RESEARCH AND MEASUREMENT ISSUES IN GAMBLING STUDIES
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RESEARCH AND MEASUREMENT ISSUES IN GAMBLING STUDIES
Garry Smith David C. Hodgins Robert J. Williams
AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic press is an imprint of Elsevier
Academic Press is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WC1X 8RR, UK This book is printed on acid-free paper. Copyright © 2007, Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, E-mail:
[email protected]. You may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting “Support & Contact” then “Copyright and Permission” and then “Obtaining Permissions.” Library of Congress Cataloging-in-Publication Data Research and measurement issues in gambling studies / [edited by] Garry Smith, David Hodgins, Robert Williams. p. cm. Includes bibliographical references and index. ISBN 978-0-12-370856-4 1. Gambling—Research. 2. Gambling—Social aspects. 3. Gambling—Law and legislation. I. Smith, Garry, Dr. II. Hodgins, David, Ph. D. III. Williams, Robert, Dr. HV6713.R47 2007 306.4'82072—dc22 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. ISBN 13: 978-0-12-370856-4 ISBN 10: 0-12-370856-7 For information on all Academic Press publications visit our Web site at www.books.elsevier.com
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DEDICATION To our patient and understanding wives, Dodie, Roslyn, and Susan.
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CONTENTS List of Contributors Preface Acknowledgments
xxiii xxvii xxxv
PART I NATURE AND SCOPE OF GAMBLING STUDIES CHAPTER 1
Situating Gambling Studies Gerda Reith
Introduction The Historical Context of Gambling The Paradigm Shift The Emergence of “Gambling Studies” Research Domains Interpretivist Approaches Sociological, Anthropological, and Psychological Research Positivist Approaches Economic and Social Cost–Benefit Analyses Biomedical Approaches Clinical Psychological Research Cognitive Psychological Research Epidemiological Research and Public Health Perspectives Conclusions: Current Trends and Future Directions
3 4 6 7 8 8 8 10 14 16 17 19 21 24
PART II MEASUREMENT ISSUES CHAPTER 2
Population Surveys Rachel A.Volberg
Introduction Purposes of Population Surveys in Gambling Studies vii
33 34
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Problem-Gambling Prevalence and Comorbidity Sampling Issues in Population Surveys Sample Size Sampling Frame Sampling Modality Multimodal Sampling Response Rates Weighting Population Survey Samples Constraints and Choices in Population Research
35 36 36 37 39 43 44 49 51
CHAPTER 3
Questionnaire Design: The Art of a Stylized Conversation Marianna Toce-Gerstein and Dean R. Gerstein
The Interview Context What Is an Interview? Conceptual Context Physical Context: Setting and Mode Social Context Privacy Effects Response Bias Social Desirability Bias Nonresponse Basic Communicative Principles Relevance Neutrality Ambiguity Language of Administration Translating the Questionnaire Using Interpreters Minimizing Cognitive Effects Recall Problems Limited Grasp and Computability of Quantities Scaling of Attitudes, Opinions, and Behaviors Causality Age-Graded Behavior: Special Considerations for Youth Questionnaire Construction Structuring the Questionnaire Major Questions, Sections, and Section Order Pathing Through the Interview
56 56 57 58 60 60 61 62 63 63 64 64 65 65 65 66 67 67 68 69 69 70 71 71 72 72
Contents
Question Flow and Context Effects Item Response Frames Reading Level Pretesting Informed Consent Special Considerations for Gambling Research Definition of Gambling Gambling Participation Attitudes Toward Gambling Problem Gambling Diagnosis of Pathological Gambling Correlates of Problem Gambling Problem Gambling Help and Treatment
ix
73 73 74 74 76 77 77 78 78 79 79 80 81
CHAPTER 4
Experimental Methodologies in Gambling Studies Sherry H. Stewart and Steven Jefferson
Basic Components of an Experimental Research Study Internal and External Validity Types of Experimental Designs Group Experimental Designs Control Groups Randomization Single-Case Experimental Designs Withdrawal Designs Multiple Baseline Design Sample Experimental Methodologies Behavioral Observation Explicit Cognition Implicit Cognition Think Aloud Reaction Time Tasks Conclusions
88 89 91 91 92 93 93 95 97 98 98 101 102 105 105 107
CHAPTER 5
Qualitative Methodologies Robert A. Stebbins
Grounded Theory and Exploration Exploratory Concatenation
112 113
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Confirmatory Qualitative Research Validity and Reliability Methods of Data Collection Qualitative Research on Gambling Issues Conclusions
114 115 117 120 122
CHAPTER 6
Longitudinal Studies of Gambling Behavior Wendy S. Slutske
Introduction Existing Longitudinal Studies of Gambling Behavior Longitudinal Studies of Gambling Behavior Initiated During Preadolescence and Adolescence Montreal, Canada (Vitaro and colleagues) Minnesota (Winters and colleagues) New York (Barnes and colleagues) Quebec, Canada (Vitaro, Ladouceur, and Bujold) Longitudinal Studies of Gambling Behavior Initiated During Late Adolescence and Early Adulthood Missouri College Students (Slutske, Jackson, and Sher) New York (Barnes and colleagues) Dunedin, New Zealand (Slutske and colleagues) Longitudinal Studies of Gambling Behavior Initiated During Early to Late Adulthood New Zealand (Abbott,Williams, and Volberg) U.S. Casino Employees (Shaffer and Hall) Key Issues and Challenges in Longitudinal Gambling Research Statistical Techniques for Modeling Stability and Change Dealing with Missing Data The Low Prevalence of Pathological Gambling Disorder Important Questions and What We Know So Far Temporal Resolution of Gambling Correlates: Establishing Causality? The Stability of Gambling Behavior The Course of Gambling Behavior and Gambling Problems Sequential/Stage Theories of Gambling Involvement: Is There a “Gateway” to Problems? Developmental Changes Versus Cohort or Period Effects on Levels of Gambling Involvement “Natural Experiments” in Longitudinal Gambling Research Summary of What We Don’t Know (Yet)
128 129 129 129 131 132 132 133 133 134 134 135 135 135 136 136 137 139 140 140 142 145 146 148 149 150
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CHAPTER 7
Quantification and Dimensionalization of Gambling Behavior Shawn R. Currie and David M. Casey
Historical Perspectives Epidemiological Data on Gambling Expenditure, Frequency, Duration, and Type Quantification of Other Addictive Behaviors Importance of the Quantification of Gambling Variations in Sources of Data Relevant Quantitative Dimensions of Gambling Behaviors Inputs Participation Status Types of Gambling Frequency Expenditure Duration Attitudes and Cognitions Outputs Financial Legal Social and Psychological Harms Clinical Use of Quantitative Gambling Data Conclusions
156 157 159 160 161 163 164 164 168 168 169 170 170 171 171 171 172 173 173
CHAPTER 8
A Review of Screening and Assessment Instruments for Problem and Pathological Gambling Randy Stinchfield, Richard Govoni, and G. Ron Frisch
Introduction Instruments Gamblers Anonymous 20 Questions (GA-20) South Oaks Gambling Screen (SOGS) Massachusetts Gambling Screen (MAGS) DSM-IV-MR (MR = Multiple Response) Diagnostic Interview for Gambling Severity (DIGS) Gambling Treatment Outcome Monitoring System (GAMTOMS) National Opinion Research Center DSM-IV Screen for Gambling Problems (NODS) Lie/Bet Questionnaire
180 181 181 190 193 193 194 195 196 198
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Gambling Assessment Module (GAM) Canadian Problem Gambling Index (CPGI) Gambling Behavior Interview (GBI) Clinical Global Impression Scale (CGI) Pathological Gambling Adaptation of the Yale Brown Obsessive-Compulsive Scale (PG-YBOCS) Gambling Symptom Assessment Scale (G-SAS) Structured Clinical Interview for Pathological Gambling (SCI-PG) Conclusions and Future Research Directions
199 200 201 202 203 204 204 205
PART III EMERGING GAMBLING STUDIES RESEARCH ISSUES CHAPTER 9
The Role of Structural Characteristics in Gambling Jonathan Parke and Mark Griffiths
Background Payment Characteristics Suspension of Judgment and Cashless Gaming Smart Cards, Spending Limits, and Cashless Gaming Maximum Bet Size and Bill Acceptors Cash Versus Credit Display Playability Characteristics Feature Games and Other Specialist Play Features Stop Buttons Gamble Buttons The Near Miss The Psychology of Familiarity Speed and Frequency Characteristics Bet Frequency and Event Frequency Event Duration In-Running Betting Payout Interval Autoplay Educational Characteristics Clocks Transparency of Expenditure and Statements Warnings/Pop-Up Messages
218 223 223 224 224 225 226 226 227 229 230 231 231 232 232 233 233 234 234 235 235 235
Contents
Ambient Characteristics Light and Color Effects Sound Effects General Sound Verbal Interaction Music Reward Characteristics Multiplier Potential and Betting Lines Payout Ratios and Randomness Jackpots Reward Schedules and Reinforcers Mandatory Cashouts Issues Relating to Research and Measurement Ecological Validity: Laboratory Versus Natural Setting Experiments in the Natural Setting Ethics in Gambling Experiments Conclusions
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236 236 236 237 238 238 239 239 240 241 242 242 243 243 244 245 245
CHAPTER 10
Situational Factors That Affect Gambling Behavior Max W. Abbott
Introduction Availability, Accessibility, and Exposure Dimensions of Accessibility and Exposure Availability and Participation Studies Using Official Data Surveys and Other Gambling Studies Availability, Participation, and Problem Gambling The Agent Gambling Prevalence Studies Replication Surveys Prevalence Changes in Population Sectors Other Exposure Concentrations Prospective Studies Other Location and Contextual Factors Credit Cards and ATMs Alcohol Tobacco Marketing and Advertising Conclusion
251 252 253 255 256 257 259 259 260 262 263 264 265 266 268 269 270 271 272
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CHAPTER 11
Individual Characteristics and Problem Gambling Behavior Tony Toneatto and Linda Nguyen
Introduction Demographic Variables Age Gender Socioeconomic Status Marital Status Early Childhood Experiences Influence of Parental Gambling Motivation to Gamble Personality Factors Arousal and Sensation Seeking Sensation Seeking Arousal Impulsivity Drive Reduction Mood Regulation Dissociation Choice of Gambling Activity Cognitive Variables Illusion of Control Illusory Correlation: Superstitious Beliefs Interpretive Biases Attributional Biases The Gambler’s Fallacy Chasing Illusory Control over Luck Conclusion
280 280 280 281 282 282 283 283 284 286 286 286 287 288 289 289 290 290 291 292 292 293 293 294 294 294 295
CHAPTER 12
Comorbidity and Mental Illness Nancy M. Petry and Jeremiah Weinstock
Introduction Current State of Knowledge of Comorbidities with Pathological Gambling Substance Use Disorders Mood Disorders Anxiety Disorders Other Disorders
305 306 306 309 310 311
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Diagnostic Measurement Issues Structured Instruments for Assessing Psychiatric Disorders Assessment of Psychiatric Symptoms Research Concerns and Summary
311 312 315 318
CHAPTER 13
Research and Measurement Issues in Gambling Studies: Etiological Models Alex Blaszczynski and Lia Nower
Introduction Overview Etiological Models Psychoanalytic and Psychodynamic Public Health Social Reward Image Social Validation Behavioral Models Cognitive Conceptualizations Neurobiological, Genetic, and Biobehavioral Integrated Models General Theory of Addictions Biopsychosocial Pathways Summary
323 324 325 325 327 329 329 330 331 333 335 337 337 338 338 339
CHAPTER 14
The Neurobiology of Pathological Gambling Judson A. Brewer, Jon E. Grant, and Marc N. Potenza
Introduction Conceptualization Biochemistry Serotonin Dopamine Norepinephrine Monoamine Oxidase Stress Pathways Opioid System
345 346 347 347 348 350 351 352 353
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Other Neurotransmitters Neuroimaging Structural Lesions and Decision Making Genetics Population Genetics Molecular Genetics Conclusions
353 354 356 357 357 358 359
CHAPTER 15
Treatment of Problem Gambling David C. Hodgins and Alice Holub
Introduction Major Approaches to Treatment Psychodynamic Approaches Theoretical Rationale and Therapeutic Model Psychodynamic Efficacy Research Unresolved Psychodynamic Issues Gamblers Anonymous Theoretical Rationale and Therapeutic Model Gamblers Anonymous Efficacy Research Unresolved Gamblers Anonymous Issues Behavioral Therapies Theoretical Rationale and Therapeutic Model Behavioral Efficacy Research Unresolved Behavioral Issues Cognitive and Cognitive-Behavioral Therapies Theoretical Rationale and Therapeutic Model Cognitive-Behavioral Efficacy Research Unresolved Cognitive-Behavioral Issues Brief Treatments and Self-Directed Treatments Theoretical Rationale and Therapeutic Model Brief Treatment Efficacy Research Unresolved Brief Treatment Issues Pharmacological Treatments Theoretical Rationale and Therapeutic Model Pharmacological Efficacy Research Unresolved Pharmacological Issues Alternative Approaches or Adjuncts to Therapy
372 373 373 373 373 373 374 374 374 375 375 375 377 378 379 379 380 382 382 382 383 385 385 385 386 387 388
Contents
Eye Movement Desensitization and Reprocessing Therapy Inpatient Programs Family Approaches Measurement/Evaluation Issues Summary and Conclusions
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388 388 389 390 391
CHAPTER 16
Prevention of Problem Gambling Robert J.Williams, Beverly L.West, and Robert I. Simpson
Introduction Educational Initiatives to Prevent Problem Gambling Upstream Interventions Information/Awareness Campaigns More Sustained and Directed Educational Initiatives Policy Initiatives to Prevent Problem Gambling Restrictions on the General Availability of Gambling Restricting the Number of Gambling Venues Restricting More Harmful Types of Gambling Limiting Gambling Opportunities to Gambling Venues Restricting the Location of Gambling Venues Limiting Gambling Venue Hours of Operation Restrictions on Who Can Gamble Prohibition on Youth Gambling Restricting Gambling Venue Entry to Nonresidents Casino Self-Exclusion Contracts Restrictions or Alterations on How Gambling Is Provided On-Site Intervention with At-Risk Gamblers Problem Gambling Awareness Training for Employees of Gambling Venues Automated Intervention for At-Risk Gamblers at Gambling Venues On-Site Information/Counseling Centers Modifying Parameters of Electronic Gambling Machines Maximum Loss Limits Restricting Access to Money Restrictions on Concurrent Use of Alcohol and Tobacco Restricting Advertising and Promotional Activities Gambling Venue Design Independence Between Gambling Regulator and Gambling Provider Summary and Recommendations
400 401 401 401 404 406 406 406 410 411 412 413 414 414 415 415 417 417 417 418 418 420 420 421 422 423 424 424 425
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CHAPTER 17
Adolescent Gambling: Current Knowledge, Myths, Assessment Strategies, and Public Policy Implications Jeffrey L. Derevensky and Rina Gupta
Introduction Adolescent Gambling Behavior Adolescent Problem Gambling Measurement Issues Related to Adolescent Problem Gambling Instruments Used to Assess Youth Problem Gambling Perspectives on the Adolescent Prevalence Data Understanding Adolescent Problem Gambling Behavior Is Pathological Gambling an Enduring Disorder? Are All Forms of Gambling Equally Dangerous? Correlates and Risk Factors Associated with Adolescent Problem Gambling Game Features, Technological Advances, and Environmental Factors Psychiatric and Mental Health Correlates Protective Factors Individual, Situational, and Environmental Factors Treatment Prevention Initiatives Concluding Remarks
438 438 439 440 440 441 442 443 443 444 447 448 448 449 450 453 456
CHAPTER 18
Cross-Cultural Comparisons Jan McMillen
Introduction Understanding the Relationship Between Gambling Research and Culture: The Importance of Theory Contending Theoretical Perspectives Normative Theories Behavioral Theories Sociological and Comparative Perspectives Problem Gambling: A Case Study of Research Cultures Theoretical Reflections The Way Forward
465 467 468 468 471 475 476 480 483
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CHAPTER 19
Internet Gambling: Past, Present, and Future Robert T. Wood and Robert J. Williams
Introduction History of Internet Gambling Current Situation Prevalence of Internet Gambling The Comparative Legality of Internet Gambling United Kingdom Other European Countries Australia New Zealand Canada United States Demographic Characteristics of Internet Gamblers Game-Play Patterns Why Do People Gamble on the Internet? Problems with Internet Gambling Unfair or Illegal Business Practices Unfair or Illegal Player Practices Internet Gambling by Prohibited Groups Problem Gambling Lack of Responsible Gambling Practices Future of Internet Gambling Researching Internet Gambling
492 492 494 495 496 496 497 497 497 497 498 499 500 501 501 501 502 502 503 505 506 508
CHAPTER 20
Social and Economic Impacts of Gambling Earl L. Grinols
Introduction Social Harm Economic Development The Eleven Components of Economic Development Cost–Benefit Analysis The Two-Sector Economy Revisited Measurement Summary and Conclusions
515 518 521 522 531 531 537
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CHAPTER 21
Gambling and Crime Colin S. Campbell and David Marshall
Introduction Categorizing Gambling and Its Relationship(s) to Crime Illegal Gambling Crimes Correlated to Problem Gambling Crimes Associated with Legal Gambling Expansion Crimes Correlated to Gambling Venues Crimes Distinct to Legal Gambling Operations Graft and Corruption Researching Illegal Gambling Researching Crime Correlated to Problem Gambling Researching the Impact of Gambling Facilities Methodological Challenges of Researching Gambling and Crime Data Problems The Social Construction of “Official Statistics” Demarcation of Gambling Concluding Observations
542 544 544 545 546 546 547 548 548 549 550 552 553 554 557 558
PART IV POLICY IMPLICATIONS OF GAMBLING RESEARCH CHAPTER 22
Values, Objectivity, and Bias in Gambling Research Jennifer Borrell and Jacques Boulet
Introduction Ontological and Epistemological Premises of Various Research Approaches Positivist (Empiricist) Approaches Interpretationist (Phenomenology) Approaches Critical-Dialectical (Structural/Structuralist) Approaches Critical-Participatory (Action-Oriented) Approaches Postmodernist (Postrelativist) Approaches Transpersonal-Ecological Approaches The Use of the Various Approaches in Existing Gambling Research Context and Corruption of Research: How Values Come to Influence Research Activities Some Contemporary Systemic Influences on Research Processes
568 568 569 570 571 571 572 573 574 575 577
Contents
Organizational and Institutional Influences Neo-Liberal Colonization of Universities and Research Institutions and the Primacy of Commercial Imperatives Government/Industry/Research Institution Collusion in Protecting Positions of Privilege Corruption of Science by Corporate Interests Within a Neo-Liberal Culture Governmental Influences Industry/Research-Institute Partnerships and Influence on Research Programs Values in Gambling Research and Their Relationship with Ideological, Cultural, and Systemic Influences Individualism Individual Pathology and Marginalization of the Problem Neo-Liberal Ideology, Individuals as Freely Choosing Consumers, and Utilitarianism Hiding Behind Putative Neutrality and Objectivity The Way Forward Multilevel Framework Understanding Harm Production Researcher Embeddedness Precautionary Principle
xxi
577 577 578 579 579 580 581 581 582 583 584 585 585 586 587 588
CHAPTER 23
Legalized Gambling: The Diffusion of a Morality Policy Patrick A. Pierce and Donald E. Miller
Introduction History of Legalized Gambling Theoretical Issues Data and Methodological Issues The Diffusion of Lotteries and Casinos The Diffusion of Innovations and Temporal Diffusion of Gambling Policies The Diffusion of Innovations and External Diffusion of Gambling Policies Lotteries Casinos Internal Diffusion of Gambling Policies The Changing Symbolic Weight of the “Sinfulness of Gambling” The Puzzle of Indian Casinos
593 594 595 596 598 598 602 603 605 606 609 609
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The Future of Legalized Gambling Future Research
610 612
CHAPTER 24
Gambling and Governance Peter Collins
Introduction A Free Market in Gambling? Two Types of Arrangement for Regulating Gambling Government Objectives in Relation to Gambling Policy Vice Crime and the Gambling Industry Organized Crime and the Gambling Industry Defrauding Customers Money Laundering and Gambling Increases in Gambling Opportunities and in General Levels of Crime Problem Gambling Gambling Opportunities and Problem Gambling Numbers Economic Benefits Taxation Conclusion: Achieving Democratic Consensus About Gambling Policy
617 618 621 624 625 627 628 629 629 630 630 633 635 636
Index
641
637
CONTRIBUTORS Max W. Abbott (251), Health & Environmental Sciences, Auckland University of Technology, Auckland, 1020, New Zealand Alex Blaszczynski (323), School of Psychology, Department of Medical Psychology, University of Sydney, Sydney, New South Wales, 2150, Australia Jennifer Borrell (567), Borderlands Cooperative, Melbourne, Victoria, 3123, Australia Jacques Boulet (567), Borderlands Cooperative, Melbourne, Victoria, 3123, Australia Judson A. Brewer (345), Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 6511 Colin S. Campbell (541), Department of Criminology, Douglas College, New Westminster, British Columbia, V3L 5B2, Canada David M. Casey (155), Addiction Centre, Foothills Medical Centre, University of Calgary, Calgary, Alberta, T2N 2T9, Canada Peter Collins (617), School of Accounting, Economics and Management Science, University of Salford, Salford, Greater Manchester, M5 4WT, United Kingdom Shawn R. Currie (155), Calgary Health Region, Calgary, Alberta, T2W 3N2, Canada Jeffrey L. Derevensky (437), International Centre for Youth Gambling Problems and High-Risk Behaviours, McGill University, Montreal, Quebec, H3A 1Y2, Canada G. Ron Frisch (179), University of Windsor, Windsor, Ontario, N9B 3P4, Canada Dean R. Gerstein (55), Claremont Graduate University, Claremont, California 91711 Richard Govani (179), University of Windsor, Windsor, Ontario, N9B 3P4, Canada Jon E. Grant (345), Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota 55455 Mark Griffiths (217), Division of Psychology, Nottingham Trent University, Nottingham, NG1 4BU, United Kingdom xxiii
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Contributors
Earl Grinols (515), Department of Economics, Hankamer School of Business, Baylor University, Waco, Texas 76798 Rina Gupta (437), International Centre for Youth Gambling Problems and High-Risk Behaviours, McGill University, Montreal, Quebec, H3A 1Y2, Canada David C. Hodgins (371), Department of Psychology, University of Calgary, Calgary, Alberta, T2N 1N4, Canada Alice Holub (371), Department of Psychology, University of Calgary, Calgary, Alberta,T2N 1N4, Canada Steven Jefferson (87), Department of Psychology, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, B3H 2E2, Canada David Marshall (541), Research and Community Engagement Division, Queensland Office of Gaming Regulation, Brisbane, Queensland, 4002, Australia Jan McMillen (465), Centre for Gambling Research, The Australian National University, Canberra, Australian Capital Territory, 0200, Australia Donald E. Miller (593), Department of Mathematics, Saint Mary’s College, Notre Dame, Indiana 46556 Linda Nguyen (279), Faculty of Nursing, University of Toronto, Toronto, Ontario, M5S 251, Canada Lia Nower (323), Center for Gambling Studies, Rutgers University, New Brunswick, New Jersey 08854 Jonathan Parke (217), Division of Psychology, Nottingham Trent University, Nottingham, NG1 4BU, United Kingdom Patrick A. Pierce (593), Center for Academic Innovation, Saint Mary’s College, Notre Dame, Indiana 46556 Nancy M. Petry (305), Department of Psychiatry, University of Connecticut Health Care Center, Farmington, Connecticut 6030 Marc N. Potenza (345), Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 6511 Gerda Reith (3), Department of Sociology, Anthropology, and Applied Social Science, University of Glasgow, Glasgow, Scotland, G12 8RT, United Kingdom Robert I. Simpson (399), Ontario Problem Gambling Research Centre, Guelph, Ontario, Canada Wendy S. Slutske (127), Department of Psychological Sciences, University of Missouri–Columbia, Columbia, Missouri 65211
Contributors
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Robert A. Stebbins (111), Department of Sociology, University of Calgary, Calgary, Alberta, T2N 1N4, Canada Sherry H. Stewart (87), Department of Psychiatry and Psychology, Queen Elizabeth II Health Sciences Centre, Dalhousie University, Halifax, Nova Scotia, B3H 2E2, Canada Randy Stinchfield (179), Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota 55455 Marianna Toce-Gerstein (55), Georgetown University, Washington, DC 20009 Tony Toneatto (279), Clinical Research Department, Center for Addiction and Mental Health; Departments for Psychiatry and Public Health Sciences, University of Toronto, Toronto, Ontario, M5S 2S1, Canada Rachel A. Volberg (33), Gemini Research Ltd., Northampton, Massachusetts 01061-1390 Jeremiah Weinstock (305), Department of Psychiatry, University of Connecticut Health Care Center, Farmington, Connecticut 6030 Beverly L. West (399), School of Health Sciences, University of Lethbridge, Lethbridge, Alberta, T1K 3M4, Canada Robert J. Williams (399, 491), School of Health Sciences, University of Lethbridge, Lethbridge, Alberta,T1K 3M4, Canada Robert T. Wood (491), Department of Sociology, University of Lethbridge, Lethbridge, Alberta, T1K 3M4, Canada
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PREFACE Garry Smith Alberta Gaming Research Institute University of Alberta
Humankind’s experience with gambling shows the activity to have been variously regarded as a sin, a vice, an unfortunate but tolerable weakness of human nature, an evolutionarily appropriate behavior, an adult form of play, a release from daily routines, a means of increasing arousal, an intellectual challenge, a buffer to existential anxieties caused by chance events, a prism of cultural values, and an activity that fosters preoccupation, escapism, conditioned responses, illusion of control, and addictive behavior. Gambling dates back to early recorded history, yet across societies past and present, gambling has varied considerably with respect to organization, social meanings, and how it is regarded in moral terms (Binde 2005). Moreover, contrasting degrees of tolerance, prohibition, and ambiguity have been shown toward the activity depending on the culture and historical era (Wykes 1964). For example, receptivity toward gambling in the United States has been cyclical, with the legitimacy of gambling having oscillated from prohibition to approval and back again several times. “Twice before in American history players could make legal bets in almost every state, but these waves of legal gambling came crashing down in scandal and ruin” (Rose 1991, p. 71). At the time of his writing, Rose speculated that America was in the midst of the third wave, with a reversal of gambling’s favored status expected to occur in a few more decades. While perhaps less pronounced than the American experience, public attitudes in most Western cultures toward gambling have tended to be cyclical. Despite the ubiquity and persistence of gambling through the ages, there is little evidence to suggest that any society has discovered an exemplary way to regulate it—that is, fair games offered with the proceeds going toward important societal benefits, gambling-related corruption and crime kept in check, and social and economic damages minimized. In line with this observation, David Allen (1952) posed the question: “What ought to be thought about gambling?” Referring to the role of government and commenting over a half century ago, Allen described American gambling policy as a matter of “first class importance,” ranking in gravity with “foreign policy and domestic tax issues.” While no doubt a hyperbole at the time, the exponential growth of legal gambling over the past few decades now makes Allen’s concern especially prescient. According to Allen, a consideration of best practices in gambling regulation should start by addressing three salient questions: (1) Is gambling a natural activity (i.e., rooted in the human psyche)? (2) Is gambling harmful (i.e., injurious to society or its members, and if so, under what circumstances)? and (3) Is gambling suppressible? xxvii
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In the first two-thirds of the twentieth century, the role of gambling in society was addressed in influential works by Mackenzie (1928), Asbury (1938), Devereux (1949), Ploscowe and Lukas (1950), Allen (1952), Bergler (1958), Ezell (1960), David (1962), Reid and Demaris (1963), and Turner (1965). These works served as a foundation for the emergent field of gambling studies but were written at a time when most forms of gambling were illegal and the activity still had a tainted reputation. Hence, they tended to be anti-gambling tracts, attempts to explain the appeal of gambling through the ages, efforts to fathom the minds of problem gamblers, or descriptions of particularly sordid gambling scenes. The point is that learned individuals have struggled with these questions through the ages, yet we still lack definitive answers. On a more positive note, we are moving closer to disentangling the mystery of gambling’s impact on society. Prior to a general trend that saw liberalized gambling laws and modest gambling expansion beginning in the 1970s, the study of gambling was a decidedly lowprofile academic pursuit. Gambling expansion in the 1970s occurred at a moderate pace under tightly regulated conditions and was driven by an attempt to restrict illegal markets and generate revenues for social programs such as welfare, sports, the arts, and others identified as “worthy causes” (Kingma 2004). A gambling policy paradigm shift occurred in the 1980s as a result of legislation lagging behind aggressive gambling practices, thus creating a situation whereby “politics gave in to market demands without convincing and conclusive (legal) justification” (Kingma 2004, p. 55).The upshot of this policy inversion was (1) accelerated gambling expansion, especially via electronic formats, (2) diversion of gambling proceeds away from decentralized social welfare programs and toward government treasuries, and (3) government application of corporate principles and strategies to market gambling offerings. Suddenly gambling had become a powerful economic, political, and cultural force that augmented government and gaming industry coffers but also posed significant economic and public health risks for individuals and communities. Academic interest was piqued by this rapid and radical transformation of gambling as scholars were keen to know how it was that a recently stigmatized and circumscribed activity had suddenly become a legitimate entertainment option. Why were previously outlawed activities now being sanctioned and promoted by government? And, how did this profusion of legal gambling opportunities affect citizen and community well-being, for better or worse? (Now, some twenty years later, legal gambling has gone viral and operates on a scale that was unimagined then.)1 1 While gambling is certainly big business, it is not an essential service in the same way as traditional government responsibilities, such as education, agriculture, and health care. Presumably, governments exist to make a jurisdiction a better place to live; government involvement in sanctioning and promoting gambling is thus an anomaly, because not only is gambling not an essential service, but public attitude surveys indicate that a majority of citizens feel that some forms of gambling actually detract from a community’s quality of life (Azmier 2000).
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In Chapter 1 of this volume, Gerda Reith details how this rampant expansion of gambling facilitated the emergence of gambling studies as an accepted academic subfield in well-established disciplines such as medicine, law, business, psychology, economics, political science, and so forth. This nascent subfield quickly advanced through several growth phases highlighted by compulsive gambling being recognized as a “disorder of impulse control” by the American Psychiatric Association in the third edition of the Diagnostic and Statistical Manual of Mental Disorders; the development of assessment tools to identify pathological gamblers; the emergence of problem gambling prevalence surveys and national gambling impact studies; and the growing availability of research funding, which had the salutary effect of attracting new scholars to the field and, in some cases, helped to establish gambling-related institutes/centers. Current advancements in gambling studies include universities offering tenure-track faculty positions and postdoctoral fellowships in gambling studies and including gambling studies courses in the curriculum; the advent of longitudinal mega-projects featuring sophisticated research designs and interagency collaboration designed to establish broad and rich databases; and the appearance of Internet websites specializing in gambling studies topics, thus allowing scholars to easily interact with their colleagues and to access gambling reports and gambling news items from around the globe. While much has been accomplished by the dedicated scholars who have created a critical mass of knowledge in gambling studies, much remains to be done. For example, the field remains encumbered by imprecise terminology, a consensus has yet to emerge on the best way to measure out-of-control gambling in general population surveys, and little headway has been made on conducting valid and reliable cost/benefit analyses. The idea for this book originated in discussions among the editors concerning the need for more detailed information on gambling studies research. The book’s premise is that the more we research and the more precise our measuring tools, the better we will be able to comprehend gambling and its effects on individuals and society at large. Accordingly, in planning the book, we assumed two objectives: first, to expose readers to the most widely used research approaches and methodologies in gambling studies, and second, to highlight critical research issues that currently challenge scholars in the field. Research and Measurement Issues in Gambling Studies is intended as a reference text for advanced students and academics working in the field of gambling studies but should also interest problem gambling treatment providers, gambling policy makers, and laypersons interested in gambling as a cultural phenomenon. Gambling studies measurement and methodological concerns are addressed by experts who have provided state-of-the-art synopses and critiques of their specialty areas. In recruiting authors, the editors sought internationally recognized gambling studies scholars and asked them to synthesize the generally accepted knowledge in their
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areas, comment on methodologies and measurement strategies used (what does and does not work), and expound on research trends, information gaps, and future research prospects. To engage this task, talented and versatile contributors from Australia, Canada, England, New Zealand, Scotland, and the United States who collectively represent a diverse array of academic disciplines (psychology, sociology, economics, cultural studies, political science, biochemistry, addiction studies, and health sciences) were enlisted. The result, we hope, is a timely and comprehensive compendium of research and measurement concerns in gambling studies. The book has four sections: (1) the nature and scope of gambling studies, (2) measurement approaches in gambling studies, (3) emergent gambling studies research issues, and (4) ethical considerations and policy implications related to gambling studies research. The first section consists of just one chapter, Gerda Reith’s “Situating Gambling Studies.” Reith’s chapter sets the stage for the remainder of the book, as she explains how gambling studies evolved, describes the research domains that formed to analyze and interpret gambling and problem gambling behavior, and speculates on how gambling studies might unfold in the future. The second section comprises seven chapters, each dealing with an important area of measurement in gambling studies research. In Chapter 2, Rachel Volberg provides a primer on the ways and means of conducting gamblingrelated population surveys. Volberg delineates why population surveys are an expedient and acceptable way to collect data on citizens’ gambling proclivities, then guides the reader through the process, indicating potential pitfalls and best practices. Chapter 3, by Marianna Toce-Gerstein and Dean Gerstein, deals with questionnaire design and development as applied to gambling studies. The authors discuss the interview context (e.g., different types of interviews, factors related to how and where interviews are conducted and response bias); communication principles pertaining to the relevance, neutrality, and ambiguity of questions; ways of minimizing cognitive effects; and how to structure questionnaires so that they flow smoothly and elicit valid responses. Sherry Stewart and Steven Jefferson review the key components of experimental research design in Chapter 4 and show how this approach is used in studies of gambling behavior.The advantages and drawbacks of this method are discussed, and the primacy of this method is noted because it is the only approach that allows the researcher to infer causality. In Chapter 5, Robert Stebbins shows how qualitative research methods can be used to improve our understanding of gambling scenes and behaviors and can ultimately lay a foundation for grounded theory. Qualitative methods are most commonly used in exploratory studies of unknown social phenomena, but Stebbins also recommends this approach for verification studies.
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In Chapter 6, Wendy Slutske explores the challenges and benefits of conducting longitudinal studies. This approach allows the researcher to resolve temporal relations between gambling behavior and its correlates and to trace gambling behavior and problem gambling development; however, it has been sparingly used in gambling studies research, mainly because of the high cost and need for experienced, academically diverse research teams. Chapter 7 focuses on the quantification and dimensionalization of gambling behavior. Shawn Currie and David Casey discuss applications to gambling from studying the quantification of other addictive behaviors and highlight the difficulties in epidemiological data collection related to gambling frequency, duration, and expenditure. The authors note that the lack of a standard unit of gambling (e.g., an equivalent to the number of alcohol drinks or number of cigarettes smoked) has hindered gambling research in this area. In Chapter 8, Randy Stinchfield, Richard Govoni, and G. Ron Frisch guide readers through the maze of assessment tools that have been used to screen for problem gambling behavior in clinical, general, and special populations. The measurement of problem gambling has been beset by controversy and a lack of precision, as evidenced by the dozen or more instruments that have been designed for this purpose. Refinements to the currently used instruments related to psychometric properties, cut scores, reliance on self-report data, and the time period being assessed are recommended. There are thirteen chapters in the third section, each dealing with a topic that has attracted scholarly attention in recent years. Chapter 9 features a discussion by Jonathan Parke and Mark Griffiths on the structural characteristics of electronic gambling machines (EGMs) and their effect on player behavior. EGMs are singled out because theirs is reputed to be the most hazardous gambling format. In addition to categorizing the structural features of EGMs, the authors call for more ecologically valid studies and note how the competing interests of gambling purveyors, gaming machine manufacturers, and gambling consumers have limited research in this area. Chapter 10, by Max Abbott, elaborates on how situational factors impact gambling behavior. These factors include the availability of various gambling formats, accessibility to gambling formats, hours of operation, pricing, alcohol and tobacco consumption while gambling, on-site automated teller machines (ATMs), and the amount and type of advertising deployed. Abbott claims there is growing evidence to link gambling availability to increased gambling participation, and in some cases, to problem gambling. Also influencing a person’s response to gambling opportunities are idiosyncratic factors such as coping styles, self-esteem, genetic makeup, faulty cognitions related to the mechanics of gambling, and demographic profile (e.g., age, gender, ethnicity, education level).The effect of these characteristics on gambling behavior is examined by Tony Toneatto and Linda Nguyen in Chapter 11. The authors
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suggest measuring the way that individuals perceive and interpret the act of gambling as a promising new research strategy. In Chapter 12, Nancy Petry and Jeremiah Weinstock probe the links between problem gambling and accompanying psychiatric conditions such as substance abuse, depressive mood states, and anxiety disorders. Diagnostic measurement issues and the assessment of psychiatric symptoms are discussed, along with implications for treatment. In Chapter 13, Alex Blaszczynski and Lia Nower describe and analyze the models used to explain the development and persistence of problem gambling. The merits and shortcomings of nine models are discussed, leaving the authors to conclude that no single empirically validated model decisively accounts for all problem gambling behavior. Researchers Judson Brewer, Jon Grant, and Marc Potenza elaborate on the neurobiological basis of problem gambling in Chapter 14. Included in their discussion is the relative influence of brain functioning and genetic factors in contributing to problem gambling behavior. Also reviewed are neuroimaging studies that examine the regions of the brain activated during gambling participation for the purpose of differentiating between problem gamblers and control subjects. Chapter 15 covers treatment issues related to problem gambling. David Hodgins and Alice Holub review the theoretical rationale and therapeutic models for seven common treatment approaches. The authors discuss the challenges related to evaluating treatment outcomes and suggest research avenues that would improve our knowledge in this area. In Chapter 16, Robert Williams, Beverly West, and Robert Simpson thoroughly review the educational and policy initiatives designed to prevent problem gambling. The authors note that some approaches are more efficacious than others, but they maintain that all efforts have some additive value and that, for maximum impact, a coordinated approach is needed. Jeff Derevensky and Rina Gupta investigate adolescent gambling in Chapter 17. Topics addressed include measuring youth problem gambling, interpreting prevalence data, and identifying correlates and risk factors associated with adolescent gambling behavior, along with treatment and prevention issues. Jan McMillen employs a sociocultural framework to review key theoretical and methodological issues pertaining to the cross-cultural study of gambling in Chapter 18. Her contention is that gambling research has been dominated by North American–based psychiatry, clinical psychology, and quantitative methodologies. The limitations of this orientation are discussed and it is suggested that gambling studies research needs a unifying rationale, which can best be achieved through a multitheoretical approach using a plurality of methods. Robert Wood and Robert Williams lead us through the murky world of Internet gambling in Chapter 19. Here, we learn about the history and prevalence of Internet gambling, its legal status in various worldwide jurisdictions, who is
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playing the games, and their game preferences. Because Internet gambling research is a relatively recent phenomenon, the authors provide a section on the unique challenges of studying this topic. Social and economic impact studies of gambling have long been debated among gambling studies scholars; the general belief is that they are desperately needed but that there is no consensus on how to execute such studies. In Chapter 20, Earl Grinols identifies the shortcomings of previous research in the area and specifies what he believes the principles of sound empirical research to be, as well as the social cost categories that must be addressed. Chapter 21 expands on a major gambling-related social cost; namely, increased crime rates. Colin Campbell and David Marshall summarize the historical association between crime and gambling, categorize and comment on typical gambling-related crimes, and outline methodological strategies to improve our knowledge in the area. The fourth section is concerned with policy issues related to gambling studies research and includes three chapters. In Chapter 22, Jennifer Borrell and Jacques Boulet examine the underlying premises of mainstream research positions; comment on the assumed relationships between gambling studies researchers and their subjects and subject matter; and show how personal and cultural values influence the topics studied, how they are studied, and how research findings are interpreted. Chapter 23, by Patrick Pierce and Donald Miller, is a case study on the politics of gambling that details how lotteries and legal casino gambling spread from state to state in America between 1966 and 2004. Using diffusion of innovations theory, the authors identify the presence of large religious fundamentalist populations in a state as the main barrier to gambling expansion and the availability of similar gambling formats in neighboring states as the key to adoption. In Chapter 24, Peter Collins compares and contrasts the ways that governments regulate gambling and argues that commercial gambling is subject to much broader and more rigorous regulation than are most other business enterprises. Collins assesses the reasons given for this stricter regulation and concludes that sound gambling policy is a delicate matter of judging public attitudes and balancing competing interests. We wish to thank the contributors to this volume for their collegiality and enthusiasm for the project. Although the individual chapters reflect considerable variations in mode and emphasis, we appreciate the professionalism of these scholars in meeting our deadlines and adapting their writing styles and conceptualizations of the subject matter to satisfy our specific requirements. The editors realize that important gambling studies–related methodological and research issues may have been overlooked in our gathering of topics for this book. If that is the case, the blame is ours. We hope that readers of this collection will develop a better understanding of the field of gambling studies and that they will consider applying the insights generated here to their own research initiatives.
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REFERENCES Allen, D. (1952). The Nature of Gambling. New York: Coward-McCann. Asbury, H. (1938). Sucker’s Progress: An Informal History of Gambling in America from the Colonies to Canfield. New York: Dodd, Mead and Co. (Reprinted 2003 by Thunder’s Mouth Press) Azmier, J. (2000). Canadian Gambling Behavior and Attitudes. Calgary, AB: Canada West Foundation. Bergler, E. (1958). The Psychology of Gambling. London: Bernhard Harrison Ltd. Binde, P. (2005). Gambling across cultures: Mapping worldwide occurrence and learning from ethnographic comparison. International Gambling Studies, 5, 1–27. David, F. N. (1962). Games, Gods and Gambling: A History of Probability Theory and Statistical Ideas. New York: Charles Griffin. Devereux, E., Jr. (1949). Gambling and the Social Structure: A Sociological Study of Lotteries and Horse Racing in Contemporary America. Ph.D dissertation, Harvard University. Ezell, J. (1960). Fortune’s Merry Wheel. Cambridge, MA: Harvard University Press. Kingma, S. (2004). Gambling and the risk society:The liberalization and legitimation crisis of gambling in the Netherlands. International Gambling Studies, 4, 47–67. Mackenzie,W. D. (1928). The Ethics of Gambling. Garden City, NY: Doubleday, Doran & Company. Ploscowe, M., and Lukas, E. J. (eds.). (1950, May). Gambling. Annals of the American Academy of Political and Social Science, 269. (Special issue) Reid, E., and Demaris, O. (1963). The Green Felt Jungle. New York: Trident Press. Rose, I. N. (1991). The rise and fall of the third wave: Gambling will be outlawed in forty years. In Gambling and Public Policy: International Perspectives (W. Eadington and J. Cornelius, eds.), pp. 65–86. Reno, NV: Institute for the Study of Commercial Gaming. Turner,W. (1965). Gamblers’ Money. New York: Signet Books. Wykes,A. (1964). The Complete Illustrated Guide to Gambling. Garden City, NY: Doubleday & Company.
ACKNOWLEDGMENTS We would like to acknowledge the support of the Alberta Gaming Research Institute (AGRI) for providing us with the opportunity and resources to study gambling issues and the Alberta government for its foresight in establishing the AGRI eight years ago. Finally, we owe a debt of gratitude to the encouragement and support provided by Scott Bentley and Kathleen Paoni, our Elsevier contacts; Scott, for suggesting the project and handling the administrative details, and Kathleen, for guiding us efficiently and painlessly through the process. We consider ourselves most fortunate to have partnered with them to produce this publication.
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PART I
Nature and Scope of Gambling Studies
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CHAPTER 1
Situating Gambling Studies Gerda Reith Department of Sociology, Anthropology, and Applied Social Science University of Glasgow Glasgow, Scotland, United Kingdom
Introduction The Historical Context of Gambling The Paradigm Shift The Emergence of “Gambling Studies” Research Domains Interpretivist Approaches Sociological, Anthropological, and Psychological Research Positivist Approaches Economic and Social Cost–Benefit Analyses Biomedical Approaches Clinical Psychological Research Cognitive Psychological Research Epidemiological Research and Public Health Perspectives Conclusions: Current Trends and Future Directions
INTRODUCTION Gambling is a nearly ubiquitous activity that has been practiced throughout history and across cultures by various social groups. For hundreds of years, individuals have gambled for excitement and escapism, to win money, to gain status, to be sociable—the list is almost as diverse as the variety of games. However, in spite of this heterogeneity, one feature that is constant in all forms 3
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of gambling is the redistribution of something of value (usually money) by the operation of chance. Although the measurement of value and the degree of chance may vary substantially, the involvement of both is fundamental for a wager to take place. Explanations, criticisms, and, more recently, scientific analyses of gambling and problem gambling have consistently been embedded in their particular sociohistorical climates. Their concerns and preoccupations and even the language they are articulated in emerge out of wider cultural processes.Today,“gambling studies” is a uniquely complex, as well as contested, field. It comprises a variety of “ways of seeing” its subject, some of which are complementary, others not, and none of which have as yet fully accounted for the myriad complexities of the topic. This introductory chapter does not attempt to answer the “big questions” that still surround the study of gambling, but rather undertakes the more modest tasks of mapping, in admittedly partial form, development of the field and outlining some other major themes. The chapters in this volume then provide detailed analysis of the central research and measurement issues in gambling studies. And so, to begin, this chapter provides a brief historical introduction to criticisms and conceptualizations of gambling before moving on to examine the socioeconomic climate of the proliferation of the activity in the twentieth and twenty-first centuries. It then examines the epistemological heritage of the various research domains that have developed around gambling and problem gambling, before finally providing an overview of possible future trends.
THE HISTORICAL CONTEXT OF GAMBLING Throughout history, perhaps the only thing that has been as ubiquitous as gambling has been condemnation of the activity, a feature that has cast a persistent shadow over its popularity. Although the terminology of the debate has changed according to its sociohistorical climate, generally the tone has been one of open hostility to what has been considered a “deviant” activity—especially when the gamblers in question have been members of economically marginal groups. From the Protestant Reformation onward, games of chance have been condemned as sinful for their undermining of the work ethic, whereby diligence and effort were supposed to be rewarded with an appropriate level of wealth and success. The rewards of gambling, on the other hand, are distributed by pure chance, and entirely disconnected to effort or merit, in a way that seemingly threatens to disrupt the social hierarchy and the ideology of meritocracy and hard work that it was based on. In the more secular period of the Enlightenment,Western societies criticized the element of irrationality that appeared to be embodied in games of
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chance. Beliefs in mystical forces such as luck and fate, rather than dynamic ideas about personal agency and effort, contradicted ideals of human reason and, along with the vast amounts of waste and loss that were often involved in games, seemed to be anathema to contemporary social values. Although gambling had been widely condemned, specific explanations for gamblers’ behavior that went beyond criticisms of sin and irrationality did not appear until around the nineteenth century. At that time, the economic activities of industrializing nations such as Great Britain and the United States threw the apparently haphazard economic actions of gamblers into sharp relief and led to a renewed focus on the “wasteful” and “immoral” aspects of players themselves. With the Industrial Revolution, discussions about gambling separated slightly from broader ideological discourses of religion and reason and started to focus on individual moral defects instead. In this era of social Darwinism, biomedical “theories” sought to explain supposedly immoral or deviant behavior with reference to defects in individual physiology. Gambling was caught up in discourses about other vices, such as alcoholism, addiction, and prostitution, with concerns expressed over the possible hereditary effects of the “disease.” During this period, gambling moved into the clinic, with the psychoanalyst Sigmund Freud (1928) turning his attention to a disorder that was defined as a compulsive neurosis. Freud related gambling to the Oedipus complex, declaring it to be grounded in unresolved childhood conflict and based on guilt and masochistic self-punishment. The influence of the psychoanalytic perspective continued well into the twentieth century, with some writers, such as Edmund Bergler (1970), focusing on the dysfunctional, masochistic qualities of “degenerate gamblers,” and others, such as Robert Lindner (1976) and Ralph Greenson (1974), declaring gamblers to be sick individuals in the grip of a regressive disease. The activity fared little better in the sociological literature, dominated at that time by functionalist perspectives that attempted to explain human action in terms of its latent function within broader social structures. In these, gambling was viewed as a mechanism for releasing the tensions and frustrations of everyday life–– a cathartic role that was particularly compelling for those at the bottom of the social hierarchy, and for whom such pressures were regarded as more acute. This link between gambling and socioeconomic deprivation influenced much of the American sociology of gambling of the 1950s, 1960s, and 1970s, with researchers portraying gambling as a compensatory activity within which the frustrations of the outside world could be worked out in symbolic form (Bloch 1951; Devereaux 1949; Herman 1967; Newman 1972). These psychoanalytic and functionalist perspectives dominated explanations of gambling at a time when the activity was, although undoubtedly widespread, largely illegal throughout Europe and America. Up until the late 1960s, when not outlawed altogether, most forms of gambling were tightly regulated by restrictive legislation and were frequently dogged by scandals involving corruption and fraud, lending games of chance the air of an underground and morally dubious enter-
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prise. It is perhaps not surprising then that attempts to describe and explain what would have been a largely invisible activity were colored by the prohibitory climate in which the latter was embedded.
THE PARADIGM SHIFT In the mid-twentieth century, economic, social, political, and cultural changes transformed Western societies and encouraged a paradigm shift out of which the activity of gambling and various forms of problem gambling emerged as serious objects of study in their own right. From around the 1970s onward, neo-liberal economic policies have encouraged a reduction of state intervention in both public and private life and reluctance on the part of policymakers to regulate markets or to impose high levels of taxation. Such policies have encouraged alternative forms of revenue generation, of which taxing the profits of commercial gambling is an attractive option. At the same time, relatively increasing affluence and the spread of consumerism have begun to undermine arguments about the “immorality” of gambling and have created instead a cultural climate that if not conducive, is not exactly hostile, to the proliferation of gambling as a mainstream leisure activity. And so, since the 1980s in particular, the political economy of gambling has been increasingly deregulated, resulting in a period of dramatic expansion and the proliferation of commercial gambling as a global, multibillion-dollar enterprise. Today, between 60 and 80% of the populations of Western societies gamble regularly, including increasing numbers of younger people, women, and the middle class—the latter being the group traditionally most hostile to gambling.The normalization brought about by such a demographic shift is reflected in changing nomenclature.The euphemism “gaming,” with its connotations of play and leisure, is increasingly favored (at least by the industry) over the traditional term, “gambling,” with all its connotations of financial loss. By the start of the twenty-first century, the fortunes of gambling have been transformed, and the gambling industry has become a profitable business, selling hope, excitement, and thrills to ever-larger numbers of consumers.The industry’s task has been aided by the increased availability of credit to larger sections of the population. In particular, middle-class consumption of credit has expanded at rates similar to gambling, greatly increasing access to the activity beyond the levels imposed by “hard” money.This expansion also taps into a wider “consumerist” ethos within culture as a whole. Not only are games of chance a product of consumer culture, they also express some of its most fundamental characteristics, such as the values of instant gratification, self-fulfillment, and conspicuous consumption. Gambling can thus be described as a leisure activity with a high degree of “cultural capital” (Bourdieu 1984/1979).
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It can be seen, then, that by the end of the twentieth century a range of political, economic, and cultural factors had contributed to the dramatically changed status and expansion of commercial gambling.
THE EMERGENCE OF “GAMBLING STUDIES” Responses to the shift in the status and availability of gambling ranged from curiosity to concern and hostility as a range of groups, including health professionals, community leaders, politicians, religious organizations, and the general public, began to consider the potentially negative impacts of gambling on individual and social life. The expansion also set the stage for the study of gambling as an important and legitimate field of academic inquiry.The topic of gambling touched on many existing disciplines, including sociology, psychology, economics, law, and medicine, and as well as forming small but distinct subfields in these areas, it also established one of its own.This development is apparent in the increasing number of specialized journals, conferences, research institutes, academic departments, and funding bodies dedicated to the study of gambling that have come into being in the past two decades or so. Broadly speaking, the general area of “gambling studies” can be divided into two approaches. On the one hand, what can be loosely termed interpretivist perspectives are concerned with the interpretations of the meanings, cultures, and contexts of gambling. Such an approach is based on the premise that social meanings are created through the intentions and understandings of individuals, which in turn are embedded in culturally and historically specific conditions. Utilizing qualitative, ethnographic methods, such as interveiws, participant observation, focus groups, and case studies, researchers in disciplines such as sociology, anthropology, and sometimes psychology have attempted to understand the meanings and roles of gambling in the everyday lives of participants, as well as the attitudes and motivations of various groups of players. The focus of such approaches is frequently, although not exclusively, on recreational or nonproblematic gambling. On the other hand, what can be loosely described as positivist perspectives have developed out of the methodologies utilized by the natural sciences. In general, these methods are assumed to be value free and aim to establish causal relations between various factors that will allow the prediction and ultimately the control of behavior.These approaches are concerned with the measurement and quantification of gambling, and particularly problem gambling, in both clinical and population-based samples. Such approaches tend to utilize quantitative methodologies such as surveys, questionnaires, and laboratory-based research, and are found in psychology and sociology, as well as in disciplines such as medicine and economics. The distinction between these two approaches is by no means absolute, nor is it always clear-cut. For instance, disciplines such as psychology and sociology can
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encompass both interpretivist and positivist methodologies. The techniques and focus of qualitative and quantitative sociology are quite distinct, as are those of, for example, clinical psychology compared with social psychology, psychoanalysis, or counseling-based approaches. Moreover, many approaches to the study of gambling are interdisciplinary in nature, utilizing insights from a number of disciplines and methodologies. The remainder of this chapter now moves on to discuss developments in the field of gambling studies. For the purposes of clarity and structure, it uses the distinction between interpretivist and positivist perspectives to organize these into two research domains, although, as it is by far the most dominant approach, the discussion of the latter is considerably longer and is subdivided into a number of smaller fields. It should be noted here that the material included in this chapter is a highly selective sample. Such selectivity is justified not only by the constraints of length, but also by the fact that this essay is designed to provide an overview of broad themes and trends, rather than an exhaustive analysis of the literature on gambling. Therefore, it is not intended as a literature review, but should rather be read as a thematic overview of the development of various research domains.
RESEARCH DOMAINS INTERPRETIVIST APPROACHES Sociological, Anthropological, and Psychological Research In disciplines such as sociology and anthropology, a variety of fairly disparate studies have focused both on gambling’s structural determinants and on its meanings for participants. The subject of these types of approach tends to be gambling settings rather than gamblers as individuals, and on the sociocultural factors that influence patterns of behavior (Volberg 2001). Although relatively few in number, some studies have shed light on structural factors, such as socioeconomic class and gender, that influence patterns of gambling and preferred types of games (Chinn 1991; Clotfelter and Cook 1989; Downes et al. 1976). At the same time, the issue of individual agency has been of interest to sociologists and anthropologists, who have examined gambling as a form of leisure with its own unique experiential frame. Many writers in this tradition have been influenced by the pathbreaking studies of Erving Goffman (1969) and Clifford Geertz (1975), who utilized symbolic interactionist and interpretivist anthropological perspectives to provide “thick” accounts of the meanings and values of gambling for participants. By focusing on the role of games in the lives of individuals as well as the local cultures within which they were embedded, these authors
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highlighted the nonmaterial gains to be had from gambling and the rewards of risk taking. These included the display of character, the expression of individual and community identity, and the earning of status and prestige within one’s peer group. Insights from studies such as these have been utilized in various ways, often in research on the cultures that surround specific games and groups of players. Here, the focus is on the social contexts of gambling behavior and the social roles and rewards that make it meaningful for players. Such studies often use participant observation, ethnographic research, or various other qualitative techniques in an attempt to uncover the “social worlds” of gambling. Early studies of this type focused on the distinctive forms of sociation that made up various gambling subcultures, including horse-race betting and poker playing. Through in-depth interviews and immersion in the everyday lives of their subjects, researchers such as Henry Lesieur (1984), Marvin Scott (1968), David Hayano (1982), and John Rosecrance (1985) were able to provide firsthand accounts of the shared values, attitudes, and systems of knowledge that distinguished gambling from other forms of social behavior. More recent studies in this tradition have continued to examine the motivations, meanings, and cultures of gambling involvement, often focusing on particular social groups and sociocultural contexts and settings of play.They include studies of (mostly male) participation in horse-race betting, the social and gendered nature of bingo, the dynamics of fruit (slot) machine playing—especially among youth—and the meanings of games for lottery players (Bruce and Johnson 1992; Dixey 1996; Falk and Maenpaa 1999; Fisher 1993; Neal 1998). More recent studies include indepth ethnographic accounts of gambling in specific settings, such as the culture and kinship of the British racecourse (Cassidy 2002) and the local rituals of gambling in a Greek community (Malaby 2003). Other studies have utilized ethnographic methods to document the social and gendered meanings of gambling in traditional societies (Goodale 1987; Sexton 1987; Zimmer 1987). By outlining the meanings that games have for participants, such studies have remained within the interpretivist tradition associated with anthropologists such as Geertz and have also demonstrated how gambling is adapted to the particular needs of local communities. At the same time, many have noted the absence of “Western” concepts of problem gambling within such cultures. Finally, more theoretical accounts have used documentary analysis to describe the social and historical construction of gambling in Western societies, exploring its changing status and relation to broad social movements and global processes.These have sought to show that gambling is both historically situated and culturally specific, and also that responses to it are frequently embedded in particular political and ideological contexts.The broad sweep of such accounts has analyzed gambling on both a wide, global scale (Brenner and Brenner 1990; McMillen 1996; Reith 1999) and in terms of its development in specific Western nations, such as the United States (e.g., Abt, Smith, and Christiansen 1985), Canada
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(e.g., Morton 2003), Australia (e.g., Caldwell et al., 1985), and Great Britain (e.g., Dixon 1991).
POSITIVIST APPROACHES However, by far the greatest attention to gambling as an activity has been concerned with positivist approaches toward the measurement and quantification of gambling activity, and within that approach, has been directed largely toward gambling that is problematic. Although the potentially deviant and/or problematic nature of gambling had long been subject to a range of critical discourses, the notion of “pathological gambling” as a phenomenon in its own right did not appear until the 1980s. At that time, it was introduced into the third edition of the reference manual published by the American Psychiatric Association, the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) (APA 1980), where it was described as an impulse control disorder—a compulsion characterized by an inability to resist overwhelming and irrational drives. Focus soon shifted to its addictive characteristics, and subsequent editions of the manual saw it reclassified in terms similar to those for psychoactive substance dependency, with the term “pathological gambling” consistently used to reflect its chronic, progressive character (APA 1987, 1994). Around the same time, the South Oaks Gambling Screen (SOGS) was developed—again, with loss of control as a defining category, and it became widely used for the measurement of gambling problems throughout populations (Lesieur and Blume 1987). Since then, over 20 screens have been developed for a range of purposes, including screening, diagnosis, population monitoring, and treatment planning. In general, they define “pathological” and “problem” gambling as behavior that is out of control and that has come to disrupt personal, family, social, and vocational life, with the former regarded as a more severe condition than the latter.1 With the development of a system of classification and nomenclature, these evaluations introduced a distinct “type” of individual—a pathological gambler.This individual was assessed against a checklist of formal symptoms which could be measured and compared against a norm, so distinguishing them as different in some way from other players. Commentators on this development have drawn on the theory of Michel Foucault (1976) to analyze the generation of notions of problem and pathological gambling as a distinctive social—or “discursive”—process (Castellani 2000; Collins 1 In diagnostic tests,“pathological” defines the behavior of individuals who score more than five criteria on the DSM, fourth edition (DSM-IV), and “problem” less than five. In reality, however, the terms are often used interchangeably, and it can moreover be difficult to distinguish between them because pathological gamblers will undoubtedly have been problem gamblers at some point, and both types of players can experience fluctuations in the severity of their condition.
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1996; Reith 2007). Utilizing Foucault’s notion of the “constitution of subjects,” whereby new social types are created through the process of classification, these authors note how the process of observation and measurement itself made the phenomenon of problem gambling increasingly visible and consequently “real” to scientific inquiry. It is in this way, they note, that a new area of study—problem gambling—and a new research subject—the problem gambler—came into being in the 1980s. Having noted the theoretical and discursive creation of the research subject, it is also worth pointing out that a variety of more mundane, pragmatic factors were involved in the emergence of the area of gambling studies. Since the 1950s, a handful of individuals had regarded gambling as an illness, among them the founders of Gamblers Anonymous, which was formed at that time, and a few psychiatrists. During the 1970s, insurance for psychiatric treatment expanded rapidly, which encouraged the inclusion of various mental conditions as bona fide illnesses in order to secure insurance payments for treatment. In practical terms, the formal recognition of problem gambling as a mental disorder meant that insurers could be persuaded to pay for treatment, thus increasing access to treatment for sufferers. And of course, for gamblers themselves, recognition of their problem removed some of the stigma associated with their behavior (Volberg 2001). In the 1970s, a branch of Gamblers Anonymous approached the head of an addictions treatment program, Dr. Robert Custer, for help in establishing a treatment program for gamblers. It was information from this program that contributed to the inclusion of pathological gambling in the DSM-III, as well as to the development of the criteria used to measure it (Custer and Milt 1985). So, although it is the case that the topic of “gambling studies” as an epistemological field or body of knowledge is interdependent with the wider culture in which it is embedded, it should also be noted that in many instances, the development of a new topic of inquiry is also frequently subject to more mundane pressures. Quite simply, it is often less the grand march of human knowledge than the exigencies of everyday life that provide the impetus for the development of a new branch of research. The development of the diagnostic evaluations not only helped to define the subject of problem-gambling research, but also introduced new social groups to the issue—medical, legal, academic, and treatment professionals, as well as lay groups and formal organizations. These groups brought their own distinct backgrounds and concerns to the issue and developed the field still further, stimulating debate and demands for research, which in turn encouraged the provision of funding and the creation of more specialist organizations and committees, most of which leaned in the general direction of psychology and medicine. These conditions gave rise to an increase in gambling-related research commissioned by various stakeholders, such as state or local government agencies, nongovernmental organizations (NGOs), problem-gambling and community groups, and academic bodies. In general, such policy-directed research attempted to esti-
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mate the impacts of gambling and the extent of problem gambling, analyze which factors exacerbated problems, and develop strategies to combat them. In recent years, large-scale technical reviews have been conducted on behalf of policymakers and stakeholders, with reports from the United States and Australia collating information from a wide range of sources to provide comprehensive reviews of the nature and extent of gambling and problem-gambling behavior (National Research Council 1999; Productivity Commission 1999). At this point it can be noted that the interests of specific groups—and in particular groups who have access to resources—often play a part in the shaping of emergent research fields and, through the allocation of funding, can influence which areas get the greatest attention. Research goes on in a politically charged climate where stakeholders’ interests may involve competing claims relating to, for example, expanding or limiting gambling opportunities, increasing funds for treatment and/or research, or lobbying for legislative change. Similarly, the results from research can be interpreted selectively according to prior interests, as is the case when, for example, industry-related groups emphasize the lowest estimates of prevalence rates while treatment-related groups for problem gambling emphasize higher ones. In this way, the most powerful stakeholders can (sometimes inadvertently) contribute to the establishment of particular research agendas, define the parameters of debate, and thus influence the creation of a knowledge base. As an example of the shaping of a research climate (although one that has more to do with simple expediency rather than the play of vested interests), we can look to recent developments in the United Kingdom. The passage of the 2005 Gambling Act was intended to regulate the rapidly developing gambling economy. Prior to legislative change, the government’s Department for Culture, Media and Sport was given the task of preparing the groundwork for the new law. Prior to legislative change, the state government’s Department for Culture, Media, and Sport was given the task of preparing the groundwork for the new law. As part of this, it commissioned an independent body—the Gambling Review Body—which recommended the establishment of an independent trust to oversee research, treatment, and prevention programs on problem gambling, and which would be funded by voluntary contributions from the gambling industry. In this way, the charitable, nongovernmental body, The Responsibility in Gambling Trust (RIGT), was created. Until the passage of the act and the creation of RIGT, U.K. policymakers had paid little attention to gambling as a serious object of study, and as a consequence, the area had been consistently underfunded and underresearched. It was in this context that RIGT set out to expand the slender base of gambling research in that country by explicitly advertising the funding that would be made available for it. In order to do this, they proactively targeted known researchers in the field of drug and alcohol addiction, encouraging them to apply their knowledge and skills
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in that area to what was described as a very similar field.2 Naturally, such a policy stimulated interest; but more than this, it gave a very clear indication of what “type” of thing the funders believed problem gambling to be, and what type of research they expected would be best suited to investigate it. In this way, the research agenda for a new field of inquiry was formed out of a convergence of the interests and preconceptions of a range of policymakers, governmental organizations, and NGOs, thus defining the field and outlining the parameters of the future debate. In the United Kingdom in the twenty-first century, as with Custer and the DSM-III in the 1980s, we can see that although economic, social, and cultural changes might create a climate conducive to the study of gambling, it is a combination of pragmatic concerns and expedient networks that provide the impetus for a research agenda to actually form and get off the ground. To sum up the current state of such research, we can note that today, the bulk of the study of gambling is focused on the measurement and quantification of specifically problematic behavior. Generally, when we talk about “gambling,” we are talking about problem gambling. Indeed, gambling itself has come to be defined in terms of problem gambling, with “normal” or recreational gamblers generally referred to as “nonproblem gamblers”—that is, in terms of what they are not. It could be argued that this focus on problematic or deviant aspects in gambling studies is a continuation of the emphasis on the disruptive effects of gambling, rooted in particular philosophical and religious perspectives, as traditionally expressed by the church or other guardians of public morality. In some ways, this line of thought is justifiable:The negative aspects of gambling are still being articulated today, albeit in different ways. However, where a significant break with the past has occurred is in the conception of gambling itself. The critical approaches reviewed earlier did not possess a distinctive notion of “problem” gambling as an activity separate from “recreational” gambling—instead, it viewed all gambling as inherently problematic, both as an activity in its own right and as one that could lead to further vice and disruption. Today, however, it is no longer the case that gambling per se is presented in negative terms. Indeed, after a lengthy history, it is at the precise moment when gambling is moving into the mainstream as a legitimate form of recreation that the scientific vocabulary to quantify its harmful effects is being developed. Now it is regarded as a legitimate form of consumption, albeit one that can, under certain circumstances, become problematic for some individuals. Furthermore, today the type of problem it represents is found in the domain of medicine, and formed within neutral discourses of sickness, risk, and vulnerability rather than pejorative ones of weakness, vice, and dysfunction. Within a broadly positivistic tradition, a range of explanations for the syndrome of problem or pathological gambling have been proposed, each with its own methods of research and its own conception of the subject under investigation, that 2 This is not to overlook the fact that they also addressed gambling researchers in the international community and made clear that they were eager to have input from a wide range of academic disciplines to enrich the field of gambling studies in the United Kingdom.
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is, its own idea of what “type” of thing problem gambling might be.As we shall see over the following pages, such differing conceptions have implications for the development of policy, the regulation of the gambling industry, and the treatment of problem gamblers themselves. What follows is an overview of research domains within this tradition. It should be pointed out, however, that separate presentations are undertaken simply for narrative clarity and should not be taken to imply their total independence from each other, since many domain research agendas, methodologies, and aims overlap. Furthermore, our discussion is of necessity a partial account: There are many contributions to the domain of gambling research, not all of which can be covered here, and many ways of viewing the themes which make it up, of which this outline is only one possibility. Economic and Social Cost–Benefit Analyses Within the general tradition of positivism are various analyses of the economic and social costs and benefits of gambling.These studies involve attempting to quantify, measure, and compare the effects of the activity by balancing what many argue to be its positive impacts with its negative ones, described as “negative externalities.” In the former camp are economic benefits such as commercial profits, increased employment, taxation revenues, and the stimulation of local and national economies. Meanwhile, negative impacts include the degradation of local communities and increased problem gambling, with its attendant costs of crime, bankruptcy, social disruption, and personal hardship. Although many such studies have been undertaken, findings remain generally contested and inconclusive. In many cases, studies use different criteria for measurement, measure different things, utilize different methodologies, and approach the subject from quite different perspectives. For example, not all forms of gambling have the same impacts. While widely dispersed forms of “convenience gambling” such as electronic machines and Internet games create few jobs and have little economic impact on local communities, it has been argued that other, more concentrated types such as casinos and the racing industry do stimulate economic growth by creating jobs and encouraging consumer spending. However, such claims have been contested. Some studies have found that the direct and indirect effects of, for example, casinos tend to be not so much the creation but rather the transfer of wealth away from other businesses and areas, resulting in little or no economic gain overall (Eadington 1984; Grinols and Omorov 1996; National Gambling Impact Study Commission 1999). In addition, approaches to estimating the impacts of gambling can hold different conceptions of their subject, with some conceiving it in terms of social impacts and others in terms of economic ones. For example, “gross impact stud-
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ies” tend to focus solely on calculating the economic benefits of gambling, generally quantifying basic features such as revenues, taxes, and employment gains without consideration of any corresponding costs. Such studies have frequently been funded by, or otherwise associated with, the gambling industry itself, thus attracting criticisms of research bias (Fahrenkopf 1995; see National Research Council 1999 for a review). On the other hand, some studies have attempted to measure the social costs associated with problem gambling, including costs associated with job loss, bankruptcy, divorce, ill health, arrest and incarceration, and increased uptake of unemployment and welfare benefits (Gerstein et al 1999). Studies that estimate social costs have tended to use one of two types of methodology. In one, the effect of a particular form of gambling, such as a casino, is estimated through calculating a number of variables, such as employment and crime rates. In the second, the costs generated by individual problem and pathological gamblers are calculated and combined with estimates of the prevalence of gambling problems in the general population in order to come to a figure of the total cost of gambling-related problems. A number of studies have attempted to assign financial values to the negative externalities associated with gambling in this way (Dickerson et al. 1995; Lesieur 1992; Thompson, Gazel, and Rickman 1996). One of the largest of these was conducted on behalf of the National Gambling Impact Study Commission (NGISC) (Gerstein et al. 1999). By controlling for a variety of sociodemographic factors, the study was able to estimate the financial impacts of problem gambling on individuals and to extrapolate from this economic costs to society as a whole. In this way, the researchers calculated that the annual costs of problem and pathological gambling to the United States totaled about $4 billion (Gerstein et al. 1999). This approach has also highlighted that the bulk of gambling losses come from problem and pathological gamblers, and that this group accounts for around a third of the entire gambling industry’s market (Lesieur 1998; NGISC 1999; Productivity Commission 1999). These kinds of cost-benefit analyses are examples of the application of quantitative methodologies to social phenomena—a process involving the measurement and comparison of numerical variables in order to establish “objective” information. The undertaking is based on an attempt to assign financial values to various aspects of gambling (from the profits enjoyed by industry to the social costs suffered by problem players) and rests on a conception of its subjects (i.e., gamblers and problem gamblers) as socioeconomic units whose behavior as workers, consumers, spouses, and community members is similarly quantifiable. From this premise it aims to come to some sort of conclusion as to the overall “value” of gambling. However, although economic factors such as jobs and profits are tangible and measurable, social and personal ones such as individual, familial, and community well-being are far less so and may simply not be amenable to such types of analysis at all. And so, despite the hope that methodological developments will produce more refined and therefore more accurate and more “true” calculations, the possibility that this kind
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Research and Measurement Issues in Gambling Studies
of approach will rest on an attempt to compare phenomena which are ultimately resistant to such quantitative approaches should always be borne in mind. Indeed, two large-scale investigations into the social and economic impacts of gambling could not reach a decisive conclusion on this.The NGISC stated that an accurate cost-benefit analysis was impossible to calculate, while the report of the Australian Productivity Commission argued that since economic benefits were canceled out by economic transfers, the impact of gambling should be estimated in terms of social factors instead. The implications of cost-benefit accounts are highly significant, for if social costs were conclusively found to outweigh the benefits of gambling, then policymakers would have a responsibility to take steps to limit the spread of commercial gambling and curtail the operation of the industry. However, in a highly charged, politicized climate, the evidence base for such scenarios remains unresolved and hotly contested. Meanwhile, it appears that the daunting task of quantifying social costs presents an ongoing challenge to even the most committed positivist. The remainder of this section now turns to look at research from psychological–medical perspectives that have, since the publication of the DSM-III, dominated the field of problem gambling research. Biomedical Approaches At the most extreme end of medicalized discourses are biomedical approaches to pathological gambling.An early emphasis on its similarities with dependent substance use encouraged a view of problem gambling as a discrete pathological entity, with a material, physiological basis. As victims of a chronic condition, pathological gamblers are regarded as qualitatively different from nonproblem gamblers and subject to a lifelong medical condition. A range of biochemical, genetic, and neurological studies have attempted to explain the material bases of the disorder. For example, neurological studies have utilized magnetic resonance imaging (MRI) to attempt to identify the physiological profiles of gamblers’ brains (Breiter et al. 2001; Potenza et al. 2003), while the relationship of substances such as noradrenalin and serotonin to impulsive disorders and craving has been investigated. In addition, genetic predispositions have been implicated in pathological gambling, especially those that control neurotransmitters responsible for mood and temperament, which are also argued to be implicated in other conditions, such as drug and alcohol addiction (DeCaria, Begaz, and Hollander 1998; Comings 1998). As with discourses of addiction in general, this kind of approach can be considered to be essentialist in that it depicts pathological gamblers as distinct “types” of individuals, whose actions are reducible to primarily physiological processes. Although many researchers emphasize the role of psychological and environmental, as well as biological, factors in contributing to the development of gambling
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problems, in general the focus of such approaches is on internal, material processes within the bodies of individual subjects themselves. Such a perspective challenges ideas about free will and self-determination, since, for these individuals, behavior is often unwilled and determined by biology.These kinds of biomedical approaches have been criticized for their reductive view of human behavior and for an incorrect assumption of causation, whereby physiological factors are regarded as causes rather than simply the observable effects of behavior (Peele 1985). Such biomedical perspectives also have implications for approaches to the treatment of problem gambling. These tend to be founded on pharmacological interventions, such as the prescription of lithium and selective serotonin reuptake inhibitors (SSRIs), and/or on abstinence from all forms of gambling. Because pathological gambling is regarded as a chronic type of disorder, abstinence is frequently presented as the most effective way of preventing relapse. This is also the approach adopted by self-help groups such as Gamblers Anonymous (GA), which subscribes to a disease model of addiction, claiming that “compulsive gambling is an illness, progressive in its nature, which can never be cured, but can be arrested” (Gamblers Anonymous 2007). Support for biomedical models can arise from groups of gamblers themselves—such is the case at least of GA, whose philosophy actually converges with the epistemological foundations of some of the strictest medical accounts, and whose members may adopt the language of medicine to articulate, and in some cases lend authority to, their perceptions of their condition. The notion of pathology involved in these discourses has implications for notions of legal accountability. The loss of both reason and self-control caused by the presence of disease implies an abnegation of responsibility, which means that morally—and sometimes legally—pathological gamblers should not be held responsible for their actions, far less take charge of their future well-being (Rose 1986; Castellani 2000). At the same time, it can be interpreted in ways that would relieve the industry of at least some responsibility for gambling problems: the rationale being that it is not the product—gambling—itself that causes harm, but rather the biological makeup of a small number of unfortunate individuals which predisposes them to vulnerability. Clinical Psychological Research A range of psychological studies, often conducted in clinical settings, have examined the subjective states, motivations and cognitions of gamblers, and focus on the role of impulse, sensation, arousal and conditioning in the development and maintenance of gambling behavior.The fourth edition of the DSM (DSM-IV) classifies problem gambling as an “impulse control disorder,” along with kleptomania and pyromania, referring to an inability to resist impulsive drives and a general loss of control over behavior. It is frequently associated with the characteristics of sensation seeking—the drive for novelty and new experiences—and is related to arousal—the
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Research and Measurement Issues in Gambling Studies
quest for excitement rather than money. In such models, uncontrolled gambling is a result of an individual’s personal inability to resist or overcome particular responses generated by games themselves. Behavior is driven by the quest to seek intense experiences in a spiraling quest for ever more exciting sensations without consideration of the long-term consequences (Blaszczynski, McConaghy, and Frankova 1990; Zuckerman 1979). Behavioral approaches have focused on the processes through which gambling is learned and reinforced, and in particular the ways in which it becomes conditioned through variable rewards, such as heightened levels of arousal and/or winning.These models of operant conditioning argue that an early gambling win provides an extremely influential reward that creates positive associations with the gambling behavior and encourages repeated play (Griffiths 1995; Orford 2001). Such approaches often highlight the similarities between the behavior of animals who can be conditioned to act in certain ways to obtain rewards (e.g., monkeys pressing levers for food) and gamblers, who repeat their behavior in the expectation of favorable results. Indeed, it has been remarked that slot machines present an almost perfect example of such operant conditioning—in other words, that players are simply responding to biopsychological cues that compel them to repeat their actions (Knapp 1997). There is some convergence between these psychological models and biomedical research, with some writers pointing to the biochemical nature of arousal and impulsivity, and some research suggesting an underlying genetic basis for a variety of impulsive-addictive-compulsive behaviors ( Jacobs 1993; Slutske et al. 2000). Again, this approach can be seen as adopting an essentialist epistemology in that its underlying assumptions are based on the idea that there exist qualitative differences between problem and nonproblem gamblers. Therefore, treatment approaches may include pharmacological interventions, as well as forms of therapy and counseling that attempt to alter particular styles of behavior and cognition. However, despite the epistemological foundations of such models, evidence for the validity of these assumptions is often ambivalent or lacking. Somewhat ironically, given the focus of much of this research (with the exception of behaviorism) on gamblers’ subjective states, many studies remain committed to the positivist paradigm of “objective” observation and measurement, an approach which has nevertheless failed to provide clear-cut findings about the internal processes of gambling behavior. The methods adopted include psychometric testing through the application of questionnaires and the use of cardiographic devices to measure physical processes such as heart rates. Some empirical studies have found pathological gamblers scoring higher than controls on sensation-seeking tests, while other studies have found no difference (Blaszczynski et al. 1990; Kuley and Jacobs 1988). Attempts to gauge arousal by measuring the cardiovascular activity of gamblers in laboratory-based experiments have failed to demonstrate raised levels of arousal, while those in genuine gambling environments have done so (Anderson and Brown
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1987). Such findings throw the “ecological validity” of artificial research settings into question, and perhaps also raise issues about the feasibility of testing, measuring, or otherwise “knowing” the subjective states of any human research subject with any degree of accuracy. At the same time that subjective states can suffer from attempts to “capture” them scientifically, the supposed objectivity of the problem-gambling assessment instruments themselves are underlined by a subjectivist bias. Despite their rigorous approach to their subject, the classifications created by the DSM-IV and the SOGS are not value free, but are formed by criteria that are socially relative and subjective. Both instruments diagnose pathological gambling on the basis of individuals’ evaluations of their motivations and moods, including states such as preoccupation, excitement, and loss of control, with the indirect effects of excess gambling inferred from negative states such as guilt, anxiety, and depression that result from disrupted personal, vocational, and financial affairs. Ultimately, then, it is gamblers’ evaluations of their own subjective feelings, rather than the measurement of more verifiable factors, that form the basis for a diagnosis of pathological gambling. In a similar vein, biomedical accounts, with which some clinical psychological perspectives overlap, can also be underlined by cultural assumptions, which turn to biological processes to explain deviation from what are essentially social norms. For example, it has been suggested that an inability to make informed decisions quickly is caused by neurobiological deficits in the prefrontal cortex and that a disregard for future consequences—“myopia”—has roots in neuroanatomical systems (Bechara 2003; Damasio 1995). It is clear that these types of investigations are heavily influenced by notions of what constitutes an “informed” decision, and how people should plan for the future. Cognitive Psychological Research It has been suggested that problem gamblers differ from nonproblem gamblers at the level of the mind: in the way that they think about gambling.This type of research has investigated the assumed irrationality of problem-gambling behavior, as revealed by the possession of a range of cognitive distortions and superstitions which the DSM-IV classifies as “disorders in thinking.” Many gamblers utilize “systems” and hold a range of superstitious beliefs which are used to attempt to control the outcomes of games and frequently justify continued, losing behavior. For example, they might look for patterns and meanings in random events when there are none and see connections in independent events which are then regarded as controllable. Clinicians and researchers have identified a range of such misperceptions and superstitions, including “biased evaluations of outcomes” (Gilovich 1983), notions of “near misses” (Reid 1986), and “illusions of control” (Langer 1975). These describe players’ tendency to overestimate their own influence in games, to
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Research and Measurement Issues in Gambling Studies
believe that losses are attributable to external factors, to hold out unfounded optimism in their chances of winning, and to believe in mystical forces such as “luck” (Gadboury and Ladouceur 1989;Wagenaar 1988).These misperceptions have commonly been identified through the “thinking aloud” method, whereby gamblers articulate their thoughts as they are playing, and these are analyzed and interpreted by the researcher. In general, these types of accounts are based on the assumption that some type of “cognitive deficit,” whether a lack of knowledge about odds and risk or a faulty system of perception, underlies problematic behavior. Implicit in such approaches are notions of “rational economic action,” whereby individuals are assumed to be risk averse and to make informed decisions based on calculations of the benefits and risks of various forms of activity. Gambling is regarded as an activity with negative expected value (i.e., gamblers can expect to lose), which is therefore antithetical to the self-interest of rational consumers. Such a conception of problem gambling assumes the fundamental irrationality of at least the repetitive behavior that drives it, if not of the subjects themselves. The type of treatment approach that flows from this is based on rectifying irrational cognitions through the provision of accurate information about the nature of games of chance and/or various forms of therapy to modify behavior and beliefs. However, it should be pointed out that, again, the assumptions involved in these kinds of approaches rest on particular cultural values and expectations about what constitutes “rational” behavior and that some writers have pointed to other interpretations. These include the possibility that since not all gamblers are motivated purely by winning, their beliefs surrounding how best to play a game should not be regarded as necessarily irrational. Other interpretations have argued that in situations of uncertainty, belief in forces external to the self is a common and frequently functional means of asserting agency and control and, furthermore, of generating enjoyment from risk taking (Lyng 1990; Reith 1999). All the approaches in the positivist tradition examined so far embrace a conception of pathological gamblers as qualitatively different, in various ways, from social or recreational gamblers.Through their observation, measurement, and classification of subjects, often within clinical or laboratory environments, researchers subscribe to a view of pathological gambling as a mental and/or physical disorder. Their focus is on the individual subject as the locus of the problem, whether in terms of their physiological makeup, psychological characteristics, internal states, mental cognitions, or a combination of them all. Such conceptions have implications for treatment and policy. If the focus of gambling problems is the individual player, then the solution to those problems similarly lies within the individual: in various forms of treatment that rectify faulty impulses, thoughts, and even genes.To some extent, such a conception of problem gamblers can be interpreted in ways that shift the policy focus away from the gambling industry: If the roots of “the problem” lie less within the product and more with players themselves, then the issue becomes
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less a case of how to regulate or limit a potentially damaging product, and more a case of how to treat and cure a minority of vulnerable individuals. Epidemiological Research and Public Health Perspectives Recently, views of problem gambling as a discrete pathological entity have been modified by broader epidemiological approaches that examine the prevalence and incidence of gambling problems throughout the population. Contrary to broadly essentialist notions which regard problem gambling as a preexisting predisposition within the individual, such research has encouraged a focus on the interrelation of the individual with a broad range of environmental factors which together contribute to the development of gambling problems of greater or lesser severity.This type of approach expands the conception—as well as the numbers— of problem gamblers to include individuals with less severe and transient problems, with consequent implications for policy and treatment. This approach is based on the information gathered from wide-ranging surveys, which have been carried out increasingly frequently in the past two decades, to estimate the prevalence and incidence of gambling and problem gambling throughout the population. They utilize quantitative methodologies such as telephone surveys and questionnaires, which incorporate variations of such diagnostic screening tools as the DSM-IV and SOGS to produce numerical and statistical forms of knowledge. A variety of national and subnational surveys have been conducted throughout Western nations, mapping patterns of behavior among the general population as well as particular subgroups, such as adolescents, which allows rates and trends to be calculated and international comparisons to be made. Through the collection of vast amounts of demographic data on individuals’ lifestyles, relationships, incomes, and health, as well as their gambling activity, the characteristics of gamblers and problem gamblers can be compared with those of the general population. From this mass of quantifiable data, patterns of behavior, as well as relationships between various factors, can be isolated and identified, and trends in gambling behavior can be seen to emerge. Ultimately, this process of observation and identification of patterns and traits through survey research makes the subject of gambling research increasingly visible and consequently—as is revealed in increasing amounts of statistical data, charts, and tables—increasingly “real.” The information from epidemiological research provides the foundation for public health perspectives on gambling.These adopt the terminology of communicable disease, distinguishing between the agent (exposure to gambling opportunities), the host (the gambler), and the environment (the wider physical and social setting in which gambling goes on) to describe the occurrence of gambling problems throughout the population. It also utilizes statistical analyses to display the associations among various environmental, social, and physical factors, thus revealing the sociodemographic patterns of risk and vulnerability that cut across the pop-
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ulation. These relations are assessed by a continuum of harm and are expressed in terms such as “probable” and “potential” problem gambler. From this, particular features emerge as being more or less significant for the development of gambling problems. For instance, certain social groups, such as males, those under thirty years of age, those on low incomes, and those who are unmarried, are revealed to be especially vulnerable to the development of gambling problems. Situational factors such as easy availability and opportunities and early exposure via family members, as well as structural factors such as the quick, continuous action of electronic machines also put a person at risk for the development of problematic gambling behavior. At the same time, associations of all these factors with other types of problematic behaviors, such as those of mental disorders, substance abuse, and criminality, can also be revealed in what are described as “comorbid” relations. One clear emphasis that has emerged from this kind of research is on the diversity and complexity of gambling and problem gambling. Just as there is no single “type” of gambling, there is no single “type” of gambler. Rather, gamblers constitute a heterogeneous group whose behaviors are influenced by a variety of factors, including the type of game played and the psychological and social characteristics of the players themselves. In the increasingly complex relations that define it, the “nature” of problematic gambling comes to unravel beyond the individual and becomes entangled in the web of relations that tie people to the wider world. Related to this type of research has been a focus on longitudinal, dynamic models that examine the ways that gambling behavior changes over time, and the pathways through gambling behavior as “stages of change” over an individual’s life course. Such studies suggest that “regular” and “problem” players are not necessarily always distinct and separate groups, but rather can be individuals whose behavior exists on a continuum, with recreational playing at one end,“pathology” at the other, and various degrees of problematic behavior in between. It is thought that many gamblers go through cycles of behavior, when they move between extremes of playing over time: from regular to problem playing and back again; in and out of a problematic status in recurring and transient phases. A range of factors, including the influence of social networks and the availability of gambling, have been associated with gambling “pathways,” which in turn impact on individuals in different ways, making them more or less resistant to behavioral change (Abbott, Williams, and Volberg 1999; Blaszczynski and Nower 2002). Similar pathways to problematic behavior among adolescents have been investigated as stages of change (DiClemente, Story, and Murray 2000), a model originally applied to the process of change for addictive behaviors such as smoking, drinking, and drug misuse. Research has also begun to suggest that it is likely that the majority of players who experience problems, especially those of a less severe nature, recover from them on their own, without recourse to formal treatment. Such processes of “natural recovery” have been the subject of research that has not yet identified factors that predict change, but nevertheless suggests that structural factors such as
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changing social relationships and financial problems, as well as experiential factors such as life crises, may act as catalysts for change (Abbott et al. 1999; Hodgins and el-Guebaly 2000; Nathan 2003). Such a perspective challenges the view of problem gambling as a chronic, progressive disorder affecting a minority of players who for various reasons are predisposed to develop the problem. Rather, this perspective focuses attention on the wider environment in which gambling goes on and the actual gambling product itself as sources of harm that have the potential to create problems for far greater numbers of recreational players. Through both types of research, we can see a widening of the research stage: Epidemiological surveys expand it across populations, while longitudinal studies expand it over time. The result is a much broader and more fluid conception of problem gambling as a type of behavior that is integrated with and influenced by a range of factors external to the individual. However, although this focus begins to move away from deterministic models of pathology, it should be remembered that it is still located within a medicalized framework. Although stages-of-change models provide dynamic accounts of human behavior, they are borrowed from the conceptual field of “addiction research,” which rests on a clear-cut medical and normative distinction between behavior that is a sign of “sickness” and behavior that is indicative of being “well.” These differing conceptions of the nature of problem gambling have very different implications for treatment modalities.The view that problem gambling is part of a continuum of behavior and that many gamblers recover on their own or suffer from less severe problems at regular intervals in their lives supports a philosophy that is less restrictive and more responsive to the requirements of large numbers of the population than are models based on the goal of abstinence for a sick minority. Primarily throughout Australasia, parts of Europe, and Canada, public health approaches tend to be committed to the twin ideals of problem prevention and harm reduction, which are based on providing information and skills to allow informed choice and responsible play. Such a philosophy underlies strategies to raise awareness and inform players about the potential risks of gambling and the best ways to play without encountering harm.These include educational programs about the characteristics and potential hazards of games and the dissemination of information on counseling and self-exclusion programs. To a large extent, these preventative measures are based on the assumption that decisions about whether and how much to gamble should be largely left to the individual, and also that informed choice will result in rational, and therefore responsible, behavior. The tropes of “responsible gambling” found in these models reflect a continued focus on the individual as the site of gambling problems, as well as their resolution.The emphasis is on players’ responsibility to arm themselves with information, to regulate their behavior, to make appropriate decisions, and to limit how long and how much they play.At the same time, the industry is exhorted to behave
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Research and Measurement Issues in Gambling Studies
responsibly toward its customers, by, for example, discouraging excessive play by vulnerable people who may lose more than they can afford and providing realistic estimates of the chances of losing. Most sectors of the industry pay at least lip service to such standards of “social responsibility.”
CONCLUSIONS: CURRENT TRENDS AND FUTURE DIRECTIONS It seems somewhat premature, at this point in what is only the first chapter in the collection, to begin to sum up the state of gambling research and attempt to predict future directions of study. In large part, that is part of the challenge for the readers: having read the book, to decide for themselves. However, some initial comments can nevertheless be made here. We have seen that two research domains exist—interpretivistic and positivistic—each with a different approach to its subject and different ways of studying it, the latter of which has long been dominant in the field. Cutting across these domains are very different conceptions of what gambling and problem gambling actually are, although in the main, these have been defined within a medical-psychological framework in terms of physical and/or mental disorder. In turn, such conceptions have implications for treatment and policy. So, for example, notions of individual pathology lie behind therapeutic and pharmacological treatments for relatively small numbers of individuals, while ideas about the more fluid nature of gambling problems stimulate policies and interventions to protect larger numbers of “regular” players from harm with ongoing programs of prevention and harm reduction. It is worth noting here that one commonality found in the varying conceptions of gambling and problem gambling discussed here is an ongoing discussion around the idea of responsibility. This emphasis on “responsibility”—whether in terms of the individual player or the gambling provider—rather than on, say, state regulation dovetails with wider ideologies of neo-liberalism, with its emphasis on individual freedom and choice. As the state increasingly moves away from the regulation of gambling, as laws are liberalized and the gambling industry is allowed greater freedoms, another paradigm shift seems to be coming into being. Rather than restrictive legislation, now the onus is on both individual and corporate responsibility. Gamblers are considered rational, sovereign consumers and the gambling industry a legitimate, mainstream leisure provider, and the interests of both are assumed to come together in responsible self-regulation. Debates around responsibility illuminate some of the wider issues that surround different conceptions of the nature of gambling problems, such as legal culpability, industry regulation, and the role of government, and in this debate, the stakes are high. Recently, for example, the threat of gambling to public health has been compared to that posed by alcohol or tobacco, with the director of a U.S. problem-
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gambling council warning, “The [U.S. Problem Gambling] report will act like the Surgeon General’s 1964 report on smoking and health—a wake-up call for America on the dangers of gambling.This report makes it very clear that gambling is not just another form of recreation—it is a very addictive and destructive activity. In short, gambling is the new tobacco” (Grey, in Kindt 2001). Lawsuits against gambling companies for misleading consumers on the risks of their products, akin to the class actions against tobacco, may be the result of this approach. No doubt the industry will be closely following the recent French case in which a gambler sued a casino for allowing him to lose over four million Euros in eight years. While the player claimed that the casino had a duty to provide him “information, advice, and loyalty,” the casino argued that “the idea of gambling is that one runs the risk of losing” (The Guardian, November 15, 2005, p. 25). Whichever side has the stronger case could set the parameters for legal notions of responsibility for years to come. In recent years, advances in research have substantially increased understanding of the nature of gambling and its problematic forms. In particular, the kind of information that has been produced by epidemiological and longitudinal research and formulated around policies of public health is starting to show us that gambling and problem gambling are multicausal and heterogeneous. Behaviors are fluid and shifting, encompassing different types of players who play in a variety of contexts and possess a range of motives. If there are commonalities, they appear to lie in an understanding that “gambling” is not a discrete entity but is rather part of a complex web of human behavior and that problem gamblers do not play in a vacuum, but act out behavior that is embedded in wider socioeconomic contexts. Although advances in understanding have been made, we need to acknowledge the limitations that still exist in the ways that gambling and problem gambling are studied and in the research methodologies used to examine them. Much gambling research is committed to the positivist tradition of “objective” observation, measurement, and classification of the subject by the researcher, is wedded to a medical-psychological epistemology of mental and/or physiological disorder, and is circumscribed by a highly individualistic focus. Although this tradition has informed knowledge, it provides a very particular kind of knowledge, and a very particular “way of seeing,” sometimes to the detriment of other perspectives. It is in this context that we need to bear in mind the value-laden nature of even supposedly objective measurement and to recognize that simply quantifying the subject is not always enough. We need more of the kinds of information that numbers alone cannot give us; to go beyond counting and begin to look at processes, meanings, and social contexts. At present, there is surprisingly little overlap between research into “regular,” or recreational, gambling and studies of problematic gambling, with the two tending toward separate foci of study and methods of research. Greater dialogue between problem-gambling research and investigations into normal/recreational gambling is badly needed, especially if, as some have argued, problem gambling is
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a stage within wider processes of more general gambling behavior. It is likely that many of the same individuals are, have been, or will be both problem and recreational players. Similarly, gambling researchers need to be aware of the close relationship between the two realms of study and attempt to build bridges between them in order to generate an understanding of the “big picture” of gambling as a whole. Fruitful avenues of future research point to the continued investigation of the motivations, attitudes, and beliefs of players and the social contexts of their behavior; the routes and pathways through which individuals move into gambling and problem gambling and perhaps out of them again; and a focus on which factors are associated with increased susceptibility to developing problems and which with resisting or overcoming them. All of this involves a commitment to longitudinal research and an investment in qualitative studies in order to build up a detailed picture of the meanings of gambling and the motivations of gamblers as dynamic phenomena embedded in socioeconomic contexts and subject to change over time. These are challenging areas for the future of the discipline, but, looking back at how much has been achieved in only the past 20 years, there is every reason to be optimistic for the continued progress of gambling studies in the future.
GLOSSARY Discourse forms of language and expression that construct and articulate particular worldviews and that are rooted in relations of power. Epistemology theories of knowledge and ways of knowing the world. Interpretivism a research approach that looks for culturally derived and historically situated interpretations of the social world, recognizing the role that the human creation of meaning has on this process. Positivism a research approach that rests on the assumption that the methods of the social sciences should be based on those of natural science and which thus utilizes (supposedly) value-free methods to observe and measure phenomena, whose relations can be expressed as universal laws and theories. Qualitative a research methodology that rests on the observation and interpretation of the subject by the researcher. Quantitative a research methodology that results in data being expressed in numerical form.
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REFERENCES Abbott, M. W., Williams, M., and Volberg, R. A. (1999). Seven Years On: A Follow-up Study of Frequent and Problem Gamblers Living in the Community.Wellington, NZ: Department of Internal Affairs. Abt, V., Smith, J. F., and Christiansen, E. M. (1985). The Business of Risk: Commercial Gambling in Mainstream America. Lawrence: University of Kansas Press. American Psychiatric Association. (1980). Diagnostic and Statistical Manual of Mental Disorders, third ed. Washington, DC: Author. —— . (1987). Diagnostic and Statistical Manual of Mental Disorders, 3rd ed., revised. Washington, DC: Author. —— . (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington, DC: Author. Anderson, G., and Brown, R. I. F. (1987). Some applications of reversal theory to the explanation of gambling and gambling addictions. Journal of Gambling Behavior, 3, 179–189. Bechara,A. (2003). Risky business: Emotion, decision making and addiction. Journal of Gambling Studies, 19, 23–51. Bergler, E. (1970). The Psychology of Gambling. New York: International Universities Press. Blaszczynski, A., McConaghy, N., and Frankova, A. (1990). Boredom proneness in pathological gambling. Psychological Reports, 67, 35–42. Blaszczynski,A., and Nower, L. (2002).A pathways model of gambling and problem gambling. Addiction, 97, 487–499. Bloch, H. (1951).The sociology of gambling. American Journal of Sociology, 57, 215–221. Bourdieu, P. (1984/1979). Distinction: A Social Critique of the Judgement of Taste. London: Routledge. Breiter, H., Aharon, I., Kahneman, D., Dale, A., and Shizgal, P. (2001). Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron, 30, 619–639. Brenner, R., and Brenner, G. (1990). Gambling and Speculation: A Theory, a History and a Future of Some Human Decisions. Cambridge: Cambridge University Press. Bruce, A. C., and Johnson, J. E.V. (1992). Toward an explanation of betting as a leisure pursuit. Leisure Studies, 11, 201–218. Caldwell, G., Haig, B., Dickerson, M., and Sylvan, L. (eds). (1985). Gambling in Australia. Sydney: Croom Helm. Cassidy, R. (2002). The Sport of Kings: Kinship, Class and Thoroughbred Breeding in Newmarket. Cambridge, UK: Cambridge University Press. Castellani, B. (2000). Pathological Gambling:The Making of a Medical Problem. Albany: State University of New York Press. Chinn, C. (1991). Better Betting with a Decent Feller: Bookmakers, Betting and the British Working Class, 1750–1990. Hemel Hempstead, Herts, UK: Harvester Wheatsheaf. Clotfelter, C.T., and Cook, O. J. (1989). Selling Hope: State Lotteries in America. Cambridge, MA: Harvard University Press. Collins, A. F. (1996). The pathological gambler and the government of gambling. History of the Human Sciences, 9, 69–100. Comings, D. E. (1998).The molecular genetics of pathological gambling. CNS Spectrums, 3, 20–37. Custer, R., and Milt, H. (1985). When Luck Runs Out: Help for Compulsive Gamblers and Their Families. New York: Facts on File Publications. Damasio, A. (1995). On some functions of the human prefrontal cortex. Annals of the New York Academy of Sciences, 769, 241–251. DeCaria, C. M., Begaz,T., and Hollander, E. (1998). Serotonergic and noradrenergic function in pathological gambling. CNS Spectrums, 3, 38–47. Devereaux, E. (1949). Gambling and the social structure. Unpublished PhD thesis, Harvard University.
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Dickerson, M., Allcock, C., Blaszczynski, A., Nicholls, B., Williams, J., and Maddern, R. (1995). An Examination of the Socio-economic Effects of Gambling on Individuals, Families and the Community. Sydney: Australian Institute for Gambling Research. DiClemente, C., Story, M., and Murray, K. (2000). On a roll:The process of initiation and cessation of problem gambling among adolescents. Journal of Gambling Studies, 16, 289–313. Dixey, R. (1996). Bingo in Britain:An analysis of leisure and class. In Gambling Cultures: Studies in History and Interpretation (J. McMillen, ed.). London: Routledge. Dixon, D. (1991). From Prohibition to Regulation: Bookmaking, Anti-Gambling and the Law. Oxford: Clarendon Press. Downes, D., Davies, B., David, M., and Stone, P. (1976). Gambling,Work and Leisure:A Study Across Three Areas. London: Routledge and Kegan Paul. Eadington, W. (1984). The casino gaming industry. Annals of the American Academy of Political and Social Science, 474, 23–35. Fahrenkopf, F. (1995). Hearing on the Gambling Impact Study Commission Committee on Governmental Affairs. U.S. Senate.Washington DC: U.S. Government Printing Office. Falk, P., and Maenpaa, P. (1999). Hitting the Jackpot: Lives of Lottery Millionaires. Oxford: Berg. Fisher, S. (1993). The pull of the fruit machine: A sociological typology of young players. Sociological Review, 41, 446–474. Foucault, M. (1976). The History of Sexuality, vol. 1 (R. Hurley, trans.). Harmondsworth: Penguin. Freud, S. (1928). Dostoevsky and parricide. In Collected Papers, vol. 5 (J. Strachey, ed.). London: Hogarth Press. Gadboury, A., and Ladouceur, R. (1989). Erroneous perceptions and gambling. Journal of Social Behavior and Personality, 4, 411–420. Gamblers Anonymous. (2007). What is compulsive gambling? “Q & A” page, website, www.gamblersanonymous.org Geertz, C. (1975).The Interpretation of Cultures. London: Hutchinson. Gerstein, D., Hoffmann, J., Larison, C., Engelman, L., Murphy, S., Palmer, A., Chuchro, L., Toce, M., Johnson, R., Buie, T., and Hill, M. A. (1999). Gambling Impact and Behavior Study: Report to the National Gambling Impact Study Commission. Chicago: National Opinion Research Center. Gilovich, T. (1983). Biased evaluation and persistence in gambling. Journal of Personality and Social Psychology, 44, 1110–1126. Goffman, E. (1969). Where the Action Is:Three Essays. London: Allen Lane. Goodale, J. (1987). Gambling is hard work: Card playing in Tiwi society. Oceania, 58, 6–21. Greenson, R. (1974). On gambling. In The Psychology of Gambling (J. Halliday and P. Fuller, eds.). London: Allen Lane. Griffiths, M. (1995). Adolescent Gambling. London: Routledge. Grinols, E., and Omorov, J. (1996). Development or dreamfield delusions? Assessing casino gambling’s costs and benefits. Journal of Law and Commerce, 16, 49–87. Hayano, D. (1982). Poker Faces:The Life and Work of Professional Card Players. Berkeley and Los Angeles: University of California Press. Herman, R. (ed.). (1967). Gambling. London: Harper and Row. Hodgins, D. C., and el-Guebaly, N. (2000). Natural and treatment-assisted recovery from gambling problems: A comparison of resolved and active gamblers. Addiction, 95, 777–789. Jacobs, D. (1993). Evidence supporting a general theory of addiction. In Gambling Behavior and Problem Gambling (W. Eadington and J. Cornelius, eds.). Reno, NV: Institute for the Study of Gambling and Commercial Gaming. Kindt, J. (2001). The costs of addicted gamblers: Should the states initiate mega-lawsuits similar to the tobacco cases? Managerial and Decision Economics, 22, 17–63. Knapp,T. (1997). Behaviorism and public policy: B. F. Skinner’s views on gambling. Behavior and Social Issues, 7, 129–139.
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Kuley, N., and Jacobs, D. (1988). The relationship between dissociative-like experiences and sensation seeking among social and problem gamblers. Journal of Gambling Behavior, 4 197–207. Langer, E. (1975).The illusion of control. Journal of Personality and Social Psychology, 32, 311–328. Leiseur, H. (1984). The Chase: Career of the Compulsive Gambler. Cambridge, MA: Schenkman Books. —— . (1992). Compulsive gambling. Society, 29, 43–50. —— . (1998). Costs and treatment of pathological gambling. Annals of the American Academy of Political and Social Science, 556, 153–171. Leiseur, H., and Blume, S. (1987).The South Oaks Gambling Screen (SOGS):A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188. Lindner, R. (1976).The psychodynamics of gambling. In The Psychology of Gambling ( J. Halliday and P. Fuller, eds.). London: Allen Lane. Lyng, S. (1990). Edgework: A social psychological analysis of voluntary risk taking. American Journal of Sociology, 95, 851–886. Malaby, T. (2003). Gambling Life: Dealing in Contingency in a Greek City. Urbana: University of Illinois Press. McMillen, J. (ed.). (1996). Gambling Cultures: Studies in History and Interpretation. London: Routledge. Morton, S. (2003). At Odds: Gambling and Canadians, 1919–1969. University of Toronto Press. Nathan, P. (2003). The role of natural recovery in alcoholism and pathological gambling. Journal of Gambling Studies, 19, 279–286. National Gambling Impact Study Commission. (1999). Final Report.Washington DC: U.S. Government Printing Office. National Research Council. (1999). Pathological Gambling: A Critical Review.Washington, DC: National Academy Press. Neal, M. (1998). “You lucky punters!” A study of gambling in betting shops. Sociology, 32, 581–600. Newman, O. (1972). Gambling: Hazard and Reward. London: Athlone Press. Orford, J. (2001). Excessive Appetites: A Psychological View of Addictions. Chichester, UK:Wiley. Peele, S. (1985). The Meaning of Addiction. San Francisco: Jossey-Bass. Potenza, M., Steinberg, M., Skudlarsky, P., Fulbright, R., Lacadie, C.,Wilbur, C., Rounsaville, B., Gore, J., and Wexler, B. (2003). Gambling urges in pathological gambling:A functional magnetic resonance imaging study. Archives of General Psychiatry, 60, 828–836. Productivity Commission. (1999). Australia’s Gambling Industries. Canberra. Reid, R. (1986).The psychology of the near miss. Journal of Gambling Behavior, 2, 32–39. Reith, G. (1999). The Age of Chance: Gambling in Western Culture. London: Routledge. —— . (2007, in press.). Gambling and the contradictions of consumption: A geneaology of the “pathological” subject. American Behavioral Scientist. Rose, I. N. (1986). Gambling and the Law. Hollywood, CA: Gambling Times/Lyle Stuart. Rosecrance, J. (1985). The Degenerates of Lake Tahoe:A Study in Persistence in the Social World of Horse Race Gambling. New York: Peter Lang. Sexton, L. (1987).The social construction of card playing among the Daulo. Oceania, 58, 38–46. Scott, M. (1968). The Racing Game. Chicago: Aldine. Slutske,W. S., Eisen, S.,True,W. R., Lyons, M. J., Goldberg, J., and Tsuang, M. (2000). Common genetic vulnerability for pathological gambling and alcohol dependence in men, Archives of General Psychiatry, 57, 666–673. Thompson,W., Gazel, R., and Rickman, D. (1996).The social costs of gambling in Wisconsin. Wisconsin Policy Research Institute Report, 9, 1–44. Volberg, R. A. (2001). When the Chips Are Down: Problem Gambling in America. New York:The Century Foundation Press. Wagenaar,W. (1988). Paradoxes of Gambling Behaviour. London: Lawrence Erlbaum. Zimmer, L. (1987). Playing at being men. Oceania, 58, 22–37. Zuckerman, M. (1979). Sensation Seeking: Beyond the Optimal Level of Arousal. Hillsdale, NJ: Erlbaum.
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PART II
Measurement Issues
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CHAPTER 2
Population Surveys Rachel A.Volberg Gemini Research, Ltd. Northampton, Massachusetts
Introduction Purposes of Population Surveys in Gambling Studies Problem-Gambling Prevalence and Comorbidity Sampling Issues in Population Surveys Sample Size Sampling Frame Sampling Modality Multimodal Sampling Response Rates Weighting Population Survey Samples Constraints and Choices in Population Research
INTRODUCTION Population surveys of gambling participation and problem-gambling prevalence play an important role in monitoring the impacts of legal gambling. In this chapter, we consider some of the critical decisions that researchers must make in planning population research on gambling and problem gambling. Some of these decisions relate to sample size, sampling frame, and sampling modality. Other decisions relate to the challenges of achieving an acceptable response rate, one important measure of the representativeness of the sample, and of properly weighting the results of the survey to yield unbiased information about gambling and problem gambling in the population. Ultimately, population surveys are always constrained 33
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by the available resources, and it is up to the researcher to decide how to most effectively deploy those resources. The material presented here is intended to assist researchers in making these decisions and in understanding possible choices.
PURPOSES OF POPULATION SURVEYS IN GAMBLING STUDIES In the 1980s, following rapid expansion in the availability of legal gambling in North America, state and provincial governments began to establish services for individuals with gambling problems.Almost immediately, questions arose about the number of problem gamblers in the general population who might seek help for their gambling difficulties. These questions required population research to identify the number (or “cases”) of problem gamblers in a jurisdiction, ascertain the demographic characteristics of these individuals, and determine the likelihood that they would utilize treatment services if these became available. In the 1990s, population surveys of gambling and problem gambling became an essential component in the monitoring of legal gambling in many countries (Abbott and Volberg 1999; Abbott,Volberg, Bellringer, et al. 2004). Population surveys of gambling participation and problem-gambling prevalence play an important role in monitoring the impacts of legal gambling. From a policy and planning perspective, population surveys are useful in tracking changes in attitudes toward gambling and gambling participation over time. Population surveys can also be helpful in assessing the proportion of gambling revenues derived from problem gamblers, an important factor in the rational calculus of public gambling policy (Volberg, Gerstein, et al. 2001). From a public health perspective, population surveys are valuable for identifying which sectors of the population contain the highest concentrations of problem gamblers and are thus important in the development and refinement of problem-gambling prevention and treatment services. By comparing problemgambler profiles from population surveys with client records and records of calls to helplines, it is possible to ascertain how well services are reaching those most in need and to introduce measures to enhance outreach. From a basic research perspective, population research is important in identifying risk factors that problem and pathological gambling have in common with other disorders, as well as those specific to the disorder. Population research is needed to improve our understanding of the relative contribution of different risk factors in the development of problem and pathological gambling. Finally, population research is needed to improve our understanding of the impact that prevention and treatment efforts may have on different types of problem gamblers.
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PROBLEM-GAMBLING PREVALENCE
AND
COMORBIDITY
Some forms of gambling have a particularly strong association with problem gambling, most notably those that are continuous in nature and involve an element of skill or perceived skill (e.g., electronic gambling machines, casino table games) (Abbott,Volberg, Bellringer, et al. 2004). Population surveys in a number of countries have found that people with preferences for, frequent involvement in, and substantial expenditures on these forms of gambling have a high probability of being problem gamblers. For example, while it is generally estimated that between 2% and 5% of the adult population are problem or pathological gamblers in jurisdictions with “mature” gambling markets, prevalence rates among regular machine players and track bettors can be as high as 25% (Abbott and Volberg 2000; Gerstein et al. 1999; Productivity Commission 1999; Schrans, Schellinck, and Walsh 2000; Smith and Wynne 2004). Early adult population surveys conducted in the United States, Canada, Australia, Spain, and New Zealand found that male gender, age under 30 years, low income, and single marital status were almost universally risk factors for problem gambling. Low occupational status, less formal education, non-Caucasian ethnicity, and residence in a large city were additional risk factors in a number of studies.Youth surveys in North America found that people in their mid- to late teenage years had higher prevalence rates than adults (Abbott,Volberg, Bellringer, et al. 2004). In recent years, some jurisdictions have seen a marked increase in the proportion of women problem gamblers, while in other jurisdictions the proportion of men has expanded. In some jurisdictions where tribal casinos have become operational, there have also been increases in the proportion of problem gamblers who are non-Caucasian. From these studies, it appears that change in the availability of particular types of gambling is instrumental in altering the sociodemographic characteristics of problem gamblers (Volberg 2004). While research generally supports the notion that problem-gambling prevalence is associated with greater exposure to high-risk gambling activities, there are some groups in the population with interesting “bimodal”gambling patterns. In comparison with other groups, they contain large proportions of people who do not gamble or gamble infrequently, as well as moderate to large proportions of frequent, high-spending gamblers. Groups in this category include some ethnic minorities and recent immigrant groups (e.g.,African Americans in the United States, Pacific Islanders in New Zealand, and eastern European immigrants in Sweden). These appear to be sectors of the population in the early stages of introduction to high-risk forms of gambling, and some of these groups have exceedingly high levels of problem gambling (Abbott 2001;Abbott,Volberg, Bellringer, et al. 2004). Like others with addictive disorders, pathological gamblers have much higher rates of co-occurring psychiatric conditions and substance abuse than are found in
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the general population.The two most recent national surveys in the United States found rates of alcohol and substance dependence among problem and pathological gamblers in the general population that were approximately ten times higher than among low-risk gamblers and nongamblers (Gerstein et al. 1999;Welte et al. 2001). There is also evidence that mood disorders, primarily major depression and anxiety, frequently co-occur with problem and pathological gambling (Gerstein et al. 1999; Specker et al. 1996). The co-occurrence of pathological gambling and attention deficit/hyperactivity disorder (ADHD) parallels research reporting ADHD in people with other addictions (Ozga and Brown 2000; Rounsaville et al. 1991; Rugle and Melamed 1993). Finally, high rates of comorbidity between pathological gambling and alcohol, nicotine and drug-use disorders, mood disorders, anxiety disorders, and personality disorders were identified in the recent National Epidemiologic Survey of Alcohol and Related Conditions (Petry, Stinson, and Grant 2005).
SAMPLING ISSUES IN POPULATION SURVEYS In the most general terms, the aim of a sampling design is to recruit a representative set of the eligible population into the study. A representative sample is necessary to enable the findings of a study to be generalized beyond just the people who are included in the study.There are many decisions that researchers must make in planning a population survey. Some of the most important questions relate to sample size,sampling frame, and sampling modality.
SAMPLE SIZE With regard to sample size, the low base rate of problem gambling in the general population poses a particular challenge. Sample sizes in population surveys of gambling and problem gambling have typically been too small to detect differences between subgroups in the population that are at highest risk for gambling problems. Given small sample sizes, the margins of error associated with problemgambling prevalence estimates tend to be quite large. In the case of many subgroups within these studies, error terms may be so large that little confidence can be placed in findings pertaining to them. An important consideration in deciding on the number of completed cases required for a survey is the ability to detect differences in prevalence rates between groups.While it is generally desirable for sample sizes to be as large as possible, statistical power calculations can be helpful in determining whether a simple random sampling strategy is adequate or a more complex sampling strategy, which trades overall power for adequate representation of specific subgroups, is needed. Statistical power is the probability of rejecting the null hypothesis that the
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prevalence rates of two groups are the same. An important point is that the appropriate size of samples for subgroups is substantially affected by differences in prevalence rates for different groups. It is easier to establish whether differences in prevalence rates are statistically significant if these rates differ substantially. Even if the final sample includes fewer respondents from specific subgroups than desired, it is still possible to assess the significance of differences in prevalence rates, albeit with a lower degree of statistical confidence (e.g., 90% rather than 95%). There are numerous texts that provide assessments of statistical power (e.g., Cohen 1988), as well as publicly available computer programs. In practice, it is helpful to consult with a sampling statistician who can examine scenarios for difference tests by subgroups of particular interest and determine the most efficient sample size for a survey—that is, a sample size that permits the most testing without greatly exceeding thresholds of statistical adequacy.
SAMPLING FRAME Fundamentally, there are two methods for sampling from populations: probability and nonprobability. Probability sampling is any method that uses some form of random selection—a process that ensures that each member of the population of interest has an equal probability of being chosen. Nonprobability sampling does not involve random selection, which means that the probability that the population is well represented cannot be calculated and that confidence intervals for statistical tests on the resulting data are difficult to estimate. In general, researchers prefer probabilistic or random sampling methods. However, there may be circumstances where random sampling methods are not feasible, practical, or theoretically sensible (Trochim 2000). Where the objective of the study is to be able to generalize the results to the general population, the best approach is to utilize random sampling. Random sampling is generally done by assigning a random number to each member of the population and then selecting the requisite number of respondents or by systematically selecting every nth member of the population. Use of random sampling makes it reasonable to generalize the results from the sample to the population. However, random sampling may not provide adequate representation when certain small subgroups in the population—such as ethnic minorities or problem gamblers—are of particular interest. Gambling researchers have used a variety of alternate sampling approaches to overcome the challenge of low base rates of problem gambling in the population. These approaches include stratified sampling and quota sampling. Stratified random sampling involves dividing the population into homogeneous subgroups and taking a random sample from within each.The use of stratified random sampling means that there is adequate representation of both the overall population and key subgroups within the population.As long as the groups
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that are sampled are homogeneous, stratified random sampling will generally have more statistical precision than simple random sampling. Finally, stratified random sampling means that the study will include enough cases from each subgroup to allow for meaningful subgroup inferences (Trochim 2000). Stratified sampling is appropriate in situations where the population is heterogeneous, with subgroups that vary considerably in behaviors of interest (e.g., gambling participation or problem-gambling prevalence). Proportional allocation is generally used to determine the size of the sample in each stratum based on the relative size of the strata in the population.When stratified sampling is employed, poststratification weighting can be used to adjust the achieved sample to reflect the population as a whole. Stratified sampling has been used in a number of studies of gambling and problem gambling, including prevalence surveys in Australia, Nevada, and North Dakota and in a study funded by the National Institute of Drug Abuse (NIDA) one of prevalence and predictors of pathological gambling (CunninghamWilliams et al. 2005; Productivity Commission 1999; Volberg 2001, 2002). Stratified sampling was also used in the patron survey component of the National Gambling Impact and Behavior Study in the United States (Gerstein et al. 1999). Another approach that is sometimes used in population surveys of gambling is quota sampling. In quota sampling, the population is segmented into mutually exclusive subgroups, as for stratified sampling. However, quota sampling does not involve random selection procedures within strata. Instead, researchers use their judgment to establish ahead of time the number of respondents they wish to interview within each stratum. Once the desired number of interviews is achieved, interviewers stop trying to recruit individuals in that stratum. The use of quotas means that the sample can no longer be considered representative of the population, since the probability of selection differs for individuals or households with different characteristics. In the case of quota sampling, poststratification weighting can be much more problematic and may not fully adjust for the differential selection probabilities associated with the original sample. It is worth noting the use of one other sampling strategy in population surveys of gambling and problem gambling: convenience sampling. Selection of respondents in this approach is based on their availability and willingness to participate in the study. Examples of convenience samples in the gambling studies field include college students in introductory psychology courses, clients in a clinical practice, and people who respond to an invitation from the researcher for volunteers (Blackman, Simone, and Thoms 1989; Hodgins and el-Guebaly 2000; May et al. 2003). The problem with all of these types of samples is that there is no evidence that the participants are representative of the populations to which the research is interested in generalizing (Trochim 2000). Convenience sampling trades ease of recruiting for representativeness of the sample. However, convenience sampling can be a good choice in situations where researchers are conducting exploratory research rather than attempting to represent an entire population.
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SAMPLING MODALITY The most expensive sampling method is area probability sampling. Area probability sampling methods were developed to permit random sampling of a population distributed across a large geographic area. Area probability sampling methods are not a concern in postal or telephone surveys, where geographic distances have little impact on the cost of recruiting participants. Area probability studies typically involve in-person interviews, with respondents residing at randomly selected residences in a geographically representative selection of neighborhoods. A variety of approaches can be used to collect data in face-to-face interviews, including computer-aided personal interviews (CAPI), paper-and-pencil interviewer-completed questionnaires, and self-completion questionnaires that the respondent fills out in the interviewer’s presence or on his/her own. Area probability sampling is generally employed in situations where it is necessary to administer lengthy, complicated questionnaires or where no other adequate means of sampling the target population exists. While area probability surveys are generally superior to telephone surveys in terms of coverage of subgroups in the population and higher response rates, such studies are costly, generally requiring at least five times the budget per completed case. Only a few area probability surveys have been completed in the gambling studies field (Cunningham-Williams et al. 1998; Orford et al. 2003;Volberg and Vales 2002). The most widely used approach to obtaining a random sample of the population is a random-digit-dial (RDD) telephone survey. Although RDD sampling can achieve cost-effective probability samples of households with telephones, households without telephones will not be covered. Coverage rates also tend to be lower for rural areas, large households, households with unemployed persons, households with young heads, African Americans, Hispanics, single persons, and persons with low income (Groves et al. 1988). However, given the constraints on time and resources generally available for population surveys of gambling and problem gambling, telephone sampling designs are the approach most commonly used in surveys of gambling and problem gambling worldwide. Telephone surveys typically use the household as the unit of analysis. The Scandinavian countries are an exception to this rule; because of the availability of official registers in Sweden and Norway, population surveys of gambling in these countries are able to use individuals as the unit of analysis (Lund and Nordlund 2003;Volberg, Abbott, et al. 2001). An emerging challenge to conducting population surveys by telephone relates to the growing number of cell-phone–only households in developed countries. As of 2004, approximately 6% of U.S. households were not covered by RDD sampling methods because they did not have landline services (Tucker et al. 2004). Individuals with mobile telephone service are generally considered outside the
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scope of such studies unless they also have access to landline telephone service.The primary challenge of mobile phone usage to survey sampling is that mobile phones tend to be associated with individual persons, while landline telephones tend to be associated with households.This means that samples based on differential access to telephone service in fact represent different units of analysis. Another difficulty in considering sampling frames based on mobile phone versus landline telephone usage relates to multiple opportunities for recruitment among individuals with access to both types of service. To date, only one population survey of gambling and problem gambling has included a sample of mobile phone users (Kavli and Berntsen 2005). Another significant recent development in the telecommunications industry relates to telephone number portability.While wireless-to-wireless and wireless-towireline provisions do not impact RDD sampling strategies, wireline-to-wireless does because it creates the possibility of inadvertently including wireless telephone numbers in the RDD sample. Broadly, evidence suggests that the results of surveys conducted by telephone compared with self-completion questionnaires (whether completed in the presence of an interviewer or mailed in later) can sometimes be incompatible. Responses to self-completion surveys tend to be skewed toward those who have a particularly strong opinion on the subject of the survey. Another challenge posed by self-completion of questionnaires is that respondents can view all of the questions as well as all of the response options prior to completing the questionnaire and may change how they answer specific items based on this information. Most significantly, the quality of data from self-completion surveys is often poor, with questions left blank and with indecipherable or “out-of-range” responses as well as limited responses to open-ended questions. Conversely, there are some advantages to self-administration (whether by postal questionnaire or using computerized aids in face-to-face interviews), particularly in surveys of sensitive behaviors.The most significant advantage is improved validity, since self-administered surveys consistently elicit higher and more accurate reports of socially sensitive behavior than do other methods (Aquilino 1997; Fendrich et al. 1999; Tourangeau and Smith 1996; van der Heijden et al. 2000). Other advantages of self-administration include the ability for respondents to proceed at their own pace, elimination of variability due to inter-interviewer administration, and minimization of interaction effects between the interviewer and the respondent (Fendrich et al. 1999; Johnson et al. 2000). Given that so many of the population surveys of gambling and problem gambling have been conducted by telephone, there is little information in the gambling studies field about the possible impact of interview modality on the results of such surveys. Only a few gambling surveys have used a self-completion approach; these include national surveys in Great Britain, Norway, and Sweden (Lund and Nordlund 2003; Orford et al. 2003; Rönnberg et al. 1999; Volberg, Abbott, et al. 2001).
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Other large health surveys that have included gambling modules in recent years include the Canadian Community Health Survey (CCHS) and the U.S. National Epidemiologic Survey of Alcoholism and Related Conditions (NESARC) (Petry, Stinson, and Grant 2005; Statistics Canada 2002). The gambling and problemgambling questions in these two surveys were administered in face-to-face interviews but did not involve computerized self-administration. Williams and Wood (2004) argue that the method of survey administration can significantly affect reporting of problem-gambling prevalence rates because respondents are less likely to acknowledge problems related to their gambling in a face-to-face interview than in a more anonymous telephone interview or in a face-to-face interview that includes computerized self-administration of questions about socially sensitive topics. They note that prevalence rates for problem gambling attained in the CCHS were less than half the rates obtained in several provincial surveys in the same time period. Similarly, substantially lower rates of problem and pathological gambling were identified in the NESARC (completed in 2001) than in the most recent national survey of gambling and problem gambling carried out by telephone in 1999 and 2000 (Welte et al. 2001). While self-administration may produce more valid estimates of sensitive behaviors because it offers greater anonymity, it is also possible that the much higher response rates in both the Canadian and U.S. health surveys contributed to lower prevalence rates through the inclusion of much larger numbers of nongamblers and infrequent gamblers than is usual in surveys with lower response rates (see the section Response Rates below). In the British Gambling Prevalence Survey—in which respondents were recruited face-to-face but the questionnaires were self-completed and collected later—4% of the questionnaires could not be used in determining the prevalence of problem gambling because more than half of the responses were missing (Orford et al. 2003). This supports the view that the data from self-completion questionnaires tend to be of lower quality than data from telephone surveys, in which automatic routing through the questionnaire and controls for inappropriate responses can be employed to ensure that respondents answer all of the questions within an established format. The first Swedish Prevalence Survey provides additional information on the comparability of self-completion versus telephone administration in problemgambling prevalence surveys (Rönnberg et al. 1999). Prior to the main survey, a pilot study was carried out with a randomly selected sample of 3,000 weekly gamblers to assess the impact of interview modality on problem-gambling prevalence estimates. Half of the respondents were interviewed by telephone and half were interviewed via a postal questionnaire. Overall, response rates for both the telephone interview (80%) and the postal questionnaire (70%) were quite high, and there was no significant difference in the problem-gambling prevalence rates in the two samples.
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Rather different results were found in the main part of the Swedish survey. Out of the 9,917 individuals selected for the survey, telephone contact was made with 8,845 (89% of the total sample). The remaining eligible respondents (1,071, or 11% of the total sample) could not be reached by telephone and were sent a postal questionnaire. In contrast to the pilot study, the response rate in the main survey was considerably lower with the postal questionnaire (31%) than with the telephone interview (77%). Also in contrast to the pilot study, the prevalence rate of problem gambling among respondents surveyed by mail was significantly higher than among those surveyed by telephone. Furthermore, the respondents to the postal questionnaire were significantly younger and less likely to have been born in Sweden than the telephone sample. A national prevalence survey conducted by the Norwegian Institute for Alcohol and Drug Research provides further information on the comparability of self-completion versus telephone administration in problem gambling prevalence surveys (Lund and Nordlund 2003). In the Norwegian survey, out of the 9,529 individuals selected, telephone contact was made with 5,484 individuals (58% of the total sample) and interviews were completed with 3,581 individuals (65% of those contacted). The remaining eligible respondents (4,045, or 42% of the total sample) received the postal questionnaire, and 1,651 questionnaires (41%) were received (S. Nordlund, personal communication, January 12, 2006). As in the Swedish survey, the response rate for the telephone sample was significantly higher than that for the postal questionnaire, and the prevalence of problem gambling was significantly lower. As in Sweden, the respondents in the postal sample in Norway were significantly younger and less likely to have been born in Norway than the telephone sample (I. Lund, personal communication, January 13–19, 2006). Finally, a recent survey of Norwegian youth aged 12 to 18 provides additional information on the comparability of self-completion versus telephone administration in problem-gambling prevalence surveys ( Johansson and Götestam, 2003). Both telephone and postal methods were used in the survey of adolescents in Norway. The majority of the sample of 3,237 adolescents (59%, n = 1,913) was interviewed by telephone, and the remainder (41%, n = 1,324) completed a postal questionnaire. In contrast to the adult surveys in Norway and Sweden, the rate of problem gambling was significantly higher among adolescent respondents interviewed by telephone in Norway compared with those who returned postal questionnaires ( Johansson 2006). As yet, Internet and email approaches have received little attention from gambling researchers, although increased exploration of these methods is likely in the near future. Online survey methods offer several advantages, including the ability to invite large numbers of potential respondents to participate at a much lower cost than those associated with mail, telephone, or face-to-face recruitment; automated routing of respondents through the questionnaire with concomitant improvements in data quality and reductions in interview length; and instant data capture and rapid availability of results.
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The critical challenge to Internet and email approaches in population research lies in ensuring the representativeness of the sample. For the most part, Internet and email surveys rely on panels of volunteers who are willing to complete surveys because of their interest in a topic or for various incentives, including merchandise. Internet surveys are advantageous where the target population is known and available through email or where the target population is difficult to reach using more traditional survey methods. It is important to note that although Internet surveys incur virtually no coding or data-entry costs, the labor costs for design and programming can be high (Schonlau, Fricker, and Elliott 2002). One recent study examined the potential of online methods for gambling surveys among adolescents (Lahn, Delfabbro, and Grabosky 2006). Comparing results from a school-based survey with the same questionnaire administered in an online format, these researchers found that the principal advantages of the online approach were greater flexibility in the timing of the survey and reductions in the amount of teacher time required for administration. Disadvantages of the online method included difficulties in obtaining adequate response rates, lack of control over the administration context, and difficulties obtaining detailed open-ended responses.
MULTIMODAL SAMPLING Finally, it is worth considering the use of multimodal sampling strategies in conducting population surveys of gambling and problem gambling. As noted above, the relative infrequency of problem and pathological gambling in the general population means that surveys must either recruit and screen very large numbers of respondents to identify adequate numbers of problem gamblers for subsequent analysis or use one of a variety of strategies to prescreen or filter respondents to reduce the number of people assessed. A variety of measures have been used to filter respondents, including regular gambling or reported gambling expenditures. “Dual frame” sampling has been used in one survey in the United States, and multiple interviews per household has been used in one survey in the United Kingdom (Gerstein et al. 1999; Orford et al. 2003). As described above, a strategy used in surveys in Norway and Sweden involves obtaining selfcompleted postal questionnaires from respondents who cannot or will not complete a telephone interview (Lund and Nordlund 2003;Volberg, Abbott, et al. 2001). Dual frame sampling was used in the U.S. national survey to capture large numbers of frequent (hence more likely to be problematic) gamblers efficiently relative to their prevalence in the household population. This approach was taken because the overarching goal of the study was to examine the socioeconomic impacts of problem gambling rather than problem-gambling prevalence alone.This survey used two separate sampling frames and interview modalities. One component consisted of a national random sample of residential telephone numbers designed to
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proportionately represent adult household residents; these respondents were interviewed by telephone.The other component consisted of a random stratified sample of individuals sampled proportionally to their frequency of entering commercial gambling venues; these respondents were interviewed in person at the venues. Since the questionnaires for the two samples were nearly identical, it was possible to use statistical procedures to combine the data from the two different frames. A statistical approach that took into account the differential opportunities for respondents to be included in the final sample was used to combine the samples and reweight the resulting file. In other words, the intercepted patrons shared the sample weights assigned initially to the telephone cases whom they most resembled in terms of gender, age, and past-year gambling behavior. In the British Gambling Prevalence Survey, a large, random, nationally representative sample of 7,680 British residents aged 16 and older was recruited from 4,619 households, using postal addresses as the sampling frame. Interviewers visited each of the randomly selected household addresses, completed a brief interview with the highest-income householder, and then asked each member of the household aged 16 and over to complete a self-administered questionnaire (Orford et al. 2003).While the data were weighted to correct for nonresponse and for differing probabilities of household selection, no adjustments were made for possible differential coverage of the population due to factors besides age and gender. Nor does it appear that adjustments were made to account for the clustering of interviews within households. Despite the use of more elaborate sampling procedures, researchers rarely make subsequent adjustments to the data for factors such as differential likelihood of inclusion in the sample or for clustering of interviews within households. Furthermore, few researchers take into account the impact of such sampling strategies on the effective sample size, despite the fact that design effects due to clustering and multimodal approaches can be substantial.
RESPONSE RATES There are benefits and drawbacks to any research approach. Postal surveys are inexpensive but generally have low response rates and long completion times. Online surveys take relatively little time to complete but, like postal surveys, tend to have low response rates and are difficult to assess with regard to representativeness. Face-to-face surveys typically achieve high response rates but may miss people who are infrequently at home and are far more costly than other sampling strategies.Telephone surveys are less expensive than face-to-face surveys, generally obtain comparable results, and, for some sensitive topics, provide a higher degree of anonymity. The great majority of population surveys in the gambling studies field have used this method.
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Although response rates alone should not be the sole measure of survey data quality, they are a crucial indicator of potential nonresponse bias and, hence, the representativeness of the sample. Given the importance of achieving a representative sample in most population surveys, response rates are also an important measure of the reliability of the estimates of gambling participation and/or problem-gambling prevalence identified in general population samples. Response rates, particularly for telephone surveys, have declined rapidly in recent years as people become increasingly reluctant to participate in this type of research and as technological barriers proliferate. Another challenge is that RDD samples of telephone numbers generally include substantial proportions of numbers that ring when dialed but are potentially out-of-scope for the survey. These numbers include businesses and public telephones as well as banks of numbers activated by telephone companies but not yet in service (as in geographic areas experiencing rapid increases in population) (Volberg 2002). While there is great uncertainty about the characteristics of individuals who choose not to participate in gambling surveys, it has generally been assumed that people who are not contacted or decline to be interviewed in gambling surveys include disproportionate numbers of problem gamblers (Lesieur 1994). However, it has been suggested that both people with little involvement and/or interest in gambling and problem gamblers may be overrepresented among nonrespondents in surveys with low to medium response rates (Abbott and Volberg 1991). If this is the case, the effects of their omission may partially or totally cancel each other out. It is possible that surveys that attain relatively high response rates pick up disproportionately more people with low involvement and/or interest in gambling with concomitantly lower prevalence estimates (Abbott, Volberg, and Rönnberg 2004). Abbott (2001) examined this possibility by comparing the most recent New Zealand problem gambling prevalence estimates with those obtained from the national Australian survey conducted at about the same time (Productivity Commission 1999). Data collection for the New Zealand Prevalence Survey was carried out in 1999 by Statistics New Zealand. The nationally representative random sample included 6,452 adults aged 18 years and older interviewed by telephone.The response rate for the New Zealand survey, conservatively defined, was 75%.This is probably because of the involvement of the country’s official statistics agency (Abbott and Volberg 2000; Abbott,Volberg, and Rönnberg 2004). In contrast, like most previous gambling prevalence surveys, the Australian study was undertaken by a private research company.The Australian survey used a two-phase sampling strategy with a brief “screening” questionnaire administered to identify broad patterns of gambling participation and a more detailed questionnaire completed by respondents using a selective strategy based on the intensity of gambling involvement. Although the final study included a stratified sample of 10,500 respondents selected by geographic area, age, and gender, the full
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questionnaire was administered to only 3,498 respondents, and the survey had a response rate below 50%. The data were subsequently weighted to generate nationally representative results (Productivity Commission 1999). The New Zealand prevalence estimate was very similar to those obtained by the two Australian states that had similar expenditure on continuous forms of gambling and markedly lower than those from Australian states and territories with higher expenditure. In other words, the prevalence estimates from these surveys were consistent with expectations based on known associations between expenditures on continuous forms of gambling and problem-gambling prevalence. On the basis of this analysis, Abbott (2001) concluded, like Shaffer, Hall, and Vander Bilt (1997), that problem gambling is a “robust phenomenon” largely impervious to differences in researcher and research methodology and quality. One consequence of the decline in response rates for telephone surveys has been that these rates are now calculated in a variety of ways. Although all of these approaches involve dividing the number of respondents by the number of contacts believed to be eligible, there can be substantial differences in response rates that result from different ways of calculating the denominator—that is, the number of individuals deemed eligible to respond. The most liberal approach to calculating response rates includes in the denominator only the total valid sample (e.g., households known to be eligible for inclusion in the sample).This approach is probably based on the response rate calculation long accepted as the standard for face-toface surveys. Using this approach—more properly called the completion rate in telephone surveys—the response rate is calculated by dividing the number of completed interviews by the sum of completes, refusals, and terminations. A more conservative approach is the method adopted by the Council of American Survey Research Organizations (CASRO). The CASRO method for calculating response rates entails determining the resolution rate, the screening rate, and the completion rate and multiplying these together. The resolution rate includes the status of the entire released sample of telephone numbers, including nonworking, nonresidential, and known and likely households that were nevertheless not screened.The screening rate involves calculating the proportion of eligible and ineligible households compared with the proportion of the sample determined to be known and/or likely households that were not screened. The completion rate is the number of completed interviews divided by the sum of completes and known eligibles that did not result in a completed interview. Researchers seldom include enough information in published reports to allow others to assess the accuracy of reported response rates. However, detailed information about the final disposition of the entire sample is valuable and should be included in methodological reports on population surveys of gambling and problem gambling to enable readers to assess the quality of the data for themselves. Table 2.1 presents information about what is needed to calculate response rates using CASRO and other methods.
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Overall, given uncertainty about the characteristics of individuals who choose not to participate in surveys, it is important to attain the highest possible response rates (within the constraints of available resources) in population surveys of gambling and problem gambling. There are a variety of steps that survey researchers can take to achieve better response rates. One important measure for improving response rates is budgeting for and completing as many attempts as possible to recruit respondents. In telephone surveys, it is important that calls be made at different times of the day and evening and on different days of the week, including weekends. Another related measure is to space these attempts out over the full period of data collection to allow for contact with respondents who may be away from their place of residence for an extended period. However, the most important measure for improving response rates in gambling surveys is to plan for an extended period of data collection in order to complete a sufficient number of call attempts to telephone numbers that are believed or determined to be eligible. Williams and Wood (2004) report on the results of an investigation into the appropriate number of call attempts to make in RDD surveys of problem gambling. Contrary to expectation, problem gamblers were no more difficult to reach than nonproblem gamblers. However, the investigators found that 95% of the contactable and cooperative sample was obtained within 15 to 16 attempts (with the majority of attempts made in the evenings/weekends and spread out over a minimum of 6 to 8 weeks) (Williams and Wood 2004).
Table 2.1 Information Required to Calculate Population Survey Response Rates. Total Sample Preresolved Nonworking Noncontact Nonresidential Answering machine Known household (unscreened) Likely household (unscreened) Ineligible (known household) Eligible (not complete) Complete Resolution rate (RR) Screening rate (SR) Completion rate (CR) CASRO response rate
D NC NR I U1 U2 J Ks Kc (D+NR+U1+U2+J+Ks+Kc)/total sample ( J+Ks+Kc)/( J+Ks+Kc+U1+U2) Kc/(Kc+Ks) RR*SR*CR
CASRO; Council of American Survey Research Organizations.
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Research and Measurement Issues in Gambling Studies
Another helpful approach to improving response rates is the use of computerized survey management systems. Computerized survey management systems can be updated daily throughout the data collection period, allowing for tight control over the sample and efficient monitoring of response rates. These systems are capable of producing a variety of detailed reports that allow researchers to monitor progress in the field and reallocate resources, if necessary, to ensure that specific targets are met. Survey management systems are also useful in managing “reissues” of telephone numbers, where interviewers have previously recorded no contact with a household or a soft refusal that might be converted. Interviewer training is an important component in ensuring the quality of the data collection effort in any population survey. It is important that new interviewers go though general training to become acquainted with the principles and practice of survey research interviewing. General training typically consists of an overview of telephone interviewing procedures, including the use of computeraided telephone interviewing (CATI) technology and the role of the interviewer in the research process.Training should cover such issues as probing for clarity and quality of response, gaining cooperation, and avoiding refusals. Project-specific training is also important, for both new and experienced interviewers. Projectspecific training includes orientation to the questionnaire as well as mock interviews before interviewers are ready to begin data collection in earnest. Experienced interviewers are an especially important resource for ensuring the highest possible response rates in population surveys. Experienced interviewers are adept at averting refusals and at converting refusals into completed interviews. In large population surveys, it can be helpful to maintain a small team of experienced interviewers who are specially trained in techniques for converting soft refusals into interviews and thereby improving the overall response rate. Survey researchers have found that sending advance letters to potential respondents or households prior to the beginning of data collection can help boost response rates. Advance letters explain the purpose and importance of the survey and reassure respondents about anonymity and confidentiality. In general, it is best to send out advance letters in waves to minimize the gap between receiving the letter and an interviewer calling or coming to the respondent’s house. It is important to craft advance letters with care so as not leave respondents in any doubt about what the survey involves or how the data will be used. In North America, commercial services using multisource databases are able to link business and residential telephone numbers (both listed and unlisted) with current names and addresses. These links can be updated on a regular basis and provide a cost-effective way to contact potential respondents and solicit their participation. Another helpful measure for enhancing response rates is mailing conversion letters midway through the data collection period. Using survey management
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systems and well-trained interviewers means that over the course of data collection, researchers are able to analyze the reasons that potential respondents who are reluctant to participate have given to interviewers. Using interviewer call notes and address matching, it is possible to tailor refusal conversion letters to fit respondents’ reasons for refusing to participate and increase the likelihood that additional efforts will result in a completed interview. Along with scrupulous planning and monitoring of the sample, the measures discussed here can be expected to result in higher response rates. However, survey researchers generally agree that the most effective steps for increasing response rates are (1) to keep the average interview to an acceptable length (e.g., no more than 20 minutes for a telephone survey and no more than an hour for face-to-face interviews) and (2) to establish as long a data collection period as possible to provide the greatest opportunity to space out attempts on each piece of sample and maximize the chances of reaching each potential respondent.And regardless of the uncertainties, the available data suggest that achieving a high response rate must be balanced against other important objectives in conducting surveys of gambling and problem gambling in the population.
WEIGHTING POPULATION SURVEY SAMPLES The ultimate goal of a survey is to generate unbiased estimates of behaviors in the target population. Before analyzing the results of population surveys, it is important to ensure that the profile of the sample mirrors the profile of the population it is meant to represent. Otherwise, the results would be biased or skewed toward subgroups that are overrepresented in the sample. Sample weighting can quickly become very complex, and it is wise to involve a sampling statistician in the design of a population survey at the earliest possible stage. In general, survey data must be weighted prior to analysis to account for differential probabilities associated with selection, response rates, and population coverage rates. The latter includes an allowance for noncoverage of the eligible population in nontelephone households and underreporting of the eligible population in telephone households. A comprehensive weighting scheme for a telephone survey will include adjustments for nonresolution of telephone numbers, screener nonresponse, multiple telephone lines in a single household, withinhousehold selection probability, interview nonresponse, and poststratification to align the achieved sample with the known characteristics of the population. Until quite recently, only large, national population surveys of gambling and problem gambling included such comprehensive weighting schemes. Poststratification weighting (sometimes called calibration) is a far more common feature of population surveys in the gambling studies field.
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Poststratification weighting corrects, as far as possible, any observable bias in the data by examining key characteristics of the sample and comparing these characteristics to reference data such as the census. If the difference is considered significant, the survey data are weighted to reflect the reference data. The most common form of corrective weighting is to compare the gender, age, and ethnicity profile of respondents with the population profile and weight the sample to match the population. However, poststratification weighting cannot account for differential nonresponse within subgroups in the population (Abbott and Volberg 2000). There is a price to pay for weighting, since these procedures can increase the statistical error of the survey results—for example, by widening confidence intervals. Each type of weighting will contribute separately to the effect on the survey results, and the extent to which this occurs is measured as the design effect, or DEFF. Design effect is more commonly considered in the development phase of a survey when researchers consider the question of sample size. In general, sample size calculations assume the use of simple random samples and the impact of alternative sample designs must be taken into account when determining the sample size for a study. Complex sampling designs are used more often than simple random sampling in population research, mainly as a means of saving money. However, complex sampling strategies result in less variability in the final sample than if random sampling is used, with the result that the effective sample size is reduced.This loss of power is measured as the ratio of the actual variance, under the sampling method actually used, to the variance under the assumption of simple random sampling.The design effect is a direct way of assessing the impact of the sampling design on sampling variability. In general, the smaller the design effect, the more reliable the results of the survey are considered to be. However, estimates of design effect are usually based on past experience, since the statistical literature provides little guidance (Rosander 1977). A separate challenge from the corrections required to account for complex sampling design relates to the appropriate calculation of confidence intervals around very low or high prevalence estimates. Statistical inference, used to examine the strength and significance of relationships between different variables or change over time on the same measure, requires the construction of accurate confidence intervals.When sample sizes are large and the proportion not near zero or one, confidence intervals can be determined using conventional approximations. However, if proportions are very small or very large, orthodox methods of calculating errors of measurement and confidence intervals may be inappropriate. This is because estimators are often not distributed normally at these extremes. In this situation, alternative approaches are needed to provide more accurate and stable confidence intervals.
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CONSTRAINTS AND CHOICES IN POPULATION RESEARCH Population surveys are always constrained by the available resources, and researchers must make numerous decisions about how to most effectively deploy them (Groves 1989). Over the course of a study, researchers must continuously balance the conflicting demands of achieving an adequate sample size, achieving adequate coverage of subgroups in the population, obtaining reliable answers from respondents to a reasonable number of questions, and attaining an acceptable response rate. The general message is that costs and sources of error in surveys are related. Cutting corners in any one area will always affect some other important property of the study. Resources that are spent on recruiting a large sample into the study cannot be spent on increasing the length of the questionnaire to obtain information on a topic that may be of great interest but is tangential to the main goals of the study. Reducing the number of attempts to contact eligible respondents may permit a researcher to increase the sample size but only at the cost of increasing nonresponse. If the emphasis is on attaining the highest possible response rate, the researcher will have to minimize the length of the questionnaire or accept a smaller sample size. Population research will continue to be an important tool in efforts to monitor gambling and problem gambling in the coming decades. Given the limited resources that have been available for conducting population research on gambling and problem gambling, we need (1) ways to pool resources for gambling research so that larger and more complex studies can be conducted and (2) ways to pool expertise so that gambling studies can benefit from developments in social science, survey methodology, and statistics. Despite the challenges of conducting population research on gambling and problem gambling, the pragmatic demands imposed by the rapid expansion of legal gambling have led researchers to carry out surveys that are often limited in terms of sample size, coverage of the population, response rate, and reliable data. While much of the research on gambling and problem gambling can be criticized, this does not mean that the findings are without validity and practical utility. Such studies are generally an advance on anecdotal evidence or nonscientific approaches to gather information and reach decisions in a highly politicized arena. In practice, all research falls along a spectrum in terms of quality and fallibility. Too little of it, in gambling studies as in many other disciplines, is found near the quality end of the continuum. Probably no single study will ever excel in all respects. However, few studies are likely to be totally devoid of information, and reviewers as well as researchers must use reasoned, but ultimately personal, judgment to decide whether the methodological weaknesses of a study entirely undermine its results.
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GLOSSARY Design effect this is the ratio of the true variance of a statistic (taking the complexity of the sample design into account) to the variance of the statistic for a simple random sample of the same size. Design effects differ for different subgroups and different statistics but can affect confidence in the results of the study. Population research studies that use questionnaires and quantitative techniques to collect information from large groups of people. Such studies are generally used to describe populations or to test hypotheses and examine relationships between variables of interest. Response rate the proportion of individuals invited to participate in a study who actually do so.The method of collecting data has an important impact on the response rate, and response rates can be calculated in a variety of ways. Sampling frame the list of members of the population of interest from which a sample for study can be drawn or the procedure used to ensure that each member of the population has an equal probability of being chosen for study. Sampling modality the method used to collect information in a survey. Sampling modalities vary widely in cost but can also affect the reliability and validity of the information collected. Weighting statistical procedures used to adjust the achieved sample to reflect the population.While weighting can correct for nonresponse and differing probabilities of selection, such procedures cannot account for differential nonresponse within subgroups in the population.
REFERENCES Abbott, M.W. (2001). What Do We Know About Gambling and Problem Gambling in New Zealand? Report Number Seven of the New Zealand Gaming Survey.Wellington: Department of Internal Affairs. Abbott, M.W., and Volberg, R.A. (1991). Gambling and problem gambling in New Zealand: Report on Phase One of the National Survey. Research Series No. 12.Wellington: Department of Internal Affairs. ———. (1999). Gambling and Problem Gambling in the Community:An International Overview and Critique. Report Number One of the New Zealand Gaming Survey.Wellington: Department of Internal Affairs. ———. (2000). Taking the Pulse on Gambling and Problem Gambling in New Zealand: Phase One of the 1999 National Prevalence Survey. Report Number Three of the New Zealand Gaming Survey. Wellington: Department of Internal Affairs. Abbott, M.W.,Volberg, R. A., Bellringer, M., and Reith, G. (2004). A review of research on aspects of problem gambling. London: Responsibility in Gambling Trust. Abbott, M. W., Volberg, R. A., and Rönnberg, S. (2004). Comparing the New Zealand and Swedish National Surveys of gambling and problem gambling. Journal of Gambling Studies, 20, 237–258. Aquilino, W. S. (1997). Privacy effects on self-reported drug use: Interactions with survey mode and respondent characteristics. NIDA Research Monograph, 167, 383–415.
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Blackman, S., Simone, R. V., Thoms, D. R. (1989). The Gamblers Treatment Clinic of St. Vincent’s North Richmond Community Mental Health Center: Characteristics of the clients and outcome of treatment. International Journal of the Addictions, 24, 29–37. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates. Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M., and Spitznagel, E. L. (1998). Taking chance: Problem gamblers and mental disorders—results from the St. Louis Epidemiological Catchment Area (ECA) study. American Journal of Public Health, 88, 1093–1096. Cunningham-Williams, R. M., Grucza, R.A., Cottler, L. B.,Womack, S. B., Books, S. J., Przybeck,T. R., Spitznagel, E. L., and Cloninger, C. R. (2005). Prevalence and predictors of pathological gambling: Results from the St. Louis Personality, Health and Lifestyle (SLPHL) study. Journal of Psychiatric Research, 39, 377–390. Fendrich, M., Johnson, T., Shaligram, C., and Wislar, J. (1999). The impact of interview characteristics on drug use reporting by male juvenile arrestees. Journal of Drug Issues, 29, 37–58. Gerstein, D. R.,Volberg, R.A.,Toce, M.T., Harwood, H., Palmer,A., Johnson, R., Larison, C., Chuchro, L., Buie, T., Engelman, L., and Hill, M. A. (1999). Gambling Impact and Behavior Study: Report to the National Gambling Impact Study Commission. Chicago: National Opinion Research Center at the University of Chicago. Groves, R. M. (1989). Survey Errors and Survey Costs. New York:Wiley & Sons. Groves, R. M., Biemer, P. P., Lyberg, L. E., Massey, J.T., Nicholls,W. L., and Waksberg, J. (1988). Telephone Survey Methodology. New York:Wiley & Sons. Hodgins, D. C., and el-Guebaly, N. (2000). Natural and treatment-assisted recovery from gambling problems: A comparison of resolved and active gamblers. Addiction, 95, 777–789. Johansson, A. (2006). General Risk Factors for Gambling Problems and the Prevalence of Pathological Gambling in Norway. Doctoral dissertation, Norwegian University of Science and Technology,Trondheim. Johansson, A., and Götestam, K. G. (2003). Gambling and problematic gambling with money among Norwegian youth (12–18 years). Nordic Journal of Psychiatry, 57, 317–321. Johnson,T. P., Fendrich, M., Shaligram, C., Garcy,A., and Gillespie, S. (2000).An evaluation of the effects of interviewer characteristics in an RDD telephone survey of drug use. Journal of Drug Issues, 30, 77–102. Kavli, H., and Berntsen, W. (2005). Gambling Habits and Gambling Problems in the Population. Report to Norsk Tipping. Oslo: MMI. Lahn, J., Delfabbro, P., and Grabosky, P. (2006). Classroom or cyberspace: Ethical and methodological challenges of online gambling surveys for adolescents. Journal of Gambling Issues, 16. Available at http://www.camh.net/egambling. Lesieur, H. R. (1994). Epidemiological surveys of pathological gambling: Critique and suggestions for modification. Journal of Gambling Studies, 10, 385–398. Lund, I., and Nordlund, S. (2003). Pengespill og Pengespillproblemer i Norge [Gambling and problem gambling in Norway]. Oslo: Norwegian Institute for Alcohol and Drug Research. May, R. K., Whelan, J. P., Steenbergh, T. A., and Meyers, A. W. (2003). The Gambling Self-Efficacy Questionnaire: An initial psychometric evaluation. Journal of Gambling Studies, 19, 339–357. Orford, J., Sproston, K., Erens, B.,White, C., and Mitchell, L. (2003). Gambling and Problem Gambling in Britain. Hove, UK: Brunner-Routledge. Ozga, D., and Brown, H. J. (2000). ADHD Screening of Adult VLT/Slot Machine Pathological Gamblers. Presented at the 11th International Conference on Gambling and Risk Taking, Las Vegas, NV. Petry, N. M., Stinson, F. S., and Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Clinical Psychiatry, 66, 564–574. Productivity Commission. (1999). Australia’s gambling industries, Report No. 10. Canberra: AusInfo. Rönnberg, S.,Volberg, R. A., Abbott, M. W., Moore, W. L., Andrén, A., Munck, I., Jonsson, J., Nilsson, T., and Svensson, O. (1999). Gambling and Problem Gambling in Sweden. Stockholm: National Institute of Public Health.
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Rosander, A. C. (1977). Case Studies in Sample Design. New York: M. Dekker. Rosenthal, R. J., and Fong,T. (2004). The Etiology of Pathological Gambling. Report to the California Office of Problem Gambling. Sacramento: Department of Alcohol and Drug Programs. Rounsaville, B. J., Anton, S. F., Carroll, K., Budde, D., Prusoff, B. A., and Gawin, F. (1991). Psychiatric diagnoses of treatment-seeking cocaine abusers. Archives of General Psychiatry, 48, 43–51. Rugle, L. J., and Melamed, L. (1993). Neuropsychological assessment of attention problems in pathological gamblers. Journal of Nervous and Mental Disease, 181, 107–112. Schonlau, M., Fricker, R. D., and Elliott, M. N. (2002). Conducting Research Surveys via Email and the Web. Rand Monograph MR-1480-RC. Santa Monica, CA: Rand Corporation. Schrans, T., Schellinck, T., and Walsh, G. (2000). Technical Report: 2000 Regular VL Players Followup: A Comparative Analysis of Problem Development and Resolution. Halifax, Nova Scotia: Focal Research Consultants Ltd. Shaffer, H. J., Hall, M. N., and Vander Bilt, J. (1997). Estimating the Prevalence of Disordered Gambling Behavior in the United States and Canada: A Meta-Analysis. Boston: Harvard Medical School Division on Addictions. Smith, G. J., and Wynne, H. J. (2004). VLT Gambling in Alberta:A preliminary analysis. Lethbridge, Canada: Alberta Gaming Research Institute. Specker, S. M., Carlson, G. A., Edmonson, K. M., Johnson, P. E., and Marcotte, M. (1996). Psychopathology in pathological gamblers seeking treatment. Journal of Gambling Studies, 12, 67–81. Statistics Canada. (2002). Canadian Community Health Survey, Mental Health and Well-being Section (CCHS 1.2). Tourangeau, R., and Smith,T.W. (1996).Asking sensitive questions:The impact of data collection mode, question format, and question context. Public Opinion Quarterly, 60, 275–304. Trochim, W. M. (2000). The Research Methods Knowledge Base, 2nd ed. Cincinnati, OH: Atomic Dog Publishing. Tucker, C., Brick, J. M., Meekins, B., and Morganstein, D. (2004). Household Telephone Service and Usage Patterns in the U.S. in 2004. Unpublished monograph. van der Heijden, P.,Van Gils, G., Bouts, J., and Hox, J. (2000). A comparison of randomized response, computer assisted interview, and face-to-face direct questioning: Eliciting sensitive information in the context of welfare and unemployment benefit. Sociological Methods and Research, 28, 505–537. Volberg, R.A. (2001). Gambling and Problem Gambling in North Dakota:A Replication Study, 1992 to 2000. Bismarck: Office of the Governor. ———. (2002). Gambling and Problem Gambling in Nevada. Carson City: Department of Human Resources. ———. (2004). Fifteen years of problem gambling research: What do we know? Where do we go? Electronic Journal of Gambling Issues, eGambling Issue 10. Volberg, R. A., Abbott, M. W., Rönnberg, S., and Munck, I. M. (2001). Prevalence and risks of pathological gambling in Sweden. Acta Psychiatrica Scandinavica, 104, 250–256. Volberg, R. A., Gerstein, D. R., Christiansen, E. M., and Baldridge, J. (2001). Assessing self-reported expenditures on gambling. Managerial and Decision Economics, 22, 77–96. Volberg, R. A., and Vales, P. (2002). Estimados de prevalencia sobre el juego patológico en Puerto Rico [Prevalence estimates of pathological gambling in Puerto Rico]. Revista Puertorriqueña de Psicología, 13, 71–98. Welte, J., Barnes, G.,Wieczorek,W.,Tidwell, M.-C., and Parker, J. (2001). Alcohol and gambling among U.S. adults: Prevalence, demographic patterns and comorbidity. Journal of Studies on Alcohol, 62, 706–712. Williams, R., and Wood, R. (2004). Final Report: The Demographic Sources of Ontario Gaming Revenue. Toronto: Ontario Problem Gambling Research Centre. Available at http://www.gamblingresearch.org.
CHAPTER 3
Questionnaire Design:The Art of a Stylized Conversation Marianna Toce-Gerstein
Dean R. Gerstein
Georgetown University Washington, DC
Claremont Graduate University Claremont, California
The Interview Context What Is an Interview? Conceptual Context Physical Context: Setting and Mode Social Context Privacy Effects Response Bias Social Desirability Bias Nonresponse Basic Communicative Principles Relevance Neutrality Ambiguity Language of Administration Translating the Questionnaire Using Interpreters Minimizing Cognitive Effects Recall Problems Limited Grasp and Computability of Quantities Scaling of Attitudes, Opinions, and Behaviors Causality Age-Graded Behavior: Special Considerations for Youth Questionnaire Construction Structuring the Questionnaire Major Questions, Sections, and Section Order Pathing Through the Interview Question Flow and Context Effects Item Response Frames 55
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Reading Level Pretesting Informed Consent Special Considerations for Gambling Research Definition of Gambling Gambling Participation Attitudes Toward Gambling Problem Gambling Diagnosis of Pathological Gambling Correlates of Problem Gambling Problem Gambling Help and Treatment
The principal issue in developing a research questionnaire is how to minimize the extent to which the information that is collected is invalid or inaccurate—that is, to minimize the extent of missing, imprecise, biased, or unreliable data.There are not many published studies on the accuracy of measurement of gambling, in terms of the specific motives, attitudes, knowledge, experiences, and behaviors of those who engage in it. However, a substantial literature exists on survey1 methodology in general, based on experimental and other systematic studies, and it has yielded firmly proven results as well as expert-judgmental “best advice” for many aspects of questionnaire design dealing with similar subjects, such as substance use, sexual and reproductive behavior, and income and wealth.We recommend that anyone interested in developing a questionnaire first review one or more of the many excellent texts on this topic, which cover these issues in much more detail than we are able to here (see, e.g., DeVellis 2003; Groves et al. 2004; Schwarz 1996; Sudman, Bradburn, and Wansink 2004; Tourangeau, Rips, and Rasinski 2000). In order to help both producers and consumers of gambling data, this chapter first outlines major design principles and then identifies and recommends key practices for developing questionnaires on personal gambling behaviors and attitudes, their correlates, and their individual and social impacts.
THE INTERVIEW CONTEXT WHAT IS
AN INTERVIEW?
The research interview—and for simplicity, we will henceforth use the term interview to refer to all individual data collection episodes, including the 1 By “survey,” we are referring to a method of collecting data from a sample of individuals, with the intent of generalizing the findings to a population of interest.
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completion of self-administered questionnaires by the respondent—is a complex social-psychological event or sequence of communicative behaviors. Although in most cases the interview involves direct communication between only the interviewer/instrument and the respondent, there are actually three other types of implicit participants who need to be taken into account in designing the questionnaire: the researchers, the intended audience, and unintended auditors. The question-and-answer format of interviews is deceptively simple. For design purposes, each interview is best viewed as a brief, stylized, dramatic conversation, partly scripted and partly improvised, in which a set of interconnected story lines are rapidly sketched out and recorded. Developing these story lines and making them as clear and accurate as possible can require a lot of work—mental concentration and sometimes emotional control—on the part of the respondent as well as the interviewer.
CONCEPTUAL CONTEXT Surveys come in two polar types, what we might call “portrait” and “landscape.” The portrait survey focuses on a single subject matter against a selective background of its most important contexts. The landscape survey incorporates a broad sweep of subjects, with limited details or contexts discernible for any one subject. Landscape surveys are often composites in which the question modules are sponsored by different clients. Surveys of either type may be strung together as longitudinal panels, meaning that the same questions are given to the same persons repeatedly over time. This permits one to look at change over time in persons or cohorts, and such longitudinal studies can provide the strongest available evidence about sequence, cause, and effect. In general, putting a small number of key gambling questions into a landscape survey is less expensive than devoting an entire survey to gambling issues, and that approach can be cost-effective if the right questions are asked and interest is limited to certain domains in which not many questions need be asked to get useful information. For example, this approach works well for measuring general attitudes toward gambling, awareness of gambling-related public messages or services, or the recency/frequency of participation in the most popular games, such as lotteries, casino slot machines, poker, and betting on major sports events. Portrait surveys are much more expensive to mount but permit far more questions to be asked about gambling, which is necessary for acquiring useful information about gambling-related problems or gambling expenditures. Perhaps the most problematic surveys attempt to be both portrait and landscape—to cover quite a number of topical areas, each in great depth.This type of survey is generally considered “too long” and can badly compromise the extent and quality of information obtained, especially for questions in the latter parts of the questionnaire.
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Researchers should be aware that the way in which gambling-related questions are contextualized will influence how respondents perceive the research and their role. The philosopher Gilbert Ryle made the observation (in Geertz 1973) that in the absence of context, we are unable to interpret the meaning of a wink.The winker might be attracted to us, she might be trying to communicate something secretly, or something else might be going on. As the context changes, the meaning of the wink changes. And so it is with questions; asking about gambling participation in a survey focused on alcohol use will send a different message than would the same questions in a casino customer satisfaction survey. This is a neutral and unavoidable fact of any communication—the way talk is framed influences participants’ understanding of its meaning.
PHYSICAL CONTEXT: SETTING
AND
MODE
Surveys can be administered—that is, the survey questions given to and answers received from the respondent—through various modes of communication. The classical mode is face-to-face, one-to-one, paper-and-pencil interviewing (PAPI), in which the interviewer reads each question in person to a single respondent from a printed questionnaire script and writes each spoken answer, or a categorical code representing it, in a designated answer blank, usually on the same printed page as the question.Variations of PAPI are nearly endless, and can include the following, in almost any combination: ●
●
●
●
●
●
The interviewer, instead of reading questions from a paper questionnaire, may read questions from a computer screen. The interviewer, instead of filling in answer blanks on paper, may key the verbal or precoded answers into a computer, which may in turn be a laptop, desktop, or handheld device. The respondent, rather than listening to questions spoken by a live interviewer, may read the questions on a printed page or computer screen (that is, self-administer the questions), or the questions may be audio or video recorded and administered to the respondent through headphones, a telephone, or a computer. The respondent, instead of speaking or writing down her answers, may enter them into a computing device using a keypad, keyboard, or pointing device (such as a mouse). Contact, instead of being face-to-face, may be via telephone, Internet connection, fax machine, or mail. Contact, instead of being one-on-one, may be in a group session.
The selection of a survey’s mode (or mixture of modes) is typically governed by balancing survey cost—usually calculated as average cost per completed case—against
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(1) data quality, that is, the extent of missing items, errors, and invalid answers, (2) sample quality, or how well the surveyed sample represents the targeted, inferential population, and (3) sample size, that is, the number of completed cases. A review of the role of mode in measurement error can be found in Lyberg and Kasprzyk (1991). Usually the mode(s) is(are) chosen before the detailed questionnaire is developed. In most respects, there is little interaction between mode choice and questionnaire content—that is, the mode choices do not make any difference in designing the questions or in the answers that would be obtained. (Note that mode choices can dramatically affect sample coverage and response rates.) There are, however, a few specific implications for questionnaire development. First, it is much easier to successfully implement complicated skip patterns or other inter-item contingencies (such as wording variations) when using computers instead of paper to generate questions and record answers. As a rule, selfadministered paper questionnaires are the least desirable mode choice in this respect. Second, all other things being equal, different modes of data collection have different psychological effects (Tourangeau et al. 2000). For example, sensitive information tends to be disclosed more fully and accurately under intrinsically more impersonal circumstances, such as computer-assisted selfadministered interviews (Epstein, Barker, and Kroutil 2001;Tourangeau and Smith 1996). Finally, information that involves many categories or compartments, including information about changes over time, tends to be more readily obtained in person, where one can display and use visual aids such as calendars, lists, or graphics. Of course, there are benefits and drawbacks to each possible mode. For example, postal surveys are inexpensive but usually have relatively high rates of missing items and very low response rates; Internet surveys may fare even worse (Kwak and Radler 2002). However, with careful multiple-wave approaches that involve additions but not multiples to the cost, postal and Web surveys may produce much better than usual response rates (see Dillman 1999 and Couper; Traugott, and Lamias 2001). On-site surveys—for example, of patrons at gambling venues—cannot produce a representative sample of all gamblers or of the general population without extensive statistical adjustments that may not be possible to properly calibrate.Area probability surveys, although generally the “gold standard” for representing the general household population, may seriously underrepresent people who are infrequently at home, and even area probability surveys can have high refusal rates if insufficient persistence and persuaders are deployed. For example, Dickerson and colleagues (1996) reported that a remarkable 49% of the householders that they attempted to recruit in a “door knock” survey in New South Wales refused to participate. Telephone surveys, in which the interviewer reads questions from a computer screen and keys in the answers, are widely used in studies of health and social
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behavior and are the most common mode used today for gambling surveys. This mode is generally called computer-assisted telephone interviewing (CATI). It is less expensive than an area probability survey, generally obtains comparable results in general population studies, and for some sensitive topics provides a higher degree of anonymity, which, as mentioned above, can obtain more accurate data. However, survey research professionals in the United States and Canada report that response rates for telephone surveys in the general population have declined rapidly since their introduction in the 1970s.
SOCIAL CONTEXT Generally, respondents to a survey do their best to be cooperative partners, attempting to infer what the researcher means by a question and even the kind of response that is expected (Schwarz 1996). Nevertheless, there are predispositions and characteristics of respondents—some stable and others more transient—that influence not only the way in which one participates in a given interview, but whether one participates at all. Privacy Effects A privacy effect is a bias that results from participants’ desire to keep certain aspects of their lives private from persons either directly or tangentially involved in the interview. Typically the term refers to bias created by third-party observers, although bias may also result with regard to the interviewer.Third-party observers might include other members of the respondent’s household, strangers in proximity to where the interview is taking place, and even interpreters and interviewer supervisors. In fact, observers may not even be present during the interview; for example, cognitive interviews are often videotaped for later analysis. Finally, third parties may be nonexistent; the respondent may simply provide inaccurate information because he believes that someone might try to obtain information about him in an unethical fashion. Obviously, privacy effects will vary depending on a variety of factors, including the respondents’ personalities, their status in the community, personal events in their lives, and even events in the news. Studies of the role that third-party observers play with regard to sensitive issues such as drug use and sexual activity have had mixed results, some showing that the respondent is likely to provide less accurate information, others showing no effects, and a small number indicating that more accurate information is obtained by having a third party present (when data are collected from cohabitating couples). The one exceptional group is adolescents, for whom the evidence repeatedly shows that the presence of a parent results in the underreporting of sensitive behaviors (Aquilino 1997). In fact, even the mere possibility
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of a known third party viewing the information has resulted in underreporting, such as that created by the practice of asking youth to write their names on questionnaires (ibid.). A key issue, obviously, is anonymity. As mentioned above in the section Physical Context, the more impersonal the mode of data collection, the more accurate responses tend to be (Epstein et al. 2001; Tourangeau and Smith 1996). A recent Dutch survey of adolescents found that they were more likely to report on certain sensitive behaviors such as alcohol use when the survey was anonymous than when it was simply confidential (van de Looij-Jansen, Goldschmeding, and Jan de Wilde 2006). Similarly, a study of adolescent smoking found that the survey mode that produced the most accurate estimates was in fact the most impersonal— that where participants used a telephone keypad to indicate their responses to computer-generated questions (Currivan et al. 2004). The important lesson to take from this body of work is that an individual’s responses may differ depending on the perceived audiences for those responses. The underlying motivators appear to be fear of reprisal and social desirability bias (see the next section). In practical terms, since gambling behaviors often create tension between family members, we recommend that interviews take place where the respondent cannot be overheard; (it may be worth rescheduling a time when the respondent will be alone). If interviews are conducted via telephone, care should be taken so that answers which might be spoken aloud do not reveal personal information. Response Bias Two response styles are of particular concern in surveys: acquiescent responding and extreme responding. Extreme responding is the tendency to choose one or another extreme when offered any ordinal or polar scale of values. Acquiescent responding is the tendency to be agreeable and positive to whatever the researcher asks, that is, to “yea-say.” Acquiescent respondents, when asked to evaluate persons or events, tend to downplay negative feelings or attitudes and accentuate the positive in their responses. In effect, acquiescent respondents adopt a higher midpoint. Research has suggested that this tendency may vary by demographic characteristics such as age, education, culture, and gender (Knäuper 1999; O’Muircheartaigh et al. 2000; Smith 2003). Acquiescent and extreme responding can coexist; for example, consider the case where, on a customer satisfaction survey, a person rates every service without exception as “excellent.” Jon Krosnick’s theory of survey satisficing builds on the notion of acquiescent responding; it states that some respondents may shortcut the cognitive process of responding to questions in two ways.The first way he calls weak satisficing, in which the respondent executes all cognitive steps involved in formulating responses, but incompletely and with bias. The second form is strong satisficing, in which the
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respondent offers responses that will seem reasonable to the interviewer without any memory search or information integration. Satisficing will result in the respondent typically selecting a no-opinion response option when it is offered, choosing socially desirable responses, failing to differentiate when a battery of questions asks for ratings of multiple objects on the same response scale, and/or tending to agree with any assertion (Krosnick, Narayan, and Smith 1996). Research on response effects has most typically focused on how they influence rating scales; such scales are typically used to measure attitudes and opinions but can also be used to measure such concepts as general health status and relative frequency of certain behaviors.They typically have an odd number of categories, with strong ones at either end (e.g.,“very favorably” to “very unfavorably”) and a neutral category in the middle. To avoid response bias from satisficing, some have argued that researchers should abandon the general practice of offering middle or neutral alternatives (Converse and Presser 1986). But this comes at a risk, particularly in attitude and opinion questions, since this response frame communicates to the respondent that she should have an opinion; the consequence (seen repeatedly across studies) is that very few people will volunteer that they do not (e.g., Schuman and Presser 1981)2; instead, we have seen that respondents who would have put themselves in the middle of the scale randomly select from one of the alternative categories (O’Muircheartaigh et al. 2000). (For more information on response scales, see the section Scaling of Attitudes, Opinions, and Behaviors.)
Social Desirability Bias Studies on survey response have identified another major type of response bias: social desirability bias. Social desirability bias is the motive to appear socially desirable, even if means bending or denying the facts in order to achieve or appear to meet socially desirable goals. Social desirability bias is a statistical tendency, not a feature of every person, but it colors enough responses in surveys to require attention in the design, operational, and analytical phases of research. Most respondents want to appear socially desirable, and many to some extent treat the survey experience like a job interview or a test on which they will be graded. Hence, many respondents tend to minimize or evade giving information about themselves that falls short of normative standards that they think the interviewer or survey team holds, or standards they hold for themselves (Tourangeau et al. 2000).The classic epigram in consumer research is that people drink half the amount of alcohol sold and use twice the amount of soap. 2 Attitude questions that do not have a middle alternative will usually give the option to the interviewer of coding “No Opinion” if this response is volunteered by the respondent.
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Social desirability bias cannot be eliminated, but its influence on results can and should be rigorously minimized. Interviewers should be trained out of conventional conversational cueing behavior, such as nodding or smiling (or headshaking or frowning) in response to answers (see Fowler and Mangione 1990; van der Zouwen, Dijkstra, and Smit 1991). Wordings should be sifted carefully to find the most value-neutral and nonpresumptive alternatives—for example, “Next I have some questions about the people you live with” instead of “Next I would like to know about your family.” The selection of mode should also take issues of respondent “image management” into consideration (see Tourangeau et al. 2000).
NONRESPONSE Participation in an ethically conducted survey is completely voluntary, meaning that the respondent is free to decline outright, decline to answer individual questions or groups of questions, or stop the interview early. Participants are also free to be lazy or inconsistent in their answers, to invent stories or opinions, and to shade or deny the truth. A well-designed questionnaire is not proof against such outcomes, but a poorly designed questionnaire will increase their frequency, aside from the problems it may create in analyzing the data. Several factors can affect the response rates of a survey. A well-designed questionnaire lets the respondent know why her participation is important and that her privacy and safety are not at risk. A thoughtfully crafted introduction that addresses these issues is essential in averting initial refusals. Response rates can also be negatively affected if the initial questions lose the respondent’s interest or the instrument is too lengthy. Questions should make sense to the respondent both individually and in the way they are ordered, should not dwell at length on matters that are irrelevant to his life or interests, should not create discomfort—or if they must, should cover unpleasant topics discreetly, quickly, and without reproach—and should not seek any information that is not needed for the stated research purposes.
BASIC COMMUNICATIVE PRINCIPLES Within the questionnaire itself, a great many factors can influence how individual questions are approached by the respondent, including item wording, sequencing of questions, and complexity.The questionnaire designers also needs to take into account various cognitive, linguistic, and emotional barriers to effective and accurate communication, and the statistical tools and interpretive strategies that the survey’s analysts will likely use.We can only touch briefly on some of these
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issues here; for more detail and breadth on writing survey questions, we recommend Fowler (1995), Schaeffer and Presser (2003), and Sheatsley (1983).
RELEVANCE Human communication is the exchange and interpretation of information, and such communication processes are remarkably intricate and subject to many kinds and degrees of success and failure. Substantial fields of study have focused on the way that survey questions are understood and addressed by respondents, especially how comprehension of and response to questions are influenced by prior questions. Perhaps the cardinal principle of communication that pertains to survey response is relevance—what experiences, persons, norms, attributes, and/or subjective states the respondent is mentally focusing on when providing the descriptive or evaluative information that a particular question evokes. Respondents constantly search for and interpret cues about what should be considered relevant for each item of a questionnaire. Effective questionnaire design is the mastery of this cueing process, so that the respondent considers relevant to each question only and exactly what the researcher does. One key aspect of relevance is that for any particular item, the personal or partisan desires or preferences of the researcher (or interviewer) are not made relevant to the respondent. The only relevant preference that should be cued by the questions is that the researcher desires the respondent’s unblinkered, unedited opinions, beliefs, perceptions, accounts, or estimates.
NEUTRALITY Neutrality of perspective is a second critical principle in designing questions— that is, neither questions nor response frames should be phrased or sequenced so as to “lead” the respondent to select answers that the respondent would consider forced or not quite accurate. Non-neutral questions provide less valid data in themselves and annoy respondents, making them less cooperative with the survey. Neutrality is partly a matter of avoiding invidious or normatively loaded wording (such as “Are you married, not yet married, or did your marriage break up?”), but also a matter of acknowledging the nearly infinite variability and continuing evolution of social arrangements and attitudes.This often means that the designers should develop a logical series of branching questions, each with very simple choices, rather than trying to pack numerous categorical possibilities into a single question. For further discussion on how question wording and response frames can intentionally or unintentionally bias results, see Knäuper (1998) and Schwarz and Hippler (1991).
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AMBIGUITY Words are very flexible instruments for conveying meaning, and that flexibility includes the fact that many words and phrases can convey different meanings depending on the context, the listener’s previous experience with the word or phrase, or the intonation as spoken or read. It is critically important that questions convey the same meaning to each respondent and that it is the same meaning that the researcher intends. Researchers cannot always be there to make sure their respondents understand them, or to answer questions when they do not. Studies have found that terms one might consider equivalent can, in fact, produce different outcomes (Smith 1987;Tourangeau et al. 2000). More problematic, though, is when a respondent asks the interviewer what she means by a particular concept—for example, betting on a game of skill (“Does it have to be on a game that I was playing, or could it also be on a game my friends were playing?”); the typical response of “Whatever it means to you” would come up short here, resulting in not only frustration on the part of the respondent, but an over- or underestimation of what the researcher is seeking to measure (Fowler 1992; Schober, Conrad, and Fricker 2004). A related issue concerns what are known as vague quantifiers—words and phrases like “hardly” and “very.” Vague quantifiers have been the subject of numerous investigations in experimental psychology and psycholinguistics; not only has this research revealed differences in how individuals understand these terms, but some work has shown that these differences may vary by race, education, and age. For more information, we refer the reader to Bradburn and Miles (1979), Schaeffer (1991), and Wallsten et al. (1986). Researchers typically attempt to avoid ambiguity by using questions whose clarity and stability of meaning have already been tested successfully in prior studies and through careful pretesting of the instrument (see the section Pretesting). The importance of this phase of questionnaire development cannot be overstated.
LANGUAGE
OF
ADMINISTRATION
Monolingual respondents must be interviewed in their own language, and bilingual or multilingual speakers in their language of greatest fluency and/or situational comfort. Study investigators can choose to tailor a given questionnaire for multiple linguistic groups through translation of the instrument, live interpretation, or both. Translating the Questionnaire Very few studies have been conducted on best practices for translating questionnaires for survey research. Nevertheless, many general-population surveys are developed in the official or most commonly spoken language first (the “source”
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language), with the written questionnaire then being translated into one or more additional languages (the “target” languages). This helps ensure comparability among instruments to the extent that this is possible. In addition to requiring accuracy and semantic equivalence in the target language, the instrument will also typically require some degree of tailoring to the cultural context; this will require consideration of the kinds of question-and-answer formats that respondents will be familiar with, culture-specific norms of politeness, and norms regarding the sharing of sensitive information (Harkness, van de Vijver, and Johnson 2003). When administering a questionnaire in two or more languages, one can only speculate for any given question the extent to which between-group differences actually exist or are artifacts of the translation. A number of techniques have been developed for translating questionnaires. The most basic is a simple direct translation that is performed by a single bilingual individual who translates the questionnaire from the source language into the target language. Another method of translation is called back translation; this iterative technique uses direct translation but then retranslates the translated text back into the source language to check for semantic equivalence. If necessary, changes are made to the source items and the process is repeated. While this method increases the likelihood that the meaning of the original question is more accurately conveyed, it unfortunately does not respect the cultural translations that should also take place. One preference is for the committee approach to instrument translation (Behling and Law 2000; Harkness et al. 2003; Pan and de la Puente 2005). In this method, the committee ideally comprises at least one arbitrator, several translators, translation reviewers, subject matter specialists, the questionnaire designers, and the person who will be in charge of conducting the pretests. Several translators work simultaneously and independently of each other to translate the full instrument. When finished, the team convenes to compare and discuss the translations. A new version is then created, which is submitted to the arbitrator to review and for any final decisions. Pretesting then commences for the translated version. Once the pretest is completed, the team reconvenes to discuss the findings and make any additional changes before main data collection begins. Using Interpreters Nonstandardized, “on-the-fly” interpretation of interviews by bilingual interviewers and third-party interpreters are problematic and should be avoided whenever translation is possible. In fact, it is better, prima facie, to explicitly omit a linguistic group from a survey than to incorporate and report on data that may or may not be valid. Despite this, we are aware that circumstances (and funding agencies) sometimes require that interpretation be used; therefore, we will describe three methods for improving the quality of data from these interviews.
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First, interviewers must be fluent in the respondents’ target language, and preferably also the dialect. Second, training is essential for both interpreters and the interviewers who will work with them. Interpreters require training similar to that which the interviewers receive, including information about specific requirements of the data collection and a copy of the questionnaire. Additionally, interviewers should be trained in how to work with interpreters—for example, in maintaining a moderate pace (Gerver 1969) and in communicating adequately to ensure that the interpreter does not get overtired. Third, we recommend that telephone interviews be monitored in real time to determine whether the interpreter is following good survey protocol and doing a satisfactory job of interpreting the questions.
MINIMIZING COGNITIVE EFFECTS RECALL PROBLEMS The human mind is a remarkable instrument, but it did not evolve for the express purpose of responding to research surveys.The most common recall problem is certainly outright forgetting, but a second frequent problem, which may be just as important, is backward or forward telescoping, in which a particular event is misplaced in time—thought by the respondent to have occurred more recently or less recently than it actually happened. A third problem is confuting one event with another; that is, taking features of two or more different events or points in time and ascribing them to the same event or point in time. Of course, there is a counterpart called splitting, in which features of one event or point in time are ascribed to separate occasions. All of these issues pose challenges to questionnaire designers and call for investigators to know the relevant research literature and technologies in order to maximize the validity and usefulness of the data. Questions about time frames need be sensitive to findings on the average trajectories of forgetting and the directions of telescoping bias for different types of events. A very important class of tools includes calendar aids, which help anchor events in time by first establishing salient markers such as birthdays and major holidays or, for longer time frames, major life events such as births, weddings, graduations, and so forth.When asking respondents to recall and date events in the distant past, interval approximations should be offered. One cannot get blood from a stone, but at least a stone will not mind the effort; however, the human respondent becomes frustrated and resentful of repeated questions that are too demanding and cannot be answered.
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LIMITED GRASP
AND
COMPUTABILITY
OF
QUANTITIES
Just as people are not digital imaging devices, they also are not digital computing devices. In general, for example, people perform poorly when asked to produce frequency reports (e.g., Burton and Blair 1991).Training and aptitude in basic arithmetic varies, and very few people of any aptitude keep running accounts and dated logs of everything they do. So when asked during an interview to sum up the number of instances of a behavior or to compute ratios of behavior over time (two very common forms of survey measurement), the results will nearly always be a rough estimate. Further, these estimates will become rougher—that is, more approximate and more subject to recall errors and biases—the longer the period covered. In methodological studies, participants have been shown primarily to use two methods to calculate the frequency of events: (1) episode enumeration, in which interviewees attempt to recall specific events of the behavior in question and sum across time, and (2) rule-based estimation, in which they calculate how often they usually perform the activity in question in a given time frame and then multiply this to determine the frequency for the time period that has been requested. Burton and Blair (1991) suggest that rule-based estimation is superior for events that are numerous, similar, and regular in frequency, whereas episode enumeration will likely produce the most accurate estimates for events that are less frequent and more distinctive and occur with greater irregularity.While all gamblers play differently, this recommendation does suggest that we may achieve better results by encouraging respondents to use rule-based estimation for lottery play (e.g., how many tickets are purchased in a typical week), and episode enumeration for less routinized activities. Questions that ask respondents to count detailed frequencies should never extend more than 12 months into the past, and even this will likely still produce underestimation of nonregular events.Although research has not yet pinpointed an ideal time frame, it likely would lie somewhere between the past month and past six months (assuming that seasonal variation were not an issue). For regular events, it can be expected that respondents will rely on memory of recent typical behavior and extrapolate to longer time frames, with recent habits overlaid on earlier ones; therefore, there would likely be no loss of accuracy were a shorter time frame used. Other well-established methods for obtaining estimates of behavioral frequency and expenditures go beyond the question itself. The best known and most effective of these is the use of prospective diaries, in which respondents make a daily record of the behavior in question and then provide these records to the researcher at predetermined intervals. To our knowledge, there have been no general population studies of gambling that have used this method. However, researchers have achieved some success using a second method, known as the
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timeline follow-back, which uses calendar aids (described above) to enhance recall. Recently this method was found to achieve good to excellent reliability and fair to good validity for report of gambling behaviors in a small treatment outcomes study of problem gamblers (Hodgins and Makarchuk 2003).
SCALING
OF
ATTITUDES, OPINIONS, AND BEHAVIORS
A wide variety of scales have been used in the history of studies to measure degrees of experience. For example, one might be asked by an interviewer, “On a scale of one to five, how would you describe your general health? ‘One’ is poor and ‘five’ is excellent.” Scales can include as few as two points to as many as the researcher would like. However, studies have shown that cross-sectional reliability, test–retest reliability, and criterion validity are optimal for scales of between five and eight points (Smith 1994). Likewise, interrater agreement is best, and susceptibility to context effects is lowest, when rating scales have between five and eight points (Wedell, Parducci, and Lane 1990).We recommend using the shortest scale within this range that is appropriate, keeping in mind that the length of time required to respond will increase with the number of items in the scale. If the survey mode permits visual aids, one can represent the scale along a horizontal axis. In telephone interviews, the questionnaire designers can include the endpoints as part of the question, while providing wording for other points on the scale for the interviewer to use as prompts when necessary.
CAUSALITY The challenge of finding and proving causal relationships is a serious one for the social and behavioral sciences. This is attributable mainly to the complicated objective/subjective nature of nearly all psychological and social phenomena and the many ways that people can get into any particular kind of trouble, whether financial indebtedness, family discord, criminal culpability, deep mental distress, or something else. The major diagnostic screens for pathological gambling tap a variety of gambling-specific behaviors, feelings, and motives, as well as adverse consequences of gambling for work, school, finances, and family relationships, in order to determine whether—and with what overall severity—gambling is a personal problem for the respondent. A different line of questioning in many surveys asks respondents whether certain specific current or past adverse events or circumstances may apply to them, such as bankruptcy, job loss, arrest, or the like, and then, for any items affirmed, asks whether their own gambling contributed causally to this result.
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In our own work (Gerstein et al. 1999), we found some disjunction between results for the last form of attribution, on the one hand, and statistical correlations between overall diagnostic status and the occurrence of adverse events and circumstances, on the other.That is, we found that respondents whose self-reports of gambling-related signs and symptoms classified them as problem and pathological gamblers were also much likelier—by multiples of between two and five to one—to report bankruptcy, arrest, and other problems. Yet when asked directly, few of the same respondents directly attributed these adverse events to their gambling. Of course, statistical correlation and cause-and-effect are not the same thing. The correlational strength in such findings may be due in whole or in part to a reverse causal path (other problems leading to gambling disorders) or to gambling disorders and other problems sharing one or more underlying causes. Also, the apparent attributional deficit may be due to systematic underreporting of adverse linkages, similar to the minimization of gambling losses. Sorting out these causal pathways is very difficult in a cross-sectional study.We recommend that researchers continue to use multiple approaches, asking about specific diagnostic markers of gambling and suspected adverse consequences of gambling, with follow-up questions of attribution.
AGE-GRADED BEHAVIOR: SPECIAL CONSIDERATIONS FOR YOUTH As a rule, studies of minors pose special difficulties for questionnaire designers. Minor children are subject in many jurisdictions to extra protections and requirements, such as the need for parental consent either implicitly (the parent is offered a fair and clear chance to opt the child out of participation, and does not do so) or explicitly (by signing a form or giving an interviewer direct verbal approval). However, consent is only the first hurdle. Children’s responsibilities, routines, language, and levels and modes of comprehension and emotional control are very different from those of adults and change substantially as children age. By and large, researchers do not sample children under the age of 9 or 10—that is, younger children are not asked to provide self-reports on their own or others’ behavior or to express their attitudes or opinions for statistical analysis. This is not due to lack of interest in child behavior, but distrust that younger children have the cognitive skills, emotional maturity, and social dexterity needed to provide useful and valid data. Nevertheless, some methodological studies with children have achieved some success. For example, Amato and Ochiltree (1987) compared the data quality obtained from children aged 8–9 and adolescents aged 15–16 and concluded that while the data from the younger children were inferior, the differences were minimal, with data
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quality from both groups being high in absolute terms. The researchers recommended that studies with younger children focus (1) on the present, as past events are difficult for younger children to recall, and (2) on subjects that are within the children’s direct realm of experience—for example, what they do at school, rather than what a parent does at work.The article also suggested a number of strategies for interacting with young respondents to improve participation and data quality. A study by Backett and Alexander (1991) confirmed some of these findings and additionally suggested that researchers avoid items regarding opinions and evaluations. By and large, adolescents do have the skills needed to complete surveys. However, many questions that are framed for adult respondents change meaning or lack relevance when asked of youngsters, and so are often incommensurable or of doubtful comparability. For example, few adolescents provide all or an appreciable fraction of the income that supports them, but most adults do. Most adolescents are enrolled in school, but their routines change substantially during summer months when school is out of session. Adolescents change their behavior much more rapidly than adults. Finally, adolescents appear to be more cautious in their responses when the data are not anonymous or the possibility exists that a parent may be listening in (see the section Privacy Effects). In short, although the same basic principles of communication and survey technique apply to studies of youngsters and adults, the applications can be quite different, and all questions and procedures need separate age-specific testing. We recommend that readers interested in this topic review Fontana (2002).
QUESTIONNAIRE CONSTRUCTION STRUCTURING THE QUESTIONNAIRE It is natural to surveys—as opposed to detailed case studies, for example— that the same overall set of questions be asked of each participant, although some subset of questions may be selected or omitted based on individual response patterns (see the section Pathing Through the Interview). The ways and degrees to which questions and answers are constrained toward uniformity is usually called the structuring of the interview. Less structured interviews—with less uniform, more improvisational questioning and more open response frames—lend themselves much more readily to qualitative analysis, while more structured interviews are better suited to quantitative (statistical) analysis, particularly when collected from large samples of respondents. Neither approach is intrinsically superior to the other, but they have different strengths and weaknesses, and limitations and
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potentials.While the present authors are versed in both qualitative and quantitative interviewing, our focus in this chapter is on the development of highly structured interviews characteristic of modern quantitative survey research. However, many of the design principles discussed here also apply to other types of interviews.
MAJOR QUESTIONS, SECTIONS, AND SECTION ORDER With limited exceptions for experimental or other special purposes, individual questions should not be distributed randomly across a survey, but grouped into topical sections.The most general rule to apply in deciding how to organize questions by topic is conventionality—the topics should accord with the commonsense ways that most respondents conceive the world, such as job, home, health, and recreation, and each section should begin with an introduction of the topic, such as “Next I have some questions about your sources of income.” In other words, topics should not be organized by the technical concepts of the researcher. The ordering of sections should follow four principles. First, items most essential for survey interpretation or analysis should come early in the sequence; second, survey order, when possible, should be arranged so as to minimize the need to repeat items; third, the designers should take into consideration the difficulty and interest of different topics and try to alternate between tedious/interesting and hard/easy sections; and fourth, the most sensitive questions should be placed as close to the end of the interview as possible.
PATHING THROUGH THE INTERVIEW Pathing refers to the sequence of specific questions that a given respondent is offered during an interview, taking into account the structure of logical contingencies that may be present in the questionnaire—such as branches, gates, filters, fills, and skip patterns. A questionnaire with no contingencies, in which every respondent is asked the identical set of questions, is usually said to have no pathing (strictly speaking, it has a single path). Pathing is generally based on very straightforward logic—a respondent’s answer to one question (“Have you ever been married? ‘No’”) may logically provide the answer to one or more additional questions, which can therefore be skipped without losing any information. Sometimes path options are simple, such as skipping a single question depending on the answer to the one immediately before it. They can be highly complex, making the pathing choice contingent on the answers to multiple, widely spaced items in complicated Boolean formulas. Pathing permits the tailoring of questions more precisely to respondent characteristics and can minimize the total number of questions or average length of interviews needed to obtain the desired information
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(pathing is the complement of adaptive testing in psychometrics). However, the more elaborate the pathing, the greater the challenge in completing the interview correctly and the greater the challenge to analysts in correctly interpreting the resulting pattern of answers and nonanswers. Elaborate pathing is greatly facilitated by computer-assisted administration, which can automate nearly all pathing decisions, and by the postsurvey step of data imputation—that is, replacing answer codes that mean “question was not administered” with the logically imputed answer.
QUESTION FLOW
AND
CONTEXT EFFECTS
There is a substantial literature in survey methodology devoted to understanding how one question affects the questions that follow, or more exactly, how the answer to one question can depend on not only the contents of that question but also the contents of questions and related survey information that preceded it (this group of phenomena is also known as context effects). This has to do both with communicative principles such as relevance and its determination and with other workings of human cognition, especially with how people learn to pay attention, retrieve memories, and calculate and estimate numerical values. Putting questions in the right order can make it easier for respondents to understand correctly what a question is about, to remember correctly things that they have done or observed, and to compute quantities more accurately. Study practices well informed by these methodological studies will yield more valid and useful results than practices that ignore them. For more information on this important subject, we recommend Moore (2002), Tourangeau et al. (2000), and Sudman, Bradburn, and Schwarz (1996).
ITEM RESPONSE FRAMES The possible ways in which an answer to a question can be recorded are referred to as response frames. The major types of response frames are fixed, open, and partially open. In a fixed frame, only a small number of all possible responses may be recorded, usually corresponding to exclusive logical possibilities that are either directly presented to the respondent for selection or else interpreted and confirmed by the interviewer from what the respondent offers (“yes/no,” “zero/one/two/three or more,” etc.). An open frame permits any response to be recorded that is clearly relevant to the question. Partially open frames permit any relevant response that falls within certain limits, with a fixed procedure for encoding responses outside those limits (e.g., in a computer-administered survey, the question “How many natural or adopted children of your own have you raised?” may have an upper limit, beyond which the user would be prompted to revise the response).
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Generally, fixed and partially open response frames are supplemented by standardized reserve codes, including “Don’t Know,” “Refused,” “Legitimate Skip,” and sometimes an all-purpose miscellaneous category such as “Other—Specify: [verbatim].”“Legitimate Skip” would be used as a response when the respondent is not asked a question due to his response to an earlier question (as discussed in the section Pathing Through the Interview).
READING LEVEL Another key element to be considered when constructing the questionnaire is achieving maximum clarity for the target audience.A prime consideration is the level of education that will be assumed for respondents; this should be determined in advance, and the questionnaire prepared accordingly. Next, the questionnaire should be tested for readability according to grade-level targets.The questionnaire designers should continue to be on the lookout for issues throughout the pretest, so that they can be resolved prior to the main data collection. Probably the most important aspects of readability pertain to syntax, grammar, and vocabulary. Even though two sentences that convey the same information may have the same number of words with the same number of syllables (hence, the same reading level), one may still be much more difficult to understand than the other. Questions are more comprehensible if they do not use technical terms and have a conversational tone. One should avoid unnecessary and uncommon words and make questions as simple as possible. In addition, keep in mind that even though a word may count as “big” because it has more than two syllables, if a word is encountered regularly in day-to-day life, it is likely that a literate adult will grasp it quickly (Tefki 1987). While there is clearly more to the readability of a document than measuring the frequency of big words or long sentences, nearly all of the readability formulas that are available (more than 40) use algorithms based on these qualities to provide a reasonably accurate estimate of grade level. The four most commonly used indices are the Flesch Reading Ease Score, Flesch-Kincaid Index, Fog Index, and SMOG (Simplified Measure of Gobbledygook). Even though different formulas tend to result in different scores, they have been found to correlate highly with one another (Meade and Smith 1991). As a practical note, Microsoft Word uses the Flesch-Kincaid Index to determine the grade level of a text, while the Fog and the SMOG are calculated by hand.
PRETESTING Any questionnaire, no matter how brief, simple, or apparently foolproof, should be pretested prior to going into the field. The pretest should examine,
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where relevant, any training materials, calling respondent selection procedures, and the overall design, flow, and respondent comprehension of the questionnaire. Before the pretest, a number of steps need to be taken. First, if the questionnaire has been programmed into a software system, the programming needs to be carefully tested so that interviewers can use the pretest to familiarize themselves with the correct instrument. Testing the questionairre pathing is critical to ensuring that respondents are not skipped over questions they should be asked, as well as for preventing respondents from being asked inappropriate questions.In many studies we have worked on, we have seen numerous crises due to insufficient skip-pattern testing, where valuable data were lost due to incorrect programming and rigorous quality control procedures not having been in place.We strongly recommend that the questionnaire designer take part in this phase, since no one will know the instrument as thoroughly. Second, it is important for the design team to record and distribute notes for interviewers on specific questionnaire items; such notes typically include clarifications about specific words or concepts within a question that some respondents may not understand.These are usually introduced during the interviewer training. Lastly, interviewer training should take place prior to the pretest. In our experience, the questionnaire designers should work as part of a team with the lead interviewers for this training, since it is during this time that the most egregious oversights are usually identified; these should be handled before the main data collection begins. One of the additional ways researchers pretest specific questionnaire items is through cognitive testing. This method is used to gather detailed information from individuals who have not previously encountered the interview and to examine how they formulate their responses. Extensive probing usually takes place following the interview or while the respondent is answering the questions.A number of styles of cognitive testing are available (see Willis [2004] for an overview). For example, the concurrent think-aloud method asks individuals to discuss their thought processes during and immediately following each question. They may be asked to provide feedback on their reactions to each question’s structure and wording and, as part of this process, repeat back each item for the interviewer in their own words. Respondents may also describe the experiences or behaviors which they believe qualify or do not qualify them to offer a particular response. The interviewer then probes as needed for additional information and for sources of confusion or misunderstanding. Research has shown that cognitive interviewing of only a few respondents— even fewer than ten—can detect major ambiguities in question wording (Fowler 1992). However, if cognitive interviews are not to be used, we recommend that as part of the pretest, interviewers ask respondents to comment on the questionnaire introduction, question wording, and the content and flow of the instrument. Interviewers should then fill out a “thumbnail sketch” summarizing the respondent’s comments during the interview, identifying problem questions, and indicating any instrument programming issues (where relevant).
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Once the pretest has been completed, the questionnaire designers will need to incorporate what has been learned from the pretest into the instrument. For this reason, the questionnaire designers should participate along with the interviewers and other team members in a pretest debriefing in order to review major findings and discuss any additional observations.Taken together, interviewers’ thoughts and observations will provide important insight in determining whether a question or answer option is working as designed, and if not, why. An excellent and thorough work on testing questionnaires is Presser and colleagues (2004). This useful guide documents a wide variety of methods for evaluating and improving instrument design.
INFORMED CONSENT Any given study typically presents both benefits and risks to participants. Benefits might include exploring an interesting topic, enjoying the social exchange of the interview, and knowing that one is making an important contribution to the body of knowledge in a particular area. However, certain information could cause harm to the respondent were it to be revealed, such as past suicide attempts and stealing to pay gambling debts. One needs to inform participants of such risks and maximize protection of their interests. Researchers need to adopt as part of every data collection effort a set of measures for informing prospective participants about possible and likely risks and benefits and obtaining clear, unforced, verifiable agreement to participate. Outlines of such measures have been codified in professional customs and in legislation in many jurisdictions. These measures differ depending on the status of the research group (university-, government-, or other-affiliated), as well as the legal status of the respondent; for example, additional consent may need to be gained from the parents of minors (Coyne [1998] provides an interesting discussion of informed consent issues with children) or from institutions in the case of studies of prisoners, patients, and the like. As soon as a respondent is contacted, and prior to the administration of any part of the interview, the interviewer should read a consent script and obtain a positive expression of informed consent.The script should identify the sponsoring institution and/or survey organization, the purpose and nature of the survey, and whether the data are anonymous and how confidentiality will be protected. In the event there will be any later contact with the respondent or her information, such as a request to participate in further data collection, this possibility should also be mentioned. Respondents should be explicitly told that they have the right to discontinue the interview at any time.They may also be provided with the name and telephone number of the principal investigator or equivalent in case they have any questions, concerns, or complaints about the study.
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SPECIAL CONSIDERATIONS FOR GAMBLING RESEARCH This section discusses specific issues the researcher may want to explore with regard to gambling.The discussion here is meant to provide guidance on some of the more common variables, based on the authors’ experience conducting their own gambling studies, as well as working with data from other studies in the field. The topical areas described are not meant to be exhaustive, nor will all of them be relevant to all of our readers. They illustrate many of the questionnaire design principles discussed in earlier sections.
DEFINITION
OF
GAMBLING
Folk definitions of gambling vary widely. For example, many people would consider buying stocks as gambling, and many people would not consider buying lottery tickets as gambling—although these are not the majority’s ideas. Due to these divergent definitions, any questionnaire items about gambling, no matter how few or how many, must be preceded by a very clear definition, followed by specification of exactly what kinds of activities the questionnaire designers want the respondent to cover when answering questions. Although there is no single standard, an example of current practice in definitions is as follows: I would like to begin by asking about your experience with various kinds of wagering or betting, including what kinds of gambling facilities are located near you. By “betting,” I mean placing a bet on the outcome of a game of skill or chance, or playing a game in which you might win or lose your money.
Then the interviewer should name specific games or venues as each one is queried; or, if general questions are being asked about gambling, such as global positive or negative attitudes, the interviewer should provide at the end of the definition a list of four or five specific examples, preferably ones that nearly all people in the places being sampled will understand and recognize—for example,“betting on slot machines or table games in a casino, betting on horse races off-track or at the track, buying lottery tickets, playing in a bingo hall.” If the interest is strictly in publicly licensed games and not private betting, or only betting within certain geographic locations (a region or province), that needs to be specified. One should avoid examples that involve proper or trade names, specialized jargon, less common games, or ambiguity about whether the activity actually involved gambling—for example, “betting at a simulcast . . . betting on a class II device . . . going to Las Vegas.” We also recommend that researchers use multiple words to describe gambling for clarity.The word “gambling” itself carries negative connotations based on
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its historical ties with organized crime.“Betting” and “wagering” are two substitutes we have used to ask about gambling, with no apparent loss of understanding. We do not recommend “gaming,” as this term is often used to describe videogame playing and other types of fantasy play (e.g., Dungeons and Dragons) that typically do not involve financial risk.
GAMBLING PARTICIPATION The most accurate data about participation comes when it is framed by venue, with respondents being asked about their wagering and their playing of specific games by venue.We find it helps respondents if they can visualize a specific place as a recall aid, before describing their activities within it. In contrast, gambling participation has been found to be substantially underreported when researchers have asked global questions (e.g., “Have you ever gambled?”) (Volberg and Banks 1990). New types of gambling that cross standard venue boundaries are created frequently. Furthermore, different jurisdictions will have different types of venues and games. Therefore, the venues and game descriptions should be tailored to reflect local contexts. Decisions should be made about how to characterize relatively rare types of gambling (e.g., purchasing lottery tickets over the Internet). Where necessary in a written questionnaire, this information may be conveyed as a parenthetical note at the end of the question. It is desirable to keep spoken questions concise, so unusual permutations may be described in materials provided to the interviewers discussed in interviewer training, then provided to respondents when clarification is needed.
ATTITUDES TOWARD GAMBLING While a number of variables can be used to learn about public opinion with regard to gambling, researchers should be warned that looking at gambling participation rates and patterns alone tells us little besides how common a behavior is. In the United States, opinion polls and surveys have demonstrated since the 1970s both an increasingly positive view toward gambling as well as increases in the number of individuals who gamble (Gallup Organization 1999; Kallick et al. 1976; Scripps Howard News Service 2006; Volberg, Toce, and Gerstein 1999). However, it is critical to look beneath the surface of general approval data. Though most Americans view gambling’s effects on society as “neutral” or a balance of positive and negative, it is still the case that far more Americans believe that gambling’s effects on society are negative rather than positive (Gerstein et al. 1999). A 1996 Gallup poll found that even though 70% of respondents disagreed that gambling
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was immoral, 67% agreed that it encouraged people who could least afford it to “squander” their money, and 61% agreed that it could make a “compulsive gambler” out of someone who would never gamble illegally (Heubusch 1997). Another important attitudinal variable is problem typology—gambling attitudes vary significantly depending on the degree of problems an individual has experienced.Volberg and coauthors (1999) found that individuals who reported a modest to moderate problem with gambling were much more positive about gambling’s overall effects on society than current gamblers who reported no problems. However, nearly half of pathological gamblers reported that the overall effect of legalized gambling on society was “bad” or “very bad.” Another common way that polls and surveys have explored attitudes toward gambling is by asking individuals why they have or have not gambled recently. The first national survey we are aware of that did this was the 1975 U.S. National Commission survey (Kallick et al. 1976). Unfortunately, while polls tend to ask why people approve or disapprove of gambling, they seldom inquire about why people do or do not gamble. However, an understanding of why people do and do not gamble can play an important role in the prevention and treatment of gambling problems. In this regard, understanding such differences on a group level can contribute to our knowledge about vulnerabilities and protective factors (see Gerstein et al. [1999] for further discussion).
PROBLEM GAMBLING Diagnosis of Pathological Gambling Administering any of the existing screens for problem and pathological gambling takes at least several minutes of interview time, which is costly in large-scale general population surveys. Researchers therefore typically use filtering mechanisms to skip low-frequency gamblers over diagnostic screening items. For example, surveys have skipped respondents who never lost more than $100 gambling in a single year of their lives (Toce-Gerstein, Gerstein, and Volberg 2003) or never gambled more than five times in any given year of their lives (Petry, Stinson, and Grant 2005). Alternative brief screens to filter out unlikely problem gamblers and reduce survey costs include the two-item Lie/Bet ( Johnson et al. 1997) and three-item NODS-CLiP (NORC Diagnostic Survey—Control, Lies, and Preoccupation) (Toce-Gerstein and Volberg 2003). Another way that questionnaire designers attempt to save time is by reducing the time frame covered by screening questions. The Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV), puts the entire lifetime in play (“Have you ever. . .”), but many researchers ask only about more recent periods (e.g., the past 6 or 12 months). Shaffer, Hall, and Vander Bilt (1997) argue
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that the lifetime measure inflates estimates of problem gambling, and they recommend that researchers rely instead on past-year (or other “current”) time frames “as the most accurate measure of the existence of clustered indicators of a gambling disorder” (p. 64). However, it is equally plausible and consistent with the DSM-IV that an active case be defined as anyone with a history of pathological gambling who exhibits one or more criteria in the past year, as was true of 83% of pathological gamblers in the NORC Gambling Impact and Behavior Study sample (Toce-Gerstein and Gerstein 2004). We recommend that researchers continue to estimate lifetime prevalence in the diagnosis of pathological gambling, since no data exist from which we can judge how many “current” DSM-IV criteria are needed for a “current” diagnosis. We also, however, recommend that researchers continue to assess which criteria respondents have experienced in a recent time frame for the following reasons: (1) Recall is best for more recent events; (2) the criteria can be linked to each other as well as to other characteristics in a fixed period of time; and (3) eventually research may suggest that a certain number of criteria, or specific criteria, are indicative of acute episodes of pathological gambling. Finally, researchers should look at the clustering of problems across longer periods of time, to further our understanding of precursors to problem gambling and aids to recovery. Correlates of Problem Gambling Gambling behaviors, including problems, can vary by a wide variety of respondent characteristics.The most basic of these are demographic characteristics that are collected as part of virtually all studies, such as gender, age, and ethnicity/ culture. Variables that have been shown to interact with gender include venue most often frequented, favorite game, age first gambled, age first had gambling problems, and age first sought treatment (Abbott,Volberg, Bellringer, et al. 2004; Grant and Kim 2002; Ladd and Petry 2002; Potenza et al. 2001;Tavares et al. 2001). Ethnicity and culture items should be defined or categorized comparably to census data in order to assess sample representativeness. Further detail may be added to reflect local contexts or specific study questions. Ethnicity and culture have been shown to be related to problem gambling in that disproportionate numbers of disadvantaged group members tend to experience gambling problems (Abbott, Volberg, Bellringer, et al. 2004; Volberg 2001; Volberg and Abbott 1997; Welte et al. 2001; Zitzow 1996). Recent immigration to a country may also play a role in gambling problems (Abbott,Volberg, and Rönnberg 2004; Petry et al. 2003). Furthermore, game preferences and help-seeking behavior can vary by group (Raylu and Oei 2002). Depending on which of these areas are of interest, one may ask a series of demographic items, including whether the respondent was born in the region of study; if not, how long ago he moved there; whether his parents were born there; and his primary language.
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Employment status and income are also typically collected as part of demographic data; this information is especially important in studies of economic impacts of gambling. Again, these questions should be asked in such a way that the data are comparable to census standards. Respondent’s job title is important for constructing more nuanced social class variables in conjunction with education and household income variables (see, e.g., Nakao and Treas 1994), but this requires specialized coding skills. Job title will also identify respondents in gaming occupations, which has been shown to be an important risk factor (Shaffer,Vander Bilt, and Hall 1999). Income questions should seek precision and include nonwage sources such as alimony, welfare, unemployment compensation, disability pay, pensions, annuities, and so forth. It is essential to determine household composition—minimally, the number of adults and minor children who live in the household, and their relationships to one another. Ideally, one should collect both respondent’s own income and all household income; however, there will be appreciable missing data on the latter unless all income-generating household members are interviewed. It is advisable to place income questions late in the questionnaire, after the interviewer has had time to establish rapport with and a pattern of responsiveness from the respondent. Another important issue is access; the evidence that increased gambling availability leads to an increase in problem gambling is appreciable (Abbott 2001; Abbott and Volberg 2000; Gambling Review Body 2001; National Research Council 1999; Productivity Commission 1999; Shaffer, LaBrie, and LaPlante 2004; Volberg 2002). Increased gambling opportunities sometimes create more problem gamblers by increasing the risk of exposure; as more people gamble, the risks that individuals with vulnerabilities will gamble and develop problems increase. Capturing the influence of gambling facilities requires correlating residence and venue data with map coordinates. In most countries, locations and revenues of venues such as casinos, racetracks, cardrooms, and lottery outlets can be obtained or derived from public sources; distances, densities, and gross revenue flows can be associated with respondent locations using residential locators such as self-reported street addresses or postal codes. Finally, it is well known that depression and problem gambling co-occur often in individuals (Petry et al. 2005). A number of depression screens are in use with excellent psychometric properties; researchers can choose the one that best suits their needs. Problem Gambling Help and Treatment General population surveys generally capture very few individuals who have sought treatment for gambling problems—in fact, only a tiny percentage of the individuals who indicate serious gambling problems in their lives report having
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sought out treatment (Gerstein et al. 1999). Report of help-seeking behavior should enumerate the main sources of help, including seeing a doctor or other health or counseling professional, enrolling in a residential treatment program, visiting an online group or website for problem gamblers, attending a Gamblers Anonymous meeting, and seeking gambling-related help from another mutual support group. Barriers to treatment are an important element for inquiry (Hodgins and el-Guebaly 2000; Rockloff and Schofield 2004). Specific questionnaire items about treatment barriers might ask whether there was ever a time when the respondent thought she should see a doctor, counselor, or other health professional or seek any other help for her wagering but did not go, and then follow up affirmative responses with an item probing why. Response categories, which should be tailored to the local context and ideally be pretested in a treatment population, might include that health insurance did not cover treatment, respondent was afraid she would have to stop gambling, respondent did not have transportation, respondent was too embarrassed to discuss the problem, the hours were inconvenient, respondent could not find a program in her preferred language, and/or respondent stopped gambling on her own. Finally, for those who have received treatment, its perceived effectiveness should be of paramount importance. A single question whether (or the extent to which) the treatment helped the person stop or cut back on gambling behavior is a good solution if survey time is at a premium.
GLOSSARY Questionnaire a written script used to ask questions and record responses.The goal of a questionnaire is to obtain information that is valid. Response effects partially or wholly invalid data resulting from issues related to questionnaire construction or contexts; examples include the desire to present a good image, fatigue or annoyance with the questions, and cultural or linguistic inconsistencies in understanding the meaning of questions.
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CHAPTER 4
Experimental Methodologies in Gambling Studies Sherry H. Stewart
Steven Jefferson
Departments of Psychiatry and Psychology, Queen Elizabeth II Health Sciences Centre, Dalhousie University Halifax, Nova Scotia, Canada
Department of Psychology Queen Elizabeth II Health Sciences Centre Halifax, Nova Scotia, Canada
Basic Components of an Experimental Research Study Internal and External Validity Types of Experimental Designs Group Experimental Designs Control Groups Randomization Single-Case Experimental Designs Withdrawal Designs Multiple Baseline Design Sample Experimental Methodologies Behavioral Observation Explicit Cognition Implicit Cognition Think Aloud Reaction Time Tasks Conclusions
Behavioral scientists can study gambling behavior and gambling disorders in the same way that scientists study other phenomena, such as the development of viral infections or novel methods of generating power—namely through the scientific or experimental method.Through experimental methods, behavioral scientists can help answer such questions as “Why do some people develop gambling problems?” and “What is the best way to treat someone suffering from pathological gambling?” Many ingenious methods have been developed for studying the behaviors that constitute problem gambling, why people develop gambling problems, and 87
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how to treat gambling disorders. In this chapter, we focus on one such set of methods for studying gambling behaviors and disorders: scientific or experimental research methods. The experimental method has the major advantage of being the only type of research design that can be used to draw conclusions about causation.
BASIC COMPONENTS OF AN EXPERIMENTAL RESEARCH STUDY We begin by examining three basic components of an experimental research study: the research hypothesis, the independent variable, and the dependent variable.When designing a research study, scientists begin with a research hypothesis, or an educated guess about the outcome of the study that will be tested empirically (i.e., through the collection of data). For example, you might hypothesize that availability of gambling establishments causes increases in gambling disorders. Or you might hypothesize that the best way to treat a problem gambler is by challenging his or her beliefs about the benefits of gambling. Once a researcher has decided what to study, the next step is to state the hypothesis clearly and in a form that is testable. For example, consider a study of the effects of gambling on drinking behavior (Stewart et al. 2002).The researchers observed a sample of 30 regular gamblers in a simulated bar in a laboratory environment which contained two video lottery terminals (VLTs). The researchers posed the hypothesis that engagement in VLT play, relative to engagement in a control activity (i.e., movie watching) in the same environment, would lead to an increase in purchases of alcoholic beverages from the “bar.” Phrasing the expected outcome in this manner made it testable—a characteristic that is important for the advancement of science. For example, it was possible that involvement in VLT play would have no impact whatsoever on purchase of alcoholic beverages; this alternative statement is called the null hypothesis in that it predicts no relationship between gambling and drinking behaviors. The researchers stated the hypothesis in a manner such that the results would lead to one of two possible conclusions. Either (1) alcohol consumption behavior is adversely affected by VLT gambling, so let’s study this more and examine the policy implications (e.g., the advisability of the common practice of housing VLTs in bars), or (2) alcohol consumption behavior is not affected by VLT gambling, so let’s look elsewhere for risk behaviors that might be affected by gambling. The next two important components of a research study are the independent and dependent variables. When a behavioral scientist develops a research hypothesis, he or she also specifies the independent and dependent variables. The dependent variable is the outcome variable or factor that the researcher expects to be influenced or to change in the study. In the area of gambling and gambling disorders, the dependent variable might be overt gambling behaviors, gamblers’ thoughts or feelings, physiological variables (e.g., heart rate while gambling;
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see Stewart et al. 2005), or symptoms of a gambling disorder. For example, in a recent study by Doiron and Nicki (in press) which investigated the effectiveness of a new problem gambling prevention program, the main dependent measures included levels of symptoms of pathological gambling, self-reported gambling behaviors, and gambling-related cognitions. Independent variables are those factors thought to affect the dependent variables. The independent variable is the factor that is manipulated by the experimenter. In the Doiron and Nicki study, the independent variable was the new intervention (participants either did or did not receive the new intervention). Statistical tests are then conducted to determine if the independent variable (e.g., exposure to the intervention) does indeed have an effect on the dependent variable (e.g., levels of gambling problems). If statistical tests do support an effect of the independent variable on the dependent variable, then the researcher must reject the null hypothesis of no relationship between these variables.
INTERNAL AND EXTERNAL VALIDITY A challenge faced by researchers conducting experimental research in the area of gambling behavior and gambling disorders is balancing ecological validity against the need for strict experimental control. In designing any research study, the researcher must balance concerns for internal validity against concerns that the findings are externally valid as well. Internal validity refers to the degree of experimental control within the study design and consequently the degree to which one can be confident that the manipulation of the independent variable is responsible for causing the outcome on the dependent variable(s). External validity, on the other hand, is the degree to which the findings can be generalized beyond the particular experiment—to individuals who were not engaged in the research study in question, and to settings beyond the laboratory (i.e., to the real-world gambling context).The more stringent the controls exerted within the research design, the greater the internal validity of the study. But internal validity is often achieved at the expense of internal validity, and vice versa. To illustrate the importance of external validity in gambling study design, research by Leary and Dickerson (1985) showed that gambling studies conducted in sterile laboratory environments that used mock gambling machines and participants who played for nonmonetary incentives did not generalize to gambling behavior in the real world. The findings of Leary and Dickerson suggest that it is important to set up the gambling situation within experimental gambling studies to be as close to the real-world gambling situation (i.e., as naturalistic) as possible. For example, one might choose to use real-world games such as commercially available gambling machines in the research, as opposed to simulated gambling tasks. And one might design the study to have participants playing for real money as opposed to using nonmonetary incentives. As another example, environmental cues of the testing
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environment can be matched as closely as possible to the real-world gambling context (e.g., testing in a bar-lab type of situation for VLT gambling research or mocking up a casino environment for a study of casino betting). These types of design features enhance the ecological validity of the study and increase its external validity, or the chances that the results of the research will generalize to the real-world gambling situation (see Stewart, Blackburn, and Klein 2000 for a review). A study by Ladouceur et al. (1991) confirms the importance of attending to issues of ecological validity of the testing environment. These authors found few significant differences on gambling cognitions and behaviors between nonpathological gamblers (video poker players) examined in a laboratory versus natural settings. However, the authors carefully simulated many aspects of the real-world gambling situation within their laboratory environment. For example, in both environments, participants played with real money, the amount of which was determined by each participant’s regular weekly bet; participants were allowed to keep all winnings from the study gambling situation; and participants played on commercially available video poker machines. On the other hand, one must attend to internal validity in experimental gambling research design. In experimental research, it is important to exert control over extraneous, potentially confounding variables.Without a reasonable degree of control of irrelevant variables, it is impossible to draw confident conclusions about the important factors underlying an interesting behavioral finding. Let us look at a sample experimental gambling study to illustrate the issue of balancing concerns about external validity against the need for internal validity within the study design. In a study by Ellery, Stewart, and Loba (2005), on the effects of alcohol intake on risk taking during VLT play, regular VLT players were randomly assigned to one of two beverage conditions. In the experimental condition, the beverage was a fixed, mildly intoxicating dose of vodka mixed with orange juice. A second condition was included where participants consumed only the orange juice, to control for the effects of drinking per se. Drinking only orange juice while playing VLTs may not be a typical experience for many VLT players, so the decision to include this control group may have compromised ecological validity of the study and reduced external validity. However, this control was necessary to determine whether risk taking observed among VLT players who drank vodka and orange juice was specifically due to alcohol intake. Another control exerted in this study was alcohol dose. All of those in the experimental group were administered a fixed dose of vodka that was based on their body weight and was designed to target a specific blood alcohol concentration (i.e., 0.06%, which is equivalent to mild intoxication).This particular dose was chosen because it is the usual dose of alcohol reportedly consumed by regular VLT players when they are gambling at the machines (Focal Research 1998). In other words, this dose was chosen to maximize ecological validity, and it was standardized across participants to enhance internal validity (i.e., so the researchers could make confident statements about the effects of this particular mildly intoxicating dose of alcohol on risk taking during
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VLT gambling). However, the decision to standardize dose across participants may have enhanced internal validity at the expense of external validity; presumably, this particular chosen dose is not the dose of alcohol that would normally be consumed by some of the regular-gambler participants while playing VLTs in the real world (i.e., some drink more heavily and some drink less than the chosen dose). Another threat to external validity pertains to alcohol restrictions in simulated experiments. For example, Diskin and Hodgins (2001) conducted a study designed to assess the presence of dissociative-like states in VLT players. Participants, who were community-based VLT players, were required to respond to a flash of light emitted in their periphery while they were using the machines.The time required to respond to the light was one of the dependent variables in this experiment. One participant was not permitted to take part in the study because he was under the influence of alcohol. From the perspective of internal validity, this seems reasonable, given that reaction time is considerably influenced by alcohol (e.g., Holloway 1995). In terms of external validity, however, the decision to exclude this participant might seem inappropriate when one considers that the majority of VLT players drink while playing the VLT machines (Focal Research 1998). Upon first consideration, the task of enhancing the ecological validity of the experimental testing environment may not seem difficult; lighting, music, and other nuances of gambling settings can generally be emulated rather easily in laboratories. However, particularly for regular gamblers who tend to gamble in one location (e.g., a local bar), there are likely to be myriad stimuli that have come to be associated with gambling (e.g., through higher-order conditioning processes), and such stimuli may be difficult or impossible to reproduce in an experimental laboratory setting. One such stimulus, for example, might be particular staff members of an individual’s regular gambling setting. Thus, it is important to recognize that even though one can go to great lengths to enhance the ecological validity of the experimental laboratory testing environment in gambling research, there will always be limits that can impact the external validity of the research findings.
TYPES OF EXPERIMENTAL DESIGNS GROUP EXPERIMENTAL DESIGNS The most common type of experimental research design used in gambling studies is the group experimental design, which involves comparisons of groups of individuals. To enhance the internal validity of the study (i.e., the degree to which we can be certain that changes in the dependent variable are actually due to changes in the independent variable), participants in an experimental group (who receive one level of the independent variable) are compared with another group of individuals, referred to as the control group, or comparison group, who receive the other level of the independent variable. In the Ellery et al. (2005) study on the effects of alcohol
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on VLT play, those in the experimental group received a moderate dose of alcohol, while those in the comparison group received a control (i.e., nonalcoholic) beverage. The inclusion of a control group for comparison helps the researcher rule out alternative explanations for the change in the dependent variable. Control Groups There are several types of control groups that are available to gambling researchers depending on the purposes of the study. For example, in the area of gambling interventions research, there are at least four types of control groups that can be employed. First, there is the no-treatment control, where participants randomized to the experimental intervention are compared against a control group of individuals who receive no intervention. A no-treatment group controls for any influences of the passage of time, since individuals in both groups complete the dependent variables at pretreatment and again at posttreatment (and/or follow-up) after the intervention is completed.The major drawback of this type of design is an ethical one: Should treatment be withheld from an individual who needs it for a gambling problem? The remaining three types of control groups do not pose this type of ethical concern. Another control group for the passage of time is the wait-list control group (or delayed treatment control group). In this type of design, control group participants have treatment withheld but only temporarily, until the two sets of dependent measures have been completed. In the Doiron and Nicki (in press) intervention study, those in the experimental group were exposed to the novel preventive intervention, while those in the control/comparison group were not. Rather, the control participants were assigned to what is referred to as a wait list, where they completed the same dependent measures as those in the experimental group and then were exposed to the intervention at a later time. By including a wait-list control group in their study design, Doiron and Nicki (in press) were able to conclude that changes in levels of gambling problems, gambling behaviors, and gambling cognitions were due to their new intervention itself rather than just the passage of time per se. Otherwise, the researchers would have seen similar changes on the various dependent variables among those assigned to the wait-list control group. This type of design takes advantage of the unfortunate fact that many addiction treatment centers have long wait lists, where potential patients/clients have to wait for services until a counselor becomes available. A third type of comparison group controls not only for the passage of time, but for expectations that clients may hold about the treatment. For example, although it was clear in the Doiron and Nicki (in press) study that some aspect of the intervention other than simply the passage of time was responsible for the observed changes in gambling behaviors, cognitions, and problems, it was not clear whether the observed improvements were caused by the contents of the intervention itself, by patient expectations for changes, or by a combination of the two. A placebo control treatment is designed to control for patient expectancies about treatment effects. A placebo (from Latin, “I shall please”) such as a harmless sugar cube is typically given to members of control groups in drug studies to make them believe that they are
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getting the real medication being given the experimental group (e.g., MacDonald et al. 2001). Although this control for expectancy is relatively easy to achieve in medication studies (e.g., Kim et al. 2001), it is not always so easy to “pull off ” in studies examining psychological treatments for gambling problems because it is not always easy to devise a placebo “treatment” that problem gamblers believe would help them but that does not include the component the researcher thinks will be effective (see Barlow, Durand, and Stewart 2006). Most often, placebo psychological treatments involve therapist attention, time, and concern but do not involve any active provision of the components of therapy the researchers believe are important for change in gambling behaviors (e.g., cognitive or behavioral techniques). Finally, a fourth type of control group involves comparing a new treatment (experimental group) with an existing, established treatment (control group) in a type of study called comparative treatment research, also commonly referred to as a “race horse” study, because it will determine which treatment comes in first. For example, the results on various dependent measures in a cognitive-behavioral treatment for problem gambling might be compared against those in treatments with a medication like naltrexone. This type of research study answers the clinically important question of whether a new treatment performs as well or better than other, established treatments. Randomization Another method used to increase internal validity in group experimental designs is random assignment to the experimental or the control group (or randomization). Random assignment, where membership in the experimental or control group is determined by chance (e.g., by flipping a coin or by using a random number table), means that each participant has an equal chance of being placed in any group within the research design. This method improves internal validity by eliminating any systematic bias in assignment (Asmundson, Norton, and Stein 2002). For example, if a researcher examining a new treatment for pathological gamblers at a gambling clinic did not assign participants to the experimental and control groups randomly, but instead assigned the first 50 who presented to the clinic to the control group and the second 50 to her new treatment, she might introduce bias if referral patterns changed over time (e.g., if the gamblers referred to treatment had more severe problems over time). This type of confound might seriously affect the internal validity of her study; thus, the process of randomization is extremely important in group experimental designs.
SINGLE-CASE EXPERIMENTAL DESIGNS When large groups of patients are not available, or when a researcher has first developed a new treatment strategy, the single-case experimental design can be considered as an alternative to the traditional group design. B. F. Skinner formalized
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the scientific research method of single-case experimental designs (see Barlow et al. 2006 for more information).This methodology involves the systematic study of individuals under various experimental conditions. Single-case experimental designs differ from case studies (i.e., the detailed description of individual cases) in that the researchers use a number of different strategies to improve internal validity, thus reducing the influence of potential confounds. Single-case experimental designs have both advantages and disadvantages relative to traditional group experimental designs, described in the previous section. Single-case designs allow us to learn a lot about the behavior of one individual, whereas traditional group designs involve making only a few observations of a large group, allowing us to make conclusions about the “average” response.The single-case experimental methodology has greatly helped us understand the factors involved in individual psychopathology (Barlow et al. 2006). In terms of applications to the study of gambling, single-case experimental designs can help us explain why individual people engage in gambling behavior, as well as how to treat those with problem or pathological gambling. One of the most important features of single-case experimental designs is repeated measurement. In contrast to the traditional group design, where the behavior of interest is measured only once before and once after the independent variable is applied, in single-case experimental designs the behavior is measured several times. More specifically, the researcher takes the same measurements over and over to learn how variable the behavior is (How does it change day to day?) and whether it shows any obvious trends (Is it getting better or worse?). Suppose a young man, Devin, comes into the therapist’s office complaining about a preoccupation with gambling. When we ask him to rate the level of his gambling preoccupation on a scale from 1 to 10, he gives it a 9, indicating a severe level of preoccupation. After several weeks of treatment, Devin rates his gambling preoccupation at 6, indicating a moderate level of preoccupation. Can we say that the treatment reduced his preoccupation with gambling? Not necessarily. Suppose we had measured Devin’s preoccupation with gambling each day during the weeks before his visit to the office (repeated measurement) and observed that it differed greatly. On particularly good days, he rated his preoccupation from 5 to 7. On bad days, it was up between 8 and 10. Suppose further that even after treatment, his daily ratings continued to range from 5 to 10.The rating of 9 before treatment and 6 after treatment may only have been part of the daily variations he experienced normally. In fact, Devin could just as easily have had a good day and reported a 6 before treatment, and then had a bad day and reported a 9 after treatment, which would imply that the treatment made him worse! Repeated measurement is part of each single-case experimental design. It helps identify how a person is doing before and after intervention and whether the treatment accounted for any changes. Consider yet another possibility with respect to Devin’s preoccupation with gambling. Maybe Devin’s preoccupation
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with gambling was on its way down before the treatment.This alternative pattern would also have been obscured with just a single before-and-after treatment measurement. Maybe he was getting better on his own, and the treatment didn’t have much, if any, effect on his gambling preoccupation. Although the first scenario shows how the variability from day to day could be important in an interpretation of the effect of treatment in Devin’s case, the second scenario shows how the trend itself can also be important in determining the cause of any change in his gambling preoccupation. There are thus three important parts of repeated measurements in singlecase experimental designs (see Barlow et al. 2006): (1) the level or degree of behavior change with different interventions, (2) the variability or degree of change over time, and (3) the trend or direction of change. Again, before-and-after scores alone do not necessarily show what is responsible for behavioral changes. For example, the researcher would feel much more confident that the treatment was responsible for Devin’s change in gambling preoccupation if (1) there were consistently high scores over repeated assessments at baseline (prior to treatment) and consistently lower scores over repeated assessments after the treatment was applied (the issue of variability); (2) the trend in the data suggested no change at baseline, versus his gambling preoccupation showing a downward trend only after the treatment was applied (the issue of trend); and (3) the degree of change in gambling preoccupation from pre- to post-treatment were fairly substantial when collapsed across the repeated measurements (the issue of level of change in the variable of interest). Now we will examine two of the most common types of single-case experimental designs and illustrate how they might be applied to the study of gambling behavior or gambling problems.The two types we will examine here are the withdrawal design and the multiple baseline design (see Barlow et al. 2006 for further detail). Withdrawal Designs One of the more common strategies used in single-case experimental research is a withdrawal design, also known as an ABAB design. In this design, the researcher tries to determine whether the independent variable is responsible for changes in behavior.The effect of Devin’s treatment could be tested by stopping it for a period of time to see whether his gambling preoccupation increased.A simple withdrawal design has four parts. First, a person’s condition is evaluated before treatment, to establish a baseline, which is the first “A” in the ABAB design.Then comes the first change in the independent variable—in Devin’s case, the beginning of treatment, or the first “B” in the ABAB design.Third, treatment is withdrawn (the return-tobaseline phase, or the second “A” in the ABAB design) and the researcher assesses whether Devin’s level of preoccupation with gambling changes again (i.e., increases)
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as a function of this step. Finally, the independent variable is manipulated once again; in Devin’s case, the treatment is applied once more: the second “B” in the ABAB design. If with the first application of the treatment, Devin’s gambling preoccupation lessens in comparison to baseline, then worsens again after treatment is withdrawn, and then improves once more when the treatment is reapplied, the researchers can confidently conclude that the treatment has reduced Devin’s gambling preoccupation. An important difference between this design and that of a case study is that the change in treatment is designed specifically to show whether treatment caused the changes in behavior. Although case studies often involve treatment, they don’t include any effort to learn whether the person would have improved without the treatment.A withdrawal, or ABAB, design gives researchers a better sense of whether or not the treatment itself caused behavior change. In spite of their advantages, withdrawal designs are not always appropriate. Two main arguments have been presented against withdrawal designs: one ethical and the other practical. First, the researcher is required to remove what might be an effective treatment, a decision that is sometimes difficult to justify for ethical reasons. In Devin’s case, a researcher would have to decide that there was a sufficient reason to deliberately produce his gambling preoccupation again. Suppose we knew that high levels of gambling preoccupation have often been accompanied by marked depression and suicidal thinking in Devin’s case. Would it be ethical to remove what might be an effective treatment for his gambling preoccupation at the risk of inducing suicidal thoughts, just to learn whether it was really the treatment that was responsible for Devin’s decreased gambling preoccupation? Barlow et al. (2006) have presented several counterarguments to this particular concern about the use of withdrawal designs. First, they note that treatment is routinely withdrawn when medications are involved so clinicians can determine whether the medication is responsible for the treatment effects and thus avoid any unnecessary medications. Second, they note that the withdrawal phase does not have to be prolonged; a very brief withdrawal may still clarify the role of the treatment. The second problem identified with withdrawal designs is a practical one. A withdrawal design is unsuitable when the treatment can’t be removed by the researcher. Suppose Devin’s treatment involved having him visualize himself by a pool at a southern resort. It would be very difficult—if not impossible—to stop Devin from imagining this situation, making it very difficult to institute the treatment withdrawal phase. Similarly, some treatments involve teaching clients skills which might be impossible to unlearn. If, through treatment, Devin learned skills that helped him be less preoccupied with gambling, it would be difficult to reverse this even if the treatment itself were withdrawn.Another single-case experimental design, the multiple baseline design, which we will examine next, addresses this particular limitation.
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Multiple Baseline Design Another commonly employed single-case experimental design strategy that doesn’t have some of the drawbacks of a withdrawal design is the multiple baseline design. Rather than stopping the intervention to see whether it is effective, the researcher starts treatment at different times across settings (e.g., bar vs casino), behaviors (e.g.,VLT gambling vs. racetrack betting), and/or participants (e.g., different people with a gambling disorder). For example, after waiting a period of time and taking repeated measures of Devin’s gambling preoccupation both at a bar and at the casino (the baseline), the researcher/clinician could treat him first in the bar setting. When the treatment begins to be effective in the bar setting, intervention could then begin in the casino setting. If he improves in the bar setting only after beginning treatment and improves in the casino setting only after treatment is also used there, we can confidently conclude that the treatment was effective. This is an example of using multiple baselines across settings. Internal validity improves with a multiple baseline design because other explanations for the results can be ruled out. Devin’s gambling preoccupation improved only in the settings where it was treated, which rules out competing explanations. For example, if Devin got married to a very supportive spouse at the same time that treatment started and his gambling preoccupation decreased in all gambling-related situations, we couldn’t conclude that his condition was affected by treatment. Suppose a researcher wanted to assess the effectiveness of a treatment for a problem gambler who appeared to be addicted to multiple forms of gambling behavior.Treatment could first focus on his VLT gambling, then on a second problem, such as racetrack betting, and then a third problem, such as casino blackjack. If the treatment was first effective only in reducing VLT gambling behavior, then was effective for his racetrack betting only after the second intervention, and then was effective for his blackjack gambling only after the third intervention, the researcher could conclude that the treatment, not something else, accounted for the improvements.This is an example of a multiple baseline design conducted across behaviors. Single-case experimental designs are sometimes criticized because they tend to involve only a small number of cases, leaving their external validity in doubt. In other words, we can’t say that the results we saw with a few people would be the same for everyone. However, although they are called single-case designs, researchers can and often do use them with several people at once, in part to address the issue of external validity. Symes and Nicki (1997) recently studied the effectiveness of a behavioral treatment called exposure and response prevention (ERP) in the treatment of problem gambling. Briefly, the theory behind ERP, drawn from the treatment of obsessive-compulsive disorder, is that exposing a problem gambler to the stimuli associated with gambling (e.g., the VLTs and the associated setting) while preventing the response of gambling will allow the urge to gamble to extinguish. Using multiple baselines across participants, the researchers
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introduced this treatment to a set of two pathological gamblers—a 23-year-old female and a 20-year-old male. Their dependent variables were self-reports of gambling behavior and urges to gamble. Only when treatment began did each pathological gambler’s gambling urges and behaviors improve. This design let the researchers rule out coincidence or some other change in the problem gamblers’ lives as an explanation for the improvements. Among the advantages of the multiple baseline design in evaluating treatments is that it does not require withdrawal of treatment, since, as we’ve seen, withdrawing treatment is sometimes difficult or impossible. Furthermore, the multiple baseline design typically resembles the way treatment would naturally be implemented. A clinician can’t help a client with numerous problems simultaneously (e.g., one who is addicted to multiple forms of gambling or one who is addicted to both gambling and drugs/alcohol) but can take repeated measures of the relevant behaviors and observe when they change.A clinician who sees predictable and orderly changes related to where and when the treatment is used can conclude that it is the treatment that is causing the change.
SAMPLE EXPERIMENTAL METHODOLOGIES In the next section of the chapter, we turn our attention to several types of research methodologies that researchers can make use of in experimental gambling studies. Specifically, we review techniques involving behavioral observation and those involving the measurement of cognition (both explicit and implicit cognitive measures, and both “offline” and “online” cognitive measures).
BEHAVIORAL OBSERVATION Generally speaking, scientific knowledge is accrued through observation. In many cases, it is possible to observe directly the variable of interest. For example, an animal researcher may believe that overcrowding leads to aggression in rats. In order to test this hypothesis, he or she may place rats in densely populated quarters and then observe their behavior. Instances of aggression (the variable of interest) can be directly observed, provided that they are appropriately operationally defined. Gambling behaviors that can be observed directly include engaging in superstitious rituals and “chasing” losses. In other cases, the variable in question cannot be observed directly. Its presence and/or strength, however, can be inferred by observing its purported effects. For example, astronomers have discovered dozens of planets outside of our solar system by observing the manner in which their gravitational pull causes nearby stars to “wobble” as they rotate on their axes. Detection of these planets via direct
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observation is not currently possible because they are too small and too far away. In the field of gambling studies, irrational beliefs concerning the outcome of gambling events cannot be observed directly.Their existence, however, may be inferred by observing gamblers’ behavior during a gambling session. For instance, Henslin (1967) observed that experienced dice players modified the force with which they rolled the dice in order to obtain desired numbers. Specifically, they rolled the dice hard for high numbers and softly for low ones. From this behavior, one might infer that dice players believe that there is an association between the force of the throw and the outcome of the roll. In gambling studies, researchers can utilize two different types of observation: Naturalistic observation and simulated observation. In naturalistic observation, participants are observed in their usual gambling setting, such as in a bar or casino. As an example of this method, King (1990) observed superstitious behaviors exhibited by bingo players while they played in a community bingo parlor. Simulated observation consists of observing gamblers in an “artificial” setting, typically in the researcher’s laboratory. Quite often, efforts are made by the researcher to ensure that the setting does not differ markedly from “natural” gambling venues, as alluded to at the beginning of the chapter.Thus, a simulated gambling setting may include soft lighting or music playing in the background. One of the main problems with naturalistic observation is reactivity, which refers to changes in behavior that occur as a result of being monitored. In gambling research, participants may feel the need to “sanitize” some of their gambling-related behaviors in an effort to avoid embarrassment and/or maintain social desirability. For example, individuals who frequently swear at VLTs may be reluctant to do so when they know that they are being watched. Reactivity may attenuate over time, as participants become increasingly accustomed to the observer’s presence (Spiegler and Guevremont 1998). An alternative is for the experimenter to be as unobtrusive as possible to reduce reactivity. A rather extreme example is a naturalistic observation study by Parke and Griffiths (2004) in which they monitored a sample of 303 slot machine players who were engaged in gambling. The site chosen for this study was a gambling arcade that housed 54 slot machines. The principal investigator obtained employment as an arcade supervisor at this establishment to minimize the experimenter’s obtrusiveness. In this role, he observed gamblers over four 6-hour periods. The “participants” in this study were unaware that their behavior was being monitored for research purposes.This lack of awareness, combined with the fact that the principal investigator actually appeared to be an arcade employee, maximized the ecological validity of the study and prevented reactivity. Nonetheless, in making decisions about how to best minimize experimenter obtrusiveness in naturalistic observation studies, one must balance these concerns against the participants’ ethical right to informed consent. Another problem with naturalistic observation is that it is sometimes difficult to define operationally a construct of interest. For example, consider dissociation
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during VLT gambling. It has been suggested that VLT play is particularly appealing to gamblers who seek escape from their difficulties (Dickerson 1993; Wynne 1994) and that VLT players in particular become highly engrossed while using the machines. However, while observing VLT gamblers in their usual setting, what behaviors would lead one to suspect that they were experiencing dissociative-like states? One might propose that obliviousness toward surrounding stimuli (e.g., music, others’ attempts to make conversation with the gambler) suggests the presence of dissociation.This may be valid, but how could one operationally define such behaviors? Observational studies in which target behaviors are vaguely defined (or not amenable to clear operational definitions) are likely to result in low levels of interobserver reliability (i.e., agreement between observers) and therefore poor replicability. Finally, as a research method, naturalistic observation is descriptive, not explanatory. Controlled conditions are not present, which makes it difficult or impossible to discover cause-and-effect relationships. For example, in the naturalistic observation study by Parke and Griffiths (2004), slot machine players were monitored for instances of aggression while gambling. A total of 165 instances of aggression were noted over the observation periods, including verbal aggression aimed at arcade staff, slot machines, and other gamblers. Physical aggression against the machines (e.g., kicking) was also noted.The authors speculated that introjected anger was the cause of aggression in this study (particularly the verbal aggression meted out against the arcade staff).Although this may be a tenable statement (especially among proponents of Freudian theory!), the observational nature of this study did not allow the researchers to draw conclusions about the underlying causes of the “aggressive behavior” with confidence. Simulated observation in gambling research is more consistent with the experimental method in that it carries the notable advantage of allowing the experimenter to control important variables. Examples of variables that have been controlled in simulated gambling settings include alcohol intake (Stewart et al. 2006), payout rate of gambling activities (Bechara et al. 1994), and structural characteristics of VLTs (Loba et al. 2001). Although experimental control is generally desirable, it may pose serious threats to external validity, as discussed earlier in this chapter. A difficulty with both naturalistic and simulated observation is that behavioral data gleaned from these methods may occur relatively infrequently—that is at a low base rate. For example, in a bar-based study of the effects of VLT machine manipulations (e.g., of the apparent speed at which the reels spin in a spinningreels game; see Loba et al. 2001), some of the behaviors of interest occurred at a very low rate (e.g.,“cashing out” occurred on average substantially less than once per hour). Thus, researchers need to plan appropriately long observation intervals to capture the phenomena of interest when conducting naturalistic observation studies.A more practical alternative can be the use of simulated observation where the situation is designed to elicit the phenomenon of interest. If one is interested in examining players’ responses to wins, and winning is a low-base-rate phenomenon
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(which it often is in gambling!!), a researcher may decide to manipulate the win rate (see Ladouceur et al. 1988) to ensure that the player experiences a win during the period of observation. In fact, means exist of adjusting win frequency experimentally. For example, MacLin, Dixon, and Hayes (1999) have developed a computerized slot machine simulation that was designed to examine many of the potential variables involved in gambling behavior. This simulation program is designed to run on a computer and allows the experimenter to manipulate a number of variables, including probabilities of payoffs. It also allows the researcher to program specific sequences of losses and wins. Data are recorded on a trial-by-trial basis and can be easily imported into many statistical analysis packages.
EXPLICIT COGNITION Individuals with gambling problems often exhibit distorted beliefs related to the outcome of gambling events. One of these beliefs, for example, is the gambler’s fallacy, which is the notion that the longer one has gambled without winning, the greater become the chances of placing a winning bet. This line of reasoning is incorrect in any gambling activity in which there is independence among the outcomes. In order to assess the magnitude of gambling cognitive distortions, a number of methods have been employed. These include self-report inventories, think-aloud procedures, and behavioral observation. In behavioral observation, an individual’s conviction in a particular gambling belief is inferred from his or her behavior, as we discussed in the behavioral observation section above. Self-report inventories, on the other hand, require the examinee to reflect on a particular gambling belief and then indicate whether and/or the extent to which they hold it to be true (this is typically accomplished through true-false questionnaires or inventories involving Likert scale ratings). For instance, the Informational Biases Scale (IBS) ( Jefferson and Nicki 2003) requires examinees to rate their conviction regarding 25 statements related to VLT gambling. Sample items include the following: “After a long string of wins on a VLT, the chances of losing become greater” and “There are certain strategies (e.g., betting all of your credits at once) that one can use with VLTs to help him or her win.” An implicit assumption often made by researchers is that beliefs influence behavior. For example, an individual who wholeheartedly endorses the gambler’s fallacy would be expected to continue gambling in spite of mounting losses. However, it appears that behaviors and beliefs are not always congruent. For example, Steenbergh et al. (2004) examined the impact of warning and informational messages on irrational beliefs and gambling behavior. A total of 101 undergraduate students, all of whom had gambled at least once, were matched on self-efficacy and level of gambling distorted beliefs and then randomly assigned to receive a warning
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message, a warning message plus information about limit setting and gambling cognitive distortions, or a 10-minute film about the history of gambling. Participants then played an electronic version of roulette. It was found that the individuals in the two message conditions exhibited greater knowledge about the risks of gambling than did those who watched the video only. Also, participants who received the warning message plus the additional information described above exhibited significant reductions in their levels of gambling cognitive distortions. However, there were no differences in gambling behavior among participants across the three groups.Another example of the discrepancy between cognitions and behavior was obtained in a recent study by Ellery, Stewart, and Collins (2006).They examined the effects of alcohol versus a placebo beverage on the gambling cognitions and behaviors of high- and low-risk VLT gamblers. Consistent with the original hypotheses, the authors found that alcohol consumption increased risk-taking behaviors during VLT play, particularly among the high-risk gamblers. However, although alcohol increased irrational gambling cognitions (on the IBS), this occurred only in the low-risk gamblers! These two studies show that self-reported gambling cognitions as reported on questionnaires are not always congruent with actual gambling behaviors. Conversely, although individuals may behave as though they hold a certain belief, it does not necessarily follow that they actually endorse that belief. For example, in a study conducted by Langer (1975), participants played a card game known as “war” against either an opponent who presented as shy and awkward or one who appeared confident and debonair.War is a very simple card game—each player is dealt a card and the one with the higher card wins. Langer reported that participants in this experiment behaved as though they were more likely to beat the shy opponent than the confident one. Presumably, however, it was clear to the participants (who were university students) that luck was the only factor in this game that determined who would win and who would lose. Thus, it is highly doubtful that any of them would have responded affirmatively if asked whether personality characteristics conferred an advantage (or disadvantage) in the game of war. Given these examples of the sometimes divergent results between measures of explicit gambling-related cognitions and gambling behavior, it is advisable to include measurements of both in research studies examining explicit cognition. It might also be advisable to study another aspect of gambling-related cognition that is theoretically less susceptible to the many forms of bias involved in self-report cognitive measures—namely, measures of implicit cognition.
IMPLICIT COGNITION Implicit cognitive processes are mental activities that transpire outside of our conscious awareness and control. It is believed that such processes influence our perception, decision making, and behavior. Several branches of psychology
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have utilized implicit cognition research designs in order to obtain a deeper understanding of phenomena of interest. For example, social psychologists frequently use the Implicit Association Test (IAT), which provides information concerning the automatic associations that people make between concepts (e.g., science and the arts) and attributes about themselves or others. Investigators who study various psychological disorders have also been known to explore implicit processes in their research. To date, little research has been conducted concerning implicit processes in gamblers. However, several researchers have acknowledged the potential importance of such factors in addictive behaviors. For example, Stacy, Leigh, and Weingardt (1994) purport that implicit associations in memory mediate addictive behavior (see also Hills and Dickerson 2002). Implicit cognition paradigms that have been utilized in gambling research include Stroop interference tasks, IATs, and Lexical Salience Tasks (LSTs) (see below and review by Zack and Poulos 2006). In the original Stroop (1935) task, various stimuli (e.g., geometric shapes, a series of Xs, commonly used words) were printed in colored ink. Participants were required to name, as quickly as possible, the color of ink in which each stimulus was printed. It was consistently found that participants took longer to name the color of ink for color words (e.g., to say “red” in response the word blue printed in red ink) than to name the ink color of shapes, a string of Xs, or non–color words. Stroop’s interpretation of this phenomenon was that the automatic (i.e., unconscious) processing of the color word’s meaning interferes with the task of rapid ink-color naming. McCusker and Gettings (1997) used a Stroop-like task to explore interference with gambling-related words in problem gamblers who either used VLTs or bet on horse races. Participants were presented with colored words that were neutral in content and drug and gambling related. It was found that overall, the problem gamblers took longer to color-name gambling-related than neutral or drug-related words. A specificity effect was also noted: VLT players exhibited interference effects for VLT-related words only, and horse race gamblers displayed interference only to track-related words. The IAT requires the test-taker to rapidly sort various stimuli into four categories using two response keys, where two of the categories (e.g., alcoholic beverages and reward outcomes) are assigned to the first response key and the other two categories (e.g., nonalcoholic beverages and relief outcomes) to a second response key. It is assumed that rapid categorization when two concepts are assigned the same key is indicative of strong implicit association between the two concepts (e.g., a strong semantic association between alcoholic beverages and reward outcomes). Comparatively slow sorting indicates a lack of implicit association or a semantic dissociation between the two concepts assigned to a given key. Using an IAT paradigm, Zack et al. (2005) assessed responses to alcoholic or nonalcoholic beverage words paired with words related to gambling win outcomes
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and words related to gambling losses. All participants in the study met the South Oaks Gambling Screen (SOGS) (Lesieur and Blume 1987) criteria for problem gambling. It was found that gamblers who drank when they won exhibited faster response times on trials in which alcohol-related words were paired on the same response key with gambling win words (e.g., jackpot) than when they were paired on the same response key with gambling loss words (e.g., forfeit).The authors suggested that drinking in response to gambling wins creates associations between gambling wins and alcohol in memory and that such implicit associations may promote drinking while gambling (e.g., to celebrate winning or the anticipation of winning). Implicit cognition tasks frequently make use of the fact that reading is a highly automatic activity. In an LST, participants are asked to read a list of words as quickly as possible.The time required to read a word is taken to be an index of the word’s importance to the reader. The assumption is that faster response times for the reading of schema-relevant words are indicative of schema activation engendered by exposure to those words. In LSTs, words are often “degraded” by intersticing arbitrary symbols among the letters (e.g., j*a*c*k*p*o*t) (see Stanovich and West 1983). An example of an LST in gambling research comes from Zack and Poulos (2004), who investigated the effects of amphetamine administration on implicit cognition.They employed a factorial design that included two groups of gamblers (problem vs. nonproblem). The amphetamine administered was a 30-mg dose of d-amphetamine, a psychomotor stimulant.This drug was chosen because previous research indicated that problem gamblers tend to describe an imagined gambling session in much the same manner that psychostimulant users describe the psychoactive effects of their drug (Hickey, Haertzen, and Henningfield 1986).The idea behind examining the effects of the amphetamine on implicit gambling cognitions was that the amphetamine might induce a state similar to that experienced by gamblers when gambling and thus might automatically activate their gambling schema. The investigators used five categories of words in their LST: gambling, alcohol, positive affect, negative affect, and neutral (e.g., window). It was found that amphetamine produced different effects in problem gamblers versus nonproblem gamblers. Nonproblem gamblers exhibited faster latency times for all classes of words—a nonspecific stimulation effect. In the problem gamblers, however, it appeared that amphetamine led to rapid responding to gambling words but inhibited responses to neutral words.Amphetamine had no effect on problem gamblers’ response time to alcohol words. Thus, this study made successful use of the LST implicit cognition task to demonstrate that, consistent with hypothesis, amphetamine automatically and selectively activates problem gamblers’ gambling schema. We now turn to methodologies that allow for examination of gambling-related cognitions “online,” while gamblers are actually engaged in gambling behavior.
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The first of these is a measure that would be considered to tap explicit gamblingrelated cognitions online (i.e.,Think Aloud), and the second would be considered to tap online implicit cognition (i.e., a reaction time task).
THINK ALOUD In the Think Aloud (TA) method (Ericsson and Simon 1980), gamblers verbalize their thoughts while they are engaged in a gambling activity.These verbalizations are typically recorded by the researcher, transcribed, and then examined for the presence of irrational statements. It is assumed that irrational statements are manifestations of underlying gambling cognitive distortions. One problem with the TA method is reactivity; once voiced, a certain statement may sound dubious or surprising to the participant and may therefore influence his or her subsequent thoughts and/or actions. An illustration of this comes from a study by Griffiths (1993), who analyzed 30 VLT gamblers via the TA method. After each participant was tested, he or she was given the option of listening to the audio recording of the session. Only four wished to do so, and, according to Griffiths, all were quite surprised by what they had said during the session. Several months after the study, Griffiths accidentally encountered one of the participants, a 19-year-old male. This young man had been diagnosed as a problem gambler at the time of the study. Griffiths described the content of their brief conversation: [S]ince taking part in my study, his gambling behavior had declined and subsequently ceased. He claimed that a large factor in the cessation of his gambling was hearing the playback of his recording. He claimed he could still remember some of the things he heard on the tape but reiterated his disbelief at what he had verbalized. He further claimed it was his disbelief that prompted him to examine and monitor his [gambling] behavior more closely.Through this self-introspective process, he realized the futility of his gambling and eventually stopped playing. (p. 296)
REACTION TIME TASKS An advantage of the TA method is that it provides a means of assessing cognition in participants while they are involved in a gambling task. However, in addition to the problem of reactivity, other problems associated with the assessment of explicit cognition (e.g., assumption that gamblers are aware of their cognitive content and can report on it accurately) plague the TA method. Other methods of assessing gambling-related cognition online are not subject to these problems and fall more under the category of implicit cognitive measures, as described earlier. One such alternative method, involving reaction time (RT) was provided by
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Diskin and Hodgins (1999), who explored the tendency to become highly absorbed during gambling sessions as a function of problem gambling status. Pathological and occasional VLT players were required to respond to a light that appeared intermittently in the periphery of their vision while they were playing VLTs in a simulated laboratory setting.The investigators hypothesized that pathological gamblers would focus their attention more intensely on VLT play than would the occasional gamblers and, as a result, would take longer to respond to the light. As predicted, the problem VLT gamblers took significantly longer than the occasional gamblers to respond to the peripheral light. Additionally, pathological gamblers reported more symptoms of general dissociation than did the occasional gamblers. In a later study, Diskin and Hodgins (2001) again compared pathological and occasional VLT gamblers on their response times to the light stimulus. However, in this study, the experimental design was slightly modified. Participants were randomly assigned to one of two conditions: baseline first and VLT first.The baseline-first condition consisted of a five-minute RT test to the light stimulus while the VLT screen was covered. Immediately after the baseline-first task, the VLT was uncovered and participants were asked to play the machine in their normal manner while simultaneously responding as quickly as possible to the light. Individuals who were assigned to the VLT-first condition played the VLT first and simultaneously responded to the light, and then completed the RT test with the VLT screen covered. Although it was predicted that the pathological gamblers would take generally longer to respond to the light stimulus while playing the VLT, this hypothesis was not confirmed when data from both conditions were combined. However, when the effect of task order was analyzed, some interesting findings emerged. For instance, among participants who played the VLT first, the pathological gamblers took almost twice as long as the occasional gamblers to respond to the light while playing the VLT. However, in the baseline-first task, in which participants had the opportunity to complete the RT test before playing the VLT, the pathological gamblers responded to the light stimulus almost seven times faster than the occasional gamblers during subsequent VLT play. This suggests that even though they were playing the VLT at the time, they were able to focus intensely on the light stimulus. It appeared, then, that although the pathological gamblers exhibited a tendency to become highly absorbed in VLT play under certain circumstances, they were capable of shifting their focus away from VLT play onto other stimuli. This set of studies illustrates the utility of using online cognitive assessment methods, as they produced findings that would not likely have been uncovered by offline methods such as a self-report cognitions inventory. Moreover, these findings have important treatment implications, as they suggest that there are ways that we can modify problem gamblers’ tendencies to become highly absorbed in VLT play.
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CONCLUSIONS We hope that this chapter has provided a useful glimpse into the various ways in which experimental methodologies have been, and can be, used in the study of gambling behaviors and gambling disorders. Experimental methods are the only set of research methods that can definitively determine causality because the experiment is high in internal validity, or control of potentially confounding variables. Thus, experimental methodologies can help definitively determine the causes of gambling problems and effective interventions for pathological gamblers. However, there are limits to experimental methods, including the external validity of the findings, or their generalizability to the real-world setting, and the obvious ethical limitations to conducting experimental research on the causes of gambling disorders. For example, it clearly would not be ethical to subject participants to factors such as prolonged exposure to gambling in order to determine whether such exposure caused a gambling disorder! For these reasons, it is important that converging research methods are used in gambling research to address important gambling issues, where the experimental methodologies described in this chapter are supplemented with the more externally valid research methods discussed in other chapters of this book.
GLOSSARY Dependent variable the outcome variable or factor that the researcher expects to be influenced or to change in the study. In the area of gambling and gambling disorders, the dependent variable might be overt gambling behaviors, gamblers’ thoughts or feelings, physiological variables, or symptoms of a gambling disorder. External validity the degree to which the findings can be generalized beyond the particular experiment—to individuals who were not engaged in the research study in question and to settings beyond the laboratory (i.e., to the real-world gambling context). External validity can be enhanced in experimental studies in the gambling area by ensuring that the testing environment closely resembles the real-world gambling setting. Hypothesis an educated guess about the outcome of the study that will be tested through the collection of data. The hypothesis must be stated clearly in a form that is testable, where the null hypothesis (predicting no relationship between the independent and dependent variables) is contrasted with the experimental hypothesis (which predicts a relationship between the independent and dependent variables). Independent variable the factor that is manipulated by the experimenter and that is expected to have an effect on the dependent, or outcome, variable.
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In a pathological gambling treatment outcome study, for example, the independent variable is the application of the treatment (e.g., treatment vs no treatment) that is expected to reduce gambling problems. Internal validity refers to the degree of experimental control within the study design and consequently the degree to which one can be confident that the manipulation of the independent variable is responsible for causing the outcome on the dependent variable. Experimental methodologies have the advantage of being high in internal validity relative to other research methods.
ACKNOWLEDGMENTS The authors would like to extend their thanks to Pamela Collins, Laboratory Manager at the Dalhousie Gambling Laboratory, for her research and administrative assistance in preparing this chapter. Dr. Stewart would like to acknowledge the support she and her colleagues have received from the Nova Scotia Gaming Foundation, the Ontario Problem Gambling Research Centre, and the Canadian Institutes of Health Research; Dr. Jefferson would like to acknowledge the support he and his colleagues have received from the Atlantic Lottery Corporation, New Brunswick, and the Hospital Corporation, Region 3, New Brunswick.
REFERENCES Asmundson, G. J. G., Norton, G. R., and Stein, M. B. (2002). Clinical Research in Mental Health: A Practical Guide.Thousand Oaks, CA: Sage. Barlow, D., Durand, M., and Stewart, S. H. (2006). Abnormal Psychology, 1st Canadian ed. Toronto: Nelson-Thompson. Bechara, A., Damasio, A. R., Damasio, H., and Anderson, S. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15. Dickerson, M. (1993). Internal and external determinants of persistent gambling: Problems in generalizing from one form of gambling to another. Journal of Gambling Studies, 15, 17–28. Diskin, K., and Hodgins, D. (1999). Narrowing of attention and dissociation in pathological video lottery gamblers. Journal of Gambling Studies, 15, 17–28. —— . (2001). Narrowed focus and dissociative experiences in a community sample of experienced video lottery gamblers. Canadian Journal of Behavioural Science, 33, 58–64. Doiron, J., and Nicki, R. (in press). Prevention of pathological gambling:A randomized controlled trial. Cognitive Behaviour Therapy. Ellery, M., Stewart, S. H., and Collins, P. (2006). An evaluation of irrational beliefs as possible mediators of the behavioral effects of alcohol consumption on video lottery terminal (VLT) play among probable pathological and non-pathological gamblers [Summary]. Alcoholism: Clinical and Experimental Research, 30, 96a. Ellery, M., Stewart, S. H., and Loba, P. (2005). Alcohol’s effects on risk-taking during video lottery terminal (VLT) play among probable pathological and non-pathological gamblers. Journal of Gambling Studies, 21, 299–324. Ericsson, K. A., and Simon, H. A. (1980).Verbal reports as data. Psychological Review, 87, 215–251.
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Focal Research. (1998). Nova Scotia Video Lottery Players’ Survey 1997/98. Halifax, Canada: Nova Scotia Department of Health, Problem Gambling Services. Griffiths, M. D. (1993). Pathological gambling: Possible treatment using an audio playback technique. Journal of Gambling Studies, 9, 295–297. Henslin, J. M. (1967). Craps and magic. American Journal of Sociology, 73, 316–360. Hickey, J. E., Haertzen, C. A., and Henningfield, J. E. (1986). Simulation of gambling responses on the Addiction Research Center Inventory. Addictive Behaviors, 11, 345–349. Hills, A. M., and Dickerson, M. (2002). Emotion, implicit decision making, and persistence at gaming. Addiction, 97, 598–599. Holloway, F. A. (1995). Low-dose alcohol effects on human behavior and performance. Alcohol, Drugs, and Driving, 11, 39–56. Jefferson, S., and Nicki, R. (2003). A new instrument to measure cognitive distortions in video lottery terminal users: The Informational Biases Scale (IBS). Journal of Gambling Studies, 19, 387–401. Kim, S. W., Grant, J. E., Adson, D. E., and Shin, Y. (2001). Double-blind naltrexone and placebo comparison study in the treatment of pathological gambling. Biological Psychiatry, 49, 914–921. King, K. M. (1990). Neutralizing marginally deviant behavior: Bingo players and superstition. Journal of Gambling Studies, 6, 43–61. Ladouceur, R., Gaboury, A., Bujold, A., LaChance, N., and Tremblay, S. (1991). Ecological validity of laboratory studies of videopoker gaming. Journal of Gambling Studies, 7, 109–116. Ladouceur, R., Gaboury, A., Dumont, M., and Rochette, P. (1988). Gambling: Relationship between the frequency of wins and irrational thinking. Journal of Psychology: Interdisciplinary and Applied, 122, 409–414. Langer, E. J. (1975).The illusion of control. Journal of Personality and Social Psychology, 32, 311–328. Leary, K., and Dickerson, M. (1985). Levels of arousal in high- and low-frequency gamblers. Behaviour Research and Therapy, 23, 635–640. Lesieur, H. R., and Blume, S. B. (1987).The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188. Loba, P., Stewart, S. H., Klein, R. M., and Blackburn, J. R. (2001). Manipulations of the features of standard video lottery terminal (VLT) games: Effects in pathological and non-pathological gamblers. Journal of Gambling Studies, 17, 297–320. MacDonald, A. B., Stewart, S. H., Hutson, R., Rhyno, E., and Loughlin, H. L. (2001). The roles of alcohol and alcohol expectancy in the dampening of responses to hyperventilation among high anxiety sensitive young adults. Addictive Behaviors, 26, 841–867. MacLin, O. H., Dixon, M. R., and Hayes, L. J. (1999). A computerized slot machine simulation to investigate the variables involved in gambling behavior. Behavior Research Methods, Instruments, and Computers, 31, 731–734. McCusker, C. G., and Gettings, B. (1997). Automaticity of cognitive biases in addictive behaviours: Further evidence with gamblers. British Journal of Clinical Psychology, 36, 543–554. Parke, A., and Griffiths, M. (2004). Aggressive behaviour in slot machine gamblers: A preliminary observational study. Psychological Reports, 95, 109–114. Spiegler, M. D., and Guevremont, D. C. (1998). Contemporary Behavior Therapy, 3rd ed. Pacific Grove, CA: Brooks/Cole Publishing Company. Stacy, A.W., Leigh, B. C., and Weingardt, K. R. (1994). Memory accessibility and association of alcohol use and its positive outcomes. Experimental and Clinical Psychopharmacology, 2, 269–282. Stanovich, K. E., and West, R. F. (1983). On priming by a sentence context. Journal of Experimental Psychology: General, 112, 1–36. Steenbergh,T. A.,Whelan, J. P., Meyers, A.W., May, R. K., and Floyd, K. (2004). Impact of warning and brief intervention messages on knowledge of gambling risk, irrational beliefs, and behaviour. International Gambling Studies, 4, 3–16.
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Stewart, S. H., Blackburn, J. R., and Klein, R. M. (2000, Spring). Against the odds: Establishment of a video lottery terminal research laboratory in a naturalistic setting. Nova Scotia Psychologist, 3–6. Stewart, S. H., Collins, P., Blackburn, J. R., Ellery, M., and Klein, R. M. (2005). Heart rate increase to alcohol administration and video lottery terminal (VLT) play among regular VLT players. Psychology of Addictive Behaviors, 19, 94–98. Stewart, S. H., McWilliams, L. A., Blackburn, J. R., and Klein, R. M. (2002). A laboratory-based investigation of relations among video lottery terminal (VLT) play, negative mood, and alcohol consumption in regular VLT players. Addictive Behaviors, 27, 819–835. Stewart, S. H., Peterson, J. B., Collins, P., Eisnor, S., and Ellery, M. (2006). Heart rate increase to alcohol administration and video lottery terminal (VLT) play among probable pathological gamblers and nonpathological gamblers. Psychology of Addictive Behaviors, 20, 53–61. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. Symes, B.A., and Nicki, R. M. (1997).A preliminary consideration of cue-exposure, response-prevention treatment for pathological gambling behaviour: Two case studies. Journal of Gambling Studies, 13, 145–157. Wynne, H. J. (1994). A description of problem gamblers in Alberta: A secondary analysis of the Gambling and Problem Gambling in Alberta study. Edmonton, Canada: Alberta Alcohol and Drug Abuse Commission. Zack, M., and Poulos, C. X. (2004). Amphetamine primes motivation to gamble and gambling-related semantic networks in problem gamblers. Neuropsychopharmocology, 29, 195–207. —— . (2006). Implicit cognition in problem gambling. In Handbook of Implicit Cognition and Addiction (R.W.Wiers and A.W. Stacy, eds.), pp. 379–391.Thousand Oaks, CA: Sage. Zack, M., Stewart, S. H., Klein, R. M., Loba, P., and Fragopoulos, F. (2005). Contingent gamblingdrinking patterns and problem drinker status moderate implicit gambling–alcohol associations in problem gamblers. Journal of Gambling Studies, 21, 325–354.
CHAPTER 5
Qualitative Methodologies Robert A. Stebbins Department of Sociology University of Calgary Calgary, Alberta, Canada
Grounded Theory and Exploration Exploratory Concatenation Confirmatory Qualitative Research Validity and Reliability Methods of Data Collection Qualitative Research on Gambling Issues Conclusions
Let us be clear from the outset what we mean by some of the key terms routinely used in the field of qualitative methodologies. The broadest concept in this area is that of qualitative research, defined by Norman K. Denzin and Yvonna S. Lincoln (1994, p. 2) as data collection that employs one or more interpretive and naturalistic methods to study its subject matter.Theirs is a generic definition, however, meant to embrace the multitude of more specific definitions of the concept that abound in this field. In addition, and in harmony with the preceding definition, I see qualitative research as composed of three elements: (1) the various data collection techniques (e.g., participant observation, semi-structured interviews), (2) the process of collecting data (e.g., the use of these techniques in a particular study), and (3) the development of grounded theory from the collected data.The order of these elements, as just presented, is chronological in that once a research problem is determined, one or more appropriate techniques of data collection are then selected, and the study gets under way by using those techniques. Next, through analysis of the data collected, grounded theory is developed. These three will be discussed here in reverse order, however, for by doing so we can accent the primordial goal of the first two elements, which is to develop grounded theory. 111
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GROUNDED THEORY AND EXPLORATION The second element may be more precisely understood as exploration, the aim of which is to generate new ideas (using the techniques in the first element) and weave them together to form what Glaser and Strauss (1967) call grounded theory.This is the set of generalizations derived inductively from data collected directly through a third element, exploratory research. Robert A. Stebbins (2001, p. 3) defines and describes social scientific exploration as follows: a broad-ranging, purposive, systematic, prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life. Such exploration is, depending on the standpoint taken, a distinctive way of conducting science—a scientific process—a special methodological approach (as contrasted with confirmation), and a pervasive personal orientation of the explorer.The emergent generalizations are many and varied; they include the descriptive facts, folk concepts, cultural artefacts, structural arrangements, social processes, and beliefs and belief systems normally found there.
The various generalizations about the group, process, activity, or situation under study discovered while exploring it, when logically integrated with one another, become the components of a grounded theory of that area. Hayano’s (1984, pp. 158–161) work on types of professional poker and blackjack players illustrates well the development and integration of generalizations (or types) by way of qualitative exploration. He identifies four types of professional poker and blackjack players. The “worker-professional” is a man or woman who holds a nondeviant job but is dedicated to a career in gambling and spends a good deal of time pursuing it.The nondeviant job, however, provides the steady income needed to live from day to day. For this type, playing poker or blackjack is, thus, leisure. The “outside-supported professional” has an even better extragambling income, derived from savings, investments, retirement funds, or a similar source. In this category we find retired people, housewives, and the independently wealthy. Hayano (1984) includes here students who, presumably, use their gambling winnings as a source of pin money. These players can also be classified as leisure participants. The “subsistence professional” is a consistent winner, but of only relatively small bets. He or she uses the winnings to live on, to pay the bills of everyday living.There is little interest here in a career in gambling or in moving on to higher stakes and stiffer competition. This, however, is precisely the orientation of the “career professional.” Such a person is much more likely to be a man than a woman, compared with the first three types, and is most highly committed to gambling as an occupation. Even when broke, he prefers to borrow money in order to return to gambling. Hayano (1977) found in his study of poker professionals in Gardena, California (one of the
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few places in North America where commercial poker gambling is legal), that they see their occupation as arduous. They emphasize a work ethic, including a commitment to poker, honesty, and integrity in borrowing and lending money to pay their own or others’ gambling debts.They also pride themselves on “class” personal demeanor, whether winning or losing. Exploration in the social sciences may be further understood by discussing what it is not. It is decisively not serendipity. Serendipity is the quintessential form of informal experimentation, accidental discovery, and spontaneous invention (Stebbins 2001, p. 4) contrasts sharply with exploration, just described as a broadranging, purposive, systematic, prearranged undertaking.The first is highly democratic—at least in principle anyone can experience serendipity—whereas the second is more narrowly select, the province of those creative people who must routinely produce new ideas. In certain fields of serious leisure and professional work, artists, scientists, and entertainers, for example, routinely explore, while, in some forms of casual leisure, people at play (both children and adults), sociable conversationalists, and seekers of sensory stimulation never do this (Stebbins 1997), an observation that holds equally well for many nonprofessional kinds of work. For the second group, new ideas and other discoveries can come only by way of serendipity, compared with the first group, where discovery, though occasionally serendipitous, is nonetheless far more likely to flow from exploration. Exploration and the grounded theory it generates constitute the early steps of the scientific process leading to verificational, or confirmatory, research, wherein hypotheses are tested in controlled circumstances, the experiment being the archetypical example.Where hypothesis-driven research has been preceded by exploration, as it ought to be ideally, the hypotheses tested are often the generalizations that make up the grounded theory in the area under study. So confirmation has a different goal from exploration. It relies on control of variables and prediction of outcomes using hypotheses. By contrast, exploration requires flexibility in looking for data and open-mindedness about where to find them (Stebbins 2001, pp. 6, 9–10). In general, exploration is the preferred methodological approach under at least three conditions: when a group, process, activity, or situation (1) has received little or no systematic empirical scrutiny, (2) has been largely examined using prediction and control rather than flexibility and open-mindedness, or (3) has grown to maturity but has changed so much along the way that it begs to be explored anew.
EXPLORATORY CONCATENATION In social science research exploratory concatenation refers, at once, to a longitudinal research process and the resulting set of open-ended field studies,
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which are linked together, as it were, in a chain leading to cumulative, often formal, grounded theory (Stebbins 1992, 2001). Studies near the beginning of the “chain” are wholly or predominantly exploratory in scope. Each study, or link, in the chain examines or, at times, reexamines a related group, activity, or social process or aspect of a broader category of groups, activities, and so on. Where this metaphor of a chain of studies becomes inadequate is in its failure to suggest the accretive nature of properly executed, concatenated exploration. In the metaphor of the chain, each link is equally important, whereas in scientific concatenation the studies in the chain are not only linked, but are also predicated on one another; that is, later studies are guided, in significant measure, by what was found in earlier research in the same area as well as by the methods used and the samples examined there. Thus each link plays a somewhat different part in the growing body of research and in the emerging grounded theory. Furthermore, note that the earlier studies only guide later exploration; they do not control it to the point where discovery is thwarted by preconceptions. Exploration describes the nature of the overall approach to data collection that is followed especially at the beginning of the chain and, to a significant degree, all along it as well. Glaser and Strauss (1967) observe that exploration may be qualitative or quantitative, even though most researchers in this area seem to favor mixing the two, with qualitativeness being primary and quantitativeness secondary. Still, as the chain of studies lengthens, quantitative data may grow in proportion and importance vis-à-vis qualitative data. Consequently, the terms “exploration” and “exploratory research” subsume both forms of data, whatever their ratio and significance in any particular study in the chain of studies or in the entire chain itself.
CONFIRMATORY QUALITATIVE RESEARCH Although qualitative methods are used mostly in exploratory research, they may, quite logically, be employed to test hypotheses generated in earlier exploration or deduced from established theory.The open-ended nature of these methods (see next section) discourages their use in large-scale surveys, where fixed-answer questionnaires and scales are far more efficient. Nevertheless, some research problems lend themselves poorly to quantification, opening up thereby a place for the openended methods of qualitative data collection. For example, hypotheses about highly emotional matters, such as the anguish of repeated, costly losses at gaming or fractious relations with a spouse over gambling habits, are often better tested (have greater validity) using open-ended, qualitative procedures than ones that are quantitative and answered using fixed responses. One of the widely acknowledged strengths of quantitative methods is their ease of application, and hence their capacity for studying large samples of people.
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This feature is obviously lost when qualitative methods must be used. But the latter can still be used to test hypotheses, even if the question of the broader distribution of the object of verification remains a problem. Confirmatory qualitative research is relatively uncommon and appears to be next to nonexistent in gambling studies.Thus the remainder of this chapter is devoted to gambling research carried out at the exploratory level.
VALIDITY AND RELIABILITY The question of validity in exploratory research, which goes at times by the name of “credibility,” refers to whether a researcher can gain an accurate or true impression of the group, process, or activity under study, and if so, how this can be accomplished. According to McCall and Simmons (1969, p. 78), validity is problematic in this realm of social scientific inquiry in at least three ways: 1. reactive effects of the observer’s presence or activities on the phenomenon being observed, 2. distorting effects of selective perception and interpretation on the observer’s part, and 3. limitations on the observer’s ability to witness all relevant aspects of the phenomena in question. These three problems worry exploratory and, even more so, confirmatory researchers alike.Viewed from the angle of the latter, exploration appears to evoke the greatest suspicion with reference to the second problem, primarily because of the heavy subjective element involved when a lone researcher (the usual party in an exploration) observes and interviews, employing an open-ended design. Exploratory researchers try to enhance the validity of their studies in various ways. Many of them discuss their emergent generalizations with the people they are investigating to determine whether these ideas have a familiar ring and whether, in the eyes of these people, the generalizations seem plausible. Some of these researchers may even be given written text to read. Additionally, aware that personal bias can distort perception and interpretation of observed events, competent explorers look assiduously for evidence that might contradict their observations.This approach is successful to the extent that the researcher is aware of his or her biases—and it is likely that no one is fully aware of them all—and that they are not held with great, unbending conviction.Third, these researchers constantly ask themselves whether they have observed a sufficient number of occurrences of an event, process, or activity to constitute grounds for a valid generalization. There are, furthermore, three central points to be made about validity in qualitative/exploratory research:
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1. Validity in qualitative/exploratory research is, in at least one way, substantially different from validity in confirmation. In the former, the focus of concern is on the explorer’s capacity to acquire directly an accurate impression of a group, process, activity, or situation, whereas in the confirmatory validity, the focus is on the investigator’s capacity to find measures and indices that indirectly convey an accurate impression of these phenomena. In fact, the validity issue is more easily resolved in exploration—accomplished, for example, by using different methods to examine the same group or activity (known as triangulation), asking key informants to comment on the familiarity and reasonableness of observations, and finding recurrent evidence for each generalization. Unfortunately, confirmatory researchers sometimes have to evaluate exploratory work, even though they are accustomed to working only with indirect measures and often know little about how validity is achieved and assessed in the realm of discovery. In this situation, they are wont to try to force the exploratory study into the Procrustean bed of verificational validation. 2. Confirmatory researchers are inclined to use what might be called a “one-shot” approach to assessing both validity and reliability. Each study undertaken must meet established criteria on both accounts. By contrast, exploratory researchers, to the extent that they concatenate their research, take a more global approach, arguing that judgments about validity and reliability are to be made with reference to a set of studies, which together demonstrate most convincingly how these two conditions have been realized. Although validity and reliability are also important to exploratory researchers in each study they execute, they recognize that the most authoritative statement about them both can be made only down the road, in the wake of several open-ended investigations. 3. Validity in exploration has a lot to do with representativeness of the sample of groups, processes, or activities being examined. Validity is strongest when hypothetical generalizations emerge from direct, empirical study of a set of representative instances. That is, validity rests on the number of times a regularity of thought or behavior is observed in talk or action, which must be often enough to seem general to all or a main segment of the people in the group, process, or activity being examined. If, for example, a study purports to be an exploration of routine life in casinos in a particular city and every casino there is observed, then representativeness of that sample is assured by means of this 100% coverage. But should casinos in socioeconomically disadvantaged areas of town be omitted from the study, the claim of representativeness in the city being examined would be false and the validity of the project, for this reason, called into question. Reliability refers to the replicability of a researcher’s observations; it turns on the question of whether another researcher with similar methodological
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training, understanding of the field setting, and rapport with its subjects can make similar observations. The same three problems identified earlier by McCall and Simmons (1969) also affect reliability in exploratory research. Here, too, after all the fretting about reliability in a particular study, experience remains the best teacher: Sufficient concatenation with different researchers participating in the process is needed to demonstrate most convincingly that the researchers can make similar or compatible observations on the same or related groups, processes, or activities. Moreover, as with validity, great concern with reliability of the study at hand is most appropriate toward the end of the research chain, where confirmation is the rule, compared with the beginning of that chain, where exploration dominates. More extensive treatments of validity and reliability in qualitative research are available from Kirk and Miller (1986) and Stewart (1998).
METHODS OF DATA COLLECTION Before discussing some of the key methods used to collect exploratory data, let us consider what researchers actually look for when exploring an area of social life. Note first that many exploratory researchers are reluctant to be very prescriptive about what to look for. They plump for something no more constraining than the old research formula of searching for field data according to the five Ws: who, what, whom, when, and where. (I consider, as data generating devices, the five in Stebbins 2001, p. 23. See also Denzin 1970, pp. 269–284.) In exploration the researcher wants to learn who is doing (thinking, feeling) what to (with, for, about) whom and when and where. Open-ended procedures generate data on these five questions, data that in turn become the basis for generalizations in the form of concepts and their interrelationship in propositions.What the old formula neglected (and, consequently, what I neglected in Stebbins 2006) is that there is also a most important additional question of how. How do the people being observed do what they do? This is not so much a conceptual interest, however, as a descriptive one. The answer to this question gives the descriptive, ethnographic foundation on which the explorer constructs more abstract grounded theory revolving around the five Ws. Furthermore there is also another W, which however, is theoretic; it is why people do what they have been observed to do. Still, answering this question does not steer data collection. Rather it greatly aids interpretation of the data that have been gathered. In sum, the five Ws and the H combine to guide data collection, leading the exploratory researcher to ask why the data collected are as they are.
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The watchword for every method of data collection in qualitative research is open-endedness, which serves as a way of implementing the characteristics of flexibility and open-mindedness of exploration. That is, each method is designed to reveal a wide range of behavior, activity, attitude, or other object of investigation. Five widely used open-ended methods are covered here: participant observation, semi-structured interviewing, focus group, narrative, and content analysis. All five have several variants that relate to special research situations (which, because of spatial limitations, will not be discussed here). These five methods may be used to generate, or in some instances help generate, either an ethnography or what I will call here a set of problem-centered data. An ethnography is a systematic, broad-ranging description of all or a major segment of the social or cultural life of a category or group of people. As the preceding definition stated, ethnographies are developed using one or more openended methods of data collection. Problem-centered data, which also may be collected using one or more open-ended methods, bear on a smaller, more specialized domain of knowledge within an ethnographic area, such as, in gambling, perceived risk, relations with spouses, and financing debt. Participant observation is the practice of directly watching and recording (even if only in memory) what is happening in the group, activity, or situation being examined. Adler and Adler (1987) point out that such observation may be conducted by one of three types of observer: a “complete member,” or bona fide member of the group (e.g., a gambler studying gamblers); an “active member,” or outsider to the group who nevertheless has access to all group involvements; or a “peripheral member,” or someone who is known to group members but lacks access to the group’s core activities.The latter two types are, in the case of gambling, commonly nongambling social scientists who have obtained different degrees of involvement with the group. Additionally, the research role of the observer may be known to the people being studied (overt observation) or unknown to them (covert observation). The adjective “participant” is not to be taken literally, to mean that “participant observers” are actually doing the activity in question. Rather, it signifies that those observers are physically present, and thus are able to see what is going on. This method is open, because the observer is in a physical position to view much, if not all, that there is to see in naturally occurring behavior and activity. Participant observation is the classic method used to develop an ethnography of an area. All science is based on some kind of description of the phenomenon being examined. Accordingly the ethnographies, which undergird exploration in an area, are generated by way of participation observation and are necessarily highly descriptive.Were we to carry out an ethnography of a Las Vegas casino, we would start by looking at who (e.g., the gamblers, card dealers, servers of food and drink) is doing what (e.g., placing bets, dealing cards, bringing food and drink) to or for whom (e.g., with the dealer, to the gamblers, for the gamblers), when
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(e.g., evenings), and where (e.g., casino rooms in large commercial establishments in a special geographic area of Las Vegas). The next four methods provide data that help explain why ethnographic pictures like the one just sketched exist. Semi-structured interviewing is conducted using an “interview guide.” This guide consists of a set of broad questions that bear on the research problem. Each item (question) in the guide is open-ended in that no preconceived categories are available into which interviewers must place their respondents’ answers. Still the interview has some minimal structure, for the items direct interviewees to talk about particular broad areas of their lives. McCracken (1988) argues that semistructured interviews are especially well adapted for gathering personal information that only the respondent is in a position to give, which includes personal opinion, attitude, belief, accomplishments, and major life events, as well as personal background data and that person’s interpretation of all this. Focus groups consist of small numbers of participants who are assembled by the researcher for the purpose of discussing one or a few common problems, issues, situations, and the like. Morgan (1997) says that such groups are good for determining collective positions on the problems, issues, and situations, though the problem, issue, or situation must be something everyone who is present is willing and able to talk about with the others. During the interactive session, participants may be reminded by the remarks of the others of their own thoughts on the subject at hand and possibly even crystallize, then and there, their own position on it.The researcher typically directs discussion, trying to ensure that everyone present has his or her say and that, through the open-endedness of the procedure, all leads bearing on the collective position are followed up. As for the size of the focus group, Morgan (1997, pp. 42–43) recommends in general, after weighing a variety of practical and substantive considerations, no less than 6 and no more than 10 participants. Although there is scant research in gambling studies that has used focus groups, such research can be mounted to good effect. Thus, we might organize focus groups among members of several local chapters of Gamblers Anonymous (GA), with the aim of considering the question of why some problem gamblers fail to join GA. Or a focus group could be assembled to discuss how GA might better realize its goals. Recreational gamblers could be assembled into such groups to talk about the differential appeals of the many forms of gaming. Narratives are constructed by people from their own biographic material. Riessman (1993, p. 3) used, as one definition of narrative in her book on narrative analysis “talk organized around consequential events.” A narrative may be constructed, for example, about a single event (e.g., a traffic accident, a theatrical performance), about a period in a person’ life,or even about an entire life (i.e., a life story). Narratives are commonly obtained through interviewing (often in two or three sessions, if about a person’s entire life or large part of it), which has even less structure than its semi-structured cousin.
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A narrative study of gamblers might begin with a question or prompt from the interviewer addressed to each respondent, such as: “Tell me how you got into gambling.” The interviewer might then ask about when gambling became problematic and about the nature of the circumstances leading to this condition. And then a third question might be posed bearing on how the respondent came to grips with the gambling problem. Note that narratives differ from biographies, which usually take in much bigger segments of the person’s life and are not intended to be generalized or used to develop grounded theory. According to Krippendorff (2004), content analysis is the search for generalizations carried out on any kind of previously unanalyzed material, be it reports, musical recordings, books, articles, paintings, diaries, and so on. This method is open-ended in that the material selected for study has not yet been analyzed and principal themes pulled out. Unlike participant observation, neither the narrative nor content analysis is well suited as a sole method for working up an ethnography. By contrast, all five methods can be fruitfully used to generate data on a particular problem. And, to repeat, the last four are well suited for explaining (through emerging generalizations) the data collected. Just as with focus groups, research in gambling studies that employs content analysis is rare. Still the method can be used to advantage as an exploratory device, even if it must be joined with other open-ended methods to generate a full ethnography. For instance, books or newspaper articles on gambling could be analyzed for the themes that run through them. Or, on a more detailed level, printed material on how to win at different kinds of games could be so analyzed. Finally, the goal in using these five methods is to produce generalized conclusions. That is, science is a generalizing (in philosophic terms, “law-giving”) enterprise, consisting as it does of propositions (or hypotheses, general statements) about the area under study.This property of science is stressed here, because of the tendency in exploration for some researchers to get caught up with the details of, for example, the narrative, turning this method into a biography of the person interviewed. As such, no generalization is attempted, and consequently the development of ground theory, and hence of science, is also arrested. (Of course, if a biography is what the interviewer wants, as, say, a scholar in the humanities, this goal is perfectly acceptable [see Stebbins 2001, pp. 11–12].)
QUALITATIVE RESEARCH ON GAMBLING ISSUES Qualitative research on gambling has tended to center on the process of gambling, the gamblers themselves, and the social and cultural circumstances in which these people pursue gambling as leisure activity or are driven to gambling by compulsion. Note, however, that by no means is all social scientific research on these subjects qualitative; a good deal of quantitative, hypothesis-driven work has
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also been conducted on them. In this section we concentrate exclusively on some representative qualitative studies in the field of gambling.The favored methods here have been participant observation and semi-structured interviewing. Both of these methods were employed by Livingston (1974) in his classic ethnography of compulsive gamblers in a city in the United States. Livingston observed several meetings of a GA chapter and later interviewed a number of its members. His study constitutes an ethnography of the problem gambler, his personality (all research participants were male), his deviant career in gambling as the activity became more compulsive and less recreational, his career in abstaining from gambling, and the role of the organization (i.e., GA) in helping facilitate the career in abstinence. Livingston also provides a rich demographic portrait of his sample, including the variables of age, education, income, religion, and marital status. The qualitative work of Lesieur (1977)—another classic in gambling studies—revolving specifically, as it did, around the deviant career of the compulsive gambler, might lead us to classify his work as problem-centered qualitative research. Indeed, he used semi-structured interviews to discern the stages of this type of career as experienced among a sample of GA members and non-GA gamblers (including even some in prison). But his research interests also spread to gamblers’ families, the gamblers’ occupations, and, in some cases, gamblers’ criminal activities (to get money to gamble), as well as bookmakers and their business and the lending practices of certain financial institutions. In short, Lesieur’s study was sufficiently broad to be qualified as an ethnography. Turning to gambling as leisure, Hallebone (1999) used semi-structured interviews in a problem-centered study of spokespersons and counselors for non–English-speaking women from four ethnic subcultures in Victoria, Australia. She found that these women—Indochinese, Chinese, Italian, and Greek—used their beliefs in karma and predestination to justify their gambling interests. Downsizing in Australian manufacturing and retailing was found to be another factor explaining those interests. Nonetheless, for many of these women, gambling pursued in this economic situation failed to last as leisure, primarily because they came to be dependent on this activity. A companion study conducted by Hallebone in Melbourne using a similar sample showed that those women became problem gamblers because they were using the activity in hope of regaining a positive sense of identity while also trying to escape isolation, loneliness, boredom, and domestic abuse. She observed that addictive gambling worked to undermine the desires of these women, in that they lost control to an addicting activity and a burgeoning personal debt. Neal (2005) studied gambling on horse races as leisure, using participant observation to gather data in a betting shop and at a racetrack in the United Kingdom. He learned, while observing and talking informally with gamblers at these two locations, that their betting was an integral part of their routine lives.
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It was not seen by them as irrational or pathological, but as something interesting and enjoyable to do during their free time. Neal’s interest in the rationality/irrationality question makes his work too specific to be judged an ethnography. It was, rather, a problem-centered study.That having been said, he moved from his findings to a discussion of gambling as leisure, thereby expanding his scope to “an ethnography of leisure gambling.” He observed that there was an overwhelming preoccupation in the United Kingdom with the compulsive side of gaming (which represented only 0.8 to 1.2% of the gambling population there), while looking on it as leisure was far more representative of all people who gambled. In Britain the betting shops and race courses are two principal axes of the leisure gaming scene. Rosecrance’s (1985) study of the horse-racing scene at Lake Tahoe, Nevada, is similar in some ways to Neal’s work. Both are concerned with the social world of the gambler, considering it an important part of his lifestyle as well as a source of motivation, knowledge, and sympathetic understanding. Both authors also observed problem and leisure gamblers. Rosecrance’s study, however, also took in professional gamblers who bet on horse races. Both he and Neal used a combination of participation observation and semi-structured interviews to generate ethnographic accounts of the gambling scenes they studied. In a rare study in the field of gambling behavior, Pratt and colleagues (2005) organized 34 focus groups, each with four to nine members, from a sample of Ontario and Quebec adolescents aged 12 to 18 years. The object was to identify what adolescents understood as “gambling,” to explore their awareness and participation in gaming activity, to identify the benefits they saw as accruing from gambling, and to identify the risks they believed were associated with such activity. Pratt and her team found, among other things, that the sample had a broad understanding of what constituted gambling, ranging from scratch tickets and personal bets to the gaming activities found in casinos and the Las Vegas gambling establishments. Turning to content analysis—another unusual method in gambling studies— McMullan and Mullen (2001) carried out such an analysis of media coverage of casino and electronic gambling between 1992 and 1997 in Nova Scotia. The authors analyzed 234 gambling stories to learn that pro-gambling corporate and political newspaper sources had conducted a successful media campaign in support of new gaming products, services, and institutions.The media gave both form and visibility to these structured messages, thereby helping create expectations about gambling and economics as well as gambling and government.
CONCLUSIONS The principal focus of this chapter has been exploratory research leading to grounded theory, with but brief mention of qualitative research methods as a
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verificational approach.The most profound argument to be mounted in favor of exploration in the social sciences is that there may be unknown phenomena in social life (gambling included) that warrant open-ended examination to discover what they are and what significance they may hold for science and community life. The field of gambling studies has been blessed with a number of fine qualitative/exploratory investigations, some of which have been described in this chapter. Still, plenty of exploration remains to be done. For instance, earlier, Neal emphasized the need to put more effort into exploring gambling as leisure and as leisure lifestyle. Only a small proportion of all gamblers have psychological problems with an activity that, to be sure, started for them as a controllable and agreeable leisure pastime. Research is also urgently needed the matter of the stigma of gambling, whether the gambling in question is for fun or is done compulsively. Qualitative methods are well suited for casting light on this condition. The meaning to men vis-à-vis women of the different kinds of gaming is still another area begging study, as an area of human psychology that is inherently qualitative. Nevertheless, such qualitative research is time consuming. And the explorer is often the only person interested in the object of study. In the typical case, he or she is alone in the field to arrange and conduct all the necessary observations, interviews, and focus groups (see Stebbins 2001, pp. 52–55, for an account of the lifestyle of the social scientific explorer).There is no one to farm out this work to, while interviews can run from 2 to 3 hours, and a researcher can spend days or nights (or both) observing action at the casinos, racetracks, and bingo halls. It is no wonder, then, that there is a shortage of research of this kind, even while the importance of gambling as leisure and as pathology dictates that we social scientists not fail to thoroughly explore this area.
GLOSSARY Ethnography a systematic, broad-ranging description of all or a major segment of the social or cultural life of a category or group of people. Exploratory concatenation both a kind of longitudinal research (process) and the resulting set of open-ended field studies that are linked together, as it were, in a chain, leading to cumulative and often formal grounded theory (product). Exploratory research a broad-ranging, purposive, systematic, prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life. Grounded theory a set of generalizations derived inductively from data collected directly through exploratory research.
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Qualitative research data collection that uses one or more interpretive and naturalistic methods to study its subject matter. Reliability the replicability of a researcher’s observations. It turns on the question of whether another researcher with similar methodological training, understanding of the field setting, and rapport with its subjects can make similar observations. Serendipity the quintessential form of informal experimentation, accidental discovery, and spontaneous invention. Validity in exploratory research, refers to whether a researcher can gain an accurate or true impression of the group, process, or activity under study, and if so, how this can be accomplished. Verificational (confirmatory) research data collection guided by hypotheses derived from established theory and conducted to test, in controlled circumstances, those hypotheses.
REFERENCES Adler, P. A., and Adler, P. (1987). Membership Roles in Field Research. Beverly Hills, CA: Sage. Denzin, N. K. (1970). The Research Act: A Theoretical Introduction to Sociological Methods. Chicago: Aldine. Denzin, N. K., and Lincoln, Y. S. (1994). Introduction: Entering the field of qualitative research. In Handbook of Qualitative Research (N. K. Denzin and Y. S. Lincoln, eds.), pp. 1–18. Thousand Oaks, CA: Sage. Glaser, B. G., and Strauss,A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Chicago: Aldine Atherton. Hallebone, E. (1999). Women and the new gambling culture in Australia. Loisir et Société/Leisure and Society, 22, 101–125. Hayano, D. M. (1977). The professional poker player: Career contingencies and the problem of respectability. Social Problems, 24, 556–564. —— . (1984, July).The professional gambler. The Annals, 474, 157–167. Kirk, J., and Miller, M. L. (1986). Reliability and Validity in Qualitative Research (Qualitative Research Methods Series 1). Newbury Park, CA: Sage. Krippendorff, K. (2004). Content Analysis:An Introduction to Its Methodology, 2nd ed.Thousand Oaks, CA: Sage. Lesieur, H. R. (1977). The Chase: Career of the Compulsive Gambler. Garden City, NY: Doubleday Anchor. Livingston, J. (1974). Compulsive Gamblers: Observations on Action and Abstinence. New York: Harper & Row. McCall, G. J., and Simmons, J. L. (eds.). (1969). Issues in Participant Observation: A Text and Reader. Reading, MA: Addison-Wesley. McCracken, G. (1988). The Long Interview.Thousand Oaks, CA: Sage. McMullan, J. L., and Mullen, J. (2001). What makes gambling news? Journal of Gambling Studies, 17, 321–352. Morgan, D. L. (1997). Focus Groups as Qualitative Research, 2nd ed.Thousand Oaks, CA: Sage. Neal, M. (2005). “I lost, but that’s not the point”: Situated economic and social rationalities in horserace gambling. Leisure Studies, 24, 291–310.
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Pratt, L., Derevensky, J. L., Gillespie, M., and Gupta, R. (2005, August). The Development of an Instrument to Assess the Role of Gambling Outcome Expectancies for Adolescents: A Qualitative Analysis of Perceived Risks and Benefits of Adolescent Gambling. Final report to the Ontario Problem Gambling Centre, International Centre for Youth Gambling Problems and High-Risk Behaviors, McGill University, Montreal. Riessman, C. K. (1993). Narrative Analysis. Newbury Park, CA: Sage. Rosecrance, J. D. (1985). The Degenerates of Lake Tahoe: A Study of Persistence in the Social World of Horse Race Gambling. New York: Peter Lang. Stebbins, R. A. (1992). Concatenated exploration: Notes on a neglected type of longitudinal research. Quality and Quantity, 26, 435–442. —— . (1997). Casual leisure: A conceptual statement. Leisure Studies, 16, 17–25. —— . (2001). Exploratory Research in the Social Sciences (Qualitative Research Methods Series 48). Thousand Oaks, CA: Sage. —— . (2006). Concatenated exploration: Aiding theoretic memory by planning well for the future. Journal of Contemporary Ethnography, 35, 483–494. Stewart, A. (1998). The Ethnographer’s Method (Qualitative Research Methods Series 46). Thousand Oaks, CA: Sage.
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CHAPTER 6
Longitudinal Studies of Gambling Behavior Wendy S. Slutske Department of Psychological Sciences University of Missouri–Columbia Columbia, Missouri
Introduction Existing Longitudinal Studies of Gambling Behavior Longitudinal Studies of Gambling Behavior Initiated During Preadolescence and Adolescence Montreal, Canada (Vitaro and colleagues) Minnesota (Winters and colleagues) New York (Barnes and colleagues) Quebec, Canada (Vitaro, Ladouceur, and Bujold) Longitudinal Studies of Gambling Behavior Initiated During Late Adolescence and Early Adulthood Missouri College Students (Slutske, Jackson, and Sher) New York (Barnes and colleagues) Dunedin, New Zealand (Slutske and colleagues) Longitudinal Studies of Gambling Behavior Initiated During Early to Late Adulthood New Zealand (Abbott,Williams, and Volberg) U.S. Casino Employees (Shaffer and Hall) Key Issues and Challenges in Longitudinal Gambling Research Statistical Techniques for Modeling Stability and Change Dealing with Missing Data The Low Prevalence of Pathological Gambling Disorder 127
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Important Questions and What We Know So Far Temporal Resolution of Gambling Correlates: Establishing Causality? The Stability of Gambling Behavior The Course of Gambling Behavior and Gambling Problems Sequential/Stage Theories of Gambling Involvement: Is There a “Gateway” to Problems? Developmental Changes Versus Cohort or Period Effects on Levels of Gambling Involvement “Natural Experiments” in Longitudinal Gambling Research Summary of What We Don’t Know (Yet)
INTRODUCTION Longitudinal research on gambling behavior is in its infancy—the earliest study based on longitudinal data reviewed in this chapter is the paper by Winters, Stinchfield, and Kim (1995), published little more than a decade ago. As this field continues to develop, it will be important to be mindful of the wisdom of more seasoned veterans from outside of the gambling research community who have spent many decades in the longitudinal research trenches. For example, Rutter (1981, p. 334) presents his balanced perspective on the role of longitudinal research in understanding behavior: There are numerous scientific and policy questions which can only be answered effectively through the availability of longitudinal or follow-up data. In addition, there are others for which longitudinal data, although not essential, greatly improve the strength of hypothesis-testing. Nevertheless, it would be quite wrong to assume that longitudinal studies are necessarily the best means of answering psychiatric questions, or even developmental questions. All too often, longitudinal studies have been planned without any clear aims or hypotheses in mind, have involved a mindless collection of large amounts of data which are never adequately analyzed, and which continue over so many years that by the end the original measures are hopelessly inappropriate for the purposes for which they are being used.
In this chapter, I review the existing database of longitudinal studies of gambling behavior, including studies of gambling participation and gambling-related problems (problem and pathological gambling).Any study of gambling participation or gambling-related problems in which individuals from a systematically ascertained
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or representative community-based sample have been assessed on at least two separate occasions across an interval of at least 1 year have been included. I have limited this review to observational/correlational longitudinal investigations and have not covered studies in which there is some type of experimental manipulation, such as an intervention study.These are covered in Chapter 15 of this volume. I will first provide a summary of the methods used in these studies, then briefly discuss three key issues and challenges in conducting longitudinal research on gambling behavior, and then provide a review of the results of longitudinal gambling research, focusing primarily on those questions that can be answered only with longitudinal or follow-up data.
EXISTING LONGITUDINAL STUDIES OF GAMBLING BEHAVIOR Table 6.1 presents a summary of published longitudinal studies of gambling behavior, grouped according to the developmental period of the participants at the initiation of the study. Of the nine longitudinal studies listed, four were initiated in preadolescence or adolescence, three were initiated in late adolescence or early adulthood, and the remaining two studies were initiated when the participants were in early to late adulthood. My review of the existing studies will be organized around these three broad developmental groupings. The existing longitudinal studies of gambling behavior can be categorized into two basic types. The purpose of the first type of study is to examine the prospective associations between one or more predictors and later gambling behavior. The other type of study is one in which repeated assessments of gambling behavior are conducted in an effort to characterize the stability, change, and patterning of gambling behavior over time.
LONGITUDINAL STUDIES OF GAMBLING BEHAVIOR INITIATED DURING PREADOLESCENCE AND ADOLESCENCE Montreal, Canada (Vitaro and colleagues) The gambling behavior research program of Vitaro and colleagues (Vitaro, Arsenault, and Tremblay 1997, 1999;Vitaro et al. 2001, 2004) was part of a longitudinal study of 1034 boys living in disadvantaged neighborhoods in Montreal, Canada (Tremblay et al. 1994). Boys were first studied when they were in kindergarten in 1984. Given that the primary focus of the study was antisocial behavior development, the participants were recruited from the 53 schools whose students came from households with the lowest socioeconomic status in order to enrich the sample for boys at risk for the eventual development of delinquency.
Table 6.1
Setting and Investigators
Time Span of Study (years)
Number of Waves of Data Collection Included in Published Reports Total
Gambling Pathology
Measure of Gambling Pathology
Number of Subjects
% Female
Mean Age of Subjects at First Wave
903
0
11
532/305
49
16
522
57
14.5
130
Summary of Existing Longitudinal Studies of Gambling Behavior.
Gambling
Preadolescence and Adolescence 12
10
8
3
8
3
3
3
7
6
2
0
SOGS-RA/ SOGS SOGS-RA/ SOGS —
3
3
1
0
—
631
0
10
DSM-III/ III-R/IV —
468
54
18.5
597
0
18
Modified SOGS
939
49
18.0
Late Adolescence and Early Adulthood Missouri college students Slutske, Jackson, and Sher New York Barnes,Welte, Hoffman, and Dintcheff Dunedin, New Zealand Slutske, Caspi, Moffitt, and Poulton
11
4
1
4
5
2
2
0
3
2
0
1
Early to Late Adulthood New Zealand Abbott and Volberg U.S. casino employees Shaffer and Hall
7
2
2
2
SOGS-R
143
46
18–65+
2
3
3
3
SOGS
639
58
37
SOGS, South Oaks Gambling Screen; SOGS-RA, South Oaks Gambling Screen–Revised for Adolescents; SOGS-RA, South Oaks Gambling Screen–Revised; DSM, Diagnostic and Statistical Manual of Mental Disorders; III, third edition, R, revised, IV, fourth edition.
Research and Measurement Issues in Gambling Studies
Monteal, Canada Vitaro, Arsenault,Tremblay, et al. Minnesota Winters, Stinchfield, et al. New York Barnes,Welte, Hoffman, and Dintcheff Quebec, Canada Vitaro, Ladouceur, and Bujold
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Prospective predictors of gambling outcomes in this study included self-rated impulsivity (Vitaro et al. 1997, 1999, 2001, 2004), risk taking (Vitaro et al. 2004), friends’ deviancy (Vitaro et al. 2001), parental supervision (Vitaro et al. 2001), and teacher-rated impulsivity (Vitaro et al. 1997, 1999), all assessed at ages 13 and 14 years of age; self-rated delinquency, drug and alcohol use, gambling frequency, and gambling problems assessed at age 16 years (Vitaro et al. 2001); and a composite inhibition measure based on teacher ratings obtained at ages 6, 10, 13, and 14 years (Vitaro et al. 2004). In addition, at ages 13 and 14, 333 boys who were persistently high or low in teacher-rated aggressiveness or high or low in teacher-rated anxiousness were invited to participate in a laboratory-based study that included tasks designed to measure response perseveration and inability to delay gratification (Vitaro et al. 1999). Gambling outcomes in this study included problem gambling assessed at ages 17 (Vitaro et al. 1997, 2001) and 23 (Wanner et al. 2006) and an empirically derived latent gambling trajectory class membership derived from the dichotomized responses to a single-item assessment of past-year gambling obtained every year from age 11 to 16 (Vitaro et al. 2004;Wanner et al. 2006). Minnesota (Winters and colleagues) Winters and colleagues (Winters, Stinchfield, and Kim 1995; Winters et al. 2002, 2005) conducted a three-wave longitudinal study of a community-based sample of Minnesota adolescents. The study extended across 8 years from adolescence to early adulthood. Participants were recruited from across the state of Minnesota, and were nearly evenly split between those residing in urban and rural areas.They were randomly selected from a list generated by a market research firm of households expected to include an adolescent (based on high school information, state driver and voter registration records, and previous market research). At the first wave of the study, conducted in 1990, telephone interviews were conducted with 702 adolescents aged 15–18 years (Winters, Stinchfield, and Fulkerson 1993a, b). At the second wave of the study, conducted in 1992, telephone interviews were conducted with 532 adolescents and young adults 16–20 years of age. At the third wave of the study, conducted in 1997–1998, a decision was made to identify and select 160 high-risk and 190 low-risk participants for follow-up based on the frequency of their gambling and their scores on the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) obtained at the earlier two waves of the study. At the third wave of the study, telephone interviews were conducted with 305 young adults 21–26 years of age. This study has examined a rich array of gambling outcomes, including the frequency of participating in 11 different types of gambling in the past year (and a variety of indicators derived from this assessment), and past-year at-risk and problem gambling based on the SOGS-RA (Winters et al. 1995, 2003) assessed at all
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three study waves. Participants were also classified into one of five different developmental gambling groups (“resistor,”“persistor,”“desistor,”“new incidence cases,” and “other”) based on their past-year problem-gambling status (no problem gambling, at-risk gambling, or problem gambling) at the three waves of the study (Winters et al. 2005). Prospective predictors of gambling outcomes at wave 3 in this study were early onset of gambling, parental gambling history, delinquency, regular substance use, psychological distress, poor school performance, and at-risk and problem gambling assessed at wave 1 or 2. In addition to spanning an important developmental transition of the participants—adolescence to early adulthood—this study also obtained measures before and after many of the participants had reached the legal age to gamble in the state of Minnesota (18 years) and before and after the introduction of the Minnesota state lottery in 1990. New York (Barnes and colleagues) Barnes and colleagues (1999, 2002, 2005) included measures of past-year gambling involvement at waves 5 and 6 of their six-wave, 7-year longitudinal study of the development of alcohol misuse. The baseline wave of the study was conducted in 1989, when the participants were 13–16 years of age, and subsequent waves were conducted at yearly intervals. Participants were a representative household sample of adolescents (with an oversampling of blacks) from the Buffalo, New York, metropolitan area who were identified through random digit dialing. Gambling assessments were conducted in 1994–95 and 1996, when the participants were aged 17–21 and 18–22. Gambling outcomes in this study included a composite measure of past-year gambling frequency based on the frequency of participating in 11 different types of gambling assessed at wave 5 (Barnes et al. 1999), a latent gambling construct based on a confirmatory factor analysis of the same 11 gambling frequency items assessed at wave 5 (Barnes et al. 2005), and a rationally derived gambling pattern variable (“flat-low,”“increasing,”“flat-medium,” “flat-high,” and “decreasing”) based on the frequency of gambling at waves 5 and 6 (Barnes et al. 2002). Prospective predictors of gambling involvement at wave 5 (ages 17–21) were self-reported impulsivity and peer delinquency (assessed at ages 15–18), moral disengagement (assessed at ages 16–19), a composite cross-wave selfreport measure of parental monitoring, and sociodemographic characteristics (Barnes et al. 2005). Self-reported impulsivity was also examined as a prospective predictor of the rationally derived gambling pattern (Barnes et al. 2002). Quebec, Canada (Vitaro, Ladouceur, and Bujold) Participants in the prospective gambling study of Vitaro, Ladouceur, and Bujold (1996) were recruited from schools throughout the province of Quebec,
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Canada. All of the participants were male, were primarily white (94%), and represented the full range of socioeconomic status. At age 13, 631 boys completed a questionnaire in which they self-reported their past-year frequency of participating in six different categories of gambling activities and the greatest amount of money that they bet in a single day in the past year.These seven items were combined into a single scale for the gambling outcome measure and were also used to identify groups of frequent gamblers (n = 33) and nongamblers (n = 108).Teacher and mother ratings of hyperactivity, impulsivity, aggressivity, and anxiety/withdrawal obtained at ages 10 and 11 were available for 441 of the 631 boys who completed the self-report questionnaire at age 13.These teacher and mother behavior ratings were used as prospective predictors of gambling involvement and gambling group membership at age 13.
LONGITUDINAL STUDIES OF GAMBLING BEHAVIOR INITIATED DURING LATE ADOLESCENCE AND EARLY ADULTHOOD Including the three longitudinal studies reviewed above that continued into late adolescence and early adulthood, and the three studies reviewed below, there are actually six longitudinal studies altogether on gambling behaviors across the critical ages of 18–25. During a critical developmental period in which one expects a great deal of change (such as the ages that span the years when it becomes legal to gamble, or when there are several important milestones), it is especially valuable to be able to study individuals at each specific age, rather than combining them into broader age bands at the analysis stage.Two of the three studies of gambling behavior during late adolescence and early adulthood, the Missouri (Slutske, Jackson, and Sher 2003) and Dunedin (Slutske et al. 2005) studies, were “age homogeneous,” that is, all of the subjects were born within the same year; this is also true of the Montreal study of Vitaro and colleagues.Thus, these three studies are especially sensitive to developmental change. (Note that an age-homogeneous study is not the only available option for conducting sensitive analyses of developmental change. In principle, any large sample can be subdivided into agehomogeneous cohorts.) Missouri College Students (Slutske, Jackson, and Sher) Participants in the gambling study by Slutske, Jackson, and Sher (2003) were 468 first-time freshmen aged 18–19 years who were part of a longitudinal study of the development of alcohol use patterns and associated problems (Sher et al. 1991). Subjects were selected from among all incoming freshmen at the University of Missouri–Columbia based on the presence or absence of alcoholism and associated psychopathology in their first- and second-degree biological relatives.
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The subjects were predominantly white (94%), and 213 of the subjects were male (46%). Data from years 1, 4, 7, and 11 were used in the gambling study, which corresponded to ages 18–19, 21–22, 24–25, and 28–29 of the participants. All individuals from the first year of the study were targeted for contact at later years, regardless of their current college enrollment status or place of residence. Gambling outcomes in this study included lifetime problem gambling assessed at all 4 years of the study, past-year problem gambling assessed at years 4, 7, and 11, and lifetime involvement in 10 different gambling activities assessed at year 11. The measures of problem gambling at all 4 years were combined to form developmental trajectories of problem gambling. New York (Barnes and colleagues) Barnes and colleagues (1999, 2002, 2005) included measures of past-year gambling involvement at every wave of their three-wave, 5-year longitudinal study of delinquency in young men. The gambling component of this study was designed to be similar to the other New York study by Barnes and colleagues reviewed above, and the results of the two studies have been published together in the same reports in tandem in order to establish the replicability of the findings. The baseline wave of the study was conducted in 1992, when the participants were 16–19 years of age, and the two subsequent waves were conducted at 1.5-year intervals. Participants were identified through random-digit dialing of households in the Buffalo, New York, metropolitan area with an oversampling of telephone districts with high-crime rates.Two-thirds of the sample was selected because the adolescent male was identified at telephone screening as being at high risk for developing delinquency, and the remaining one-third of the sample was randomly selected. Although gambling involvement was assessed at all three waves, published reports to date have utilized the gambling measures from waves 2 and 3, which were conducted in 1994–1995 and 1996–1997, when the participants were aged 17–21 and 19–22 years. Gambling outcomes and prospective predictors in this study were the same as in the other New York study by Barnes and colleagues reviewed above. Dunedin, New Zealand (Slutske and colleagues) The prospective problem gambling study of Slutske and colleagues (2005) was part of a much larger longitudinal study based in Dunedin, New Zealand.The Dunedin Multidisciplinary Health and Development Study is an ongoing longitudinal investigation of the health and behavior of a complete cohort of consecutive births between April 1, 1972, and March 31, 1973, in Dunedin (Silva and Stanton 1996; Moffitt et al. 2001).The birth cohort was constituted when the participants were 3 years of age. The investigators were able to successfully enroll 91% of the
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eligible births, which yielded a cohort size of 1037 children (52% male).The cohort families represent the full range of socioeconomic status in the general population of New Zealand’s South Island and are primarily (~93%) of white European ancestry. Scales from a comprehensive assessment of self-reported personality traits conducted at age 18 were used to predict structured interview–based diagnoses of past-year problem gambling, as well as alcohol, cannabis, and nicotine dependence at age 21.
LONGITUDINAL STUDIES OF GAMBLING BEHAVIOR INITIATED DURING EARLY TO LATE ADULTHOOD Six of the seven longitudinal studies of gambling behavior reviewed above continued into early adulthood. Only the two published longitudinal studies of gambling behavior reviewed below have included participants who were in or beyond their fourth decade of life. New Zealand (Abbott, Williams, and Volberg) Abbott, Williams, and Volberg (1999, 2004) conducted a 7-year follow-up in 1998 of 143 adults who were selected from 4053 participants in the 1991 New Zealand National Prevalence Survey. Individuals in four different subgroups were selected for the follow-up: regular noncontinuous gamblers, regular continuous gamblers, lifetime problem gamblers, and lifetime probable pathological gamblers. Outcome measures included the SOGS-Revised; a 10-item scale based on the criteria from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV), for pathological gambling; and the frequency of participating in 21 different gambling activities. Prospective predictors of gambling outcomes at the follow-up were the frequency of participation in gambling activities, gambling subgroup classification, gambling problems, self-reported current psychological distress and past-year alcohol problems, and demographic characteristics, all assessed at baseline. U.S. Casino Employees (Shaffer and Hall) The longitudinal gambling study of Shaffer and Hall (2002) represents the only study reviewed in this chapter that focused on a high-risk population—in this case, casino employees. As part of a health survey for an employee assistance and health improvement program, the researchers assessed gambling and alcoholrelated problems via self-report questionnaires. Employees at six different casinos were invited to participate in three surveys conducted at intervals of approximately 1 year. Unfortunately, participation and retention rates in this longitudinal study were very low, in part owing to the large job turnover at the casinos and the
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inability to track individuals who were no longer employees.The response rate at the first year was 67% (n = 6067), of which 52% (n = 3174) and 19% (n = 1176) participated at years 2 and 3, respectively; there were 639 individuals who provided problem gambling information at all three years. Gambling outcomes were scores and gambling problem severity categories (i.e., levels 1, 2, and 3) derived from the SOGS, assessed at all 3 years (Lesieur and Blume 1987). Prospective predictors of gambling outcomes were a four-item measure of alcohol-related problems, a oneitem measure of depression, and a variety of demographic, health, risky-behavior, and biomedical variables culled from the larger health survey.
KEY ISSUES AND CHALLENGES IN LONGITUDINAL GAMBLING RESEARCH Given space constraints, here I will briefly draw attention to three important topics for longitudinal gambling research: (1) statistical techniques for modeling stability and change, (2) missing data, and (3) approaches for coping with the low prevalence of pathological gambling disorder.
STATISTICAL TECHNIQUES FOR MODELING STABILITY AND CHANGE Many recent advances have been made in the development and refinement of growth modeling, a general family of techniques for analyzing longitudinal data. With but one exception (Vitaro et al. 2004), growth modeling has not yet made its way into the longitudinal gambling research literature. Growth modeling does not require the same restrictive (and often implausible) assumptions as, and affords a number of advantages over, more traditional analytic approaches for analyzing longitudinal data such as repeated-measures analysis of variance (Gibbons et al, 1993;Tomarken and Waller, 2005). Hierarchical linear modeling (HLM) is a regression-based framework (see Singer and Willett 2003) for simultaneously modeling change over time at both the group and individual levels. HLM analysis of longitudinal data consists of two levels—a level 1 model that estimates (from each individual’s repeated measurements) the initial mean level (intercept) and direction and rate of change over time (slope) for each individual, and a level 2 model that estimates the group level differences in mean levels and change over time. One can think about a two-level longitudinal HLM as a systematic way of conducting individual regressions for each participant based on his repeated measurements and then using the resulting parameters obtained from these individual regressions as the input data for a regression analysis for the entire group. HLM goes beyond this simplified scenario by “borrowing strength” from the
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information provided by other participants with similar characteristics to derive a better estimate of the trend for the individual (Gibbons et al. 1993, p. 743). Latent variable growth curve modeling (LGM) is a structural equation modeling–based framework (see Duncan et al. 1999) for simultaneously modeling change over time at both the group and individual levels. LGM is similar to confirmatory factor analysis (Curran and Hussong 2003). The repeated measurements obtained over the course of a longitudinal study are treated as indicators of two correlated latent factors—a mean-level factor (intercept) and a growth structure factor (slope), with fixed factor loadings of the repeated measurements on the latent factors representing the passage of time. In LGM, the latent factor means represent the mean level and growth at the group level, and the latent factor variances represent individual differences around these factor means. HLM and LGM are flexible data-analytic approaches that could potentially be used to test a number of different hypotheses about gambling behavior over time. For example, one could examine (1) whether increases or decreases in gambling involvement with age are linear or nonlinear, (2) whether there is an acceleration in growth in gambling involvement when people reach the legal age to gamble, (3) whether changes in gambling-related problems correspond to changes in alcoholrelated problems, and (4) the predictors, correlates, and sequelae of various aspects of changes in gambling behavior. (See Curran and Hussong [2003] and Muthén and Muthén [2000] for some applications and extensions of growth modeling.)
DEALING WITH MISSING DATA Missing data are inevitable in longitudinal research. Over the course of a longitudinal study, not all of those who participate at earlier waves will participate at later waves (and some of those who participate at later waves may have been unavailable at earlier waves).There are two potential consequences of missing data in a longitudinal study. At the very least, even when the data are missing completely at random, there will be a reduction in sample size and a corresponding loss of statistical power. This can be especially consequential when accumulated over many waves of a longitudinal study. However, data are usually not missing completely at random, and this may be especially true in longitudinal studies of gambling behavior. Individuals with gambling pathology may be more likely to thwart follow-up efforts or to drop out of a longitudinal study compared with individuals without gambling pathology; they may be more likely to be geographically mobile—to move or disconnect their telephones to escape from creditors—or to be incarcerated. For example, Abbott et al. (1999) found that those who felt that they had a gambling problem were less likely to participate in the 7-year follow-up of the New Zealand study than those who did not, and Slutske et al. (2003) showed that problem gambling at the first wave of the Missouri study was a significant predictor
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of nonparticipation at subsequent waves conducted 3, 6, and 10 years later. In addition to a reduction in sample size and a loss of statistical power, nonrandom missing data can also lead to biased results and incorrect conclusions (Acock 2005; Raghunathan 2004). Experts argue strongly against the common practice of ignoring missing data at the analysis stage by including only individuals with complete data (i.e., “listwise deletion”). However, it is important to recognize that even the best missing-data techniques are not panaceas and cannot completely correct all of the biases that can result from missing data. The appropriate handling of missing data is one area of statistics in which there appears to be a general consensus among experts about the relative merits of different strategies (e.g., Acock 2005; Raghunathan 2004; Schafer and Graham 2002; Tomarken and Waller 2005). Three approaches are especially promising for dealing with missing data in the context of a longitudinal study: (1) weighting, (2) multiple imputation, and (3) full information maximum likelihood. Weighting (Raghunathan 2004) is commonly used in large-scale crosssectional epidemiologic surveys when the characteristics of the target population are known. One can compensate for the underrepresentation of certain groups in the obtained sample by assigning a sampling weight to each observation that reflects the probability of the groups’ inclusion in the sample—observations from undersampled groups will be weighted more heavily and those from oversampled groups will be weighted less heavily.The same logic can be applied to account for individuals who drop out of a longitudinal study. Those individuals from groups who are underrepresented at follow-up based on characteristics at the baseline assessment can be weighted more heavily. Weighting can also be used when data are missing by design, such as when a two-stage sampling design is used. Multiple imputation (Acock 2005; Schafer and Graham 2002;Tomarken and Waller 2005) is an extension of single imputation in which missing values in a data set are imputed based on the available nonmissing data. In multiple imputation, this process is repeated in order to generate 5–20 imputed data sets. Data analyses are conducted on each of these imputed data sets, and then pooled estimates of the parameters and standard errors are derived from the set of solutions. Multiple imputation is preferred over single imputation because the degree of uncertainty in estimating the imputed data points is taken into account in the statistical analyses. Full information maximum likelihood (Little and Rubin 2002) is generally considered the preferred approach to handling missing data.This approach does not fill in missing data, but rather uses all available data to derive proper parameter estimates.This missing-data technique is not available for all software packages and statistical routines, but when it is available, its application requires very little effort on the part of the end user. Full information maximum likelihood estimation can be found in all major structural equation modeling and HLM software packages (Acock 2005).
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THE LOW PREVALENCE OF PATHOLOGICAL GAMBLING DISORDER In a recent U.S. national survey of 43,093 adults, the number of individuals identified with past-year and lifetime DSM-IV diagnoses of pathological gambling disorder was only 74 and 185, respectively (Slutske 2006).As it is currently defined, pathological gambling disorder is no more prevalent than schizophrenia or anorexia nervosa. One of the greatest challenges to conducting systematically ascertained community-based longitudinal studies of pathological gambling disorder is the fact that it is not very common. Here I summarize two approaches to dealing with this problem. One approach for dealing with the low base rate of pathological gambling disorder is to employ strategies for developing enriched samples. High-risk prospective studies were originally proposed in response to the challenge of prospectively predicting the onset of another rare disorder, schizophrenia (Mednick and McNeil 1968). Typically, unaffected individuals have been deemed at high risk based on a history of the same disorder in their parents. Although this approach has not yet been used in studies of pathological gambling, a related approach was used in the recently completed follow-up to the Vietnam Era Twin study (Eisen et al. 1998; Slutske et al. 2000). In this study, unaffected identical and fraternal twins of men with pathological gambling disorder were selected for the 10-year follow-up with the assumption that they were at high risk for the development of pathological gambling. A strategy for maximizing the number of individuals in a sample who already are experiencing problems is to use a two-stage sampling design. The first stage involves the initial screening of a larger pool of potential participants, and the second stage involves more intensive study of the subsample consisting of those who were identified at the first stage as already having experienced problems.With the parallel collection of data from a randomly selected unaffected control sample, one can use missing-data techniques such as weighting to obtain estimates based on the larger representative stage 1 sample.A possible solution to the low base rate problem in systematically ascertained community-based research on pathological gambling disorder might be to pool data across several research sites. Another approach for dealing with the low base rate of pathological gambling disorder is to focus on continuous measures of gambling pathology and on subclinical problem gambling.There has been a recent general trend in psychopathology research to use continuous, rather than categorical, measures of pathology.This has two clear benefits for longitudinal studies of gambling pathology—a gain in statistical power and provision of a more sensitive metric of intra-individual change. Although typically used to derive categorical outcomes, all of the commonly used measures of problem and pathological gambling, such as the SOGS, can also be used to derive continuous outcome measures (Lesieur and Blume 1987).
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There are a number of reasons to focus on a broad range of gambling pathology, including subclinical problem gambling, rather than restricting the focus of research to diagnosable pathological gambling disorder: (1) Subclinical gambling pathology is still clinically significant (Slutske et al. 2003); (2) there is no solid empirical evidence supporting the current diagnostic threshold, and studies that include the entire range of gambling pathology, including subclinical problem gambling, will provide the evidence needed to determine where the diagnostic threshold should be drawn; (3) problem gambling may be an important developmental precursor in the downward spiral leading to pathological gambling disorder; and (4) if problem and pathological gambling truly represent different points on an underlying continuum of liability, then studies that identify risk factors for problem gambling will also further our understanding of the risk factors for pathological gambling disorder because the only difference between the two outcomes would be that more (but not different) risk factors would be required to develop pathological gambling disorder than problem gambling. Although we may never know whether problem and pathological gambling truly represent different points on an underlying continuum of liability, it is likely that the relevant risk factors for problem and pathological gambling substantially overlap.
IMPORTANT QUESTIONS AND WHAT WE KNOW SO FAR In this section I focus on the following five important issues that can be resolved effectively only with longitudinal or follow-up data: (1) resolving the temporal relation between gambling behavior and its correlates, (2) establishing the stability of gambling behavior, (3) characterizing the course of gambling behavior, (4) identifying sequences and stages in the development and progression of gambling behavior, and (5) understanding age differences in levels and rates of gambling behavior. To date, the main focus of nearly all of the existing longitudinal studies of gambling behavior has been on the first and third questions. I will also discuss how longitudinal data can improve the strength of hypothesis testing in the context of natural experiments.
TEMPORAL RESOLUTION OF GAMBLING CORRELATES: ESTABLISHING CAUSALITY? Longitudinal data are essential for establishing the temporal relation that is necessary for inferring a cause-and-effect association. For example, are crime-prone individuals more likely to become involved in gambling activities, or do gambling activities lead to criminal behavior in some individuals?
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Is socioeconomic disadvantage a cause or a consequence of problem gambling? When considered in isolation, correlational longitudinal data are insufficient for answering such causal questions (even when one is conducting “causal modeling” analyses) (Cliff 1983) and are best thought of as one piece of a puzzle. Although correlational longitudinal data cannot definitively prove causal relationships, they can rule out some possibilities based on a temporal relation that is inconsistent with a particular direction of causation. There are essentially two ways to establish temporal precedence of one construct over another. The most intuitively appealing is to study individuals early enough so that one or both behaviors of interest have not yet developed. In the Montreal study by Vitaro and colleagues (2004), a substantial fraction of boys were already gambling by age 11, so one would need to initiate a study even earlier than this to prospectively predict gambling involvement using this strategy. Most existing longitudinal studies of gambling behavior have not studied individuals prior to the potential development of the gambling outcomes of interest. For example, in the Dunedin birth cohort study (Slutske et al. 2005), the higher-order personality dimensions of negative emotionality (including lowerorder scale indicators of nervousness or worry, anger or aggressiveness, and feeling mistreated or victimized) and (low) behavioral constraint (including lower-order scale indicators of risk taking, impulsivity, and rebelliousness) assessed at age 18 were prospectively associated with problem gambling at age 21. Even though pastyear problem gambling was assessed as the outcome of interest at age 21, some individuals may have had problems at or before age 18, and some aspects of personality, particularly those related to negative emotionality, could conceivably be a consequence of these earlier gambling problems, rather than a potential cause of problems at age 21. This possibility could not be empirically evaluated because problem gambling was not assessed at age 18. Although such prospective associations add an important piece to the “causality puzzle,” they can be interpreted in several different ways. For instance, an association between personality at baseline and problem gambling at follow-up might be partially due to the cross-sectional association of personality with (unmeasured) problem gambling at baseline, and the temporal stability of problem gambling from baseline to follow-up. Therefore, a second way that researchers might attempt to establish the temporal precedence of one construct over another is through the use of statistical controls. Measures of the outcome variable (in this example, problem gambling) would also be included at baseline so that the effect of problem gambling at baseline on the association between personality at baseline and problem gambling at follow-up can be statistically controlled. A related technique that has been used to disentangle the direction of causality of two correlated constructs is cross-lagged panel correlation (CLPC) analysis (Kenny 1979). In CLPC analysis, both constructs of interest are each measured over two or more occasions.The premise of CLPC analysis is to compare the strength
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of the association between, for example, personality at time 1 and problem gambling at time 2 with problem gambling at time 1 and personality at time 2. The rationale is that if the former association is stronger than the latter (i.e., personality → problem gambling > problem gambling → personality), then the correlation between personality and problem gambling can be predominantly attributed to the prospective association between personality and problem gambling. Conversely, if the reverse is true (i.e., problem gambling → personality > personality → problem gambling), then the correlation between personality and problem gambling can be attributed predominantly to the prospective association between problem gambling and personality. If the associations do not differ from each other, then the correlation between personality and problem gambling is “spurious” (i.e., due to the effect of one or more common variables that causes them both) (Campbell and Kenny 1999). CLPC analysis can be a potentially useful exploratory data-analytic technique (Campbell and Kenny 1999; Kenny 1979; Rutter 1981). For example, once replicable cross-sectional and longitudinal relationships between a putative cause and effect are established, one might then examine the difference in the crosslagged associations to see if they are consistent with a causal hypothesis before proceeding to more sophisticated analyses (Campbell and Kenny 1999). There are a number of problems that limit the interpretability of CLPC analyses of manifest (rather than latent) variables (Campbell and Kenny 1999; Rogosa 1980). For example, different implementations of CLPC assume that, for example, problem gambling and personality are measured with equal reliability at both time points, or that individual differences in problem gambling and personality are equally stable across time. Many of these problems can be circumvented by examining the cross-lagged associations between latent variables estimated within the context of a structural equation model, and this is now the preferred approach to conducting such analyses (e.g., see Sher et al. 1996; Sher and Wood 1997; for a nontechnical general review of structural equation modeling, see Loehlin 1998).
THE STABILITY
OF
GAMBLING BEHAVIOR
Developmental psychologists and statisticians draw distinctions between at least three different types of trait stability—mean level stability, stability in individual differences (“rank-order” or “inter-individual stability”), and intra-individual stability (see e.g., Caspi and Roberts 2001; Roberts and DelVecchio 2000; Rogosa 1995)—that are relevant to understanding gambling behavior. Mean level stability refers to the aggregate stability over time in the average level of a trait (in the case of continuous outcomes) or the percentage of individuals possessing a trait (in the case of dichotomous categorical outcomes) and is assessed by examining group means or prevalences over time. In the context of a longitudinal study of a
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relatively age-homogeneous sample, mean level stability provides insight into developmental changes in a gambling outcome, and in the context of a natural experiment, mean level stability can reflect the effects of an important historical change. Three existing longitudinal studies present evidence relevant to the issue of mean level stability of gambling behavior across development. In the Montreal study (Vitaro et al. 2004), the prevalence of gambling at least once in the past year steadily increased among boys from age 11 until age 15, but then appeared to level off from age 15 to 16 (actual prevalences by age were not reported). In the Minnesota study (Winters et al. 2002), the prevalences of any gambling (80–88%) and regular gambling (13–18%) did not significantly differ across the three waves of the study conducted at ages 15–18, 16–20, and 21–26. This apparent mean level stability, however, masked considerable variability when individual gambling activities were considered. Involvement in unregulated forms of gambling (i.e., cards, betting on games of personal skill or on sports) significantly declined, whereas involvement in regulated forms of gambling (i.e., scratch tabs, gambling machines, and lottery) significantly increased over this developmental (and historical) period. Although there were no differences in the prevalences of problem gambling (2–4%), there was a significant increase in the prevalence of at-risk gambling from waves 1 and 2 to wave 3 of the study (12–15% vs 21%). In the Missouri study (Slutske et al. 2003), the past-year prevalences of problem gambling did not significantly differ from ages 21–22 (3%), 24–25 (3%), or 28–29 (2%). Stability in individual differences refers to the retention of an individual’s rank within a group over time and is assessed by correlating the same repeated measures of a trait obtained from a group of individuals on two separate occasions. As mentioned in the previous section, a prospective association between a correlated baseline characteristic and a gambling outcome actually may be (at least partially) due to the inter-individual stability of the gambling outcome itself. Inter-individual stability inevitably diminishes over time for psychological traits, and based on the scant evidence available, this also appears to be true for problem gambling. Only two longitudinal studies have reported the inter-individual stabilities of gambling behavior (Slutske et al. 2003;Vitaro et al. 2001), but stabilities were also calculable from the published data of another study (Winters et al. 2005). The inter-individual stabilities of problem gambling across 2 to 10 years from the Missouri and Minnesota studies (Slutske et al. 2003;Winters et al. 2005) are shown in Figure 6.1. Note that, as expected, there is a substantial correlation of −0.50 between the length of the test–retest interval and the inter-individual stability (estimated by the tetrachoric correlation). Overall, there appears to be substantial stability of individual differences in problem gambling, with an average test–retest correlation across 2 to 10 years of 0.53. (Interestingly, results from the Montreal study [Vitaro et al. 2001] are inconsistent with the Missouri and Minnesota studies [Slutske et al. 2003;Winters et al. 2005]. In the Montreal study,
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the inter-individual stability of problem gambling across 1 year from age 16 to 17 was only 0.20, but this may have been due to the fact that an abbreviated threeitem version of the SOGS-RA was used at age 16 and the full twelve-item scale was used at age 17 [Vitaro et al. 2001].) In the context of a longitudinal study of a relatively age-homogeneous sample, the patterns of inter-individual stabilities over time can provide clues to the causes of individual differences in gambling outcomes at different ages or phases of life (Cole 2006; Fraley and Roberts 2005). For example, Cole (2006, p. 22) argued that the “fact that stability estimates tend to diminish as the time interval increases suggests the influence of processes that are somewhat transitory.”The fact that the stabilities for problem gambling are greater than zero also suggests the influence of “something enduring or traitlike” (ibid.), at least over the short time span of 2 to 10 years covered in these two studies.What we don’t know yet are the longerterm inter-individual stability of problem gambling and whether the curve in Figure 6.1 will eventually reach an asymptote greater than zero or the stabilities will continue to diminish to zero.The former curve (i.e., one that asymptotes at > 0) would lead to the conclusion that individual differences in problem gambling are enduring over the life course and that problem gambling later in life can be predicted from problem gambling earlier in life. The latter curve (i.e., one that approaches 0) would lead to the conclusion that individual differences in problem gambling do not endure over the life course and that problem gambling later in life cannot be predicted very well from problem gambling earlier in life.
1
Test−retest correlation
0.9
MN 0.82
0.8
r = −50
MN 0.74
0.7
MO 0.66
0.6
MO 0.51
0.5
MO 0.57
MN 0.54
0.4
MO 0.37
0.3
MO 0.35
MO 0.2
0.2 0.1 0 0
2
4
6
8
10
12
Years between assessments
Figure 6.1. Stability of individual differences in problem gambling as a function of time from two longitudinal studies. (Data points from Slutske, Jackson, and Sher [2003] are denoted by a square; data points calculated from Winters et al. [2005] are denoted by a diamond.)
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Intra-individual stability refers to the consistency within an individual of the amount or level of a trait (for continuous outcomes) or of belonging in a particular category (in the case of categorical outcomes). Thus, studies that focus on the course of gambling behaviors and gambling problems are studies of intra-individual stability.
THE COURSE
OF
GAMBLING BEHAVIOR PROBLEMS
AND
GAMBLING
There is now consistent evidence emerging from longitudinal research suggesting that the course of problem gambling is variable—for some individuals the gambling problems are relatively transient, whereas for others the gambling problems are persisting and chronic (Slutske 2006).This is an important distinction that has been overlooked until recently because it was assumed that disordered gambling behavior was invariably progressive and chronic. The first compelling evidence came from the 7-year follow-up of the 1991 New Zealand National Prevalence Survey (Abbott et al. 1999). The New Zealand researchers found that of the 13 individuals who had a past-6-month diagnosis of probable pathological gambling in 1991, only 3 (23%) had a similar diagnosis in 1998; of the 22 individuals with a past-6-month diagnosis of problem gambling in 1991, only 3 (14%) had escalated to probable pathological gambling, and only 2 (9%) had a problem gambling diagnosis in 1998. (Somewhat surprisingly, this study also obtained mean level differences, that is, an unexpected decrease in the prevalence of current disordered gambling behavior over time in this age-heterogeneous cohort.) The Minnesota (Winters et al. 2005) and Missouri (Slutske et al. 2003) studies tracked the course of problem gambling over 8 and 11 years, respectively, among individuals ranging in age from 15 to 29 years. Both studies were consistent in demonstrating high mean level stability, moderate inter-individual stability, but low intra-individual stability of problem gambling across this age range. The intra-individual stability of problem gambling from these two studies is depicted in Figure 6.2.The figure illustrates that the persistence of past-year problem gambling across all three time points in these two studies was exceedingly rare, persistence across two consecutive time points was also uncommon, and at each time point the numbers of resistant cases and incident cases often equaled or outnumbered the number of persistent cases. Several studies have examined the course of nonpathological gambling behavior. In the Montreal study of Vitaro and colleagues (2004; Wanner et al. 2006), participants were categorized according to their pattern of gambling at least once in the past year based on assessments conducted annually from age 11 to 16. Three trajectory groups were empirically identified using discrete mixture modeling (Nagin 1999). Sixty-two percent of the boys were assigned to a
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(a) Winters et al.,
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2005
T1 in 1992 mean age = 16.0 years
PG 7
T2 in 1994 mean age = 17.6 years T3 in 1997−1998 mean age = 23.8 years
No PG 298
PG 4 PG 2
No PG 3
No PG 2
PG 0
persistent
No PG 3
PG 12 PG 5
No PG 286 PG 5
No PG 281
incident
resistant
No PG 7
desistant
(b) Slutske et al., 2003 Year 4 in 1990−1991 age = 21−22 years
PG 11
Year 7 in 1993−1994 age = 24−25 years Year 11 in 1998−1999 age = 28−29 years
PG 4 PG 1 persistent
No PG 3
No PG 377 No PG 7 PG 0
No PG 7 desistant
PG 8 PG 1
No PG 369
No PG 7
PG 5
incident
resistant
No PG 364
Figure 6.2. Course of past-year problem gambling across three time points from two longitudinal studies. (Note: PG, problem gambling; data from Winters et al. [2005] and Slutske, Jackson, and Sher [2003].)
group that was unlikely to gamble at any given age, 22% of the boys were assigned to a group that was likely to gamble at any given age, and 16% of the boys were assigned to a group that was unlikely to gamble at ages 11, 12, and 13, but likely to gamble at ages 14, 15, and 16; there were no trajectory groups identified in which the probability of gambling decreased from age 11 to 16. In the two parallel New York studies of Barnes and colleagues (Barnes et al. 2002), participants were categorized according to their pattern of gambling frequency across two study occasions that occurred 12 to 18 months apart when participants were 17–21 and 18–22 years of age, respectively (essentially picking up at the age where the Montreal study left off). The New York studies (Barnes et al. 2002) identified a subgroup of young adults (16–26%) who decreased the frequency of their gambling.
SEQUENTIAL/STAGE THEORIES OF GAMBLING INVOLVEMENT: IS THERE A “GATEWAY” TO PROBLEMS? Longitudinal studies of gambling behavior can provide interesting insights into the typical developmental sequence of participating in different gambling activities in much the same way that longitudinal substance use research has yielded important insights into the typical sequence of the use of different
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substances. An influential paper (widely cited as evidence that marijuana is the “gateway” to the use of other illicit drugs) showed that there was a typical sequence of stages of substance involvement among adolescents, starting with no use, to the use of beer or wine, to the use of hard liquor or cigarettes, to the use of marijuana, to the use of other illicit drugs (Kandel 1975).The identification of these stages was based on the observation that there were very few individuals who used a substance at a later stage who hadn’t previously used substances from the earlier stage(s). The cross-sectional 1999 U.K. gambling prevalence survey of 7680 adolescents and adults (Sproston, Erens, and Orford 2000) showed a pattern of involvement in various gambling activities that suggested a similar stage-like process that might be tested in a longitudinal study by examining the initiation of new activities over time. Participants from the U.K. study were classified into groups according to whether they had participated in one, two, three, four, five, or six or more different gambling activities in the past year. With each additional activity, all of the activities of the less involved groups were endorsed by the majority of the individuals in the more involved groups.That is, gambling activities appeared to conform to a Guttman scale progressing from participation in the national lottery, to scratch cards, to gambling machines, to private betting with friends, to horse/dog races. In the United Kingdom, lottery playing was less strongly associated with problem and pathological gambling than was betting on dog races because it was an activity that was ubiquitous across all levels of gambling involvement. Betting on dog races was an activity that was associated with an overall greater level of gambling involvement in the United Kingdom, and so was also more indicative of problem and pathological gambling. One could also examine the typical sequence in the development of symptoms of problem and pathological gambling in much the same way that symptoms of alcohol dependence have been examined. For example, Toce-Gerstein, Gerstein, and Volberg (2003) presented results from a cross-sectional 1999 U.S. prevalence survey in which they examined the frequency of endorsement of each pathological gambling symptom among those who varied in the severity of their gambling pathology based on the total numbers of lifetime symptoms they endorsed. Toce-Gerstein et al. (2003) identified the following four sets of symptoms characteristic of increasing levels of severity of gambling pathology (from least to most severe): (1) chasing losses; (2) preoccupation, lying about gambling, and gambling to escape; (3) withdrawal, loss of control, tolerance, bailout, and risked relationships; and (4) illegal acts. This ordering of symptoms might also reflect a temporal sequence that could be tested in a future longitudinal study by examining the development of new symptoms over time. In the absence of longitudinal data, a cross-sectional study with retrospectively reported ages of onset of pathological gambling symptoms might be a valuable first step (e.g., see Nelson et al. 1996).
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DEVELOPMENTAL CHANGES VERSUS COHORT OR PERIOD EFFECTS ON LEVELS OF GAMBLING INVOLVEMENT In cross-sectional epidemiologic surveys, the prevalence of problem gambling varies with age, with higher prevalence estimates typically obtained for younger than older individuals. For example, in the 1999 U.K. gambling prevalence survey (Sproston et al. 2000), there was a strong negative association (r = −0.98) between age group and past-year problem gambling, with estimates based on the SOGS ranging from 1.7%, 1.2%, 0.8%, 0.7%, 0.5%, and 0.1% among 16–24 year-olds, 25–34 year-olds, 35–44 year-olds, 45–54 year-olds, 55–64 year-olds, and those aged 65 years and older, respectively. Similarly, in a meta-analysis of 119 North American cross-sectional prevalence surveys of problem and pathological gambling, both lifetime and past-year estimates were substantially higher for adolescents than for adults (Shaffer, Hall, and Vander Bilt, 1999).What is unclear from these cross-sectional data is whether this reflects a developmental trend or an effect of different rates of problem gambling among individuals from different birth cohorts. More recently born individuals may be at greater risk for developing gambling-related problems; alternatively, this prevalence difference may be due to a developmental effect, such that gambling-related problems are at their peak during adolescence and young adulthood, with a gradual maturing-out of problems with age. There are limited longitudinal data on mean level stability to answer this important question, and the existing studies do not span the entire life course. In the Minnesota longitudinal study of Winters and colleagues (2002), the prevalence of problem gambling was unchanged from adolescence to young adulthood, although there was a significant increase during the young adult years of at-risk gambling that appeared to coincide with an increase in participation in regulated gambling activities. In the Missouri study of Slutske and colleagues (2003), the lifetime and past-year rates of problem gambling did not differ from age 18 to 29. Thus, the results from longitudinal research appears to contradict the results of the cross-sectional research by suggesting that the prevalence of problem gambling actually may be relatively stable from adolescence to early adulthood or may actually be higher among young adults than among adolescents. Taken together, the evidence from the cross-sectional and longitudinal research, coupled with the results of a “cross-temporal” meta-analysis of crosssectional prevalence surveys conducted between 1977 and 1997 indicating higher prevalences of disordered gambling behavior obtained in more recent studies than in earlier studies (Shaffer et al. 1999), suggests that age-related differences in prevalences of problem and pathological gambling may be due, at least in part, to a cohort effect or an interaction between a developmental and a period effect (i.e., effects of historical changes only influencing those at certain critical ages; see Menard 2002; Rice, Moldin, and Neuman 1991), rather than simply reflecting developmental changes.
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“NATURAL EXPERIMENTS” IN LONGITUDINAL GAMBLING RESEARCH A longitudinal study can potentially provide a sensitive test (i.e., using a within-subjects design that is unconfounded by selection factors, rather than a between-subjects design) of the effect of a new gambling-related legislation or policy or other macro-environmental event or change. In effect, one has a natural experiment comparing the same individuals before and after such an event or change has occurred. Such events or changes cannot always be anticipated, but ongoing longitudinal studies may be well positioned to seize a unique opportunity in assessing their impact.A nice example examining the possible causal connection between socioeconomic disadvantage and psychiatric symptomatology comes from an ongoing 8-year community-based longitudinal study of mental illness among urban and rural youth recruited from eleven counties in western North Carolina (“the Great Smoky Mountains Study,” Costello et al. 2003). Some of the youth were American Indians living on a federal reservation that extended into this region. In year 4 of the longitudinal study, tribal members began to receive income from a gambling casino that opened on the reservation, and there was also an increase in the number of jobs available in the newly opened casino and surrounding businesses.This new source of income raised 14% of the American Indian families from below to above the poverty line and corresponded to a reduction in externalizing psychiatric symptoms of these previously poor youth. Another ingenious use of this same longitudinal study comes from a scheduled interview that was planned to occur during the year 2001.Two-thirds of the sample were interviewed prior to the terrorist attacks on the United States on September 11, and the remaining one-third were interviewed after 9/11.The investigators examined (this time, using a between-subjects design) the effect of the September 11 attacks on reports of current substance use and psychiatric symptomatology among 19–21 year-olds (Costello et al. 2004). The only longitudinal study that has examined the effect of a change in legislation or policy on subsequent gambling behavior is the Minnesota study of Winters and colleagues (1995), who examined the effect of the introduction of lottery games into the state of Minnesota. They were introduced in two stages— instant scratch tabs first and then, 4 months later, online or number selection lottery games. The baseline survey of the Minnesota study, conducted when the participants were 15–18 years of age, occurred prior to the second stage, but due to administrative delays, after the first stage; the follow-up was conducted 1.5 years later—well after all of the stages of the introduction of the lottery were complete. Mean level wave 1/wave 2 comparisons in gambling outcomes were made for two groups: those who were of legal age (18+ years) for at least half of the interval between wave 1 and wave 2 and those who were of legal age for less than half of this interval.The legal age subsample reported a greater frequency of participating
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in regulated forms of gambling (casino machines, scratch tabs, and lottery) at wave 2 than at wave 1, whereas there was no such increase in the underage subsample. However, there were no mean-level increases in regular, at-risk, or problem gambling from wave 1 to wave 2 for either the legal-age or underage subsamples.These results were “reassuring to public health officials who were concerned that the onset of the state’s high-stakes and heavily promoted lottery would trigger a significant increase in the rate of problem gambling among youth” (Winters et al. 1995, p. 178).
SUMMARY OF WHAT WE DON’T KNOW (YET) Although the first decade of community-based longitudinal gambling research has led to a number of important insights about gambling behavior, a review of the research conducted to date highlights a number of areas in which there are gaps in our knowledge. ●
●
●
●
●
●
Longitudinal studies are just beginning to disentangle the temporal and potential causal relations between gambling behavior and important correlates. Establishing prospective prediction is an important first step but must eventually be followed by more probing analyses that can rule out alternative hypotheses. We don’t yet know the longer-term mean level, inter-individual, and intra-individual stability of gambling behavior. There are major spans of the life course and segments of the population that have not yet been the subjects of developmentally sensitive longitudinal gambling research.We know virtually nothing about the predictors, stability, and course of gambling behaviors beyond the third decade of life. We also know virtually nothing about the predictors, stability, and course of gambling behaviors during childhood and early adolescence among girls. We don’t yet know the factors that predict individual differences in the desistance from or maintenance or escalation of gambling behavior. Such questions may require the use of more contemporary statistical techniques such as growth modeling. Longitudinal research has not yet tackled important issues related to the typical sequence in which different gambling activities are initiated and whether individual differences in this pattern are related to gambling outcomes. Nor has longitudinal research examined the typical progression of problem and pathological gambling symptom development. We have not yet capitalized on the potential power of longitudinal research for examining the effect on gambling behavior of changes in gambling legislation or policy or other macro-environmental events.
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Finally, nearly all of the longitudinal gambling research has been focused on nonpathological gambling involvement or subclinical problem gambling.There is very little information currently available from communitybased longitudinal research on the predictors, stability, and course of diagnosable pathological gambling disorder.
GLOSSARY Cohort effects an interaction between a developmental and time-period effect in which the effects of historical events uniquely influence those at certain critical ages rather than simply reflecting developmental changes. Critical developmental period a period in which one expects a great deal of change (such as the ages that span the years when it becomes legal to gamble, or when there are several important milestones). Intra-individual stability the consistency within an individual of the amount or level of a trait (for continuous outcomes) or of belonging in a particular category (in the case of categorical outcomes). Longitudinal study study of individuals from a systematically ascertained or representative community-based sample who are assessed on at least two separate occasions across an interval of at least 1 year. Mean level stability the aggregate stability over time in the average level of a trait (in the case of continuous outcomes) or the percentage of individuals possessing a trait (in the case of dichotomous categorical outcomes). It is assessed by examining group means or prevalences over time. Natural experiment a study of the effect of a new gambling-related legislation or policy or other macro-environmental event or change by comparing the same individuals before and after such an event or change has occurred. Stability in individual differences the retention of an individual’s rank within a group over time assessed by correlating the same repeated measures of a trait obtained from a group of individuals on two separate occasions.
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Tomarken, A. J., and Waller, N. G. (2005). Structural equation modeling: Strengths, limitations, and misconceptions. Annual Review of Clinical Psychology, 1, 31–65. Tremblay, R. E., Pihl, R. O.,Vitaro, F., and Dobkin, P. L. (1994). Predicting early onset of male antisocial behavior from preschool behavior. Archives of General Psychiatry, 51, 732–739. Vitaro, F., Arseneault, L., and Tremblay, R. E. (1997). Dispositional predictors of problem gambling in male adolescents. American Journal of Psychiatry, 154, 1769–1770. —— . (1999). Impulsivity predicts problem gambling in low SES adolescent males. Addiction, 94, 565–575. Vitaro, F., Brendgen, M., Ladouceur, R., and Tremblay, R. E. (2001). Gambling, delinquency, and drug use during adolescence: Mutual influences and common risk factors. Journal of Gambling Studies, 17, 171–190. Vitaro, F., Ladouceur, R., and Bujold, A. (1996). Predictive and concurrent correlates of gambling in early adolescent boys. Journal of Early Adolescence, 16, 211–228. Vitaro, F.,Wanner, B., Ladouceur, R., Brendgen, M., and Tremblay, R. E. (2004).Trajectories of gambling during adolescence. Journal of Gambling Studies, 20, 47–69. Wanner, B., Vitaro, F., Ladouceur, R., Brendgen, M., and Tremblay, R. E. (2006). Joint trajectories of gambling, alcohol, and marijuana use during adolescence: A person- and variable-centered approach. Addictive Behaviors, 31, 566–580. Winters, K. C., Stinchfield, R. D., Botzet, A., and Anderson, N. (2002). A prospective study of youth gambling behaviors. Psychology of Addictive Behaviors, 16, 3–9. Winters, K. C., Stinchfield, R. D., Botzet, A., and Slutske, W. S. (2005). Pathways of youth gambling problem severity. Psychology of Addictive Behaviors, 19, 104–107. Winters, K. C., Stinchfield, R. D., and Fulkerson, J. (1993a). Patterns and characteristics of adolescent gambling. Journal of Gambling Studies, 9, 371–386. —— . (1993b). Toward the development of an adolescent gambling problem severity scale. Journal of Gambling Studies, 9, 63–84. Winters, K. C., Stinchfield, R. D., and Kim, L. G. (1995). Monitoring adolescent gambling in Minnesota. Journal of Gambling Studies, 11, 165–183.
CHAPTER 7
Quantification and Dimensionalization of Gambling Behavior Shawn R. Currie
David M. Casey
Calgary Health Region Calgary, Alberta, Canada
Addiction Centre Foothills Medical Centre University of Calgary Calgary, Alberta, Canada
Historical Perspectives Epidemiological Data on Gambling Expenditure, Frequency, Duration, and Type Quantification of Other Addictive Behaviors Importance of the Quantification of Gambling Variations in Sources of Data Relevant Quantitative Dimensions of Gambling Behaviors Inputs Participation Status Types of Gambling Frequency Expenditure Duration Attitudes and Cognitions Outputs Financial Legal Social and Psychological Harms Clinical Use of Quantitative Gambling Data Conclusions
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HISTORICAL PERSPECTIVES Gambling has been present in our society for many centuries. Nonetheless, attempts to monitor and control the amount of gambling in the general population are relatively recent. The onset of government regulation of gambling in Western countries, which occurred roughly in the late 1960s in both the United States and Canada (Campbell and Smith 1998; Petry 2005), can be reasonably viewed as the first organized effort to quantify gambling behavior.The motives at the time were largely financial. Governments were concerned about the vast amounts of untaxed income being generated by casinos, horse tracks, and other gambling venues. Regulation by the government provided the means for provincial and state participation in this new and growing economy. The onset of government regulation also signaled the first official recognition that gambling, like alcohol, drugs, and tobacco, required external controls to protect the public from potential harm. During the first 20 years or so of government regulation and monitoring, gambling failed to attract the attention of researchers (Campbell and Smith 2003). The first large-scale surveys of gambling behavior on the individual level were not conducted until the late 1970s (Smith and Wynne 2000). Systemic data on the extent of gambling in the population has been collected primarily by government regulatory bodies and the industry itself. In both cases, the focus of data collection continues to be on the economic impact of gambling. The industry, for example, monitors variables such as patron origin (e.g., locals vs tourists), frequency of visitation, the average expenditure per visit, and types of games played.This information, typically not disclosed to the general public or government, provides the industry with a primary source of data for marketing and growth initiatives. Government regulators collect similar data for monitoring purposes. Furthermore, in some countries such as Canada, the government is also the main provider of gambling opportunities, putting it in the unique position of using data for both regulatory and marketing purposes. In terms of the economic impact of gambling, the government closely monitors the number of gambling outlets (e.g., number of electronic gaming machines, lottery outlets, casinos, and racetracks), per capita revenue generated from games of chance, general populace attitudes toward gambling, and jobs created directly by gambling and by spin-off economies (tourism, hospitality, and construction). A significant historical trend in the last 20 years has been the emphasis by government- and industry-run gambling to attract local consumers rather than tourists (MacDonald, McMullen, and Perrier 2004). The expansion of gambling venues in Canada and the United States has been largely driven by a “convenience gambling” model, providing opportunities to gamble as close to consumers as possible to prevent the migration of persons across provincial, state, and national borders to the larger centers of gambling.This market trend is not limited to casino
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gambling. It has influenced all types of gambling products. For example, lottery gambling saw a rapid expansion of available products in the late 1980s, including the introduction of instant-win tickets and sports betting (MacDonald et al. 2004; Smith and Wynne 2000).The intent is to put more opportunities to gamble within reach of the average consumer. These trends have important implications for the collection and interpretation of data on gambling behaviors. Apart from known gambling hot spots (Las Vegas, Atlantic City, etc.), information gathered on individual gambling habits via population surveys, government revenues, and industry records in specific jurisdictions are now used locally to monitor socioeconomic trends. Hence, data collected on resident gambling habits can be used for policymaking purposes at the local level. Furthermore, the impact of responsible gambling initiatives can be more effectively measured when the bulk of gambling is performed by local residents.
EPIDEMIOLOGICAL DATA ON GAMBLING EXPENDITURE, FREQUENCY, DURATION, AND TYPE There have been numerous large population surveys conducted in the last 10 years across North America to measure individual gambling habits. Every province in Canada has conducted its own problem gambling prevalence survey, with sample sizes ranging from 1500 to 4000 (Williams and Wood 2004). In addition, national data on gambling habits were collected as part of a large household survey on mental health completed in 2002 (Statistics Canada 2002a). Similar surveys have been conducted throughout the United States and other countries (Shaffer, Hall, and Vander Bilt 1997). In contrast to the paucity of gambling prevalence data that existed 15 years ago, researchers are now faced with a surplus of data. Unfortunately, a variety of instruments and questions have been employed on surveys, making comparisons across regions extremely difficult. All provincial surveys in Canada conducted over the last 5 years have used a common instrument, the Canadian Problem Gambling Index (CPGI) (Ferris and Wynne 2001). Nevertheless, subtle differences in the wording of questions related to the type of gambling, amount of money spent, and frequency of play exist across the surveys. Due to space limitations, it is not possible to review the prevalence data from each population survey conducted on gambling in the last 10 years. To provide a national snapshot, Canadian data from the gambling module collected in the 2002 Canadian Community Health Survey (CCHS)–Mental Health and Well-being cycle (Statistics Canada 2002a) are presented in Table 7.1. Briefly, the CCHS-1.2 was a cross-sectional survey of a nationally representative sample of over 36,000 individuals aged 15 and older in all provinces and territories (Cox et al. 2005; Statistics Canada 2002a). Gambling was assessed using a short version of the CPGI (Ferris and Wynne 2001). Unfortunately, the CCHS-1.2 version did not ask participants about
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time invested in gambling activities, so national prevalence data on gambling duration were unavailable. In addition, because frequency varied by type of gambling, a composite index of gambling frequency was calculated based on the type occurring most often (e.g., an individual who plays the lottery once per week and electronic slot machines every day was classified as a daily gambler). Nonetheless, the frequency data are likely underestimates given that the frequency questions for each type of gambling were administered independently (e.g., an individual who engages in Table 7.1 Canadian Population Statistics for Frequency, Expenditure, Percent Income, and Type of Gambling. Dimension
% or mean
Gambling Participation % population ≥ 15 years of age who are gamblers
76
Frequency of Any Gambling Daily
1
2–6 times/week
11
About once/week
17
2–3 times/month
9
About once/month
9
6–11 times per year
7
1–5 times per year
22
Never
24 Expenditure
Self-reported Government-reported revenue % Gross income
$272 $1080 0.9
Type of Gambling1 Lotteries
65
Instant-win
36
Casinos
22
Bingo
8
Video lottery terminals outside of casinos
6
Horse racing Other
4 21
Data sources: Currie, Hodgins, Wang, el-Guebaly, and Wynne 2006a; Canadian Community Health Survey: Mental Health and Well-being (Marshall and Wynne 2003); Survey of Household Spending (MacDonald, McMullan, and Perrier 2004); and provincial government financial reports (Azmier 2005). 1 Categories not mutually exclusive.
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a different form of gambling every day might be erroneously classified as a once-aweek gambler). Despite these limitations,Table 7.1 provides, to our knowledge, the only national perspective on self-reported gambling behaviors.
QUANTIFICATION OF OTHER ADDICTIVE BEHAVIORS Progress in the measurement of gambling has been influenced by the methodologies developed to assess other addictive behaviors, most notably alcohol.There are numerous similarities between alcohol and gambling as social phenomena. Both are legal, regulated, and considered socially acceptable for both adults and adolescents (Korn and Shaffer 1999). Key concepts in the epidemiology of alcohol consumption patterns are transferable to gambling. Researchers have quantified the consumption of alcohol across dimensions such as the proportion of drinkers in the population, the proportion who drink regularly, per capita alcohol consumption (liters per person or standard drinks per person), beverage choice, the proportion of the population who drink heavily or binge-drink, frequency of consumption, source of alcohol consumed (off- or on-premise purchase), and the drinking context (home, bars, before driving, etc.). Meaningful gambling equivalents can be created for most of these indicators. Policy development in the area of alcohol has been guided by a central principle:A direct correlation exists between alcohol consumption and risk of harm in the population (Babor 2002); numerous studies show that the risk of health and psychological problems increase with greater daily consumption (Babor 2002; Babor et al. 2003). Quantitative information on alcohol use has been used to determine how the availability of alcohol, its price, restrictions on access (e.g., age limits), and drinking circumstances (measures to deter impaired driving) are related to consumption patterns.The empirical study of this relationship has influenced harm reduction strategies, including limits on blood alcohol level while drinking, restrictions on the sale of alcohol, and the promotion of responsible drinking guidelines (Bondy et al. 1999) which place specific limits on quantity, frequency, duration, and context. No such limits exist for gambling, although a set of proposed limits derived from the CCHS-1.2 data were recently published by Currie, Hodgins, Wang, el-Guebaly, and Wynne (2006). Aiding the study of alcohol consumption patterns is the means to convert different alcoholic beverages to a standard drink. The World Health Organization and most federal health agencies agree on a common set of standard drink equivalents (Bondy et al. 1999). Although a variety of beverage choices exist, the amount of alcohol can be estimated in each drink type and aggregated per person. Sources of error notwithstanding (e.g., variations in the beverage size), the ability to equate
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drink types has been instrumental to the quantification of alcohol consumption. Without standard drink equivalents, population data on per capita consumption would be meaningless.The creation of a comparable, standard unit of gambling has not occurred (Korn and Shaffer 1999). In the area of tobacco research, the most common quantitative indicator is the number of smokers in the population (Roemer 2004). The total number of regular smokers divided by the total number of persons in the target group provides the estimated prevalence of smoking, a highly relevant statistic for monitoring the health of a population (Statistics Canada 2002b).The gambling equivalent for this measure would be percent of gamblers in the population. In terms of assessing the quantity of smoking among those who smoke, cigarettes per day is the most common indicator.This is a valid measure of present level of exposure to smoking toxins (Djordjevic 2004).The total cigarette packs per year provides an estimate of total cumulative exposure (Boyle et al. 2004). However, cigarettes per day is not without limitations, as it does not accurately capture the variable pattern of smoking seen in occasional smokers. Furthermore, different tobacco products (cigarettes, pipes, cigars, and chewing tobacco) vary in terms of the concentration of nicotine, tar, and other harmful chemicals (Djordjevic 2004). Assessment measures such as the Timeline Follow-Back (TLFB) can be used to more precisely assess smoking days in a month and cigarettes smoked per smoking day (Brown et al. 1998). It should also be noted that biochemical measures of both smoking (saliva and serum cotinine levels, carbon monoxide level in expired air) and alcohol (blood alcohol level, gamma-glutamyl transpeptidase [GGT], urine screens) are also available to validate self-reported use, quantify exposure, and serve as surrogate measures of amount consumed. Biochemical measures correlate reasonably well with the actual quantity of cigarettes or alcohol consumed (Miller, Sovereign, and Krege 1988; Stevens and Munoz 2004). The means to quantify consumption via self-report and biochemistry are unique to substance abuse. In summary, most of the quantitative measures of consumption used in tobacco and alcohol monitoring can be adapted to gambling in a meaningful way. The primary intent of quantitative indicators in these areas has been to monitor the health behaviors of individuals, measure level of risk, and assess the impact of strategies aimed at reducing high-risk health behaviors.These aims are relevant to gambling research and public policy and have driven the development of comparable quantitative indicators of gambling behaviors.
IMPORTANCE OF THE QUANTIFICATION OF GAMBLING Over the last 15 years, research on the impact of gambling has shifted focus from a purely economic perspective to an individual-behavioral perspective.
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The driving force has been research on the prevalence of problem gambling in the general population (Shaffer et al. 2004). Public health officials are in need of accurate, standardized information on how much, how often, and for how long consumers gamble (Korn 2000; Korn and Shaffer 1999; Shaffer and Korn 2002). Accordingly, researchers have called for an expanded research agenda with greater emphasis on the determinants of disordered gambling, such as factors of personality, behavior, and context (Shaffer et al. 2004). “Responsible gambling” initiatives place the onus of control on the individual consumer of gambling products (Blaszczynski, Ladouceur, and Shaffer 2004). There is also now greater awareness that disordered gambling is not a simple on/off phenomenon. Rather, the risk of problem gambling exists on a quantifiable continuum. A greater understanding of individual gambling habits and their relationship to harm or risk of addiction will undoubtedly aid in responsible gambling policy development (Currie, Hodgins, Wang, el-Guebaly, and Wynne 2006). Systematic monitoring of gambling habits is highly relevant when taking a population-based perspective. However, at the level of the individual, the quantification of gambling behavior also has utility. For gamblers seeking help, the assessment of gambling quantity, frequency, and type can aid in determining the severity of addiction (see the Hodgins chapter in this volume). For example, the ratio of intended to actual expenditure on gambling (i.e., how much money the individual planned to spend versus how much he actually spent) can be a useful index of degree of control (Weinstock,Whelan, and Meyers 2004).
VARIATIONS IN SOURCES OF DATA Data on the incidence of gambling have been collected using a variety of sources. Early on, data on gambling were collected primarily by government regulatory bodies or the gambling industry. More recently, however, data on gambling have been collected by researchers using a broader range of sources and techniques, including telephone interviews, face-to-face self-reports, government reports on gambling revenue, household spending surveys, and collateral reports (Azmier 2005; Gambino 1997; Hodgins, Currie, and el-Guebaly 2001). There are strengths and weaknesses associated with each of these sources of data. A full review of these issues is beyond the scope of the present chapter.The main strengths and weaknesses of different techniques will be discussed here. Self-reports of individual gambling behavior in the form of telephone, faceto-face, or computer-based surveys provide researchers with a wealth of firsthand accounts of the extent of gambling involvement. Self-reports tend to be reasonably inexpensive and easy to use.There are a variety of instruments (e.g., the CPGI, the South Oaks Gambling Screen [SOGS], the National Opinion Research Center Diagnostic Screen [NODS]) that have been used to measure the quantity of
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gambling, with Shaffer et al. (2004) identifying 27 instruments used to identify disordered gambling alone. However, the very fact that there is such a wealth of self-reports of gambling makes it very difficult to compare the results from studies in which different instruments were used. Even when the same instruments are used, some questions have been altered for some studies. Another limitation of selfreports is the tendency for some individuals to underreport their participation in gambling activities. Underreporting can be influenced by the wording of expenditure questions or individuals’ inability to be precise (e.g., errors in recall) regarding the amount of money they spend on gambling activities. Another potential reason for underreporting is misrepresentation on the part of respondents, who may desire to provide researchers with a socially desirable answer or may even be in denial about the extent of their behavior (Stevens and Munoz 2004;Weinstock et al. 2004). Gambling revenue reports developed for governments are another source of data related to gambling.These reports tend to focus on net revenue for gambling establishments, the amount wagered, and employment benefits incurred as a result of the gambling establishments (National Council of Welfare 1996; Statistics Canada 2003). The data on household expenditures on gambling activities (Statistics Canada 2003) are particularly helpful, since they allow us to compare the household expenditures with other data collected, such as self-reports on the amount spent on gambling.There can be a large discrepancy between self-reported spending on gambling and actual revenues from gambling reported by the government. The direction of the discrepancy (overreporting vs. underreporting) varies depending on the source of the data (Wood and Williams, in press). For example, the Canadian government reported the average annual revenue from legalized gambling as approximately $1080 per household in 2004, whereas the average self-reported spending on gambling was only $272 per household (Azmier 2005). The revenue generated from tourist gambling accounts for a small part of this discrepancy. In contrast, Williams and Wood (2004) found that across the Canadian provinces, average self-reported expenditures were 2.1 times higher than actual provincial gaming revenues for the same time period when the self-reported amounts were derived from gambling behavior surveys. These data suggest that households can grossly over- or under-estimate how much family income actually goes toward gambling. Variations in the statistics related to gambling are the result of a number of factors, including the fact that different instruments are used to measure gambling in different studies. Comparable health research from the area of smoking has shown that different approaches to quantify level of risk (e.g., self-reports of cigarette consumption vs carbon monoxide levels) can impact the accuracy, sensitivity, and validity of the data gathered (Stevens and Munoz 2004).This makes it difficult to compare the statistics from different studies. For instance, prevalence rates for problem gambling vary across Canada, from a low of 2.7% in Saskatchewan to a high of 8.6% in Ontario (Williams et al. 2004). The variable prevalence rates are
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thought to be due to a variety of factors: different survey instruments, the specific wording of some questions, variable response rates, socioeconomic factors, and variations in the availability of gambling across provinces. In addition, studies use different time frames when measuring the level of gambling—for instance, within the last month, within the last year, currently, and/or in a lifetime (National Council of Welfare 1996).This makes it very difficult to compare the results across various studies. Finally, individuals may interpret the wording of the question in very different ways. Researchers have determined that a relatively simple question such as “How much do you spend gambling?” can lead to a wide variety of answers that may underrepresent or overrepresent the actual amount of money spent on gambling activities (Blaszczynski, Dumlao, and Lange 1997;Wood and Williams, in press). Individual answers appear to depend on whether the respondent believes that he should include net expenditures or turnover. It is critical that clear and concise instructions be given for each question, particularly those dealing with expenditure (Williams and Wood 2004). Finally, in other fields, the use of selfreports are usually validated with other measures. Since there is no universally agreed upon metric for gambling, the information that is gathered in separate studies is not always easily compared (Weinstock et al. 2004). As mentioned earlier, in the case of alcohol, tobacco, and drugs, it is easier to make comparisons, since there are universally agreed upon biomedical and behavioral metrics.The Gambling (G)TLFB described by Weinstock et al. (2004) may be an important step in helping to identify behavioral metrics that would be agreed upon by researchers. As stated by Gambino (1997), “In the absence of definitive tests, we cannot know the true prevalence of any condition, we can only estimate it” (p. 293).
RELEVANT QUANTITATIVE DIMENSIONS OF GAMBLING BEHAVIORS Compared with alcohol and smoking, the behavioral dimensions to gambling are far more complex.Weinstock et al. (2004) note that gambling is “a heterogeneous collection of activities, and measuring it requires the assessment of various dimensions” (p. 73).To conceptualize gambling as measurable activity, a simple input–output model is proposed.As seen in Figure 7.1, several measurable inputs to gambling can be defined.These are broadly categorized as being systemic (at the community, industry, or government level) or individual (at the person level). The systemic inputs (e.g., infrastructure costs, number of employees necessary to run venues) will not be discussed in this chapter but are shown in Figure 7.1 for illustrative purposes. At the level of individual gambling, many behavioral dimensions are possible.The most relevant for describing gambling activity are participation status, frequency, expenditure, duration, and type of game played.These are described in more detail below. Individual attitudes, knowledge, and perceptions of gambling
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can also be viewed as relevant dimensions, since they may influence both the rate and the intensity of participation. Weinstock et al. (2004) further articulates contextual dimensions to gambling, such as the setting (casino, home, bar) and concurrent alcohol consumption. A brief synopsis of the relevant inputs and outputs on an individual behavioral level is provided in Table 7.2.The core dimensions are discussed in the following sections.
INPUTS Participation Status Gambling prevalence surveys broadly dichotomize the population as being gamblers or nongamblers based on having wagered money on an event with an uncertain outcome over the past year. One of the challenges inherent in determining gambling status from self-report is that perceptions differ within the general public as to what constitutes gambling. For example, many people do not consider playing the lottery a form of gambling. Furthermore, in some surveys, playing the
• Government revenue • Growth rate • Charitable benefits • # jobs created and average salaries • Gambling-related crime rates • Spin-off economic benefits (e.g., tourism) • Cost of treating PGs • Cost of regulation
System Level
• # gaming outlets (casinos, EGMs, lottery outlets, etc.) • # of visitors/year • # jobs required • Cost of maintaining venues
Gambling
Input
Output
• Gambler status • Type of game • Context • Frequency of play • Expenditure ($ wagered, percent income) • Time invested • Knowledge and attitudes
Individual Level
• Monetary win/loss $ • Psychological benefits (pleasure, socialization, satisfaction, etc.) • Harms (financial, emotional, relationship, health, job, suicide) • Pathological gambling incidence and prevalence
Figure 7.1. Conceptual model of the quantification of gambling behavior. EGM, electronic gaming machine; PG, pathological gambler.
Table 7.2. Dimensionalization of Gambling Behavior. Definition
Quantification Schemes
Data Sources
Utility
Participation in gambling
Proportion of population who gamble
Lifetime and 12-month rates of gambling participation
Population surveys
Assess norms of gambling participation in the general population; examine socioeconomic correlates of gambling
Game type
Consumer preference for gambling type among available options
% of consumers who played specific game types in the last 12 months
Population surveys
Assess the popularity of specific game types; assess market trends in gambling and shifts in preference for high- and low-risk types; examine socioeconomic correlates of gaming preference
% of consumers who play >1 type of game
G-TLFB
Most popular game types
Industry records
% of gamblers who primarily bet in casinos, bars, racetracks, home, etc.
Population surveys
Number of different gambling venues per person
G-TLFB
Per capita expenditures, losses, and net expenditures (wins – losses) among gamblers in past 12 months
Population surveys
Average, maximum, and minimum dollar output per gambling session
G-TLFB
Setting
Expenditure and losses
Venue in which gambling occurs
Amount of money spent on games of chance
Assess consumer preferences and risk level for different venues; examine contextual factors (e.g., alcohol use) influencing gambling
Determine the spending patterns of individual gamblers; examine degree of control while gambling; assess longitudinal trends in gambling expenditures; examine socioeconomic correlates of gambling expenditure
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(Continues)
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Parameter
Parameter
Percent of annual income diverted to games of chance
Quantification Schemes
Data Sources
Largest win and loss in lifetime, past 12 months
Government revenue
Expenditure on gambling in relation to other household expenditures
Industry records
Percent of annual personal, household, and disposable income spent on gambling
Population surveys G-TLFB Government revenue
Frequency of gambling
Time spent gambling
Past year frequency of playing games of chance
Amount of time spent on games of chance
Frequency of playing specific games in the past 12 months
Population surveys
Frequency of any gambling in the past 12 months
G-TLFB
Total time spent on all gambling activity
Population surveys
Average time spent per session on specific games
G-TLFB
Average time spent per session on any gambling GamblingNegative consequences related harms of gambling (e.g., stress, financial strain, interpersonal
Number of gambling-related harms experienced per gambler
Population surveys Crime, bankruptcy, divorce rates
Utility
Determine the economic impact of gambling in relation to the individual’s economic means; assess longitudinal trends in proportion of disposable income diverted to gambling; assess the relationship between percent income and risk of harm Assess consumer habits in gambling; determine the relationship between frequency and gambling-related harms
Determine sociological trends in how the populace spends its time; assess time among family, work, and other activities in relation to gambling; assess the health implications of spending excess time in gambling venues (e.g., inactivity, smoky environments) Assess financial and nonfinancial consequences of gambling
Research and Measurement Issues in Gambling Studies
Percent income
Definition
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Table 7.2 (Continued)
conflict, suicide, legal problems)
Proportion of gamblers experiencing >1, >2, and >3 gambling-related harms
Emergency room and hospital admission statistics
Positive consequences Proportion of population of gambling (e.g., identifying specific excitement, socializing, psychological benefits as relief of stress, the main reason for satisfaction of gambling supporting charities, etc.) Average number of psychological benefits experienced per gambler
Population survey questions on main reasons for gambling
Assess nonmonetary incentives to gamble to understand individual gambling behavior
Problem gambling
Individuals who exceed the diagnostic threshold for problem gambling
Population surveys
Determine treatment needs in a community; assess the impact of increasing gambling opportunities on problem gambling prevalence; assess the overall health of a population
Lifetime and 12-month prevalence of problem gambling in the general population
Diagnostic interviews
Problem and pathological gambling rates
Treatment utilization
G-TLFB, Gambling Timeline Follow-Back interview.
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Psychological benefits
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stock market is considered a form of gambling (Ferris and Wynne 2001; Smith and Wynne, 2000).To accurately interpret gambling participation figures derived from self-report surveys, it is important to understand the definition of gambling employed and whether some activities are excluded from the definition. When using a broad definition of gambling that includes lottery play, past-year and lifetime rates of gambling participation range between 60 and 95%, respectively, in North America (Petry 2005; Shaffer et al. 2004). Types of Gambling Gambling encompasses a broad range of activities.Types of gambling include lotteries, bingo, card games, slot machines, electronic gaming machines, sports betting, casino games, horse betting, and many others. Most of these categories can be further subdivided. Casino games include blackjack, craps, and roulette; sports betting includes baseball, hockey, and football. There are literally hundreds of different gambling activities, each with its own set of rules, odds of winning, and payout schedule. Gambling types can be categorized across several dimensions: singleplayer (e.g., blackjack) versus multiplayer (e.g., bingo); passive (e.g., sports betting) versus active (e.g., poker); continuous (e.g., slot machines) versus noncontinuous (e.g., lottery). Some forms of gambling, notably electronic gaming machines, are considered to carry a high risk of addiction, while others are considered low risk (Griffiths 1993). However, the relationship between game type and risk level is confounded by the fact that most gamblers play more than one game.The majority of Canadians (67%) play more than one type of game (Currie, Hodgins,Wang, el-Guebaly, and Wynne 2006). Furthermore, problem gamblers are more likely than nonproblem gamblers to sample a variety of game types (Petry 2005). Studying the interaction of game type with standard quantitative dimensions of amount, duration, and frequency is made more complicated with the plethora of legal and illegal gambling forms available to the consumer. Frequency How often one gambles is a concrete and useful indicator of level of participation in gambling. The CPGI uses seven categories to capture frequency of gambling, ranging from one to five times per year to daily. Other surveys have used fewer categories (daily, weekly, monthly, less than monthly) to measure frequency. A finer-grain perspective of gambling frequency can be obtained with an instrument such as the G-TLFB, which provides an estimate of the exact number of days gambled in a 30-day period (Weinstock et al. 2004).The choice of a categorical or continuous scale to quantify frequency scheme would depend on the intended purpose of the data being collected. Categorical data may be sufficient for examining population level trends in gambling but inadequate for
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assessing change following treatment. Number of days gambled in the past 30 days has been shown to be reliable over time and sensitive to change for measuring the impact of treatment (Hodgins and Makarchuk 2003). Although frequency of gambling correlates with the risk of problem gambling (Currie, Hodgins, Wang, el-Guebaly, and Wynne 2006;Weinstock et al. 2004), it should not be used as a surrogate indicator of risk level. Some individuals may gamble frequently but wager small amounts (e.g., buying lottery tickets twice weekly), whereas others may wager large amounts less frequently (e.g., visiting a casino once per month). Expenditure A highly relevant dimension of gambling behavior is amount of money invested. Money spent can be subdimensionalized in terms of absolute dollars wagered, net amount gained or lost (wins minus losses), and the percent of personal or household income invested in gambling. Weinstock et al. (2004) describe an additional dimension of intent, defined as how much the gambler intends to spend in a given gambling session.The ratio of intended expenditure to actual expenditure provides an indication of the gambler’s level of control. Assessing expenditure and net money won or lost in self-report surveys can become problematic when an operational definition of this dimension is not made clear to the respondent. Most surveys ask respondents how much money they spend on gambling in a session or over the course of a specified time period.As noted, different interpretations of this question are possible, including the total money invested (not including wins or losses), net amount invested (wins minus losses), or losses only. Hence, the phrasing of expenditure questions will impact the characteristics and reliability of the data collected (Wood and Williams, in press). The percent of income spent on gambling is arguably a better indicator of gambling expenditure because it frames the amount wagered in the context of the gambler’s financial means (Weinstock et al. 2004). Spending $1000 per year on gambling has different financial implications for an individual with an annual income of $20,000 compared with $100,000. Ideally, this parameter should be expressed as a percent of disposable (after tax) income; however, it is common for household spending and gambling-specific surveys to inquire about gross income only. Because most families share expenses and income, the proportion spent on gambling should be expressed as a percentage of total household rather than personal income. Using this methodology, MacDonald et al. (2004) estimated that in 2002 Canadians spent an average of 0.9% of their total household income on gambling. Although percent income is the most useful expression of gambling expenditure, it is also the most prone to measurement error.As noted, persons responding to household expenditure surveys tend to underestimate the amount they spend on gambling compared with estimates of spending that derive from government
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revenues (Azmier 2005). In addition, some individuals are reluctant to disclose their income in surveys (Bradburn, Sudman, and Wansink 2004; Duncan and Peterson 2001), while others may not be able to provide an accurate estimate of their spouses’ income to report a total household income. Hence, both the numerator and the denominator of the percentage of income are prone to measurement error or bias (Walker et al. 2006). Duration Like frequency, time investment in gambling is game specific and often constrained by the temporal characteristics of the game. Certain forms of gambling are relatively brief in duration (e.g., buying a lottery ticket), whereas others require a longer investment of time (e.g., playing a game of bingo). Difficulty can arise when operationalizing duration for “passive” forms of gambling. For example, when an individual bets on a sporting event, should one consider duration of gambling as the time it takes to place the bet, or the full length of the sporting event until the outcome is known? Similar to expenditure, duration can be calculated in many ways: average time spent per gambling session, total time spent gambling in a month, maximum time spent gambling in a single session, and others. Surveys conducted across the United States and Canada vary in their collection of duration data. Attitudes and Cognitions An individual’s opinion of the benefits and costs associated with gambling will undoubtedly influence his or her decision to gamble. For example, an individual who believes that gambling is an opportunity to make money quickly is more likely to gamble than an individual who believes that all games of chance favor the house. Knowledge of the odds of winning should also theoretically influence gambling behavior, although not always in the expected direction (Williams and Connolly 2006). For example, there are high rates of both nonproblem and problem gambling among casino employees despite their having an excellent knowledge of gambling odds and the house advantage (Petry 2005). Other cognitions—for example, illusionary control of gambling outcome—have emerged as more important predictors of the tendency to gamble or keep gambling than has knowledge of odds (Ladouceur and Walker 1996). Self-report instruments have been developed to measure attitudes and cognitions in gamblers (Ladouceur et al. 2002).These scales have been used to research the relationship between attitudes and actual gambling behavior, as well as to measure the impact of interventions to change gambling (Williams et al. 2004). The Canadian public’s attitude toward gambling has also been assessed as part of a series of studies by the Canada West Foundation (Azmier 2000).
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OUTPUTS At the level of the individual, relevant gambling outputs can be broadly categorized as being financial, legal, social, or psychological in nature. Financial The majority of gamblers, both recreational and problem, gamble with the intent to win money. Hence, the extent of wins or losses can be viewed as the main output at the level of the individual gambler. The financial outputs of gambling can be calculated in numerous ways, including average wins and losses over a specified time period, largest win or loss ever, and total wins and losses over a specified period (Walker at al. 2006).There is no standard method for collecting this information, and surveys have differed in the types of winsand-losses questions used to gather data on financial outputs (Wood and Williams, in press). As noted, a detailed perspective on wins and losses can be obtained using the G-TLFB method (Hodgins and Makarchuk 2003; Weinstock et al. 2004). However, the G-TLFB is impractical for large population surveys. The win–loss ratio could be viewed as a surrogate measure of financial harm. Over time most gamblers lose more than they win. The impact of these losses is moderated by the gambler’s financial situation. The assessment of financial harm should also include indicators such as debt load, interest charges, inability to pay other bills or provide for basic necessities, personal bankruptcy, the need for seeking additional employment, and the cost of treatment. It is worth noting that financial harm impacts not only the individual and his or her immediate family, but also the community (e.g., in terms of treatment costs, higher interest rates for everyone, social assistance costs). Legal Few gamblers experience legal ramifications of their gambling. When legal problems do emerge, however, they are often substantial. Legal consequences arising from gambling can include divorce proceedings, bankruptcy, and criminal charges if the individual has engaged in any illegal activity (e.g., theft, robbery) to obtain money to gamble or pay off debts. Population level statistics on indicators such as divorce rates and crimes attributable to gambling are collected by the government and used to assess the impact of new gambling venues on a community. At the level of the individual, survey instruments such as the CPGI and the SOGS include several items assessing both financial and legal consequences arising from gambling (Ferris and Wynne 2001; Lesieur and Blume 1987).
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Social and Psychological Harms Gambling-related harms are not limited to financial consequences. The social and psychological outputs can be either positive or negative. Instruments such as the SOGS (Lesieur and Blume 1987) and the CPGI (Ferris and Wynne 2001) collect information on a variety of psychosocial harms from excess gambling. These are discussed in more detail in Chapter 8. The quantification of psychosocial harms is complicated, yet extremely important because, unlike alcohol or smoking, the consequences of excessive gambling cannot be measured in biomedical terms.The diagnosis of problem or pathological gambling is made on the basis of exceeding a defined threshold of harms, which include social, psychological, legal, and financial consequences. Problem gambling assessment instruments to date have adopted a rather simplistic approach to quantifying levels of psychosocial harms. Both the SOGS and the National Opinion Research Center Screen for Gambling Problems based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (Gerstein et al. 1999), use a dichotomous system for scoring harms. Gamblers indicate in a yes–no fashion whether they experience problems such as sleep difficulties or criticism about their gambling. The CPGI uses a four-point scale ranging from “never” to “almost always” to assess the extent to which gamblers have experienced any psychosocial harms over the past 12 months (the time frame can be modified depending on the purpose of the collection). The latter approach is an improvement over the dichotomous scoring system; however, all these instruments make the fundamental assumption that different harms are qualitatively equivalent and can be scored using the same metric. Such an assumption is untenable given the range of harms covered by gambling instruments. Can one assume that the psychological distress caused by being criticized about gambling is equivalent to entertaining thoughts of suicide? Is borrowing money from a friend to gamble equivalent to declaring bankruptcy? Endorsement of either of these harms could receive the same score. Only a minority of gamblers experience problems related to gambling. The majority of gamblers presumably find the experience pleasant and rewarding. The social and psychological benefits of gambling have been acknowledged (Korn and Shaffer 1999), but little work has been done to quantify this domain. Gamblers may experience psychological benefits from low-risk gambling such as stress reduction, socialization, and satisfaction with supporting charitable causes at “Las Vegas nights” (Desai et al. 2004). It is possible that gambling has a dose–response relationship similar to alcohol: High exposure is harmful or toxic to the individual, whereas low-dose levels may be psychologically beneficial or even protective (Kaiser 2003). To date, there has been no attempt to quantify the psychological benefits of gambling. Although population surveys have included broad questions on the reasons for gambling, no instrument has been specifically developed to assess
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the benefits of gambling. Recent research suggests that some older adults do experience health benefits of recreational gambling (Desai et al. 2004; Loroz 2004). Thus far, the psychological benefits of low-risk gambling have been studied only in the elderly (Shaffer and Korn 2002).
CLINICAL USE OF QUANTITATIVE GAMBLING DATA Quantitative information on gambling is used in clinical settings for a variety of purposes. First, data on the amount and frequency of gambling are used to help identify gamblers who could benefit from treatment (Hodgins et al. 2001). Second, the assessment instruments can be used to facilitate changes in behavior.Third, the assessment instruments can be used to assess effectiveness of specific clinical interventions focused on helping individuals who have difficulties with gambling. Fourth, the assessment instruments can provide meaningful feedback to clients. Finally, the assessment instruments can help to individualize treatment for specific clients (Haynes 2006). As is evident, measuring quantity of gambling is useful in a wide variety of clinical settings.
CONCLUSIONS This chapter has provided an overview of the basic principles and challenges inherent when attempting to dimensionalize a highly complex activity such as gambling. Meaningful quantitative indicators of gambling behavior can be constructed using comparable indicators from the epidemiology of other addictive behaviors as a general guide. Quantitative measures of gambling have been used mostly in prevalence surveys aimed at tracking the extent of gambling in the general population. Many also have utility in clinical research as outcome measures following treatment, and in direct clinical applications to aid in the assessment of individual gamblers. Nevertheless, there is a lack of standardization of measures in the field of gambling. Surveys vary in the types of quantitative information collected on gambling and the wording of questions to obtain this information. Thus, estimates of key indicators such as expenditure and duration of gambling episodes can vary greatly across surveys. As a final note, none of the three primary quantitative dimensions— frequency, expenditure, and duration—are used to identify disordered gambling (Hasin 2003). In other words, gambling intensity in terms of how often, how much, and for how long is not presently considered relevant to an individual’s level of risk for harm from gambling.An individual spending relatively small amounts of money or gambling infrequently could be considered a problem gambler if
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sufficient consequences or behavioral indicators of gambling were evident. Conversely, an individual spending very large amounts of money or gambling on a daily basis may not be considered disordered if he or she reports few problems related to the gambling behavior.The identification of problem gambling is made solely on the basis of behavioral problems and consequences of gambling. Similarly, the determination of problem drinking is made on the basis of drinking consequences, not on how much someone drinks. Very few population surveys, until only recently, have bothered to collect data on the intensity of gambling involvement or other possible determinants of problem gambling. There appears to be a robust relationship between gambling frequency, expenditure, and duration, on the one hand, and risk for gambling-related problems on the other (Currie, Hodgins, Wang, el-Guebaly, Wynne, and Chen 2006). Additional research on the dose–response relationship in gambling could help to answer such questions as: How much is too much gambling? The answer to this question would have extraordinary utility in responsible gambling policy and public health initiatives. Nevertheless, the complexity of gambling will make the development and dissemination of any guidelines aimed at preventing harm by limiting gambling involvement extremely challenging. For example, the main quantitative dimensions—frequency, expenditure, and duration—are correlated and highly dependent on the type of gambling.The development of a standard unit of gambling would greatly assist future research in this area but ultimately may be untenable given the heterogenic nature of gambling.
ACKNOWLEDGMENTS Work on this chapter was funded in part by the Alberta Gaming Research Institute.The investigators wish to thank Statistics Canada for data access. However, the opinions and views expressed do not represent those of Statistics Canada.
GLOSSARY Expenditure amount of money spent on games of chance after accounting for wins and losses; the net win or loss. Frequency of gambling how often an individual gambles within a specified time period (typically one month). Gambling-related harms negative consequences of gambling (e.g., stress, financial strain, interpersonal conflict, suicide, legal problems). Game type gambling type among available options (e.g., lottery, sports betting, casino game). Participation status proportion of population who have wagered money on an event with an uncertain outcome over the past year.
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Psychological benefits positive consequences of gambling (e.g., excitement, socializing, relief of stress, satisfaction of supporting charities). Setting venue in which gambling occurs (e.g., bar, casino, track, home). Time spent gambling amount of time spent gambling per session, expressed in hours or minutes.
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CHAPTER 8
A Review of Screening and Assessment Instruments for Problem and Pathological Gambling Randy Stinchfield
Richard Govoni
Department of Psychiatry University of Minnesota Medical School Minneapolis, Minnesota
University of Windsor Windsor, Ontario, Canada
G. Ron Frisch University of Windsor Windsor, Ontario, Canada
Introduction Instruments Gamblers Anonymous 20 Questions (GA-20) South Oaks Gambling Screen (SOGS) Massachusetts Gambling Screen (MAGS) DSM-IV-MR (MR = Multiple Response) Diagnostic Interview for Gambling Severity (DIGS) Gambling Treatment Outcome Monitoring System (GAMTOMS) National Opinion Research Center DSM-IV Screen for Gambling Problems (NODS) Lie/Bet Questionnaire Gambling Assessment Module (GAM) Canadian Problem Gambling Index (CPGI) Gambling Behavior Interview (GBI) Clinical Global Impression Scale (CGI) Pathological Gambling Adaptation of the Yale Brown Obsessive-Compulsive Scale (PG-YBOCS) Gambling Symptom Assessment Scale (G-SAS) Structured Clinical Interview for Pathological Gambling (SCI-PG) Conclusions and Future Research Directions 179
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INTRODUCTION Problem gambling has vexed humans for centuries, but it has only been within the last few decades that assessment instruments have been developed to identify problem and pathological gamblers. Legalized gambling as an industry has experienced unprecedented growth and expansion over the past three decades, and along with this rapid growth have come concerns about problem gambling.There is a need to identify problem gamblers in the general population in order to determine the extent of the problem in society and to aid public policy planning, such as the provision of treatment and prevention programs for problem gambling. Furthermore, mental health care agencies need to be able to accurately screen for and diagnose pathological gambling (PG) in order to provide appropriate treatment services. In 1990, a published critical review of existing instruments included only two instruments (Volberg and Banks 1990). There now exist over a dozen problem-gambling instruments that have been developed for a variety of purposes, including screening, assessment, diagnosis, epidemiological surveys, research, treatment planning, and treatment outcome monitoring. These instruments range in length from as few as two items to more than one hundred items. Since new instruments continue to be developed, this is an opportune time to examine what instruments are available, compare the strengths and limitations of existing instruments, and make recommendations for future refinement of existing instruments as well as for future instrument development. It should also be noted that there have been fairly large differences in reported prevalence rates in epidemiological surveys of problem gambling—from as low as less than 1% to as high as 10%—and at least part of this disparity may be attributed to a lack of precision in current measurement efforts. The current diagnostic criteria for PG have been established by the American Psychiatric Association (APA) (1994) in the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV). In general, the APA has taken an objective, behavioral approach to diagnostic criteria. Although it has placed PG in the impulse control disorder section of the DSM, the diagnostic criteria are very similar to substance use disorders diagnostic criteria and share a number of the signs and symptoms found in substance use disorders, such as tolerance and withdrawal. Instruments based on DSM diagnostic criteria can be expected to inquire about consequences of gambling, attempts at controlling one’s gambling, and changes in gambling behavior that may indicate tolerance and withdrawal syndromes. A number of assessment instruments included in this review are based on DSM diagnostic criteria or include some of the diagnostic criteria. Most problem-gambling instruments are relatively new and have not undergone rigorous reliability, validity, and classification accuracy evaluation (National Research Council 1999).There is also a paucity of research on the measurement of problem gambling among special populations, such as youth. At this
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point, the assessment of special populations has been conducted either by making revisions to instruments developed for adults, as in the case of youth gambling, or by assuming that existing adult instruments are appropriate, as in the case of seniors.The clinical experience of those who work with special populations suggests that the signs and symptoms of problem gambling may be somewhat different in these segments of the population. Both researchers and clinicians are confronted with the challenge of selecting from among existing instruments, many of which have little, if any, reliability or validity information for the task at hand. The primary aim of this review is to describe the instruments currently available and to provide information about each instrument, including development, author(s), year of development, content, number of items, administration method and time, intended purpose of the instrument, psychometric properties (reliability, validity, and classification accuracy), norms, scoring instructions, interpretation of scores, and strengths and limitations. See Table 8.1 for a description of each instrument. Due to space limitations, this review is confined to instruments designed for adults and that are in current use and have at least minimal evidence of reliability and validity. For reviews of other instruments, including youth instruments, the reader is referred to Stinchfield, Govoni, and Frisch (2001, 2004).
INSTRUMENTS GAMBLERS ANONYMOUS 20 QUESTIONS (GA-20) Gamblers Anonymous (GA) uses a set of 20 questions for the purpose of identifying compulsive gamblers. A score of 7 or higher indicates that the respondent is a compulsive gambler. The items address behaviors related to compulsive gambling, such as remorse over gambling, gambling to forget problems, borrowing money to gamble, and difficulty sleeping, to name a few. Although the GA-20 is commonly used, there is little psychometric and classification accuracy information available. Ursua and Uribelarrea (1998) note that there are no published reports describing the development of the GA-20 and only two studies that report any psychometric information. The earliest known validity evidence for the GA-20 was reported by Kuley and Jacobs (1988), who found that the GA-20 yielded high correlations with frequency of gambling and with dissociative experiences. Ursua and Uribelarrea (1998) conducted a study of the psychometric properties of the GA-20 in a sample of 127 problem gamblers who came for treatment at two self-help agencies in Madrid, Spain, and they also administered the GA-20 to a comparison sample of 142 nonproblem, social gamblers matched on age and gender with the problem gamblers. The internal consistency of the GA-20, using Cronbach’s (1951)
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Table 8.1 Descriptions of Instruments. Content Areas
Number of Items and Response Options
Administration Time and Method
Scoring Instructions, Score Range, Cut Scores, and Interpretation of Scores
Gamblers Anonymous 20 questions (GA-20)
Signs and symptoms of compulsive gambling; negative consequences
20 items; true/false response option
10-minute paperand-pencil or interview
One point for each item; score of 7 or more indicates compulsive gambler.
South Oaks Gambling Screen (SOGS) (1987)
Games played; signs and symptoms of problem gambling; negative consequences; sources of money to gamble
20 scored items; response options vary
10–20-minute paperand-pencil questionnaire
One point for each item; score range 0–20; score of 5 or more indicates PPG.
Massachusetts Gambling Screen (MAGS) (1994)
Signs and symptoms of pathological gambling; psychological and social problems associated with gambling; this study also includes a 12-item measure of DSM-IV diagnostic criteria
14 items (7 items are scored)
5–10-minute paper-and- 7 MAGS items are scored by multiplying pencil questionnaire each item times a discriminant function coefficient; then sum and add a constant; score range 0–2 = transitional or potential pathological gambler; score >2 = pathological gambling
DSM-IV-MR (2000)
DSM-IV diagnostic criteria
10 items, one for each criterion; four-point response options for most items
5-minute questionnaire
One point for each item; score range 0–10; score of 3–4 (including at least on point from criteria 8, 9, or 10) is a problem gambler; score of 5 or more is severe problem gambler.
Diagnostic Interview for Gambling Schedule (DIGS)
Demographics, gambling 20 diagnostic symptom involvement, treatment items to measure the history, onset of gambling, 10 DSM-IV diagnostic
30-minute interview
If respondent endorses either of the two items per criterion, the criterion is considered endorsed. One point
Research and Measurement Issues in Gambling Studies
Name of Instrument (year)
criteria.Two items for each criterion
for each of the 10 criteria. Score range 0–10; cut score of >5 indicates PG.
Gambling Treatment The Gambling Treatment Outcome Monitoring Admission Questionnaire System (GAMTOMS) includes a 10-item measure of DSM-IV diagnostic criteria for PG, as well as other measures of gambling problem severity, including the SOGS, gambling frequency, gamblingrelated financial problems, and legal problems.
142-item Gambling Treatment Admission Questionnaire has a 10-item measure of DSM-IV diagnostic criteria
30–45-minute paperand-pencil questionnaire
The DSM-IV diagnostic criteria items are one point each and are summed. Score range is 0–10; cut score of >5 indicates PG.
National Opinion Research Center DSM-IV Screen for Gambling Problems (NODS) (1999)
17 items
5–10-minute interview for NODS
NODS is scored one point for each DSM criterion. Score range 0–10; score of 0 = low-risk gambler; 1 or 2 = at-risk gambler; 3 or 4 = problem gambler; and >5 = PG
DSM-IV diagnostic criteria for diagnosing PG including lifetime and past-year time frames. A filtering question of losing $100 or more was used prior to administration of NODS.
Screening and Assessment Instruments for Problem Gambling
gambling frequency, amounts of money bet and lost, sources of borrowed money, financial problems, legal problems, mental health screen, other impulse disorders, medical status, family and social functioning, and diagnostic symptoms (lifetime and past year)
(Continues)
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Table 8.1 (Continued) Content Areas
Number of Items and Response Options
Administration Time and Method
Scoring Instructions, Score Range, Cut Scores, and Interpretation of Scores
Lie/Bet (1997)
Lie to people about your gambling; bet more and more money
2 items; yes/no response option
1-minute interview
Answering yes to one or both items indicates PG.
Gambling Assessment Module (GAM) and Computerized GAM
Structured gambling Demographics section = diagnostic interview that 27 items; gambling has three modules: section = 40 items; Demographics, Gambling, interviewer and Interviewer observations = observations.The gambling 7 items module includes items assessing gambling frequency and diagnostic criteria
30–60-minutes; interview (paperand-pencil or computerized)
A score of >5 out of 10 DSM-IV criteria indicates PG; 11 algorithms for the activity-specific diagnoses
Canadian Problem cates Gambling Index (CPGI) (2001)
Gambling involvement,
15-minute interview
Score range is 0–27. Score of 0 indi-
Gambling Behavior Interview (GBI) (2001)
Clinical interview to measure 76 items, including 20 signs and symptoms of PG, SOGS, 10 DSM-IV including gambling diagnostic criteria, and frequency, amount of time 32 research items
problem gambling, adverse consequences, family history of gambling, comorbid disorders, and distorted cognitions
31 total; 9-item problem gambling scale; four response options: never = 0; sometimes = 1; most of the time = 2; and almost always = 3
nonproblem gambling; 1–2 indicates low risk gambling; 3–7 indicates moderate risk gambling; and 8 or more indicates problem gambling. 30–60-minute interview
DSM score of 5 or more indicates PG; 20-item research scale uses item weights; 5-item screen score of >2 indicates probable PG.
Research and Measurement Issues in Gambling Studies
Name of Instrument (year)
and money spent gambling, the SOGS, DSM-IV, and 32 research items with a past-year time frame Three items: (a) severity of illness, (b) rating of improvement, and (c) efficacy index. It is used primarily in pharmacological studies
Three items; improvement Clinician-administered item is rated on a 7-point interview that takes Likert scale from very about 5 minutes much improved to very much worse
NA
Pathological Gambling Severity of pathological Adaptation of the gambling symptoms over a Yale-Brown recent time period (usually Obsessive-Compulsive within the past 1 or 2 Scale (PG-YBOCS) weeks). Gambling thoughts/ urges and behavior
Ten items, rated on a Clinician-administered 5-point Likert scale interview that takes ranging from least severe about 10 minutes (0) to most severe (4).
Each set of questions is totaled separately as well as one total score. No interpretation of scores was provided since it is used as a measure of change.
Gambling Symptom Assessment Scale (G-SAS) (2001)
Gambling urges, thoughts, feelings, and behavior
10 items; four-point Likert response options
Clinician-administered interview that takes about 10 minutes
Scores range from 0–40.
Structured Clinical Interview for Pathological Gambling (SCI-PG) (2004)
DSM-IV diagnostic criteria for pathological gambling
11 screening items; 33 PG diagnostic items
10–20-minute interview
Five or more of the ten DSM diagnostic criteria and evidence that the gambling is not better accounted for by a manic episode indicate PG (Continues)
Screening and Assessment Instruments for Problem Gambling
Clinical Global Impression (CGI) (1976)
185
Name of Instrument Reliability α = .94 (Ursua and Uribelarrea, 1998)
Classification Accuracy Indices Sample Characteristics, Criterion, Base Rate, Sensitivity, Specificity, and Hit Rate
Kuley and Jacobs (1988) Ursua and Uribelarrea (1998). Criterion is group membership, 127 problem gamblers report that the GA-20 142 nonproblem social gamblers; base rate = .47; sensitivity = .98; specificity = .99; yielded high correlations hit rate = .99. It should be noted that these classification accuracy indices are based with frequency of gambling upon a sample with a base rate of about 50%, which inflates classification accuracy and with dissociative indices. experiences; GA-20 was highly correlated with the SOGS (r = .94). Ursua and Uribelarrea (1998)
SOGS
α = .97; onemonth testretest reliability r = .71
Correlations with counselor assessments (r = .86), family member assessment (r = .60), and DSM-III-R pathological gambling diagnosis (r = .94)
GA members (n=213), university students (n=384), and hospital employees (n=152). Criterion was DSM-III-R diagnosis of PG hit rates among GA members (.98), university students (.95), and hospital employees (.99);
MAGS
MAGS 7-item scale α = .84; DSM-IV 12item scale α = .89
MAGS total discriminant score was correlated with total DSM-IV score, r = .83
NA
DSMIV-MR
α = .79
Discriminated between NA regular and nonregular gamblers and between problem and social gamblers
Research and Measurement Issues in Gambling Studies
GA-20
Validity
186
Table 8.1 (Continued) Psychometrics
α = .92
The total diagnostic score (0–10) exhibited significant correlations with the following measures of gambling problem severity: gambling frequency, r = .39; highest amount gambled in one day, r = .42; current gambling debt, r = .47; number of financial problems, r = .40; number of borrowing sources, r = .31; and legal problems, r = .50.
NA
GAMTOMS
Internal consistency reliability: DSMIV diagnostic criteria (α = 89), SOGS (α = .85), and financial problems (α = .78); 1week test–retest yielded correlations of r = .74 for DSM-IV; r = .91 for SOGS;
Validity of the DSM-IV diagnostic criteria was measured by correlations with the following measures of gambling problem severity: SOGS (r = .83); gambling frequency (r = .43); and number of financial problems (r = .40).
DSM-IV diagnosis of PG was used to classify clinical versus nonclinical cases: base rate = .20; hit rate = .96; sensitivity = .96; specificity = .95; false positive rate = .01; and false negative rate = .14. DSM-IV diagnosis of PG was used to classify SOGS PPG versus non-PPG cases: base rate = .79; hit rate = .98; sensitivity = .97; specificity = 1.00; false positive rate = .00; and false negative rate = .10.
NODS
2-4 week test–retest coefficients of r = .99 and r = .98 for lifetime and past year, respectively
NODS was administered to 40 individuals in outpatient problem-gambling treatment programs. Of these 40, 38 scored >5 on the lifetime NODS and two obtained scores of 4. For past year NODS, 30 scored >5, 5 scored 3 or 4, and 5 scored <2.
NA
Screening and Assessment Instruments for Problem Gambling
DIGS
(Continues)
187
Name of Instrument Reliability NA
Classification Accuracy Indices Sample Characteristics, Criterion, Base Rate, Sensitivity, Specificity, and Hit Rate
NA
Classification accuracy indices were computed on 191 male GA members and 171 male nonproblem gambling controls; sensitivity = .99, specificity = .91, positive predictive power = .92, and negative predictive power = .99; A second study that included females reported sensitivity = 1.00, specificity = .85, positive predictive power = .78, and negative predictive power = 1.00.
GAM/ DSM-IV diagnosis C-GAM of PG 1 week test–retest with two interviewers is κ = .79; game-specific κ ranged from .51 to .77
Concordance with clinician ratings was fair for five diagnostic criteria with κ ranging from 0.5 to 0.7 and poor for the other five criteria with κ ranging from 0.0 to 0.3
NA
CPGI
9-item problem gambling scale α = .84; 4week test–retest correlation of r = .78.
Discriminating between different groups; correlated with the SOGS (r = .83), DSM-IV (r = .83), and results of clinical interviews (r = .48)
DSM-IV was the criterion; sensitivity was .83 and specificity was 1.00.
GBI
DSM-IV α = .95; 20item research scale α = .96; 5-item screen
20-item research scale and 5-item screen correlations with DSM-IV diagnostic criteria scale (r = .90; r = .92), and with SOGS
Group membership was criterion: gambling treatment patients (n = 121) and general population who had gambled in past year (n = 138). Classification accuracy was computed for discriminating between the two groups. Base rate was .47. DSM-IV using standard cut-score of ≥5: hit rate = .91; sensitivity = .83; specificity = .98; false positive rate = .03; and false negative rate = .13; 20-item research scale, using item
Research and Measurement Issues in Gambling Studies
Lie-Bet
Validity
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Table 8.1 (Continued) Psychometrics
α = .95
score (r = .82; r = .85).
weights, yielded the following accuracy indices when: hit rate = 1.00; sensitivity = 1.00; specificity = 1.00; false positive rate = .00; and false negative rate = .00; Five-item screen with cut score of ≥2: hit rate = .99; sensitivity = .99; specificity = .99; false positive rate = .02; and false negative rate = .01.
NA
CGI correlated with PGYBOCS, r = .89
NA
PGYBOCS
α = .97; test– retest ranged from r = .29 to r = .56; interrater agreement ICC = .97
PG-YBOCS exhibited significantly different scores between a PG and a control sample. PG-YBOCS change score correlated (r = .90) with SOGS; and uncorrelated with anxiety (r = −.01) and depression (r = .08).
Pallanti et al (2005) recruited 188 pathological gamblers and 149 healthy controls. No information about classification accuracy.
G-SAS
α = .89; test– retest is r = .70
correlated with CGI, r = .78
No information about classification accuracy.
SCI-PG
Interrater agreement, κ = 1.0; test– retest reliability, r = .97
Convergent validity: correlated with SOGS (r = .78) and with PG-YBOCS (r = .38); discriminant validity: correlated with HAM-A (r = .23) and HAM-D (r = .19).
Longitudinal course of 20 gambling treatment patients was the criterion: Sensitivity = .88; specificity = 1.00; positive predictive value = 1.00; and negative predictive value = .67.
Screening and Assessment Instruments for Problem Gambling
CGI
NA, not available, not provided, or unknown; PG, problem gambling; PPG, probable problem gambling
189
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coefficient α was .94, indicating high internal consistency. In terms of validity, the GA-20 was highly correlated with the South Oaks Gambling Screen (SOGS) (r = .94), and the authors noted that both instruments have a number of items that are virtually identical.A factor analysis revealed that the GA-20 is a unidimensional instrument with one factor accounting for over 50% of the variance. The GA-20 was found to clearly differentiate problem gamblers from social gamblers, and the classification accuracy indices were high, with a sensitivity of .98, a specificity of .99, and a hit rate of .99. It should be noted that these classification accuracy indices are based upon the combined sample (i.e., both clinical and comparison samples) with a base rate of about 50%, and this base rate tends to maximize classification accuracy indices. When a screening instrument is used in a general population prevalence survey, the base rate would likely be less than 5%, in which case classification accuracy indices would be attenuated (Baldessarini, Finklestein, and Arana 1983). A strength of the GA-20 is that it was developed by problem gamblers for problem gamblers, and therefore has good external and face validity. Another strength is that it is brief and simple to administer. A limitation of the GA-20 is that it has virtually been ignored by investigators, and thus there is little psychometric information; for example, there are no published studies on how the items were developed or how the cut score of 7 or higher was determined.
SOUTH OAKS GAMBLING SCREEN (SOGS) In the mid-1980s, Lesieur and Blume (1987) developed the SOGS, a 20-item paper-and-pencil questionnaire used to screen for PG in clinical populations. At the time, both the DSM-III (APA 1980) and DSM-III-R (APA 1987) diagnostic criteria were available to assist in the development and validation of the SOGS (Culleton 1989; Lesieur and Blume 1987).The SOGS has been used for both clinical and survey research purposes, as well as in numerous studies around the world (National Research Council 1999; Shaffer, Hall, and Vander Bilt 1997).The SOGS is scored by summing the number of items endorsed out of 20, and a cut score of 5 or more indicates probable pathological gambling (PPG). The authors correctly use a probabilistic scoring interpretation, since it is not a diagnostic instrument and does not include all of the criteria required for a diagnosis of PG.The content of the SOGS includes items that inquire about hiding evidence of gambling, spending more time or money gambling than intended, arguing with family members about gambling, and borrowing money from a variety of sources to gamble or to pay gambling debts. The original development study found the SOGS to demonstrate satisfactory reliability and validity in four different samples, including GA members (n = 213), university students (n = 384), psychiatric hospital inpatients (n = 867), and
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hospital employees (n = 152). Reliability was estimated by combining the three samples of GA members, university students, and hospital employees and computing internal consistency (α = .97). One-month test–retest reliability was computed on the SOGS dichotomous classification of PPG versus non-PPG with 74 inpatients and 38 outpatients (r = .71, n = 110).Validity was examined by correlating the SOGS with counselors’ and family members’ independent assessments and DSM-III-R diagnosis for PG. The SOGS was found to be correlated with counselor independent assessments (r = .86), family member assessment (r = .60), and DSM-III-R PG diagnosis (r = .94). In terms of classification accuracy, the SOGS was compared with DSM-III-R diagnosis of PG and demonstrated satisfactory hit rates among GA members (98.1%), university students (95.3%), and hospital employees (99.3%). Since its development, the SOGS has been used for a variety of purposes and populations, but beyond the original development, little systematic research has been conducted on the psychometric properties of the SOGS under these varying conditions of use. Therefore, questions have been raised about the psychometric properties and classification accuracy of the SOGS under these new conditions. This is particularly true when the SOGS is used to estimate prevalence of PG in general population surveys. In a recent meta-analysis of PG prevalence studies, over half of the 152 studies used the SOGS as the measure of PG (Shaffer et al. 1997). The original development data on the SOGS do not provide specific information about its psychometric properties for use in general population surveys. Also, the psychometric data obtained in the development of the SOGS are now almost 30 years old, and the diagnostic criteria for PG have been revised twice from DSM-III (APA 1980) to DSM-III-R (APA 1987) to DSM-IV (APA 1994) since the development of the SOGS.The latest revision, from DSM-III-R to DSM-IV, included significant changes in the content of diagnostic criteria, an increase from nine to ten criteria, and a raising of the cut score from 4 to 5. Lesieur and Blume (1993) have reviewed the various modifications of the SOGS and have provided suggestions as to their suitability.They suggest that the initial questions, which ask about the type of gambling that subjects participate in, be modified to suit the gambling practices of the jurisdiction where the screen is being used. Such changes help the subjects define the concept of gambling before proceeding to the remainder of the screen. The time frame of the original SOGS is lifetime and the instrument does not differentiate pathological gamblers in remission from current pathological gamblers.Therefore, prevalence rates using the SOGS include both current probable pathological gamblers and those in remission. Lesieur and Blume suggest that the SOGS may be modified to cover a 6-month or 1-year time frame to identify current pathological gamblers. Stinchfield (2002) subjected the SOGS to psychometric evaluation for a 1-year time frame and found it to have satisfactory reliability and validity.
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In this study, the SOGS and a measure of DSM-IV diagnostic criteria were administered to a general population sample and a gambling treatment sample. The SOGS demonstrated satisfactory reliability with α = .69 in the general population sample and α = .86 in the gambling treatment sample. The SOGS demonstrated satisfactory validity in both samples, with correlations between the SOGS and DSM-IV criteria of r = .77 and r = .83 in the general population and treatment samples, respectively. This study also noted that while the SOGS and DSM-IV share some content, they are not identical and should not be considered equivalent. Convergent validity evidence for the SOGS included moderate to high correlations between the SOGS and other gambling problem severity measures in the gambling treatment sample, ranging from r = .33 to r = .65. On the basis of DSM-IV diagnostic criteria, the SOGS showed satisfactory classification accuracy in the gambling treatment sample, with a high hit rate (.96), high sensitivity (.99), modest specificity (.75), low false positive rate (.04), and low false negative rate (.10). The SOGS showed poorer classification accuracy in the general population, with a modest sensitivity of .67 and a high false positive rate of .50 (Stinchfield 2002). The SOGS overestimated the number of pathological gamblers in the general population, as compared with DSM-IV diagnostic criteria. Specifically, half of the cases identified as PPG by the SOGS did not meet DSM-IV diagnostic criteria for PG. The strengths of the SOGS include brevity and ease of administration and its common usage allows for comparisons across studies. Another strength is the large body of psychometric evidence that has been accumulated across different populations. Limitations of the SOGS include heavy weighting of the scale to “sources of borrowed money.” A respondent could be scored PPG on five different sources of borrowed money alone. The SOGS has also been found to yield high false positive rates in some populations; however, this is a commonly accepted foible of screening instruments, where these cases would be identified as false positives with further testing. Another limitation is that the lifetime time frame of the SOGS precludes the discrimination of current PPG from those who may be in recovery; however, this is remedied with the past-year time frame of the SOGS-Revised (R). Ladouceur and colleagues (2000) have examined the accuracy of the SOGS in terms of how children, adolescents, and adults misunderstand the items and the effect of this misunderstanding on scores.These investigators found that most participants misunderstood some SOGS items, and the misunderstanding led the respondents to endorse the misunderstood items that led to higher SOGS scores.The clarification of misunderstood items had the effect of reducing SOGS scores and reducing the number of respondents classified as PPG. In order to address one of the limitations of the SOGS, a lack of consideration of the severity of gambling problem endorsed, Strong et al. (2004) examined the utility of the SOGS with a Rasch model of measurement. They found that
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SOGS items could be ordered in terms of their level of gambling problem severity, similar to a Guttman scale. This goes beyond the simple adding of endorsed SOGS items to look at the degree of problem severity of each SOGS item and approaches a continuum of gambling problem severity.The authors suggest that to obtain a true continuum, more low and moderate problem severity items will need to be added to the scale.
MASSACHUSETTS GAMBLING SCREEN (MAGS) The MAGS, a brief screening instrument, was developed by Shaffer et al. (1994). It measures gambling problems in the past year and was designed to obtain an estimate of the prevalence of problem gambling. Although first used in a 1993 study of adolescents, it was developed for both adolescents and adults.The MAGS includes 14 items adapted from the Short Michigan Alcoholism Screening Test (SMAST), an alcoholism screen developed by Selzer, Vonokur, and van Rooijen (1975). In the MAGS development study, a measure of DSM-IV diagnostic criteria for PG was also developed, consisting of 12 items.The MAGS classifies respondents as nonproblem, in-transition, or pathological gamblers, using a weighted scoring derived from a discriminant function analysis (DFA). The 7-item MAGS scale had an internal consistency reliability coefficient α of .84. In terms of validity, the MAGS total discriminant score obtained a high correlation (r = .83) with total DSM-IV score. Strengths of the MAGS include brevity, face validity, and has good psychometric properties. Limitations of the MAGS include a subclinical category of “in-transition,” which assumes that the person is moving, and this may not be an accurate label for all persons in this score range. It is possible for some people to maintain a low problem severity level without moving in one direction or the other (see Winters et al. 2005).While item weighting provides greater precision for the sample from which the item weights were derived, it may not add precision when applied to another sample. These item weights, derived from an adolescent sample, may not be applicable to an adult sample and will require cross-validation in other samples.
DSM-IV-MR (MR = MULTIPLE RESPONSE) Fisher (2000) developed a 10-item questionnaire to measure DSM-IV diagnostic criteria of PG in adults.There is one item for each criterion and the items are paraphrased from the DSM-IV criteria. Most items have four response options: never, once or twice, sometimes, and often. Each item is scored as one point, and the score range is from 0 to 10.A score of 3 or 4, including at least one
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point from item/criteria 8, 9, or 10, is classified as problem gambling, and a score of 5 or more is classified as severe problem gambling. The DSM-IV-MR was administered to 1105 casino patrons in the United Kingdom, and internal consistency reliability was satisfactory, with an α = .79. In terms of validity, the DSMIV-MR had significantly different mean scores between regular and nonregular gamblers and between self-identified problem and social gamblers. Regular gamblers were those who visited casinos at least once a week, and nonregular gamblers visited casinos less often. Strengths of the DSM-IV-MR are that it is based on DSM-IV diagnostic criteria and it uses multiple response options. Limitations include a lack of validity evidence, Likert-style response options that mix specific counts of behavior “never” and “once or twice” with general behavioral anchors of “sometimes” and “often,” and a lack of evidence for the category of “problem gambler” with a score of 3 or 4. It is also curious why multiple response options are used, but item scoring involves collapsing response options into a dichotomous score. The scoring algorithm also departs from DSM-IV by requiring that at least one point derive from criteria 8, 9, or 10.
DIAGNOSTIC INTERVIEW
FOR
GAMBLING SEVERITY (DIGS)
The DIGS is a structured clinical interview developed by Winters, Specker and Stinchfield (2002). The DIGS was developed to assist with diagnosing PG, determining need for further assessment and treatment planning. The DIGS includes items measuring demographics, gambling involvement, treatment history, onset of gambling, gambling frequency, amounts of money bet and lost, sources of borrowed money, financial problems, legal problems, mental health screen, other impulse disorders, medical status, family and social functioning, and diagnostic symptoms (lifetime and past year). The interview also includes 20 diagnostic symptom items to measure the 10 DSM-IV diagnostic criteria. There are two items per criterion, and the items were paraphrased from the criteria. The DIGS has undergone some preliminary psychometric analyses, including internal consistency and validity estimates.The DSM-IV diagnostic criteria items demonstrated good internal consistency (α = .92). The total diagnostic score (range 0–10) exhibited moderate and statistically significant correlations with the following measures of gambling problem severity: gambling frequency, r = .39; highest amount gambled in one day, r = .42; current gambling debt, r = .47; number of financial problems, r = .40; number of borrowing sources, r = .31; and legal problems, r = .50. Strengths include interview method of administration that allows for probing and its basis in DSM-IV diagnostic criteria. Limitations include a lack of extensive psychometric evidence, particularly classification accuracy.
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GAMBLING TREATMENT OUTCOME MONITORING SYSTEM (GAMTOMS) The GAMTOMS was developed in 1992 to assess gambling problems in clients and to evaluate the effectiveness of gambling treatment programs in Minnesota (Stinchfield 1999; Stinchfield and Winters 1996, 2001). The GAMTOMS is a multidimensional assessment system that is made up of the following instruments: (a) Gambling Treatment Admission Questionnaire/Interview, (b) Gambling Treatment Discharge Questionnaire, (c) Gambling Treatment Follow-up Questionnaire/Interview, (d) Gambling Treatment Services Questionnaire, (e) Significant Other Admission Questionnaire, (f) Significant Other Discharge Questionnaire, and (g) Significant Other Follow-up Questionnaire. The Gambling Treatment Admission Questionnaire/Interview includes a ten-item measure of DSM-IV diagnostic criteria for PG, as well as other measures of gambling problem severity, including the SOGS, gambling frequency, gambling-related financial problems, and legal problems. Reliability and validity of the GAMTOMS have been evaluated in a treatment sample of over 1000 clients from the Minnesota gambling treatment outcome study (Stinchfield 1999; Stinchfield and Winters 1996, 2001), as well as more recent psychometric data on both the questionnaire and interview versions (Stinchfield et al. in press). Internal consistency reliability was measured with Cronbach’s alpha for the following scales from the Gambling Treatment Admission Questionnaire: DSM-IV diagnostic criteria (α = .89), SOGS (α = .85), and financial problems (α = .78). Convergent validity of the DSM-IV diagnostic criteria was measured by correlations with the following measures of gambling problem severity: SOGS, r = .83; gambling frequency, r = .43; and number of financial problems, r = .40. Discriminant validity of the DSM-IV was examined by measuring correlations between the DSM-IV diagnostic criteria and variables that should not be related to gambling problem severity, such as client age (r = .02), gender (r = .15), and education (r = .14). The classification accuracy indices of the 10-item measure of DSM-IV diagnostic criteria for PG in the GAMTOMS have a hit rate of .98, sensitivity of .95, specificity of .996, false positive rate of .004, and false negative rate of .05 (Stinchfield 2003). Both the questionnaire and interview versions of the GAMTOMS were recently evaluated for reliability, validity, and classification accuracy, with a sample of 85 and 150 gambling treatment clients, respectively (Stinchfield et al. in press). Two types of reliability were examined: temporal stability and internal consistency. Temporal stability of the Gambling Treatment Admission Questionnaire/ Interview was examined with a 1-week test–retest procedure. The Gambling Treatment Admission Questionnaire and Interview were administered at admission to treatment and readministered 1 week later. Gambling frequency for 14 different games showed satisfactory 1-week test–retest reliability with intra-class correla-
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tions (ICC) ranging from .46 to .98, with an average test–retest correlation of ICC = .83.The test–retest coefficients for DSM-IV diagnostic criteria score were ICC = .74 and .78; for SOGS, ICC = .90 and .87; for financial problems, ICC = .91 and .90; and for illegal activities, ICC = .92 and .86.The results indicate good to excellent temporal stability. Estimates of internal consistency were as follows: DSM-IV diagnostic criteria (α = .59 and .59), SOGS (α = .79 and .66), and financial problems (α = .78 and .71), for the questionnaire and interview versions, respectively. In terms of validity, the GAMTOMS gambling frequency section demonstrated modest correlations with a version of the Time-Line Follow-Back (Sobell et al. 1985) adapted to measure gambling frequency during the past four weeks, r = .55 and r = .47.The DSM-IV diagnostic criteria score was correlated with the SOGS, r = .63 and r = .62. Scales, including gambling frequency, DSM-IV diagnostic criteria, SOGS, financial problems, and legal problems, were also found to discriminate well between a clinical and nonclinical sample.The DSM-IV diagnosis of PG yielded the following classification accuracy indices using group membership as the criterion (clinical versus nonclinical): base rate = .20, hit rate = .96, sensitivity = .96, specificity = .95, false positive rate = .01, and false negative rate = .14. The DSM-IV diagnosis of PG yielded the following classification accuracy indices using SOGS classification as the criterion: base rate = .79, hit rate = .98, sensitivity = .97, specificity = 1.00, false positive rate = .00, and false negative rate = .10. Strengths include multidimensional assessment of a number of content domains, a large body of psychometric evidence, repeated measures that allow for assessment of change over time, and two administration methods. Limitations include a structured nature of the interview that may prevent probing and building rapport with client.
NATIONAL OPINION RESEARCH CENTER DSM-IV SCREEN FOR GAMBLING PROBLEMS (NODS) The National Opinion Research Center (NORC) (1999) conducted a national gambling survey in the United States in 1998. After reviewing existing instruments, the NORC research team developed a diagnostic measure based on DSM-IV diagnostic criteria.The 17 questions match the DSM-IV diagnostic criteria for PG (APA 1994). Some DSM-IV diagnostic criteria are measured with two items and some are measured with one item.The NODS includes both a lifetime and a past-year time frame.The past-year item is asked only if the lifetime item is answered with a positive response. The NODS score ranges from 0 to 10. Before the NODS is administered, a filter or screening question is asked first: Has the respondent’s gambling resulted in losses greater than $100 in 1 day or over the past year? Interpretation of NODS scores (for respondents who have gambled and lost more than $100) is as follows:A score of 0 is considered low-risk gambling, a score
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of 1 or 2 is at-risk gambling, a score of 3 or 4 is problem gambling, and a score of 5 or more is pathological gambling. Some information about the psychometric properties of the NODS is reported from its development. Field testing was conducted with a clinical sample prior to its use in the national survey. In terms of validity, the NODS was administered to 40 individuals in outpatient problem gambling treatment programs. Of these, 38 scored 5 or more on the lifetime NODS, and 2 obtained a score of 4. For past-year NODS, 30 scored 5 or more, 5 scored 3 or 4, and 5 scored 2 or less.The authors do not report internal consistency coefficients.The test–retest period was between 2 and 4 weeks, and the sample size was 44 subjects, some of whom were from the treatment sample used for validity analyses (the other subjects are not described).The authors report test–retest coefficients of r = .99 and r = .98 for lifetime and past year, respectively. The main concern about the NODS is that it is based on the DSM-IV; however, it diverges from the DSM-IV at important points. First, the filtering question of losing $100 or more was used because pretesting suggested that respondents who were “non-gamblers and very infrequent gamblers grew impatient with repeated questions about gambling related problems.” However, the loss of a certain dollar amount is not part of the DSM-IV criteria and it would seem that a more appropriate filtering question is whether the respondent has gambled in the past year. Second, the use of time periods and frequency parameters were added to the criteria: such phrases as “past 2 weeks” and “three or more times,” which are not present in the DSM-IV.While these changes make rational sense, they need to be justified in terms of empirical evidence. Third, the NODS lifetime prevalence rate includes both current pathological gamblers and individuals who were pathological gamblers in the past but are not now; however, the NODS also includes a past-year time frame which provides a more accurate estimate of current PG. Furthermore, and more importantly, the lifetime time frame appears to allow an individual to be classified as a pathological gambler when his or her symptoms may not have occurred contiguously within a given time period.This limitation is discussed at greater length below in recommendations for future research.The lifetime time frame makes sense only as a screen and/or if you can establish through an interview that the symptoms occurred within a contiguous time period, such as past 12 months. What about the different categories? Is there any evidence for the validity of these categories and the cut scores? Are there definitions for these subclinical categories? DSM criteria are all high problem severity symptoms. Is there any evidence that endorsing one or two high problem severity symptoms makes someone an “at-risk” gambler or that having three or four high problem severity symptoms makes someone a “problem gambler”? These subclinical categories need to be more specifically defined for people showing low and moderate problem severity signs and will require psychometric research to show that they are
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valid categories that can be accurately classified. A brief version of the NODS has been developed and is referred to as the NODS-CLiP (Control, Lies, Preoccupation).
LIE/BET QUESTIONNAIRE The Lie/Bet Questionnaire is a two-item screen for PG developed by Johnson et al. (1997).The two items were selected from a 12-item questionnaire developed by the investigators and based on DSM-IV diagnostic criteria for PG. The following items were found to be the best discriminators between PGs and controls: (1) “Have you ever had to lie to people important to you about how much you gambled?” and (2) “Have you ever felt the need to bet more and more money?” The investigators found that this 2-item screen had sensitivity of .99, specificity of .91, positive predictive power of .92, and negative predictive power of .99 in comparing 191 male GA members and 171 male nonproblem gambling controls who were employees of the Department of Veterans Affairs (VA). Additional classification accuracy data were computed on a new sample that included 295 men (116 GA members and 179 VA employees and volunteers) and 128 women (30 GA members and 98 VA employees and volunteers) (Johnson, Hamer, and Nora, 1998); sensitivity was 1.00, specificity was .85, positive predictive power was .78, and negative predictive power was 1.00. The authors acknowledge that the classification accuracy is maximized by this comparison, where the base rate is close to 50%, and that the test will not be as accurate in screening for PG among the general population because of the differences in baseline rates of PG between the development sample and the general population, where the base rate may be close to only 1%. No reliability or other types of validity information are provided. The authors state that the items are based upon DSM-IV diagnostic criteria for PG; however, the items are not exact paraphrases of the criteria and appear to add and delete content that may affect how someone responds to the item.The first question, “Have you ever had to lie to people important to you about how much you gambled?” (emphasis added), is purported to represent the DSM-IV criterion “Lies to family members, therapist, or others to conceal the extent of involvement with gambling.” However, the addition of the words “had to” would appear to change the content of question and add a notion of force, requirement, or compulsion that is not present in the criterion. There are likely to be respondents who lie about their gambling but may not feel they “had to” lie. The second question, “Have you ever felt the need to bet more and more money?,” has dropped the portion of the criterion that indicates the reason for betting more money, that is, “in order to achieve the desired excitement” (APA 1994). These additions and deletions of content may, at first glance, appear to be minor issues;
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however, the items diverge from the criteria and this needs to be acknowledged, since they may not measure the DSM-IV criterion they purport to measure. Nevertheless, a strength of this 2-item screen is its brevity and the fact that it has a place for settings and situations in which only a brief screen can be used.A positive result from this screen should be followed up with a more comprehensive assessment.
GAMBLING ASSESSMENT MODULE (GAM) The GAM is a comprehensive structured diagnostic gambling interview that employs DSM criteria. The GAM inquires about participation in 11 different types of gambling activities. For each gambling activity that the respondent participated in five or more times in his lifetime, he is then asked a set of diagnostic items. This is similar to substance use disorder diagnoses, where abuse or dependence is diagnosed for each substance. However, DSM-IV has one diagnosis for PG and does not have separate diagnoses by gambling activity, so the GAM departs from DSM-IV at this point.The GAM assesses whether diagnostic criteria have been met for previous versions of DSM, including DSM-III and DSMIII-R.The GAM assesses when the symptom was first and most recently met. In addition to diagnosis, the GAM also assesses a number of other domains, including demographics, social, psychological, and financial consequences of gambling, age of onset, convictions for crimes related to gambling, and treatment seeking (see Table 8.1 for psychometric information). Psychometrics of the GAM including 1-week test–retest reliability of diagnosis is reported as κ = .78 (Cunningham-Williams et al. 2006). The author has developed other versions of the GAM for specific assessment purposes, including a computerized version of the GAM (C-GAM), self-administered (GAM-IV-S) (Cunningham-Williams et al. 2005), brief interview (GAM-IV-I), 12-month version (GAM-IV-12), collateral informant, diagnostic items only (GAM-CI), and drug and alcohol abuse/dependence modules (GAM-DA). A chief strength of the GAM is that it identifies when the diagnostic criteria were first and most recently met, which allows for the differentiation of current cases from cases in remission.This is critical when the purpose of the research is to identify current and new cases. The GAM has a computerized version that will assist with administration and scoring.The GAM has different versions for different purposes such as self-administered and brief versions, which are helpful for different purposes. Weaknesses include a lack of psychometric information; however, the GAM is in the early stages of development. A unique element of the GAM is that it generates DSM-IV diagnoses for different types of gambling, and while this is a departure from DSM-IV, it may have value, and the author is currently pursuing research in this direction.
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CANADIAN PROBLEM GAMBLING INDEX (CPGI) The rationale for the CPGI was to develop a new, more meaningful measure of problem gambling for use in general population surveys with more indicators of the social and environmental context of gambling and problem gambling (Ferris and Wynne 2001). Problem gambling was defined as gambling behavior that creates negative consequences for the gambler, for others in his or her social network, or for the community. The CPGI was developed after a review of the literature and existing problem gambling instruments. The CPGI includes 31 items, 9 of which are scored as a measure of problem gambling.The 9 problem gambling items have four response options: never = 0, sometimes = 1, most of the time = 2, and almost always = 3.The CPGI problem gambling total score is the sum of all 9 items, and the score ranges from 0 to 27.The CPGI problem gambling scale scores are interpreted as follows: (1) no gambling and score of 0 indicates nongambling, (2) gambling and score of 0 indicates nonproblem gambling, (3) score of 1–2 indicates low-risk gambling, (4) score of 3–7 indicates moderate-risk gambling, and (5) a score of 8 or more indicates problem gambling.The cut scores and categories were determined “with respect to the distribution of scores on the problem gambling continuum . . . and more research is necessary in order to provide a strongly supported division between low and moderate risk groups” (Ferris and Wynne 2001, p. 42). The other CPGI items measure gambling involvement (types of gambling activity, frequency, spending) and correlates of problem gambling that can be used to develop profiles of different types of gamblers or problem gamblers; the social and environmental context of the gambler (e.g., family background of gambling, alcohol or drug problems, exposure to stimulus from which individual wishes to escape, and predispositions of the gambler (comorbidity, distorted cognitions). The CPGI was pilot tested on 143 people who represented three distinct groups: general population, regular gamblers, and self-designated problem gamblers in treatment.The CPGI was then further tested in a general population survey of 3,120 Canadian adults, and this included a test–retest reliability component of 417 respondents from the general population survey and clinical validation interviews with 143 respondents from the general population survey.The CPGI 9-item problem gambling scale (referred to as the Problem Gambling Severity Index) demonstrated satisfactory reliability with estimates of internal consistency of α = .84 and a 4-week test–retest correlation of r = .78. The CPGI demonstrated satisfactory validity by discriminating between different groups and strong correlations with other concurrent measures of gambling behaviors, such as gambling frequency, time spent gambling, and amount of money spent gambling. The CPGI was highly correlated with the SOGS (r = .83) and DSM-IV (r = .83) and modestly correlated with the results of clinical interviews (r =.48).The correlations of the CPGI with the SOGS and DSM-IV diagnostic criteria should be interpreted with caution, given the overlap in items between the CPGI and the DSM and
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SOGS. Specifically, five of the CPGI items are identical or similar to SOGS items, two CPGI items are identical or similar to DSM-IV PG diagnostic criteria, and one CPGI item overlaps with both the SOGS and the DSM-IV. Therefore, eight of the nine CPGI problem gambling items share content with the SOGS and DSM-IV. The CPGI problem gambling items overlap with SOGS and DSM-IV and therefore this overlap in content must be considered when interpreting the validity coefficients between the CPGI and SOGS and DSM-IV.The CPGI classification accuracy was measured against DSM-IV, with sensitivity of .83 and specificity a perfect 1.00. Again, these estimates of classification accuracy should be interpreted with caution, due to the overlap in content between the CPGI and DSM PG diagnostic criteria. The CPGI prevalence rate for problem gambling among the Canadian general population survey was 0.9%, which fell between the SOGS PPG rate of 1.3% and the DSM-IV PG rate of 0.7% for the same sample.
GAMBLING BEHAVIOR INTERVIEW (GBI) The GBI is a 76-item instrument designed to measure signs and symptoms of PG.The GBI was developed in 1995 as an instrument to collect reliability, validity, and classification accuracy data on the SOGS and DSM-IV diagnostic criteria (Stinchfield 2002, 2003). It was further revised in a study to examine the reliability, validity, and classification accuracy of the DSM-IV diagnostic criteria and a set of research signs and symptoms of PG (Stinchfield, Govoni, and Frisch 2005, 2006). This study involved an examination of 32 signs and symptoms of PG that were generated from both a literature review and focus groups of recovering pathological gamblers and their family members.The goal was to determine if there were additional signs and symptoms of PG, beyond those in the DSM-IV, that might prove useful for diagnosis, clinical assessment, and treatment planning.The GBI has a past12-months time frame and can be administered in approximately 15 minutes or much shorter time if the respondent has not gambled in the past 12 months. The GBI is made up of eight content domains: gambling attitudes (4 items), gambling frequency of different games (15 items), time and money spent gambling (4 items), gambling frequency at different venues (7 items), SOGS (25 items), DSM-IV criteria (10 items), research diagnostic items (32), and demographics (9 items). Demographics include gender, age, race, marital status, education, occupation, and employment status. Gambling frequency is measured for common forms and venues of gambling, along with measures of gambling involvement, such as days spent gambling, hours spent gambling, and money lost gambling. In terms of reliability of the ten DSM-IV diagnostic criteria, a principalcomponents analysis indicated that the ten criteria yielded a unidimensional scale with one factor with an Eigenvalue of 5.8 that accounted for 58% of the variance. All 10 diagnostic criteria had high factor loadings, ranging from .60 to .87.
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Internal consistency was excellent, with α = .92, and all ten diagnostic criteria had high corrected item-total correlations, ranging from r = .52 to r = .82. In terms of validity, DSM-IV criteria exhibited evidence of construct validity with good discrimination between the general population and gambling treatment samples.The average DSM-IV score for the general population sample was 0.6, compared with 6.8 for the gambling treatment sample.The difference between the two groups was statistically significant: independent groups t-test (t = 28, df = 257, p < .001). Convergent validity of DSM-IV criteria was exhibited by generally high correlations with concurrent problem gambling severity measures, ranging from r = .27 to r = .90. Discriminant validity was exhibited by low correlations with variables unrelated to problem gambling, ranging from r = −.02 to r = −.16. Although the standard DSM-IV cut score of 5 yielded a respectable hit rate (.91), sensitivity was low (.83) and the false negative rate was high (.13). A cut score of 4 yielded better classification accuracy, including a higher hit rate (.95), sensitivity (.93), and specificity (.96), and lower false negative rate (.06).The DFA yielded better classification accuracy than either cut score of 5 or 4, with a hit rate of .97, a sensitivity of .94, and a specificity of .99. A cut score of 5 misclassified 23 subjects, a cut score of 4 misclassified 15 subjects, and the DFA misclassified 9 subjects (Stinchfield et al. 2005). In terms of the 32 items on research signs and symptoms in the GBI, some of them were found to be strong discriminators between a gambling treatment sample and a general population sample (Stinchfield et al. 2006).The 10 DSM-IV items and the 32 research diagnostic items were entered in a DFA to determine the best set of discriminators, and a 20-item scale and 5-item screen were produced. Surprisingly, the research items outperformed the DSM-IV diagnostic criteria. The 20-item scale and 5-item screen yielded satisfactory reliability and validity and had better classification accuracy than DSM-IV diagnostic criteria. The reliability, validity, and classification accuracy indices of these two measures are reported in Table 8.1.The inclusion of these new items as diagnostic criteria may improve the accuracy of PG diagnosis.
CLINICAL GLOBAL IMPRESSION SCALE (CGI) Studies on the effectiveness of pharmacological treatments of PG have used a small number of instruments, including the CGI (Hollander et al. 1998). The CGI is a mental health clinician–report rating scale of improvement on symptoms (Guy, 1976). The CGI is made up of three items; however, most studies report only the global improvement item, “Compared with his condition at admission to the project, how much has he changed?,” which has a seven-point response option from “very much improved” to “very much worse.” The other two CGI items are ratings of severity of illness and an efficacy index. The CGI
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has also been used as a patient-rated scale (Kim et al. 2001). While the CGI is reportedly reliable and valid (Pallanti et al. 2005), we were unable to locate any information on CGI reliability or validity. While the CGI is a simple, straightforward, and commonly used scale as a measure of change/improvement in pharmacological studies, it involves a subjective rating using clinician judgment, which is open to the effects of bias and the risk of unreliable ratings, particularly in studies where the clinician is not blind to the study design. Given the subjective nature of the clinical rating, this scale should be used with caution.We would suggest that investigators first establish satisfactory interrater agreement prior to the onset of any study using this measure.
PATHOLOGICAL GAMBLING ADAPTATION OF THE YALE BROWN OBSESSIVE-COMPULSIVE SCALE (PG-YBOCS) Another commonly used scale in pharmacological studies is the PGYBOCS, which is an adaptation of the YBOCS for PG (Hollander et al. 1998; Pallanti et al. 2005).The PG-YBOCS consists of 10 items administered by a clinician that measure the severity of PG over a recent time interval. Five questions inquire about gambling thoughts and urges, and the other five inquire about gambling-related behavior. Each item is rated on a five-point Likert scale ranging from 0 (least severe) to 4 (most severe). Pallanti et al. (2005) did not explain how these ratings were assigned to the patient response; however, clinician judgment is used to make the ratings. There are two subscale scores and one total score. The psychometric properties of the PG-YBOCS have recently been examined (Pallanti et al. 2005), and both temporal stability and internal consistency were assessed.Tenday test–retest yielded item ICC coefficients ranging from .29 to .56, indicating modest test–retest reliability. High internal consistency was exhibited, with α = .97. Interrater agreement was assessed using three raters, and this yielded an ICC of .97, indicating excellent interrater agreement; however, κ would have been a more appropriate statistic. In terms of convergent validity, the PG-YBOCS was highly correlated with the SOGS (r = .90). Discriminant validity was assessed by correlations with the Hamilton Anxiety Rating Scale (r = −.01) and Hamilton Depression Rating Scale (r = .08). Other validity evidence included that PGYBOCS scores were significantly different between a PG clinical sample and a general population sample. The PG-YBOCS is a brief and easily administered instrument that shows satisfactory evidence of reliability and validity. One concern is the reliance on clinician judgment and the potential for bias and unreliable ratings. Future research will need to focus on the reliability and particularly the interrater agreement of the PG-YBOCS. It will be important to train clinical raters and establish interrater agreement prior to use of the PG-YBOCS as a measure of change.
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GAMBLING SYMPTOM ASSESSMENT SCALE (G-SAS) The G-SAS is a 10-item rating instrument developed by Kim et al. (2001) to assess frequency, intensity, and duration of gambling symptoms and was originally developed to measure outcome in PG pharmacological studies.The items are administered to the client in an interview format. The clinician scores client responses on a five-point Likert scale ranging from 0 to 4 (each response option varies by item) (see Kim et al. 2001), and scores range from 0 to 40.Time period assessed is the past seven days. Items 1–3 assess gambling urges; items 4 and 5 measure gambling-related thoughts; items 6 and 7 assess frequency and duration of gambling behavior; item 8 assesses subjective excitement in anticipating an imminent gambling experience; item 9 summarizes the subjective distress caused by gambling; and item 10 measures the amount of personal trouble caused by gambling (Kim et al. 2002).The G-SAS has demonstrated satisfactory reliability in terms of internal consistency (α = .89) and test–retest (r = .70). In terms of validity, the G-SAS was correlated with the CGI (r = .78). The G-SAS is brief and easy to administer. Clinician judgment is introduced in the assignment of numbers to the patient response, and this opens the process to bias and potential for unreliable measurement. Future research will need to focus on reliability and particularly interrater agreement. It will be important to train clinical raters and establish interrater agreement prior to use of the G-SAS as a measure of change.
STRUCTURED CLINICAL INTERVIEW FOR PATHOLOGICAL GAMBLING (SCI-PG) The SCI-PG was developed by Steinberg, Rounsaville, and Potenza, and is reported on by Grant et al. (2004).The SCI-PG is a Structured Clinical Interview for DSM-IV (SCID)–compatible instrument to diagnose PG. The SCI-PG demonstrated satisfactory interrater agreement and test–retest reliability. Evidence for the convergent validity of the SCI-PG includes a high correlation with the SOGS and a moderate correlation with the PG-YBOCS. Preliminary classification accuracy of the SCI-PG with 20 patients was satisfactory with comparison to “longitudinal course.” Strengths of the SCI-PG include: (a) It is compatible with the widely used SCID, which has demonstrated reliability, validity, and classification accuracy; (b) it is structured and standardized, which improves interrater reliability; (c) it incorporates the clinician’s impression and allows for probes and clarification; and (d) it is the only PG instrument that includes assessment of the DSM-IV PG exclusion of “gambling behavior not better accounted for by a Manic Episode.” Weaknesses include the following: (a) It has to be administered by a trained and/or experienced
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clinician/diagnostician because it relies on clinical impressions; that is, it introduces judgments as to who should be administered the interview and who should receive the PG diagnosis; (b) it does not include the Manic Episode SCID module but instructs the administration of this SCID module in order to make the exclusion; and (c) the psychometric information comes from one study with a small sample size, that is, the classification accuracy information is based on 20 patients.
CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS In response to the need for instruments to detect and measure problem gambling, a number of instruments have been developed.The SOGS is the most commonly used assessment instrument and has accumulated the largest volume of psychometric research to date, but new DSM-based instruments are generating a good deal of research momentum. DSM-IV diagnostic criteria have been paraphrased into questions that are used to diagnose clients in gambling treatment programs (e.g., DIGS), measure prevalence rates of PG in epidemiological surveys (e.g., DSM-IV-MR, NODS, and GAM), and measure gambling treatment outcome (e.g., GAMTOMS).This review provides information on existing screening and assessment instruments to assist clinicians and researchers in selecting an appropriate instrument for their specific purposes and populations. This review also shows that most existing instruments are found to be lacking in regard to complete information about psychometric properties and classification accuracy for different purposes and populations.This may, in part, explain the wide range of prevalence estimates reported in the meta-analysis of gambling surveys (Shaffer et al. 1997). Most instruments described above have been developed for clinical purposes but have often been used for other purposes and populations. There are a number of recommendations for future research. First, existing instruments need to be put to rigorous psychometric evaluation for each purpose and population where it is employed. For example, the classification accuracy of an instrument is affected by the base rate of the disorder within the population of interest, and therefore an instrument developed to measure PG in a clinical sample (high base rate) will likely have different classification accuracy when applied to the general population (low base rate) (Gambino 2005). The current lack of information makes it difficult for a clinician or researcher to select a psychometrically sound instrument for a specific purpose or a specific population, other than assessment in a clinical setting, and this needs to be remedied. This research will build a body of evidence for (or against) the reliability, validity, and classification accuracy of existing instruments for specific purposes and populations; will justify the continued use of those instruments found to be reliable, valid, and accurate; and will serve to revise and refine those instruments found lacking.
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Second, new instruments need to be developed for the assessment of problem gambling among specific populations, such as youth and seniors. Current research shows that there are significant differences in gambling behavior among populations, and this requires instruments designed specifically for these populations, or at minimum the development of norms with existing instruments for these different populations. In developing new instruments, investigators need to be methodical and use scientific standards for test development. It is recommended that investigators and test users follow the standards for testing set forth by the American Educational Research Association, American Psychological Association, and National Council on Measurement in Education (1999). These guidelines describe technical standards for test construction and evaluation, including reliability and validity, and will facilitate the development of psychometrically sound instruments. Third, DSM-IV diagnostic criteria for PG are used to make clinical, scientific, and public policy decisions, and there has been little research on these criteria. In order to make these decisions, the diagnostic criteria need to show evidence of reliability, validity, and classification accuracy.The DSM-IV is the accepted standard for the identification of PG, but it is based primarily on clinical experience and expert group consensus.We need to move beyond consensus to empirical evidence. There continues to be debate about the adequacy of definitions and diagnostic criteria of PG (National Research Council 1999, Rosenthal 1989, Shaffer et al. 1997).Although the DSM-IV criteria are the primary tool for diagnosing PG and are regularly revised with each new edition of the DSM, there is little empirical evidence of the reliability and validity of the DSM-IV diagnostic criteria for PG. Lesieur and Rosenthal (1991) conducted a comprehensive literature review of PG for the DSM-IV committee and found little data other than clinician opinions and anecdotal reports about the diagnostic criteria. In the recent meta-analysis of disordered gambling prevalence in the United States and Canada by Shaffer et al. (1997), it was reported that although the DSM diagnostic criteria had been used to measure the prevalence of PG in different samples, no studies reported the reliability or validity of the diagnostic criteria. In contrast, the diagnostic criteria of other disorders in the DSM-IV, such as substance use disorders, have extensive reliability and validity information.Therefore, the most pressing questions in the field of problem gambling are:What diagnostic criteria should be used to diagnose PG? Are the criteria reliable, valid, and accurate? and,What cut score should be used to diagnose PG? There continues to be debate about what DSM cut score yields the most accurate classification. The cut score was raised from 4 to 5 between DSM-III-R and DSM-IV. A recommended research direction is to explore whether there are other important signs and symptoms of PG that if added to the current diagnostic criteria would improve reliability, validity, and classification accuracy. DSM-IV diagnostic criteria include the condition to rule out a manic episode as a better
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explanation for the gambling behavior; however, no current PG instruments include this condition, with the exception of the SCI-PG, but only when used in conjunction with the full SCID (Grant et al. 2004).This condition should be put to empirical test to determine how often a manic episode is a better explanation for gambling behavior than is PG and whether the condition is important to an accurate diagnosis of PG. The debate about DSM cut scores also raises the issue of those individuals who exhibit some signs and symptoms of PG but do not satisfy the requirement of five or more diagnostic criteria. Lesieur and Rosenthal (1993) suggested lowering the cut score to 4 and exploring the creation of another category for individuals who show symptoms of but are subthreshold for PG, similar to the “abuse” category in substance use disorders. More information is needed to specifically define and operationalize this subclinical category and determine the appropriate criteria and cut score. Is the set of symptoms in DSM-IV comprehensive and complete? Are there other signs and symptoms that are not in the current set of diagnostic criteria but which are relevant to the PG diagnosis? A fourth issue is validity of self-report. Self-report is the “bread and butter” of both clinical and empirical work, and therefore it is critical that we have evidence of the validity and accuracy of self-report, particularly in assessing PG. In conducting surveys of the general population, there is a different set of demand characteristics, such as impression management. Lesieur (1994) has addressed some of these issues. In clinical settings, anecdotal evidence suggests that many pathological gamblers are also pathological liars (in fact, this is one of the diagnostic criteria), and this raises the question of whether or not we can believe their self-report. In order to answer this question, we need to know if the client is motivated to withhold or distort information.What are the potential costs and/or benefits to the client for accurate versus inaccurate self-disclosure? Are clients motivated to hide their gambling problem? Do they want to avoid treatment? Clinicians also report that some clients do not report the full extent of their legal and financial gambling-related problems at the time of intake assessment and disclose more legal and financial details later in treatment. Clients may be motivated to withhold some details, particularly financial and legal details that may be selfincriminating, until they have developed trust with their therapist. The evidence thus far shows that most clients are forthright and provide accurate information to clinicians and researchers (Hodgins and Makarchuk 2003; Stinchfield and Winters 1996). Although pathological gamblers hide their gambling from their family with deception and lies, by the time they get to a mental health provider, they are often desperate for help, are tired of the deception, and therefore are willing to admit they have a problem. Additional research needs to be conducted on the validity of self-report and particularly on the conditions that yield accurate self-report. A fifth measurement issue is the time period assessed.What is the ideal time period to inquire about gambling behavior and pathology? Lifetime? Past year?
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Past month? The time period used should be determined by the purpose of the assessment. Do you want to know if the respondent has ever been a pathological gambler or do you want to know if he is currently a pathological gambler? Gambling assessment often covers the respondent’s lifetime, with question stems that begin with “Have you ever ….”. While this time period indicates whether the respondent has ever had a gambling problem, it does not indicate whether he is currently a pathological gambler; therefore, some instruments inquire about current time periods such as past year or less.The other element here is determining when the symptoms first occurred and the most recent occurrence of symptoms. This is critical for discriminating between current PG cases and those in remission or recovery. It also plays a role in determining whether or not the symptoms clustered around the same time. For example, we would not want to diagnose someone with PG if during their teens they bet more than they intended and this subsided and during their 20s they chased their losses and this subsided and during their 30s they had someone bail them out and this subsided and so on. Using some existing instruments, this person would be diagnosed with PG; however, this person did not experience the set of symptoms within a relatively short time period, such as the past year. Although the DSM-IV does not specifically require that the symptoms occur contiguously during a specified time period, it does use the language of “pattern,” “persistent,” “recurrent,” “progressive,” and “chronic,” all of which indicate that the symptoms need to occur fairly close in time and for extended periods of time, and that the appearance and then subsequent disappearance of symptoms over a lifetime does not result in an “accumulation” of symptoms and a diagnosis of PG. If at any given time, the person does not exhibit five or more symptoms within 6 to 12 months, he should not be diagnosed with PG, as is the time period for DSM-IV Substance Dependence criteria, upon which the Pathological Gambling criteria are modeled (American Psychiatric Association, 1994). That is why it is a mistake to conduct a prevalence survey of the general population using a lifetime period and not identify when the symptoms first appeared and when they most recently appeared. This can result in an inflated prevalence rate, because it will include respondents who may have been pathological gamblers but no longer meet the diagnostic criteria for PG. A related mistake is using a screening instrument alone to measure prevalence. The purpose of a screening instrument (such as the SOGS) is to cast a broad net with a small mesh and catch anyone who may have a problem. If the screen is positive, this should trigger a more comprehensive clinical assessment, including a diagnostic interview, that would clarify the number of symptoms and when the symptoms occurred and if they satisfy diagnostic criteria for a current diagnosis of PG. A sixth measurement issue is whether or not diagnostic criteria can be used to measure a continuum of gambling problem severity and identify subclinical cases, that is, those individuals who exhibit some symptoms of PG but not enough to yield a diagnosis. This range of behaviors is typically referred to as subclinical
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or subthreshold. DSM-IV diagnostic criteria were developed to diagnose PG and yield a dichotomous diagnosis—the person either does or does not have the disorder. While this method is utilitarian in that it identifies individuals who are most in need of clinical services, it fails to acknowledge the construct of a continuum of gambling problem severity. Investigators have paraphrased these diagnostic criteria into items and scales to measure PG. All of these criteria measure PG, and respondents who satisfy five or more are considered pathological gamblers, regardless of which five symptoms they satisfy.That is, all ten DSM-IV diagnostic criteria may be considered “high problem severity” symptoms/items (Strong et al. 2004). Conversely, investigators typically conceptualize problem gambling on a continuum rather than as a dichotomy, and therefore have added subclinical categories for gamblers falling below the clinical threshold, referring to them as “in-transition” (MAGS) or “at-risk” and “problem gambler” (NODS).While these subclinical categories are of interest, measuring them with high problem severity items is less than ideal. In order to adequately measure a problem gambling continuum, a pool of symptoms/items that reflect low, moderate, and high problem severity are needed, and future research should examine the value of including low and moderate severity symptoms/items to more precisely measure the underlying continuum. The objectives of this review were to provide a resource for research and mental health professionals regarding the issues involved in screening and assessment of problem gambling, to inventory the types of instruments that are available, to present strengths and limitations of each instrument, including information on reliability, validity, and classification accuracy, and to make recommendations for further instrument refinement and development. We hope that these recommendations will lead to more rigorous psychometric research on existing measures of PG and to refinement of measurement tools and greater precision, which is the mark of good science.
AUTHOR NOTES This manuscript was supported by a grant from the Ontario Ministry of Health. The authors would like to thank instrument developers who provided information about their instruments.
GLOSSARY Classification accuracy addresses the question, how well does the instrument identify those with, and without, the disorder? Classification accuracy is typically assessed with a number of coefficients, including sensitivity, specificity, false positive rate, false negative rate, positive predictive power, and negative predictive power. Sensitivity is the true positive rate, that is, the rate of pos-
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itive test results among those with the disorder. Specificity is the true negative rate, that is, the rate of negative test results among those without the disorder. False positive rate is the percent of positive test results among those without the disorder, and false negative rate is the percent of negative test results among those with the disorder. Positive predictive power is the rate of true-positive results among all positive test results. Negative predictive power is the rate of true-negative results among all negative test results. Convergent validity is commonly assessed by measuring correlations between the scale of interest and other scales that measure the same construct. In order to demonstrate validity, a new scale should be moderately to highly correlated with existing scales of the same construct that have already demonstrated satisfactory psychometric properties. Nunnally (1978) suggests that one should expect modest correlations, in the r = .30 to r = .40 range, when computing validity correlations. Internal consistency is the concept that a set of items all measure the same construct. One way of assessing internal consistency is by comparing the score on one half of the items with the score on the other half of the items. Another approach to measuring internal consistency is to utilize statistical techniques that measure the homogeneity of the scale, commonly measured by Cronbach’s α, a coefficient that ranges from 0 to 1.The higher the alpha, the greater the internal consistency of the scale. As a criterion, Nunnally (1978) recommends .70 or higher for the “early stages of research” (p. 245), whereas the criterion for “applied settings where important decisions are made with respect to specific test scores, a reliability of .90 is the minimum that should be tolerated, and a reliability of .95 should be considered the desirable standard” (p. 246). Reliability is defined as consistency, repeatability, and stability. Reliability can be influenced by factors such as the number of items in the scale, number of subjects used in the evaluation, and the type of subjects utilized in the development and evaluation of the instrument.There are two types of reliability: temporal stability and internal consistency. Temporal stability addresses the question of whether the instrument yields the same or similar scores when administered at different times.Temporal stability is measured by test-retest procedures, that is, administering the test to the same individual at two points in time, typically within a few days or one week. It is assumed that the characteristics of interest have not changed over the time period and therefore the instrument is considered stable if test scores are similar between the two assessments. In order to demonstrate satisfactory temporal stability, a test-retest correlation of r = .70 or higher needs to be obtained.
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Validity addresses the question of whether the instrument measures the construct it purports to measure.
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––––– . (2004). Screening and assessment instruments. In Pathological Gambling: A Clinical Guide to Treatment (J. E. Grant and M. N. Potenza, eds.), pp. 207–231.Washington, DC:American Psychiatric Publishing, Inc. ––––– . (2005). DSM-IV diagnostic criteria for pathological gambling: Reliability, validity, and classification accuracy. American Journal on Addictions, 14, 73–82. ––––– . (2006). Evaluation of Research Diagnostic Criteria for Pathological Gambling and Comparison to DSMIV Diagnostic Criteria for Pathological Gambling. Manuscript submitted for publication. Stinchfield, R., and Winters, K. (1996). Effectiveness of Six State-Supported Compulsive Gambling Treatment Programs in Minnesota. St. Paul: Minnesota Department of Human Services. —— . (2001). Outcome of Minnesota’s gambling treatment programs. Journal of Gambling Studies, 17, 217–245. Stinchfield, R.,Winters, K. C., Botzet, A., Jerstad, S., and Breyer, J. (in press). Development and psychometric evaluation of the Gambling Treatment Outcome Monitoring System (GAMTOMS). Psychology of Addictive Behaviors. Strong, D. R., Lesieur, H., Breen, R., Stinchfield, R., and Lejuez, C.W. (2004). Using the Rasch model to examine the utility of the South Oaks Gambling Screen across clinical and community samples. Addictive Behaviors, 29, 465–481. Ursua, M. P., and Uribelarrea, L. L. (1998). 20 questions of Gamblers Anonymous:A psychometric study with population of Spain. Journal of Gambling Studies, 14, 3–15. Volberg, R. A., and Banks, S. M. (1990). A review of two measures of pathological gambling in the United States. Journal of Gambling Studies, 6, 153–163. Winters, K. C., Specker, S., and Stinchfield, R. (2002). Measuring pathological gambling with the Diagnostic Interview for Gambling Severity (DIGS). In The Downside: Problem and Pathological Gambling (J. J. Marotta, J. A. Cornelius, and W. R. Eadington, eds.), pp. 143–148). Reno: University of Nevada, Reno. Winters, K. C., Stinchfield, R., Botzet, A., and Slutske,W. S. (2005). Pathways of youth gambling problem severity. Psychology of Addictive Behaviors, 19, 104–107.
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PART III
Emerging Gambling Studies Research Issues
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CHAPTER 9
The Role of Structural Characteristics in Gambling Jonathan Parke
Mark Griffiths
Division of Psychology Nottingham Trent University Nottingham, United Kingdom
Division of Psychology Nottingham Trent University Nottingham, United Kingdom
Background Payment Characteristics Suspension of Judgment and Cashless Gaming Smart Cards, Spending Limits, and Cashless Gaming Maximum Bet Size and Bill Acceptors Cash Versus Credit Display Playability Characteristics Feature Games and Other Specialist Play Features Stop Buttons Gamble Buttons The Near Miss The Psychology of Familiarity Speed and Frequency Characteristics Bet Frequency and Event Frequency Event Duration In-Running Betting Payout Interval Autoplay Educational Characteristics Clocks Transparency of Expenditure and Statements Warnings/Pop-Up Messages Ambient Characteristics Light and Color Effects Sound Effects General Sound Verbal Interaction Music 217
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Reward Characteristics Multiplier Potential and Betting Lines Payout Ratios and Randomness Jackpots Reward Schedules and Reinforcers Mandatory Cashouts Issues Relating to Research and Measurement Ecological Validity: Laboratory Versus Natural Setting Experiments in the Natural Setting Ethics in Gambling Experiments Conclusions
Ever since the first slot machine was introduced to the general public in 1895, the gaming industry has employed numerous design features to entice people to gamble and keep them gambling. It is likely that many of these have arisen spontaneously or fortuitously without in-depth empirical psychological analysis into their impact on behavior and how they work (Griffiths 1993; Parke and Griffiths 2006). However, the effectiveness of these methods suggests that there is much to be learned about the psychology of gambling from an analysis of structural characteristics (i.e., those characteristics that facilitate the acquisition, development, and/or maintenance of gambling behavior irrespective of the individual’s psychological, physiological, or socioeconomic status).The principal aims of this chapter follow: 1. To categorize the structural aspects of gambling in terms of their function in order to inform research, treatment, and policy; 2. To provide an overview of these structural factors whilst describing in more detail some of those factors that have received limited attention elsewhere; 3. To consider relevant issues relating to the study and measurement of structural characteristics; 4. To suggest directions for future research; and most importantly, 5. To recommend a plan for the difficult task of tackling the conflicting aims of interested parties.
BACKGROUND Over the past three decades, a number of authors have in some way referred to the role of structural characteristics in the acquisition, development, and maintenance of gambling behavior (e.g., Abt, Smith, and Christiansen 1985;
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Cornish 1978; Griffiths 1993, 1995; Royal Commission on Gambling 1978; Weinstein and Deitch 1974). Some of the analyses of structural characteristics have been in the form of general overviews (e.g., Cornish 1978;Weinstein and Deitch 1974). However, others have been more specific and have concentrated on the structural characteristics of individual gambling activities such as lotteries (Abt et al. 1985; Griffiths 1997a; Griffiths and Wood 2001), casino games (Abt et al. 1985; Royal Commission on Gambling 1978), scratchcards (Griffiths 1997b) and/or electronic gaming machines (EGMs) (Griffiths 1993, 1995; Parke and Griffiths 2006). Griffiths (1999a) outlined a list of common structural and situational characteristics, including: ●
● ● ● ● ● ● ● ● ● ●
●
●
● ● ●
Stake size (including issues around affordability, perceived value for money) Event frequency (i.e., time gap between each gamble) Amount of money lost in a given time period (important in “chasing”) Prize structures (i.e., number and value of prizes) Probability of winning (e.g., 1 in 14 million on a 6/49 Lotto game) Size of jackpot (e.g., millions of pounds, dollars, etc., in a lottery) Skill and pseudo-skill elements (actual or perceived) Near miss opportunities (number of near winning situations) Light and color effects (e.g., use of red lights on EGMs) Sound effects (e.g., use of buzzers or musical tunes to indicate winning) Social or asocial nature of the game (i.e., individual and/or group activity) Accessibility (e.g., opening times, membership rules, number of outlets) Location of gambling establishment (e.g., out of town, next to workplace) Type of gambling establishment (e.g., betting shop, amusement arcade) Advertising (e.g., television commercials) Rules of the game (e.g., complicated, easy)
Those of us who have examined this growing area of research have argued that structural characteristics appear to be an increasingly important factor in the maintenance of gambling behavior. The structural characteristics of a particular gambling activity may act as reinforcers for a gambling behavior, may satisfy gamblers’ needs, and may actually facilitate excessive gambling (Cornish 1978; Griffiths 1993). Furthermore, we have also argued that by identifying particular structural characteristics, it may be possible to understand more about gambling motivation and behavior, which may have useful clinical, academic, or even commercial implications. By identifying and understanding how games are structured (i.e., game design and associated features), we are really trying to unravel what makes a game
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addictive or what makes a game playable or fun and therefore engaging and commercially successful.The identification of these factors may have real and important implications for clinicians, policymakers, the gambling industry, and players to do the following: 1. Educate clinicians.To ensure that clinicians have the appropriate knowledge to help problem gamblers through education and/or challenging cognitive biases and irrational beliefs. Structural awareness of games may also help identify information about a player’s motivation by examining the type and form of gambling preferred. For example, if players place riskier bets using EGMs offering praise or compliments (see section on Verbal Interaction), this may indicate that a player has low levels of self-esteem or social interaction. In a clinical sense, this may yield new information regarding player motivation that could inform any subsequent intervention. 2. Educate and inform players. Information may empower players to gamble in a responsible way if we can help them understand, identify, and even adjust to such cues (i.e., risky games or addiction features) by either avoiding or exercising caution when playing high-risk machines. For example, players engaging in games that have high event frequencies could aim to be more cautious if they know that they could spend money faster or chase their losses easier. 3. Initiate and inform policy. More importantly, empirical evidence that identifies risky games and features might initiate or inform government policy and legislation in an attempt to minimize harm. 4. Inform the gaming industry. Finally, it is inevitable that the gaming industry will continuously want to know more about what makes games more attractive and, ultimately, more profitable. Usually, the industry will have more resources and more ecologically valid settings to research these structural factors than will academics or clinicians. For this reason, the gaming industry tend to be ahead of the clinical and research communities. However, it should be noted that more operators and software providers appear to be taking a more socially responsible approach. EGM gambling has been identified as one of the world’s major gambling problems (Griffiths 1995, 1999a). It has also been argued that EGM gambling comprises more gambling-inducing structural characteristics than all other forms of gambling (Griffiths 1995). Consequently, many of the structural factors considered in this chapter will be in the context of EGM gambling. Griffiths and Delfabbro (2001) have noted that the etiology of social, professional, and problem gambling behaviors has tended to focus on individual or social factors rather than structural and/or situational factors. These have
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included psychological variables (e.g., personality factors, attitudes, expectations/ beliefs), biological and/or genetic predispositions (e.g., polygenetic influences, dopaminergic neurochemistry), and sociological factors (e.g., peer influences, social heredity) (Griffiths and Delfabbro 2001). Despite the brief acknowledgment of the role that structural factors may have had in gambling behavior over 30 years ago (Weinstein and Deitch 1974), it was not until recently that any critical consideration has been given to their role in the acquisition, development, and maintenance of gambling behavior. Indeed, as we will show, empirical investigations aimed at examining the effects of structural factors have been even more recent. We argue that critical to the development of this research is the ability to classify these structural factors according to the role they play in the gambling behavior itself. Therefore, not only can we attempt to understand the consequences of the presence of certain factors in EGMs (e.g., spending may be reduced by limiting the availability and denominational values of bill acceptors and removing or limiting near misses), but we can also acknowledge the aspect of the gambling behavior that is being affected (e.g., limited-value bill acceptors affect how people pay to gamble but removing or limiting near misses may reduce how engaging or fun gambling may be). In doing this, we begin to have a more specific understanding of the role of game structure in player behavior. As a starting point, this chapter proposes the following taxonomy of structural factors: ●
● ●
●
●
●
Factors that make gambling fun, interactive, and/or engaging (which, for the purposes of parsimony, we will refer to as playability); Factors which relate to how one pays to gamble (payment); Factors relating to how one receives financial rewards or winnings (reward); Factors relating to the frequency, duration, and expediency of the game or reward speed; Protective features that educate or provide information to players (educational); and, Ambient features which may influence the immediate situation of the game or may contribute to other factors already mentioned (e.g., the use of color and sound).
The following categories and the factors presented in this chapter are summarized in Table 9.1. It should be noted that like most classifications of social phenomena, some factors could arguably be included under a different heading, and therefore each set is not entirely mutually exclusive. Each of these factors will be discussed in more detail, with particular consideration being given those factors receiving limited attention elsewhere.
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Table 9.1 Taxonomy of Structural Factors in Gambling Games.
Payment
Characteristics
Authors
Suspension of judgment (cashless gaming) Bill acceptors
Griffiths 1993, 1999a, 2003; Griffiths & Parke 2002; Nisbet 2005 Blaszczynski, Sharpe, & Walker 2001; Schellinck & Schrans 2002; Brodie, Honeyfield, & Whitehead 2003 Blaszczynski, Sharpe,Walker, Shannon, & Coughlan 2005 Walker 2004; Delfabbro 2006 Loba, Stewart, Klein, & Blackburn 2002 Nisbet 2005
Spending limits Betting lines Credit versus cash display Smart card technology: Precommitment Near miss
Playability
Verbal interaction Familiarity Feature games and bonus games Specialist play features (e.g., nudges, shuffles, etc.) Stop buttons Gamble buttons Originality/novelty (new) Event duration and frequency
Speed
Autoplay Clocks/time awareness
Educational
Player information Transparency of expenditure and statements/counters Warning Limit-setting information
Strickland & Grote 1967; Moran 1979; Reid 1986; Griffiths 1991, 1994, 1997a, 1997b, 1999a, 1999b; Chantal,Vallerand, Ladouceur, & Ferland 1995; 1999; Kassinove & Schare 2001; Griffiths & Wood 2001; Ladouceur & Sévigny 2002; Parke & Griffiths 2004; Parke & Griffiths 2006 Griffiths & Parke 2003; Parke & Griffiths 2006 Griffiths & Dunbar 1997; Parke & Griffiths 2006 Parke & Griffiths 2006 Griffiths 1990a, 1994, 1995; Parke & Griffiths 2006 Loba, Stewart, Klein, & Blackburn 2002; Ladouceur & Sévigny 2005 Griffiths 1990, 1994, 1995;Walker 2004 Schellinck & Schrans 2002 Griffiths 1993, 1995, 1997a, 1997b, 1999a; Blaszczynski, Sharpe, & Walker 2001; Griffiths & Wood 2001; Loba, Stewart, Klein, & Blackburn 2002; Parke & Griffiths 2004; Blaszczynski, Sharpe,Walker, Shannon, & Coughlan 2005; Delfabbro 2006 Parke & Griffiths 2006 Schellinck & Schrans 2002; Blaszczynski, Sharpe, & Walker 2003 Loba, Stewart, Klein, & Blackburn 2002 Loba, Stewart, Klein, & Blackburn 2002 Steenbergh,Whelan, Meyers, May, & Floyd 2004 Steenbergh,Whelan, Meyers, May, & Floyd 2004
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Table 9.1 (Continued) Characteristics
Authors
Light
Caldwell 1974; Griffiths & Swift 1992; Griffiths 1993; Delfabbro 2006; Parke & Griffiths 2003, 2006 Stark, Saunders, & Wookey 1982; Griffiths & Swift 1992; Griffiths 1993 Griffiths & Parke 2003, 2005; Parke & Griffiths 2006 Hess & Diller 1969;White 1989; Griffiths 1993; Loba, Stewart, Klein, & Blackburn 2002; Griffiths & Parke 2006; Delfabbro 2006 Cornish 1978; Dickerson 1993; Griffiths 1993, 1997a, 1997b; Delfabbro & Winefield 1999; Griffiths & Wood 2001; Griffiths & Parke 2005; Delfabbro 2006 Cornish 1978; Griffiths 1993, 1997a, 1997b; Griffiths & Wood 2001 Royal Commission 1951;Weinstein & Deitch 1974; Cornish 1978; Griffiths 1993, 1997a, 1997b; Griffiths & Wood 2001;Walker 2004; Delfabbro 2006 Dickerson 1991, 1992, 1993; Delfabbro & Winefield 1999; Griffiths 1999b; Delfabbro 2006 Delfabbro 2006 Blaszczynski, Sharpe, & Walker 2003
Ambient Color Music Sound
Jackpot size
Payout ratio Win probability/multiplier/ betting lines Reward Schedules
Immediacy Mandatory cashouts
PAYMENT CHARACTERISTICS Usually, the first interaction a player has with an EGM is the purchase of credit to begin play. Several factors are contributing to the changing face of payment, including industry profitability, technology, and pressure from various groups to make EGMs more socially responsible. Each structural aspect of payment will be discussed.
SUSPENSION
OF JUDGMENT AND
CASHLESS GAMING
Suspension of judgment refers to a structural characteristic that temporarily disrupts the gambler’s financial value system and potentially stimulates further gambling (Griffiths 1993). Old-style U.K. EGMs (otherwise referred to as fruit machines) often used tokens instead of coins (rather like the casinos’ use of chips), which “disguised” the money’s true value (i.e., decreased the psychological value of the money to be gambled).Tokens are put straight back into the machine
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without hesitation because they cannot be exchanged back into money by the machine’s owner. For most gamblers, it may be that the psychological value of virtual representations of money will be less than “real” money (and similar to the use of chips or tokens in other gambling situations or the use of e-cash in Internet gambling).This is a well-known concept that is exploited both by those in commerce (i.e., people typically spend more on credit and debit cards because it is easier to spend money using plastic) and by the gaming industry (Griffiths and Parke 2002). Anecdotal evidence would seem to suggest that people gamble more using e-cash than they would with real cash (Griffiths 1999a). This needs to be tested in an empirical setting. It should be noted that tokens have been phased out of EGM gambling in the United Kingdom, although many casinos around the world have started to use smart cards that are also representational forms of money and have the capacity to psychologically lower the real value of the money.
SMART CARDS, SPENDING LIMITS, AND CASHLESS GAMING Nisbet (2005) acknowledges some of the negative aspects of cashless gaming, such as isolated and uninterrupted play. However, she also emphasizes that cashless payment can be used to promote responsible gambling. In particular, she suggested that card-based cashless systems could be used to encourage players to limit how much they spend in a given period of time and to inform players of their spending habits through regular statements of their transactions. It is worth pointing out that Nisbet also found that despite supply-side informants (i.e., operators, manufacturers, etc.) suggesting that a $200 spending limit would restrict player acceptance, approximately three-quarters of players who were interviewed (n = 134) agreed that the limit was sufficient for their needs.This is an area that requires more empirical investigation, although acceptance of cashless systems still appears to be low, with less than 5% of New South Wales’s EGMs employing this technology.
MAXIMUM BET SIZE
AND
BILL ACCEPTORS
An Australian study carried out in ecologically valid settings (hotels and clubs housing EGMs) reported that changing maximum bet sizes from $10 to $1 had no significant effect on perceived enjoyment for either recreational or problem gamblers (Blaszczynski, Sharpe, and Walker 2001; Blaszczynski et al. 2005). The authors concluded that if modifications promoted harm minimization, then this could be enjoyed with limited impact on player satisfaction. Blaszczynski et al. (2001) found that only 7.5% of the 20% who were problem gamblers bet above the $1–$10 limit, and therefore, limits on bet size may be an effective harm minimization measure for these individuals, despite their making up a small proportion of the overall sample. However, the authors warn that since most bets are under $1,
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“problems caused by gambling losses result not so much from excessive bet size over shorter periods, but relatively standard bet sizes for longer periods of time in play” (p. 76).Therefore, limits to maximum bet size may be more useful as a harm minimization tool for a minority of gamblers either playing with higher stakes or playing with time constraints within a shorter session.Therefore, given the limited impact of such a feature on player satisfaction, it may be useful in a comprehensive social responsibility program. Blaszczynski et al. (2001) suggested that bill acceptors may increase spending in a number of ways, such as: ● ●
●
Suspending judgment, whereby more cash is transferred to credit at once; Minimizing breaks (and thereby allowing less time to consider expenditure), as players do not need to leave the machine to get change; and Increasing privacy (e.g., avoid the potential embarrassment of letting gaming staff, friends, family, or even other customers know how much a gambler is spending).
Schellinck and Schrans (2002) found supporting empirical evidence for the claim that the amount initially put into an EGM was twice as high on machines that included bill acceptors. Consistent with this, Blaszczynski et al. (2001) found that limiting bill acceptors to the $20 denomination reduced overall revenue on modified EGMs by 42%. However, they also concluded that limiting bill acceptors is “likely to be an inconvenience that has no impact on length of time spent playing” (p. 76), because players may simply move along to an unmodified machine. Further research needs to be carried out, in environments where there are no alternative machines with higher bill acceptors. Brodie, Honeyfield, and Whitehead (2003) interviewed 359 individuals following the introduction of an upper limit of $20 into bill acceptors in EGMs operating in Queensland, Australia. Although the majority reported no change in their gambling behavior, a minority of players (15–20%) reported reductions in number and duration of visits, size of bet, and overall expenditure, with the greatest changes coming from the problem gambling group. However, such reductions were not corroborated by their empirical inquiry revealing that EGM metered win or consumer net loss remained unchanged overall in the affected jurisdiction following the change in note acceptors.Although such findings may be explained by an increase in number of people of gambling, more empirical evidence is needed to support this claim. (Betting lines and credits per play [i.e., multipliers] will be discussed in the section Reward Characteristics, as they are interlinked with payout and reward.)
CASH VERSUS CREDIT DISPLAY In a laboratory experiment, Loba et al. (2002) reported that when a visual display counter showed money won or lost in dollars rather than credits, pathological gamblers found it easier to stop playing. This result was not found among
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nonpathological gamblers. However, this is only one experiment and needs replicating and generalizing to other forms of EGM gambling.
PLAYABILITY CHARACTERISTICS By definition, “gaming” machines, viz., EGMs, need to provide a game that is “playable.” A game needs to some extent to be interactive, engaging, and fun. Although EGMs have traditionally achieved this through matching symbols on reels, they are evolving to be more exciting and interactive through using more features, more ploys (e.g., near misses), and better marketing.These factors are now discussed in more detail.
FEATURE GAMES
AND
OTHER SPECIALIST PLAY FEATURES
It has been noted by a number of authors (Cornish 1978; Griffiths 1993; Langer 1975) that the degree of personal participation (i.e., bettor involvement) and the exercise of skill are interrelated.The gradual introduction of specialist play features such as buttons for “nudge,” “hold,” and “gamble” meant the creation of perceived skill (real or imagined). Early work by Griffiths (1990a) argued that the introduction of specialist play features promoted the illusion of control through personal involvement, perception of skill, and familiarity with a particular machine. Over the last decade in the United Kingdom, there has been an increase in the number of specialist play features introduced into EGMs. One of the biggest changes in the format of the U.K. EGM over the last decade has been the rise of the feature game (Parke and Griffiths 2004, 2006). The feature game is a more elaborate and complex extension of the specialist play features associated with earlier machines, such as the “nudge,”“gamble,” and “hold” buttons (features that have traditionally been viewed as promoting “idiot skill”—see Griffiths 1994, 1995). Feature games vary extensively by machine and manufacturer but typically include a core variety of different types, such as: ●
●
●
The “lapper,” with which prizes are won by doing circuits (“laps”) on the game board The “trail,” with which prizes are won by progressing up the “trail” in the hope of winning the jackpot or top feature The “hi-lo ladder,” with which prizes are won by advancing up the prize ladders by successful gambles (i.e., gamblers having to guess whether the next number on the game board will be higher or lower).The top of such ladders usually represent jackpot wins or above.
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●
227
The “grid,” which is a variation of the hi-lo ladder, whereby progression is made by successful (higher or lower) gambles. The jackpot can be obtained by reaching the corners of the grid. (Parke and Griffiths 2006)
The easiest way to conceptualize the feature game is to imagine a basic board game such as Monopoly, Cluedo, or Snakes and Ladders (which are, in fact, all types of U.K. EGMs), in which essentially, the player rolls the dice (usually twelve-sided instead of six-sided) and plays against the machine instead of another player. The concept, of course, allows for many variations and combinations. We would argue that a higher required level of bettor involvement may increase the illusion of control and the level of excitement experienced by players. From our observational analysis (Parke and Griffiths 2006), we concluded that for the regular gambler, participating in a feature game is the preferred way to win. In addition to the development of the feature game, there have been other significant specialist play features that appear to exploit the illusion of control.These include bonuses and secret functions such as “skill stop,” the “shuffle,” the “superhold,” the “trail boost,” the “feature hit,” the “reel skill,” the “win spin,” the “selector,” the “respin,” and the “stopper,”’ among others (see Parke and Griffiths 2006 for a detailed overview of these features). Whereas at one point these feature games and specialist play features were limited mostly to the United Kingdom and some European jurisdictions that did not use a random number generator (RNG), these kinds of features are being slowly phased into EGMs in other jurisdictions using RNGs, such as the United States, Canada, and Australia. Figures 9.1 and 9.2 show examples of how the feature game has been introduced into EGMs in other jurisdictions using RNGs. Although we speculate that such features may increase illusion of control, player involvement, and excitement among players, this needs to be supported by empirical research. Furthermore, research needs to explore the exact impact of such effects. For example, does increased illusion of control and player involvement lead to problem gambling in this context, or does this simply increase player experience and playability in a relatively harmless way?
Stop Buttons Another factor that may affect player interaction and playability is the option or requirement to “stop” a reel spin. In other words, by pressing a button or the equivalent, one may be able to stop the reels or symbols, thereby further involving the player in the outcome of the game. In effect, the player makes the decision when the reels or symbols come to a halt. This function might foster an illusion of control among players and, in some situations, might reduce event duration or reel spin speed. Loba et al. (2002) found support for the claim that players have a stronger desire to play machines with a stop function regardless of whether they are
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Figure 9.1. Example of a lapper and trail feature game in a U.S. electronic gaming machine using a random number generator.
Figure 9.2. Example of a newer kind of feature game (similar to old-style “duck shoot” games often found at fairs) in a U.S. electronic gaming machine using a random number generator.
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pathological gamblers. However, it is questionable whether the presence of this function is vitally important, as players they studied did not notice any significant difference when moving from machines without this function to play machines with this function.This might suggest that although a stop function is considered more fun or enjoyable, the absence of a stop button is not enough for players to lose interest and inhibit further play. In another laboratory study, Ladouceur and Sévigny (2005) found that a stopping device increased illusions of control and cognitive biases and increased actual gambling persistence at video lottery terminals (VLTs, which are a specific form of EGMs). Gamble Buttons Arguably, all buttons found on an EGM could, by definition, be referred to as a “gamble” button, as they facilitate gambling behavior. However, the term “gamble button” is unique in that it refers to a mechanism that allows players the opportunity to risk winnings by gambling it further.There are several variations of this feature, differing in terms of risk (double or nothing, increase or lose a small percentage), in terms of randomness (some are random, some are not), and in terms of form (some gambles relate to going higher or lower on a numbered reel; some result from a simple press of a button).What they all have in common is that they give the gambler more interaction, more risk, a chance to win a greater prize, more opportunities to experience a near miss, and (in some cases, it could be argued) more illusion of control. Griffiths (1993) was among the first to acknowledge the presence of the gamble button in U.K. fruit machines (a form of EGM), claiming that this was a “pseudo-skill” feature which allowed players to believe they had more skill over the outcome of the game than they did in actuality. U.K. fruit machines have the widest variety of forms of gamble buttons and options, owing to their current pseudo-random technology, as well as relatively flexible British legislation. Walker (2004) examined the use of a gamble button at a large club in Sydney, Australia, and found that less than 5% of players were willing to use the gamble button, and reluctance to use this feature increased as the initial win size increased. As Walker pointed out, this was further evidence of irrational player behavior, as machines in this jurisdiction offered a payback ratio of 100% (i.e., a 50/50 chance of winning or losing) when a player used the gamble button.This is, of course, higher than the payout ratio of EGMs generally, which is 70–95%. Using the gamble button on an EGM would thus appear to be more profitable in the long run than regular play. This finding could also be used to support the claim that gamblers play for time rather than money (Griffiths 1990b, 1995). In other words, here the gamble button represents better value, albeit with more risk, which may ultimately lead to shorter gambling sessions. Walker (2004) postulated that the lack of popularity of the gamble button could be explained by the fact that only problem gamblers tended to use this
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facility. Walker subsequently reported that this was not the case, having surveyed gamblers (n = 120) in a large Sydney club and found that there was no correlation between number of hours of play each week and reported use of the gamble button. Future research should focus on other indicators of problematic play, such as stake and bet levels, expenditure, chasing, affordability, and/or perhaps scores on the South Oaks Gambling Screen or criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM), fourth edition. The Near Miss A near miss is any nonwinning outcome of a gamble that is “perceived” as being almost successful.The notion of player perception is important to this definition, since “near misses” are the same as other losing outcomes in that they do not pay out any winnings and they do not affect the payout ratio (see the section Reward Characteristics)—the difference exists only in how the visual representation is perceived on screen. A near miss (e.g., two matching symbols on a win line with the third matching symbol just off the win line) may still be reinforcing and fun, even though it may not cost the operator anything. Essentially, players perceive that they are frequently nearly winning, as opposed to frequently losing (Griffiths 1994). As early as 1967, Strickland and Grote demonstrated that having a greater number of winning symbols on the first two EGM reels (and fewer on the third reel) did indeed prolong gambling in experimental gambling situations. This suggests that some commercial gambling activities (particularly EGMs and instant scratchcards) are formulated to ensure a higher-than-chance frequency of near misses. Several authors (Griffiths 1991, 1994, 1999b; Parke and Griffiths 2004; Reid 1986) have argued that near misses may have an effect on the development and maintenance of gambling behavior. Reid (1986) suggested that the near miss can be explained in terms of frustration theory (Amsel 1958) or cognitive regret (Kahneman and Tversky 1982). According to Amsel, failing to fulfill a goal (e.g., not winning on an EGM) produces frustration, which energizes ongoing behavior. Subsequent wins then reinforce high-rate behavior. According to Kahneman and Tversky’s theory, the frustration produced by “nearly winning” induces a form of cognitive regret.The elimination of regret can be achieved by playing again, and this in turn encourages future play. Parke and Griffiths (2004, 2006) have argued that where the psychology of the near miss was previously limited to matching symbols on the reels, there are now several aspects of EGM features that manipulate the gambler through the near miss.The gaming industry appears to have adapted and strengthened the near miss by connecting it to the feature game (rather than symbol matching).The more features incorporated into an EGM, the more opportunities to use different types
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of near miss (see Parke and Griffiths 2004 for an overview of the relationship between the feature game and the near miss). Given the lack of empirical research on such a fundamental structural factor, we suggest that future research address the impact of the near miss on the various factors relating to EGM play, including engagement, spending (of time and money), and other indicators for problem gambling and customer satisfaction. Research should also explore players’ perceptions of the near miss and locus of control (e.g., do players feel that a near miss is the result of something they failed to do correctly, or do they consider the near miss to be a ploy that does not approximate a winning outcome in any way?). The Psychology of Familiarity Other recent innovations in game design (including EGMs and scratchcards) tap into the psychology of familiarity (Griffiths and Dunbar 1997). Parke and Griffiths (2006) highlighted various areas with relevance to familiarity, such as naming, trust, experience, fun, and persuasion.The affiliation of familiarity with a machine can be very play inducing. Why would a gambler play on one machine more than another if both had exactly the same chances of winning? Some speculative reasons forwarded by Parke and Griffiths (2006) include: ●
●
●
Trust.With an international “quality” brand such as The Simpsons, a player might think that she is unlikely to lose a lot of money. She might also think that the jackpots are likely to be generous. Experience. Long-time regular viewers of The Simpsons might think they know the television program inside out. They might think that their knowledge will help them in the playing of the machine. Fun. It might simply be that the game play of The Simpsons is more exciting and that the sound effects and features are novel, cute, and/or more humorous than those of other machines.
It may be the case that if themes are increasingly “familiar,” an individual might be more likely to persevere with the complexities of a machine (Griffiths and Dunbar 1997). Players may find it more enjoyable because they can easily interact with the recognizable images they experience. Again, such suggestions need empirical testing.
SPEED AND FREQUENCY CHARACTERISTICS When considering speed and frequency of gambling, concepts such as event frequency and payout interval can often be misunderstood and applied in the wrong context. Often, these are mistaken for having the same meaning.
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Furthermore, concepts such as bet frequency and event duration are often ignored despite the importance of their role in the speed and frequency of betting. All of these concepts involve slightly different aspects of gambling, although they are all implicated factors that affect speed and frequency.
BET FREQUENCY
AND
EVENT FREQUENCY
Event frequency refers to the number of events that are available for betting in any given time period. For example, a lottery draw may occur twice a week, but an electronic keno lottery draw may occur 100 times per hour. In this example, a keno lottery draw has a higher event frequency. Bet frequency, on the other hand, refers to the number of bets or gambles placed in any given time period. Using lottery again as an example, multiple tickets (e.g., 10) can usually be purchased as frequently as desired before any single lottery draw. So, here bet frequency would be equal to 10, but event frequency would be equal to 1. Therefore, bet frequency can often be higher than event frequency, and hence it is possible to spend more than one can afford even with a low-event frequency. Upward limits on the number of bets or the absence of multipliers (e.g., limitations on number of bets and amounts wagered by EGM players—see the section Reward Characteristics) will influence the relationship between bet frequency and event frequency. The relationship between bet frequency and event frequency needs further empirical investigation.As researchers and clinicians, we often make the assumption that the two have a strong relationship: The higher number of betting events, the higher the frequency of betting. Until more research is forthcoming, it may useful to comment on this relationship further here. Although players can place many bets on just one gambling event, the outcome of this event can influence future betting activity. By outcome, we are essentially referring to winning or losing. Losing can often create financial and emotional motivation to continue betting, that is, “chasing” (for a detailed exposition of chasing, see Lesieur 1994). It can be speculated that the satisfaction from winning may reduce motivation for further betting in the short term or that it may increase betting as a result of an increased bankroll, illusions of control, and/or cognitive biases.Therefore, a higher event frequency not only offers more opportunity and choice for betting, but also affects motivation for betting through revealing consequential wins and losses at the end of each event.
EVENT DURATION This essentially refers to how fast the “event” is (e.g., a reel spin might last 2.3 seconds). It is important to acknowledge that duration of the betting event is different from event frequency. However, they may be inextricably linked in that
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the length of a betting event will obviously limit the frequency with which it can take place. For example, a betting event lasting two hours (e.g., a sports game) could not have an event frequency greater than one in any 2-hour period, but a roulette spin (lasting approximately 5–6 seconds) may have an event frequency of several hundred in the same 2-hour period. Furthermore, as a result of the introduction of in-running betting (see next section), this relationship is even less clear.
IN-RUNNING BETTING This refers to the relatively new betting option of wagering on an event that has started but has not yet finished—what bookmakers commonly refer to as “inplay.” This means that players can continue to bet on an event and, perhaps more importantly, adapt their bets according to how the event is progressing. For example, when betting on a sporting event, gamblers who initially bet that Team A will win could theoretically place subsequent bets with different predictions (i.e., the game will end in a tie or Team B will win) based on their interpretations of the game thus far. Of course, gamblers could also maintain their original predictions but place further bets if they are encouraged or further convinced by the backed team’s performance. Given that this is a relatively new betting concept, it certainly requires further empirical research. It could be speculated that in-running betting may contribute to excessive, prolonged, or unplanned gambling as a result of: ●
●
●
Within-session chasing (i.e., on the same event or series of events). For example, an individual may make an incorrect bet selection but then choose to recoup past losses by placing more bets on the same game; An increase in perceived skill (through watching, analyzing, or even attending a betting event); or An increase in the “juice,” the “action,” of a sporting event.
Bet frequency is therefore not a structural factor but is determined to some extent by payout interval and event frequency, which are indeed structural factors.
PAYOUT INTERVAL This is the time between the end of the betting event (i.e., the outcome) and the winning payment. This is beginning to change with cashless systems (see Nisbet 2005).The frequency of playing when linked with two other factors—the result of the gamble (win or loss) and the actual time until winnings are received— exploits the psychological principles of learning (Moran 1987).This is a process of operant conditioning of habits by rewarding (i.e., reinforcing) behavior (i.e., through presentation of a reward such as money). To produce high rates of
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response, those schedules which present rewards intermittently (random and variable ratio schedules) have been shown to be most effective (Skinner 1953). Since EGMs operate on random and variable ratio schedules, it is unsurprising that high rates of response (i.e., excessive gambling) occur. Cornish (1978) notes that promoters appear to acknowledge the need to pay out winnings as quickly as possible, thus indicating that receiving winnings is seen by the gaming industry as a reinforcement to winners to continue gambling. Rapid event frequency and short event duration also mean that the loss period is brief, with little time given over to financial considerations; and, more importantly, winnings can be regambled almost immediately. Games that offer a fast, arousing span of play, frequent wins, and the opportunity for rapid replay are those most associated with problem gambling. Frequency of opportunities to gamble (i.e., event frequency) appears to be a major contributory factor in the development of gambling problems (Griffiths 1997a, 1997b).The general rule is that the higher the event frequency, the more likely it is that the activity will cause gambling problems.Addictions are essentially about rewards and the speed of rewards. Therefore, the more potential rewards there are, the more addictive an activity is likely to be. Given the time, money, and resources, a vast majority of gambling activities are “continuous” in that people have the potential to gamble again and again.This is now true more than ever as a result of in-running betting options.
AUTOPLAY Parke and Griffiths (2006) have outlined recent developments to U.K. EGMs that have implications for event frequency, most notably the “autoplay” feature. More specifically, it has been increasingly popular for U.K. EGMs to have a built-in autoplay feature. In these instances, the machine plays for the gambler trying to make the optimum (i.e., best) choices. This might be interpreted as helping the customer in terms of convenience and also in terms of acting as a model of best practice. However, it may also result in an increased event frequency that may be more profitable to the machine owners and manufacturers. This is achieved by reducing the level of human interaction (i.e., compared with a machine, human choice inevitably slows down overall playing time). For the machine operator, the more plays, the greater the player turnover, and the greater the profit.The autoplay stops only when the player needs to make a decision regarding features, holds, nudges, or wins.
EDUCATIONAL CHARACTERISTICS Educational structures are characteristics that exist purely for the purpose of harm minimization. Ambient features and features relating to payment, playability, speed, and reward can contribute to both engagement (making games more fun,
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enjoyable, and interesting) and harm minimization. Educational features are included to provide information regarding the player’s behavior and how the EGM actually works in an attempt to ensure more responsible play.
CLOCKS As one would expect, the use of clocks as a feature is aimed at assisting the player to keep track of time. In their evaluation of an introduction of new EGMs using responsible gaming features, Schellinck and Schrans (2002) found that clocks had no measurable impact on time or expenditure but did improve players’ ability to keep track of time and play within set limits.
TRANSPARENCY
OF
EXPENDITURE
AND
STATEMENTS
As previously discussed, Loba et al. (2002) found that when pathological gamblers played EGMs using counters showing money won (or spent) rather than credits, they found it easier to stop gambling overall.This suggests that factors improving awareness of expenditure may be useful in harm minimization. Such a finding makes intuitive sense: Gamblers may operate using cognitive processes that distort their awareness or recollection of how much money has been won or lost. For example, Wagenaar (1988) identified heuristics that gamblers may use that might contribute a biased interpretation of expenditure, such as fixation on absolute frequency. Fixation on absolute frequency refers to when gamblers focus on how much they are winning and ignore the levels of expenditure needed to get those wins. In other words, they do not take into consideration the overall financial outcome (e.g., wins minus losses)—they simply refer to win frequency and amount but, to some extent, may ignore the level of expenditure needed to secure such wins. With such cognitive processes in mind, features that facilitate accurate recall of expenditure may have positive implications for harm minimization and for keeping the gambler informed. One method is by cash displays as opposed to credit displays. In addition, we suggest that future research be done on the efficacy of gamblers’ receiving regular statements of expenditure (wins and losses)—either within session during play, which would tend to be onscreen, or between sessions in weekly or monthly statements in the mail or as an e-mail via account membership.
WARNINGS/POP-UP MESSAGES In the same way that alcohol- and nicotine-related products carry mandatory warnings in various jurisdictions, warning messages have been recommended
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on gambling products to warn and inform players of the risks involved with, and time spent, gambling. These are often referred to as pop-up messages, given the nature of how they are introduced to players onscreen during play. Schellinck and Schrans (2002) reported that exposure to a 60-minute pop-up message (informing players of time spent) resulted in small but significant decreases in length of session and in expenditure of problem gamblers. In another study examining the impact of brief intervention messages and information on limit setting, Steenbergh et al. (2004) reported that the provision of messages to college-age gamblers significantly improved their knowledge of the risks and rewards of gambling. However, although these findings may support the use of intervention messages and limit-setting information in manifesting cognitive change, the authors reported that this intervention did not significantly affect actual gambling behavior.Therefore, more research is needed to examine the link between gambling-related knowledge and problematic play, particularly in relation to higher-risk players whose baseline behavior has more scope for being influenced by the experimental manipulations.
AMBIENT CHARACTERISTICS LIGHT
AND
COLOR EFFECTS
Griffiths (1993) offered an overview on the psychology of light and color effects in relation to EGMs. The psychology of color in EGMs is still being utilized, although there is still little research into the differential effects of color stimulation on gambling behavior in ecologically valid settings. The limited empirical data suggest that people gamble more under red lighting (because it is more arousing) and that most U.K. gambling arcades utilize colors toward the red end of the spectrum in their lighting and decor (Griffiths and Swift 1992; Stark, Saunders, and Wookey 1982). Parke and Griffiths (2006) reported that on many new U.K. EGMs (e.g., Cleudo, The Great Escape, Eastenders, Trivial Pursuit), some structural features (e.g., the white letters of the feature board) will turn red when playing on the feature, indicating that the jackpot or “top feature” will be won. All the player has to do is continue playing the feature until the end.
SOUND EFFECTS Sound effects in gambling (which mainly affect EGMs) have long been used, although there is very little empirical evidence assessing exactly what impact the use of sound has on gambling behavior. This section briefly assesses the role of general sound, verbal interaction, and music.
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General Sound A number of authors over the last 30 years (e.g., Griffiths 1993; Hess and Diller 1969; White 1989) have argued that the sound effects of EGMs are gambling inducers. Constant noise and the sound of money falling into a metal payout tray give the impression of a noisy, fun, and exciting environment in which winning is more common than losing (Griffiths and Parke 2003; Parke and Griffiths 2006). However, these are very general effects that create an overall impression.To regular players, the sound of a payout is obviously a distracter to playing an individual machine, as this payout signals the perception that this machine is less likely to pay out again in the short term. Sounds and music from EGMs may act as reinforcers. Of particular relevance are the sound effects after losing (which could be termed “acoustic frustration”). Many machines make loud or antagonistic noises after a player has lost. Antagonistic sounds invoke frustration and disappointment. For example, on The Simpsons EGM, Mr. Smithers smugly informs Homer Simpson, “You’re fired!” or Chief Wiggam says: “You’re going away for along time, Ha!” At present, we can only speculate about the consequences of such sound effects. In line with hypotheses supporting frustration theory and cognitive regret (Amsel 1958; Kahneman and Tversky 1982), this might make the EGM more enticing. However, it may be that antagonistic sound effects perpetuate gambling only in the short term or within a session. In the long term or between sessions, regular EGM players might avoid machines that induce frustration, cognitive regret, and/or aggression. Loba et al. (2003) found that a pairing of decreased reel speed and no sound effects reduced ratings of enjoyment, tension, and excitement for pathological gamblers in their study.They also found that pathological gamblers found it more difficult to stop gambling when no sound was paired with faster reel speed and a stopping device. However, it is difficult to tease out the extent to which sound accounts for these findings, as this manipulation was paired with other variables. Delfabbro (2005) found empirical evidence suggesting that players preferred machines with sounds used to indicate wins and rated these machines as significantly more attractive. Further empirical research investigating sound as a structural factor in betting must focus on the role that sound may play in a particular game. As outlined, we suggest that sound used to reinforce a win will facilitate gaming behavior, while sounds reinforcing a loss will have an inhibiting effect. Another sound effect that has been used on EGMs is pulsating sound, where the pitch of the sound increases and the sound becomes faster when a gambling decision needs to be made. This appears to increase the tension and also tends to make players react more quickly to the machine. Utilization of sound in this particular manner has been termed “perceived urgency” (Edworthy, Loxley, and Dennis 1991). However, sound effects of EGMs have dramatically changed and improved over the last decade. Basic pulsating (“beeping”) sounds have been replaced by complex musical pieces (classical or pop), intricate sound effects, and verbal interaction
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(see next section).The aim of these developments has not really changed over the years. Sound effects still appear to heighten emotional states. In addition, improved sound effects might make the EGM more appealing on a general level. Verbal Interaction The sound effects of EGMs have advanced significantly over the past decade. In particular, verbal interaction in the form of commands and reinforcers are now central to many EGMs. A number of factors might be involved in verbal interaction that could make EGMs more play inducing, including the raising of selfesteem, the giving of hints and guidance, and, of course, making these games more fun (Parke and Griffiths 2006). Several U.K. EGMs make explicit appeals to self-esteem when positive play is made on the EGM by the player. For instance, on PsychoCashBeast, the machine says: “You’re cool!” (in a seductive female voice); on Top Tenner, the machine says: “You are a genius!”; and on Viva Las Vegas, there are cheers from a crowd.We return again to The Simpsons, which is one of the most popular series of EGMs on the U.K. market.This EGM has been so successful in the United Kingdom that there have been at least three different versions of the machine in operation. One of the attractive features of the machine is that there is a substantial level of verbal interaction from the popular television characters. For instance: ●
●
●
● ●
When a player leaves the machine, the character Apu says, “Thank you, come again.” When a player starts playing the machine again, the character Krusty the Clown says, “I knew you’d come crawling back!” When a player experiences any losses on the feature play, the character Homer does his “D’oh” catchphrase. When a player wins, the character Bart says, “Wow, cool, man.” When a player wins the jackpot, The Simpsons theme song plays.
In addition to increasing the novelty and fun associated with game play, these simple verbal and musical interactions may have the capacity to reinforce a sequence of wins, making them more memorable, or may “soften the blow” within losing sequences. Of course, such speculation calls for more empirical research. (For other effects of verbal interaction, such as player guidance or friendship, see Parke and Griffiths 2006). Music Another speculative area worthy of further investigation is that of background music on the machine. It could be the case that such music facilitates, stimulates, maintains, and/or exacerbates gambling behavior in some individuals. This will
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obviously depend on the musical preferences of the fruit machine players themselves. It is likely that “pop music” will be the most effective, since it is popular by definition. Griffiths and Parke (2005) highlighted that empirical research would be useful in the following areas, as the music might (a) increase confidence in fruit machine players, (b) increase arousal in fruit machine players, (c) relax the fruit machine player, (d) help the EGM player disregard previous losses, and (e) induce a “romantic” affective state, leading the player to believe that his chances of winning are better than they are.
REWARD CHARACTERISTICS MULTIPLIER POTENTIAL
AND
BETTING LINES
Multiplier potential is a structural characteristic that refers to the range of odds and stakes that the form of gambling offers and can be viewed as a primary inducement to play (Griffiths 1993). In essence, gamblers can choose the rate at which their wins and/or losses multiply. In the United States, this can be achieved on the same EGM, which are called “multipliers” or “progressives” (e.g.,“progressive slots” in relation to slot machines), whereas traditionally in the United Kingdom the amount gambled per play has been dependent upon the machine, that is, each EGM has a particular gambling stake. Traditionally, machines have offered the option of betting on 1 coin, 2 coins, or 3 coins, but there are now options for multiplying the stake by as much as 500 coins per spin on some newer machines (usually one-cent machines). New EGMs in the United Kingdom are now starting to incorporate multiplier stake systems, although there is limited empirical evidence that such systems have any effect on how inducing an EGM might be (Parke and Griffiths 2006). Bettor involvement and choice may be increased, but the effect is expected to be marginal. However, players may be more motivated to select a higher or maximum multiplier, as they may perceive such play to represent better value, such as: ● ● ● ●
Greater than proportional increases in potential wins, Eligibility to win a progressive jackpot, Eligibility to play certain feature-games win bonuses, and Eligibility to play certain win bonuses.
However, it is important to note that the overall percentage return on investment is ultimately determined by a payback ratio (e.g., 80% of credits paid back) that is the same regardless of how many coins/credits are played. Therefore, over the long run, both methods should yield the same return on investment. However, it may be that the more engaging options potentially afforded by a higher multiplied stake (such as feature games, progressive jackpots, etc.) influence the player’s decision to multiply his or her stake.
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Furthermore, with the introduction of video screens in the place of the traditional reel sequence displays, there are now more options for betting on more than one line.Video screens can now allow a variety of betting options, as much as 30 lines per play. Where most reel-based EGMs offered prizes on the “horizontal matching” of symbols, video screens now have the option to reward matching of symbols on various horizontal, diagonal, vertical, and even zigzag configurations. This, combined with the option to multiply credits per play, significantly increases the stake size and potentially the size and/or frequency of rewards/prizes. For example, if you choose to play 20 lines at 9 credits per line on a 10-cent EGM, then each spin or play will cost $18 per spin (e.g., 10 cents × 20 × 9 = $18). However, you will also have 20 times as many opportunities to win a prize at a rate 9 times higher than if the gambler played one credit and one line per spin. Walker (2004) refers to the behavioral interaction between multiplying stake and betting lines.The strategy whereby a player bets on one line for the minimum bet per line is referred to as the “minimin” strategy. In contrast, a “maximin” strategy means betting on the maximum amount of lines available but only betting the minimum amount per line. The utility in making this distinction is that players who may fear “missing out” on potential wins may prefer the maximin strategy, whereas players who wish to play for a longer period of time may prefer to use the minimin strategy. From their observational study,Williamson and Walker (2000, as cited in Walker 2004) reported a preference for the maximin strategy and suggested that maximin players were motivated to bet on the maximum number of lines so that no winning combination was missed. Although further research is needed, it would seem that the option to bet on more than one line per spin and more than one credit per line may increase the likelihood of spending more within a given time frame. Furthermore, since neither affects the percentage return on investment in the long term, such strategies of play may be initiated and perpetuated as a result of biased reasoning or by gamblers’ motivation to take more risk in their betting in a shorter period of time. EGMs offering multiple stakes and multiple betting lines may increase excitement and minimize the chance of missing a winning combination or a feature game but will inevitably increase expenditure within a given period of time.There is no evidence to date that confirms whether strategies requiring higher expenditure simply mean that players spend the same amount of money in a shorter period of time or that they spend more money over a similar period of time. More empirical research is needed to determine the likely effects of overall expenditure and their links to the development and maintenance of problem gambling.
PAYOUT RATIOS
AND
RANDOMNESS
The payout ratio is the ratio of the winnings paid out to players in relation to the money taken in in the form of stakes. Payout ratios for EGMs are usually in
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the region of 70–90% (i.e., on average 70–90% of the original stake is paid back to the gambler over the long run). It is worth noting that EGMs around the world differ in how payout ratios are allocated. For instance, unlike North American EGMs, U.K. and some European EGMs do not operate via RNGs. This difference is described best via the UK patent GB 2 165 386 A, which states: Fruit machines are usually designed to give overall a preselected payout, i.e., a preselected ratio of the coins (or tokens) paid out in winning games to the coins (or tokens) inserted into the machine and credited for the playing of games. With the aim of ensuring a relatively even distribution of payouts (e.g., to avoid long runs of winning games followed by long runs of losing games), it is known to provide a compensator which monitors the payout ratio game by game and initiates action, as necessary to, to influence the random selection of wins and thereby attempt to hold the ratio at all times close to the preselected level. (GB Patent GB 2 165 386A [P1:L9-L24] )
Therefore, while each spin on a North American EGM is an independent random event, spins on U.K. EGMs are manipulated by a “negative feedback control” principle (otherwise referred to as “compensation” or “adaptive logic”). This is designed to ensure that machines make a profit in return for offering entertainment while still obeying the payback percentage promised to the player (Parke and Griffiths 2006).These differences in structural characteristics have significant implications for gambling behavior. Most notably, EGMs operating under “adaptive logic” may indirectly create an element of skill and/or perceived skill in EGM gambling. Such skill refers to players who use this lack of randomness to their advantage. Consider the following example: EGM 1 has taken in a large amount of money in the purchase of credits but has paid out little in winnings. EGM 2 has paid out a large amount of money in winnings but has taken little money in the purchase of credits. According to the above patent, players will rationalize that EGM 1 will always be the most profitable machine to play, since the odds of winning will have increased (and decreased on EGM 2) as a result of the adaptive logic. Therefore, the skill is in selecting the correct machine, a procedure referred to as “skimming” (Parke and Griffiths 2006).While the exact utility of skimming for profitable play is unknown, it is clear that some players will be attracted to the potential advantage of knowing the short-term payout history of an EGM.
JACKPOTS While it is acknowledged that there is currently little research exploring structural factors generally, it is perhaps most surprising that there is a paucity of empirical research into the effect of jackpot size on gambling behavior. There is some evidence to suggest that a higher jackpot will attract more participation in
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that gambling activity. For instance, more lottery tickets are sold on “rollover” weeks because the potential jackpot is very large (Griffiths and Wood 2001). However, what is less clear is the motivation behind such a trend. Although, this might be explained by the obvious appeal of getting more money, other, more subtle factors may be at work. It could be the case that the higher jackpots attribute higher status to gambling activity and that higher jackpots offer more opportunity for chasing losses. Like many of the other structural factors discussed here, more empirical research is desperately needed to learn about the impact of jackpot and win sizes on gambling.
REWARD SCHEDULES
AND
REINFORCERS
One of the most important (and earliest identified) structural aspects of any gambling activity is the nature of reinforcement that occurs from engaging with that game (Skinner 1953). This refers to more than monetary reinforcement and may include other reinforcers, such as social reinforcement, escape, or even classically conditioned stimuli such as auditory cues in a betting shop (Dickerson 1984). Although monetary reinforcement is traditionally considered to operate under a variable ratio schedule of reinforcement, some reinforcers are argued to operate under other schedules, such as fixed interval schedules. (See Dickerson [1979, 1993] or Delfabbro and Winefield [1999] for an account of the role of reinforcement as a structural factor in gambling.)
MANDATORY CASHOUTS A mandatory cashout is a forced withdrawal of all remaining credits or banked winnings on an EGM. The aim of such a feature is to “interrupt” play in an attempt to either discontinue a dissociative state caused by excessive play or to create a short timeout that may facilitate players to take stock of their expenditure. There is, to date, no evidence to support the effectiveness of this technique in harm minimization. Schellinck and Schrans (2002) reported from their Nova Scotia study that a mandatory cashout feature had no effect on either duration of play or actual expenditure regardless of whether participants have the status of pathological gamblers. However, this research is inconclusive, since the mandatory cashout in the Nova Scotia study occurred after 150 minutes, and, as Blaszczynski et al. (2003) point out: “Since session length for problem players is 144.5 minutes and since problem players report cashing out of one machine and continuing to play on another machine 65% of the time, the actual occurrences of mandatory cash out must be quite rare” (p. 30).
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ISSUES RELATING TO RESEARCH AND MEASUREMENT ECOLOGICAL VALIDITY: LABORATORY VERSUS NATURAL SETTING In the Reno model, Blaszczynski, Ladouceur, and Shaffer (2004) suggested that only scientifically and ecologically valid research should guide decision making regarding problem gambling and harm minimization. Of course, this presents a problem for those researching the impact of structural factors on gambling. Firstly, there is the problem of access. There are gatekeeper1 issues that must be navigated whereby operators are reluctant to permit researchers into the gambling environment.The reasons for such reluctance could be any of a variety, including maintaining privacy of customers, minimizing the potential disruption at gambling venues, and/or limiting the exposure of the potentially problematic nature of their gaming devices. This reluctance contributes to the second problem, that of carrying out nonecologically valid research. It would seem reasonable to propose that if we cannot research gambling in a real gambling environment, then our next best option should be to research gambling in a simulated laboratory setting. However, despite suggestions to the contrary (e.g., Ladouceur et al. 1991), what we can actually learn from nongamblers in situations where they cannot lose or win money (or a significant amount of money) and in an environment that does not resemble a real gambling venue is highly questionable. Furthermore, even if we were able to start to address some of these issues by creating real outcomes in gambling simulations (e.g., increasing risk by allowing participants to lose or win more), as researchers we are strictly bound by an ethical code not to put participants at risk. Research by Ladouceur and colleagues (1991) reported that there were no significant differences between the cognitive and behavioral components of video poker players in a natural setting compared with a laboratory setting, and they consequently suggested that in some cases, laboratory-based experiments can have good ecological validity. This empirical investigation represents one of the few attempts to examine differences in validity in this way and thus is an important step forward in confirming the value of laboratory-research investigation of structural characteristics in gambling. However, such confidence in the validity of laboratory experiments should be viewed with caution. In this particular experiment, none of the respondents met the 1 In this context, a “gatekeeper” refers to any individual or organization in a position of control or responsibility for an environment or a business and/or its customers (e.g., casino manager, online gaming operator).
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criteria of the DSM, revised third edition, for pathological gambling.Therefore, such findings cannot be generalized to problem gamblers, arguably the key population for which harm minimization initiatives are targeted. Also, we would suggest that such experiments might have utility in investigating cognitive variables (e.g., erroneous beliefs), since these may already exist and may be manifested regardless of the experimental manipulation. However, behavior is a better yardstick by which we can judge laboratory-based ecological validity. In the investigation by Ladouceur et al. (1991), monetary risk was significantly lower within the natural setting.This may have been because any incurred losses were the participants’ own money and not money provided by the experimenters, as was the case for the laboratory group. Finally, the situational aspect of this particular “natural” setting (a grocery store) is very different from many other forms of natural settings such as casinos, slot parlors, amusement and Internet gambling, and so on.Therefore, these findings are limited not only to certain populations, but also to certain gambling situations and forms. It is clear that more research like that conducted by Ladouceur and his colleagues (1991) should be carried out. However, until more robust research on this issue is available, researchers must continue to find ways to conduct research and experiments investigating gambling in more realistic situations. For the time being, there seems to be two main ways in which this can be achieved: by conducting experiments in real environments, and (2) perhaps more controversially, by challenging the ethical stance on permitting research participants to win and lose their own money.
EXPERIMENTS
IN THE
NATURAL SETTING
As stated earlier, researchers are met with resistance from gatekeepers for a variety of reasons when trying to conduct research in real gambling environments. Perhaps it is time for more pressure to be placed on regulatory bodies that could push for researcher access in actual gambling locations. Just as an example, this might be a stipulation for licensing or could be considered part of an operator’s drive to become more socially responsible. For example, demonstrating commitment to responsible gambling is something that has been given precedence by the new U.K. regulatory body, the Gambling Commission. Alternatively, relationships between the gambling industry and the research/clinical community must be forged and/or further developed. Clear information should be provided for industry stakeholders from researchers, setting out a thoughtful and organized approach to any proposed research, which would not significantly affect the gambling environment or its customers. Furthermore, discussions need to begin and/or develop to confirm what kind of priority stakeholders should give to understanding how games actually work and what effect they may have on gambling behavior, including (but not limited to) problematic behaviors.
The Role of Structural Characteristics in Gambling
ETHICS
IN
245
GAMBLING EXPERIMENTS
For researchers, the safety and well-being of participants in experiments should always be one of the highest priorities.This principle in itself is rarely in debate. What is often in debate, however, is how we interpret safety and wellbeing and how we satisfy the cost/benefit ratio of our research and its potential impact on participants. Conditions and financial incentives used in gambling experiments fit squarely into this category and attract much debate. If we believe that valid experiments are more likely to be conducted under conditions whereby gamblers lose their own money without reimbursement and win money they can keep, with limited constraints imposed by the experimenter, then we are presented with an ethical conundrum, one that we feel has not been appropriately addressed. While it is beyond the remit of this chapter to make a significant contribution to this debate, we wish to identify this as an issue that needs further attention if research on the structural factors of gambling is to progress in the way that is recommended in the Reno model (Blaszczynski et al. 2004).
CONCLUSIONS This chapter has demonstrated that some structural characteristics are still capable of producing psychologically rewarding experiences, even in financially losing situations (e.g., the psychology of the near miss). It has been widely accepted that certain structural factors of gambling games influence the acquisition, development, and maintenance of gambling behavior. However, it would appear that the role of such factors has become even more significant within the past decade. Interactive feature plays, increased skill orientations and bettor involvement, and the manipulation of familiarity and sound effects are now combined to produce sophisticated and psychologically immersive EGMs. It is clear from this overview that our knowledge and understanding of the structural aspects of gambling is inadequate. Some factors (e.g., feature games, jackpot size, cashless gaming), argued to be influential in governing player behavior, have been subject to little or no empirical inquiry. And findings for those factors which have received more attention (e.g., near misses, payment features, event frequency) have been inconsistent or inconclusive. Therefore, more research is needed. Moreover, the conditions for research—experiments in particular—need to be suitable. More focus needs to be given to the ecological validity of experiments, and improved access to valid environments (e.g., casinos, online gaming software) needs to be afforded researchers. Opportunities for precision in research and measurement of structural factors rest heavily on building relationships with the
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gambling industry and putting pressure on regulators to prioritize this issue. In addition, we may even need to revisit the ethical position relating to spending, winning, and losing in gambling-related experiments. One of the original aims of this chapter was to categorize structural factors in a meaningful way.The motivation for doing so, in addition to contributing to the organization of this research area, was to develop a starting place to consider the competing interests of industry profits, consumer satisfaction, and consumer protection.2 As identified throughout this chapter, several researchers are now examining the role that various structural characteristics have for harm prevention and minimization. However, features or precautions relating to potential harm may be inextricably linked to profitability and fun. For example, reducing the speed of reel spins on EGMs may reduce the chances of sustained losses and chasing, but it may also make the game less enjoyable and ultimately less profitable. Given the newly proposed categorization of structural factors, we would like to propose a way forward. One option for future research and policy initiatives may be to focus on regulating factors relating to payment (spending) and player awareness/education and focus less on factors relating to playability (which may also include reward, ambient, and speed characteristics). In this way, EGMs can continue to be fun, exciting, and play-inducing, even with the eventual aim of minimizing harm. By targeting spending, game transparency, and, perhaps most importantly, the education of players, the competing objectives can begin to be addressed. In addition to giving a brief overview of the current state of knowledge in this area, this chapter reevaluates what we know about the structural aspects of gambling and attempts to offer a fresh perspective from which to engage in the challenges inherent in research and consumer protection.
GLOSSARY Bet frequency the number of bets or gambles placed in any given time period Bill acceptor a device used to accept cash payment in the form of bills rather than coins Electronic gaming machines (EGMs) a blanket term for electronic devices used for gambling, whereby the aim is to match symbols on real or virtual reels. Cultural variants included under this term are “fruit machines” (U.K.);
2 It is important to note that consumer protection and consumer satisfaction are not always the same thing. Players may be enjoying the game but still be at risk or spending more money than they can afford.
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video poker machines, or “pokies” (Australia); and “video lottery terminals,” or “VLTs” (Canada). Event frequency the number of events that are available for betting in any given time period Feature game an interactive gambling opportunity incorporated into some EGMs, which is distinct from symbol matching Near miss a losing outcome of a gamble that is perceived to be almost successful, which may cause players to overvalue losing gambles Payout ratio the ratio of the winnings paid out in relation to the money staked Random number generator (RNG) a device used to ensure that outcome of an EGM is always random and unpredictable. EGMs in some jurisdictions use pseudo-random devices that are argued to be predictable to a limited extent and may allow for the use of more complex feature games. Structural characteristics the properties of a gambling game that determine how players interact with it, enjoy it, understand it, pay for credits, and receive rewards Suspension of judgment a temporary disruption in the gambler’s financial value system whereby the psychological value of virtual representations of money will be less than “real” money Symbol matching the principal gambling game in an EGM, whereby wins are offered for matching symbols in sequential order on the reels
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CHAPTER 10
Situational Factors That Affect Gambling Behavior Max W. Abbott Health & Environmental Sciences Auckland University of Technology Auckland, New Zealand
Introduction Availability, Accessibility, and Exposure Dimensions of Accessibility and Exposure Availability and Participation Studies Using Official Data Surveys and Other Gambling Studies Availability, Participation, and Problem Gambling The Agent Gambling Prevalence Studies Replication Surveys Prevalence Changes in Population Sectors Other Exposure Concentrations Prospective Studies Other Location and Contextual Factors Credit Cards and ATMs Alcohol Tobacco Marketing and Advertising Conclusion
INTRODUCTION Gambling includes multiple activities, undertaken in diverse settings, appealing to different sorts of people, and perceived in various ways by participants and observers (Abbott and Volberg 1999;Walker 1992).While far from universal, it was present in ancient civilizations and widely dispersed indigenous societies (Binde 2005; Gabriel 1996). Although gambling is widespread, attitudes toward it have 251
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fluctuated from acceptance to rejection. Major world regions have experienced long-term alternating cycles of liberalization and restriction (Rose 2003). Although diminished in some jurisdictions, participation differences remain among gender, ethnic, religious, and socioeconomic groups in most modern societies (Abbott,Volberg, Bellringer, et al. 2004; Raylu and Oei 2002). Sociocultural contexts clearly exert strong influences on gambling behavior and the way that gambling activities and their consequences are conceptualized and responded to (Abbott and Volberg 1999). During the past two decades, gambling availability and expenditure have expanded dramatically. It has been argued that this expansionary phase is quantitatively and qualitatively unprecedented and integrally connected to broad interrelated trends that continue to shape the evolution of commercial gambling globally (Abbott and Volberg 1999; McMillen 1996).The major focus of this chapter is on relationships among gambling availability, exposure, participation, and problems— an important and controversial topic. Consideration is also given to some other situational factors, including advertising, location, and setting. When considering gambling accessibility and exposure, it is important to take account of the gambling form involved. A substantial body of research indicates that some gambling types are much more strongly associated with problem gambling than others (Abbott and Volberg 1999, 2000; Shaffer, Hall, and Vander Bilt 1997;Walker 1992;Welte et al. 2001;Wildman 1998).There are also indications that problems develop more rapidly in association with electronic gaming machines (EGMs) than other forms (Evans 2003) but may be more transient (Abbott, Williams, and Volberg 1999, 2004). Despite this diversity, most studies continue to treat gambling as a unitary entity and inappropriately generalize findings from one form to another (Raylu and Oei 2002).
AVAILABILITY, ACCESSIBILITY, AND EXPOSURE It is widely believed that greater gambling availability, especially to continuous forms like EGMs, casino table games, and track and sports betting, has led to increased gambling participation and gambling-related problems. Major reviews (e.g., Abbott and Volberg 1999; Raylu and Oei 2002; Shaffer et al. 1997;Wildman 1998) have, with varying degrees of qualification, concluded that research findings are generally consistent with this viewpoint. National official review bodies in the United States (National Research Council 1999), the United Kingdom (Gambling Review Body 2001), and Australia (Productivity Commission 1999) have agreed. In contrast, Shaffer et al. (2004) more recently commented: To our knowledge, no scientific research has established a causal link between disordered gambling and either literal or figurative proximity to gambling. Similarly, no
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scientific research has established a direct link between community cues for gambling and increased urges to gamble—although this is one of the objectives of advertising. (p. 43)
Abbott et al. (2004) and Shaffer et al. (2004) maintain that relationships between availability and problems are complex and that consideration needs to be given to the duration of exposure as well as to individual and wider environmental factors that moderate exposure effects.They propose that in particular circumstances the relationship between rising gambling exposure and increasing problems will attenuate or reverse. When this “adaptation hypothesis” was first articulated, it was thought that the process would be slow, “perhaps only after decades and generations of social learning” (Shaffer et al. 1997).Abbott et al. (1999) suggested that adaptation could be more rapid and would probably vary across gambling forms and population sectors. Increased public awareness of problem gambling and its early warning signs, the development of informal social controls, expansion of treatment and self-help, and regulatory changes were considered likely to be implicated. Abbott (in press), building on the foregoing, advances and reviews research relating to the following hypotheses: ●
●
● ●
During exposure to new gambling forms, particularly EGMs and other continuous activities, previously unexposed individuals, population sectors, and societies are at high risk for the development of gambling problems. Over time—years rather than decades—adaptation (“host” immunity and protective environmental changes) typically occurs and problem levels reduce, even in the face of increasing exposure. Adaptation can be accelerated by regulatory and public health measures. While strongly associated with problem development (albeit comparable to some other continuous forms when exposure is held constant), EGMs give rise to more transient problems.
DIMENSIONS
OF
ACCESSIBILITY
AND
EXPOSURE
Examination of relationships among gambling availability, participation, and problems requires accurate and reliable measurement. It also requires inclusion of sound measures within robust studies that examine variation in the nature and extent of exposures while taking account of other factors that may affect outcomes. Accessibility is a widely used term in a variety of academic disciplines. It is complex and difficult to define and measure (Handy and Niemeier 1997). Access to any facility, service, or product is multiply determined. Economic, social, cultural, and legal factors may be implicated in addition to spatial location.
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The Productivity Commission (1999) identified nine accessibility or exposure dimensions in gaming in Australia (see Figure 10.1).The most widely studied is the total number of opportunities to gamble (e.g., number of EGMs per capita). Some others, namely, number and location of venues/outlets and opportunities to gamble within venues/outlets, relate to proximity (distance and time).The remaining dimensions are opening hours, conditions of entry, ease of use, size of initial outlay, and social accessibility. The accessibility dimensions may be regarded as measures of dose, i.e., the degree of exposure to gambling.As indicated earlier, exposure impacts are strongly influenced by the specific nature of the exposure. For example, in the Australian context, while EGMs and lotteries are both highly accessible, their behavioral and social impacts differ markedly (Productivity Commission 1999). Potency refers to the strength, virulence, or toxicity of an agent. It seems reasonable to view the
Opportunities to gamble per venue
Number of venues
Number of opportunities to gamble
Accessibility
Opening hours
Conditions of entry
To what?
To whom?
Location of venues
Ease of use Social accessibility
Initial outlay
Figure 10.1. Dimensions of accessibility. Source: Productivity Commission. (1999). Australia’s Gambling Industries. Final Report No. 10. Canberra: AusInfo, p. 84.
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strength of associations between participation in particular gambling activities (and perhaps some aspects of gambling settings) and problem gambling as an indicator of potency. Duration is the length of time that a population or individual has been exposed.Apart from the gambling form (including its properties, such as potency), the extent of exposure to it (dose), and the duration of this exposure, gambling behavior and other effects will depend on the nature of the exposed population or individual. In the case of problem gambling, a variety of individual and societal risk and protective factors will moderate exposure effects. Conceptualization and measurement of gambling exposure is at an early stage. Studies vary in the aspects of exposure selected and the way they are measured. While contributing to an appreciation of the complexity of exposure, this compromises integration of information about the effects of changes in accessibility on behavior. Shaffer et al. (2004) piloted an assessment method that takes some account of exposure complexity. It produces a quantitative “standardized exposure gradient” made up of the number of casinos/casino hotels and people working in the gambling industry (“dose”), the number of different gambling modalities available (“potency”), and the time that casinos have been legalized (“duration”). These authors are currently developing a personal exposure model that takes account of individual variation within particular locations. Suggested components include family gambling background, current household members’ gambling involvement, gambling industry–related employment, and aspects of the community gambling setting.
AVAILABILITY
AND
PARTICIPATION
Participation, like availability, has been indexed in various ways. Research using official data sources and general population surveys has examined relationships between various indices of availability and expenditure. Other studies, using a variety of methodologies, have examined changes in availability and participation over time. Official agencies and industry sources frequently provide aggregate expenditure data on types of legal gambling available within particular jurisdictions. Availability data are also often provided including the number and location of venues/outlets and particular gambling activities.This information is of variable accuracy, and caution is required when making comparisons over time or between jurisdictions. Population surveys enable more detailed participation data to be obtained from individuals. Survey data are subject to errors of measurement and sampling. As with the availability data, the nature and quality of samples and methods differ and caution is necessary when making comparisons between studies (Abbott and
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Volberg 1999). Question phrasing, particularly in relation to expenditure, has a marked influence on findings (Volberg et al. 2001).
STUDIES USING OFFICIAL DATA Jurisdictional datasets almost invariably indicate substantial increases in expenditure following the introduction of a new gambling form.There are numerous instances of casinos and EGMs dominating gambling markets within a few years. In some cases, previous leaders such as lotteries and track betting have declined in absolute as well as relative terms.Where EGMs are widely distributed outside casinos, strong covariation is typically evident between increased EGM numbers and EGM expenditure (Abbott and Volberg 1999; Productivity Commission 1999). In Victoria, Australia, from 1992 to 2003, EGM numbers and expenditure increased markedly (South Australian Centre for Economic Studies 2005). However, expenditure continued to grow at a similar rate following imposition of a cap on numbers in late 1995.This suggests that the availability-expenditure relationship broke down. The cap, however, while effective in preventing the total number of machines from increasing in Victoria, did not dictate where they would be located within the state. Some other jurisdictions, including New Zealand (Ministry of Health 2006a), have experienced similar expenditure increases despite a leveling off or reduction in machine numbers. Associations between availability and expenditure have also been found across jurisdictions. Abbott (2001a) examines relevant data for Australian states and territories. A strong linear relationship is evident, suggesting that greater availability has been a major factor in increased expenditure. However, while the relationship is strong overall, Victoria is anomalous. Per capita EGM expenditure in this state is similar to that of jurisdictions with much higher machine densities. Approximately twice as much is spent per machine in Victoria than elsewhere. It might be that increased expenditure per machine in Victoria reflects unmet demand which people go to additional lengths to satisfy. This would be consistent with economic “consumer demand” theory, which posits that providers of products and services are essentially passive and that consumption is largely driven by customer/client need or demand (Pass, Lowes, and Davies 1993). This view of consumer demand runs counter to “producer sovereignty” theory, which proposes that providers stimulate demand and influence consumer behavior by product development, marketing, and strategic geographic placement (Marshall 2005; Pass et al. 1993). Although consumer demand cannot be totally discounted, there is evidence that EGMs have been strategically relocated within Victoria to maximize financial returns (Productivity Commission 1999). Subsequent studies elsewhere (Livingston 2001; Marshall 2005; Marshall and Baker 2002; South
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Australian Centre for Economic Studies 2005) have found that machine (and venue) densities are strongly associated with expenditure at local and regional levels.Thus, while the relationship between machine density and expenditure breaks down at the state level in Victoria, it apparently applies at more local levels. Aggregate data from official sources do not include prohibited and informal gambling or allow determination of expenditure by nonresidents, or expenditure by residents in other jurisdictions. Studies using these data cannot provide information about individual gambling participation and expenditure.This means, among other things, that it is not known whether increased expenditure is a consequence of more people gambling or a similar (or smaller) number of people gambling more intensively. Investigation of these and related matters requires social surveys.
SURVEYS AND OTHER GAMBLING STUDIES Many gambling surveys have examined differences between regions and population sectors. Repeat surveys have also been conducted, assessing changes in participation over time. Few studies have been national in scope, and small sample size and various methodological deficiencies limit the relationships that can be examined. Some surveys have investigated associations between availability and participation. A number have also assessed problem gambling prevalence and will be considered later. Some have monitored changes following the introduction of a new form or forms of gambling or significant change in provision. Abbott and Volberg (1999, 2000) and Volberg (2001) critically review North American, Australian, and New Zealand studies. More recent research is considered in Abbott, Volberg, Bellringer, et al. (2004). A few studies have tracked the same individuals prospectively (Abbott and Clarke, in press). The first comprehensive national survey of gambling behavior was conducted in the United States in 1975 (Kallick et al. 1976). It showed that gambling participation was substantial even before legal gambling was widely accessible. When this survey was undertaken, only 13 states had lotteries, two had legal offtrack betting, and casinos were confined to Nevada. The second national survey, over twenty years later (Gerstein et al. 1999), found considerable change in lifetime participation (85%), but little change in past-year gambling (63%). The report’s authors commented that this finding was surprising given the vast expansion in availability and expenditure since 1975. However, similar findings have come from other jurisdictions. In New Zealand, seven national surveys were conducted between 1985 and 2000 (Abbott 2001a). A shorter series of multiple replications was conducted in Victoria from 1992 to 1997 (reviewed in Abbott and Volberg 1999). In both jurisdictions, availability increased markedly and included the introduction of casinos and widespread distribution of EGMs. In New Zealand, past-year participation was
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fairly stable, ranging from 85 to 90%. There were, however, marked changes for some gambling forms. In some cases, including EGMs and casinos, initial increases in participation frequency and duration and reported expenditure were followed by decreases. Studies elsewhere have also reported significant participation and expenditure increases following the introduction of new forms of gambling, such as the U.K. National Lottery (Grun and McKeigue 2000) and casinos (Abbott and Volberg 2000; Ladouceur et al. 2005; Room, Turner, and Ialomiteanu 1999). However, as in New Zealand and Victoria, reductions following initial increases have also been reported, even when gambling opportunities continue to expand. Recent studies in North America (reviewed in Abbott,Volberg, Bellringer, et al. 2004; Volberg 2001) and the United Kingdom (Creigh-Tyte and Lepper 2004) found particularly significant reductions in regular (weekly or more often) participation. Some studies indicate highly differential rates of change across population sectors. Long-standing sociodemographic differences have diminished in jurisdictions where casinos and EGMs have been widely introduced (Abbott, Volberg, Bellringer, et al. 2004; Abbott, Volberg, and Ronnberg 2004; Productivity Commission 1999). Marshall (2005) observed that while it has been assumed (and often demonstrated) that increased availability and expenditure go together, attempts to understand how and why this is the case have been scant. He attempted to redress this, using time-geography concepts. Participation data were obtained by interviewing random samples of adult residents. Many people fail to access services or engage in particular activities because their space–time constraints prevent or restrict them from doing so (Hagerstrand 1982). People with limited finances and limited access to transport have smaller space–time activity boundaries (Weber and Kwan 2002). Consistent with time–geography postulates, Marshall (2005) found a strong linear relationship between per capita EGM numbers and gambling behavior. Specifically, adults living in regions with the highest EGM concentrations had substantially elevated past-6-months and past-week participation rates. Not only did higher proportions of residents in heavily provisioned locations gamble on EGMs, those who did so participated more often, had longer sessions of play, and lost more money per session. Similar findings have been obtained elsewhere (Marshall et al. 2004). It will be recalled that Victoria was an outlier in Australia. Marshall (2005) similarly identified a suburb with EGM participation rates similar to suburbs with much higher machine densities. In this instance it may have been a consequence of the main EGM venue’s being located next to the suburb’s major shopping, service, and recreational facilities and sharing a large parking area with them.This highlights the potential importance of availability features additional to density. Studies have also found relationships between proximity to other forms of gambling, including casinos, and increased gambling participation (Gerstein et al. 1999).
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A number of U.K. surveys found that adolescents living near fruit machines had significantly higher levels of participation (Fisher 1991, 1993, 1999; Ide Smith and Lea 1988). If there is a casual relationship between gambling availability and participation, not only should participation increase following increased availability, it should reduce when availability decreases.This has been little investigated.The case of Victoria, where EGM numbers were capped in the mid-1990s, has been examined. More recently, the Victorian government imposed sinking local caps in five socially deprived regions with high EGM concentrations. Within these regions, EGM numbers dropped from 5494 to 5088. This initiative was evaluated using a quasi-experimental design (South Australian Institute for Economic Studies 2005). It was found that “experimental” and matched “control” regions experienced comparable changes in expenditure and that there was no “spillover” (displaced expenditure to adjacent regions). It was concluded that the reductions, which still left the capped regions with higher machine densities than the state average, were too small to reduce access and that there were sufficient EGMs per venue to prevent “crowding out” of people who wanted to use them. During the course of the regional caps evaluation, a smoking ban was introduced throughout Victoria, which included EGM venues.Twenty-four-hour EGM trading was also phased out by the imposition of a minimum four-hour closedown. The former measure was estimated to reduce EGM expenditure by between 13 and 19% in the study regions; the latter by an average of 3%. The findings of most, but not all, studies considered to this point have been consistent with availability theory. Others, particularly those involving repeat surveys some years apart, indicate reduced participation rates and/or other changes in gambling behavior over time. These changes may reflect or play a role in adaptation.While many studies demonstrate various associations between availability and participation, the methodologies used do not allow strong causal inferences to be made. The recent Victorian study suggests that situational factors additional to gambling density and location can have significant impacts on participation.
AVAILABILITY, PARTICIPATION, AND PROBLEM GAMBLING THE AGENT GAMBLING Numerous general and special population studies have found that people with preferences for, frequent involvement in, and high expenditure on gambling activities, especially continuous forms involving an element of skill or perceived skill, have a high probability of experiencing gambling problems (Abbott,Volberg,
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Bellringer, et al. 2004). Participation in multiple gambling activities is an additional risk factor (Welte et al. 2001). In a number of jurisdictions, between 15 and 30% of weekly or more frequent EGM participants have significant gambling problems (Abbott and Volberg 1999; Productivity Commission 1999; Schrans, Schellinck, and Walsh 2000). Links between participation in the above-mentioned forms of gambling and problem gambling are also evident in clinical presentations. In multiple jurisdictions where per capita EGM expenditure has risen markedly, substantial increases have been found in EGM-related problems, in absolute terms and relative to problems with other forms of gambling (Abbott et al. 2004; Productivity Commission 1999). This change is commonly accompanied by a large rise in the number of women seeking help for gambling problems, predominantly in relation to EGMs. Much has been postulated about EGM attributes (and those of other continuous forms) that make them particularly “addictive” (Walker 1992; Griffiths 1995). However, it is only recently that regular players have been tracked prospectively, in real-life settings, to identify factors associated with problem development. A study by Dickerson, Haw, and Shepherd (2003) is of particular note.They found that most regular participants lost control over session spending and frequency of venue visits and that this was due primarily to the number of hours spent gambling per week. Some individual characteristics (nonproductive coping, depression, and impulsivity) contributed, but these accounted for only a modest outcome variance. Most participants needed to use active and planned strategies to prevent losing control. However, even when they did, about half still lost control on some occasions. The researchers concluded that impaired control and subsequent problem development are “natural” consequences of regular, high-intensity EGM play, rather than something confined to a small minority of mentally disordered problem gamblers. These findings suggest that the participation/problem link is causal, with regular EGM participation contributing to loss of control and problem development.They further suggest that increased availability and regular participation and expenditure on these forms will give rise to elevations in problem gambling incidence and prevalence.
PREVALENCE STUDIES Analysis of North American state and provincial surveys carried out between 1975 and 1996 found that past-year adult probable pathological gambling rates averaged 0.8% for surveys prior to 1993 and 1.3% for those conducted post-1993 (Shaffer et al. 1997). While statistically significant, the total variability explained by time was small, and the authors noted that it was unclear what other factors explained prevalence changes. Rates for youth, students, and institutional
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populations did not change. Contrary to expectation, despite considerable variation in gambling history, availability, and expenditure, regional differences were not apparent. Some national surveys have found elevated prevalence in areas adjoining casinos (Gerstein et al. 1999) and in cities with casinos compared with cities without casinos (Abbott and Volberg 2000). In the latter case, casinos had been introduced into two cities a few years prior to the survey. In some analyses, prevalence differences remained when other factors associated with problem gambling were controlled statistically. U.K. youth surveys, referred to earlier, found that both increased participation and higher problem levels in locations with access to fruit machines. The 1998 national survey commissioned by the Productivity Commission enabled differences between individual states and territories to be meaningfully examined.The Commission concluded that prevalence was higher in jurisdictions with greater accessibility (EGMs per 1000 adults) and expenditure (both EGM and total nonlottery expenditure per 1000 adults). However, careful examination of the data suggests nonlinear relationships. Although the two states (WA and TAS) with the lowest machine densities and expenditures (and New Zealand, with a similar density) had the lowest prevalence (see Figure 10.2), within the more exposed jurisdictions increased exposure does not appear to be associated with further prevalence increases. Overall the data suggest that increased prevalence is associated with greater exposure but that the relationship starts to break down somewhere between six and ten machines per 1000 adults and when average EGM expenditure reaches about Aus$200 per adult.
25
ACT NSW
20 15
QLD NT
10
TAS NZ
5
SA VIC
WA
0 0
0.5 1.0 1.5 2.0 2.5 Prevalence rate (SOGS 5+)
3.0
Estimated machine expenditure per adult ($)
Machines per 1000 adults
30
900 800
NSW
700 VIC
600
ACT
500 400
SA
300
TAS
200
NZ WA
100
QLD NT
0 0
0.5 1.0 1.5 2.0 2.5 3.0 Prevalence rate (SOGS 5+)
Figure 10.2. Current probable pathological gambling prevalence estimates and the EGM density and expenditure by jurisdiction: Australia and New Zealand.
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REPLICATION SURVEYS Replication surveys provide a more direct method to assess relationships between changes in availability and problem gambling. Pre-1991 baseline studies used only “lifetime” measures. In the three North American studies of this type, gambling availability increased between studies, and, consistent with exposure theory, there were substantial prevalence increases at replication (see Abbott and Volberg 1999). New Zealand is the only country where repeat national surveys have been conducted using comparable methods and measures. During the three years prior to the 1991 baseline survey (Abbott and Volberg 1996;Volberg and Abbott 1994), per capita expenditure more than doubled following introduction of a national lottery, other lottery products, and noncasino EGMs.The initial survey found that 48% of adults gambled weekly or more. The probable pathological gambling prevalence estimate was 1.2%. A 1996 survey (North Health 1996) obtained an estimate of 0.4%, despite increased availability and expenditure. A third survey was conducted in 1999 (Abbott and Volberg 2000), a few years after casinos were opened in the two major metropolitan areas.Total gambling expenditure had doubled since 1991. The current prevalence rate remained low at 0.5%. Frequent participation was also lower than in 1991 (40%), a consequence of fewer people taking part this often in continuous forms. Although only one truly national study has been conducted in Australia (Productivity Commission 1999), in 1991 four major cities were surveyed using the same measure employed in the 1999 New Zealand study (Dickerson et al. 1996). The estimate was 6.6%, substantially higher than the later 1998 national estimate of 2.1%. Although Australia already had higher per capita gambling expenditure relative to other countries in 1991, rapid growth occurred throughout the 1990s, associated primarily with increased availability of high-intensity EGMs in clubs, hotels, and casinos. None of the more recent state-level surveys have obtained rates higher than the 1998 national estimate (Banks 2003). While using different problem gambling measures, national surveys were conducted in 1997 and 2002 in Norway (Gotestam and Johansson [2003] and Lund and Nordlund [2003], respectively). Noncasino gaming machines were widely deployed in Norway during the 1990s. Given that the measure used in 1997 typically generates lower rates than those used in 2002, there appears to have been little or no change in prevalence. Replications have been completed in North America at state and provincial levels. A review by Abbott (2001a) found that seven obtained higher rates at replication than baseline, and eight obtained lower estimates. Examination of the five most recent studies (Volberg 2001) revealed that, as in New Zealand, despite substantial increases in various forms of continuous gambling, in every instance there were significant reductions in the number of regular gamblers. In some cases
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prevalence increased, and in others it decreased. A major difference between the two was that in the latter case, there were a range of services for people with gambling problems. New Zealand and Australia, and to a lesser extent Norway, also developed specialist services during the 1990s. Reference has been made to local surveys conducted before and after the introduction of specific forms of gambling. Some of these studies (e.g., Govoni et al. 1998; Shepherd, Ghodse, and London 1998) found no increase in gambling problems, whereas others did (e.g., Room et al. 1999). There is also some information from locations where particular gambling forms have been removed or reduced. In 1994 all EGMs in South Dakota were shut down for 3 months. During this period there was a marked reduction in the number of people seeking help from gambling treatment centers. Following reactivation, numbers increased but remained below what they had been prior to the closedown (Carr et al. 1996). While problem gambling was not assessed in the Victorian regional caps study (South Australian Centre for Economic Studies 2005), counseling case numbers fell during the study period from 5309 (2001) to 3508 (2003), reflecting reductions in almost all regions across the state.
PREVALENCE CHANGES
IN
POPULATION SECTORS
In recent years gambling patterns have changed significantly in a number of jurisdictions. Regular participation has increased in some sectors and decreased in others. To varying degrees, these changes are reflected in shifting problem gambling risk profiles. Of particular note is the erosion of long-standing gender differences.This change was first observed in Nevada (Hunter 1990).The Productivity Commission (1999) concluded that the formation of a large group of female problem gamblers with EGM-related problems in Australia, following the introduction of EGMs, “is the most powerful evidence in favour of a connection between problem gambling and the availability of gaming machines” (p. 8.22). Long-standing age and sociodemographic differentials have also diminished in some populations (Abbott,Volberg, Bellringer, et al. 2004).While gambling participation and problems have become somewhat more evenly spread, a number of groups, including some ethnic minority and recent migrant groups, remain at high risk.These groups typically have bimodal participation patterns, with high proportions of both nongamblers and high-intensity regular gamblers. They are population sectors that are beginning to enter the gambling (and problem gambling) “market” (Abbott,Volberg, and Ronnberg 2004). While participation and problem rates have changed together in some groups, in others prevalence decreases appear to have occurred with minimal or no change in participation (Abbott and Volberg 2000). This suggests that lower prevalence
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may be both a consequence of behavior changes that reduce participation in highrisk forms (lower exposure) and an adaptation that provides protection when people continue to take part regularly (“immunity”).
OTHER EXPOSURE CONCENTRATIONS Apart from jurisdictions, communities, and localities there are other, more local, contexts within which gambling exposures can vary, including families, workplaces, and reference groups. Some studies have asked participants how they were first introduced to gambling. While there is variation across social groups, families of origin are typically mentioned most often, especially by problem gamblers.Whereas most people who gambled during childhood or adolescence report being introduced to gambling by family members, those who commenced later more often refer to external socializing agencies, including friends, advertising, work colleagues, and partners/spouses (Abbott 2001b). Reasons most often given for past increases in gambling included more money being available, more gambling options (particularly in the case of problem gamblers), and advertising (Abbott 2001a). Many children learn to gamble with their parents and siblings (Cornish 1978). Griffiths (1990), from observational studies of slot-machine play among U.K. adolescents, reported parental facilitation of gambling behavior. Parents and their children have also been found to have similar gambling cognitions and behaviors (Oei and Raylu 2002). Adolescents and adults who gamble frequently, particularly problem gamblers, report much higher levels of gambling in the families they grew up in and in their current families. Schellinck and Schrans (2003) found that when two regular gamblers lived in the same household and one was a problem gambler, the probability was greater than 50% that the other person also had a gambling problem. Problem gamblers more often report having commenced gambling at an earlier age and having been introduced to gambling by family members (Abbott 2001a; Raylu and Oei 2002) relative to nonproblem gamblers. The preceding findings are predominantly from retrospective accounts and are thus subject to recall deficiencies.They might reflect social learning and genetic and other familial influences additional to exposure per se. Partners/spouses and other family members are most often mentioned as current gambling companions, although this varies across venues, gambling forms, and population sectors (Abbott 2001b). Partners/spouses were more often mentioned being present during casino than noncasino EGM participation (ibid.). EGM play in contexts other than casinos was much more often solitary. Abbott and Volberg (2000) found that usually gambling alone was a strong risk factor for problem gambling, whereas gambling with friends or work colleagues (but not family members) was protective.This may, at least in part, explain why regular non-casino EGM participation was more
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strongly associated with problem gambling in that study than similar levels of engagement with casino EGMs. Although Abbott and Volberg (2000) found that adults who reported gambling before the age of 13 years had significantly elevated problem gambling levels, so too did the group that first gambled at age 25 years or older. Introduction to gambling between the ages of 18 and 24 years was associated with very low prevalence, raising the possibility that initial exposure at this time is protective. Lack of exposure and socialization might increase vulnerability when gambling is subsequently encountered during adult years. More needs to be done to advance understanding of these and no doubt other, often complex, relationships among families, gambling behavior, and problem gambling. From the beginnings of public and occupational health research there has been interest in occupational groups with high exposure to particular risk factors. With respect to gambling, it might be anticipated that gambling industry employees would have elevated rates of problem gambling.This appears to be so for casino employees in the United States (Shaffer and Hall 2002). However, younger and more recent employees have more problems than long-term employees. Shaffer and Hall interpret this as indicating elevated risk during early exposure, followed by adaptation.While possible, alternate explanations including differential attrition cannot be discounted. Substantial variations in gambling participation and problem gambling prevalence rates have also been found across other occupational categories (Abbott and Volberg 2000). Walker (1992) cites sociological research going back to the 1950s that suggests ways in which work and other reference groups can encourage and discourage gambling. Religious affiliation and social activities can also have significant impacts on gambling participation and problem gambling (Abbott and Volberg 1999;Tse et al. 2004).
PROSPECTIVE STUDIES With the exception of the study by Dickerson et al. (2003), to this point links between availability, participation, and problems have been considered by reference to studies that are retrospective or cross-sectional or assess change by drawing distinct samples from the same population at different points in time. During the past few years prospective studies have been conducted (reviewed in Abbott and Clarke, in press; Slutske, this volume) that have found, contrary to the conceptualization in the Diagnostic and Statistical Manual of Mental Disorders, that problem gambling is transitory, especially in its less severe forms. It appears that EGM-related problems, relative to problems associated with track betting and perhaps other continuous forms, are less persistent (Abbott, Williams, et al. 2004). While reductions in incidence appear to be important in explaining prevalence reductions in some populations and population sectors, increased problem remission may also be implicated.
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A few studies have commenced during subjects’ childhood or adolescence (Slutske, Jackson, and Sher 2003;Vitaro et al. 2004; Winters et al. 2002). Winters et al. (2002) found no significant change overall in infrequent or regular gambling rates from age 16 to 24 years. This was contrary to expectation given the large increase in legal gambling availability during the study period. However, the mix of activities did change, with legal forms increasingly replacing informal and unregulated games such as playing cards for money.While problem rates were also stable,“at-risk” gambling increased from age 18 to 24 years. Males had higher participation and problem rates than did females throughout the study. Early gambling onset and parental gambling problems were associated with problem development, although these factors were not assessed directly or prospectively. Similar findings are reported by Slutske et al. (2003). Vitaro et al. (2004) examined gambling behavior in a male sample from age 11 to 17 years. Three distinct trajectories were identified. A significant minority began gambling by age 11 and maintained or increased their involvement subsequently. A slightly smaller group did not start gambling prior to age 13 but rapidly increased their participation thereafter. By age 17, their respective problem gambling prevalence rates were 4%, 20%, and 15%. While gambling involvement was assessed only outside the family, the researchers proposed that family and/or peerrelated factors were likely to be more strongly involved in problem development among the late-onset gamblers. One adult study (Abbott et al. 1999) is of interest because it included a natural “experiment,” a consequence of casinos being introduced in two major cities two to three years prior to the final assessment point. Participant gambling involvement and problems were assessed relative to other locations while controlling statistically for many potentially confounding factors. No effect was detected.
OTHER LOCATION AND CONTEXTUAL FACTORS A wide variety of contextual factors additional to the number and distribution of particular forms and venues/outlets influence gambling behaviour and may also have an impact on problem gambling.They include venue type and a number of more specific venue characteristics. Further availability factors mentioned by the Productivity Commission (1999) include number of opportunities to gamble per venue, opening hours, conditions of entry, ease of use of gambling form, initial outlay required, and social accessibility. Few of these have been formally investigated. An exception is opening hours. As indicated earlier, it was estimated that the introduction of a 4-hour closedown of EGM venues per 24 hour period in Victoria resulted in a 3% reduction in expenditure (South Australian Centre for Economic Studies 2005). It is not known what impact, if any, this had on at-risk or problem gambling. Mention has
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also been made of increased female gambling involvement and gambling-related problems in a number of jurisdictions with high densities of EGMs. There are, however, exceptions, such as parts of Spain, where EGMs are widely available but female problem gambling prevalence remains low relative to that of males. This may reflect negative social attitudes toward women gambling in venues where machines are located and/or their lack of appeal to Spanish women. Age restrictions, while variously enforced, are also an obvious factor with respect to youth access. Membership requirements, dress codes, and other social criteria also have implications for access by particular social groups. Ladouceur et al. (2005) recently conducted focus groups and a laboratory study to assess the influence of the format, arrangement, and availability of EGMs in bars outside casinos on gambling behavior and perceptions. It appears that locating machines in an isolated area may facilitate loss of control and excessive gambling.The Productivity Commission (1999) also expressed the view that arranging gambling activities so that they are less anonymous and auxiliary to other activities may enhance social interaction and convey a sense of social ambivalence or disapproval. However, the former cautioned that potential moderating effects might be counteracted if increased visibility were associated with greater access to alcohol. Both Ladouceur et al. (2005) and the Productivity Commission (1999) also expressed the view that under some circumstances, concentrating more EGMs in fewer venues may be beneficial, in part because this could reduce exposure to nongamblers and facilitate the implementation of harm minimization strategies. There is some indication that EGM participation in casinos may be less strongly associated with problem gambling than EGM participation in clubs and pubs (Abbott and Volberg 2000). Further research is required to assess these possibilities as well as the impacts of other venue features on gambling and problem gambling. In recent years many jurisdictions have introduced measures intended to promote moderation, prevent problem development and protect problem gamblers (Abbott, Volberg, Bellringer, et al. 2004; Independent Pricing and Regulatory Tribunal of New South Wales 2004). Messerlian, Derevensky, and Gupta (2004) outline approaches, within a public health framework, focused on youth. Hing (2003) assessed the perceived adequacy of responsible gambling measures in Sydney clubs. In descending order of agreement, patrons indicated that the following were likely to promote responsible gambling: 1. 2. 3. 4. 5. 6. 7.
minors and intoxicated persons prevented from gambling, credit or cash advances not made for gambling, big wins paid by check, staff trained in responsible gambling practices, gambling advertising and promotions conducted responsibly, checks not cashed for more than $200, operation of a self-exclusion program,
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8. gambling facilities shut down for at least a few hours each day, 9. clocks and natural lighting in gambling areas, and 10. remote location of automated teller machines (ATMs) and machines for electronic funds transfer at point of sale (EFTPOS). Participants in Hing’s (2003) study were also asked whether or not they considered that responsible gambling measures in clubs had changed their attitudes and behavior. Forty-four percent believed that they had modified the way they thought about gambling, and 12% believed that such measures had made their gambling less enjoyable. Eighteen percent considered that they had reduced their gambling frequency, 17% reduced the length of time they usually gambled, and 19% reduced their usual gambling expenditure. While these effects were somewhat more often reported by problem and borderline problem gamblers than by nonproblem gamblers, high proportions of patrons remained in the former categories. It is beyond the scope of this chapter to fully assess research that addresses the actual or likely efficacy of purported preventative and responsible gambling measures. However, it is evident that relevant, soundly based studies are rare and that little of substance can be concluded from the available literature (Abbott, Volberg, Bellringer, et al. 2004; Hing and Dickerson 2002; South Australian Centre for Economic Studies 2005). Attention is confined to measures and areas that appear most promising to date, namely, access to credit cards and ATM usage, smoking, alcohol availability, and advertising.
CREDIT CARDS
AND
ATMS
Studies have found that while most people seldom use ATMs or credit card facilities at gambling venues, many problem gamblers do (Volberg 1996; Abbott 2001b; Productivity Commission 1999). Abbott (2001b) found that 17% of problem gamblers but only 2% of all adults interviewed in a national survey considered that greater access to these facilities led to an increase in their gambling.This was predominantly in relation to noncasino EGMs. McMillen, Marshall, and Murphy (2004) conducted a community survey that replicated previous findings of greater ATM usage at gambling venues by problem gamblers than nonproblem gamblers.They also found that problem gamblers withdrew larger amounts. Money accessed in this way was most often for the purchase of alcohol and gambling, predominantly on EGMs, horse/dog races, and casino table games. They concluded that convenient access to ATMs in gambling venues contributed to greater expenditure and was implicated in the development and persistence of gambling problems. Findings from other studies are consistent with this conclusion (NFO Donovan Research 2003). A national Australian study (McDonnell-Phillips Pty Ltd. 2006) found that access to ATMs at EGM and TAB
Situational Factors That Affect Gambling Behavior
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(Totalizer Agency Board) venues (Tabcorp 2007) was one of the most frequently mentioned reasons for regular gamblers exceeding intended gambling expenditure limits. Problem gamblers were particularly likely to cite this reason, and both regular and problem gamblers rated removal of ATMs from venues or restrictions on withdrawals as a measure that would be most effective in enabling them to control expenditure. It appears likely that the co-location of ATMs and credit facilities with certain forms of gambling contributes to at-risk and problem gambling. Further research is required to assess the impact of such measures.
ALCOHOL Some forms of gambling are accessible only in locations that are licensed to serve alcohol. Links between alcohol, alcohol problems, gambling, and problem gambling are multiple, complex, and not well understood (Abbott et al. 2004; Stewart and Kushner 2003). Some surveys have found high levels of alcohol consumption among regular, heavy gamblers (Potenza, Maciejewski, and Mazure 2006). Numerous clinical and community studies indicate that problem gamblers have significantly elevated levels of alcohol use, misuse, and dependence (Abbott 2001b; Abbott and Volberg 1992; Gerstein et al. 1999; Ministry of Health 2006b; Welte et al. 2001). Relative to nonproblem gamblers, problem gamblers much more often report overspending their alcohol and tobacco budgets (McDonnell-Phillips Pty Ltd. 2006). In the present context, interest is confined to the role that alcohol availability and consumption play in gambling settings. Studies have found that high percentages of regular gamblers report consuming alcohol while gambling (Focal Research 1998; Stewart and Kushner 2003), perhaps especially during EGM participation (Stewart et al. 2002).A recent general population survey of 30,000 adults found that greater problem gambling severity was associated with increased likelihood of reporting alcohol use while gambling. More than twice as many at-risk and problem gamblers than nonproblem gamblers reported having gambled while “under the influence” of alcohol (Queensland Government 2005). McDonnell-Phillips Pty Ltd. (2006) found that alcohol consumption during gambling sessions (EGMs and TAB track betting) was one of the most frequently mentioned “triggers” that sent gamblers over their intended expenditure limits. This was more often the case for males and problem gamblers and in hotel settings (ibid.). Although many studies indicate a relationship between gambling and alcohol use, there has been relatively little research on the effects of alcohol consumption on gambling behavior. An early survey involved brief interviews with regular EGM participants while they were gambling, then subsequent completion
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of questionnaires (Baron and Dickerson 1999). It was found that two or more alcoholic drinks increased reports of difficulty in resisting urges to gamble. Continued consumption during a gambling session also predicted unplanned, extended gambling. Kyngdon and Dickerson (1999) followed up Baron and Dickerson’s (1999) survey by undertaking an experimental study involving regular EGM players who also regularly consumed alcohol. Participants received three alcoholic (experimental group) or nonalcoholic drinks (control group) prior to playing a computerbased analogue of a card game.This and a subsequent experimental study (Ellery, Stewart, and Loba 2005) demonstrate that alcohol consumption is associated with riskier styles of gambling (using analogue and real gambling forms) among regular and problem gamblers. Experimental studies have not adequately balanced their relatively strong internal validity with high external validity. Nevertheless, considered in conjunction with observational and survey findings, it appears that as with behavioral control more generally (e.g.,Vogel-Sprott et al. 2001), alcohol reduces inhibition and leads to more intensive and riskier gambling. Further research is required to assess the extent to which this contributes to the development and maintenance of problem gambling and whether reducing access to alcohol is an effective harm minimization strategy.
TOBACCO General population surveys (Abbott 2001b; Ministry of Health 2006a) and studies of help seekers (Rodda, Brown, and Phillips 2004) indicate that problem gamblers have very high rates of tobacco use. For example, a recent New Zealand national health survey found that 58% of problem gamblers were daily smokers compared with 22% of nonproblem gamblers.This study also found that 44% of people who gambled and smoked reported that they increased the amount they smoked when gambling; 8% said they smoked less. Problem gamblers much more often reported increasing the amount they smoked while gambling than nonproblem gamblers (61% vs 32%; OR = 4.5). Rodda et al. (2004) found significant linear relationships between problem gambling severity and both smoking frequency and nicotine dependency. Anxiety was associated with problem gambling and smoking. It was suggested that anxiety might contribute to the maintenance of both activities. Smoking bans have been introduced into public areas, including gambling venues, in a number of jurisdictions. A mention was previously made of Victoria, Australia, where a ban was estimated to have a significant impact on EGM expenditure (South Australian Centre for Economic Studies 2005). Statewide, for the 12 months following the ban, EGM expenditure fell by almost 20% and was
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reduced by a further 2% in the following year. The impact appears to have been much greater in clubs and pubs than in casinos.This may, at least in part, be a consequence of smoking areas being provided in the casino in close proximity to EGMs. EGM expenditure also dropped in New Zealand during 2004/2005, following the introduction of a smoking ban (Ministry of Health 2006c). Some caution is required in attributing these decreases entirely to the bans, as, in both jurisdictions, other regulatory measures were being introduced at the same time. There does not appear to be any published research that has assessed the impact of smoking bans on problem gambling. However, it is of interest that in Victoria, and more recently in New Zealand, substantial reductions in help seeking for problem gambling (helpline calls and counseling of clients) occurred in the year following the introduction of smoking bans. In both jurisdictions the large majority of callers and clients reported problems primarily with EGMs. It seems highly likely that the smoking bans played a role, and research is required to examine the ways in which smoking and smoking bans are related to gambling, problem gambling, and help seeking for gambling problems.
MARKETING
AND
ADVERTISING
As with other products, a variety of methods are used to attract new customers to gambling activities, increase or maintain market share, and enhance sales. This includes advertising in various media; sponsorship of sports, cultural, and social events; point-of-sale promotions; merchandising; and (well-publicized) provision of grants to valued community causes and organizations (Perese, Bellringer, and Abbott 2005). In recent years various approaches have also been taken to promote “responsible” gambling, raise awareness of problem gambling and encourage help seeking (see Abbott, Vogel, Bellringer, et al. 2004 and Perese et al. 2004 for reviews). Although rigorous evaluations using control groups are rare, some interventions appear to modify gambling-related attitudes and behaviors. Expenditure on social marketing and other interventions has been compared with expenditure on gambling advertising.Amounts spent on the latter invariably greatly exceed the former. Codes and regulations governing advertising and other forms of marketing have also been introduced and tightened. Research is lacking with respect to both compliance and outcomes. A Canadian study examined the content of gambling advertisements (Korn, Hurson, and Reynolds 2004). Participation was portrayed as a normal and enjoyable form of entertainment involving fun and excitement and often centered on friends and social events.The likelihood of large financial gain was a central theme (“It can happen to you!”) and gambling participation was portrayed as a way to escape the pressures of daily life. A number of commentators have maintained that
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advertising plays an important role in normalizing gambling, increases participation, and contributes to problem development (Adams 2004). Adams (2004) and Dyall (2004) claim that high-risk populations, including ethnic minorities, are often targeted by gambling advertising. Gambling advertising and other forms of promotion are conducted on a large scale, and there is a high level of public awareness of these activities. For example, with regard to awareness, a national study found 89% of adults recalled some form of gambling advertising in the last 12 months (Amey 2001). Lottery products were most often mentioned, followed by horse/dog racing and casinos. Young adults and frequent and heavy (high expenditure) gamblers had the highest recall rates. However, there is very little published research on the impacts of gambling advertising and marketing on gambling and problem gambling. In another national survey (Abbott 2001b), advertising was the fourth most often spontaneously mentioned way in which adults considered they had been introduced to gambling (mentioned by 9% of participants). This was much more often the case for younger adults, people of non-European ethnicity, and those who didn’t start gambling until they were adults.There was no difference between problem and nonproblem gamblers. Just under a quarter of adults believed that advertising had led to an increase in their gambling at some time—the same as the proportion that mentioned the introduction of new forms of gambling. In most cases the link was with Lotto or other lottery products. Problem gamblers (32%) more often attributed increased gambling to advertising than did nonproblem regular (25%) or infrequent (22%) gamblers. Other research has found that problem gamblers often mention advertising as a trigger to gambling (Grant and Kim 2001). The foregoing findings are consistent with the view that advertising and marketing can affect gambling behavior in various ways. However, for the most part they derive from cross-sectional surveys and/or involve retrospective accounts of distant experiences. Given the potentially important impacts of industry and social marketing and related activities, further research is warranted, preferably employing more conceptually driven and robust designs than those deployed hitherto.
CONCLUSION There is little doubt that increased availability of and exposure to gambling activities have contributed to increases in gambling participation and, in some instances, problem gambling. It is highly probable that other situational factors, including venue characteristics, social context, access to cash or credit, availability of alcohol, and industry marketing and advertising also influence gambling behavior. However, in most instances, there is insufficient research of a type that enables strong causal inferences to be made.There are indications that in some situations,
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relationships among increased availability of high-risk forms of gambling, increased participation, and problem gambling have attenuated or broken down. Further research is required to advance understanding of the roles and relative importance of situational factors in determining changes in gambling and related behaviors. This research has potentially important implications for policy and practice directed toward minimizing gambling-related harms.
ACKNOWLEDGMENT Jason Landon located and summarized studies that examined advertising, alcohol, and ATM access in relation to gambling behavior. This material was selectively drawn on and incorporated into the chapter. His contribution is gratefully acknowledged.
GLOSSARY Adaptation changes in individuals, population sectors, and populations that decrease exposure to gambling activities and/or reduce adverse consequences of participation Gambling availability the availability of gambling activities to populations or population sectors Gambling exposure the extent to which populations or population sectors come into contact with gambling activities Gambling participation involvement in gambling activities (type, frequency, duration, expenditure) Harm minimization measure directed toward reducing the incidence and prevalence of a disorder and related harms Incidence the number of new cases of a disorder during a specified period Prevalence the number of cases of a disorder present in the population at a given point in time during a specified period Situational factors proximal environmental factors that influence gambling exposure, participation, and consequences of participation
REFERENCES Abbott, M. W. (in press). Do EGMs and problem gambling go together like a horse and carriage? Gambling Research. ––––– . (2001a). What Do We Know About Gambling and Problem Gambling in New Zealand? Wellington: Department of Internal Affairs.
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CHAPTER 11
Individual Characteristics and Problem Gambling Behavior Tony Toneatto
Linda Nguyen
Clinical Research Department Center for Addiction and Mental Health Departments of Psychiatry and Public Health Sciences University of Toronto Toronto, Ontario, Canada
Faculty of Nursing University of Toronto Toronto, Ontario, Canada
Introduction Demographic Variables Age Gender Socioeconomic Status Marital Status Early Childhood Experiences Influence of Parental Gambling Motivation to Gamble Personality Factors Arousal and Sensation Seeking Sensation Seeking Arousal Impulsivity Drive Reduction Mood Regulation Dissociation Choice of Gambling Activity Cognitive Variables Illusion of Control Illusory Correlation: Superstitious Beliefs Interpretive Biases Attributional Biases The Gambler’s Fallacy Chasing Illusory Control over Luck Conclusion 279
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INTRODUCTION Over the past several decades many individual differences in problem gambling have been studied, including the role of negative affects (e.g., depression and anxiety), personality factors (e.g., seeking, impulsivity, boredom proneness, extroversion, locus of control, narcissism, antisociality), concurrent disorders (e.g., gambling pathology and mood disorders, substance misuse or attention deficit/hyperactivity disorder [ADHD]), gender differences, and the role of cognitive factors (Aasved 2002; Petry 2005). In addition to a multitude of methodological weaknesses that characterize the empirical research, our knowledge of problem gambling has been hindered by a lack of distinction between different subtypes of gambling behavior.The field has labored under a “homogeneity myth” that has glossed over individual differences among diverse forms of gambling. This has introduced a significant source of variance in the research results, which have typically been reported in an aggregated fashion across gambling type (e.g., Blaszczynski and Nower, this volume; Dickerson 1993; Petry 2005; Raylu and Oei 2002; Toneatto 2005; Toneatto and Millar 2004). An implicit assumption of this approach to problem gambling research is that the diverse phenomenology of gambling behavior may represent unimportant variation of more fundamental underlying process variables (e.g., Shaffer 1997).The result of this methodological approach has been the accumulation of a flawed knowledge base that poses a significant barrier to a more coherent understanding of problem gambling, its prevention and treatment. As increasingly diverse types of gambling become available within, and to broader segments of, our society, gambling choices will likely reflect multiple biological, psychological, social, and cultural variables. Fortunately, in recent years research has begun to focus on specific gambling subtypes—for example, bingo (King 1990), lotteries (Hardoon et al. 2001), and slot machines (Delfabbro and Winefield 2000)—or have reported results by gambling type (Petry 2005). Space limitations do not permit an exhaustive or complete critical review of the voluminous literature on individual differences. As a result, the focus of this chapter will be primarily on the research literature for which there is a considerable body of empirical work.
DEMOGRAPHIC VARIABLES AGE Age and gambling disorders have consistently been shown to be negatively correlated in North America, Australasia, and Europe, with younger people tending to show higher rates of problem gambling than older adults (see review by Petry 2005; Shaffer, Hall, and Vander Bilt 1999). Keeping in mind the variability of assessment methodologies, the rates of problem gambling among younger people may be as
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much as three times that of adults (National Research Council 1999). Other evidence suggests that the earlier the onset of gambling, the greater the probability that one will also develop a more severe gambling disorder (e.g., Ladouceur 1991;Volberg 1994). Vitaro and colleagues (2004) examined the trajectory of gambling involvement longitudinally in a sample of over 900 males of low socioeconomic status (SES) who were assessed annually during their adolescent years beginning at age 11.Three groups were distinguished: (1) early-onset/high gambling involvement, (2) late-onset/high gambling involvement, and (3) low gambling involvement. Anxiety and impulsivity (assessed via teacher ratings) during childhood and early adolescence distinguished the early-onset and the nonproblem groups, with the late-onset group in between. Individuals classified with early-onset/high gambling involvement between ages 11 and 16 were found to have higher rates of problem gambling at age 17 (i.e., 10.7%) compared with those who were classified with low gambling involvement (i.e., 3.1%). The late-onset/high gambling involvement group fell in between (i.e., 6.0%). The prevalence of problem gambling appears to be lowest among older adults. The National Opinion Research Center (Gerstein et al. 1999) estimated the rates of pathological gambling in a U.S. national study to be between 0.2% and 0.3% in the elderly, with 0.6% characterized as problem gamblers (i.e., three or four symptoms from the criteria in the Diagnostic and Statistical Manual of Mental Disorders [DSM] for pathological gambling).These rates, however, appear to vary depending on the sample studied. For example, about 10% of a sample of individuals 60 years of age and older recruited from bingo halls, seniors residences, and community centers were found to have a gambling problem as defined by the South Oaks Gambling Screen (SOGS) (Erickson et al. 2005). McNeilly and Burke (2000) studied 315 adults over the age of 65 recruited either from gambling venues (55% of whom had at least one symptom on the SOGS, with 11% scoring in the pathological range) or the community (15% of whom scored at least one SOGS symptom, with 2.7% scoring as pathological gamblers).The gambling-venue sample (compared with the community sample) tended to have higher rates of smoking (14.8% vs 3.8%, respectively), borrowing money from credit cards to gamble (11.3% vs 1.2%), and arguing about handling money (10.7% vs 3.6%). Risk factors for problem gambling among older adults include accessibility of gambling opportunities, marketing practices by the gambling industry, depression and anxiety, and social isolation (Desai 2004).
GENDER Men have consistently been shown to make up a higher proportion of problem gamblers in studies conducted in North America (e.g., Ladouceur 1991; Nower, Derevensky, and Gupta 2004), the United States (e.g., Welte et al. 2001), Sweden (Volberg et al. 2001), New Zealand (Volberg and Abbott 1997), Hong Kong (Wong and So 2003), and Korea (Lee et al. 1990).The effect of male gender
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on rates of problem gambling may interact with age (e.g., Gupta and Derevensky 1998; Stinchfield 2000). The prevalence rate among younger males is approximately three times that for females (e.g., Chantal and Vallerand 1996; Hraba and Lee 1996) compared with a male-to-female ratio among adult problem gamblers of approximately 2:1 (Volberg and Abbott 1997). An important gender difference has been the “telescoping effect,” that is, the observation that women appear to experience a different illness course than men. The interval between the onset of gambling and its recognition as a problem appears to be shorter for women (e.g., Grant and Kim 2002; Ladd and Petry 2003; Tavares et al. 2001).Tavares and colleagues (2003) found the progression of a gambling disorder to be twice the rate in women compared with men in an outpatient problem gambling treatment sample in Brazil. In a treatment-seeking sample in Spain, women were found to have a later age for placing their first bet but a more rapid onset of a gambling problem (Ibanez et al. 2003).
SOCIOECONOMIC STATUS Although interpreting SES data can be difficult due to its confounding relationship with other variables that may be related to problem gambling such as ethnicity, mental health issues, and educational level, lower SES, unemployment, low educational achievement, and low income have been consistently associated with higher rates of problem gambling (e.g., Shepherd, Ghodse, and London 1998; Hraba and Lee 1995).The National Research Council (1999) found that individuals with incomes under $25,000 were overrepresented among the gambling disordered. In a study of risk factors for gambling with a representative sample of the U.S. population, Welte et al. (2004) found minority status and SES to be significantly associated with gambling problems after controlling for gambling behavior, addictive behavior, and other sociodemographic variables. Ladouceur (1991) found similar results in Quebec, while Volberg and Steadman (1989) found high school dropouts to be overrepresented among problem gamblers in New Jersey and Maryland. SES may also interact with type of gambling behavior. Kroeber (1992) showed that players of electronic gaming machines (e.g., slot machines) tended to be of lower SES than roulette players, who in turn were more psychosocially unstable (e.g., loneliness, financial debts, criminal lifestyle) than slot machine players.
MARITAL STATUS Marital status has consistently been shown to be related to the prevalence of problem gambling. Problem gamblers are more likely to be divorced than nonproblem gamblers (Cunningham-Williams et al. 1998;Volberg 1994). Lower rates
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of problem gambling among married individuals compared with the divorced or separated have been shown in studies in Sweden (Volberg et al. 2001), Norway (Gotestam and Johansson 2003), Australia (Productivity Commission 1999), and New Zealand (Abbott and Volberg 2000) However, the causal direction of marital status and gambling pathology is still unclear. There is also evidence that married problem gamblers may be more likely to seek treatment than the unmarried (e.g., Petry and Oncken 2002).
EARLY CHILDHOOD EXPERIENCES Rates of abuse and neglect among problem gamblers appear to be higher than among social or nonproblem gamblers. Kausch, Rugle, and Rowland (2006) studied the lifetime history of trauma among pathological gamblers. They found that almost two-thirds of gamblers attending a residential treatment facility reported a history of emotional trauma, 40% reported a history of physical trauma, and 24% reported a history of sexual trauma. The majority of these traumatic episodes occurred during childhood. Abuse rates were higher among women, as all females reported some form of abuse compared with 61% of the males. A positive history of trauma was associated with suicide attempt frequency, addictive behavior, and psychiatric symptoms. Similar findings have been reported by Specker et al. (1996) and Ciarrocchi and Richardson (1989). Taber, McCormick, and Ramirez (1987) found that about 25% of 44 recovering male gamblers experienced physical or sexual trauma. Ladd and Petry (2003) and Petry and Steinberg (2005) also found that female pathological gamblers have a more unstable and troubled domestic environment and often endured some form of childhood abuse, especially emotional and sexual abuse and physical neglect. Petry and Steinberg (2005) showed that the severity of maltreatment was associated with severity of gambling problems, as well as an earlier age of onset of gambling.
INFLUENCE
OF
PARENTAL GAMBLING
Although the majority of this research has been retrospective, adult problem gamblers have reported higher rates of gambling behavior by their parents compared with nonproblem gamblers (Clark and Rossen 2000; Gambino et al. 1993; Lesieur and Heineman 1988). Jacobs (2000) has also shown excessive parental gambling to be associated with problem gambling among adolescents. In a study of high school students who completed the SOGS-Revised for Adolescents (RA) (Langhinrichsen-Rohling et al. 2004), probable pathological gamblers reported higher likelihood of parental gambling, suicide attempts, alcohol and drug misuse, and peer pressure susceptibility. An association between purchase
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of lottery tickets by parents for their children and the subsequent level of gambling severity among their children compared with nongamblers or social gamblers was reported by Felsher, Derevensky, and Gupta (2003). Oei and Raylu (2004) studied the role of gambling-related parental cognition and behavior on offsprings’ gambling behavior in a sample of 189 families (546 individuals). They found that the father’s gambling-related cognitions and behaviors were most strongly correlated with the offspring’s gambling behavior. In an interesting demonstration of the transmissibility of gambling-related irrational beliefs, Caron and Ladouceur (2003) randomly assigned individuals who played video lotteries regularly but nonproblematically to a condition where (a) an accomplice expressed erroneous or irrational thoughts about gambling, (b) the accomplice expressed rational thoughts about gambling, or (c) the accomplice did not speak.The results showed that the subjects took greater monetary risks by betting more credit while playing video lotteries in the presence of the accomplice who had expressed erroneous cognitions about gambling.These results may help explain how childhood exposure to gambling environments may impact gambling behavior later in life.
MOTIVATION TO GAMBLE When gamblers are asked why they gamble, the most common responses include social facilitation, emotional regulation, fun and excitement, money, social approval, and acceptance. For example, blackjack gamblers cited enjoyment or excitement (50%), to win money (8.5%), to be sociable (33%), and to pass the time (8.5 %) (Anderson and Brown 1984). The elderly may gamble in order to fulfill social needs or to alleviate boredom, but are less likely to gamble for purposes of excitement or to win money (McNeilly and Burke 2000). Based on such reasons, several researchers have formulated typologies to summarize the various motivations to gamble. Cocco, Sharpe, and Blaszczynski (1995) have suggested that there at least two main subtypes of gamblers: those who are overstimulated and are attracted to types of gambling that may soothe or diminish aversive emotional states by promoting dissociation or narrowing of attention (e.g., slot machines, electronic gaming), and those who are understimulated and are attracted to games that provide stimulation and arousal (e.g., sports betting, race track betting, craps). These two models have been incorporated into the “pathways” model (Blaszczynski and Nower 2002 and this volume), a synthesis of the biopsychosocial determinants of problem gambling distinguishing three primary pathways: behaviorally conditioned gamblers, emotionally vulnerable gamblers (i.e., overstimulated), and antisocial impulsivist gamblers (i.e., understimulated).The emotionally vulnerable gambler relies on gambling to cope with aversive emotional states
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and is associated with generally poor coping skills. For example, Borsoi and Toneatto (2003) found pathological gamblers to demonstrate poorer coping skills compared with subclinical and asymptomatic gamblers as measured by the Problem-Solving Inventory. Specifically, the pathological gamblers reported reduced confidence, diminished sense of personal control, and an avoidance-based coping style. Getty,Watson, and Frisch (2000) found a sample of gamblers recruited from Gamblers Anonymous (GA) to be more reactive and less reflective and to employ suppressive coping strategies. The impulsivist gambler has difficulty tolerating boredom and delaying gratification and is prone to sensation seeking.The behaviorally conditioned gambler is more likely to develop a gambling problem as a result of poor decision making, irrational thinking, and bad judgments, often due to sensitivity to the schedules of reinforcement governing the ratios of wins and losses. The behaviorally conditioned problem gambler may often be impacted by early significant gambling wins. Custer and Milt (1985) reported large wins early in their subjects’ gambling careers as an important factor in developing a gambling problem. Some studies have reported a disproportional number of problem gamblers who reported a sizable win early in their gambling careers (e.g., Walker 1992). Coventry and Norman (1997) showed that even among individuals who experienced the same number of gambling wins over time, those who experienced them sooner in their careers judged themselves to be more successful and played for longer periods. Early wins may activate an attributional process which attributes subsequent losses to external causes but wins as the result of their own skill or ability. Chantal and Vallerand (1996) and Chantal, Vallerand, and Vallieres (1995), using the Gambling Motivation Scale, identified three types of motivation for gambling: intrinsic motivation (such as gaining knowledge, experiencing stimulation, or realizing an accomplishment), extrinsic motivation (such as gambling for rewards, relief of negative emotional states, or social validation and approval), and amotivation (lack of awareness between gambling outcomes and gambling behavior). In a sample of problem gamblers identified among university students, ratings of intrinsic motivation (i.e., experiencing stimulation) and extrinsic motivation (i.e., emotional regulation, social approval) were found to be higher than for the non-problem gambling students. Chantal and Vallerand (1996) also demonstrated an interaction between motivation and skill/chance types of gambling. The “skill” players (i.e., horse racing) showed elevations on intrinsic motivation, whereas the “luck” players showed elevations on extrinsic regulation (i.e., winning money). Gender differences in motivation have also been reported. In a study by Grant and Kim (2002), women’s motivation to gamble was more likely to be associated with feelings of loneliness, dysphoria, and escape from personal or family problems, whereas men gambled more for action, excitement, arousal, and to win money (Raylu and Oei 2002). Grant and Kim (2002) and Ladd and Petry (2003)
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reported evidence showing that women may gamble as a form of emotional selfregulation (e.g., of depression, marital dysfunction). Electronic gaming may induce states of dissociation that allow the gambler, usually a woman, to escape from other life problems (e.g., Jacobs 1988). A study by Scannell et al. (2000) found that women adopted more emotion-focused coping strategies, such as avoidance and self-blame, which in turn led to reduced control over gambling behavior.
PERSONALITY FACTORS AROUSAL
AND
SENSATION SEEKING
The hypothesis that gambling acts as a stimulant by providing an intensification of desirable emotions, sensations, and feelings has often been proposed as an important individual difference between problem gamblers. Individuals who are underaroused, bored easily, hypomanic, or depressed may find the stimulation and excitement associated with waging higher amounts of money and participating in higher-risk gambling activities arousing, exhilarating, and reinforcing. Gambling becomes a means of maintaining an optimal level of arousal. When such persons are underaroused or understimulated, the corrective effect of gambling may be highly valued and sought. High-sensation seekers would then be more likely to be vulnerable to gambling in order to maintain this optimal level of arousal and excitement (e.g., Brown 1986; Leary and Dickerson 1985). For sensation seekers, the primary motivation for gambling is to experience arousal. Risking money becomes the means of creating the optimal level of arousal rather than the reason for gambling. Of course, since the arousal may be fleeting, repetition and persistence of gambling may be necessary (Boyd 1976). The sensation-seeking hypothesis comprises two components: (a) sensation seeking itself, which hypothesizes that hypoarousal, boredom, or the need for excitement is pleasurably reinforced by gambling, and (b) arousal, which suggests that gambling generates autonomic or physiological excitement (Aasved 2002). Sensation Seeking Sensation seeking has been among the most commonly investigated personality factors among problem gamblers. While some studies have concluded that problem gamblers score higher on measures of sensation seeking (e.g., Breen and Zuckerman 1999), others have not (e.g., Allcock and Grace 1988; Blaszczynski, Wilson, and McConaghy 1986; Zuckerman 2005). On paper-and-pencil tests such as the Sensation-Seeking Scale (SSS) (Zuckerman 1979), correlation between higher needs for arousal and stimulation and preference for riskier bets in a sample
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of undergraduates has been reported (Zuckerman 1979). Kuley and Jacobs (1988) also found that problem and pathological gamblers scored higher on the higher SSS than social gamblers. In a sample of community-recruited problem gamblers, the Boredom Susceptibility subscale of the SSS (as well as the total score), but not the Thrill and Adventure-Seeking subscale, was found to correlate significantly with severity of gambling (Kuley and Jacobs 1988).The Boredom Proneness Scale and Boredom Susceptibility subscale of the SSS, however, were negatively correlated in another study of pathological gamblers (Blaszczynski, McConaghy, and Frankova 1990). Coventry and Norman (1997) suggested that gambling-related arousal may result from the influence of irrational cognitions and the structural characteristics of the gambling activity on sensation seeking. There is some evidence for an interaction between sensation seeking and the specific gambling activity. Dickerson (1991) suggested that sensation seeking is game specific, with casino games and illegal activities correlated with this construct but not slot machines. Cocco et al. (1995) found that horse race gamblers (“skill” gamblers) sought heightened arousal, while electronic gaming machine players (“chance” gamblers) appeared to seek dampened arousal. Among youth, sensation seeking was associated with gambling frequency but not with the development of a gambling disorder (Moore and Ohtsuka 1999). Male and female youth problem gamblers were also more likely to endorse higher preference for intensity seeking than the nonproblem gambling subjects in a sample of 1339 students attending junior colleges in Quebec (Nower et al. 2004). However, sensation seeking did not distinguish between problem gamblers and nonproblem gamblers in another study of Canadian adolescents (Powell et al. 1999). Arousal The experience of a physiological “rush” or excitement has often been proposed as a major reinforcing stimulus for gambling (Anderson and Brown 1984). This appears to be supported by research with sports and horse/dog race gamblers who showed increases in blood pressure and heart rate (e.g., Blanchard et al. 2000; Carroll and Huxley 1994; Coventry and Norman 1997; Griffiths 1993). Many studies have objectively and subjectively demonstrated increased levels of arousal in problem gamblers, especially in ecologically valid settings (Diskin, Hodgins, and Skitch 2003; Griffiths 1995). In these studies, the effects were enhanced when the gamblers played in a real casino versus a laboratory setting. However, not all studies have found a relationship between arousal and problem gambling (e.g., Brown 1986; Coulombe et al. 1992; Sharpe et al. 1995) or even between objective and subjective reports of arousal (e.g., Diskin and Hodgins 2003; Diskin et al. 2003). Coventry and Hudson (2001) found that winning during slot machine play was associated with an increased heart rate. In the Coventry and Norman (1997) study of horse race bettors, heart rate escalated from baseline (65 beats per minute)
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until the bet was placed (88 bpm), leveled off until the beginning of the race, and then escalated again until the last 30 seconds of the race (92 bpm). Heart rate decreased after the race until the individual left the track (83 bpm), but did not return to baseline levels, suggesting an enduring impact of the gambling experience on heart rate. However, there were no differences in heart rate after the race and when leaving the track between those who won vs those who lost their race. A difference was observed during the last 30 seconds of the race (i.e., 102 bpm for those who won; 90 bpm for those who lost). In another study, Coventry and Constable (1999) observed an increase in heart rate during electronic machine gambling, but only when winning. Coventry and Hudson (2001) suggested that the positive correlation between winning and heart rate did not require that the gamblers actually win money. Increased arousal may occur repeatedly for brief periods of time throughout the gambling session despite a net financial loss. However, Sharpe (2004) found an elevation in skin conductance levels whether or not the poker machine problem gamblers in the sample were winning or losing, whereas the social gamblers showed an increase only when winning. Ladouceur and colleagues (2003) also found that the expectancy of winning at video lotteries was the most important factor in determining levels of arousal. Heart rate was higher among those expecting to play with money compared with those who were not playing for money but for fun. Ladouceur et al. (2003) concluded that gambling is significantly less stimulating when it does not involve the expectation of winning money. The inconsistent results in the arousal/sensation-seeking literature, similar to other constructs discussed in this chapter, can be accounted for by a multitude of methodological differences (i.e., measure of arousal, type of gambling, setting, psychometric instruments, demographic, gender). As we have noted elsewhere, the inclusion of heterogeneous samples of gamblers often introduces variability in the results that may not always be apparent. In addition, sensation seeking may be a state rather than a trait, peaking at certain times during the gambling process when gambling is particularly satisfying or arousing for the individual (Coventry and Norman 1997). Dickerson, Hinchy, and Fabre (1987) have suggested that sensation seeking may not influence decision to gamble but may affect persistence once gambling begins.
IMPULSIVITY Impulsivity has consistently been shown to be associated with problem gambling in adults (Alessi and Petry 2003; Blaszczynski, Steel, and McConaghy 1997; Castellani and Rugle 1995; Steel and Blaszczynski 1998), as well as among youth problem gamblers (e.g.,Vitaro, Arsenault, and Tremblay 1999). Nower et al. (2004) found that both impulsivity and intensity seeking (an aspect of sensation seeking) were related to problem gambling in youth attending junior colleges in Quebec.
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Impulsivity during adolescence (e.g., suicide attempts, risky behaviors) was also frequent in the history of an adult sample of pathological gamblers in treatment (Martins et al. 2004). Paper-and-pencil measures of impulsivity have generally demonstrated higher impulsivity among pathological gamblers than among control groups and are associated with more severe gambling pathology (e.g., McCormick et al. 1987; Steel and Blaszczynski 1998). Clark (2004) found impulsivity, as measured by the Eysenck Impulsiveness Questionnaire, to be associated with problem gambling in a sample of university students. An association between problem gambling and ADHD, a disorder in which impulsivity plays a major role, has often been reported (e.g., Carlton and Manowitz 1992; Rugle and Melamed 1993). Gamblers in treatment also appear to score higher on measures of impulsivity (e.g., Blaszczynski et al. 1997; Steel and Blaszczynski 1998). Breen and Zuckerman (1999) demonstrated a relationship between impulsivity and chasing behavior in a laboratory gambling task. The vast majority of studies of impulsivity have been retrospective or crosssectional.Vitaro,Arsenault, and Tremblay (1997) prospectively followed males during their adolescence and found that impulsivity scores in early adolescence were correlated with gambling status later in their adolescence. Vitaro et al. (1999) also found that poor delay of gratification and perseveration (both indicators of impulsivity), as measured on a card-playing task at ages 13–14, predicted gambling at age 17. Slutske et al. (2005), in a longitudinal population-based study in New Zealand, found risk taking and impulsivity at age 18, as assessed by the Multidimensional Personality Questionnaire, to be associated with problem gambling measured at age 21. This personality profile was similar to that for substance use disorders such as alcohol, cannabis, and nicotine dependence.
DRIVE REDUCTION Mood Regulation Problem gambling has been conceptualized as negatively reinforced behavior through its ability to provide emotional relief or escape from unpleasant or aversive stimuli (Orford, Morrison, and Somers 1996). Supportive evidence comes from the high rates of dysphoric emotions, especially anxiety and depression, reported by gamblers (Petry and Weinstock, this volume).The rates of mood disorder as determined by structured diagnostic measures among treatment-seeking gamblers have been shown to be high whether they were treated in residential settings (e.g., McCormick et al. 1984), in outpatient settings (e.g., Ibanez et al. 2001; Specker et al. 1996), or from the community (Black and Moyer 1998). Significant stressful life events such as marital breakdown are also reported more frequently among problem gamblers (Taber et al. 1987; Roy et al. 1988). Dickerson et al. (1991) showed that dysphoric mood and thoughts prior to gambling were an important trigger for
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chasing. However, most studies that have assessed directionality between mood and gambling find that the gambling disorder precedes the onset of depression (e.g., 86% of the treatment-seeking gamblers in the McCormick et al. [1984] study). Hodgins, Peden, and Cassidy (2005) found the report of affective symptoms to be equally likely to either precede or follow the onset of a gambling problem in a sample of treated pathological gamblers (compared with substance use, which generally always preceded problem gambling onset). While the evidence is less consistent with anxiety disorders, generalized anxiety and some phobias may be elevated among treatment-seeking gamblers but not obsessive-compulsive or posttraumatic or anxiety disorder (Petry and Weinstock, this volume; e.g., Black and Moyer 1998; Ibanez et al. 2001; McCormick, Taber, and Kruedelbach 1989). However, Henry (1996) found that pathological gamblers with a history of trauma reported a reduction of their gambling behavior following successful therapeutic resolution of the traumatic anxiety. Dissociation Symptoms of dissociation involving altered experience of time and self have often been reported among problem gamblers. In a study of adolescents, gambling led to subjective loss of the sense of time, depersonalization, escape, or dissociation (Allcock 1985). Jacobs (1988) assessed dissociation in a sample of 121 pathological gamblers drawn from GA and residential treatment settings compared with control samples of normal adults and adolescents. The prevalence of dissociative experiences was high as measured through each of four items related to: trance (79%), shift in personal identity (79%), out-of-body experiences (50%), and amnesiac episodes (38%) while gambling; the rates of these symptoms were very low in the control groups. These results were replicated by Kuley and Jacobs (1988) in a community sample of problem gamblers and by Diskin and Hodgins (2001) in a sample of video lottery terminal (VLT) players. Diskin and Hodgins (2001) found the score on items specifically measuring losing track of time and amnesiac experiences to be significantly higher among the problem gamblers compared with the occasional gamblers. However, neither Diskin and Hodgins (2001) nor Grant and Kim (2003) found differences between problem and nonproblem gamblers using a psychometric measure of general tendency toward dissociation, the Dissociative Experiences Scale (Bernstein and Putnam 1986).
CHOICE OF GAMBLING ACTIVITY Given the wide availability of different forms of gambling, the choice of which games(s) to play is affected by a host of developmental, psychosocial, and demographic variables. Increasingly, researchers are noting significant differences in the characteristics of those who participate in, and those who develop problems
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with, specific types of gambling. Petry (2003) suggested that each type of gambling was conceptually distinct from the other and may require different approaches to treatment. In the Petry (2003) study, treatment-seeking gamblers were compared based on their preferred gambling activity (i.e., sports, racing, cards, slots, lotteries). Reported were differences in SOGS scores (racing gamblers were the highest), in gambling frequency (lottery players were the highest), in money wagered (racing gamblers were the highest), in years of problem gambling (racing gamblers were the highest), in GA participation (lottery players were the least likely to attend), and in patterns of psychiatric illness and addiction. Horse/dog race gamblers tended to be less educated, men, and older, with an earlier onset of gambling. Sports gamblers tended to be younger men with relatively higher rates of addiction comorbidity. Gamblers on electronic machines (e.g., slot machines) were more likely to be female and older with higher rates of psychiatric comorbidity and later onset of gambling. The lottery gamblers tended to have both alcohol and other psychiatric comorbidity and the highest frequencies of gambling. Arousal may also interact with type of gambling. Poker machines (e.g., Sharpe et al. 1995),VLTs (Diskin and Hodgins 2003), horse racing (e.g., Coventry and Norman 1997), and casino games (e.g., Anderson and Brown 1984) have all been shown to potently increase arousal.Welte et al. (2004) also found that certain types of gambling (e.g., casino gambling, lottery, pulltabs, bingo, sports) were more likely to be associated with the progression toward problem gambling than other types (e.g., Internet, track, dice, gambling machines, keno). Gender appears to affect the choice of gambling activity. Women preferred nonskill/nonstrategic forms of gambling such as bingo and slot machines, whereas men preferred skill/strategic types of gambling such as sports and track betting. Gambling choice may also reflect gender differences in motivation for gambling (i.e., emotional dampening vs excitation). Gender differences in gambling preferences have also been found among adolescents. Boys appear to prefer wagering on skill games such as card games and sports, whereas girls tend to prefer lotteries (e.g., Stinchfield 2001). In a study of treatment-seeking gamblers, Crisp et al. 2004 found a higher proportion of the electronic gaming machines and bingo gamblers to be women and a higher proportion of the horse race, legalized betting, and card gamblers to be men. Among older adults, there may also be a shift to playing games that are less competitive and which demand fewer cognitive and attentional resources, such as bingo, lotteries, and slot machines (Mok and Hraba 1991).
COGNITIVE VARIABLES Within the gambling literature there has been a substantial body of work devoted to the role of cognitive factors in pathological gambling (e.g., Gaboury and Ladouceur 1989; Griffiths 1995; Ladouceur and Dubé 1997; Toneatto 1999; Walker 1992).There is little question that problem gamblers exhibit unique cognitive
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psychopathology that distinguishes them from social gamblers ( Joukhador, Maccallum, and Blaszczynski 2003).Two major beliefs appear to describe the problem gamblers’ irrational cognition: beliefs that gambling outcomes can be correctly (1) predicted and (2) controlled (Letarte, Ladouceur, and Mayrand 1986). Letarte et al. (1986) distinguish primary illusory control, the irrational belief that the gambler can control the outcomes of gambling events, versus secondary illusory control, the irrational belief that the gambler can predict the outcomes of gambling events. Even games that are ostensibly completely random, such as slot machines, can elicit irrational beliefs about control and prediction (e.g., Griffiths 1995; Langer 1983; Toneatto et al. 1997). These core beliefs yield a wide array of irrational or maladaptive beliefs about gambling outcomes that have been well described in the literature and will be briefly described here (e.g., Petry 2005; Toneatto 1999).
ILLUSION
OF
CONTROL
Illusion of control refers to the belief that the probability of winning is greater than would be dictated by random chance. Such a belief may be more apparent in games where skill or knowledge may operate (e.g., horse racing, cards, sports lotteries) (Ceci and Liker 1986), but may also be present in nonskill games as well (e.g., bingo, lotteries) (e.g., Coulombe et al. 1992; Griffiths 1993; Langer 1983).The converse of the illusion of control is the belief that one may be able to control or predict gambling outcomes, a belief that has been observed in both gamblers and nongamblers (Langer 1983). For example, lottery tickets chosen by individuals will be accorded a higher value than lottery tickets assigned to them (Dixon, Hayes, and Ebbs 1998). Some gamblers believe that luck oscillates between periods of good and bad “streaks” and cannot be manipulated directly. The best strategy is to wait for the next streak of good luck and wager during those times (Gaboury and Ladouceur 1989; Li and Smith 1976; Rogers 1998); conversely, they should avoid gambling during unlucky streaks.The challenge for gamblers is to determine when their luck is shifting (Walker 1992). Illusory Correlation: Superstitious Beliefs Talismanic superstitions include beliefs that the possession of certain objects increases the probability of winning (Toneatto et al. 1997). These may include objects believed to confer good luck (e.g., a ring, a hat), specific attributes associated with winning (e.g., the color green), or objects that have personal significance (e.g., a family heirloom) (King 1990). Numbers often take on talismanic properties (Rogers 1998;Wagenaar 1988).
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Behavioral superstitions include beliefs that certain actions or rituals can increase the probability of winning. Many gamblers believe that luck can be manipulated in their favor through superstitious behaviors or systems (Bersabé and Arias 2000).As a result, individuals may gamble only when they feel they have been able to attract the influence of good luck using whatever strategy they believe is most efficacious (King 1990). Games that require behavioral involvement (e.g., lotto, keno, slot machines) tend to elicit skill-like behaviors (Langer 1983) and induce beliefs that the outcome can be controlled even when it is clear that such behavior can have no effect on the outcome (Griffiths 1995). One widespread behavioral superstition is entrapment (Brockner and Rubin 1985; Walker 1992), or the belief that one must continue to gamble or wager in the event (albeit unlikely) that the winning outcome takes place.This is frequently observed in lotto or lottery players who bet on every draw and in slot machine players who do not leave a machine. Cognitive superstitions include beliefs that certain mental states can influence the probability of winning. These can include prayer, hope, positive expectations and attitudes, and a strong conviction that a win is imminent (Gaboury and Ladouceur 1989;Toneatto et al. 1997).
INTERPRETIVE BIASES The problem gambler may explain severe or repeated gambling losses in ways that encourage continued gambling. Since losses tend to outnumber wins, concerted effort is necessary to explain losses despite the use of gambling systems and strategies.These include attributional biases, the gambler’s fallacy, and “chasing.” Attributional Biases Gamblers may use attributional biases to overestimate dispositional factors (e.g., skills, abilities) and to underestimate situational factors (e.g., luck, probability) as explanations for wins (Gaboury and Ladouceur 1989; Ross and Sicoly 1979). In the study by Gaboury and Ladouceur (1989), successive wins at slot machines in which the outcomes could not be influenced by the gamblers rapidly led to a belief that skill was involved. “Near misses,” in which a gambling outcome falls just short of a win (e.g., one number missing from a winning lottery number), are common in various types of gambling (e.g., slot machines,VLTs) and are often reframed as near wins rather than as losses by problem gamblers (Parke and Griffiths 2004; Reid 1986). Such outcomes may encourage irrational thought processes that lead to persistent gambling. In a sample of video lottery players, the perception of near wins was found to extend playing time compared with the control group, which did not experience
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near wins (Cote et al. 2003). Kassinove and Schare (2001) found that near wins led to a greater persistence in playing a computerized slot machine game. The reverse attributional process may also explain losses or near losses—this is often termed hindsight bias. Bad luck or unexpected idiosyncratic events (e.g., “My horse stumbled”) may be used to explain losses, but dispositional explanations (e.g.,“I’m lousy at gambling”) are discounted (Gilovich and Douglas 1986; Lau and Russell 1980). The Gambler’s Fallacy This fallacy is a belief that a gambling outcome is more likely to occur simply because it has not occurred for a period of time (e.g., Rogers 1998). Wagers may thus increase after a loss, in the anticipation that a win is “due.” The gambler’s fallacy can also include beliefs that (a) even a brief sequence of gambling events will express a random process (the representativeness bias) (Spanier 1994); (b) chance is self-correcting, so that losses will be followed (i.e., balanced) by wins and vice versa (Spanier 1994); and (c) there is dependence between independent events (e.g., lotteries, coin tosses, roulette spins) (Ladouceur & Dubé 1997; Rogers 1998). Keren and Lewis (1995) have distinguished Type I gambler’s fallacy, the belief that a series of outcomes will soon change (e.g., a run of black outcomes in roulette will be followed by red outcomes) from Type II gambler’s fallacy, which states that a series of outcomes will continue (e.g., a run of black outcomes in roulette will continue). For example, Burns and Corpus (2004) found that the Type I fallacy (the belief that a run of independent events will be interrupted) was more likely to affect judgments when the subjects were led to believe that the task was random (e.g., spin of a roulette wheel). If they were led to believe that the task was nonrandom (e.g., basketball free throws), they were more likely to believe that a run would continue (e.g., a hot streak,Type II fallacy). Chasing Chasing describes the responses of gamblers to a sequence of serious losses in which they have lost a significant amount of money. These gamblers may believe that only by continuing to gamble is there any chance of recovering it (Lesieur 1984; Walker 1992). Breen and Zuckerman (1999) showed in a laboratory study that gamblers who chased their losses tended to be more impulsive.
ILLUSORY CONTROL
OVER
LUCK
Several studies have examined the cognitive psychopathology specific to different types of gambling (e.g., Delfabbro and Winefield 2000 for slot machines;
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Hardoon et al. 2001 for lottery players). For example, in a review of cognitive distortions among lottery players, Rogers (1998) showed that the gambler’s fallacy, belief in hot and cold numbers, entrapment, belief in personalized luck, superstitious behaviors, and many others were common. In a study of electronic gambling machine players, Joukhador, Blaszczynski, and Maccallum (2004) found that problem gamblers endorsed more erroneous cause–effect beliefs between two independent events (i.e., superstitious beliefs) than did nonproblem gamblers. Superstitious belief ratings were also correlated with gambling behaviors such as time spent gambling, number of gambling sessions per week, and years of problem gambling. A study by Ladouceur (2004) suggested that the raw frequency of erroneous perceptions may not distinguish problem from nonproblem gamblers, at least in a sample of video lottery players. Rather, pathological gamblers were more convinced of the truth of their erroneous perceptions than were the nonproblem gamblers. Most interestingly, while the nonproblem gamblers demonstrated a reduction in confidence in their erroneous perceptions as their play continued, the pathological gamblers showed a trend toward increasing their confidence over time. These results suggest a role for a metacognitive analysis of gambling cognition. Moore and Ohtsuka (1999) in a sample of high school and university students showed that several irrational beliefs related to gambling (i.e., manipulation of luck, beating the “system,” illusion of control, internal control over gambling) predicted gambling frequency and problem gambling. However, Williams and Connolly (2006) found that educating university students on probability theory (e.g., odds) through the use of gambling examples produced differences in the ability to calculate gambling odds and resistance to irrational gambling-related mathematical beliefs compared with those who were instructed on probability theory generically (i.e., without the aid of gambling-related examples). Surprisingly, there was no effect on gambling behavior.Williams and Connolly (2006) concluded that learning mathematical knowledge related to gambling did not translate into a modification of gambling behavior. In contrast, Benhsain,Taillefer, and Ladouceur (2004) instructed 31 occasional gamblers that using previous outcomes to predict future events was a common error and not an accurate or reliable method to predict gambling outcomes. The researchers found that individuals who were reminded about the independence of events reported a decreased motivation to pursue playing roulette and made fewer erroneous perceptions.
CONCLUSION In this chapter a selective representation of the research literature on the individual differences among problem gamblers was reviewed.The general trend of the findings demonstrate problem gambling rates to be correlated with age (negatively), male gender (positively), SES (negatively), and divorced/separated relationship
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status (positively). Unfortunately, the majority of this research does not investigate the mediating variables that may connect a specific demographic variable and gambling pathology. In other words, what are the processes by which age, SES, or gender, for example, affect the development of gambling problems? For an understanding of the “mechanisms” linking demography and problem gambling, it is necessary to investigate the relevant biopsychosocial variables. This chapter has reviewed some of the more well-studied mediators of problem gambling. However, due to the many methodological differences among studies, especially the tendency to group results independently of the types of gambling, there was considerable variability in the empirical findings. Nevertheless, the available evidence suggests that early life experiences (e.g., parental modeling of gambling behavior, traumatic childhood experiences) are important factors in the development of adult problem gambling behavior. The contribution of psychobiological factors was evident from the fairly consistent evidence for the mediating role of gamblingrelated arousal, impulsivity, sensation seeking, and mood regulation in the motivations for, and reinforcement from, excessive gambling. The pathways model is an important development in the conceptual understanding of the manner in which these diverse variables interact to produce different types of problem gambler.The interplay of these variables may also help explain the preference for different types of gambling activity. Although the evidence is still limited, the choice of gambling type, like the choice of psychoactive substance, reflects an interaction of a wide array of intrapersonal, interpersonal, and environmental variables. Among these variables, cognitive factors appear to be particularly important, as they reflect the interaction between the structural characteristics of the gambling type, psychobiological factors, and sociodemographic variables. Thus, the way gamblers perceive and interpret the act of gambling is increasingly becoming an important means of measuring the complex interplay of these variables. As mentioned earlier, the variability in the empirical findings for any one factor prevents the drawing of any firm conclusions about the individual differences between gamblers at this time.To this end, it would be prudent for problem gambling researchers in the future to include the necessary information (e.g., demographic characteristics, description of the gambling behavior, description of the assessment methodology) and present the data by subtype, demographic variable, or other significant classificatory variable.This will accelerate our understanding of the role of individual differences in the onset and development of problem gambling.
GLOSSARY Arousal activity in the cortico-reticular loop of the brain’s ascending reticular activating system; a state of activation, either behavioral, affective, or physiological; increased sensory, behavioral, and emotional responsivity.
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Cognition the process of receiving, processing, storing, and using information; conscious intellectual activity (e.g., thinking, reasoning, remembering, imagining, learning). Dissociation a psychological process involving alterations in identity or sense of self, which can include: a relatively mild and transient sense that the world or the self is “unreal” (derealization, depersonalization), more permanent states such as amnesia (loss of memory), and a disruption in the usually integrated functions of consciousness, memory, identity, and perception of the environment. Illusion of control the tendency for human beings to believe they can control or influence outcomes that they demonstrably have no influence over; the belief that chance events are accessible to personal control. Impulsivity decreased sensitivity to negative consequences of behavior; rapid, unplanned reactions to stimuli before complete processing of information; and lack of regard for long-term consequences, often to ease tension or gain pleasure. Sensation seeking the need for new and varied experiences through disinhibited behavior, a nonconventional lifestyle, and a rejection of monotony; may include engaging in physically risky activities; pursuing new experiences through travel, music, and drugs; seeking social stimulation through parties and a variety of sex partners; and avoiding boredom produced by unchanging circumstances.
REFERENCES Aasved, M. (2002). The Psychodynamics and Psychology of Gambling. Springfield, IL: Charles C.Thomas. Abbott, M.W., and Volberg, R.A. (2000). Taking the Pulse on Gambling and Problem Gambling in New Zealand: A Report on Phase One of the 1999 National Prevalence Survey.Wellington: Department of Internal Affairs. Alessi, S. M., and Petry, N. M. (2003). Pathological gambling severity is associated with impulsivity in a delay discounting procedure. Behavioural Processes, 64, 345–354. Allcock, C. C. (1985). Psychiatry and gambling. In Gambling in Australia (G. Caldwell, B. Haig, M. Dickerson, and L. Sylvan, eds.), pp. 163–171. Sydney: Croom Helm. Allcock, C. C., and Grace, D. M. (1988). Pathological gamblers are neither impulsive nor sensation-seekers. Australian and New Zealand Journal of Psychiatry, 22, 307–311. Anderson, G., and Brown, R. I. F. (1984). Real and laboratory gambling, sensation-seeking and arousal. British Journal of Psychology, 75, 401–410. Benhsain, K., Taillefer, A., and Ladouceur, R. (2004). Awareness of independence of events and erroneous perceptions while gambling. Addictive Behaviors, 29, 399–404. Bernstein, E. M., and Putnam, F.W. (1986). Development, reliability, and validity of a dissociation scale. Journal of Nervous and Mental Disease, 174, 727–735. Bersabé, R., and Arias, R. M. (2000). Superstition in gambling. Psychology in Spain, 4, 28–34. Retrieved January 22, 2007, from http://www.psychologyinspain. com/content/full/2000/3.htm.
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CHAPTER 12
Comorbidity and Mental Illness Nancy M. Petry
Jeremiah Weinstock
Department of Psychiatry University of Connecticut Health Care Center Farmington, Connecticut
Department of Psychiatry University of Connecticut Health Care Center Farmington, Connecticut
Introduction Current State of Knowledge of Comorbidities with Pathological Gambling Substance Use Disorders Mood Disorders Anxiety Disorders Other Disorders Diagnostic Measurement Issues Structured Instruments for Assessing Psychiatric Disorders Assessment of Psychiatric Symptoms Research Concerns and Summary
INTRODUCTION Data are emerging that pathological gambling is a disorder that rarely occurs in isolation, but instead is often related to other psychiatric conditions. In this chapter, we first review data related to comorbidities between pathological gambling and substance use, mood, anxiety, and other psychiatric disorders. We then describe measurement issues pertinent to these studies, including the types of instruments most often employed, as well as methods for assessing symptom severity. Finally, issues related to consistency across studies, sample sizes, and recruitment sources are discussed, along with available data addressing whether or not the existence of other psychiatric conditions should impact the manner in which pathological gamblers are treated in clinical settings. 305
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CURRENT STATE OF KNOWLEDGE OF COMORBIDITIES WITH PATHOLOGICAL GAMBLING Comorbidity is a term used to describe the co-occurrence of two or more disorders in the same person. Each disorder can occur independently, a pattern that would represent lifetime comorbidity. Alternatively, the disorders can occur at the same time, a pattern that would be considered current comorbidity. Some psychiatric conditions have overlapping symptoms, so in order for multiple diagnoses to be made, the disorders must express their usual etiology and characteristic symptom presentation independently. As reviewed below, evidence is mounting that pathological gambling rarely occurs alone.
SUBSTANCE USE DISORDERS The strongest case for evidence of comorbidity among pathological gamblers relates to substance use disorders. In all known surveys in which both pathological gambling and substance use disorders were assessed, a positive association between the conditions was noted (Crockford and el-Guebaly 1998; Petry 2005).We review the larger epidemiological studies that assessed comorbidity of pathological gambling and substance use disorders. In the United States, three nationally based epidemiological studies have been conducted that evaluated the presence of both gambling and substance use disorders. The National Opinion Research Center surveyed 2417 randomly selected adults by telephone (Gerstein et al. 1999). Individuals who were identified with a lifetime diagnosis of pathological gambling had approximately nine times the rate of substance use disorder (9.9%) compared with nongamblers (1.1%). Around the same time as this survey,Welte et al. (2001) were conducting another independent survey of 2638 randomly selected adults in the United States.The rate of current alcohol dependence among the lifetime pathological gamblers identified was 25.0%, compared with a rate of 1.4% among the nongamblers. More recently, results from an even larger survey from the United States were published (Petry, Stinson, and Grant 2005).The National Epidemiologic Survey of Alcohol and Related Conditions (NESARC) surveyed over 43,000 randomly selected respondents.The focus of the study was on alcohol use diagnoses, as well as other psychiatric conditions, including pathological gambling.The lifetime rate of alcohol abuse or dependence was 73.2% among those identified as lifetime pathological gamblers versus 25.0% among nongamblers. The statistically significant odds ratio of 6.0 suggested that pathological gamblers had a six-fold increased risk of having an alcohol use diagnosis in their lifetimes. Only the latter of these nationally based surveys also assessed diagnoses of other drug use disorders specifically. In the NESARC sample, Petry et al. (2005)
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found that 38.1% of lifetime pathological gamblers had a disorder with one or more other substances: sedatives, tranquilizers, opiates, stimulants, hallucinogens, cannabis, cocaine, inhalants/solvents, heroin, and other drugs. The corresponding rate of other illicit substance use disorders in the nongamblers was only 8.8%, with a 4.4-fold increased risk of illicit drug dependence in the pathological gamblers. While the other two nationally based surveys did not include diagnoses of other substance use disorders, some regional surveys have. Cunningham-Williams et al. (1998) evaluated data from the Epidemiologic Catchment Area (ECA) survey in the early 1980s from the St. Louis, Missouri, site, which assessed pathological gambling as well as other illicit drug abuse and dependence diagnoses in 2954 respondents. In a combined group of problem (a subdiagnostic threshold condition) and pathological gamblers, 39.9% had an illicit drug use disorder versus 23.8% of nongamblers. However, the 95% confidence interval associated with the odds ratio overlapped 1.0 (0.5 to 3.3), indicating that these differences in prevalence rates of illicit drug use disorders were not statistically significant. In Canada, Bland et al. (1993) surveyed 7214 randomly selected adult residents of Edmonton, Alberta. Rates of lifetime alcohol abuse or dependence were almost four times higher in the lifetime pathological gamblers (63.3%) compared with the nonpathological gamblers (16.5%), consistent with the U.S. studies and with a statistically significant 3.8-fold increase risk. Rates of other substance use disorders in this Canadian sample were also significantly elevated in the pathological gamblers (23.3%) compared with nongamblers (6.3%). Data from these surveys are reported in Table 12.1. While epidemiological data are clearly most appropriate for understanding relationships between disorders, data from treatment-seeking samples corroborate high rates of comorbidity between pathological gambling and substance use disorders. For example, Stinchfield and Winters (2001) evaluated 592 consecutive admissions to a gambling treatment program in Minnesota, and 35% of the treatment-seeking gamblers had a lifetime diagnosis of a substance use disorder. In Spain, Ibanez et al. (2001) evaluated 69 treatment-seeking gamblers, and 35% had a history of an alcohol use disorder, with 23% reporting a current alcohol use disorder. In an evaluation of 75 gamblers in treatment in Australia, Maccallum and Blaszczynski (2002) found that 24% had a current alcohol use diagnosis and 11% had marijuana abuse or dependence. These rates are clearly much higher than general population rates (Regier et al. 1990). The converse relationship has also been examined, and individuals seeking treatment for a substance use disorder have been classified as to whether they have a gambling problem. Many such studies are available, so only a few of the larger ones are highlighted. Among 512 substance abusers, Cunningham-Williams et al. (2000) reported rates of lifetime pathological gambling to be 10%. Langenbucher et al. (2001) and McCormick (1993) each reported rates of pathological gambling to be 13% among 372 and over 2000 substance abusers, respectively. In Canada,
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Table 12.1 Prevalence Rates and Odds Ratios of Psychiatric Conditions Identified Among Pathological Gamblers. Petry et al. (2005)
Bland et al. (1993)
Cunningham-Williams et al. (2005)
Pathological Nongamblers* Odds Gamblers (%) Ratio (%) (95% CI)
Pathological Nongamblers Odds Gamblers (%) (%) Ratio ( p value)
Problem/ Nongamblers Odds Pathological (%) Ratio Gamblers (%) (95% CI)
Alcohol
73.2
25.0
6.0 (3.8–9.7)
63.3
16.5
3.8 (<.001)
44.5
6.8
3.3 (1.9–5.6)
Drug
38.1
8.8
4.4 (2.9–6.6)
23.3
6.3
3.7 (<.001)
39.9
23.8
1.3 (0.5–3.3)
Major depression
37.0
12.3
3.3 (2.3–4.9)
20.0
12.4
1.6 (n.s.)
8.8
5.2
3.3 (1.6–6.8)
Dysthymia
13.2
3.8
3.3 (1.9–5.6)
20.0
4.9
4.1 (<.001)
4.2
3.4
2.1 (0.8–5.7)
Mania (0.8–15.1)
22.8
2.5
8.0 (4.7-13.7)
3.3
0.6
5.3 (n.s.)
3.1
0.8
3.4
Generalized anxiety
11.2
3.6
3.1 (1.8–5.3)
—
—
—
7.7
9.0
1.1 (0.5–2.6)
Panic (0.8–12.5)
13.1
4.2
4.2 (2.4–7.5)
3.3
1.8
1.9 (n.s.)
23.3
1.6
3.2
Agoraphobia
5.1
1.0
5.2 (2.6–10.5) 13.3
2.4
5.5 (<.001)
—
—
—
Phobia
23.5
7.8
3.5 (2.2–5.5)
10.0
5.4
1.9 (n.s.)
14.6
9.5
2.3. (1.2–4.3)
Obsessivecompulsive
—
—
—
16.7
2.3
7.2 (<.001)
3.9
1.1
3.5 (1.5–9.7)
Schizophrenia
—
—
—
0.0
0.7
n.s.
0.9
2.1
0.6 (0.1–2.9)
*
Actual prevalence rates are not presented for nongamblers. Percentages are derived from odds ratios. CI, confidence interval; n.s., not significant. Blanks indicate that disorder was not assessed.
Research and Measurement Issues in Gambling Studies
Diagnosis
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Toneatto and Brennan (2002) found current rates of pathological gambling to be 11% in 580 individuals seeking residential addiction treatment. In methadone maintenance samples, rates of gambling disorders are similarly high.The largest sample of 220 methadone patients found that 7% were pathological gamblers (Feigelman et al. 1995). Even higher rates of pathological gambling are noted in some of the smaller surveys.With 62 methadone patients, Ledgerwood and Downey (2002) found rates of current pathological gambling to be 18%, and Spunt et al. (1996) reported rates of pathological gambling to be 16% among 167 methadone patients.These rates are substantially higher than the 0.42% to 2% rates of pathological gambling in general population surveys (Bland et al. 1993; Gerstein et al. 1999; Petry et al. 2005; Welte et al. 2001). Thus, substance use disorders and pathological gambling are clearly linked.
MOOD DISORDERS Only a few studies have evaluated the presence of other psychiatric disorders along with pathological gambling in general population surveys. As shown in Table 12.1, the NESARC study found that rates of major depression were about three times higher in pathological gamblers relative to nongamblers (Petry et al. 2005). Similarly, in the ECA survey of St. Louis–area residents in the early 1980s, Cunningham-Williams et al. (1998) found that being a problem or pathological gambler significantly increased the chances of having major depression by about three-fold. Bland et al. (1993), in Canada, found that 20.0% of pathological gamblers, compared with only 12.4% of the nonpathological gamblers, met criteria for major depression, although in this study the odds ratio was not statistically significant. Rates of dysthymia, a disorder characterized by chronically depressed mood for over two years, were significantly elevated in pathological gamblers in the NESARC study, with about a three-fold elevated risk. Bland et al. (1993) also found significantly higher rates of dysthymia in pathological gamblers compared with nongamblers: 20.0% versus 4.9%, respectively. However, prevalence rates of dysthymia were not significantly higher among problem and pathological gamblers in the Cunningham-Williams et al. (1998) study, although trends were noted. Bipolar disorder is generally considered an exclusionary criterion for pathological gambling, unless the two disorders occur independently. Neither Bland et al. (1993) nor Cunningham-Williams et al. (1998) found significantly elevated risk of bipolar disorder in their samples. However, in the much larger NESARC sample (Petry et al. 2005), rates of a manic episode were eight-fold higher in pathological gamblers compared with nongamblers. Given the high rates of co-occurrence, perhaps the exclusionary criterion should be reconsidered. Only a few studies have systematically examined rates of mood disorders in treatment-seeking pathological gamblers.While all these studies suffer from relatively
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Research and Measurement Issues in Gambling Studies
small sample sizes, they do point to high rates of depression among those who seek treatment for gambling problems. Four studies (Linden, Pope, and Jonas 1986; McCormick et al. 1984; Ramirez et al. 1983; Taber et al. 1987) evaluated major depression in pathological gamblers who were treated on inpatient units, and lifetime rates ranged from 32% to 76% across these samples. Specker et al. (1996) found that gamblers seeking outpatient treatment had about three times the rate of major depression compared with nonpathological-gambling matched controls, with lifetime rates of major depression of 70% and 23%, respectively. Two studies (Linden et al. 1986; McCormick et al. 1984) also found elevated rates of hypomania (38%) and manic episodes or bipolar disorder (8–24%) in treatment-seeking gamblers.These rates are clearly higher than general population rates of mania and bipolar disorder (Regier et al. 1990), but the rates are confounded by the fact that the majority of these patients were receiving inpatient psychiatric treatment. Patients receiving outpatient treatment, by definition, are likely to have less severe problems, but few studies of prevalence rates of mood disorders in outpatient gamblers exist. Nevertheless, the available data do suggest a strong link between pathological gambling and mood disorders, whether epidemiological or treatmentseeking samples are studied.
ANXIETY DISORDERS Anxiety disorders have also been evaluated with respect to comorbidities with pathological gambling, albeit in relatively few studies. Cunningham-Williams et al. (1998) found that being a problem gambler significantly increased the risk of phobias but not of any other anxiety disorders. Bland et al. (1993) found that pathological gamblers were significantly more likely than nonproblem gamblers to have any anxiety disorder, with rates of 26.7% versus 9.2%. In terms of specific anxiety disorder, agoraphobia occurred at significantly higher rates in the gamblers, but social phobia, simple phobia, and panic disorders did not differ significantly based on gambling status. Both Bland and colleagues (1993) and CunninghamWilliams et al. (1998) also noted an increased risk for obsessive-compulsive disorder among pathological gamblers. The NESARC study also addressed rates of anxiety disorders among pathological gamblers and nongamblers (Petry et al. 2005). Every anxiety disorder assessed occurred at significantly higher rates among pathological gamblers, including generalized anxiety disorder, panic disorder with and without agoraphobia, specific phobias, and social phobia, each with odds ratios greater than 3. In terms of treatment-seeking gamblers, only three known published studies have evaluated rates of anxiety disorders. In 25 Gamblers Anonymous (GA) members, Linden et al. (1986) found that 4% exhibited symptoms of social phobia, generalized anxiety disorder, or agoraphobia with panic disorder; 8% had simple phobia; and
Comorbidity and Mental Illness
311
16% had panic disorder.These diagnoses were not mutually exclusive, and 28% of the sample had one or more anxiety disorders. Ibanez et al. (2001) found lifetime rates of any anxiety disorder of 7.2% in pathological gamblers compared with 4.3% in controls, but these rates did not differ significantly. Specker et al. (1996) likewise did not find statistically significant differences in a small sample of 40 outpatient gamblers compared with controls. Rates of any anxiety disorder were 37.5% versus 22.5%. While these small studies did not necessarily reveal significant differences between groups, the lack of statistical significance may be related to power. Overall, anxiety disorders appear to occur more frequently in pathological gamblers than in the general population.
OTHER DISORDERS The Cunningham-Williams et al. (1998) and Bland et al. (1993) studies are the most informative for determining co-occurrences of other psychiatric disorders and pathological gambling in the general population. Cunningham-Williams et al. (1998) found that being a problem or pathological gambler significantly increased the odds of schizophrenia 3.5-fold. Bland et al. (1993) did not uncover any pathological gamblers with schizophrenia in their study, nor did they find a relationship between pathological gambling and eating disorders. Similarly, there is little evidence of high rates of eating disorders in treatment-seeking samples of pathological gamblers (Specker et al. 1996), although few studies have yet evaluated this issue. Furthermore, very few individuals with schizophrenia and gambling problems seek treatment at the few gambling treatment clinics that have systematically collected data on this psychiatric disorder (e.g.,Taber et al. 1987). In sum, data are lacking regarding the association between pathological gambling and other psychiatric conditions. However, given the strong and consistent relationships noted between pathological gambling and substance use, mood, and anxiety disorders assessed in the NESARC study, it is likely that other psychiatric conditions are elevated in pathological gamblers as well.
DIAGNOSTIC MEASUREMENT ISSUES Data reviewed above clearly indicate that psychiatric comorbidities are common among pathological gamblers, but a number of measurement issues affect interpretations of these findings. One important consideration is the reliability and validity of instrument(s) used to evaluate psychiatric diagnoses, including gambling. Assessment instruments for pathological gambling are covered thoroughly in Chapter 8, so the present chapter will focus instead on measurement issues specific to the classifications of other psychiatric conditions.
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Research and Measurement Issues in Gambling Studies
STRUCTURED INSTRUMENTS FOR ASSESSING PSYCHIATRIC DISORDERS Different classification systems are used throughout the world, and across these classification systems, symptoms and methods of psychiatric diagnoses vary. For example, the diagnosis of anxiety disorders varies depending upon use of either the International Classification of Diseases, tenth revision (ICD-10), or the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (Gavin and Slade 2002). Compounding the complications with the diagnostic classification system are cultural or ethnic issues, such that psychiatric symptoms and even classifications can vary, sometimes quite substantially, cross-culturally. For instance, in some cultures major depression symptoms may be experienced and reported in more somatic terms (e.g., as aches, weakness) than emotional terms (e.g., in terms of sadness, guilt) (American Psychiatric Association 1994). Also, cultural idioms of distress, such as “feeling blue” for sadness or “butterflies” for anxiety, are not necessarily relevant or easily translated to other languages (Minsky et al. 2003). Finally, the language used in the interview and the ethnicity of the assessor may influence clinical judgment of symptom severity and ultimately diagnosis (Malgady and Costantino 1998). Combined, these issues impact the accuracy of diagnoses across samples and populations. In addition to cultural differences and variations in classification systems themselves, a variety of instruments are available to diagnose psychiatric conditions.These instruments vary, in part, upon the classification system used. Some of the most common include the Diagnostic Interview Schedule (DIS) (Robins et al. 1988) and the Structured Clinical Interview for DSM-IV (SCID) (First et al. 1996). Other instruments are available—such as the Composite International Diagnostic Interview (CIDI) (World Health Organization 1997) and the MiniInternational Neuropsychiatric Interview (MINI) (Sheehan et al. 1998)—but have not yet been applied to gamblers. The DIS is a structured interview for Axis I disorders designed to be administered by nonprofessionals and used primarily in epidemiological studies. Administration can take between one and three hours, with questions read verbatim and response choices for the DSM, third edition, revised (III-R), being yes and no. A computer-based scoring system then yields both current and lifetime diagnoses. In comparison with other diagnostic interviews, the DIS is less expensive in terms of training, supervision, and data management costs, and it has been translated into other languages. Unfortunately, it has only modest psychometric support (e.g., Helzer, Spitznagel, and McEvoy 1987).Test–retest studies find that coefficient kappa (i.e., agreement beyond chance) is at best “fair,” especially for diagnosis of some lifetime disorders. Psychometric studies have also found only modest agreement in regard to diagnostic status between lay interviewers and psychiatric professionals (Helzer et al. 1987). Nevertheless, the DIS appears to correlate well with other clinical measures of psychiatric severity (Whisman et al. 1989).
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The SCID is a flexible, semi-structured interview that assesses current and lifetime DSM-IV Axis I and Axis II disorders for clinical, research, and training purposes. The instrument is organized into modules that assess different disorders, so that it can be customized depending on need and time available. Administration time of the SCID can range from 1–4 hours. However, interviewers must have background training in psychopathology, the DSM-IV, and basic interviewing skills, as symptoms are classified along a continuum ranging from “not present” to “present—clinically significant.” In contrast to the DIS, the SCID has more extensive psychometric support, including test–retest reliability, interrater agreement, and concurrent validity (Kidorf et al. 1998; Maziade et al. 1992). However, because of the costs associated with training and personnel, the SCID is rarely used in epidemiology studies and is more often used in clinical research settings. The DIS and SCID were most often used in the aforementioned studies evaluating comorbid psychiatric conditions associated with pathological gambling. For example, the Bland et al. (1993) study used a version of the DIS from the DSM-III, as did the Cunningham-Williams et al. (1998) study. The Welte et al. (2001) and Gerstein et al. (1999) studies, which focused primarily on association of gambling with alcohol use disorders, used versions of the DIS based on the DSM-IV. The NESARC study also utilized a DSM-IV successor to the DIS, the National Institute on Alcohol Abuse and Alcoholism Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM-IV (AUDASIS). Presence or absence of a disorder is clearly affected by the psychometric properties of the instruments used. The SCID has more extensive evidence of reliability and validity in classifying psychiatric disorders than the DIS, but the DIS is simpler to administer. Further, some studies used skip-out criteria, so that individuals who responded negatively to a “gatekeeper” item were not administered the remainder of the items. While this practice decreases administration time, it may also hinder validity, because some individuals with the disorder may not be administered the full battery. Despite differences in psychometric properties and administration across studies, more similarities than differences are noted in terms of patterns of comorbities outlined above. Differences between pathological gamblers and nongamblers in prevalence rates sometimes varied with respect to statistical significance, but variations in significance levels may reflect issues with power rather than actual differences in relationships across conditions. Sample sizes varied vastly across epidemiology studies, ranging from 2,417 to over 43,000 respondents. Disorders that occur at relatively low baseline rates, such as pathological gambling, are likely to have wide confidence intervals when samples are small. When examining comorbidities between conditions which both occur at low rates (e.g., pathological gambling and bipolar disorder), these confidence intervals are expected to be quite large. In fact, data on prevalence rates of
314
Research and Measurement Issues in Gambling Studies
other psychiatric conditions among pathological gamblers were drawn from between 30 (Bland et al. 1993) and 195 (Petry et al. 2005) pathological gamblers in total, who were identified within the larger epidemiological surveys. In the Cunningham-Williams et al. (1998) study, the number of pathological gamblers identified was so low that both problem and pathological gamblers were combined into one group for analyses. Patterning of comorbidity clearly may vary when individuals with the subthreshold condition are included. Differences in prevalence rates of psychiatric conditions across studies were not limited to the problem and pathological gamblers but also extended to the larger group of nongamblers used as comparisons. A number of hypotheses can be invoked to explain variations in prevalence rates of psychiatric conditions obtained across samples. Obvious explanations include the use of different samples, diverse instruments, in-person versus telephone interviews, and response biases or participation rates.The NESARC survey (Petry et al. 2005) was conducted with a much larger sample than the other studies, used in-person evaluations, and had a very high response rate (81%).The Bland et al. (1993) and Cunningham-Williams et al. (1998) surveys were also conducted in-person, and had relatively high response rates of 71% and 80%, respectively. In contrast, Gerstein et al. (1999) and Welte et al. (2001) had response rates of 56% and 65%, respectively, and these latter surveys were done over the telephone. Another issue that complicates understanding of the relationships between these psychiatric conditions relates to time frames. Studies of diagnostic instruments and prospective studies of pathology have found that the reporting of lifetime disorders is less reliable than assessment of current or past-year disorders. For example, using the DIS in an 11-year prospective study of college students followed into young adulthood, Slutske, Jackson, and Sher (2003) found the lifetime rate of problem gambling to be grossly underreported. By the last year of the study, about 50% of the respondents who had, at some point earlier, endorsed problem gambling now denied ever having problems with gambling in their lifetimes. Similar inconsistencies are noted with other Axis I disorders (e.g., Ross et al. 1995), raising concerns about the accuracy of lifetime diagnoses. Complicating matters further, some studies focused on evaluating lifetime rates, while others examined current rates, of either gambling or the other psychiatric conditions. Most of the data presented in Table 12.1 and throughout the first half of this chapter related to lifetime rates of both pathological gambling and the other conditions. However, these data do not reflect the proportions meeting current diagnostic criteria, of either or both disorders.Therefore, the question that remains unanswered concerns the temporal relationships between pathological gambling and other psychiatric conditions. That is, are people with depression or anxiety disorders more likely to develop pathological gambling than people without these (or other) psychiatric conditions? Or, does pathological gambling increase the propensity of developing depression and anxiety disorders? If one condition abates
Comorbidity and Mental Illness
315
naturally or in response to treatment, do other symptoms and conditions emerge or do they also subside? Few studies have evaluated these issues in regard to pathological gambling, and those that have (e.g., el-Guebaly, 2005; McCormick et al., 1984; Specker, et al., 1996) generally suffer from sample-size issues, with too few participants to accurately determine temporal associations. The onset and patterning of pathological gambling and other psychiatric disorders is variable. Examples abound of pathological gambling being a secondary disorder to other psychiatric disorders, especially substance use disorders (e.g., Cunningham-Williams et al. 2000). In contrast, the onset of major depression was found to be equally likely to precede or to follow the development of pathological gambling in one study (Hodgins, Peden, and Cassidy 2005) and more often followed the onset of pathological gambling in others (Taber et al. 1987).As legalized gambling becomes more readily available to current and future generations, different patterns may emerge, presumably with pathological gambling being more likely to co-occur or precede other conditions. Clearly, these reports highlight that our knowledge is limited concerning the temporal relationship between pathological gambling and other psychiatric disorders. The cross-sectional nature of most studies further confounds interpretation of results. Cross-sectional studies yield estimates from one specific point in time and rely on retrospective reports. Prospective studies are needed to elucidate the pattern and relationship between pathological gambling and other psychiatric disorders. Few such studies have been undertaken, and they usually focus on the broader definition of problem gambling (Abbott, Williams, and Volberg 2004; Slutske et al. 2003), due to the low base rate of pathological gambling. An advantage of longitudinal studies is that individuals are followed up in real time over a course of several years, but these types of studies are very costly to administer. Some studies have focused instead on symptom assessment, which is typically simpler to evaluate, as described later in this chapter.
ASSESSMENT
OF
PSYCHIATRIC SYMPTOMS
The study of psychiatric symptoms has some benefits over the study of diagnostic categories. Persons (1986) highlights many of these advantages, including avoidance of diagnostic misclassification; isolating single elements of pathology for study, including those ignored by a diagnostic category; and recognition of the continuum of clinical phenomena with normal phenomena. Symptom assessment instruments go beyond measuring the presence or absence of a condition to assess severity. Severity is the extent to or intensity at which a symptom is present. For example, the Beck Depression Inventory (BDI) (Beck et al. 1979) measures depression along a continuum from no depression to severe depression.Assessment of a variable along a continuum allows for a finer grain of measurement and may more accurately
316
Research and Measurement Issues in Gambling Studies
reflect individual differences than would dichotomous assessment (Nunnally and Bernstein 1994). A number of instruments are available to assess psychiatric symptoms, the most commonly used inventories being the Short Form-12 (SF-12) (Ware, Kosinski, and Keller 1995), the BDI, the Beck Anxiety Inventory (Beck and Steer 1987), the Brief Symptom Inventory (BSI) (Derogatis 1993), and the Spielberger State Trait Anxiety Inventory (STAI) (Spielberger 1983). These are self-report assessments and therefore are easy to administer, have substantial psychometric support, and are sensitive to changes over time. In addition, norms exist for both inand outpatient psychiatric samples and for gender and age community controls, allowing for comparison with normative groups. However, these instruments do not assess diagnoses. Another frequently used measure with good psychometric support is the Addiction Severity Index (ASI) (McLellan et al. 1992). The ASI assesses severity over the past 30 days in seven problem domains often impacted by gambling, including medical, employment, legal, drug, alcohol, psychiatric, and family/social difficulties (Lesieur and Blume 1992; Petry 2003). It also assesses some lifetime problems in these areas. Composite scores are derived for the past-month items, and values range from 0 = no problems in the area to 1 = severe problems. The ASI has been extensively used in substance abuse treatment research, and composite scores are sensitive and responsive to treatment (e.g., Alterman et al. 1994). A potential drawback of the ASI includes a dearth of studies in community samples. In addition, the instrument is usually administered in an interview format, although computerized and self-report forms are now being evaluated. Both epidemiology and community studies have investigated psychiatric symptoms in pathological gamblers. Epidemiological studies have found that psychiatric symptoms are greater in pathological gamblers relative to nonpathological gamblers.The NESARC study (Petry et al. 2005) found that pathological gamblers reported greater psychiatric disability in comparison with nonpathological gamblers using the SF-12. Gerstein et al. (1999) found similar results in their study with pathological gamblers endorsing more severe emotional/mental health problems than nonpathological gamblers. Interestingly, both of these studies involved a lifetime diagnosis of pathological gambling and still found significant differences between pathological and nonpathological gamblers on indices of current psychiatric symptom severity. Studies involving treatment-seeking pathological gamblers also found increased psychiatric symptom severity. Petry (2003) found that pathological gamblers seeking treatment reported greater psychiatric distress in comparison with frequent gamblers, as assessed by the ASI. Getty, Watson, and Frisch (2000), using the BDI, found that pathological gamblers involved in GA had elevated depression scores in comparison with matched controls. Pathological gamblers in that study scored in the mild to moderately depressed range, while the controls scored in the
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minimally depressed range. Other studies have also noted significant symptoms such as depression and anxiety (Blaszczynski, McConaghy, and Frankova 1991a; Hodgins and el-Guebaly 2004; Leblond, Ladouceur, and Blaszczynski 2003; Milton et al. 2002).Together, these studies indicate that gamblers entering treatment are experiencing significant psychiatric symptoms in addition to or as a result of gambling. Fortunately, intervention studies also find that treatment is associated with reductions in psychiatric symptoms. In a study examining the efficacy of cognitivebehavioral therapy for pathological gambling (Petry et al. 2006), BSI scores decreased over time among participants in all conditions, even those receiving minimal interventions. However, between group differences emerged such that patients who received individual cognitive-behavioral therapy evidenced the greatest reductions in BSI scores.That study followed participants for 1 year. Another study (Blaszczynski et al. 1991a) reinterviewed pathological gamblers about five years after treatment and found that those in remission from gambling scored in the minimally depressed range on the BDI, while those still experiencing gambling problems scored in the mild to moderately depressed range. Differences were also noted between groups on measures of anxiety and psychiatric functioning. Overall, these findings suggest that gamblers who seek help experience reductions in psychiatric distress, and those who abate gambling show the greatest improvements. However, questions remain as to the nature and mechanisms of these changes. Some treatment studies have gone further and investigated whether severity of psychiatric symptoms is a moderator of treatment outcome. A moderator is a variable that influences the direction and strength of a dependent variable, such as retention in treatment or reductions in gambling problems. Several studies investigated psychiatric symptom severity as a moderator of retention and found no association. For example, Leblond et al. (2003) and Milton et al. (2002) found that baseline scores on the BDI were not associated with treatment dropout. In a study of GA attendance, Brown (1986) also found no differences between dropouts and continuers on occurrence of adverse life situations, a proxy for psychiatric symptom severity. Studies have also investigated symptom severity in relation to gambling outcomes. Petry et al. (2006) found that baseline BSI scores were not associated with gambling abstinence during the treatment period or at a 12-month follow-up evaluation in 231 pathological gamblers. In a smaller sample of 40 gamblers, Milton et al. (2002) also found that baseline anxiety and depression scores did not predict outcomes. These data appear to suggest that treatments work equally well for gamblers with substantial versus minimal psychiatric symptom severity. The relationship between other psychiatric phenomena and pathological gambling is also starting to garner attention. Reductions in alcohol and drug abuse across time have been noted for pathological gamblers entering treatment (Blaszczynski, McConaghy, and Frankova 1991b; Taber et al. 1987), and drug and alcohol use have been related to treatment dropout in at least one study (Milton et al. 2002). Further evaluation of psychiatric and substance use problems both
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Research and Measurement Issues in Gambling Studies
preceding and during treatment may help to identify gamblers who may require more intensive or comprehensive services and the ways in which other symptoms interact with gambling. The assessment of psychiatric symptom severity is typically limited to past month or past week.These brief time frames provide an accurate assessment of current frequency and intensity of transitory states such as depression and anxiety. Other measures are usually required to assess other time frames, such as past year or lifetime. The STAI measures current anxiety symptoms (i.e., state) and also measures stable individual differences in anxiety (i.e., trait) and is more akin to a lifetime measure of anxiety. Likewise, the ASI measures both lifetime and recent problems, allowing for a more comprehensive evaluation across a number of dimensions. The time frame of the assessment (e.g., past week, past month, past year) is noted because it is a critical issue in the measurement of psychiatric symptomatology. Recent time frames are beneficial for measuring change across time and demonstrating efficacy of interventions, while longer time frames represent more stable individual differences (Maruish 1994). Another concern is that many studies either did not assess relevant psychiatric symptoms, such as anxiety and depression, or used measures with little or unknown psychometric support. Evaluating psychiatric symptoms lengthens assessment time, but it also allows for a more detailed analysis of very common co-occurring problems in gamblers, which ultimately may guide and improve treatments.
RESEARCH CONCERNS AND SUMMARY In sum, pathological gambling is highly comorbid with other psychiatric conditions, yet a number of measurement issues impact understanding of these relationships. In terms of psychiatric diagnoses, there is a need to better delineate similarities and differences across classification systems, including use of culturally sensitive instruments that are reliable and valid across cultures, ethnicities, and age groups (see Chapters 17 and 18). Unfortunately, psychiatric classification is a lengthy process. Brief instruments are not yet available for diagnoses, but development of computerized versions may aid in reducing personnel time of administration. Another issue that impacts our understanding of comorbidity relates to sample sizes. As reviewed earlier, pathological gambling is a disorder that occurs at a low baseline rate, and hence evaluating its relationship to other psychiatric conditions, especially those that are also relatively rare, requires very large sample sizes. The NESARC study is the largest psychiatric epidemiology study conducted to date, and it demonstrated conclusively that pathological gambling is comborbid with a range of conditions. Nevertheless, only 195 pathological gamblers were identified among over 43,000 persons surveyed.The second wave of the NESARC is under way but does not include a module on pathological gambling, so it may
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be a long time before additional large-scale epidemiological data are available about pathological gambling. Evaluating comorbidity in treatment-seeking samples may be easier to do, but few pathological gamblers seek or receive treatment services (National Research Council 1999).Thus, treatment-seeking samples may be skewed toward those with more severe gambling and/or psychiatric problems. Nevertheless, examination of psychiatric comorbidities in treatment-seeking gamblers may aid in guiding treatment recommendations.To date, little research is available about the best ways to treat pathological gamblers who do present for treatment (Petry 2005; Toneatto and Ladouceur 2003). Available data suggest that gamblers reduce gambling with no or minimal interventions and that more extended interventions may improve outcomes relative to less intensive interventions (Hodgins et al. 2001; Petry et al. 2006). While most pathological gamblers present with psychiatric comorbidity and significant psychiatric symptomatology, severity of these problems appears to reduce over time with treatment. Fewer data are available on actual psychiatric diagnoses and how or if diagnoses abate in response to gambling treatment. Thus, much research remains to be conducted to better understand psychopathology in pathological gamblers and how best to treat their condition.
GLOSSARY Comorbidity presence of two or more psychiatric disorders within an individual. Psychometric studies research studies investigating reliability and validity.
REFERENCES Abbott, M.W.,Williams, M. M., and Volberg, R. A. (2004). A prospective study of problem and regular nonproblem gamblers living in the community. Substance Use and Misuse, 39, 855–884. Alterman, A. I., O’Brien, C.P., McLellan, A. T., August, D. S., Snider, E. C., Droba, M., Cornish, J. W., Hall, C. P., Raphaelson, A. H., and Schrade, F. X. (1994). Effectiveness and costs of inpatient versus day hospital cocaine rehabilitation. Journal of Nervous and Mental Disease, 182, 157–163. American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington, DC: Author. Beck, A. T., Rush, A. J., Shaw, B. F., and Emery, G. (1979). Cognitive Therapy of Depression. New York: Guilford Press. Beck, A. T., and Steer, R. A. (1987). Beck Depression Inventory Manual. San Antonio, TX: The Psychological Corporation/Harcourt Brace Jovanovich. Bland, R. C., Newman, S. C., Orn, H., and Stebelsky, G. (1993). Epidemiology of pathological gambling in Edmonton. Canadian Journal of Psychiatry, 38, 108–112. Blaszczynski, A., McConaghy, N., and Frankova, A. (1991a). Control versus abstinence in the treatment of pathological gambling: A two to nine year follow-up. British Journal of Addiction, 86, 299–306.
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CHAPTER 13
Research and Measurement Issues in Gambling Studies: Etiological Models Alex Blaszczynski
Lia Nower
School of Psychology Department of Medical Psychology University of Sydney Sydney, New South Wales, Australia
Center for Gambling Studies Rutgers University New Brunswick, New Jersey
Introduction Overview Etiological Models Psychoanalytic and Psychodynamic Public Health Social Reward Image Social Validation Behavioral Models Cognitive Conceptualizations Neurobiological, Genetic, and Biobehavioral Integrated Models General Theory of Addictions Biopsychosocial Pathways Summary
INTRODUCTION A primary function of research in gambling is to identify the risk and protective factors that predispose some individuals to continue gambling despite serious adverse consequences. It is well accepted that statistical principles of probability applied to gambling indicate that payout rates and overall advantage always favor the house. The cost of each gamble is a combination of the proportion of each bet retained for taxes and the “house edge,” with the remainder allocated to 323
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a prize pool for distribution to winners.This means that the longer one gambles, the greater the likelihood of losing. Under these conditions, an economically prudent person would minimize risk by limiting exposure to gambling. Yet a minority of the adult population manifest patterns of recurrent excessive gambling behavior that cause significant functional impairment across a range of domains (Walker 1992a). This chapter will provide a brief outline of early psychological explanatory accounts of the origins of problem gambling, followed by a description of more recent unitary and multifactorial etiological models.
OVERVIEW Etiological models attempt to describe the causal processes contributing to a condition or phenomenon. In the field of gambling, researchers have identified specific internal and external predictive risk factors associated with the development of pathological gambling: age, gender, impulsivity, family history, genetic vulnerabilities, and ecological variables (for a comprehensive review, see National Research Council 1999 and the chapters by Toneatto and Nguyen and Abbott in this volume). Setting these individual variables aside, it is evident that there is no single empirically validated theoretical model of gambling that effectively integrates the multivariate biological, psychological, sociopolitical, and environmental factors and processes into a cohesive conceptual framework sufficient to explain the critical pathways leading to the development of problem or pathological gambling. Indeed, debate continues as to whether the condition represents a unidimensional construct with arbitrary boundaries delineating levels of gambling according to intensity of expenditure of time and/or money, or a categorically discrete syndrome differentiated by biological and/or personality factors causing impaired control (Shaffer, LaBrie, and LaPlante 2004;Walker 1992a). Adherents of both the public health (Korn and Shaffer 1999; Shaffer et al. 2004) and dimensional models (Dickerson 1991; Walker 1992a) set aside individual psychological elements in favor of a need to attend to aggregated population, environmental, and social variables that expose community members to risk of excessive gambling. The development of problem and pathological gambling is considered the inevitable outcome of the interactive influences of the degree and duration of exposure to gambling, “addictive” or toxic structural characteristics of the nature of the gambling activity, and vigor of commercial promotions. Public health initiatives designed to promote responsible gambling, therefore, take into consideration elements of accessibility, availability, and acceptability, then attempt to modify these through government policy initiatives and legislated regulatory strategies that restrict access, limit venues and opportunities, and educate and inform participants of the risks associated with gambling.
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The categorical or “disease” model (American Psychiatric Association 2000; Blaszczynski and Nower 2002; Oldham, Hollander, and Skodol 1996) maintains that there is an underlying pathogenesis at the individual level that produces an inherent biological or intrapsychic vulnerability or predisposition that under specific circumstances results in impaired control over behavior. Pathological gambling is conceptualized as the behavioral outcome of intrapsychic conflicts, affective dysregulation coupled with poor problem-solving and coping skills, obsessive-compulsive spectrum features, addiction linked to neurotransmitter dysregulation in brain reward centers, behavioral disturbances resulting from schedules of reinforcement and/or irrational and erroneous belief schemas regarding probabilities and estimates of winning, and adverse personality traits (Blaszczynski and Nower 2002). It is important to appreciate that theoretical models explaining the etiology of gambling are not mutually exclusive but share many common overlapping elements. For example, principles of learning and contingencies of reinforcement fundamental to conditioning theories are relevant to the addiction, affective dysregulation, cognitive, and biological reward deficiency models. Essentially, all conceptual models acknowledge the interaction of key biopsychosocial variables in the etiology process. However, they assume that pathological gamblers form a homogeneous population, and these models are therefore limited by a set of fundamental principles in their search for a single, narrow, theoretically oriented explanatory cause or set of processes to account for the motivation to participate initially through to the transition to impairment in control and consequent persistence over time. There is increasing evidence that although sharing common attributes, pathological gamblers differ among themselves psychologically and demographically in many important respects, which precludes application of the same etiological processes to all members of the gambling population. Converging lines of emerging research suggest the existence of distinct subgroups of pathological gamblers. Although the clinical manifestations and presentations may appear superficially similar, there are significant differences in their causal pathways that offer important distinctions with a major influence on management, course, and prognosis. The following sections will describe the predominant etiological models.
ETIOLOGICAL MODELS PSYCHOANALYTIC
AND
PSYCHODYNAMIC
The primary focus of early concerns with gambling rested with social and political agencies attempting to curtail moral corruption, crime, and public and social disorder associated with gambling, or to protect vulnerable gamblers
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from exploitation and cheating by unscrupulous operators. The moral weakness view of gambling, prevalent until the twentieth century, failed to distinguish social from immoderate and excessive gambling and accepted all forms of the behavior as sinful and a regression to primitive instincts (France 1902). Heralding contemporary explanations of the motives underlying excessive gambling and its perception as an addiction, early formulations emphasized avarice and the pursuit of wealth or held the premise that “the true temptation [underlying gambling] is the desire which prompts most men to drink hard,” that is, excitement, escape from boredom, and “a desire to forget self ” (France 1902, p. 383). In contrast, sociological accounts showed a tendency to emphasize broader ecological aspects of industrial capitalism, in which gambling represented an escape from regimentation, poor quality of life, and a concomitant sense of alienation and anomie (Bloch 1951; Goffman 1969; Herman 1976). Commencing with von Hattinger’s (1914) case study, the earliest formal etiological theories were psychoanalytic and psychodynamic in orientation. Freud (1945) equated gambling to a compulsive neurotic state which was conceptualized as an addiction in the same context as alcoholism and substance abuse (Galdston 1951; Rosenthal and Rugle 1994). Gambling was seen as the manifestation of an addiction with masturbation, which was considered the “primal addiction for which all later addictions are substitutes” (Herman 1976, p. 94). Freud (1945) contended that as a result of unfulfilled desires for his mother and wishes for his father’s death, the gambler remained stationary in a compulsive neurotic state engaging in self-punishment as in the form of masturbatory obsession played out in the arousal and stimulation of gambling. Although there is no detailed account of his direct involvement in the treatment of a case of pathological gambling, Freud (1945) provided a fascinating psychoanalytic analysis of the Russian novelist and pathological gambler Fyodor Dostoevsky founded on the content of the novelist’s letters and writings. Freud hypothesized that gambling provides a substitute for unresolved sexual conflict. Fueled by guilt and depression, the gambler bets, not for the money, but for excitement generated by ambivalence.The gambler commits moral masochism to punish himself by losing, while, alternately, continuing the activity in hopes of receiving the “love” of the symbolic parent while winning. McCormick and Taber (1987) hypothesized that this ambivalence is generated by “the early presence in the patient’s life of a parent figure who was perceived as punishing and demanding, yet evocative of respect and fear” (McCormick and Taber 1987, p. 10); for example, Dostoevsky’s father, who died suddenly when the novelist was 18 (Herman 1976). Gambling becomes an unconscious reenactment of childhood rebellion against parental figures and propels the gambler toward life situations of rejection that need to be overcome (Bergler 1957). These situations result in an ever-present balancing of a pleasure–pain tension level fueling the gambling. The gambler masks that tension with narcissistic charm that actually belies hostile
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and aggressive personality features that are postulated to underlie the addiction (Lindner 1950). Freud’s analysis laid the foundation for the myriad of subsequent psychodynamic explanations, emphasizing a range of intrapsychic processes and conflicts: sexualization of gambling, the presence of underlying psychoneuroses related to regressions to pregenital psychosexual phases of development, regressive infantile conduct and attempts to obtain longed-for erotic satisfaction, fulfillment of primitive superego demands, and challenges and provocations to destiny and fate (Fenichel 1945; Kris 1938; Laforgue 1930; Reik 1942; von Hattinger 1914). Fenichel (1945) observed that gamblers present as narcissists, feeling entitled to privilege and exempt from negative consequences and fate’s dire circumstances. In contrast, Galdston (1951) suggested that the weak ego state of the gambler caused him to reach out to a surrogate parent—Lady Luck—in a symbolic attempt to garner favor and approval. This effort, of course, meets with continual frustration. Harris (1964) summarized the varying psychoanalytic positions by concluding that all possess three characteristic conceptions of gambling, as: (a) an unconscious substitute for pregenital libidinal and aggressive outlets associated with unresolved Oedipal conflicts, (b) the wish for punishment emerging as a reaction to guilt associated with indulgences in forbidden impulses, and (c) a medium for repeated reenactments—but not resolution—of these conflicts. It is important to note that psychoanalytic reports prior to the 1980s described clinical observations of single cases or small samples of patients whose primary diagnoses or referrals were not specifically gambling related, conducted before diagnostic criteria for the disorder were established. In addition, treatment goals and levels of gambling severity were left unspecified. Only Rosenthal and Rugle (1994) have postulated a clear rationale for the effectiveness of integrated insight-oriented supportive therapies, though their argument is largely theoretical. In general, psychoanalytic explanations are impossible to empirically validate and are therefore of limited utility in explaining the pathogenesis of pathological gambling (Cornish 1978; Rosecrance 1985). As a result, the scientific standing of psychoanalytic and psychodynamic frameworks as empirically supported models for pathological gambling has failed to advance significantly over the last 20 years, as public health, behavioral, cognitive-behavioral, biological, and addiction models have assumed greater importance.
PUBLIC HEALTH Gambling occurs within a social context in which availability, accessibility, and acceptability are influenced by a myriad of socioeconomic and political influences. Government legislation dictates the nature and range of legalized gaming
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products, their distribution and location within communities, age restrictions, and enforcement procedures to ensure that commercial advertising and promotional activities comply with appropriate codes of conduct and legislative requirements. In addition, the structural characteristics of gaming products and environmental contingencies in the venues have implications for their toxic or “addictive” quality and likelihood of creating problems (Dowling, Smith, and Thomas 2005; Griffiths 1993;Walker and Dickerson 1996). The public health model of gambling, therefore, adopts a critical position in pursuing a population-based prevention orientation. Accordingly, this model shifts the focus of attention away from micro-level individual intrapsychic and personality variables to aggregated data describing elements at a macro level. It is important to stress that the model does not ignore micro (psychological) factors. Rather, it focuses on mapping global risk and protective factors that contribute to the transition from recreational to problem gambling and the distribution of gambling within the community to identify vulnerable ethnic and other subcommunities (Shaffer et al. 2004). The public health model, derived from an epidemiological framework, is geared toward assessing and monitoring harm caused by gambling and shaping government policies designed to reduce harm and guarantee equitable and timely access to treatment. Korn and Shaffer (1999) and Korn (2001) were the first to formally apply the communicable disease control paradigm to gambling in describing the complex interrelationship between the host (gambler), agent (gambling opportunity), and vector (money) operating within a specific environment (family, sociopolitico-economic, and cultural). The central tenet of this model, as eloquently described in a 2004 article by Shaffer et al., is that exposure to gambling (by analogy, equivalent to exposure to germs) is a necessary but not sufficient factor that “infects” individuals insofar as it influences patterns of gambling behavior and problem gambling; increased risk for problem gambling is functionally related to duration of exposure and toxicity of the product. Social, cultural, religious, and ethnic attitudes toward gambling may serve as protective factors—for example, contributing to low prevalence rates among Islamic and some fundamentalist Christian communities (Seventh-Day Adventists, Mormons). Evidence supporting the public health model is sourced from epidemiological studies suggesting a link between gambling availability and higher prevalence rates (see Abbott, this volume; Petry 2005), although Shaffer et al. (2004) assert that there is minimal scientific data to demonstrate a definitive causal relationship between the two. These authors point to studies that produce conflicting data on this topic. Nevertheless, there is evidence that the introduction of opportunities into a virginal gambling environment is associated with increased participation rates with diminishing marginal increases after the market reaches saturation (Abbott, this volume).
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Shaffer et al. (2004) also advanced the concept of social adaptation to gambling.This social adaptation model is based on the notion that individuals and communities learn to adapt to changes within their environment in such a way that a cohort follows a progression, beginning with escalating involvement in the early, introductory phase of gambling in a community, which levels to a plateau as the market matures, which in turn is followed by environmental adaptation. The next cohort generation fails to experience novelty or “honeymoon” effects of gambling and/or may be exposed to or experience gambling-related harm within the family or society; thus, they become more cognizant of the negative aspects of gambling. This theory is supported by longitudinal data, suggesting lower rates of problem gambling among Nevada adolescents and the reduction in prevalence rates in Canada (Wynne, Smith, and Jacobs 1996) and Minnesota (Stinchfield 2001). The public health model is predominantly concerned with the external societal determinants of gambling, suggesting that strategies designed to ameliorate gambling-related harms are best pursued through public policy decision-making initiatives. The model does not address concerns at the individual psychological and biological level that explain why only some individuals develop problem gambling behaviors.
SOCIAL REWARD A few theorists have provided etiological conceptualizations based on social reward. There is little empirical validation for these theories; however, two of the more creative hypotheses derived from these theories provide constructs that could yield interesting and important information on the role of the social system in pathological gambling. Image Holtgraves (1988) asserted that the urge to gamble is rooted in a need to present a desired image to others. He theorized that that desire, labeled “selfpresentation,” would encompass other explanations for gambling, like the gambler’s desire to be punished (Bergler 1957), to affirm his existence (Kusyszyn 1977), and to escape boredom (Herman 1976). Self-presentation theory holds that actions carry messages about the image of the social actor. As a result, the actors attempt, consciously or unconsciously, to use their actions to control their image. The image of the gambler is multifaceted. Gambling involves risk taking and is usually conducted in a social setting (e.g., casino, racetrack) that promotes extravagance and a “carefree, reckless, freewheeling image” that bestows prestige (Holtgraves 1988, p. 81). Holtgraves (1988) added that “fateful” activities like
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gambling allow for an expression of character: Even when losing, the gambler can display “courage, gameness, integrity and composure” (p. 82). In two preliminary studies, Holtgraves (1988) found that four ego ideals similar to those suggested by Goffman (1969) emerged as motivations for gambling behavior: 1. 2. 3. 4.
competence (perceptive, skilled responsive, observant), confidence (venturesome, positive outlook), control (deliberate, consistent, composed), and sociability (personable, sportsmanlike).
In addition, despite the fact that all gambling outcomes are determined by chance, gamblers rated their peers who won as more competent (i.e., perceptive, skilled, responsible) than those who lost. Betting style also affected perceptions of confidence and control. Gamblers who continued to increase their wagers were perceived as more confident but less controlled than those who bet the same amount each time. Holtgraves (1988) claimed that these findings suggest that “riskiness” can result in the “imputation of both positive qualities (venturesome, confident, competitive, positive outlook) and negative qualities (impulsive, inconsistent, and excitable)” (p. 86). Social Validation Ocean and Smith (1993) broadened the concept of social reward by positing a theoretical model to explain how the gambling venue as an institution perpetuates the need for social validation, which ultimately may result in pathological behavior. These authors hypothesized that a casino environment offers social rewards which include “group affiliation, emotional and moral support, selfesteem, social status and salient identity” (p. 334). At the same time, “outside society” fosters stigmatization, disculturation, and value conflicts, particularly for members like disenfranchised minority groups and those who are unwilling or unable to conform to societal norms. Gambling provides double reinforcement: Social rewards (positive reinforcers) increase the gambler’s commitment to the casino, while the negative reinforcers from society are removed when the gambler enters the casino. As the gambler increases play, her problems with the outside world increase and thus she is more likely to seek escape in the casino. Most research on pathological gambling has focused on the personality and family of the gambler or on the gambler’s physiological or psychological state (Ocean and Smith 1993).The theory advanced by Holtgraves (1988) suggests that it would be wise to devote research to examining the seductive power of gambling institutions and to devising new ways of decreasing the alienation gamblers feel from society. Future study of minority-group and senior-citizen gamblers should provide valuable information in this area. As with the public health model, the social reward model focuses on (a) macro-level factors, with an implicit two-tiered
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assumption of homogeneity, (b) variables influencing gambling that apply equally to all gamblers, and (c) the underlying psychological dynamics as being similar for all problem gamblers.
BEHAVIORAL MODELS The underlying assumption in behavioral models is that gambling is a learned maladaptive behavior governed by principles of Skinnerian operant and Pavlovian classical conditioning. As noted by Petry (2005), variable ratio and random contingencies of immediate reinforcement where the intertrial interval (placing a bet and learning of its outcome) is short are instrumental in maintaining high-frequency repetitive behaviors that are resistant to extinction. Dickerson (1979) extended Skinner’s operant conditioning model to postulate the presence of two available reinforcers: (a) money won, reinforced on a random reinforcement schedule, and (b) excitement associated with cognitions and environmental stimuli reinforced on a fixed interval schedule to account for observed betting shop behaviors such as delayed placement of bets. Anderson and Brown (1984) further suggested a two-factor neo-Pavlovian model to account for the maintenance of pathological gambling patterns, emphasizing the classical conditioning of environmental cues and autonomic/cortical arousal, together with the negative reinforcement associated with a reduction in aversive emotional states produced by the narrowing of attention and distraction from awareness of life problems. Within these behavioral models, positive and negative reinforcement are crucial determinants.Winning money (reward) coupled with its concomitant physical and subjective arousal represents a positive reinforcement that increases the probability that a behavior (gambling) will be repeated. The unpredictability of receiving a reward delivered on a random or variable ratio schedule maintains gambling behavior, since the gambler anticipates that the next bet will result in a positive outcome. In addition, as suggested by Anderson and Brown (1984) and Jacobs (1986), a proportion of gamblers experience dissociative states that enable them to emotionally escape from stresses resulting in negative reinforcement, that is, the removal of an aversive (stress or affective) state or stimulus. Negative reinforcements similarly increase the likelihood that future behaviors will increase in frequency. Through classical conditioning, the association of gambling-related environmental stimuli with excitement through repeated pairings of conditional (gambling cues) and unconditional (winning money) stimuli becomes such that, over time, conditional stimuli are sufficient to elicit the unconditional stimuli. Accordingly, subsequent exposure to any environmental gambling cue will elicit a state of excitement that is interpreted as an urge, drive, or craving to gamble or will
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provoke a reaction that is interpreted as a withdrawal state.Any positive (wins) and negative reinforcement (elimination of the urge, drive, or craving and withdrawal state) will consolidate the operant and classical conditioning processes. McConaghy (1980) advanced the behavior completion mechanism model, which suggests that habitual gambling results from a process of cortical excitation in which repeated occurrences of complex sets of behaviors establish a neuronal representation of those habits in the cortex.With each repetition, the set of behaviors becomes increasingly consolidated to form a habitual pattern of behavior. Consistent with Sokolov’s orienting reflex model, formulated in 1963, incoming stimuli are compared against cortical representations of habitual behavior patterns, and if matching occurs, the drive to complete the habit is inhibited. If mismatching occurs, the behavior completion neuronal model initiates a drive for the individual to continue engaging in the sequence of behaviors until the habit is carried out to its completion. Consequently, interruption of the habit results in a state of aversive physical arousal or tension that is experienced as a persistent drive to carry out the habit. Once the habit is successfully completed, the drive is satisfied and the aversive state of arousal dissipates. This model is consistent with the principles underlying operant conditioning in that positive reinforcement associated with the gambling behavior and negative reinforcement produced by the removal of the aversive arousal strengthen the neuronal model of behavior. These learning models have led to the application of a variety of operant or classical conditioning–based aversive techniques to countercondition the arousal/ excitement associated with gambling.The most frequent forms are electric shocks or nausea-producing pharmacological agents (Barker and Miller 1968; Koller 1972; McConaghy et al. 1983; Salzmann 1982; Seager 1970), covert sensitization (Bannister, 1977; Cotler 1971), and stimulus control and exposure (Greenberg and Rankin 1982). The behavior completion mechanism model led to the innovative application of imaginal desensitisation as a successful intervention (McConaghy et al. 1983). Although behavioral treatments can be effective (see Hodgins, this voume), the behavioral etiological models fail to explain (a) the reason that fewer than 5% of gamblers persist in excessive gambling or (b) the transition from an extended period of recreational gambling to excessive habitual patterns. These behavioral models also fail to explain the effects of punishment (accumulating losses) on human behavior. In animal paradigms, pigeons or rats persist in pressing levers for food even to the point of exhaustion under conditions of variable reinforcement. However, persistence does not result in other concomitant costs to the animal. In contrast, persistence in gambling is associated with significant direct response costs to the individual: emotional distress, remorse, lack of finances, and anticipated marital conflicts and legal/social consequences during or toward the end of sessions as funds are depleted and the gambler realizes the consequences of his/her action. Unlike alcohol abuse, where there is a delay in the negative effects (a hangover)
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following excessive consumption, negative emotions are experienced during or immediately after completion of gambling. Behavioral models have all but neglected the role of punishment, focusing instead on the role of positive and negative reinforcement. Though valuable in explaining persistence in gambling and underpinning addiction paradigms, pure behavioral etiological models are of limited utility as a conceptual framework explaining problem and pathological gambling. In contrast, cognitive-behavioral models acknowledge limitations by combining learning principles with additional elements.
COGNITIVE CONCEPTUALIZATIONS Without doubt, the cognitive model is most frequently utilized to guide treatment interventions for problem gambling. A number of studies have found that gamblers who receive cognitive restructuring treatment (Echeburua, Baez, and Fernandez-Montalvo 1996) in conjunction with social skills training, problem solving, and relapse prevention (Sylvain, Ladouceur, and Boisvert 1997) report decreased levels of problem gambling. (For reviews, see Hodgins in this volume, and Petry 2005.) The cognitive model is predicated on the assumption that erroneous and irrational belief structures, misunderstanding of probabilities and concepts related to randomness and mutual independence, and the drawing of causal associations between independent chance events form the basis for persistence in gambling despite accumulating losses (Ladouceur and Walker 1996; Toneatto et al. 1997; Walker 1992a).Toneatto et al. (1997) and Ladouceur and his colleagues (Gaboury and Ladouceur 1989; Ladouceur and Gaboury 1988; Ladouceur et al. 1988) have consistently found that up to 80% of the verbalizations made by a sample of problem gamblers seeking treatment were irrational or contained cognitive distortions, with a mean number of 3.5 cognitive distortions per participant. Experiences of social learning, such as vicarious exposure to parental gambling, participation in family and peer-related gambling activities, and positive parental attitudes, engender in children and adolescents positive acceptance of gambling as a legitimate recreational pastime. Experientially, early wins are instrumental in shaping beliefs that winning is possible and that gambling represents a convenient and easy source of supplementary income. Increased frequency and intensity of gambling invariably lead to a progressive, downward spiral of accumulating losses (Lesieur 1984), which in turn produces cognitive dissonance, as the gambler justifies behaviors and overestimates the probabilities of winning. There is clear evidence that irrational and erroneous beliefs are prevalent among not only problem but also recreational gamblers. Studies have identified a myriad of erroneous and irrational perceptions and cognitive distortions, identified
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by psychometric measures, experimental manipulations, and audiotaped verbalizations elicited using the “thinking aloud” technique, whereby uncensored thoughts are recorded during play. Toneatto (2002) and Ladouceur et al. (2002) provide a detailed account of the common types of cognitive distortions. These can be broadly categorized into those related to personal skill and judgment (illusions of control: Langer 1975), ability to influence outcomes (superstitious rituals and beliefs: Joukhador, Maccallum, and Blaszczynski 2003; Joukhador, Blaszczynski, and Maccallum 2004), selective recall and biased evaluation of outcomes (Gilovich 1983; Gilovich and Douglas 1986), and erroneous perceptions regarding randomness and the independence of events (Coulombe et al. 1992; Gaboury and Ladouceur 1989;Walker 1992b).Toneatto et al. (1997) reduced 13 such identified cognitive distortions into five classifications under three similar higher-order categories: control, reframing, and prediction. Other studies have also identified a positive relationship between gambling severity and erroneous cognitions related to illusions of control and expectancy of winning (see e.g., Felscher, Derevensky, and Gupta 2004; Ladouceur 2004). Overall, problem gamblers exhibit greater levels of irrational beliefs compared with recreational gamblers, although the direction of causality is yet to be established. By some mechanism, possibly cognitive dissonance, the problem gambler maintains an “illusion of control”—a belief that he has a degree of skill or mastery over the outcome of a game of chance—despite mounting losses and other adverse consequences. In addition, he discounts losses through biased evaluations in which failure is attributed to external factors rather than deficits in personal skills (Gilovich 1983). The illusion of control is supplemented by overvalued beliefs in luck as a personal attribute, unrealistic optimism, and superstitious ideations that coalesce to reinforce the concept that winning is possible. Selective recall of past wins over losses and patterns of intermittent wins reinforce this optimism, as does the gambler’s fallacy (Cowan 1969), which maintains that a win is “due” following a series of losses. Structural characteristics of electronic gaming machines foster the gambler’s fallacy and other related erroneous perceptions. In particular, the phenomenon of the “near miss”—in which a gambler perceives that he or she almost won based on the visual presentation of slot machine symbols—has been found to correlate with increased persistence in gambling (Dixon and Schreiber 2004; Kassinove and Schare 2001). Gamblers misinterpret the randomly generated sequences of symbols displayed on the screen, nearly matching a paying combination, to suggest that the machine is about to pay out. In reality, symbols on the “virtual reel”—the visual representation of outcome on the screen—bear little relation to the “real reel,” a random computer generator of numbers with infinite combinations, housed within the machine. Erroneous perceptions related to near misses increase levels of arousal, which in turn reinforce the belief that winning is imminent (Griffiths 1991).
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Evidence from cognitive studies consistently demonstrates that irrational and erroneous belief schemas are prevalent among problem gamblers (Ladouceur and Walker 1996). However, cognitive theories have yet to adequately explain the functional interaction between arousal and cognitive activity as a causative factor in the onset and maintenance of problem gambling.
NEUROBIOLOGICAL, GENETIC, AND BIOBEHAVIORAL Pathological gambling is frequently described as an “addiction without the drug” (Potenza et al. 2001), the assertion being that with the exception of chasing losses, all diagnostic criteria “have their counterpart in alcohol, heroin, cocaine, and other forms of substance drug dependence.” Evidence for this assertion is based, in part, on the repetitive nature of gambling, persistence despite adverse consequences, preoccupation, impaired control, and features of tolerance and withdrawal. An increasing number of investigations have implicated abnormalities in brain regions associated with decision-making processes, biobehavioral functioning, specifically dysregulation in noradrenergic, serotonergic, dopaminergic, and opioidergic neurotransmitters, and genetic mechanisms, notably twin concordance data and dopamine D2 receptor genes, which may contribute to the development or maintenance of gambling disorder (for comprehensive reviews, see Goudriaan et al. 2004 and Potenza, this volume). Neurobiological and genetic models maintain a common thread in adopting sensitivity to reward and the reward deficiency concept in explaining correlates of problem gambling: craving, relapse, decision making, impulsivity, delayed discounting, and failure to learn. Neuropsychological studies (Carlton and Manowitz 1992; Rugle and Melamed 1993) and performance on tasks such as the Iowa Gambling Test (Bechara et al. 1994; Petry 2001) suggest that pathological gamblers display deficits in higher executive functioning, delay discounting, fluency, and interference control, as well as higher levels of impulsivity and disinhibition often seen in patients suffering attention deficit and ventromedial prefrontal cortex disorders. Using functional magnetic resonance imaging (fMRI), Reuter et al. (2005) found that reduced ventral striatal and ventromedial prefrontal activation on a guessing game were negatively correlated with gambling severity in a sample of pathological gamblers.This pattern of decreased activation, similar to findings in drug addiction, led these authors to conclude that pathological gambling was a non–substance related addiction. Several studies have investigated the role of heritability in gambling disorder (Black, Moyer, and Schlosser 2003; Daghestani, Elenz, and Crayton 1996; Gambino et al. 1993). A meta-analysis by Walters (2001) found wide variation, 16 to 46%, in the proportion of family members of gamblers who also reported disordered
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gambling, substantially less than the predisposition for alcohol or crime. Findings indicated a stronger effect for higher-severity cases in support of a continuum model of gambling, at least for males, suggesting that learning variables and genetics fostered observed gender differences in familial transmission. Likewise, twin studies have reported similarities in the gambling behaviors of male monozygotic twins (Winters and Rich 1998) and identified an association between genetic predispositions to both alcohol dependence and gambling disorder (Slutske et al. 2000). Genetic studies have reported associations between dopamine-related gene sequences and pathological gambling (Comings et al. 1996, 1997, 2001). Gambling acts to release dopamine, a chemical that mediates pleasure responses in the brain. As further evidence, some patients suffering from Parkinson’s disease, which depletes the brain of dopamine, begin gambling pathologically when prescribed medication to increase dopamine production (Seedat et al. 2000). In addition to dopamine, dysregulation in two other brain chemicals largely responsible for mood—serotonin and norepinephrine—have been implicated in the dysphoric mood states that are often cited as predisposing factors for problem gambling (DeCaria et al. 1996; Moreno, Saiz-Ruiz, and Lopez-Ibor 1991; Roy, DeJong, and Linnoila 1989). These studies suggested that genetic variants implicating dopaminergic systems set the foundation for sensitivity to certain types of rewards, such as the excitement associated with winning and risk taking inherent in gambling.Through cortical excitatory processes (winning and environmental stimuli), which involve the meso-limbic reward circuit structures, ventral tegmental area, amygdala, and nucleus accumbens, the reinforcing effects of gambling are consolidated in hippocampal memory. Conditioned responses (amygdala) to relevant cues and/or withdrawal in response to the absence of cues subsequently triggers feelings of craving to resume gambling. Deficits in response inhibition caused by the action of neurotransmitters on the prefrontal cortex result in the decreased capacity of higher frontal lobe executive functioning to inhibit urges, which, when combined with (a) erroneous cognitive belief schemas, (b) cravings generated by reward memories, and (c) anticipatory reinforcement, inevitably leads to continued gambling. Withdrawal and tolerance phenomena also play central roles in the maintenance of the addictive cycle (Bozarth 1994). Initially, the euphoric effects of gambling are highly reinforcing and lead to a shift in incentive-salience that is described variously as “wanting,” “craving,” or “toxic motivation” (Bozarth 1990; Koob and Moal 1997). With repetition, greater incentive salience to gamblingrelated stimuli occurs in line with the increasing sensitization of the dopamine system (Robinson and Berridge 1993). Concomitantly, the strength of the appetitive effects of drugs is sufficient to supplant those elicited by other reinforcers, precipitating a downward spiral whereby the gambler focuses on gambling to the exclusion of all other social, personal, and familial inclinations and obligations.
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Over time, the need to reduce or avoid aversive gambling-withdrawal symptoms, fostered by neuroadaptational changes and motivational shifts, emerge as the predominant reason for continued behavior and relapse episodes (Baker et al. 2004).Withdrawal-induced negative affective states become potent motivators for relapse (Baker et al. 2004), and the drive to reduce withdrawal fosters dependence in negative reinforcement models of addiction (Cappell and Le Blanc 1981; Hershon 1977; Robinson and Berridge 1993). As Goudriaan (2005) notes, methodological difficulties and inconsistent findings in the limited number of neurobiological and genetic studies conducted to date preclude a conclusive interpretation of the causative role these factors play in pathological gambling. It yet remains to be demonstrated that observed neurotransmitter dysregulation and neuropsychological deficits are the causes or the effects of pathological gambling. Nevertheless, the concepts of reward deficiency (Blum et al. 2000) and impaired response inhibition and salience attribution (see Heidbreder 2005 for a review) propose addiction models that may be productively applied to gambling. Data from neurobiological models suggest commonalities between gambling and substance abuse disorders, a fruitful area for further research but one fraught with many unchallenged assumptions (see Potenza, this volume).
INTEGRATED MODELS The complex and multifactorial nature of biopsychosocial findings in research suggest that problem gambling results from the interplay of a variety of factors that coalesce to result in disorder. In response, the general-theory-ofaddictions (Jacobs 1986), biopsychosocial (Sharpe 2002; Sharpe and Tarrier 1993) and pathways models (Blaszczynski and Nower 2002) attempt to explain and integrate salient factors.
GENERAL THEORY
OF
ADDICTIONS
The general theory of addictions was the first attempt to account variously for physiological and psychological factors in the etiology of pathological gambling and other addictive behaviors (Jacobs 1986). Jacobs proposed that pathological gamblers possess two interrelated sets of predisposing factors: (a) a physiological resting state of arousal that is either hypotensive/depressed or hypertensive/anxious and (b) a psychological predisposition toward feelings of inadequacy, low selfesteem, and low self-efficacy, influenced by early rejection from caregivers and peers, which fosters a need for recognition and approval (Jacobs 1986). The theory maintains that predisposing factors combine with a need for wish fulfillment and escape and cause the gambler to seek chance encounters with
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substances, behaviors, or both that foster dissociation from reality and a feeling of being “alive” and “normal.” Gambling maintains the fantasy in which anxiety becomes excitement, depression transforms into relaxation, and the gambler feels successful and accepted. In keeping with learning theory, such an experience establishes a “reciprocal relationship between positive and negative reinforcement” (Jacobs 1986, p. 28).The gambler continues in this pattern in order to avoid returning to the prior aversive resting state and resists attempts to discontinue the behavior. Ultimately, mounting adverse consequences propel the desperate gambler into a “state of physical and psychological exhaustion” (p. 28) that may, in the best circumstances, lead to treatment. Gupta and Derevensky (1998) reported empirical support for the model in a cohort of adolescents; however, the study was limited by use of a convenience sample and existing survey instruments to test the constructs.
BIOPSYCHOSOCIAL Based on research findings, Sharpe and Tarrier (1993) proposed a heuristic cognitive-behavioral model to account for the development and maintenance of problem gambling. The model, figuratively depicted as a systemic feedback loop, suggests that gambling behavior initiates from a trigger, usually an early monetary win that causes a physiological reaction in the form of autonomic arousal. The gambling is then reinforced by the desire for increased arousal amplified by gambling, by a partial reinforcement schedule of wins, and by arousal-generating cues in the gambling environment. The model further postulates that problem-solving and stress-coping skills serve as mediators for problem gambling behavior, such that individuals with poor problem-solving and coping skills, particularly those exposed to environmental factors that reduce the efficacy of those skills, are more likely than others to develop gambling problems. A reformulation of the model by Sharpe (2002) adopted a diathesis-stress perspective on predisposing factors, maintaining that adverse experiences in the early psychosocial environment, combined with genetic vulnerabilities such as deficits in the dopaminergic and serotonergic systems, result in psychological and/or biological vulnerabilities that trigger a loss of control in gambling situations.
PATHWAYS In response to etiological conceptualizations that perceive pathological gambling either as a categorical or as a spectrum disorder and assume that problem gamblers are a homogeneous population, Blaszczynski and Nower (2002) proposed the pathways model, a conceptual framework that identifies three primary subgroups/clusters of gamblers: behaviorally conditioned, emotionally vulnerable,
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and biologically based impulsive pathological gamblers. All three groups have common exposure to related ecological factors (e.g., availability, accessibility, acceptability), cognitive processes and distortions, and contingencies of reinforcement. However, according to the proposed model, predisposing emotional stressors and affective disturbances for some individuals and biological impulsivity for others represent significant additive risk factors. The differential pathways model has significant implications for treating adults and adolescent pathological gamblers (Blaszczynski and Nower 2002; Nower and Blaszczynski 2004). Based on the research literature, the model proposes three different etiological pathways. Pathway 1 gamblers are characterized by an absence of specific premorbid features of psychopathology, and their gambling results largely from the effects of conditioning, distorted cognitions surrounding probability of winning and disregard for the notion of independence of events, and/or a series of bad judgments/poor decision making rather than because of impaired control. Cognitive therapy and brief interventions are treatments of choice.The Pathway 2 gamblers share similar ecological determinants, conditioning processes, and cognitive schemas; however, these individuals also present with premorbid drug abuse, anxiety, and/or depression, a history of poor coping and problem-solving skills, problematic family background experiences, and major traumatic life events that fuel gambling participation motivated by a desire to modulate affective states and/or meet specific psychological needs. For this group, cognitive-behavioral interventions require the addition of stress management, problem-solving, and other appropriate adjunctive therapies designed to address premorbid psychopathology. Finally, Pathway 3 gamblers possess psychosocial and biologically based vulnerabilities similar to Pathway 2 gamblers but are distinguished by a high degree of impulsivity, antisocial personality disorders, and attention deficit, manifesting in severe multiple maladaptive behaviors. Given the putative role of biological factors linked to neurotransmitter dysregulation and traits of impulsivity, psychopharmacological interventions should be considered as an important supplementary treatment. While these pathways purport to account for differences among problem gamblers, it is important to note that empirical testing is needed to validate, refute, or supplement proposed variables within the model and to further elucidate differences among subgroups of problem gamblers with regard to age, gender, ethnicity, and other relevant factors.
SUMMARY Problem gambling is a complex disorder with a variety of biopsychosocial risk factors. Theoretical conceptualizations ranging from Freudian and neurobiological explanations to cognitive-behavioral theories attempt to explain persistence
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in behavior despite unfavorable odds and mounting adverse consequences. Due to variants in individual differences, family history, psychobiology, and cognitive interpretations, no single theory can fully account for the etiology of disorder in all problem gamblers. Future research is needed to further identify salient risk and resiliency factors and to refine methods of identification and treatment that are tailored to individual differences among problem gamblers. It is imperative to pursue efforts to develop an integrated conceptual model of gambling and pathological gambling that acknowledges the heterogeneity of the gambling condition.
GLOSSARY Etiology factors and processes contributing to the development of pathological gambling. Pathways model a model based on the notion of three subgroups of pathological gamblers each having similar phenomenological features but different factors contributing to its development. Theoretical models conceptual models explaining how pathological gambling develops and factors that may contribute to its maintanence. Explanatory models derived from specific theoretical orientations of psychology include sychodynamic, behavioral, cognitive, biobehavioral, and public health.
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Ladouceur, R., and Gaboury, A. (1988). Effects of limited and unlimited stakes on gambling behavior. Journal of Gambling Behavior, 4, 119–126. Ladouceur, R. (2004). Perceptions among pathological and non-pathological gamblers. Addictive Behaviors, 29, 555–565. Ladouceur, R., Gaboury,A., Dumont, M., and Rochette, P. (1988). Gambling: Relationship between the frequency of wins and irrational thinking. Journal of Psychology: Interdisciplinary and Applied, 122, 409–414. Ladouceur, R., Sylvain, C., Boutin, C., and Doucet, C. (2002). Understanding and Treating the Pathological Gambler. New York: John Wiley and Sons. Ladouceur, R., and Walker, M. (1996). A cognitive perspective on gambling. In Trends in Cognitive and Behavioural Therapies (P. M. Salkovskis, ed.), pp. 89–120. Chichester and New York: John Wiley and Sons. Laforgue, R. (1930). On the eroticization of anxiety. International Journal of Psycho-analysis, 11, 312–321. Langer, E. J. (1975).The illusion of control. Journal of Personality and Social Psychology, 32, 311–321. Lesieur, H. R. (1984). The Chase: Career of the Compulsive Gambler. Cambridge, MA: Schenkman. Lindner, R. M. (1950). The psychodynamics of gambling. Annals of the American Academy of Political Science, 269, 93–107. McConaghy, N. (1980). Behavioural completion mechanisms rather than primary drive maintain behavioural patterns. Activas Nervosa Superior (Praha), 22, 138–151. McConaghy, N., Armstrong, M., Blaszczynski, A. P., and Allcock, C. (1983). Controlled comparison of aversion therapy and imaginal desensitization in compulsive gambling. British Journal of Psychiatry, 142, 366–372. McCormick, R.A., and Taber, J. I. (1987).The pathological gambler: Salient personality variables. In The Handbook of Pathological Gambling (T. Galski, ed.), pp. 9–39. Springfield, IL: Charles C.Thomas. Moreno, I., Saiz-Ruiz, J., and Lopez-Ibor, J. J. (1991). Serotonin and gambling dependence. Human Psychopharmacology, 6, S9–S12. National Research Council. (1999). Pathological Gambling: A Critical Review.Washington, DC: National Academy Press. Nower, L., and Blaszczynski, A. (2004). A pathways approach to treating youth gamblers. In Youth Gambling Problems:A Current Perspective (R. Gupta and J. L. Derevensky, eds.), pp. 189–209. Norwell, MA: Kluwer Academic Publishers. Ocean, G., and Smith, G. J. (1993). Social reward, conflict and commitment:A theoretical model of gambling behavior. Journal of Gambling Studies, 9, 321–339. Oldham, J. M, Hollander, E., and Skodol, A. E. (1996). Impulsivity and Compulsivity. Washington DC: American Psychiatric Association. Petry, N. M. (2001). Pathological gamblers, with and without substance use disorders, discount delayed rewards at high rates. Journal of Abnormal Psychology, 110, 482–487. –––– . (2005). Pathological Gambling: Etiology, Comorbidity and Treatment. Washington DC: American Psychological Association. Potenza, M. N., Steinberg, M. A., McLaughlin, S. D., Wu, R., Rounsaville, B. J., and O’Malley, S. S. (2001). Gender-related differences in the characteristics of problem gamblers using a gambling helpline. American Journal of Psychiatry, 158, 1500–1505. Reik,T. (1942). From Thirty Years with Freud. London: Hogarth Press. Reuter, J., Raedler,T., Rose, M., Hand, I., Glascher, J., and Buchel, C. (2005). Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nature Neuroscience, 8, 147–148. Robinson,T. E., and Berridge, K. C. (1993).The neural basis of drug craving:An incentive-sensitization theory of addiction. Brain Research Reviews, 18, 247–229. Rosecrance, J. (1985). Compulsive gambling and the medicalization of deviance. Social Problems, 32, 275–284. Rosenthal, R., and Rugle, L. (1994).A psychodynamic approach to the treatment of pathological gambling: Part I. Achieving abstinence. Journal of Gambling Studies, 10, 21–42.
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Roy, A., De Jong, J., and Linnoila, M. (1989). Extraversion in pathological gamblers: correlates with indexes of noradrenergic function. Archives of General Psychiatry, 46, 679–681. Rugle, L., and Melamed, L. (1993). Neuropsychological assessment of attention problems in pathological gamblers. Journal of Nervous and Mental Disease, 181, 107–112. Salzmann, M. M. (1982).Treatment of compulsive gambling. British Journal of Psychiatry, 141, 318–319. Seager, C. (1970).Treatment of compulsive gamblers using electrical aversion. British Journal of Psychiatry, 117, 545–553. Seedat, S., Kessler, S., Niehaus, D. J. H., and Stein, D. J. (2000). Pathological gambling behaviour: Emergence secondary to treatment of Parkinson’s disease with dopaminergic agents. Depression and Anxiety, 11, 185–186. Shaffer, H. J., LaBrie, R. A., and LaPlante, D. (2004). Laying the foundation for quantifying regional exposure to social phenomena: Considering the case of legalised gambling as a public health toxin. Psychology of Addictive Behaviors, 18, 40–48. Sharpe, L. (2002). A reformulated cognitive-behavioral model of problem gambling: A biopsychosocial perspective. Clinical Psychology Review, 22, 1–25. Sharpe, L., and Tarrier, N. (1993). Towards a cognitive-behavioural theory of the problem gambler. British Journal of Psychiatry, 162, 407–412. Slutske,W. S., Eissen, S.,True,W. R., Lyons, M. J., Goldberg, J. and Tsuang, M. (2000). Common genetic vulnerability for pathological gambling and alcohol dependence in men. Archives of General Psychiatry, 57, 666–673. Stinchfield, R. (2001). A comparison of gambling by Minnesota public school students in 1992, 1995, and 1998. Journal of Gambling Studies, 17, 273–296. Sylvain, C., Ladouceur, R., and Boisvert, J.-M. (1997). Cognitive and behavioural treatment of pathological gambling: A controlled study. Journal of Consulting and Clinical Psychology, 65, 727–732. Toneatto, T. (2002). Cognitive therapy for problem gambling. Cognitive and Behavioral Practice, 9, 191–199. Toneatto, T., Blitz–Miller, T., Calderwood, K., Dragonetti, R., and Tsanos, A. (1997). Cognitive distortions in heavy gambling. Journal of Gambling Studies, 13, 253–266. von Hattinger, H. (1914). Analerotik, Angslust und Eigensinn. Internationale Zeitschrift fur Psychoanalyse. Walker, M. B. (1992a). The Psychology of Gambling. Sydney: Pergamon Press. —— . (1992b). Irrational thinking among slot machine players. Journal of Gambling Studies, 8, 245–261. Walker, M., and Dickerson, M. G. (1996).The prevalence of problem and pathological gambling:A critical analysis. Journal of Gambling Studies, 12, 233–249. Walters, G. D. (2001). Behavior genetic research on gambling and problem gambling: A preliminary meta-analysis of available data. Journal of Gambling Studies, 17, 255–272. Winters, K. C., and Rich, T. (1998). A twin study of adult gambling behaviour. Journal of Gambling Studies, 14, 213–135. Wynne, H., Smith, G., and Jacobs, D. F. (1996). Adolescent Gambling and Problem Gambling in Alberta. Edmonton: Alberta Alcohol and Drug Abuse Commission.
CHAPTER 14
The Neurobiology of Pathological Gambling Judson A. Brewer
Marc N. Potenza
Department of Psychiatry Yale University School of Medicine New Haven, Connecticut
Department of Psychiatry Yale University School of Medicine New Haven, Connecticut
Jon E. Grant Department of Psychiatry University of Minnesota School of Medicine Minneapolis, Minnesota
Introduction Conceptualization Biochemistry Serotonin Dopamine Norepinephrine Monoamine Oxidase Stress Pathways Opioid System Other Neurotransmitters Neuroimaging Structural Lesions and Decision Making Genetics Population Genetics Molecular Genetics Conclusions
INTRODUCTION Despite relatively high prevalence rates and significant morbidity associated with pathological gambling (PG), the neurobiological basis of PG is still poorly understood. Mounting data suggest that multiple neurotransmitter systems are involved in the pathophysiology of PG (Potenza 2001; Potenza and Hollander 2002; Potenza, Kosten, and Rounsaville 2001). In this chapter, we review evidence 345
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that supports the involvement of serotonergic, noradrenergic, dopaminergic, and opioid systems, as well as genetic factors contributing to PG.
CONCEPTUALIZATION Pathological gambling has been hypothesized by some researchers to lie along a compulsive-impulsive spectrum (McElroy et al. 1992), representing an obsessive-compulsive (OC) spectrum disorder (Hollander and Wong 1995a, 1995b). Although individuals with PG engage in repetitive behaviors, often with strong associated urges, gambling (and actions in other impulse control disorders [ICDs]) is often related as pleasurable or egosyntonic, whereas repetitive behaviors or rituals in OC disorder, for example, are generally egodystonic (Blaszczynski 1999; Grant and Potenza 2006a). Individuals with PG typically score high on measures of impulsivity (Blaszczynski 1999; Cavedini et al. 2002; Kim and Grant 2001; Petry 2000; Potenza, Steinberg, et al. 2003). Consistent with these observations, PG is currently grouped with other ICDs in the text revision of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR), in the category of “ICDs Not Otherwise Specified” (American Psychiatric Association 2000). Included in this category are kleptomania, pyromania, trichotillomania, and intermittent explosive disorder. Additional ICDs have been proposed, including compulsive shopping, compulsive computer use, and compulsive sexual behaviors (Grant and Potenza 2004; Potenza and Hollander 2002). Proposed definitions for impulsivity share features with core elements of addiction:“a predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions to the impulsive individual or others” (Moeller et al. 2001).Thus, PG and other ICDs may aptly be described as “behavioral addictions” because they share similar features with substance dependence (Holden 2001; Potenza 2006; Shaffer 1999). Consistent with the notion that PG may be a behavioral addiction, studies have highlighted the comorbidity of PG with other substance use disorders (SUDs), with rates of nicotine dependence approaching 70% (Crockford and elGuebaly 1998), alcohol abuse or dependence in the range of 50 to 75% (McCormick et al. 1984; Petry, Stinson, and Grant 2005), and other drug use problems nearing 40% (Cunningham-Williams et al. 1998). Individuals with SUDs are up to tenfold more likely to have PG (Spunt et al. 1995). High rates of other psychiatric disorders have also been described, especially mood, attention-deficit, and antisocial personality disorders (Blaszczynski and McConaghy 1994; Crockford and el-Guebaly 1998; Rugle and Melamed 1993). Such studies suggest a functional if not underlying mechanistic overlap between PG and SUDs as well as between PG and other psychiatric disorders. Specific pathways and neurotransmitter systems will be discussed below.
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BIOCHEMISTRY SEROTONIN Pharmacological challenge studies suggest serotonin (5-HT) dysfunction in PG. Eight men and women with PG and matched controls were given a low dose of clomipramine (CMI), a 5-HT and norepinenprhine (NE) reuptake inhibitor. This dose of CMI is thought to preferentially inhibit 5-HT reuptake. Individuals with PG compared with control subjects displayed at baseline lower prolactin levels and exhibited significantly blunted prolactin responses to CMI one hour after challenge, suggesting diminished 5-HT transporter availability (Moreno, Saiz-Ruiz, and Lopez-Ibor 1991). Metachlorophenylpiperazine (m-CPP), a metabolite of trazodone, which acts as a partial agonist and has high affinity for 5-HT receptors (especially 5HT2c, which has been implicated in the mediation of aspects of mood, anxiety behavior, and neuroendocrine function [Kennett and Curzon 1988]), was administered to ten men with PG and ten healthy male control subjects.The PG group reported an associated “high” after m-CPP administration.This response is similar to those reported in other disorders in which impulsive or compulsive behaviors are prominent, including antisocial personality disorder (Moss, Yao, and Panzak 1990), borderline personality disorder (Hollander et al. 1994), and alcohol abuse or dependence (Benkelfat et al. 1991). Additionally, subjects with PG demonstrated increased prolactin levels compared with controls, with greater responses correlating with increased gambling severity. Further support for 5-HT dysfunction in PG comes from analysis of cerebrospinal fluid (CSF) and blood. Although studies comparing men with PG with healthy controls have shown no significant differences in 5-HT or its metabolite 5hydroxyindolacetic acid (5-HIAA) (Bergh et al. 1997; Roy et al. 1988; Roy, De Jong, and Linnoila 1989), when controlling for tapping time (which was increased in the PG group), levels of 5-HIAA were found to be lower in those with PG (Nordin and Eklundh 1999).These data are consistent with findings of low levels of 5-HIAA in individuals with impulsive characteristics (Coccaro et al. 1989; Linnoila et al. 1983).Additional evidence for 5-HT in the pathophysiology of PG and other ICDs comes from studies of monoamine oxidase (MAO) from peripheral blood samples, as described in the section Monoamine Oxidase in this chapter. Clinical trials in PG have shown mixed support regarding the efficacies of drugs that operate through serotonergic mechanisms (see Hodgins, this volume). Several open-label trials suggested efficacy of serotonin reuptake inhibitors (SRIs) in the treatment of PG (Hollander et al. 1998; Zimmerman, Breen, and Posternak 2002). Additionally a single-center, eight-week, double-blind, placebo-controlled safety and tolerability study using paroxetine in 45 individuals with PG showed superiority of active drug at the 6- and 8-week timepoints (Kim et al. 2002).
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A follow-up, 16-week, multicenter study (n = 76) did not observe statistically significant differences between paroxetine and placebo, with high rates of clinical improvement observed with both active drug (59%) and placebo (48%) (Grant et al. 2003). Recently, 60 patients with PG were treated for 6 months in a doubleblind, flexible-dose, placebo-controlled study of sertraline (50–150 mg daily).The investigators found no difference in response rate (measured by the Criteria of Pathological Gambling Questionnaire [CCPGQ]) between groups (74% vs. 72%) (Saiz-Ruiz et al. 2005). Finally, a study evaluated the efficacy of escitalopram in 13 subjects with PG and co-occurring anxiety in a 12-week open-label trial with double-blind discontinuation (Grant and Potenza 2006b). There was a 62% response rate (defined as >30% reduction of the Yale-Brown Obsessive Compulsive Scale Modified for Pathological Gambling). Concurrent decreases were also observed in the scores on the Hamilton rating scales for anxiety and depression (HAM-A and HAM-D, respectively), and this finding is clinically important given the frequency of co-occurring affective symptoms in PG (Black and Moyer 1998). Further studies should consider the influences of natural recovery (Slutske 2006) and co-occurring disorders when determining the role of 5-HT modulation in the treatment of PG. Additionally, further characterization of a role for 5-HT in the initiation and maintenance of PG is warranted, and the specific influences of genetic, gender, and ethnic factors that influence treatment outcome need to be identified.
DOPAMINE Multiple lines of evidence support the mesocorticolimbic (MCL) dopamine (DA) system in the mediation of reinforcing behaviors (Hyman 2005; Kalivas and Volkow 2005; Nestler 2002; Nestler and Aghajanian 1997). Human studies of individuals with cocaine dependence have shown MCL regional activation after viewing cocaine-related videotapes (Kosten et al. 2006; Wexler et al. 2001), and occupancy of the DA transporter has been correlated with cocaine’s euphorigenic effects (Volkow et al. 1997). It has also been proposed that DA may mediate some of the rewarding or reinforcing aspects of gambling (DeCaria, Begaz, and Hollander 1998).To this end, Bergh and colleagues (1997) measured the levels of 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA), two DA metabolites in CSF of ten males with PG compared with sex-matched controls. They found decreased levels of DA and increased levels of DOPAC and HVA in PG subjects compared with controls, concluding that these findings were consistent with an increased rate of DA neurotransmission. However, these findings were no longer observed when correcting for CSF flow rate (Nordin and Eklundh 1999). A common process implicated in drug priming is release of DA in the nucleus accumbens (Kalivas and Volkow 2005). Similarly, gambling has been
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shown to produce priming-like effects in problem gamblers (Loba et al. 2001). Drugs with similar mechanisms of action can “cross-prime” for reinstatement of other drugs within that class (i.e., amphetamine for cocaine) (Shalev, Grimm, and Shaham 2002). Amphetamine increases extracellular catecholamine and serotonin concentrations via vesicular depletion, reuptake inhibition, enhancement of DA synthesis, and MAO inhibition (Sulzer et al. 2005). To assess whether amphetamine might prime motivation to gamble, Zack and Poulos (2004) compared problem gamblers with and without problem drinking with controls (four groups: “gamblers,” n = 10; “gambler/drinkers,” n = 6; “drinkers,” n = 8; and “controls,” n = 12) in a double-blind, placebo-controlled, counterbalanced, between/within design of self-reported motivation for gambling and a modified rapid reading task of relevant and irrelevant semantic domains. The researchers found that amphetamine increased motivation for gambling in gamblers, which could be predicted by the severity of reported gambling problems, although few effects were seen in motivational priming for alcohol in drinkers. Additionally, reading speed to motivationally irrelevant words was found to be slowed in the gambling group given amphetamine, but was improved in nongamblers.These results suggest that amphetamine can cross-prime for gambling behavior, lending further evidence for the involvement of dopaminergic and/or other aminergic pathways in the pathophysiology of PG. Several reports link DA agonist use in Parkinson’s disease (PD) with PG (Dodd et al. 2005; Driver-Dunckley, Samanta, and Stacy 2003; Kurlan 2004; Szarfman et al. 2006;Weintraub and Potenza 2006). In one of the largest case series published to date (n = 11), PG was observed in 7 of 11 patients within 3 months of maintenance dose or dose escalation of DA agonist therapy (Dodd et al. 2005). In PD patients in which PG develops, excessive gambling often resolves within months of discontinuing agonist therapy (Dodd et al. 2005). In many patients, other compulsive behaviors (eating, sex) developed concurrently with PG, and these also frequently resolved with cessation of DA agonist treatment. Early reports also suggest a role for DA antagonists in the treatment of ICDs associated with DA agonist treatment in PD, although controlled trials are lacking (Dodd et al. 2005; Klos et al. 2005;Weintraub and Potenza 2006). Dodd et al. (2005) found that a preponderance of patients (9 of 11) developed PG with pramipexole therapy, which has particularly high affinity for the D3 receptor (Gerlach et al. 2003).These findings suggest limbic mechanisms in the association between PG and DA agonist treatment given the relative localization of the D3 receptor to the limbic system (in rat brain, olfactory > hypothalamus > substantia nigra > striatum) (Sokoloff et al. 1990) and decreases in regional cerebral blood flow (rCBF) seen in primates in bilateral orbitofrontal cortex, thalamus, cingulate cortex, and insula given pramipexole (Black et al. 2002). Consistently, the top five higher-than-expected drug-event reporting ratios involving PG from the Food and Drug Administration’s Adverse Event Reporting System were all DA agonists, with
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pramipexole having an adjusted reporting ratio of over 380 times the “expected” (Szarfman et al. 2006). However, the preferential association between pramipexole and PG has been questioned in that pramipexole is often prescribed more frequently than other DA agonists (Lu, Bharmal, and Suchowersky 2006), and at higher doses (Morgan, Iyer, and Sethi 2006). Similar to findings in Canada (Lu et al. 2006), a recent study of 272 patients with PD who were screened and assessed for ICDs did not observe a difference between specific dopamine agonists in their associations with PG and other ICDs (Weintraub et al. 2006). In a multivariate model, treatment with a DA agonist and a history of an ICD prior to PD onset predicted current ICD (ibid.). Additionally, daily doses of DA agonist were higher in patients with an ICD than in those without. Therefore, existing data suggest that DA agonists, particularly at high dosages and in individuals at risk for ICDs, are associated with PG and other ICDs. Limitations of these studies include possible effects of ascertainment bias and either under- or over-reporting. Future prospective studies should investigate the nature of the relationship between DA therapy in PD and ICD symptomatology, and improved prevention and treatment strategies should be developed and tested. For example, although case reports suggest that DA/5-HT antagonists such as olanzapine or quetiapine can be helpful for DA agonist–associated ICD symptomatology (Klos et al. 2005), empirical data are largely lacking and should be examined further, particularly for individuals whose PD severity necessitates continued DA therapy.
NOREPINEPHRINE Norepinephrine (NE) has been hypothesized to mediate arousal, attention, and sensation seeking in PG (DeCaria, Begaz, and Hollander 1998; Roy et al. 1988; Roy, De Jong, and Linnoila 1989). To test this hypothesis, CSF, plasma, and urine were collected for quantitation of NE and its metabolites 3-methoxy-4hydroxyphenyl glycol (MHPG) and vanillylmandelic acid (VMA) (Roy et al. 1988). Individuals with PG were noted to have higher CSF levels of MHPG and higher urine levels of NE. Significant positive correlations were found between scores of extroversion on the Eysenck Personality Questionnaire and CSF MHPG, plasma MHPG, urine VMA, and the sum of NE and NE metabolites (Roy et al. 1989). Increased levels of CSF NE and MHPG were also found in a separate study of men with PG, although subsequent analysis reported decreased MHPG when correcting for CSF flow (Bergh et al. 1997). A third study measured plasma levels of NE in subjects playing Pachinko, a gaming machine that combines elements of pinball and slot machines (Shinohara et al. 1999).The researchers found that plasma NE rose during play, with statistically significant differences in levels in six regular players at the beginning and end of a winning streak.
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Meyer et al. (2004) measured physiologic and chemical responses in 15 male problem versus nonproblem gamblers in a balanced crossover study in which participants used their own money to play blackjack (with a control session using a point score instead of money). The researchers found significantly higher levels of DA in problem gamblers during casino gambling compared with controls. Peripheral DA is derived predominantly from the gastrointestinal tract and/or other peripheral sources as the blood–brain barrier limits CNS contributions (Goldstein et al. 1999; Greene and Faull 1989; Kopin 1985; Lambert et al. 1993; Van Loon and Sole 1980). Increased heart rate and NE levels were observed in both gambling groups, with problem gamblers showing significantly higher levels across the entire gambling session.Among all participants, NE levels correlated significantly and positively with the desire to start or continue gambling. With the caveats of confounding variables (social context, course of gambling wins and losses) related to the study venue (a casino), Meyer et al. (2004) concluded that higher levels of DA and NE and increased heart rate support the hypothesis of greater activation of the sympathetic nervous system during gambling in problem gamblers as compared with nonproblem gamblers.
MONOAMINE OXIDASE MAO, with its A and B subtypes, metabolizes NE, 5-HT, and DA. MAO-A appears to act primarily on 5-HT, NE, and DA, while MAO-B mainly metabolizes phenylethylamine (Shih, Chen, and Ridd 1999). Associations between polymorphisms in the promoter region of the MAO-A gene have been linked to PG (see Genetics section) (Perez de Castro et al. 2002). MAO-B, the main isoform found in platelets (Denney et al. 1982), has been suggested to reflect 5-HT function based on positive correlations with CSF HIAA and HVA in healthy volunteers (Oreland et al. 1981). However, in the same study, no correlation was found between MAO-B activity and CSF HIAA in subjects who had demonstrated impulsive behavior (attempted suicide). A recent study using fenfluramine-induced prolactin response in ten healthy men showed a positive correlation between delta-prolactin and platelet MAO-B activity, leading the authors to conclude that MAO-B activity on platelets may be a marker of central serotonergic activity (Eriksson et al. 2006). It remains to be determined whether this correlation will hold for populations other than healthy men (especially when smoking history is considered). A recent review noted that studies of platelet MAO-B activity in suicidal individuals found inconclusive results that “did not support the hypothesis of an altered platelet MAO-B activity in suicidal patients” (Muller-Oerlinghausen, Roggenbach, and Franke 2004), with notable lack of control for smoking (a known MAO inhibitor) (Muller-Oerlinghausen et al. 2004; Shih et al. 1999). Nonetheless, decreased MAO activity has been associated with eating disorders such as anorexia nervosa
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(Diaz-Marsa et al. 2000) and bulimia nervosa (Carrasco et al. 2000; Hallman, Sakurai, and Oreland 1990), high levels of sensation seeking (Fowler, von Knorring, and Oreland), and PG. Two studies involving men with PG showed decreases of 26% and 41% of platelet MAO activity compared with matched controls (Blanco et al. 1996; Carrasco et al. 1994). However, in both studies, no correlation between MAO activity and sensation seeking was found. Further studies are needed to examine: (1) the validity of platelet MAO activity as a marker of central serotonergic activity, and (2) correlations observed between low MAO activity and PG in other populations (e.g., women) and specific characteristics (e.g., impulsivity).
STRESS PATHWAYS Cortisol changes have been associated with gambling behaviors. A study of male and female Kimberley, Australia, aborigines whose urine was collected during a 2-day period just after receiving their wages (after which the community members typically partook in or watched gambling) showed significantly higher rates of cortisol and epinephrine excretion than in separate volunteers whose urine was collected several days later (Schmitt, Harrison, and Spargo 1998). These data support the possibility of stress pathway involvement in gambling, or that gambling invokes the stress pathway, but should be interpreted cautiously as they reflect cross-sectional correlative results. Additionally, they were analyzed without knowledge of field conditions (e.g., what volunteers were actually doing on those days) and are confounded by self-selection bias. A study of 21 men with PG found no differences from control subjects in cortisol responsivity on a dexamethasone suppression test (DST) (Ramirez, McCormick, and Lowy 1988). Similarly, no differences were found in CSF levels of corticotrophin-releasing hormone and corticotrophin between men with PG and healthy volunteers (Roy et al. 1988). Meyer et al. (2000) measured salivary cortisol and heart rate in 19 volunteers recruited from casinos during blackjack versus card play without monetary stakes.They found statistically significantly elevations in both measures during the blackjack compared with the control condition. More recently, in a counterbalanced crossover study of 29 male volunteers recruited from casinos, the same investigators noted statistically significant increases in heart rate and cortisol levels during blackjack gambling compared with a control cardplaying condition (Krueger, Schedlowski, and Meyer 2005). In subgroup analyses, increases in heart rate but not cortisol level were noted in those with high compared with low impulsivity (split into two groups at the median) as measured by the Eysenck Impulsivity Questionnaire. As the authors note, this study has high external validity but is limited by confounders such as social context and the course of gambling events (especially since subjects wagered their own money in the experimental condition). These findings contrast with those from a similarly
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designed study of 15 problem gamblers by the same investigators (Meyer et al. 2004). Further study should examine the extent to which differences in these findings are attributable to specific group differences (e.g., genetic constitutions or environmental exposures influencing stress responsiveness), insufficient power, or other explanations. Additional studies investigating stress hormone function in gambling and PG are warranted given the foregoing data and reports of increased rates of physical health problems in subjects with PG (Erickson et al. 2005; James et al. 1999; Morasco,Vom Eigen, and Petry 2006; Pietrzak et al. 2005; Scherrer et al. 2005;Weinstock, Blanco, and Petry 2006).
OPIOID SYSTEM The endogenous opioid system influences experiencing of pleasure. Opioids modulate mesolimbic DA pathways via disinhibition of gammaaminobutyric acid (GABA) input in the ventral tegmental area (Johnson and North 1992). Gambling or related behaviors have been associated with elevated blood levels of the endogenous opioid beta-endorphin (Shinohara et al. 1999). Given its mechanism of action (Tamminga and Nestler 2006) and efficacy in the treatment of alcohol and opiate dependence (O’Brien 2005), opioid receptor antagonists have been tested in the treatment of PG. Naltrexone showed superiority to placebo in a double-blind single-site study (Kim et al. 2001). More recently, nalmefene, a long-acting opioid antagonist, showed superiority to placebo in a 16week, randomized, dose-ranging, double-blind, multicenter study of 207 subjects with PG (Grant et al. 2006). In addition to their possible utility in the treatment of PG, these studies suggest that the opioid system plays an important role in the pathophysiology of this disorder.
OTHER NEUROTRANSMITTERS Dysregulation of other neurotransmitter systems has been hypothesized in PG. Studies of CSF in PG and control subjects have found nonsignificant differences in levels of neuropeptide Y (Roy, De Jong, and Linnoila, 1989), galantine (Roy et al. 1990), GABA (Roy, De Jong, Ferraro, et al. 1989), neurotensin (Roy, De Jong, and Linnoila 1989), somatostatin (ibid.), or growth-hormone releasing hormone (ibid.). Recently, taurine levels from CSF of eleven male pathologic gamblers were compared with healthy controls 8 hours after fasting (Nordin and Sjodin 2006). Significantly higher taurine levels were observed in the gambling group both in total and concentration gradient and in three fractionated samples.These findings contrast with those from a previous study in which the authors found reduced levels of taurine (Nordin and Eklundh 1996), although this may reflect methodological differences.
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Other neurotransmitter systems such as glutamate have been proposed as being involved in PG and other addictive disorders (Chambers and Potenza 2003; Chambers,Taylor, and Potenza 2003), and direct investigation of roles for glutamate and GABA appear warranted. To summarize, pharmacological challenges implicate serotonergic systems in the pathophysiology of PG. Treatment studies involving serotonergic reuptake inhibition have shown mixed results. Mounting evidence exists from amphetamine challenge studies and dopamine agonist treatment for the involvement of dopaminergic systems in PG, and these data are consistent with the pathophysiology of addictive disorders. Similarly, with the available knowledge on opiate system physiology and large multicenter trials demonstrating benefit from antagonist therapy, substantial evidence exists for opioid system involvement in PG.Although supportive, less evidence is available on NE systems, with much of it coming indirectly via amphetamine challenge and metabolite measurement. The involvement of stress pathways in PG is logical given the importance of stress on relapse in other addictive processes. However, much of the data generated to date are based on a single clinical paradigm and have generated some contradictory results. As with all of the above neurotransmitter systems, some promising data have been generated looking at other neuropeptide systems, but results need to be replicated using larger sample sizes to more precisely determine their involvement.
NEUROIMAGING An increasing number of neuroimaging studies exist involving subjects with PG or other formal ICDs.The ventromedial prefrontal cortex (vmPFC) has been implicated as a critical component of decision-making circuitry in risk-reward assessment in addiction (Kalivas and Volkow 2005) and specifically PG (Bechara 2003). Decreased activation of vmPFC has been observed in PG subjects during presentation of gambling cues (Potenza, Steinberg, et al. 2003), performance of the Stroop Color-Word Interference Task (Potenza, Leung, et al. 2003), and simulated gambling (Reuter et al. 2005). In Reuter et al. (2005), among PG subjects, activation of the vmPFC correlated inversely with gambling severity. Diminished activation of vmPFC has also been found to distinguish individuals with bipolar disorder from control subjects during Stroop performance (Blumberg et al. 2003), and in association with impulsive aggression in depressed individuals (Dougherty et al. 2004) and other clinical samples, such as New et al. (2002) and Siever et al. (1999). In these last studies, responsiveness of the vmPFC to serotonergic drug challenges (m-CPP, fenfluramine) was found to be blunted in individuals with impulsive aggression, similar to findings in individuals with alcohol dependence (Hommer et al. 1997). Taken together, these data support a role for vmPFC dysfunction, possibly of a serotonergic nature, in PG and other ICDs.
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DA and DA-related neurocircuitry have been implicated in addiction (Hyman 2005; Kalivas and Volkow 2005) and PG (Chambers and Potenza 2003; Chambers,Taylor, and Potenza 2003).The MCL DA system has been examined in gambling or related monetary reward processes. In a study in which subjects were paid increasing amounts of money based on the skill level reached while playing a video game, imaging by positron emission tomography (PET) using 11C-raclopride (a ligand with high affinity for D2 receptors) showed a 13% decrease in ligand binding compared with control conditions, which the authors suggested indicated a twofold increase in DA release in the striatum (Koepp et al., 1998). Additionally, using [11C]n-methylspiperone, a ligand for D2, 5-HT2a and 5-HT2c receptors (Nyberg et al. 1999), researchers noted decreased binding of the ligand in PG subjects compared with controls (Goyer et al. 1999). Decreased binding in these studies may reflect increased DA release, decreased receptor affinity, and/or decreased receptor availability. In a simulated gambling task, PG subjects were distinguished from healthy control subjects by demonstrating diminished ventral striatal activation, and activation of this region correlated inversely with gambling severity amongst the PG subjects (Reuter et al. 2005). Similarly, in a study of gambling urges and cocaine cravings in PG and cocaine dependence, diminished activation of ventral striatum distinguished addicted (PG or cocaine dependent) from control subjects during viewing of gambling and drug videotapes, respectively (Potenza et al. 2005). In a study of unmedicated subjects with PG, investigators measured relative metabolic rate using 18F-deoxyglucose in PET imaging, to compare computer blackjack for monetary rewards versus points only (Hollander et al. 2005).They noted significantly higher relative metabolic rates in the primary visual cortex, cingulate gyrus, putamen, and prefrontal cortex in the monetary condition compared with points only, suggesting heightened sensory and limbic activation with increased valence/risk. Other imaging studies have implicated brain regions involved in attentional processing as distinguishing PG and control subjects when viewing gambling-related material (Crockford et al. 2005). Together, these findings suggest a complex network of brain regions is activated during gambling and related behaviors and that activity within certain regions differentiate PG from control subjects. Consistent with its dysfunctional role in other addictive processes and psychiatric conditions (i.e., impulsivity in bipolar disorder), multiple studies using different experimental paradigms provide consistent evidence for the involvement of the vmPFC in PG. Similarly, studies suggest a role for the ventral striatum in PG as well, which is not surprising given the importance of the nucleus accumbens in addictive processes. PET studies provide evidence for the possible involvement of DA and 5-HT, although more extensive research is needed to confirm and extend these preliminary studies. Limitations of present studies include small samples that typically comprise young men. Furthermore, commonly occurring allelic variants have been shown to substantially influence brain activation patterns (Egan et al. 2004), and imaging studies
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involving PG subjects published to date have not included genetic assessments. Additional studies with larger, diverse, and better-characterized samples will be important. Furthermore, the nature of brain activation differences in studies using functional magnetic resonance imaging (fMRI) should be coordinated with task performance measures and ligand-based imaging in order to better understand the nature of observed activation differences between groups. For example, diminished blood oxygen level dependent (BOLD) signal (activation) in fMRI can reflect improved neuronal efficiency (Egan et al. 2001) or impaired processing (e.g., from a stroke), and measures that provide additional information as to the nature of the observed BOLD signal are important.
STRUCTURAL LESIONS AND DECISION MAKING The Iowa Gambling Task (GT) was developed as a tool to investigate decision making (Bechara et al. 1994). It involves selection from different piles of cards with predetermined patterns of rewards and punishments:Two are associated with lower rewards and punishments with ultimate long-term gain, while the other two are associated with higher rewards and punishments ultimately resulting in loss. Patients with vmPFC lesions performed worse in the GT than did control subjects.Additionally, though they generated skin conductance responses similar to controls in response to rewards and penalties of selections, in contrast to control subjects, they failed to generate anticipatory skin conductance responses while pondering their next selection (Bechara et al. 1996, 1997; Bechara,Tranel, and Damasio 2000). Subjects with substance use disorders similarly display impaired performance on the GT (Bechara and Damasio 2002), and this poor performance has been correlated with decreased blood flow to the vmPFC and other cortical regions (Adinoff et al. 2003; Grant, Contoreggi, and London 2000; London et al. 2000; Tucker et al. 2004). Furthermore, subjects with PG have been shown to choose disadvantageously in the GT (Cavedini et al. 2002). Finally, individuals with PG (n = 60) more readily chose lower monetary rewards promised immediately over higher monetary rewards promised after delayed intervals (termed “delay discounting”) compared with control subjects (n = 26) (Petry 2001). Petry (2001) also showed that temporal discounting of rewards was more rapid when concurrent substance use disorders were present in individuals with PG. Using an experimentally derived gambling task in individuals with vmPFC lesions, although limited by small numbers of subjects with naturally heterogeneous lesions, the preceding studies provide physiologic and empiric evidence regarding the role of the vmPFC in real-time gambling-type decision making. These studies approach the phenomenon by investigating a group of individuals with known structural brain dysfunction and apply a gambling task, rather than starting from a gambling behavioral outcome (PG) and investigating for underlying
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neural characteristics. Importantly, clear links are beginning to be made between the GT (as well as the related discounting of delayed rewards) and dysfunction in PG and other addictions, although it should be emphasized that these findings are preliminary and need replication.Taken together, these findings lend further evidence for the role of frontal cortical regions and the MCL system in PG and strengthen arguments regarding commonality in brain regions with other addictions.
GENETICS With the completion of the Human Genome Project and the rapid rate of discovery of the genetic contributions to psychiatric disorders, the importance of genetics in understanding the neurobiology of PG is becoming increasingly apparent.
POPULATION GENETICS Samples of twins with known zygosity allow for the estimation of genetic and environmental contributions to a behavior or disorder of interest (Shah et al. 2005). A study examining “high-action” forms of gambling (lottery, gambling machines, casino cards) showed significantly higher rates of similarities in male monozygotic (MZ) than dizygotic (DZ) twins (Winters and Rich 1998). Using data from the Vietnam Era Twin (VET) registry, genetic factors were estimated to account for between 35 and 54% of the liability for DSM-III-Revised PG symptomatology (Eisen et al. 1998). This degree of heritability is similar to those of other psychiatric disorders, including SUDs; in the same sample, 34% of variance in the risk for drug dependence was attributable to genetic factors (Tsuang et al. 1996). Additional studies of the VET sample have identified common genetic and environmental contributions to PG and alcohol dependence (Slutske et al. 2000) and PG and antisocial behaviors (Slutske et al. 2001). A more recent investigation found that shared genetic contributions for PG were not limited to externalizing disorders but included major depression (Potenza et al. 2005). Interestingly, the shared genetic contribution to PG and major depression was as large as or larger than those for alcohol dependence or antisocial behaviors, highlighting the need for additional research into the specific biological mechanisms underlying this association. Although the VET studies have multiple strengths, including a large sample of over 7000 twins with known zygosity and gambling behaviors, limitations also exist and include a predominantly well-educated, middle-aged, male cohort that was assessed in the early 1990s at a time when different criteria were used to define PG, and legalized gambling was less accessible and socially acceptable than it is presently.
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MOLECULAR GENETICS Investigations into specific genes relating to the NE, 5-HT, and DA neurotransmitter system contributions to PG have been performed. As the D2A1 allele of the D2R has been implicated in compulsive/addictive behavior such as drug abuse, compulsive eating, and smoking (Blum et al. 1995), investigators found that in a sample of 171 non-Hispanic Caucasians with PG, 51% carried the D2A1 allele compared with 26% of controls (Comings et al. 1996).When gamblers were stratified by median split according to PG severity, 64% of the more severe half carried the D2A1 allele compared with 41% in the less severe half. Allelic variants have also been found with the D1 receptor (D1R). Frequency of homozygosity of the Dde I allele of the D1R has been found to be elevated compared with controls in PG, tobacco smokers, and Tourette’s syndrome probands (Comings et al. 1997). Additionally, allelic variants of the D4 receptor (D4R) gene, which differ in the number of 48-base pair nucleotide repeats (previously shown to have functional differences in terms of ligand binding [Van Tol et al. 1992]), have been implicated in studies of novelty-seeking behavior (Benjamin et al. 1996; Ebstein et al. 1996) but not others (Gelernter et al. 1997; Jonsson et al. 1997; Malhotra et al. 1996; Sullivan et al. 1998). Allelic variants of the D4R gene have also been implicated in mood disorders (Lopez Leon et al. 2005).Two independent studies found a role for the D4R gene in PG (Comings et al. 1999; Perez de Castro et al. 1997). Specifically, in 68 Caucasian Spanish individuals with PG, an association with the longest D4 variant (seven-repeat) was noted in females but not males (Perez de Castro et al. 1997). In contrast, a study of seven-repeat D4 variant alleles in PG showed a nonsignificant difference using a Bonferroni corrected α of 0.125. However, when criteria were broadened to include copies of five to eight repeats, the difference became significant (p < 0.0002) (Comings et al. 1999). Allelic variants of 5-HT-related genes have also been explored in association with PG. A report has found a statistically significant association between allelic variants of the tryptophan 2,3-dioxygenase gene, the protein product of which is involved in 5-HT metabolism (Comings 1998).Additionally, a functional polymorphism in the promoter region of the human serotonin transporter gene (SLC6A4) (the short variant produces functionally less protein) has been associated with several dimensions of psychopathology, including neuroticism, anxiety, and depression (Caspi et al. 2003; Hariri et al. 2002; Lesch et al. 1996; Lesch and Gutknecht 2005; Surtees et al. 2006). An association has been reported between the short allele and PG in males but not females (Perez de Castro et al. 1999). These findings further suggest the involvement of the 5-HT system in the pathogenesis of PG. Polymorphisms in the MAO-A promoter have been reported, with a 30– base-pair region being represented 3, 3.5, 4, or 5 times. The MAO-A genes with 3.5 or 4 copies of the repeated sequence appear to be transcribed two- to ten-fold more efficiently than the variants with three or five copies, based on in vitro studies
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(Sabol, Hu, and Hamer 1998). In a study of 68 subjects with PG, the three-copy allele was significantly higher in gamblers compared with controls (45% vs. 33%), with a relative risk of the three-copy allele ranging from 1.7 to 2.1, depending on gambling severity (Perez de Castro et al. 2002). Allelic variants relating to other neurotransmitter systems have been explored. Polymorphisms of 31 genes involved in DA, 5-HT, NE, and GABA pathways were analyzed in 139 individuals (106 males and 33 females) with PG (53% of whom had concurrent substance abuse) and age, race, and sex-matched controls (Comings et al. 2001).Although only 15 of the genes were included in the regression analysis, several (DRD2, DRD4, DAT1, NMDA1, among others) showed significance in conferring risk for PG, although the fraction of variance was less than 0.02 for most genes. Interestingly, DA, 5-HT, and NE pathways contributed relatively equally to the risk of PG, suggesting a possible additive role for these neurotransmitter systems. To summarize, twin studies show a substantial genetic contribution to PG, and these findings are consistent with genetic contributions to other addictions. Although the best twin studies of PG performed to date have particular strengths (large sample, diagnoses based on structured clinical interviews), there also exist notable limitations (all-male sample). Molecular genetic studies have probed components of the multiple neurotransmitter system. Compared with biochemical and neuroimaging studies that implicate DA systems in PG, molecular genetic studies to date have yielded more modest results in DA and other neurotransmitter systems. Molecular genetic studies published to date are limited by relatively small samples and lack of stratification by racial/ethnic groups (Ibanez et al. 2003). Genome-wide studies that provide more conclusive data have identified genetic factors in SUDs and other psychiatric disorders (Chen et al. 2005; Gelernter, Liu, et al. 2004; Gelernter, Page, et al. 2004; Williams, Brown, and Langefeld 2005). In constrast to studies of major depression and other psychiatric disorders (Caspi et al. 2003), no studies of PG have examined gene–environment interactions. As genetic contributions to PG become better understood, their utility in developing and targeting treatment strategies should be examined.
CONCLUSIONS Biochemical, functional neuroimaging, and genetic studies have implicated 5-HT, DA, and opioid systems, among others, in the pathophysiology of PG. Multiple limitations exist in current studies. Most studies include small samples that are predominantly or exclusively male, thus limiting the generalizability of the findings. Other samples are of limited racial/ethnic diversity, and many studies do not include measures in ancillary domains that likely have substantial influence on findings (e.g., genetic information in brain imaging studies). Although biological
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measures in some domains are very precise (e.g., identity of a specific allelic variant in an individual), related measurements (behavioral measures) are often not precise. Other biological measures (e.g., BOLD signal in fMRI) can have multiple etiologies, and understanding the precise meaning of findings often requires additional information. Biological studies in psychiatry have frequently been hindered by heterogeneity within the subject group being studied, and the identification of intermediary phenotypes or endophenotypes has been forwarded as a strategy for limiting the variability and improving measurements therein (Gottesman and Gould 2003). Endophenotypes have not been adequately investigated in PG, and identification of relevant endophenotypes in PG could help improve measurement variances in biological studies.
GLOSSARY Biochemistry the chemistry of living matter. Dopamine a monoamine neurotransmitter formed in the brain and essential to the normal functioning of the central nervous system. It is also an intermediate in the formation of epinephrine. DSM Diagnostic and Statistical Manual of Mental Disorders, a comprehensive classification of officially recognized psychiatric disorders generated by the American Psychiatric Association. Genetic affecting or affected by genes. Iowa Gambling Task a clinical laboratory tool developed to investigate decisionmaking processes. It involves selection from different piles of cards with predetermined patterns of rewards and punishments:Two are associated with smaller immediate rewards and intermittent punishments with ultimate long-term gain, while the other two are associated with larger immediate rewards and intermittent punishments, ultimately resulting in loss. Neurobiology the biological study of the nervous system. Neurotransmitter a chemical substance that transmits nerve impulses across a synapse to a postsynaptic element, such as another nerve, muscle, or gland. Norepinephrine a catecholamine neurotransmitter produced by neurons of the sympathetic nervous system and in some parts of the central nervous system. It also is a vasopressor hormone of the adrenal medulla, and is a precursor of epinephrine. Opioid a drug, hormone, or other chemical substance having sedative or narcotic effects similar to those containing opium or its derivatives. Similar to opiate. Serotonin an organic compound formed from tryptophan and found in animal and human tissue, especially the brain, blood, and gastric mucous mem-
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branes, which is active in vasoconstriction, stimulation of the smooth muscles, and transmission of impulses between nerve cells. Also called 5-hydroxytryptamine.
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CHAPTER 15
Treatment of Problem Gambling David C. Hodgins
Alice Holub
Department of Psychology University of Calgary Calgary, Alberta, Canada
Department of Psychology University of Calgary Calgary, Alberta, Canada
Introduction Major Approaches to Treatment Psychodynamic Approaches Theoretical Rationale and Therapeutic Model Psychodynamic Efficacy Research Unresolved Psychodynamic Issues Gamblers Anonymous Theoretical Rationale and Therapeutic Model Gamblers Anonymous Efficacy Research Unresolved Gamblers Anonymous Issues Behavioral Therapies Theoretical Rationale and Therapeutic Model Behavioral Efficacy Research Unresolved Behavioral Issues Cognitive and Cognitive-Behavioral Therapies Theoretical Rationale and Therapeutic Model Cognitive-Behavioral Efficacy Research Unresolved Cognitive-Behavioral Issues Brief Treatments and Self-Directed Treatments Theoretical Rationale and Therapeutic Model Brief Treatment Efficacy Research Unresolved Brief Treatment Issues Pharmacological Treatments Theoretical Rationale and Therapeutic Model Pharmacological Efficacy Research Unresolved Pharmacological Issues
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Alternative Approaches or Adjuncts to Therapy Eye Movement Desensitization and Reprocessing Therapy Inpatient Programs Family Approaches Measurement/Evaluation Issues Summary and Conclusions
INTRODUCTION A variety of treatment approaches have been developed for gambling disorders, each of which reflects an assumed etiology of the disorder.A meta-analysis of psychological treatments for pathological gambling was recently completed (Pallesen et al. 2005). The authors included 22 outcome studies, conducted between 1968 and 2004, that targeted pathological gambling and reported outcomes directly related to gambling (e.g., diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition [DSM-IV], number of hours/days/sessions gambled, dollars spent, desire or urges to gamble). Pallesen and colleagues concluded that in general, psychological interventions for pathological gamblers are associated with favorable short-term outcome (overall effect size = 2.01, p < 0.01) and long-term improvement (overall effect size = 1.59, p < 0.01) compared with no treatment. These authors did not assess which types of treatments were more effective than others, although most of the studies included in their meta-analysis were behavioral or cognitive-behavioral in nature. Pharmacological treatments were not included in the meta-analysis. However, reviews of medication trials suggest that treatments with some classes of drugs may be effective for reducing gambling urges and behavior (Grant and Kim 2002; Grant, Kim, and Potenza 2003). This chapter will review these various therapeutic approaches.The theoretical rationale and therapeutic model is introduced for each, followed by a review of the available outcome literature. Measurement, evaluation, and conceptual issues are also addressed. We use the term disordered gambling to refer to gambling problems in a broad sense, while the term pathological gambling is reserved for studies that used DSM criteria to describe participants. The chapter concludes by presenting priorities for future research directions in the area of treatment of gambling disorders.
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MAJOR APPROACHES TO TREATMENT PSYCHODYNAMIC APPROACHES Theoretical Rationale and Therapeutic Model Early writings on gambling, published in the 1950s and 1960s, understood the problem of “compulsive gambling” from a psychodynamic point of view, explaining gambling behavior in terms of the expression of internal conflicts or unconscious motivations (e.g., Bergler 1957; Lindner 1950). Formulations generally include acting on id impulses to achieve pleasure and gratification from the sinful or forbidden act of gambling, leading to guilt and then self-punishment via financial loss by continued gambling.The person may gamble as a symbol of acting out against a parent (Rosenthal and Rugle 1994). Psychoanalytic treatments for gambling generally consist of individualized, long-term individual or group therapy, aimed at increasing the client’s insight into the origins or unconscious drives behind the gambling behavior and resolving underlying conflicts to reduce the need for gambling. Psychodynamic approaches have included traditional psychoanalysis (free association, interpretation of transference, exploration of childhood), object-relations-interpersonal therapy, and unstructured process groups. No guidelines have been developed in terms of how to deal specifically with the problem of gambling, and no psychodynamic treatment approaches specific to gamblers have been developed. Psychodynamic Efficacy Research Randomized controlled outcome studies of psychodynamic approaches have not been conducted, due to the individualized nature of psychodynamic approaches and the infrequent use of standardized outcome measures (Lopez Viets and Miller 1997). Psychodynamic approaches are not standardized and vary according to the specific orientation of the therapist, making comparisons difficult. A small number of case reports continue to be published, indicating that psychodynamic treatments are still in use (e.g., Rosenthal and Rugle 1994). However, conclusions about efficacy, and comparisons with other forms of psychological treatment, have not been made and psychodynamic therapies have not been included in meta-analyses of gambling treatment. Unresolved Psychodynamic Issues With the current leaning in the field of psychological research toward randomized controlled trials (RCTs), manualized therapies, and standardized outcome measures, it is less clear how one should evaluate the efficacy or effectiveness of
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treatments such as psychodynamic therapies that are less amenable to those research designs. As these treatments continue to be used, research identifying therapeutic elements, and characteristics of clients that predict success with psychodynamic approaches, would be beneficial.
GAMBLERS ANONYMOUS Theoretical Rationale and Therapeutic Model Gamblers Anonymous (GA) stems from the traditions and spiritual principles of Alcoholics Anonymous (AA) and considers that “gambling for certain individuals is an illness called ‘compulsive gambling’ ’’ (Gamblers Anonymous 2007). Gambling is conceptualized from a medical perspective, in that individuals with pathological gambling behavior are suffering an illness, which leads to a permanent predisposition for losing control over their gambling. GA promotes complete abstinence as a goal, and achievements of periods of abstinence are celebrated and marked with tokens (e.g., 30 days, 60 days, 6 months, 1 year). GA’s spiritual focus is explicitly nondenominational, and groups are led by volunteers from the membership. The therapeutic rationale for GA is that the twelve steps to recovery will lead gamblers to attain abstinence, in addition to several other aims that are considered lifetime goals: recognition and acceptance of lifetime powerlessness over the disease of gambling; acceptance of spirituality in daily recovery; enhanced insight and awareness of the impact of gambling on one’s self-esteem and interpersonal relationships; taking responsibility and making amends to those harmed by gambling; and giving back to other problem gamblers.The group format is intended to provide a sense of common purpose and understanding, validation, emotional and spiritual support, and hope. Anonymity allows for members to feel safe in sharing openly with other members.
Gamblers Anonymous Efficacy Research Outcome studies on the effectiveness of GA are few, and well-controlled efficacy research has not been conducted. It is clear that attrition rates are high (Preston and Smith 1985; Rosecrance 1986; Stewart and Brown 1988). On the other hand, correlational data show that individuals who affiliate with GA have better gambling outcomes than those who do not (Hodgins, Peden, and Cassidy 2005), even when they are engaged in professional treatment concurrently (Petry et al. 2006; Russo et al. 1984; Taber et al. 1987). Specific outcome data are limited, although one Scottish study found that of 232 individuals who attended GA,
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7.5% were abstinent at 1 year, and 7.3% were abstinent 2 years later (Stewart and Brown 1988). Unresolved Gamblers Anonymous Issues The nature of the GA program presents a number of challenges to examining its efficacy. The value of scientific evidence for effectiveness is inconsistent with the faith-based nature of the GA program. Moreover, research participation can conflict with the anonymity principle, and solicitation of research participation is seen as breaching the primary purpose of the group—to help gamblers stop gambling. In addition, the group is, by design, facilitated by members who are not professionals, which makes the standardization of the experience that is needed for rigorous evaluation difficult. These challenges make the conduct of large RCTs of GA unlikely in the near term. However, it is important that we continue to collect systematic quasiexperimental and correlational data to identify client factors impacting affiliation and effectiveness of GA. It is also feasible to design randomized evaluations of professional treatments that are based on GA principles and/or are designed to encourage GA attendance, modeled after AA 12-step facilitation approaches (Project Match Research Group 1997).
BEHAVIORAL THERAPIES Theoretical Rationale and Therapeutic Model Behavioral explanations became popular beginning in the 1960s, conceptualizing gambling in terms of learned patterns of reinforcement (e.g., financial, thrill) and understanding gambling as part of an overall functional pattern. Behavioral models conceptualize behaviors as having three components: antecedents (factors that precede/trigger problem behavior), the overt or covert behavior itself, and the consequences of the behavior (patterns of reinforcement and punishment). In the area of disordered gambling, several categories of antecedents have been identified, including financial pressure, external gambling cues, positive or negative emotions, interpersonal factors, and unplanned or impulsive urges to gamble (Hodgins and el-Guebaly 2000, 2004). The overt behaviors that disordered gamblers display include loss of control over time and money spent gambling, chasing of losses, and use of gambling as a means of coping with feelings of stress, anxiety, or depression (American Psychiatric Association 2000; Blaszczynski 2000; Breen and Zuckerman 1999). Covert behaviors include increased time thinking about gambling, strategizing to attain money or hide gambling from others, and ruminating about losses.
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In terms of consequences, gambling is associated with both positive and negative effects. Negative effects include psychological distress (stress, anxiety, depression, guilt, low self-esteem, and suicidal ideation), relationship conflict, financial loss, and difficulty meeting work or other obligations. However, gambling is also highly reinforcing and provides thrills and excitement, opportunities to socialize, escape from stress or personal problems, and of course, the potential for monetary gain. Research has confirmed the common observation that a large win early in a gambler’s experience is associated with an increased likelihood of developing gambling difficulties (Turner et al. 2001).The variable pattern of reinforcement from gambling means that the timing of a win is impossible to predict, a situation known to be associated with developing persistent behaviors. The positive reinforcement from gambling comes not only from winning money, but also from the thrill of a perceived near win (which may increase gambling). Furthermore, autonomic arousal experienced during gambling is reinforcing and provides a sense of pleasure or gratification that is similar to that felt with addictive substances. Gambling may also be negatively reinforcing, in that by alleviating stress, anxiety, or other negative affect, gambling becomes sought out as an escape from negative emotions or becomes a conditioned response to stress.A related idea in the behavioral literature regards gambling as part of a behavioral completion mechanism (BCM). The BCM perspective holds that gambling becomes part of an established behavioral pattern or habit, whereby a variety of internal and external events can come to elicit the drive to gamble. Gambling provides excitement and tension relief (Blaszczynski 2000; McConaghy et al. 1988). The theory further holds that individuals will begin to experience tension or anxiety if the behavior (i.e., gambling) is not completed in response to triggers. Treatments following the behavioral learning model seek to change gambling by intervening with one or more of the components of the functional relationship. First of all, some behavioral therapies may focus on changing antecedent conditions, thereby reducing the likelihood of the individual having an urge to gamble. Such therapy, termed “stimulus control,” would raise awareness of antecedent conditions, such as emotional distress or financial pressure, and teach clients to become more aware of situations that are related to urges to gamble. Stimulus control also involves modifications such as limiting access to money, not visiting venues that offer gambling, and not spending time with people associated with heavy gambling. Other behavioral techniques that may change antecedents to gambling include providing assertiveness training or other social skills to reduce interpersonal stress; money management information to reduce financial pressure; relaxation training; and anger, stress, or other emotional coping skills. Treatments can also aim to alter individuals’ behavioral responses to the antecedents, such as teaching clients to use alternate methods of relieving stress (e.g., meditation, exercise) or by limiting their gambling in terms of time or money available. Reduction in gambling is expected if clients can successfully develop and use alternative coping responses in situations that are associated with urges to gamble.
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Some behavioral therapies are aimed at changing the reinforcement patterns of gambling. For example, aversive conditioning aims to change the associations of gambling with pleasure, excitement, or relief, to become associated with aversive reactions. Aversion treatments use stimuli that are either physically aversive, such as electric shocks or drugs causing nausea, or mentally aversive, such as envisioning the worst conceivable consequences of gambling. Aversion therapy is aimed at developing negative associations with gambling in the expectation that desire to gamble will decrease. An alternate behavioral approach is exposure with response prevention (ERP), which is a treatment that has been shown to be effective with obsessive-compulsive disorder, in that behavioral responses strongly associated with anxiety cues are changed. As gambling has both obsessive qualities (e.g., preoccupation with gambling) and compulsive behaviors, it was thought that ERP could reduce urges to gamble and decrease the likelihood of gambling when the person was feeling anxious or tense (McConaghy et al. 1983, 1988; McConaghy, Blaszczynski, and Frankova 1991). ERP involves being exposed to gambling cues, which will lead to an increase in anxiety or urge to gamble. Clients are prevented from their normal response of gambling to reduce the tension and are taught alternative strategies to cope with their increased anxiety, such as deep abdominal breathing or physical activity. Behavioral treatment can also be focused on the maintenance of abstinence from gambling and on other treatment gains. Relapse prevention techniques involve identifying high-risk situations and acting either to avoid such situations or to cope more adaptively. It is thought that relapse prevention techniques reduce the likelihood of experiencing urges to gamble and increase the likelihood of engaging in adaptive coping behaviors (e.g., Echeburua, Fernandez-Montalvo, and Baez 2000a). Behavioral Efficacy Research Early published literature indicated that aversion therapies were the first types of behavioral approaches used for treating what was termed “compulsive gambling.”Two case reports investigated apomorphine, a drug that induces vomiting, to generate an aversive physical reaction to tape-recorded and photographic gambling stimuli (Cross 1966) or actual gambling (Salzmann 1982). Most other studies used electrical aversion (e.g., Barker and Miller 1968; Goorney 1968; Koller 1972; Seager 1970). Painful electrical shocks were applied to the playing arm of gamblers while they viewed slides depicting casino games, read horse race results in the newspaper, or played a poker machine. These studies indicated that about 30% of individuals reported abstinence after several sessions of such treatment, but as this was early in the life span of gambling research, no diagnostic guidelines were available to describe the severity of gamblers’ problems, and no standardized measures of symptoms were available to allow for objective analysis.
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Later studies compared different behavioral approaches. McConaghy et al. (1983) compared 20 gamblers randomly assigned to receive aversion (electric shock) therapy or imaginal desensitization over a 1-week period.The results indicated that both treatments had positive outcomes but that imaginal desensitization was more effective than aversion therapy in reducing both urges to gamble and actual gambling. The authors were the first to use collateral data to verify patient reports of gambling, lending more support to their findings of positive outcomes. Overall, 70% of participants reported abstinence 1 year posttreatment. The same investigators (McConaghy et al. 1988) then compared imaginal relaxation with imaginal desensitization. There were no differences between groups—both experienced significant reductions in gambling and anxiety when exposed to gambling cues. A later study comparing imaginal desensitization, aversion therapy, imaginal relaxation, and in vivo exposure therapy included a long-term (2–9 years) followup (McConaghy et al. 1991). Imaginal desensitization showed the most improvement over the one-month and 2- to 9-year follow-ups. In a post-hoc comparison of outcomes for individuals self-reporting complete abstinence versus periodic relapses (Blaszczynski, McConaghy, and Frankova 1991), no differences in outcomes were identified across behavioral interventions. Gambling disorders are associated with high relapse rates (Hodgins and el-Guebaly 2004), prompting strategies aimed at preventing relapse. Echeburua et al. (2000b) evaluated the efficacy of providing relapse prevention strategies as a follow-up to behavioral treatment. Sixty-nine pathological gamblers received behavioral treatment (stimulus control and exposure with response prevention), followed by random assignment into one of three conditions: individual relapse prevention, group relapse prevention, or no treatment. Both group and individual relapse prevention were associated with complete abstinence or greatly reduced gambling and a decrease in money spent gambling. Whether individuals still met criteria for pathological gambling at the end of treatment was not addressed. The observed differences were maintained over a 12-month follow-up period (Echeburua et al. 2001). This study reported a dropout rate of 15%, and analyses suggested that dropouts were more anxious than completers. Unresolved Behavioral Issues The literature on behavioral therapies includes several well-controlled, randomized trials demonstrating significant advantages of treatment compared with no treatment, with outcomes that seem on average to be maintained for 6 months or longer. A general conclusion from this body of research is that interventions involving developing relaxation skills, exposure to gambling cues, and direct behavioral action are effective in improving gambling urges, time and money spent, and abstinence (Pallesen et al. 2005). Aversion therapies, in comparison, appear to be less effective. The unpleasant nature of aversive techniques may lower clients’
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willingness to engage in therapy work, further limiting treatment effectiveness. A second conclusion is that behavioral relapse prevention techniques aimed at maintaining treatment gains also appear to be effective (e.g., Echeburua et al. 2000a, 2001), which indicates the need for implementing and evaluating follow-up care. In terms of modality, there are no well-controlled comparisons of group versus individually administered behavioral therapy. Echeburua, Baez, and FernandezMontalvo (1996) used stricter criteria, such as diagnoses from the DSM, revised third edition (III-R), exclusion of those with other mental disorders, random assignment, and a wait-list control group. Unfortunately, participants in the individual and group formats received different therapies (individual behavior therapy but group cognitive therapy), precluding conclusions about their relative efficacy. The field has moved toward a more integrated understanding of disordered gambling and its treatment. As many behavioral interventions are actually multifaceted, often including cognitive components, it may be difficult to isolate the most potent agent of change. Therapeutic benefit may stem from practicing new behaviors, gaining insight into one’s contextual triggers, or learning new associations.There remains a need for dismantling studies to determine what component(s) of behavioral therapy are effective.
COGNITIVE
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Theoretical Rationale and Therapeutic Model Cognitive-behavioral formulations of gambling recognize the importance of both the cognitions underlying behavioral choices and the link between behavioral experimentation and cognitive change. As indicated in the previous section, gambling serves one or more functions, such as relief from boredom, gratification through thrill seeking, social contact, and escape from negative emotions or life stresses. Cognitive and cognitive-behavioral formulations of pathological gambling hold that excessive gambling is driven by maladaptive cognitions (Sharpe 2002; Sharpe and Tarrier 1993; Chapter 11, this volume). Maladaptive cognitions that have been supported by research include themes such as having skill or personal control over the outcome of a gamble, superstitions involving luck or other factors, and memory biases in favor of remembering wins while discounting losses (Gaboury and Ladouceur 1989; Griffiths 1994; Holub 2003; Ladouceur et al. 1995). Both social and disordered gamblers have also been found frequently to misunderstand probability principles, which may lead them to believe that a win is imminent and persist despite mounting losses (Kelly et al. 2001). Distorted cognitions have long been believed to originate from an individual’s more fundamental underlying beliefs about oneself, the world, and the future. A core belief proposed to underlie much excessive gambling behavior is that the gambler is able to predict, influence, or control the outcome of his or her gambling (Toneatto 2002).
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This belief is maintained regardless of actual outcome because gamblers continue to process information in ways consistent with their belief. Distorted cognitions about gambling are reinforced by gambling because wins are regarded as evidence of skill, while losses are regarded as random, uncontrollable events (Griffiths 1994). Biases in decision making have also been identified in gambling. Studies have characterized gamblers as less sensitive to punishment, particularly delayed punishment, in that they continue to gamble despite high losses. Gamblers are found to be more strongly influenced by immediate reward than potential future consequences (Alessi and Petry 2003; Bechara, Tranel, and Damasio 2000). Some have suggested that these data reflect gamblers’ tendencies to be focused on the present (Hodgins and Engel 2002), and it is unclear how and to what extent these decisionmaking styles are related to distorted thinking about gambling. Several authors (e.g., Bechara et al. 2000) have suggested that disadvantageous decision making in gamblers is related to biologically based characteristics that would influence sensitivity to reward and reduce behavioral inhibition. Cognitive-behavioral therapy (CBT) for gambling aims to increase the individuals’ awareness of their thoughts and of the link among thoughts, behavior, and emotion.Therapy involves eliciting problematic thoughts about gambling, guiding the client to question the validity of those thoughts, and providing corrective information through education, logical discussion, or behavioral experimentation (Ladouceur et al. 2001a; Toneatto 2002). Such cognitive restructuring is expected to lead to improved control over gambling and a more realistic understanding about the likelihood of winning. In addition, cognitive strategies also aid gamblers in coping with urges to gamble and managing negative emotions. Modification of core beliefs underlying distorted thinking is also the aim of some therapies.As individuals become more adept at challenging cognitive distortions, therapy can focus on collecting information that is inconsistent with the belief about gambling. In addition to addressing problematic cognitions, CBT also employs strategies aimed at directly modifying behavior. Behavioral strategies may include reducing avoidance and developing clients’ skills in various areas (e.g., social communication, assertiveness, problem solving, relaxation or anxiety reduction, adaptive behavioral coping). Therapy provides an opportunity to practice new behaviors and receive feedback. In addition, CBT includes exposure to anxiety-provoking situations.
COGNITIVE-BEHAVIORAL EFFICACY RESEARCH The majority of the published evaluations of psychosocial treatment for gambling disorders have been cognitive or cognitive-behavioral. Although a number of the uncontrolled trials have evaluated purely cognitive treatments (e.g., Bujold et al. 1994; Toneatto and Sobell 1990), the more recent randomized trials have focused on combined cognitive and behavioral approaches. Importantly,
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gambling CBT trials have been conducted by different research groups in different parts of the world in a number of different languages. Ladouceur and colleagues completed three randomized trials in Quebec with French-speaking pathological gamblers (Ladouceur et al. 2001b, 2003; Sylvain, Ladouceur, and Boisvert 1997). In the earlier two of these trials, individual therapy was compared with a control condition in which participants waited several months prior to initiating treatment. Treated participants reported less gambling and increased perceived self-control over gambling at 12 months, compared with those on the wait list, and gains were maintained in the treated group. In the latter study, the same treatment offered in group format, plus a relapse prevention component, was compared with a wait-list control. Participants receiving treatment met fewer DSM-IV criteria for pathological gambling and reported higher self-efficacy and perceived self-control over gambling posttreatment, although not less frequent gambling.Treatment gains were maintained over 24 months. Hodgins and Petry (2004) noted that although these randomized trials represent an improvement in methodology compared with previous reports, intent-to-treat analyses were not conducted. Only participants who completed treatment—63%, 53%, and 74%, respectively—were included in the reports, which limits the generalizability of the results. Results of a randomized trial are available from Spain (Echeburua et al. 1996). Pathological slot machine gamblers were randomly assigned to individual behavioral therapy (stimulus control and exposure with response prevention), group cognitive therapy, a combination of the cognitive and behavioral therapies, or a wait-list control group. The individual behavioral therapy was found to be more effective than the group cognitive restructuring and the combined therapies in terms of reducing gambling frequency at 1 year. All three treatment groups had better outcomes than the control group at 6 months in terms of reduced time spent gambling and on subjective report of gambling severity, but the magnitude of differences between treatments was found to be minimal. Petry (2006) evaluated an eight-session individual treatment for pathological gamblers in the United States. The cognitive-behavioral therapy was either delivered by a professionally trained counselor or completed alone by the patient in a self-help workbook, and both conditions were compared with a group referred to GA. Participants in all groups reduced gambling. The individual/workbook cognitive-behavioral conditions in particular had significantly better improvement in terms of days gambled and number of gambling criteria met compared with the GA referral alone group. Investigators used well-defined intent-to-treat analyses, using all available data for each participant at every follow-up. No differences between groups on gambling variables were identified at baseline. At baseline and follow-ups, the groups did not differ in number of GA meetings attended; however, attendance at GA was positively associated with abstinence from gambling. An enhanced version of group cognitive-behavioral therapy, also conducted in the United States, compared standard group CBT, CBT enhanced with interactive
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written exercises, and a wait-list control group (Melville et al. 2004). Both treatment groups were superior to control at the end of 8 weeks. In addition, individuals receiving CBT enhanced with written mapping exercises experienced more improvement than did the standard-CBT group at the end of treatment, as evidenced by shorter duration of gambling episodes, fewer DSM-IV criteria met, and reports of less depression and anxiety. It may be that mapping served to individualize the group treatment, leading to greater effect. In Australia, Milton et al. (2002) investigated the incremental benefit of adding interventions aimed at increasing compliance and motivation and reducing dropout in clients receiving individual CBT. Forty clients were provided with eight individual sessions of CBT; twenty people also received compliance-improving interventions, such as positive affirmation of their choice to seek help and reinforcement of self-efficacy, reminder phone calls, assessment feedback, and a weekly decisional balance exercise to complete. The additional interventions were associated with lower dropout rates and with higher levels of clinical change at posttreatment. However, these differences were not maintained at the 9-month follow-up. Comorbid problem drinking was identified as a significant predictor of poor gambling outcome. In sum, both individual and group cognitive-behavioral therapies appear effective for reducing gambling. The rigor of the outcome evaluations has also improved with time. Moreover, some data suggest that treatment can lead to the adoption of more adaptive cognitions and knowledge about gambling (Blaszczynski and Silove 1995; Lopez Viets and Miller 1997;Toneatto 2002). Unresolved Cognitive-Behavioral Issues Despite consistency across some studies regarding the types of cognitive distortions present in gambling (e.g., illusion of control or skill, superstitious thinking, misperception of probability), no cognitive therapy studies have included an objective measure of cognitions before and after therapy. Cognitive change is important, as persistence of maladaptive cognitions after treatment could increase the risk of relapse posttreatment (Sharpe and Tarrier 1993). Some measures of cognitive distortions have been developed (Holub 2003; Kassinove 1998; Steenbergh et al. 2001; Strong et al. 2004) and should be used in future research on cognitivebehavioral approaches. Without standardized or objective measures of distorted thinking, it is unclear to what extent treatment outcomes are related to cognitive change versus other factors (e.g., behavioral change, coping skills, insight).
BRIEF TREATMENTS
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SELF-DIRECTED TREATMENTS
Theoretical Rationale and Therapeutic Model The use of briefer treatments for gambling has been growing in recent years. As available resources for gambling-related problems are limited or nonexistent in
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many geographic areas, telephone-based support, mailed self-help manuals, and online resources can potentially increase outreach (Hodgins 2004). There are differing conceptualizations in the literature regarding brief treatment. In most studies, brief treatments for problem gambling are defined as those using less professional resources or time than usual face-to-face interventions (typically, 6 to 12 sessions of therapist contact). Using this broad definition, brief treatment includes single-session interventions, approaches with minimal therapist involvement (e.g., telephone support, self-help workbooks, bibliotherapy), or self-directed support groups. Self-help workbooks based on cognitive, behavioral, and solutionfocused approaches have been developed for overcoming gambling problems (e.g., Blaszczynski 1998; Hodgins and Makarchuk 2002).Advantages of workbook-based interventions include increased accessibility, lower defensiveness or embarrassment, and enhancement of clients’ sense of control over their own recovery. A promising area of brief treatment is motivational interviewing (MI), a style of therapy that considers the client’s stage of readiness to make the behavioral changes required by treatment protocols (Hodgins and Diskin 2007). MI is client centered, in that it views clients as holding the solutions to their distress, and the therapist is a facilitator of their behavioral change. Clients present at various levels of readiness to enact change, and MI intends to meet them at their stage of readiness and to identify and address the ambivalence. By doing so, it is thought that the client will be able to come up with solutions and enact changes (Miller and Rollnick 2002). The stance of MI is empathetic and focuses on clients’ strengths to enhance self-efficacy regarding change.The goals of MI techniques are to build on clients’ intrinsic motivation for change, strengthen their commitment to change, and help them develop a plan. Brief Treatment Efficacy Research Minimal or brief therapies have had success in a number of areas of behavior change and have been well supported in the treatment of substance addictions. Several studies have investigated the efficacy of minimal treatments for disordered gamblers. Dickerson, Hinchy, and Legg-England (1990) randomly assigned 29 disordered gamblers to receive an in-depth assessment interview plus a self-help manual containing behavioral strategies or to receive only the manual. At 3 months, gamblers receiving the interview and manual were spending fewer dollars per week on gambling than the manual-only group. However, these group differences were not maintained at 6-month follow-up. The study was limited by a small sample size, low (45%) follow-up rate, and lack of a no-treatment control group. A more recent study investigated the value of providing extended relapseprevention information to a self-help workbook intervention. Individuals meeting DSM-IV criteria for pathological gambling (n = 169) were randomly assigned to receive a workbook providing an overview of behavioural strategies for various
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aspects of relapse prevention (e.g., avoiding urges, lifestyle management, financial management) versus receiving several additional booklets each of which provided an expanded discussion of one specific topic (Hodgins, Currie, et al. 2007). At 6and 12-month follow-ups, individuals in both groups had fewer days gambling and had spent less money than at baseline, but there was no indication of any particular additional benefit of providing the detailed information booklets. The results suggested that a booklet containing an overview of change techniques may be sufficient to stimulate behavioral change in motivated individuals. However, the lack of a control group made more specific conclusions about the effect of the booklets on gambling outcomes unclear. The efficacy of MI has now received support in RCTs of gambling. Hodgins, Currie, and el-Guebaly (2001) randomly assigned 102 disordered gamblers to receive a self-help workbook only or a workbook as well as telephone motivational support or to be in a 1-month wait-list control condition. All three groups experienced improvement over the study period, with 84% of participants reporting significant improvement in gambling behavior and 25% reporting abstinence. At 3- and 6-month assessments, participants receiving the workbook plus telephone support improved significantly in terms of reduced days spent and dollars lost gambling compared with the wait-list control group. Those receiving a workbook only were not significantly more improved than those on the wait list. The authors commented that the sample appeared to be motivated and capable of self-change prior to receiving the intervention.The intake assessment done prior to randomization or knowledge that treatment would be provided within a short time may also have had therapeutic elements for participants on the wait list. A 24-month follow-up was subsequently conducted, indicating that workbook/ telephone support was associated with better long-term outcome than workbook only (Hodgins et al. 2004). Participants receiving the workbook and telephone calls reported less frequent gambling and lower severity scores than the workbookonly group. Diskin (2006) evaluated a face-to-face single-session MI. Participants recruited through advertising in media who were experiencing some concerns about their gambling but not necessarily wanting to reduce it were randomly assigned to one of two groups. Half the participants were given the MI and the other half spent a similar amount of time with an interviewer talking about their gambling and completing various semi-structured personality measures. All participants were given a copy of a self-help workbook.The gamblers who received the MI gambled less often and spent less money gambling than the control group over a 12-month follow-up. Interestingly, participants with relatively less severe gambling problems did equally well in the MI and the control conditions, but those with greater severity did better with the MI.This finding supports the notion that natural or self-directed recoveries are common among individuals with less severe gambling problems.
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Unresolved Brief Treatment Issues One potential advantage of minimal interventions is that they should be able to attract a larger number of problem gamblers into treatment than formal faceto-face treatments (Hodgins 2004). However, this advantage has yet to be confirmed, and studies of treatment systems are required to supplement the efficacy research that has been reported. Although motivational approaches seem effective compared with more minimal intervention such as workbooks, the efficacy relative to more established cognitive-behavioral interventions is yet to be confirmed. It is unclear whether certain client characteristics such as lower severity or lack of comorbid pathology predict success with brief interventions.
PHARMACOLOGICAL TREATMENTS Theoretical Rationale and Therapeutic Model Biological formulations of gambling problems stemmed from observations of the similarities between disordered gambling, other addictive behaviors, and mental health disorders. Genetic models of addiction have long implicated dysfunctional reward pathways in the brains of addicts that lead individuals to become addicted to substances or behaviors that are pleasurable or that provide tension relief. The central nervous system transmitter, dopamine, has been most strongly associated with reward pathways in gambling and other addictions (e.g., Bergh et al. 1997). Biological models of disordered gambling also implicate abnormalities in other neurotransmitter systems, particularly, serotonin, norepinephrine, endogenous opioids (Comings et al. 2001), and monoamine oxidase (Carrasco et al. 1994). Such conditions are thought to predispose certain individuals to impulsive or compulsive behavior generally, and therefore the potential to gamble and to develop gambling problems (see Chapter 14, this volume). Medications used to treat disordered gambling have aimed to alter such neurotransmitters, with different clinical aims. The observation of obsessive and compulsive symptoms in pathological gamblers (Kim and Grant 2001a) has led to the use of drugs that have been efficacious in treating obsessive-compulsive disorder—selective serotonin reuptake inhibitors (SSRIs). It is hypothesized that SSRIs will reduce urges to gamble and diminish obsessive and compulsive behaviors. Drugs that have the clinical effect of blocking subjective feelings of euphoria associated with substance use have also been used with gamblers. Such opioid antagonists block dopamine and norepinephrine, thereby dulling the pleasure experienced from gambling, reducing novelty seeking, and weakening the association between gambling and arousal (DeCaria et al. 1998). Other classes of drugs that have been used to treat disordered gambling include atypical antipsychotics and antiepileptic medications, both of which may have some mood-modulating effects and may also act to reduce urges. Some have
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also suggested that in addition to targeting gambling directly, the treatment of mood disorders is important given their high comorbidity with gambling. Treatment with mood-stabilizing drugs such as lithium may reduce the use of gambling to regulate negative mood and reduce bipolar symptoms, including excessive gambling (Blaszczynski 2000).
PHARMACOLOGICAL EFFICACY RESEARCH Although there are no approved medications for the treatment of disordered gambling in the United States or elsewhere, there has been a proliferation of case reports, open-label trials (nonblinded), and double-blind randomized clinical trials of a number of medications over the past decade. Published reports have investigated the efficacy of SSRIs, opioid antagonists, and mood stabilizers.A few reports of other classes of psychoactive drugs have also been published. Most pharmacological studies have assessed SSRIs, including fluvoxamine, paroxetine, sertraline, citalopram, and chlomipramine. In addition to several case studies, open-label trials of SSRIs have reported promising results in terms of reducing urges to gamble and lowering scores on measures of obsessive and compulsive symptoms (Hollander et al. 1998; Zimmerman, Breen, and Posternak 2002). Grant and Potenza (2006) conducted an open-label phase of escitalopram, after which 13 pathological gamblers showed improved gambling and anxiety symptoms. Following up these positive outcomes, several double-blind, randomized, placebo-controlled trials of pharmacotherapy for gambling have now been reported. Some of these studies have found significant changes in urges to gamble, severity of problem gambling, and obsessive and compulsive symptoms (Hollander et al. 2000; Kim et al. 2002), whereas most others did not find significant advantages of SSRIs over placebo (Blanco et al. 2002; Dannon et al. 2005; Grant, Kim, Potenza, et al. 2003; Saiz-Ruiz et al. 2005). Another type of antidepressant, nefazodone, has a slightly different mechanism of action than traditional SSRIs, acting to both reduce transmission and block reuptake of norepinephrine. An open-label trial of nefazodone with pathological gamblers (n = 14) led to significant improvements in gambling behavior, depression, and anxiety (Pallanti et al. 2002). However, this drug has now been pulled from the markets in Canada and the United States due to an association with liver failure. The literature on SSRI treatments suggests that large doses of SSRIs seem to be necessary for successfully improving disordered gambling. Individuals with and without comorbid mood or anxiety disorders have all benefited from SSRI treatment (Grant, Williams, and Kim 2006). However, all RCTs have reported high placebo response rates, which makes determination of the effectiveness of the drugs more difficult. Interestingly, the antidepressant effect of SSRIs does not appear to mediate outcome, as positive changes in gambling behavior have been reported despite a lack of significant changes in mood.
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Studies using mood-stabilizing drugs also seem promising in case reports and in two larger trials, a randomized, single-blind study comparing lithium and valproate (Pallanti et al. 2002) and a randomized double-blind trial of sustained-release lithium (Hollander et al. 2005). In the former study, participants in both medication groups (n = 42) showed improvement on a measure of obsessive and compulsive symptoms, and similar numbers of individuals were considered medication responders, with no differences in outcomes between groups. However, fewer individuals in the lithium group, versus the valproate group, completed the study (65% versus 84%). In the latter trial, 40 pathological gamblers with bipolar spectrum disorders were assigned to receive either lithium or placebo. Individuals receiving lithium showed greater improvement on measures of obsessive and compulsive symptoms, mood symptoms, and gambling involvement. So far, research with mood-stabilizing drugs suggests that for disordered gamblers who have comorbid bipolar symptoms, mood stabilizers have the potential to reduce problematic gambling behavior as well as bipolar symptoms. Further research using randomized controlled designs with longer follow-up intervals and using comparisons of efficacy in gamblers with and without bipolar symptoms is needed. In addition to drugs that impact mood, therapies for gambling have also targeted addictive symptoms. Three such studies have assessed the efficacy of opioid antagonists for gambling. Kim and Grant (2001b) initiated an open-label trial of naltrexone with 17 pathological gamblers. After 6 weeks, significant improvements in subjective ratings of urges to gamble and reductions in gambling behavior were noted. The investigators then conducted a double-blind, placebo-controlled trial (Kim et al. 2001). Only 45 of 83 participants completed the study, and results indicated a significant placebo effect of 25%. Despite this, greater improvement in terms of symptoms of problem gambling was found for those receiving naltrexone. Recently, a large randomized, placebo-controlled trial (n = 207) was conducted using nalmefene (Grant, Potenza, et al., 2006). Nalmefene is an opioid antagonist with a safer side-effect profile than naltrexone. Results indicated that individuals receiving nalmefene evidenced greater reductions in urges to gamble, problematic thoughts, and behaviors compared with those receiving placebo. Improvements on obsessive and compulsive symptoms were also found for those receiving the lower, but not the higher, dose of nalmefene. Thus, early results from studies of opioid antagonists show promise but also suggest high placebo response rates. Unresolved Pharmacological Issues As data on outcomes of pharmacological interventions for disordered gambling are mixed, it is necessary for research to identify factors that may be associated with positive treatment outcomes. Some have suggested that nonspecific factors, such as motivational status, may be responsible for the high placebo response rates (e.g., Petry 2005). In support of this idea, Petry (2006) found that in
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a study of treatment-seeking pathological gamblers (n = 234), higher motivation to change was significantly predictive of reductions in gambling behavior. The impact of comorbid mental health symptoms also required further study. A problem affecting medication trial outcomes is high rates of study dropout (reportedly 30–50% [Petry 2005]). Conclusions are also hampered by generally small sample sizes, short follow-up points, lack of longitudinal data, and differences in outcome measures used to determine effectiveness. Other problems inherent in medication research include aversive side effects and lack of compliance. Ecological validity of the results may also be compromised by the lack of concurrent psychotherapy, which gamblers seeking treatment may be more likely to receive. Studies assessing the efficacy of pharmacological treatment with concomitant psychotherapy are needed.
ALTERNATIVE APPROACHES OR ADJUNCTS TO THERAPY EYE MOVEMENT DESENSITIZATION THERAPY
AND
REPROCESSING
One report by Henry (1996) evaluated the use of eye movement desensitization and reprocessing therapy (EMDR), based upon an anxiety-driven formulation of disordered gambling. EMDR is a treatment developed for posttraumatic stress disorder (PTSD) that is thought to correct dysfunctional information processing and release trauma-related anxiety.The gambling application conceptualized gambling as driven by unresolved trauma-related anxiety and dysfunctional information processing in the brain. This formulation was derived from similarities between pathological gambling and PTSD in terms of obsessive thoughts, from evidence of dissociation during gambling, and from psychodynamic theories of trauma processing. Henry used EMDR as a supplement to standard cognitive-dynamic therapy for interested individuals in a small sample of self-referred gamblers (n = 22). The treatment was not manualized and was highly individualized and variable in length (1 to 8 months). No standardized outcome measures were used, but selfreports of gambling suggested reduced gambling frequency.
INPATIENT PROGRAMS Most of the therapies previously described are administered on an outpatient basis and are low to moderate in terms of time and commitment intensity. For some disordered gamblers, more intensive intervention may be necessary, whereby
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the gambler physically removes him or herself from the context associated with problematic gambling. Inpatient programs are typically modeled after substance use rehabilitation approaches, using a combination of stabilization (of suicidal intent, acute mental health disorders, or physical health issues) and psychological support (group and individual therapy). Lopez Viets and Miller (1997) identified six different uncontrolled evaluations that yielded generally positive outcomes. For example, Russo and colleagues (1984) reported the effectiveness of a 30-day inpatient treatment program for self-referred pathological gamblers that included supportive group therapy, psychoeducation, and referral to GA. Most patients (46/52 beds) had alcohol-related problems. At 1 year postdischarge, 48% of the 126 participants completed follow-up measures: 55% of these reported complete abstinence and an additional 13% reported at least 6 months of abstinence after discharge.These low follow-up rates typify the outcome research in this area and, together with the lack of a control group, preclude firm conclusions.As these programs continue to be in use, more rigorous research on effectiveness is warranted.
FAMILY APPROACHES It is well recognized that problem gambling affects not only the individual who gambles, but significantly also his/her interpersonal relationships. One of the DSM-IV criteria for pathological gambling is disruption in social relationships due to gambling or its consequences. Disruptions include lack of trust, financial stress, failure to meet family obligations, and withdrawal from the family. As such, interventions for gambling problems have also been directed at affected family and friends (e.g.,Tepperman 1985). An approach to family work developed in the field of substance addiction is the Community Reinforcement and Family Therapy (CRAFT) approach (Miller, Meyers, and Tonigan 1999). Family members are thought to be key influences for the problem gambler and are taught behavioral strategies to reinforce nongambling behaviors in their gambling family members. The goals of CRAFT include increasing coping skills in affected family members, improving family functioning, and helping family members engage the gambler into treatment. A small RCT (n = 31) assessed the efficacy of a self-help manual based on the CRAFT approach compared with an information-only control condition (Makarchuk, Hodgins, and Peden 2002), and a larger trial (n = 186) compared the self-help manual plus telephone therapist support with an information-only control group (Hodgins, Toneatto, et al. 2007). In both studies, clients receiving the workbook, with or without telephone support, felt satisfied with the program and reported that their needs had been met. In addition, they reported reduction in number of days that the gambling partner/family member had spent gambling. Participants in all groups, including the control groups, reported improved personal
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and relationship functioning, which may have reflected natural change or may have been related to contact with the study.The research did not demonstrate the attainment of the main goal of the CRAFT approach of engaging the gambler into treatment.The authors suggested that concerned significant others may need more guidance to implement the skills in the workbook and an eight-session face-to-face format of CRAFT is currently being evaluated. In contrast to CRAFT, an approach not focused on engaging the gambler into treatment was used for partners of pathological gamblers. Rychtarik and McGillicuddy (2006) developed a ten-session manualized coping skills training program for partners of pathological gamblers and conducted a randomized trial of the intervention compared with a 10-week delayed treatment condition (n = 23). The stress-coping model was emphasized and was used to understand the function of the partner’s gambling and the impact on concerned significant others. The model presented to clients was that individuals gambled in part to relieve stress and negative emotions. Gambling was framed as maintained by family members’ use of inadequate coping strategies that paradoxically increased stress and therefore gambling. If partners learned more adaptive cognitive and behavioral coping responses, they would experience less stress, and this reduction would in turn lead to a reduction in their partner’s gambling. Partners randomized to the coping skills training program demonstrated significant improvements in cognitive and behavioral coping and significantly reduced depressive and anxious symptoms compared with the delayed treatment group. Participants also reported an increase in their partners’ nongambling days. Family interventions are at an early stage of development and evaluation. It will be important to determine their efficacy and cost-effectiveness relative to treatment provided to the gambler alone, as well as their optimal placement in the treatment system.
MEASUREMENT/EVALUATION ISSUES A challenge in the evaluation of research on treatments for pathological gambling is the large variability across studies in terms of definition and assessment of gambling behaviors and problems and differences in the measures used to assess treatment outcomes. Inconsistency in research methodology makes comparisons across studies difficult, and therefore conclusions about the relative effectiveness of various therapy approaches for gambling remain unclear. In response to the need for greater consistency in the field of gambling research, a panel of treatment researchers (Walker et al. 2006) provided guidelines to systematize the reporting of outcomes. It was hoped that the Banff consensus would improve the quality and comparability of efficacy research being conducted.Three criteria were presented in the guidelines as important indicators of successful outcome. First, treatment
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must lead to a reduction in frequency or intensity of gambling behavior, ideally in terms of time spent gambling or thinking about gambling and money spent on gambling. Second, interventions should lead to a reduction in problems directly caused by problematic gambling in one or more of the following five domains: mental health (stress, depression), marital and family relationships, financial wellbeing, employment and productivity, and legal problems related to gambling.Third, improvement of gambling behavior must be a direct result of the therapy’s hypothesized mechanism of action. In addition, the consensus report suggested that researchers use appropriate control groups, conduct intent-to-treat versus completer analyses, and use collateral verification of a gambler’s self-report information whenever possible. It was also emphasized that research in gambling must use longer follow-up periods than typically employed in order to determine the frequency of relapses and the durability of various treatments for gambling. Specifically, it was recommended that follow-ups be conducted at posttreatment, 3 to 6 months postcompletion, 1 year posttreatment, and 2 years or more posttreatment.
SUMMARY AND CONCLUSIONS Although earlier treatment studies have suffered from numerous methodological limitations such as small sample sizes, high attrition, poor follow-up rates, and nonstandard outcome measurement (Hodgins and Petry 2004; Toneatto and Ladouceur 2003), it is exciting that more recent studies not only are methodologically more rigorous but also provide good evidence of treatment effectiveness generally. An interesting observation in Pallesen et al.’s (2005) meta-analysis is that the RCTs in this area had larger effect sizes than the uncontrolled trials, which is unusual in treatment research and underscores the value of rigorous research. As we previously mentioned, the low rates of treatment seeking among problem gamblers is an important issue for our field. We have begun to develop and evaluate treatments that may be more attractive to pathological gamblers and therefore may result in greater numbers of treatment seekers. Similarly, alternative formats such as telephone and online therapy are becoming popular.We have not yet, however, conducted treatment system research to assess the actual impact in our communities, although treatment seeking and its alternative, natural recovery, are receiving increased research attention (e.g., Chapter 6, this volume). To date, we have only begun to attend to individual characteristics that predict success in specific treatments. For example, although Petry et al. (2006) found that men had better outcomes posttreatment than women, no studies have found gender/treatment interactions, with one gender faring better in a particular type of treatment. Despite their high rates, mental health comorbidities have either been excluded from studies (e.g., Echeburua et al. 2000b; Grant et al. 2006) or not
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assessed (e.g., Hodgins et al. 2004; Ladouceur et al. 2001b). Similarly, the differential outcomes for subtypes of gamblers (Chapter 13, this volume), different ethnic groups, and different type of gambling are understudied.These research questions require larger sample sizes that typically involve multicenter research trials, which have not yet been conducted for pathological gambling. Multicenter trials would also allow designs comparing medication and psychological interventions as well as combined treatments.The positive but modest benefits of medication suggest that it may most effectively play an adjunctive role to psychological treatments, similar to the role of naltrexone and acomprosate in the treatment of alcohol dependence. Alternatively, medication may have no additive effect when combined with psychological treatment, although the impact on mental health symptoms (e.g., depression and anxiety for SSRIs, depression and mania for lithium) might lead to better gambling outcomes indirectly. Additionally, the impact of concurrent substance use on treatment outcome is not well known (Hodgins et al. 2005). Abstinence versus moderated gambling is another issue that deserves systematic attention. A close examination of the most recent outcome studies reveals that some studies provide abstinent focused treatments (Petry et al. 2006; Sylvain et al. 1997) and others allow participants to select a goal, ranging from cutting back the problematic type of gambling to quitting the problematic type only to quitting all types of gambling (Hodgins et al. 2004; Robson et al. 2002). The impact of abstinent versus flexible treatment goals on participation and outcome remains unclear. Strict abstinence goals may discourage gamblers from seeking treatment, and flexibility may open the doors to more treatment seekers (Hodgins and Petry 2004). Guidelines are being developed to direct “responsible” gambling involvement that can be used to help disordered gamblers set appropriate goals and to assess outcomes (e.g., Currie et al. 2006). Clinical trials comparing treatments with varying treatment goals are necessary.
GLOSSARY Behavioral therapies interventions focused on changing the contingencies that maintain problem behaviors. Brief treatments interventions that last one to three sessions or are selfdirected by participants such as self-help workbooks. Cognitive therapies interventions focusing on changing the maladaptive thinking patterns and beliefs that maintain problem behaviors. Community Reinforcement and Family Therapy (CRAFT) intervention designed to help family members use behavioral principles to modify the gambling behaviors of a close family member. Efficacy trials controlled and randomized clinical investigations of carefully characterized participants.
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Gamblers Anonymous mutual support group modified from Alcoholics Anonymous with a spiritual and disease model orientation.
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Rosenthal, R. J., and Rugle, L. J. (1994). A psychodynamic approach to the treatment of pathological gambling: Part I. Achieving abstinence. Journal of Gambling Studies, 10, 21–42. Russo, A. M.,Taber, J. I., McCormick, R. A., and Ramirez, L. F. (1984). An outcome study of an inpatient treatment program for pathological gamblers. Hospital and Community Psychiatry, 35, 823–827. Rychtarik, R. G., and McGillicuddy, N. B. (2006). Preliminary evaluation of a coping skills training program for those with a pathological-gambling partner. Journal of Gambling Studies, 22, 165–178. Saiz-Ruiz, J., Blanco, C., Ibanez, A., Masramon, X., Gomez, M. M., Madrigal, M., and Diez,T. (2005). Sertraline treatment of pathological gambling: A pilot study. Journal of Clinical Psychiatry, 66, 28–33. Salzmann, M. N. (1982).Treatment of compulsive gambling. British Journal of Psychiatry, 141, 318–319. Seager, C. (1970).Treatment of compulsive gamblers using electrical aversion. British Journal of Psychiatry, 117, 545–553. Sharpe, L. (2002). A reformulated cognitive-behavioral model of problem gambling: A biopsychosocial perspective. Clinical Psychology Review, 22, 1–25. Sharpe, L., and Tarrier, N. (1993).Towards a cognitive-behavioural theory of problem gambling. British Journal of Psychiatry, 162, 407–412. Steenbergh, T. A., Meyers, A. W., May, R. K., and Whelan, J. P. (2001). Development and validation of the gamblers’ beliefs questionnaire. Psychology of Addictive Behaviors, 16, 143–149. Stewart, R. M., and Brown, R. I. F. (1988). An outcome study of Gamblers Anonymous. British Journal of Psychiatry, 152, 284–288. Strong, D. R., Daughters, S. B., Lejuez, C. W., and Breen, R. B. (2004). Using the Rasch model to develop a revised Gambling Attitudes and Beliefs Scale (GABS) for use with male college student gamblers. Substance Use and Misuse, 39, 1013–1024. Sylvain, C., Ladouceur, R., and Boisvert, J. (1997). Cognitive and behavioral treatment of pathological gambling: A controlled study. Journal of Consulting and Clinical Psychology, 65, 727–732. Taber, J. I., McCormick, R. A., Russo, A. M., Adkins, B. J., and Ramirez, L. F. (1987). Follow-up of pathological gamblers after treatment. American Journal of Psychiatry, 144, 757–761. Tepperman, J. H. (1985).The effectiveness of short-term group therapy upon the pathological gambler and wife. Journal of Gambling Behavior, 1, 119–130. Toneatto, T. (2002). Cognitive therapy for problem gambling. Cognitive and Behavioral Practice, 9, 191–199. Toneatto, T., and Ladouceur, R. (2003). Treatment of pathological gambling: A critical review of the literature. Psychology of Addictive Behaviors, 17, 284–292. Toneatto, T., and Sobell, L. C. (1990). Pathological gambling treated with cognitive behavior therapy: A case report. Addictive Behaviors, 15, 497–501. Turner, N. E., Littman-Sharp, N., Masood, Z., and Spence,W. (2001). Final Report:Winners:Why Do Some Develop Gambling Problems While Others Do Not? Toronto: Ontario Ministry of Health: Substance Abuse Bureau. Walker, M., Toneatto, T., Potenza, M., Petry, N. M., Ladouceur, R., Hodgins, D. C., el-Guebaly, N., Echeburua, E., and Blaszczynski,A. (2006).A framework for reporting outcomes in problem gambling treatment research:The Banff, Alberta Consensus. Addiction, 101, 504–511. Zimmerman, M., Breen, R. B., and Posternak, M. A. (2002). An open-label study of citalopram in the treatment of pathological gambling. Journal of Clinical Psychiatry, 63, 44–48.
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CHAPTER 16
Prevention of Problem Gambling Robert J. Williams
Robert I. Simpson
School of Health Sciences University of Lethbridge Lethbridge, Alberta, Canada
Ontario Problem Gambling Research Centre, Guelph, Ontario, Canada
Beverly L. West School of Health Sciences University of Lethbridge Lethbridge, Alberta, Canada
Introduction Educational Initiatives to Prevent Problem Gambling Upstream Interventions Information/Awareness Campaigns More Sustained and Directed Educational Initiatives Policy Initiatives to Prevent Problem Gambling Restrictions on the General Availability of Gambling Restricting the Number of Gambling Venues Restricting More Harmful Types of Gambling Limiting Gambling Opportunities to Gambling Venues Restricting the Location of Gambling Venues Limiting Gambling Venue Hours of Operation Restrictions on Who Can Gamble Prohibition on Youth Gambling Restricting Gambling Venue Entry to Nonresidents Casino Self-Exclusion Contracts Restrictions or Alterations on How Gambling Is Provided On-Site Intervention with At-Risk Gamblers 399
400
Research and Measurement Issues in Gambling Studies
Problem Gambling Awareness Training for Employees of Gambling Venues Automated Intervention for At-Risk Gamblers at Gambling Venues On-Site Information/Counseling Centers Modifying Parameters of Electronic Gambling Machines Maximum Loss Limits Restricting Access to Money Restrictions on Concurrent Use of Alcohol and Tobacco Restricting Advertising and Promotional Activities Gambling Venue Design Independence Between Gambling Regulator and Gambling Provider Summary and Recommendations
INTRODUCTION In order to understand how to prevent something, it is first necessary to understand what causes it. The etiology of addictive behavior, including problem gambling, is best understood from a biopsychosocial perspective. Essentially this proposes a range of biological, psychological, experiential, and social factors that can contribute to, or provide protection from, the development of the addiction. The biopsychosocial model of problem gambling has three important implications for prevention. 1. First, because of the large number of risk factors, as well as the biological basis of some of them, the risk of problem gambling in a population can be reduced but probably never eliminated. 2. Second, because many risk factors also apply to other addictions and psychopathology, generic prevention initiatives targeting a wide range of problems (especially in youth) are likely both an efficient and essential component of problem gambling prevention. 3. Third, because there are a multitude of both internal and external factors that contribute to problem gambling, effective prevention will almost certainly require a sustained, multifaceted, and coordinated approach provided to a wide range of age groups. The traditional way of categorizing prevention efforts is by the type of people the efforts are directed toward. Primary prevention is aimed at individuals in the general populace, so that they will not become problem gamblers. Secondary
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401
prevention is aimed at the development of problem gambling in individuals with risk factors for the condition. Tertiary prevention is an effort to stop and potentially reverse the problems occurring in existing problem gamblers. However, another organizational approach, and the one used in the present chapter, is according to the nature of the initiative. Virtually all prevention initiatives can be categorized into either educational initiatives, intended to change internal knowledge, attitudes, beliefs, and skills so as to inoculate or deter an individual from problem gambling, or policy initiatives, intended to prevent problem gambling through the alteration of external environmental controls on the availability and provision of gambling. The purpose of this chapter is to review what is known about the nature and effectiveness of educational and policy initiatives to prevent problem gambling.
EDUCATIONAL INITIATIVES TO PREVENT PROBLEM GAMBLING UPSTREAM INTERVENTIONS It is well established that negative early childhood experience significantly influences the development of problematic behavior later in life. Accordingly, it is not surprising that interventions to strengthen families and create effective parenting are generally the most powerful way to reduce adolescent problem behaviors, which then also serves to reduce problems at later ages (Foxcroft et al. 2005; Kumpfer and Alvarado 2003). This is probably also true for the prevention of adolescent and adult problem gambling, although the approach has never been empirically tested. Nevertheless, there is good evidence that family-based programs are effective for the primary prevention of other addictive behaviors, such as alcohol and drug use, in young people (Foxcroft et al. 2005; Gates et al. 2005). It would be useful if future family/parenting interventions included the incidence of problem gambling among their outcome measures. For similar reasons, it is to be expected that exposure to well-socialized peer groups, supportive teachers, and good schools would have the same beneficial effect on prevention of problem gambling as they do on the prevention of other problematic behavior (Durlak 1997; Nation et al. 1993; Weissberg and Gullotta 1997).
INFORMATION/AWARENESS CAMPAIGNS When most people think of problem gambling prevention, they think of information campaigns targeted specifically at gambling.These are known variously
402
Research and Measurement Issues in Gambling Studies
as “information/awareness campaigns,” “mass media campaigns,” or “social marketing.” Campaigns are directed at the general public and usually contain information consisting of one or more of the following elements: ● Encouragement to “know your limits” or “gamble responsibly” ● Warnings about the potentially addictive nature of gambling ● Identification of the symptoms of problem gambling ● Information about where people can go for help or more information on problem gambling (i.e., treatment agencies, 24-hour hotlines) ● Provision of the true mathematical odds of various gambling activities ● Efforts to dispel common gambling fallacies and erroneous cognitions ● Provision of guidelines and suggestions for problem-free gambling These initiatives are usually developed and provided by governmental health or social service agencies, schools, or the gambling industry.The information itself is provided: ● On the gambling product (e.g., odds printed on the back of lottery tickets, “responsible gambling” messages on electronic gambling machines [EGMs]) ● On posters and pamphlets at gamblings venues and elsewhere throughout the community ● In the form of “public service announcements” on radio, television, and newspapers ● By means of presentations, plays, or videos (most often presented in educational settings) ● On government, social agency, and/or gambling provider websites. Examples of some teen-oriented websites include http://www.zoot2. com, http://www.luckyday.ca, http://inyaface.co.nz, http://www. wannabet.org, http://www.thegamble.org, www.responsiblegambling. qld.gov.au Information/awareness campaigns are relatively inexpensive ways of delivering preventive health messages to a large portion of the population and countering the often considerable commercial efforts to promote the product. Although awareness campaigns to prevent problem gambling are relatively common across many jurisdictions, there is limited research on their impact. The evidence that does exist suggests that improvements in knowledge and awareness are reliably produced in people who are asked to attend to these messages. For example, a brochure on pathological gambling was found to effectively convey useful new information to members of the general public in Québec who were shown it (Ladouceur et al. 2000). Similarly, videos on gambling have resulted in improved knowledge of gambling and problem gambling among eleventh- and twelfth-grade students in Québec (Ladouceur et al. 2005) as well as elementary school children (Ferland, Ladouceur, and Vitaro 2002; Lavoie and Ladouceur 2004).
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403
However, awareness campaigns appear to have a very limited impact if people are not explicitly asked to attend to the information or have no intrinsic interest in it. For example, Indiana implemented a statewide awareness campaign to promote responsible gambling using radio announcements, billboards, brochures, newspaper advertisements, posters, pens, T-shirts, press conferences, and “problem gambling town hall meetings.” Najavits, Grymala, and George (2003) found that only 8% of the general public recalled seeing or hearing any advertising. However, of that 8%, 72% reported that the advertising had increased their knowledge of problem gambling. One percent of the total sample took action based on seeing or hearing the ad, such as calling the helpline. A similar result was obtained in Ontario, Canada. Turner et al. (2005) found that 66% of the Ontario public was unaware of any initiatives to reduce problem gambling. This is notable considering that Ontario spends proportionally more on problem gambling prevention and treatment than any other jurisdiction in the world (Sadinsky 2005). In 1995 the Victoria Department of Human Services in Australia initiated a statewide problem gambling awareness program consisting of a 5-week multilanguage radio, newspaper, and billboard advertisement phase in the first year, a 14-week television advertisement phase in 1996, and a 30-week radio and television advertisement phase in 1997–1998. Jackson et al.’s (2002) evaluation of this program concluded that it produced an increased number of callers to the gambling helpline and an increase in the number of new clients entering treatment. In 2001, the Victoria government initiated a similar informational campaign, which reportedly resulted in a 70% increase in calls to the helpline and a 118% increase in clients presenting themselves to treatment (Victoria Department of Human Services 2002). It is important to note that providing support to distressed gamblers or recruiting problem gamblers into treatment is a much less satisfactory “prevention” outcome than results showing that awareness campaigns help inoculate the general public from developing gambling problems in the first place.There is no direct evidence on the effectiveness of awareness campaigns as a primary prevention tool for problem gambling, however, and the general public’s lack of awareness of these initiatives is not very encouraging. Fortunately, there is considerably more literature on the utility of public information/awareness campaigns for other health behaviors that contain lessons for prevention of problem gambling (Byrne et al. 2005). In general, research has found that sustained information/awareness initiatives have significant potential to improve people’s knowledge and/or change attitudes at a community-wide level (Grilli, Ramsay, and Minozzi 2004; Sowden and Arblaster 2005). Indeed, population surveys show that mass media are in fact the leading source of information about important health issues such as weight control, HIV/AIDS, drug abuse, asthma, family planning, and mammography (Chapman and Lupton 1994).
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Research and Measurement Issues in Gambling Studies
While knowledge and attitudinal changes have been fairly reliably obtained, the ability of awareness campaigns to produce actual changes in behavior is much less common (Grilli, Ramsay, and Minozzi 2004; Slater et al. 2005; Sowden and Arblaster 2005; Stacy, Bentler, and Flay 1994). Furthermore, the knowledge and attitudinal impact of prevention messages often declines with time, requiring that they be repeated regularly (Vidanapathirana et al. 2004). The exceptions to this failure to achieve behavior change are situations where behavioral change is comparatively easy to achieve and/or the consequences of not changing behavior are significant. For example, after extensive media reports on dietary studies relating cholesterol-rich foods with heart disease, consumption of beef, eggs, and fatty milk products in North America declined. Similarly, reports on the risks of excessive sodium consumption were associated with increased use of salt-free food products.A decline in the use of birth control pills and intrauterine devices (IUDs) between 1970 and 1975 correlated closely with publicity about their possible adverse effects (Jones, Beniger, and Westoff 1980). Vidanapathirana et al. (2004) found that mass media interventions have immediate effects in promoting HIV testing. In the gambling context, evidence of behavioral change is seen in the increase in helpline calls or treatment seeking as reported by the Victoria Department of Human Services (2002) and Jackson et al. (2002) (and, anecdotally, by agencies in other jurisdictions when promoting these services).
MORE SUSTAINED
AND
DIRECTED EDUCATIONAL INITIATIVES
Elementary and high school–based prevention programs for problem gambling are relatively uncommon. They typically have a broad scope that includes teaching statistical knowledge about gambling, providing information on the potentially addictive nature of gambling, explaining gambling fallacies, esteem building, peer resistance training, and so on. Examples of these types of programs are “Don’t Bet On It” in South Australia for ages 6 to 9; “Gambling: Minimising Health Risks” in Queensland for grade 5; “Facing the Odds” in Louisiana for grades 5 to 8; “Kids Don’t Gamble . . .Wanna Bet?” in Minnesota and Illinois for grades 3 to 8; “Youth Making Choices” for high school students in Ontario; “Count Me Out” in Québec for ages 8 to 17;“Hooked City” in Quebec for grades 7 to 12; and “Gambling: A Stacked Deck” in Alberta for grades 9 to 12. There has been little published evaluation of these programs. Gaboury and Ladouceur (1993) evaluated a three-session program in Québec that was based on an alcohol prevention model.The program covered an overview of gambling, legal issues, how the gambling industry manipulates the chances of winning, beliefs and myths about gambling, and the development of pathological gambling.A sample of 289 juniors and seniors from five high schools completed the program. Whereas the evaluation showed that the students did learn about gambling and coping skills,
Prevention of Problem Gambling
405
these effects did not significantly influence their gambling attitudes or behavior 6 months later. An unpublished study by Ferland, Ladouceur, and Jacques (2000) also obtained mixed results. This program targeted 1207 youths in grades 8, 9, and 10 in Québec, with half receiving three “interactive meetings” and the other half acting as the control school. The program provided information on knowledge and misconceptions about gambling activities, social problem solving, and excessive gambling. Results at 3 months postintervention indicated that the program produced a significant improvement in knowledge about gambling and a decrease in gambling misconceptions. However, there was no improvement in social problemsolving ability, a skill thought to be lacking in individuals at risk for problem gambling. The impact of the program on actual problem gambling behavior is unknown, as this outcome was not assessed. The International Centre for Youth Gambling Problems and High-Risk Behaviors (2004) in Montreal, Québec, undertook an evaluation of their interactive CDs for the prevention of problem gambling in youth (Hooked City for grades 7–12 and The Amazing Chateau for grades 4–6). Several months after being exposed to these interactive CDs, students had significantly improved knowledge about gambling, more awareness of the signs of problem gambling, and fewer gambling fallacies. However, there was no significant change in gambling behavior, although there was a trend in this direction. Encouraging results have been obtained from a high school curriculum in Alberta called “Gambling: A Stacked Deck.” This program was first piloted in a Calgary high school in 2001 (Davis 2003) and was revamped in 2002 and in 2003 based on its implementation in other high schools in southern Alberta (Williams et al. 2003, 2004).The nature and content of the curriculum was derived from existing programs and a careful study of what was known to be effective in other primary prevention programs (e.g., Durlak 1997;Weissberg and Gullotta 1997).The resulting program contained information about the nature of gambling and problem gambling, exercises to make students less susceptible to gambling fallacies, information on the true odds involved in most gambling activities, teaching and rehearsal of decision making and social problem-solving skills, and teaching and rehearsal of adaptive coping skills.The format of the program was as important as the content. Important elements of the format included an entertaining and engaging delivery, a strong emphasis on skill learning and application of knowledge, and five hour-long sessions. The program also targeted the social environment of the people receiving the intervention, by ensuring that all students in the targeted grade at the school received the program, so as to influence the students’ primary peer group. Over 1600 students from 12 different schools received the program, and another 400 students served as the control group. At 3 to 6 months following the end of the program, students in the intervention group had significantly more negative attitudes toward gambling, better knowledge about gambling, fewer
406
Research and Measurement Issues in Gambling Studies
gambling fallacies, and significant decreases in all measures of gambling behavior relative to both baseline and the control group. There were no changes in decision-making skills, other high-risk activity, or prevalence of problem gambling (Williams,Wood, and Currie, in preparation). While the results of this study are encouraging, its long-term effectiveness is unknown. It is also sobering to examine the literature from other fields, which indicates that even with comprehensive educational approaches, the effects on the desired behavior are often small (Merzel and D’Afflitti 2003; Sowden and Stead 2005;Thomas and Perera 2006) or nonexistent (Gates et al. 2005; Secker-Walker et al. 2002).
POLICY INITIATIVES TO PREVENT PROBLEM GAMBLING Health-oriented policies are measures taken by governments and industry intended to inhibit the adoption of risk gambling practices and the subsequent onset of problems.
RESTRICTIONS
ON THE
GENERAL AVAILABILITY
OF
GAMBLING
Evidence would suggest that gambling availability has a positive, but complex, relationship to problem gambling prevalence. First, there is a strong within-country association between the availability of gambling and the prevalence of problem gambling (Lester 1994; National Gambling Impact Study Commission [NGISC] 1999; Productivity Commission 1999; Shaffer, LaBrie, and LaPlante 2004; Welte et al. 2004). Moreover, the expansion of legalized gambling in the 1980s and 1990s was followed by significant increases in problem gambling in the United States (National Research Council 1999; Shaffer, Hall, and Vander Bilt 1997). However, it also seems clear that (a) there are many other important factors that also determine a jurisdiction’s problem gambling prevalence rate and (b) the relationship between gambling availability and problem gambling is not a linear one (jurisdictions often obtain increased rates of problem gambling initially, followed by stable or decreased rates over time) (Shaffer et al. 2004). Because of the significant relationship between availability and problem gambling prevalence, it comes as no surprise that restricting gambling availability is a policy that is often used to prevent problem gambling.
RESTRICTING THE NUMBER
OF
GAMBLING VENUES
Most countries require licenses for gambling operators but do not specify restrictions on the number of bingo halls, horse race tracks, or lottery outlets.
Prevention of Problem Gambling
407
It is much more common practice to put restrictions on casino numbers.Venue caps make theoretical sense considering the aforementioned positive association between product availability and product consumption. Specific evidence of their association with problem gambling is seen in the following: ●
●
●
The U.S. National Gambling Impact Study found that living within 50 miles of a casino was associated with a 50% higher rate of pathological gambling (NGISC 1999). Welte et al. (2004) independently demonstrated a positive relationship between problem gambling in the United States and the existence of a casino within 10 miles of the gambler’s home. Lester (1994), in a U.S.-wide study, found that the opportunity to gamble at casinos with slot machines, on sports betting, at jai alai, and in teletheaters was associated with being in a state with a greater per capita prevalence of Gamblers Anonymous (GA) chapters.
Table 16.1 demonstrates that within Canada, there is a significant positive relationship between provincial density of casinos and racinos (combined race tracks and casinos) and provincial rates of problem gambling in 2002.There are also positive relationships between problem gambling rates and the density of bingo licenses and horse racing venues. Interestingly, there is no association with the number of EGM locations, and there is a negative association with the number of lottery outlets. Before/after comparisons of the impact of venue openings is also relevant to this issue: ●
●
●
●
Room,Turner, and Ialomiteanu (1999) found that Casino Niagara’s opening in Ontario in 1996 brought an increase in gambling and reported gambling problems 1 year later among Niagara Falls residents. Toneatto, Ferguson, and Brennan (2003) also found that this casino opening was associated with increased scores on the South Oaks Gambling Screen (SOGS) for residential substance abusers who gambled most frequently on casino gambling in 1997 and 1998. Jacques, Ladouceur, and Ferland (2000) found that as opportunities for casino gambling become available in two Québec communities in 1996, there was increased participation rates and spending on casino gambling by local citizens and an increase in problem but not pathological gambling. In contrast, Govoni et al. (1998) found that Casino Windsor’s opening in Ontario in 1996 produced no significant change in Windsor residents’ gambling expenditure or rate of problem gambling one year later. Hann and Nuffield (2005) found that the opening of four casinos and one racino in Ontario in 1999 and 2000 produced an increase in the rate of probable pathological gamblers in these communities from 1.5% to 2.5% (although no change in the rate of problem gamblers [2.4%]).
408
Table 16.1 Correlates of Canadian Provincial Problem Gambling Prevalence in 2002. NB
QU
PEI
NF
BC
ONT
NS
AB
SK
MB
Correlation with PG Prevalence
1.5
1.7
1.9
1.9
1.9
2.0
2.0
2.2
2.9
2.9
C/Rs per 100,000 adults
0
.12
0
0
.59
.26
.27
.77
.94
.46
r = .74* tau-b = .63*
EGMs per 100,000 adults
433
341
388
633
102
213
591
471
758
807
r = .68* tau-b = .42
Casino table games per 100000 adults
0
3.6
0
0
12.1
5.9
7.6
17.5
13.6
7.8
r = .56 tau-b = .59*
Horse racing venues per 100,000 adults2
.68
.60
1.91
.24
.65
1.11
1.36
2.26
1.21
2.30
r = .56 tau-b = .52*
Bingo licenses per 100,000 adults
57
40
37
138
NA
22
74
105
230
56
r = .53 tau-b = .20
EGMs outside of C/Rs per 100,000 adults3
433
237
388
633
0
0
441
255
507
582
r = .35 tau-b = .22
% Revenue on prevention/treatment4
.59
1.25
.63
.38
.48
1.20
1.22
.52
1.53
.71
r = .32 tau-b = .24
# Locations EGMs occur outside C/Rs per 100,000 adults
111
62
87
138
0
0
73
50
93
67
r = −.02 tau-b = −.12
Lottery outlets per 100,000 adults
175
180
177
323
128
113
181
90
104
97
r = −.50 tau-b = −.47
Research and Measurement Issues in Gambling Studies
Problem gambling prevalence1
% Aboriginals in provincial population5
2.4
1.1
1.0
3.7
4.4
1.7
1.9
5.3
13.5
13.6
r = .93** tau-b = .52*
% ETOH dependence (high probability)
2.0
1.9
2.8
3.2
3.6
3.2
2.1
3.5
4
3.6
r = .74* tau-b =.63*
Prevention of Problem Gambling
NB, New Brunswick; QU, Québec; PEI, Prince Edward Island; NF, Newfoundland and Labrador; ONT, Ontario; NA, not available; NS, Nova Scotia; AB, Alberta; MB, Manitoba; SK, Saskatchewan; C/R, casino/racino; EGM, electronic gambling machine; ETOH, ethyl alcohol. 1 As established by a Canadian Problem Gambling Index (CPGI) score of 3 or higher.The CPGI was administered as part of the Canadian Community Health Survey (1.2) (May–Sept 2002; n = 34,770). 2 Racetracks and teletheatres. 3 EGMs outside casinos or racinos for every 100,000 adults aged 18 and older. 4 Percentage of provincial government gambling revenue spent on prevention and treatment of problem gambling. 5 From 2001 Statistics Canada census. * Correlation significant at the .05 level (two-tailed) Note: Unless otherwise stated, all data come from the Canadian Gambling Digest, published by the Canadian Partnership for Responsible Gambling (2004) and the 2001 Statistics Canada Census. All data from the Canadian Gambling Digest pertain to the period April 2002 to March 2003.
409
410
Research and Measurement Issues in Gambling Studies
●
Five to nine months after the introduction of two new casinos and one new racino into the British Columbia lower mainland in 2005, Mangham et al. (2006) found no significant change in problem gambling prevalence rates but also noted that this area already was very densely populated with eight casinos.
RESTRICTING MORE HARMFUL TYPES
OF
GAMBLING
It is a common policy to prohibit or restrict inherently more “dangerous” forms of a product. For example, in many countries handguns, assault rifles, and automatic weapons are prohibited, whereas hunting rifles are legally available. Similarly, drugs with greater perceived potential for addiction (e.g., cocaine, methamphetamine, heroin) tend to be illegal in most countries, with substances perceived as less harmful being legally available. Electronic gambling machines are the form of gambling consistently identified by problem gamblers, treatment agencies, and gambling researchers as creating the most problems in Western countries (e.g., Dowling, Smith, and Thomas 2005; Smith and Wynne 2002). Internet gambling is another form with an unusually high association with problem gambling (Chapter 19, this volume; Wood and Williams 2007). Internet gambling is prohibited in several jurisdictions because of concerns with its potential for harm (unfortunately, this usually has limited deterrent value because of enforcement difficulties). EGM gambling is prohibited or does not occur in some jurisdictions (e.g., 15/50 U.S. states did not have EGMs in 2006), and there is some empirical evidence regarding the impact of EGM bans, as follows: ●
●
In 1994, the 7859 legal EGMs in South Dakota were declared unconstitutional and shut down for 3 months, then were reinstated by public referendum (Rose 2003). In the eleven months prior to the ban, substance abuse treatment centers (n = 4) averaged 68 inquiries and 11 problem gambling clients per month. During the shutdown, there were only two inquiries and two people treated among all four centers. In the 3 months after EGM reinstatement, the centers averaged 24 inquiries and treated eight gamblers per month (Carr et al. 1996). In 2000 the 36,000 legal EGMs in South Carolina were banned. Following the ban there was a significant increase in seizures of illegal machines from 48 in 2000–2001 to 1551 in 2004–2005 (South Carolina Law Enforcement Division 2005). Nonetheless, the number of active GA groups fell from 32 to 16, with several of the remaining active groups reporting the attendance at their meetings decreasing from 40 to 1 or 2 (Bridwell and Quinn 2002).Additionally, the most active gambler’s hotline
Prevention of Problem Gambling
411
in the state reported that calls fell from 200 a month to zero.These reductions have been maintained in subsequent years. Current directory information for GA (Gamblers Anonymous 2006) indicates ten active groups in South Carolina.Additionally, less than 1% of the 4500 calls made to the South Carolina Gambling Helpline since its inception in 2004 have been related to problems with EGMs (personal communication, Jimmy Mount, August 4, 2006). Other machine bans are pending or in progress that could potentially provide more evidence on this issue. North Carolina legislated a phase-out starting in October 2006 (Eisley and Allegood 2006). Portugal and Latvia intend to eliminate EGM gambling by January 1, 2007 (Sychold 2006).The 15,000 EGMs in Norway will be eliminated in July 2007 (Honegger 2006). Placing a limit on the total number of EGMs is another variant on this policy strategy. Again, this makes theoretical sense given the strong positive relationship between EGM numbers per capita and problem gambling rates. For example, Australia has the world’s highest per capita EGM ratio (~1 machine for every 99 people) (excluding tiny tourist-oriented countries, e.g., Monaco), as well as one of the world’s highest rates of problem gambling (Productivity Commission 1999). Within Australia, there is also a significant positive relationship between number of machines and regional problem gambling rates (Productivity Commission 1999; South Australian Centre for Economic Studies [SACES] 2005). As shown in Table 16.1, the same is true in Canada, where there is a significant positive correlation between provincial problem gambling prevalence rates and EGMs per 100,000 adults (r = .68, p < .05). It appears that reductions in EGM numbers do not have a significant impact if they do not substantially change overall EGM availability.A study by the SACES (2005) investigated the impact of regional restrictions on EGM numbers in the state of Victoria. It found that gambler losses were not generally reduced, help seeking by problem gamblers did not change, and no revenue losses were sustained in venues where machines were removed. However, the study pointed out that areas with the new caps tended to have the highest EGM per capita ratios to begin with, and the magnitude of the reductions was small. Similarly, a 25% reduction in EGMs outside casinos (i.e., video lottery terminals [VLTs]) in Nova Scotia, Canada, in November 2005 is said to have resulted in a relatively small reduction in revenue (Halifax Daily News 2006).
LIMITING GAMBLING OPPORTUNITIES TO GAMBLING VENUES The availabilty of “convenience gambling,” whereby gambling opportunities are available outside of dedicated gambling venues, is sometimes cited as an
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Research and Measurement Issues in Gambling Studies
important factor in the development of problem gambling. EGM gambling is prohibited outside of dedicated gambling venues in Cyprus, France, Greece, and Luxembourg and is banned from “low barrier” locations (e.g., bars, lounges, clubs) in Lithuania, Latvia, and the Netherlands (Sychold 2006). In the United States, only Louisiana, Montana, Nevada, Oregon, and West Virginia allow EGMs outside of gambling venues (American Gaming Association 2006). Of Canada’s ten provinces, Ontario and British Columbia do not permit EGMs outside of gambling venues. In Australia, the state of Western Australia does not permit EGMs outside of its one casino. The unique impact of limiting gambling opportunities to gambling venues is difficult to determine, as jurisdictions that do this also tend to have fewer total EGMs and sometimes are less accepting of gambling in the first place. In Canada, the provinces of Ontario and British Columbia have no EGMs outside of gambling venues and also have the lowest ratio of EGMs per 100,000 adults.As seen in Table 16.1, they still have “mid-range” problem gambling prevalence rates, perhaps due to the fact that they have the highest number of casinos/racinos in the country (25 and 19, respectively, in 2002). The lack of EGMs outside of casinos/racinos also likely explains why residents of these two provinces patronize EGMs within casinos/racinos at a higher rate than any other province (28% and 22%, respectively) (Canadian Partnership for Responsible Gambling 2004; Mangham et al. 2006). In Canada, the overall relationship is relatively weak between provincial problem gambling prevalence rates and the number of EGMs outside of gambling venues per capita (r = .35, ns), and nonexistent between problem gambling prevalence rates and the number of EGM locations per capita (r = −.01, ns) (see Table 16.1) (this is true even when removing ONT and BC from the correlations). In the United States, there is no significant difference in the rates of problem gambling in states with EGMs outside of casinos (n = 4, prevalence = 4.1%) compared with states without EGMs outside of casinos (n = 24, prevalence = 3.9%), t(26) = .17, p = .86.These results are somewhat surprising considering the fact that increasing the number of alcohol outlets per capita tends to increase alcohol consumption (Wagenaar and Holder 1995; Wagenaar and Langley 1994). However, what these results perhaps indicate is that each available EGM represents an independent “outlet,” as opposed to each place EGMs are located.Thus, total EGMs per capita may continue to be a much better predictor of jurisdictional problem gambling prevalence rates. A corollary of this point is that concentrating gambling opportunities within gambling venues may simply result in corresponding local concentrations of problem gambling (e.g., Shaffer et al. 2004).
RESTRICTING THE LOCATION
OF
GAMBLING VENUES
Historically, casinos in Europe and the United States were placed in tourist destinations away from major urban centers.This is still the case in Asia and Africa.
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Prevention of Problem Gambling
The historical rationale for this was that casinos would be deleterious for urban, working-class populations and that the economic benefits of casinos are most apparent when they draw new money and wealth into the community rather than redirecting money from other local businesses (e.g., Grinols 2004).The other major benefit of “outside” patronage is that the social problems created by gambling go home with the tourist, rather than impacting the local social service and health care system. It must be said that these social and economic principles still appear to be sound, despite the tendency to locate casinos in urban centers in recent years. An additional consideration concerning placement of gambling venues is the fact that some groups of urban residents are much more vulnerable to problem gambling than others. In general, neighborhood disadvantage is positively associated with problem/pathological gambling (e.g.,Welte et al. 2004). On an individual level, Rush et al. (2005) found that substance abuse and demographic factors were the strongest predictors of problem gambling status (stronger than gambling venue proximity). In Canada, the national prevalence study of gambling in 2002 (Canadian Community Health Survey [CCHS 1.2]), found that people with less education and of Aboriginal descent had significantly higher risk of problem gambling (Marshall and Wynne 2003).As seen in Table 16.1, the Canadian provincial problem gambling prevalence rate is in fact best predicted by proportion of the population with Aboriginal ancestry (r = .93, p < .01). Almost equally strong is the relationship between provincial rates of alcohol dependence (established in the same CCHS 1.2 survey) and problem gambling prevalence (r = .74, p < .05).
LIMITING GAMBLING VENUE HOURS
OF
OPERATION
Policies to limit the number of hours that patrons may gamble in any 24-hour period vary around the world, with some venues being open 24 hours and others having nightly shutdowns. As with most other responsible gambling initiatives, information is limited regarding the effectiveness of hours-of-operation restrictions. The province of Nova Scotia’s shutdown of EGMs outside of casinos at midnight resulted in a self-reported 18% reduction in spending among a random sample of problem gamblers.Actual revenues declined only about 5.1–8.7% (Nova Scotia Gaming Corporation 2005). In Australia, hours-of-operation restrictions apply in seven states and territories. However, as reported by the Centre for Gambling Research (2005), the large majority of venue operators reported no effectiveness of the short shutdown periods (which occur at times of day when the patronage is already at its lowest). Reduced hours of operation still make theoretical sense considering (a) the general premise that reduced availability leads to reduced problems and (b) evidence in the alcohol field that restricted hours and days of operation reduce social harm (Babor et al. 2003; Chikritzhs and Stockwell 2006). However, similar to
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Research and Measurement Issues in Gambling Studies
reductions in EGMs, unless availability reductions are meaningful and substantial, it seems unlikely that an overall beneficial impact will be obtained.
RESTRICTIONS ON WHO CAN GAMBLE PROHIBITION
ON
YOUTH GAMBLING
Worldwide, it is a common policy to restrict gambling to individuals who are of legal adult age, although there are some important regional variations. For example, there are no age limits to play EGMs (“fruit machines”) with low prize limits in the United Kingdom. Sixteen-year-old youths can purchase lottery tickets in England. A few U.S. states (and Alberta, Canada) permit bingo playing at age 16 (National Research Council 1999). There is also wide variation on enforcement. In general, there tends to be good enforcement in situations where gambling occurs in adult-only venues (e.g., casinos, bars/clubs/lounges) and poor enforcement in situations where gambling opportunities are available in public locations. Consequently, North American and Australian youth tend to have low rates of casino table game and gambling machine play (available in only adult venues) but high rates of lottery and scratch-ticket play (available in public locations) (Delfabbro, Lahn, and Grabosky 2005; Felsher, Derevensky, and Gupta 2004). In contrast, gambling machine play is among the most common gambling activity among youth in Nordic countries, as these machines are available in public locations ( Johansson and Götestam 2003; Ólason, Sigurdardóttir, and Smari 2006). It is somewhat surprising to note that countries where youth have greater access to gambling opportunities (e.g., the United Kingdom, Nordic countries) tend to have lower rates of adult problem gambling. Here again, differences in instrumentation, response rates, etc. may account for apparent differences in adult problem gambling rates. Furthermore, even if these differences are real, there are alternative explanations that could account for them (e.g., European EGMs tend to have low stakes and low prize limits). However, it is also worth considering whether early exposure to gambling could, paradoxically, have beneficial effects.The analogy here is the oft-cited lower rate of adult alcoholism in many countries where children are exposed to alcohol at an early age (e.g., China, Israel, southern European countries such as Italy) (Heath 1995; Pittman and White 1991). However, it is important to note that early exposure by itself is not sufficient and in some cases is quite harmful. Indigenous populations and countries such as France have very high rates of alcoholism despite early exposure. Rather, what is common among cultures with low rates of alcoholism is early positive exposure and ongoing promotion and modeling of moderate use in the context of family, meals, and/or religious service, as well as cultural
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taboos against drunkenness (Heath 1995; Pittman and White 1991). It is also important to note that any cultural practice that promotes widespread drinking is problematic, as there is a strong association between a country’s overall alcohol consumption and its level of health problems (Swedish National Institute of Public Health 2002).
RESTRICTING GAMBLING VENUE ENTRY TO NONRESIDENTS Several countries do not permit local residents to gamble at casinos. Examples are France, the Bahamas, Malaysia, and Nepal.Australia does not permit residents to gamble at its government-licensed online casino (Lasseters) (although it does permit its citizens to wager money with Australian online sports and race books, poker rooms, lottery sites, and skill game sites). In other countries, resident access is severely restricted. For example, South Korean citizens are allowed to gamble at only one of South Korea’s 15 casinos (Back 2006).The rationale for this policy is the same as that for locating casinos in tourist areas: to ensure that casino revenues come from outside the jurisdiction and to protect the local populace from the social harm. Although theoretically sound, there is a lack of empirical evidence on the effectiveness of this policy in preventing problem gambling. It is clear that gambling is still common in some of these countries with this policy.
CASINO SELF-EXCLUSION CONTRACTS The first formal casino self-exclusion program was initiated in 1989 in Manitoba, Canada, coincident with the opening of Canada’s first permanent, yearround casino. In the Netherlands, Holland Casino developed a program in 1990. In the United States, a tribal casino in Connecticut implemented a self-exclusion program in 1994, and Missouri developed the first statewide program in 1996. Since that time, many casinos and jurisdictions around the world have adopted selfexclusion programs. Several Internet gambling sites also offer self-exclusion programs. The effectiveness of these programs can be measured in three ways: utilization, success in excluding banned individuals, and overall effect on gambling behavior. Regarding utilization, on the basis of provincial data provided to one of the authors (R.W.), between 0.6% and 7.0% of problem gamblers in Canada signed up for these programs in 2005, depending on the province.These are fairly low utilization rates, similar to what has been reported in Australia and the United States (SACES 2003). One European jurisdiction with significantly higher rates is the Netherlands, due to the proactive nature of its program, in
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which individuals with high rates of casino patronage are approached to see if they wish a “visit limitation” or casino exclusion contract (Bes 2002; Nowatzki and Williams 2002). Another measure of effectiveness concerns the percentage of people who sign contracts who do not actually reenter the casino(s) during the period of exclusion. There is very limited evidence on this topic. Ladouceur et al. (2000) studied 220 individuals self-excluded from a Québec casino. A subset of 53 went back to renew or reestablish a self-exclusion contract. Of this group, 64% reported not entering the casino during their previous exclusion period. However, the 36% who did return reported going back a median of six times. Steinberg and Velardo (2002) studied a small subset (n = 20) of the 294 excludees at the Mohegan Sun Casino in Connecticut. Here again, most reported that they did not return to the casino during the period of exclusion, but the majority of the 20% that did return went back more than nine times. A review of self-exclusion in Victoria, Australia, also concluded that a significant number of self-excluders reenter casinos without being detected (O’Neil et al. 2003). Much higher compliance occurs in the Netherlands, where personal identification is required to enter any of the 12 casinos operated by Holland Casino. A computer system registers all visits and immediately identifies anyone who has requested a ban or visit limitation (Bes 2002). A final measure of effectiveness concerns the impact that self-exclusion has on overall gambling behavior. Again, there is very little known about this. Of the 53 individuals who went back to renew a self-exclusion contract at a Québec casino, only 30% reported that they had stopped gambling completely during their previous contract (which had typically been for a period of 6 to 12 months) (Ladouceur et al. 2000). Two previous studies reported that about half of selfexcluded patrons found other ways to gamble, such as illegal gambling or EGMs outside of casinos (Bes 2002; Ladouceur et al. 2000). Furthermore, a study completed in the Netherlands found that a large percentage of people who requested a ban or visit limitation eventually returned to the casino following the period of restriction. Some had a sharp increase in visiting frequency in the ensuing 6 months, although the frequency of most people stabilized over time at fewer than eight visits per month (Bes 2002). While it is apparent that casino self-exclusion contracts have some preventive value in containing the harms to established problem gamblers, it is also apparent that they could be a lot more effective than they currently are. Nowatzki and Williams (2002) provide a detailed discussion of the changes that need to occur, including mandatory promotion, irrevocable contracts, minimum ban length of five years, application to all gambling venues within the jurisdiction, computerized identification checks for enforcement of self-exclusion contracts (as is done in Europe), legal liability and penalties for both the venue and the gambler upon
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breach of contract, and optional counseling and a mandatory gambling education seminar prior to reinstatement.
RESTRICTIONS OR ALTERATIONS ON HOW GAMBLING IS PROVIDED ON-SITE INTERVENTION WITH AT-RISK GAMBLERS Several different initiatives have attempted to provide therapeutic interventions to at-risk and problem gamblers at the gambling venue itself.This makes a lot of theoretical sense considering that a significant portion of gambling venue patronage consists of problem gamblers (e.g., Fisher 2000; Gerstein et al. 1999), and that only a small minority of problem gamblers ever seek treatment.The following describes these initiatives and what is known about their effectiveness. Problem Gambling Awareness Training for Employees of Gambling Venues In recent years problem gambling awareness training for employees of gambling venues has been initiated in many countries. Manitoba, Canada, was one of the first jurisdictions to implement a program, beginning in 1998.The purpose of these programs is to increase employee recognition of problem gambling among patrons and to direct these patrons to appropriate treatment resources. Programs are variously delivered by venue owners/operators, departments of health/addiction agencies, contracted companies, or combinations of the foregoing. Staff training is mandatory in several jurisdictions and is sometimes also required of EGM site holders/staff and lottery retailers. Front-line employees at casinos typically receive a onetime knowledge and skill development session to understand and recognize problem gambling behaviors in patrons so as to alert their supervisors to these individuals. More extensive training is typically provided for supervisory and management personnel at casinos, whose responsibilities include approaching the identified individuals to offer immediate crisis management or treatment referral. Research on the effectiveness of training programs is limited.The Addictions Foundation of Manitoba found that 98% of 1550 VLT site owners and employees reported finding the training useful (Smitheringale 2001).The only known evaluation that included any sort of behavioral measure was conducted by Ladouceur et al. (2004). These investigators found that VLT retailers in Québec reported greater confidence in recognizing and addressing problem gambling after receiving a 2-hour problem gambling awareness workshop, and also reported approaching
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Research and Measurement Issues in Gambling Studies
problem gamblers more frequently than did new retailers who had not yet attended the workshop. A comparable, well-researched initiative is training for alcohol servers to not serve intoxicated patrons. A systematic review of this evidence shows several instances where this training has resulted in the desired effect but just as many instances where compliance with the training has been poor (Ker and Chinnock 2006). Some of the main factors interfering with the effectiveness of this training include the likelihood that intervention will compromise profits, the voluntary nature of the training (in some jurisdictions), the lack of enforcement, and a lowskilled workforce with high turnover and personal drinking habits that are inconsistent with these interventions (Ker and Chinnock 2006; Mosher et al. 2002; Reiling and Nusbaumer 2006). It is important to note that these barriers to compliance also apply to the gambling industry (Shaffer and Hall 2002; Williams and Wood 2004a, b). Automated Intervention for At-Risk Gamblers at Gambling Venues A much more reliable system exists in the Netherlands.The requirement to show ID upon entering casinos allows Holland Casino to track the frequency of casino visitation. If the computer indicates a significant increase in visitation frequency or that the person has had twenty visits a month over the past 3 months, then the person is automatically approached to see whether he or she would like to sign a visit-limitation or self-exclusion contract (Bes 2002). Only 18.5% of these approaches are perceived negatively by the patron (ibid.). In 2004, a total of 21,360 interviews were held with patrons, resulting in 3155 visit restrictions and 4423 admission bans (Holland Casino 2006). Although this type of proactive intervention with at-risk gamblers has not received extensive evaluation, secondary prevention initiatives (i.e., risk reduction) that prevent problems from occurring in the first place are always going to be more effective than treatment of existing problems. An indirect measure of the utility of Holland Casino’s approach is perhaps seen in the fact that the number of people seeking help for problem gambling from the official social services is only 50% of what these numbers were in 1995 (Holland Casino 2006). On-Site Information/Counseling Centers Responsible Gambling Information Centres (RGICs) located at gambling venues are a fairly new initiative.The primary purpose of RGICs is to provide, on patron request, information and education about the risks of gambling (e.g., odds of winning and losing, demonstrations/tutorials about slot machine workings/random number generation). A second purpose is to identify, support, and refer RGIC visitors who are experiencing problems with gambling. Immediate crisis intervention
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419
and counseling may be provided, but ongoing counseling services are not necessarily included in RGIC mandates. Third, information and support are provided to venue employees to assist them with customer interactions. In all jurisdictions, operational funding for RGICs has been provided either directly or indirectly by governments. The Crown Casino in Melbourne, Victoria, Australia, has operated the Crown Customer Support Centre (CCSC) since May 2002. The CCSC is located away from gambling areas but within the Crown Entertainment Complex and is staffed by casino employees (personal communication, Bill Horman, August 11, 2006).The CCSC provides 24-hour on-site help, support, and counseling services to casino patrons. In Queensland, a trial program is currently operational at one gambling club, whereby a counselor is made available on-site once per week, with associated costs borne by the venue (personal communication, Queensland Treasury Department, May 8, 2006). The Kangwon Land casino in Korea offers on-site counseling services (Back 2006). The first RGIC in Canada opened in 2003 at the McPhillips Street Casino,Winnipeg, Manitoba. RGICs currently operate in twelve casinos and one racino in Canada. The Canadian RGICs are staffed by persons with knowledge of addictions and with counseling backgrounds employed by addiction prevention/treatment agencies (Alberta, Manitoba), the department of health (Saskatchewan), the crown corporations operating the gambling facility (Québec, Prince Edward Island), nonprofit organizations (Ontario), and for-profit organizations (Nova Scotia). Most RGICs began operations in 2005 and 2006 and are considered to be pilot projects. Effectiveness evaluations either have not yet taken place or appear to be in very early stages.There is some information on utilization rates, which appear to be fairly low by patron utilization standards, although high by treatment provider standards. Approximately 8000 customers have accessed Manitoba’s RGICs since 2003 (75% for information only and 10% for support and referral) (Mehmel 2006). However, to put this in context, over 10,000 people visit Manitoba casinos every day. It is also interesting to note that the actual number of problem gamblers who have received treatment from the Addictions Foundation of Manitoba has gone down during this time period (523 in 2003–2004 to 467 in 2004–2005) (Addictions Foundation of Manitoba 2005). Similar relatively low rates of RGIC patron utilization are reported at Alberta’s largest casino, the Palace Casino, which has averaged three to five people per day in the initial six months (Canadian Broadcasting Corporation 2006). There are two other issues concerning RGICs. First, the extent to which they simply provide information is the extent to which their utility is similar to the information/awareness campaigns discussed earlier (i.e., good at improving knowledge but weaker at changing behavior). Secondly, there is some risk that the presence of RGICs in gambling venues may take the onus off gambling employee staff to identify and intervene with at-risk gamblers.This would be unfortunate, as
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Research and Measurement Issues in Gambling Studies
gambling employees have considerably greater interaction with at-risk gamblers than do RGIC staff.
MODIFYING PARAMETERS OF ELECTRONIC GAMBLING MACHINES Because EGMs are associated with the most gambling problems, a fair bit of research has investigated initiatives to alter features of EGMs to mitigate harm. Machines with the highest revenue generation continually replace less lucrative machines. Hence it can be expected that current machines have evolved to employ a wide array of characteristics to optimize revenue generation. A detailed discussion of research that has attempted to “unravel” some of these features is contained in Chapter 9 of this volume (Parke and Griffiths). Based on the research, several feature alterations appear to have some potential to reduce harm.These include slower speed of play, elimination of early big wins (perhaps by decreasing maximum win size); reduction in frequency of near misses, the number of betting lines available, and interactive features of EGMs; and presentation of pop-up messages. There is conflicting or insufficient evidence on the importance of payback rates, maximum win size, limitation of maximum bet size, more public placement of EGMs, bill acceptor limitations, time and spending limits, mandatory cashouts, and ambient light and sound. There is no evidence that on-screen clocks or monetary rather than credit displays are effective. There are two important caveats about this research. First, almost all of these studies have been conducted on people with prior experience on EGMs. Their effectiveness as primary prevention tools is plausible but less certain. Secondly, the magnitude of the effects tends to be small.The reality is that any automated device employing a variable ratio schedule (or more properly, random ratio schedule) with significant reinforcers and an event frequency of five seconds will tend to produce very strong behavioral patterns that are resistant to extinction. Thus, EGMs will likely always be high-risk devices with a strong association with problem gambling.
MAXIMUM LOSS LIMITS In addition to loss limits that are available on some EGMs, policies to limit the amount of money a gambler can lose are found on several of the major Internet gambling sites (e.g., Chapter 19, this volume). Limits are usually placed on maximum losses or deposits. However, similar problems to casino self-exclusion
Prevention of Problem Gambling
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programs exist concerning the ability to revoke limits or having easy access to other sites where limits have not been placed. It is rare to find this policy in land-based venues. One exception is the state of Missouri, which from 1994 has restricted each gambler’s losses to a maximum of $500 during 2-hour “excursions” on its 11 riverboat casinos (patrons can buy no more than $500 in gambling chips for the slot machines and table games).There is no information on the effectiveness of this measure other than the Missouri casino industry’s repeated claims that their revenues are much smaller than those of competing riverboat casinos in neighboring jurisdictions (Brokopp 2006). Here again, there are logistical problems involved with applying this policy to more than one venue at a time.
RESTRICTING ACCESS TO MONEY House credit is banned in most countries except the United States.Automatic teller machines (ATMs) within venues are generally permitted in most countries, although sometimes with restrictions on withdrawal amounts or machine placement. There is a lack of empirical research concerning the effectiveness of monetary restrictions. However, there is considerable anecdotal and survey data that indicate this to be a potentially effective strategy. First, it is well established that problem gamblers use cash machines more frequently than regular gamblers (Caraniche Pty Ltd. 2005; Independent Pricing and Regulatory Tribunal 2004). Secondly, problem gamblers in treatment report that the most common reason for terminating a gambling session and leaving a gambling venue is because they have run out of money (Productivity Commission 1999). Indeed, self-reports of problem gamblers consistently identify easy and immediate access to cash as exacerbating gambling-related harm (e.g., Caraniche 2005; McMillen, Marshall, and Murphy 2004; SACES 2005).The majority of 418 EGM players in Victoria, Australia, were of the view that ATMs should not be located in gambling venues at all.Among this same group, this was deemed to be the most effective harm minimization measure available (Caraniche Pty Ltd. 2005). Implementation of policies to ban credit, limit ATM withdrawals, or remove ATMs from or near gambling venues is often opposed by the gambling industry as well as by some gambling researchers, due to the inconvenience it would impose on nonproblem gamblers (McMillen et al. 2004).While this concern may be legitimate, it must be said that there are several problem gambling prevention policies that have this feature. Indeed, it is fairly common that policies governing the provision or use of problematic products (e.g., alcohol, firearms) restrict the unfettered use of these products to both at-risk and non–at-risk individuals so as to benefit society as a whole.
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Research and Measurement Issues in Gambling Studies
RESTRICTIONS
ON
CONCURRENT USE AND TOBACCO
OF
ALCOHOL
Gambling and drinking often co-occur, particularly where gambling occurs at problematic levels (e.g., Grant, Kushner, and Kim 2002). Several studies have also documented that risky play and extended play increase with alcohol consumption (Baron and Dickerson 1999; Kyngdon and Dickerson 1999; Ellery, Stewart, and Loba 2005). Given this knowledge, restrictions on the use of alcohol while gambling have significant potential as a harm minimization strategy for problem gambling. While policies regarding the sale of alcohol in gambling venues vary worldwide, responsible service practices (e.g., prohibiting continued sale of alcohol to intoxicated gamblers) are generally either legislated or otherwise entrenched in government policy. Policies concerning free drinks and other complementary goods and services are less likely to be included in responsible gambling codes. Following is a brief review of alcohol policies in various gambling jurisdictions. Casinos in Canada may not provide free alcoholic beverages. In the United States, free drinks are provided to casino patrons in 6 of 11 states with commercial casinos (Colorado, Iowa, Louisiana, Nevada, Mississippi, and New Jersey). Low-cost drinks are also common. Free drinks and discounted alcoholic beverages are either banned or not commonly available in most European countries, except in some eastern European countries (e.g., casinos in Ukraine). Some casinos in Australia (e.g., the Gold Coast in Melbourne, the Crown Casino in Victoria) provide lowcost or free drinks to customers.“Host responsibility” regulations in New Zealand prohibit free drinks. The association between gambling and tobacco use is also well established. Public health campaigns have successfully led to implementation of “public place” smoking bans in growing numbers of jurisdictions around the world in order to reduce the well-known health risks associated with smoking and secondhand smoke. Smoking bans may also inadvertently act as one of the more effective policies to reduce problem gambling, given that the majority of problem gamblers are smokers (e.g., Petry, Stinson, and Grant 2005; Rodda, Brown, and Philips 2004). It is no coincidence that gambling venues are the most common places to petition for and receive exemptions from public smoking bans. Indeed, significant reductions in gambling revenues have followed gambling venue smoking bans in various jurisdictions, including Canada, Australia, and New Zealand, Australia, Canada, and Scotland (Atlantic Lottery Corporation 2006; Hospitality Association of New Zealand, 2005; Saskatchewan Liquor and Gaming Authority, 2006; Skycity Entertainment Group 2005).This is notable considering that a large proportion of gambling revenue traditionally has derived from problem gamblers (Williams and Wood 2004a,b). It is hypothesized that problem gamblers may be less likely to gamble for extended periods if they cannot smoke, thereby reducing
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harm. This is corroborated by a survey of EGM venue operators, who reported that among all the harm minimization measures, the smoking ban was the most effective (Caraniche Pty Ltd. 2005). In New Zealand, calls to the problem gambling hotline dropped by one-third in the year following the smoking ban (Smokers Club 2006). Interestingly, there is also evidence that EGM and casino revenues may return to their previous levels after some time (e.g., Buchanan 2006). There is no empirical research to indicate whether this is due to smokers (and problem gamblers) having adjusted to this requirement or nonsmokers patronizing gambling venues at higher rates because of the smoke-free environment.
RESTRICTING ADVERTISING
AND
PROMOTIONAL ACTIVITIES
Restrictions on gambling advertising and promotional activities are based on the belief that these activities may induce gambling in vulnerable groups (e.g., problem gamblers, minors) or may serve to counteract advertising that promotes responsible gambling. There is some support for these contentions. In one study, half of a sample of pathological gamblers reported that advertising triggered them to gamble (Grant and Kim 2001).Also, the amount of money devoted to gambling advertising is many magnitudes greater than the money devoted to problem gambling prevention. For example, the province of Ontario spends more money on prevention, treatment, and research than any other jurisdiction in the world, amounting to $36 million in 2003/2004 (Sadinsky 2005). By comparison the Ontario Lottery and Gaming Corporation’s budget for marketing, advertising, and promotions is over $570 million, which does not include the additional advertising budgets of the three commercial casinos.Whereas previously reviewed research indicates that most people are unaware of “responsible gambling” initiatives (e.g., Turner et al. 2005), it is the rare person who is unaware of the omnipresent lottery and casino advertisements on television and radio. With respect to alcohol and tobacco, earlier research tended to indicate that advertising influenced market share but did not influence overall consumption (e.g., Boddewyn 1994). However, more recent research has found a much stronger relationship between exposure to tobacco or alcohol advertising and subsequent use of these substances in youth (Ellickson et al. 2005; Lovato et al. 2006). Furthermore,Weiss et al. (2006) found that anti-tobacco advertising was insufficient to counteract the effects of pro-tobacco advertising. Prohibiting misleading advertising is as important as restricting the amount of advertising.Typical examples are lottery advertisements that suggest the chances of winning are better than they actually are and that a person’s overall well-being will be substantially better after winning a jackpot. Similarly, websites that provide players with information about the frequencies of winning lottery numbers
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Research and Measurement Issues in Gambling Studies
deceptively convey the impression that useful information might be gleaned from these data.
GAMBLING VENUE DESIGN Many casinos around the world employ a “Vegas-style” design.The essential elements of this design are a lack of windows, an absence of clocks, a mazelike interior, low ambient light punctuated by bright colorful lights of EGMs, and constant background noise of EGMs, particularly the sounds of winning (there is no sound of losing).The presumption is that all of these elements help induce and perpetuate gambling. However, here again, there is a lack of empirical evidence on the issue. Subjectively, many gamblers believe that alteration of these features would be useful harm minimization strategies (Caraniche Pty Ltd. 2005). Some researchers have also demonstrated that the light and sound characteristics of EGMs are arousing and attractive features to gamblers (Griffiths 1993; Griffiths and Parke 2005). Delfabbro, Falzon, and Ingram (2005) empirically demonstrated that EGMs with lower illumination significantly increased time spent playing (sound did not influence gambling behavior). There is also some tentative evidence that people gamble more under red lighting (e.g., Stark, Saunders, and Wookey 1982). However, even if it were well established that these elements promoted gambling behavior among current gamblers, a plausible mechanism might be their conditioned association to the gambling itself (lights and sounds being very salient, easily conditionable stimuli).The other observation relevant to this issue is that EGMs generate significant revenues in all sorts of different environments, including corner stores, bars, clubs, hotels, arcades, restaurants, racetracks, and boats.
INDEPENDENCE BETWEEN GAMBLING REGULATOR AND GAMBLING PROVIDER The traditional role of government has been to regulate problematic products/services. However, in many jurisdictions governments have expanded their role to include the actual provision of gambling. In some instances this is limited to state-operated lotteries, and in other instances (e.g., Canada) governments are also the major provider of EGMs, casinos, sports betting, and other forms of gambling. A conflict of interest exists when the regulator and operator are part of the same organization.The relevance with respect to the prevention of problem gambling is that this conflict of interest potentially compromises the regulator’s ability to implement truly effective prevention policies and to effectively regulate the
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operator. It seems fairly evident that total independence between the regulator and the provider is a policy much more conducive to the prevention of problem gambling.
SUMMARY AND RECOMMENDATIONS Table 16.2 summarizes the evidence concerning the effectiveness of various educational and policy initiatives to prevent problem gambling. This table makes several important points. First, there exists a very large array of prevention initiatives, many of which have been implemented in various jurisdictions. This reflects the considerable interest and effort that is being put into mitigating the harm caused by gambling in recent years. Second, much is still unknown about the effectiveness of many individual initiatives. There is not a single initiative for which the evidence is conclusive. In most cases the evidence is fairly limited, and estimations of effectiveness are tentative.There is a particular lack of well-conceived and well-designed educational initiatives that show efficacy. Considerably more research is warranted. In conducting this research it is important to focus on meaningful behavioral change as the measure of effectiveness. Improvements in awareness, knowledge, or attitudes are of value as intermediate steps in the right direction but are of very limited importance if not accompanied by behavioral change. There is also value in examining the rate of new cases (incidence) in the jurisdiction subsequent to any widespread adoption of the initiative to gauge its more global impact and its utility as a primary prevention tool. Third, the most commonly implemented measures tend to be among the less effective options (e.g., awareness/information campaigns, responsible gambling features on EGMs, casino self-exclusion). When potentially more effective initiatives are implemented, they are typically done in such an inconsequential or perfunctory fashion as to ensure a lack of impact (e.g., small reductions in number of gambling venues or numbers of EGMs, minor restrictions on access to money).There seems to be a desire to prevent problem gambling without reducing revenues, which is very difficult if not impossible to achieve, considering that problem gamblers account for a significant percentage of gambling revenue (Productivity Commission 1999;Williams and Wood 2004a, b). Indeed, a good measure of overall success in preventing problem gambling will be an overall decrease in jurisdictional gambling revenue. Jurisdictions need to accept this fact and institute policies that seek to obtain “optimal” gambling revenue as opposed to “maximal” revenue. Also contributing to the poor choice of interventions is the fact that identification and design of most of these initiatives is not done by social scientists with expertise in prevention, but usually by government or industry people. As Blaszczynski, Ladouceur, and Shaffer (2004) state, “Most public policy recommendations are not
426
Table 16.2 Estimated Effectiveness Potential of Problem Gambling Prevention Initiatives. High
Moderately High
? ✔
Restrictions on the general availability of gambling
✔1
Restricting the number of gambling venues
✔1
Restricting more harmful types of gambling
✔1
Limiting gambling opportunities to gambling venues
? ✔ ?2
Limiting gambling venue hours of operation Restrictions on who can gamble
? ?3
Prohibition of youth gambling 4
?
✔4
Casino self-exclusion contracts Restrictions on how gambling is provided Intervention with “at-risk” gamblers
? ✔5
Research and Measurement Issues in Gambling Studies
✔
More sustained and directed educational initiatives POLICY INITIATIVES
Restricting venue entry to nonresidents
Low
✔
Information/awareness campaigns
Restricting the location of gambling venues
Moderately Low
✔
EDUCATIONAL INITIATIVES “Upstream” interventions
Moderate
Problem gambling training for employees of gambling venues
?
Automated intervention for at-risk gamblers at gambling venues
✔
On-site information/counseling centers
? ✔6
Modifying EGM parameters Maximum loss limits
?
Restricting access to money ✔
Restricting advertising and promotional activities
?
Gambling venue design Independence between gambling regulator and gambling
? ?
EGM, electronic gambling machine. Question mark indicates uncertainty due to insufficient evidence. 1 If the reductions are substantial. 2 Unless the time reduction is very substantial. 3 Likely has higher potential for preventing youth problem gambling. 4 Prevention benefits limited to residents rather than nonresidents. 5 If done appropriately. 6 Primarily slower speed of play, eliminating early big wins (perhaps by decreasing maximum win size), reducing frequency of near misses, reducing number of betting lines, reducing interactive features, and presenting pop-up messages.
Prevention of Problem Gambling
Restrictions on concurrent use of alcohol and tobacco
?
427
428
Research and Measurement Issues in Gambling Studies
based on empirical data but derive instead from anecdotes, common sense and personal belief ” (p. 306). Fourth,Table 16.2 suggests that there is almost nothing that is not helpful to some extent and there is almost nothing that, by itself, has high potential to prevent harm.There is no “magic bullet” to prevent problem gambling. Even total prohibition would likely have only a moderately positive impact. Similarly, even the less effective initiatives may change the behavior of a few individuals, may at least lay the foundations for later behavior change, or may contribute to the effectiveness of other initiatives. Because of their nature, primary prevention initiatives (e.g., school-based education programs, reduction in availability) are almost always more efficacious than tertiary prevention initiatives (providing helpline numbers, casino self-exclusion, RGICs, etc.). However, the present review makes the case that external controls (policy) tend to be just as useful as internal knowledge (education).Within the gambling field, sentiments are sometimes expressed that external controls are inferior strategies (e.g., Napolitano 2003) or that the primary emphasis should be placed on educating gamblers so they can make informed choices (e.g., Blaszczynski et al. 2004, 2005). However, within the substance abuse field, research shows that mandated treatment is generally as effective as voluntary treatment (e.g., Miller and Flaherty 2002) and that contingency management approaches actually tend to be more effective than counseling (Prendergast et al. 2006). No one argues that laws concerning bicycle helmets, fencing around swimming pools, speed limits, maximum blood alcohol levels while driving, etc., are not helpful in preventing undesirable outcomes.The same logic applies to gambling policy. The corollary of this point is that effective prevention in most fields actually requires coordinated, extensive, and enduring efforts between effective educational initiatives and effective policy initiatives (Nation et al. 1993; Stockwell et al. 2005). This is consistent with the essential tenets of the biopsychosocial model, which posits that problem gambling develops through a complex interaction between many different endogenous attributes and many different exogenous stimuli. Prevention of alcohol abuse, for example, requires restructuring the total alcohol environment through education and policy to reduce young adult drinking and associated problems (Holder 2005). Comprehensive community programs tend to be more promising and cost-effective than any single approach (Foxcroft et al. 2005; Slater et al. 2005). The notion here is that behavioral change is more likely to occur if interventions are directed at the individual, group, and community level. Multiple prongs may also be synergistic, with different overlapping initiatives reinforcing the message and power of each component. Arguably, the need for comprehensive educational and policy efforts is even greater for problem gambling, as the age of onset tends to be much broader than that for substance abuse. The final point to be made is that prevention efforts have to be sustained and enduring, because behavioral change takes a long time.As indicated earlier, in other fields, even with comprehensive approaches, the immediate effects on behavior have
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sometimes been small (e.g., Merzel and D’Afflitti 2003; Sowden and Stead 2005) or absent (Gates et al. 2005; Secker-Walker et al. 2002).Tobacco use illustrates this point best. There was no dramatic reduction in tobacco use after prevention efforts began in the mid-1960s. Rather, a very slow but progressive decline has been seen over the past 40 years as educational efforts, policies, and public attitudes strengthened. These things appear to be mobilizing more quickly with gambling, so reductions in problem gambling perhaps may occur more quickly.
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CHAPTER 17
Adolescent Gambling: Current Knowledge, Myths, Assessment Strategies, and Public Policy Implications Jeffrey L. Derevensky
Rina Gupta
International Centre for Youth Gambling Problems and High-Risk Behaviors McGill University Montreal, Quebec, Canada
International Centre for Youth Gambling Problems and High-Risk Behaviors McGill University Montreal, Quebec, Canada
Introduction Adolescent Gambling Behavior Adolescent Problem Gambling Measurement Issues Related to Adolescent Problem Gambling Instruments Used to Assess Youth Problem Gambling Perspectives on the Adolescent Prevalence Data Understanding Adolescent Problem Gambling Behavior Is Pathological Gambling an Enduring Disorder? Are All Forms of Gambling Equally Dangerous? Correlates and Risk Factors Associated with Adolescent Problem Gambling Game Features,Technological Advances, and Environmental Factors Psychiatric and Mental Health Correlates Protective Factors Individual, Situational, and Environmental Factors Treatment Prevention Initiatives Concluding Remarks
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INTRODUCTION The landscape of gambling has changed dramatically in the last decade. The widespread legalization of multiple forms of gambling on an international level has been unprecedented. While once relegated to specific jurisdictions and only a small number of games, gambling opportunities have become so varied and widespread that it is difficult to find jurisdictions in which some form of gambling is not government controlled, regulated, organized, or owned. Even in those jurisdictions where gambling is prohibited, multiple forms are readily accessible, such as underground gambling, Internet gambling, and mobile gambling (i.e., gambling via wireless technology like handheld devices, laptops, etc.). Gambling, often referred to as gaming, has become mainstream and is now viewed as a socially acceptable form of entertainment in spite of the recognized potential personal and social costs associated with excessive gambling.
ADOLESCENT GAMBLING BEHAVIOR While gambling has been widely viewed as an activity engaged in by adults, there is a large and growing body of literature suggesting that it is a popular form of recreation among adolescents. In most jurisdictions legislative statutes directly prohibit children and adolescents from engaging in government sponsored and regulated forms of gambling (e.g., lottery, casinos, horse racing, machine gambling). Yet, there remains little doubt that many youth engage in both regulated and nonregulated (e.g., card games and sports wagering among peers) forms of gambling (Derevensky and Gupta 2004a). Survey results and reviews of prevalence studies examining youth gambling behavior have been remarkably consistent in their findings suggesting that in spite of restrictions adolescents and young adults have engaged in practically all forms of social, government sanctioned, and nonregulated gambling available. The most popular forms of gambling include wagering on cards (poker; in particular, Texas Hold ‘em is the current rage), dice, and board games; betting with peers on games of personal skill (pool, bowling, basketball and other sports); playing arcade or video games for money; purchasing lottery tickets; sports-betting through the lottery (where permissible), betting parlors, or bookmakers; gambling on the Internet and in mobile (wireless) venues; wagering at horse and dog tracks; gambling in bingo halls and card rooms; playing slot machines and table games in casinos; and gambling on electronic gambling machines (e.g., video lottery terminals [VLTs], poker machines [“pokies”], slot/fruit machines) (Derevensky and Gupta 2004a; Derevensky et al. 2004; Jacobs 2004). Adolescent wagering behaviors have been found to be dependent upon a number of factors, including the local availability of games, the geographical prox-
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imity of gaming locations, the child’s gender and type of game (gambling is more popular among males than females; males prefer sports wagering whereas females report engaging in lottery purchases more often), the individual’s age (older adolescents and underage young adults are more likely to engage in electronic gaming machines and casino playing), and cultural/ethnic background (see Abbott et al. 2004; Chevalier et al. 2003; Derevensky, 2007; Ellenbogen, Gupta, and Derevensky, in press; Gupta and Derevensky 1998a, 2004; National Research Council 1999; Stinchfield 2000;Volberg 1998). While there is ample evidence suggesting that a large proportion of adolescents have engaged to some degree in gambling, most do it in a socially responsible manner and suffer few gambling-related difficulties. Adolescent gambling behavior, similar to that of adults, can be conceptualized along a continuum ranging from nongambling to social/occasional/recreational gambling to problem and pathological gambling (Figure 17.1).
ADOLESCENT PROBLEM GAMBLING On the continuum, only those at the extreme end experience multiple personal, academic, mental health, social, and financial problems. While there is an overall lack of consensus as to the actual prevalence rate among adolescents experiencing severe gambling, the results of most prevalence studies, large-scale metaanalyses, and reviews conducted internationally have been remarkably consistent in their conclusion that adolescents as a group constitute a high-risk population for gambling problems (Abbott et al. 2004; Jacobs 2000, 2004; National Research Council 1999; Shaffer and Hall 1996). Reviews of prevalence studies have revealed that between 60% and 80% of adolescents report having engaged in some form of gambling during the past year, with most of these adolescents doing so on an occasional basis (Abbott et al. 2004; Adlaf and Ialomiteanu 2000; Derevensky and Gupta 2004b; Jacobs 2004; National Research Council 1999), While the vast majority of adolescents can be described as social, recreational, and occasional gamblers, between 3% and 8% have been found to have a very serious gambling problem, with another 10–15% being at risk for the development of a gambling problem (Abbott et al. 2004; Derevensky and Gupta 2000; Jacobs 2004; National Research Council 1999; Shaffer and Hall 1996). After acknowledging
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difficulties in comparisons of multiple data sets, the National Research Council (1999) concluded that “the proportion of pathological gamblers among adolescents in the United States could be more than three times that of adults (5.0% versus 1.5%)” (p. 89).
MEASUREMENT ISSUES RELATED TO ADOLESCENT PROBLEM GAMBLING Early conceptualizations of pathological gambling in general were primarily based upon clinical experience and consensus among researchers and clinicians suggesting that pathological gambling is a multidimensional problem (Govoni, Frisch, and Stinchfield 2001). These early attempts at assessing and screening for gambling severity were designed primarily for adults and focused upon both the negative behaviors associated with excessive gambling and their concomitant gambling-related problems. New screening instruments have recently been developed to identify adults with pathological gambling problems incorporating a public health approach; however, little progress has been made toward improving our instruments assessing adolescent problem gambling.The use of survey instruments, in general, has come under serious criticism (see Derevensky and Gupta 2004c, 2006; Derevensky, Gupta, and Winters 2003; Ferris et al. 1999). Nevertheless, most existing instruments and measures have continued to focus upon behavioral indicators associated with problem playing, the emotional and psychological correlates associated with pathological gambling, the adverse consequences of excessive playing, and the economic and sociological aspects directly associated with excessive gambling.
INSTRUMENTS USED TO ASSESS YOUTH PROBLEM GAMBLING Despite significant advances in our understanding of adolescent problem gambling in the last decade, new screening instruments assessing adolescent problem gambling are still lacking. Most adolescent gambling screens have been adapted from adult instruments using adult criteria while modifying the questions to make them more age/developmentally appropriate. Such instruments include the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) (Winters, Stinchfield, and Fulkerson 1993); the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, for juveniles (DSM-IV-J) (Fisher 1992) and its multiple-response revision, the DSM-IV-MR-J (Fisher 2000); and the Massachusetts Gambling Screen (MAGS) (Shaffer et al. 1994). Similar to the instruments used for the assessment of adult pathological gambling, there exist
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common constructs underlying these instruments. In addition, while the number of items and constructs may differ, each criterion item has equal weighting, and a suggested cut score is provided identifying pathological and/or problem gambling for each instrument.These variations in cut score criteria have led to serious difficulties in reliable estimation of the prevalence rates of adolescent problem gambling as well as in the ability to compare study outcomes (Derevensky and Gupta, 2006).
PERSPECTIVES ON THE ADOLESCENT PREVALENCE DATA The variability of reported prevalence rates of youth problem gambling has been reported to be generally larger compared with the variability reported for adult prevalence rates of problem gambling (National Research Council 1999) and remains somewhat troubling (Derevensky et al. 2003). As well, questions regarding the comparability of findings using different instruments have been raised, and the issue concerning the validity of the prevalence rates of adolescent pathological gambling has been recently questioned in a number of studies (e.g., Ladouceur 2001; Ladouceur et al. 2000). Ladouceur and his colleagues contend that the current reported rates of serious gambling problems among adolescents may be significantly overestimated.They have highlighted the importance of discrepancies observed in a number of screening instruments and the number of youth being clinically identified as pathological gamblers as important issues that need to be addressed. To compound the issue, items may have been included, modified, deleted, and translated into other languages without verifying their applicability, and criteria scores have been adjusted in a number of prevalence studies. Derevensky et al. (2003) and Derevensky and Gupta (2006) have argued that differences in prevalence rates are likely affected by a number of situational and measurement variables, including sampling procedures (e.g., telephone surveys vs. school-based screens, community vs. convenience samples, failure to include highrisk populations such as school dropouts and delinquents), use of different instruments and measures, varying cut-point scores associated with different instruments, the use of modified instruments (some studies have reduced the number of items administered to youth), and the inconsistency of availability and accessibility of gambling venues. As well, gender distributions within each of the studies, the age of the population being assessed, cultural differences, the time frame used for assessing gambling behavior (past year vs. lifetime), as well as the distinct possibility that adolescent reports may be more variable than their adult counterparts, may be accounting for discrepant findings.
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Compounding measurement issues, the lack of consistency in terminology used to identify adolescents with serious gambling problems (e.g., pathological gamblers, probable pathological gamblers, compulsive gamblers, problem gamblers, level 3, disordered gamblers) remains a concern.While the field continues to strive to find the gold standard measurement instrument, there remains considerable value in our continued discussions and debate over the definition and etiology of problem and pathological gambling. Such discussions will likely stimulate the development of new empirically derived criteria and the subsequent development of new and improved screening and diagnostic instruments (a more detailed treatise of measurement issues can be found in Derevensky et al. 2003 and Derevensky and Gupta, 2006).
UNDERSTANDING ADOLESCENT PROBLEM GAMBLING BEHAVIOR The current models of pathological gambling have been based predominantly upon a wide spectrum of theoretical approaches, including behavioral, cognitive, cognitive-behavioral, psychodynamic, and social learning models. Despite the recognition of varied and multiple causes associated with gambling problems and possible causal pathways, these models and theoretical approaches have been limiting in their perspective to differentiate specific typologies of problem and pathological gamblers (Blaszczynski and Nower 2002; Gupta and Derevensky 2004;Vitaro et al. 2004). As a result, Nower and Blaszczynski (2004) have suggested a conceptual pathway model that identifies three primary subgroups/clusters of adolescent gamblers while continuing to acknowledge that there may be additional pathways. It is important to note that this model was based upon clinical observations with adults and has not been empirically tested. The pathways model contends that the three groups of pathological gamblers (behaviorally conditioned, emotionally vulnerable, and biologically based impulsive pathological gamblers) have all experienced common exposure to ecological factors (e.g., availability, accessibility, acceptability of gambling opportunities), have similar cognitive processes and distortions, and have experienced common contingencies of reinforcement with respect to gambling. However, they suggest that for some individuals psychopathology plays a major role, while for other individuals biological impulsivity represents an additive risk factor.Their differential pathways model has significant implications for both the assessment and the treatment of adolescent pathological gamblers given their different etiologies (Blaszczynski and Nower 2002; Gupta and Derevensky 2004; Nower and Blaszczynski 2004).
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IS PATHOLOGICAL GAMBLING AN ENDURING DISORDER? In contrast to traditional conceptualizations, more recently Nower and Blaszczynski (2002) have hypothesized the existence of a distinct subgroup of episodic binge gamblers, whose behavior is characterized by a history of intermittent bouts of severe dyscontrol and excessive gambling accompanied by intervening and longer periods of abstinence. Accordingly, these binge gamblers are reported to experience rapid escalation of intense uncontrolled gambling binges that results in both short-term and long-term chronic negative psychosocial consequences. However, unlike individuals exhibiting other, more typical long-term periods of excessive gambling, these binges are time limited, often reaching a peak that is followed by abrupt cessation, during which time individuals report an absence of any persistent urges and/or preoccupational thoughts. The notion of adolescent binge gambling, somewhat analogous to binge drinking, is worthy of examination. The identification of such binge gamblers would have important implications for determining treatment and prevalence rates of adolescent pathological gamblers. It should be noted that the enduring and persistent gambling behaviors of some adolescents lead to a wide variety of problems. Derevensky and Gupta (2000), using the DSM-IV-J gambling screen, reported that among adolescents identified as pathological gamblers, 91% reported a preoccupation with gambling; 85% indicated chasing their losses; 70% lied to family members, peers, and friends about their gambling behavior; 61% used their lunch money and/or allowance for gambling; 61% became tense and restless when trying to reduce their gambling; 57% reported spending increasing amounts of money gambling; 52% indicated gambling as a way of escaping problems; 27% reported missing school (more than five times) to gamble in the past year; 24% stole money from family members to gamble without their knowledge; 24% sought help for serious financial concerns resulting from their gambling; 21% developed familial problems resulting from their gambling behavior; and 12% reported having stolen money from outside the family to gamble.
ARE ALL FORMS OF GAMBLING EQUALLY DANGEROUS? Abbott et al. (2004) and Griffiths (1999) have argued that some forms of gambling may be more problematic than others. They suggest that games continuous in nature and involving elements of either skill or perceived skill have been more closely associated with problematic gambling.While the research on
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adults suggests that individuals’ frequent participation in certain forms of gambling (e.g., slots, electronic gambling machines, casino games, Internet gambling) may result in more problematic pathological gambling behavior (Abbott and Volberg 2000; Abbott et al. 2004; Petry 2005; Productivity Commission 1999; Schrans, Schellinck, and Walsh 2000), no evidence is currently available suggesting that certain forms of gambling are more problematic for adolescents. Given the more restricted opportunities for gambling (accessibility, geographical distances, and financial requirements), little is currently known about the impact of the attributes of specific games upon adolescent pathological gambling. However, it is generally assumed that adolescent patterns of gambling are much less stable, are transitory, and often depend more upon age (related to accessibility) and availability of the game. Referring back to the pathways model, it remains important to remember that adolescents with gambling problems are not a homogeneous group; rather they likely differ in terms of gender and gambling preferences, but there may be specific subtypes of pathological gamblers, with each subtype having a different etiology and different accompanying pathologies (Derevensky and Gupta, 2006; Ellenbogen, Derevensky, and Gupta 2005).
CORRELATES AND RISK FACTORS ASSOCIATED WITH ADOLESCENT PROBLEM GAMBLING Problem gambling, similar to other mental health disorders and addictive behaviors, has multiple risk factors, and no single constellation of risk factors can alone predict that a particular problem will exist.The research in the field has significantly increased in the past decade, along with our understanding of the risk and protective factors associated with adolescent problem gambling. In a number of reviews (Derevensky and Gupta 2004a,b; Dickson, Derevensky, and Gupta 2002, 2004), we have indicated the commonality of risk factors associated with problem gambling which are common to other addictive behaviors and a wide variety of adolescent mental health disorders (see Romer 2003), as well as those factors that may be unique to gambling problems (Dickson et al. 2002, 2004, in press). While there are multiple constellations of risk factors that in conjunction with a lack of protective factors likely place certain individuals at high risk for a specific problem, there is also a growing recognition that there is no universal etiology underlying gambling problems, that the constellation of risk factors may be different for individuals, and that a number of pathways may exist which lead to pathological gambling. Our current knowledge of the correlates and risk factors
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associated with adolescent problem gambling can best be summarized by the following: 1. Gambling is more popular among males than among females, and more adolescent males than females exhibit pathological gambling behaviors (Abbott et al. 2004; Derevensky and Gupta 2004a; Ellenbogen et al. 2005; Gupta and Derevensky 1998a; Jacobs 2004; National Research Council 1999; Stinchfield 2000; Volberg 1998). Pathological gambling among male adolescents has been found to be anywhere from two to four times as prevalent as among females (Derevensky and Gupta 2004b; Ellenbogen et al. 2005; Stinchfield 2000). Males have also been found to make higher gross wagers, gamble at earlier ages, gamble on more games, gamble more frequently, spend more time and money, and experience more gambling-related problems than females (Jacobs 2000, 2004), with parents being more likely to encourage their son’s gambling (Ladouceur et al. 1994). 2. Among adolescents there often is a rapid movement from social gambling to problem gambling (Derevensky and Gupta 1999; Gupta and Derevensky 1998a). 3. Many youth problem gamblers indicate having had very early gambling experiences and an early big win (Griffiths 1995; Gupta and Derevensky 1997; Wynne, Smith, and Jacobs 1996). 4. Adolescent pathological gamblers’ initial gambling experiences often originate with family members in their own homes, with older siblings being an early predominant influence. As children get older, their patterns of gambling change such that they gamble less with family members and more with friends. In general population surveys, adolescents with gambling problems are also more likely to report having parents who they perceive gamble excessively, are involved in other addictive behaviors, and/or have been involved in illegal activities (Abbott and Volberg 2000; Fisher 1993; Griffiths 1995; Gupta and Derevensky 1998a; Hardoon, Gupta, and Derevensky 2004; Raylu and Oei 2002; Wood and Griffiths 1998). 5. Youth with gambling problems have a positive attitude toward gambling (Dickson, Derevensky, and Gupta, in press). While they can fail to completely understand the odds associated with winning, many are cognizant of the problems associated with excessive gambling but view them as long-term consequences and not of immediate concern (Gillespie et al. 2005). 6. Cultural differences have been reported in gambling behaviors (Ellenbogen et al. in press; Stinchfield 2000;Wallisch 1993). 7. Personality traits reveal that adolescent pathological gamblers are more excitable, extroverted, and anxious, tend to have difficulty conforming to societal
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norms, experience difficulties with self-discipline (Gupta, Derevensky, and Ellenbogen 2006; Hardoon et al. 2002), exhibit higher scores on measures of state and trait anxiety (Gupta and Derevensky 1998b; Ste.-Marie, Gupta, and Derevensky 2002), are more impulsive (Nower, Derevensky, and Gupta 2004; Vitaro et al. 1998), are greater risk takers (Abbott et al. 2004; Derevensky and Gupta 2004b; Nower, Derevensky, et al. 2004), are more self-blaming and guilt prone (Gupta and Derevensky 2000), and report having a poor self-esteem and self-image (Gupta and Derevensky 1998b, 2004). 8. Adolescent pathological gamblers have been found to have increased physiological resting states, to have a greater need for sensation seeking, and to be more likely aroused and excited when gambling (Gupta and Derevensky 1998b; Nower, Derevensky, et al., 2004). 9. Individuals with gambling problems are more likely to experience a multiplicity of school-related problems, including increased truancy and poor academic performance; are more likely to have repeated a grade in school; and report a greater frequency of attention-deficit/hyperactive disorder and conductrelated problems (Derevensky et al. 2005; Hardoon et al. 2004). 10. Peer influences remain significant in both gambling behavior and excessive gambling (Griffiths 1990; Gupta and Derevensky 1997, 2004). There also exists a substantial body of literature examining differences in cognitive processing between individuals with and without gambling disorders. Adolescent problem gamblers, like their adult counterparts, exhibit erroneous beliefs, cognitive thinking displaying a lack of utilization of independence of events when making judgments, and an exaggeration of perceived skill involved in gambling. Adolescents with gambling problems, compared with their nonproblem peers, have been found to have poor or maladaptive general coping skills (Gupta, Derevensky, and Marget 2004; Nower, Derevensky, et al. 2004). They have also been found to use more emotion- and distraction-oriented coping styles than nongamblers.As adolescents with gambling problems report more daily hassles and major traumatic life events (Bergevin et al. 2005; Kaufman, Derevensky, and Gupta 2002), their minimal use of effective coping and adaptive mechanisms likely gets them into further difficulty. It is important to note that adolescents perceive gambling as an enjoyable, socially acceptable form of entertainment. For most adolescents and adults, it is generally perceived as a relatively benign activity, significantly less harmful than use of alcohol, drugs, or cigarettes (Dickson et al. 2002).They view gambling activities positively, even while understanding its potential negative consequences (Gillespie et al. 2005, 2007).
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GAME FEATURES, TECHNOLOGICAL ADVANCES, AND ENVIRONMENTAL FACTORS While we have previously indicated that specific types of gambling may be more problematic than others, the structural characteristics associated with particular electronic gambling machines which incorporate rapid play and high event frequencies and are predicated upon intermittent reinforcement schedules may be highly addictive. Such machines lend themselves to being played continuously.Their short payout intervals result in immediate and rapid feedback, creating ideal opportunities for repeated and continuous play while simultaneously minimizing individuals’ opportunity to cognitively recognize their losses (Abbott et al. 2004). In addition to the variable ratio payout schedules, it has been argued that the “near miss” provides the player with a form of secondary reinforcement by creating an illusion that the player is not constantly losing but rather is nearly winning (Griffiths 1999). Such types of structures are frequently found not only in gambling machines but in scratch lottery tickets as well. These features may also help account for the popularity of lottery scratch/instant tickets among youth (Felsher, Derevensky, and Gupta 2004). Both electronic gambling machines and instant scratch tickets possess color and low-cost entry, while electronic machines also incorporate sounds, music, and lights, making them highly attractive and creating an aura promoting continued gambling (Griffiths and Wood 2004). Technological advances continue to provide new gambling opportunities in the form of Internet gambling, mobile gambling, more technologically advanced slot machines,VLTs, interactive lottery games, and interactive television games of chance (Griffiths and Wood 2004). Such innovations may make it harder to monitor adolescents’ playing behaviors.There is recent research suggesting that a large number of underage youth have gambled on the Internet without money on practice sites (a number of Internet gambling sites offer players the opportunity to engage in identical activities without money with the potential for acquiring points and/or a prize), with a smaller number gambling with money (Byrne, Gupta, and Derevensky 2004; Hardoon, Derevensky, and Gupta 2002). Still further, there is evidence that youth who gamble both with and without money are more likely to exhibit gambling problems. While many adolescents do not have easy access to a credit card to enable them to gamble online, there remains a concern that those Internet gambling sites which permit individuals to play free games without money may be training sites such that when adolescents do have funds to gamble, the sites will already have developed player loyalty. As well, the odds on the free play sites have been deceptively altered to favor the player (Sévigny et al. 2006), providing misleading payout rates and giving the player an illusion of control.
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Gambling venues take many different forms, depending upon the jurisdiction in which they are located. Similarly, the age restrictions and accessibility for various forms of gambling differ.While in most jurisdictions adolescents are legally prohibited from gambling in state-owned or -regulated venues, Jacobs (2004) has argued that their resourcefulness and the lack of enforcement of statutes enables many adolescents to readily engage in gambling activities.
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There is a growing body of empirical evidence which suggests strong associations with alcohol and other drug use and substance abuse disorders among adolescent pathological gamblers (Gupta and Derevensky 1998a,b; Hardoon et al. 2004; Lynch, Maciejewski, and Potenza 2004;Winters and Anderson 2000). Gupta and Derevensky (2004), through their clinical work, have long suggested that adolescents with gambling problems often use gambling as a way of escaping daily hassles and major traumatic life events (Bergevin et al. 2005). Given their poor or maladaptive general coping skills (Bergevin et al. 2005; Gupta et al. 2004; Nower, Derevensky, et al. 2004), it is not surprising to find a moderate to high correlation among high alcohol consumption, drug use, and excessive gambling. Lynch et al. (2004) have suggested that these findings need to be considered from both a neurodevelopmental framework and a public health perspective (see Messerlian and Derevensky 2005; Messerlian, Derevensky, and Gupta 2005). There remain a number of studies which have reported that both male and female adolescents with gambling problems exhibit greater depressive symptomatology compared with both nongambling adolescents and those described as social/occasional gamblers, with a large percentage reaching criteria for clinical depression (Gupta and Derevensky 2004; Gupta et al. 2004).The fact that children of adult problem gamblers exhibit a number of mental health, substance abuse, and psychosomatic problems and remain at heightened risk for long-term mental health problems, including gambling problems, is equally disturbing (Gupta and Derevensky 1998a; Jacobs et al. 1989; Lesieur and Rothschild 1989).
PROTECTIVE FACTORS Most recently, attention has begun to focus on the protective and buffering factors which are thought to reduce the incidence of adolescent pathological gambling. Dickson et al. (in press) hypothesized that merely looking at risk factors without considering protective factors is extremely limiting when trying to predict high-risk behaviors. While there are some specific unique risk factors associated with problem gambling, many of the risk factors found in adolescent pathological
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gamblers transcend it across multiple risky behaviors (e.g., drug and alcohol use and abuse, cigarette smoking, unprotected sex). Dickson-Gillespie et al. (in press) report that poor family and school connectedness was symptomatic of their adolescent problem gambling subjects, with family cohesion playing a significant role as a protective factor. Lussier, Derevensky, and Gupta (2005) examined resilience in the presence of identified risk factors as a possible protective factor for youth gambling problems. Their results revealed that adolescents perceived to be vulnerable (high risk/low protective factors) had a mean gambling severity score nine times larger than the resilient group (high risk/high protective factors), eight times larger than the safe group (low risk/low protective factors), and thirteen times larger than the insulated group (low risk/high protective factors).They concluded that those youth identified as vulnerable were at greatest risk for experiencing gambling problems. Interestingly, their results revealed that all (100%) of youth classified as pathological gamblers and 86.7% classified as at-risk (exhibiting a number of clinical problems but not reaching clinical criteria for pathological gambling) scored on the resilient measure as being vulnerable, while only 4.3% of youth identified as resilient were identified as at-risk gamblers and none were pathological gamblers despite their reporting high levels of risk exposure. These data were strongly supported by findings of Gupta et al. (2004) and Nower, Derevensky, et al. (2004) revealing poor coping and adaptive behaviors among adolescent pathological gamblers.
INDIVIDUAL, SITUATIONAL, AND ENVIRONMENTAL FACTORS While a number of individual, situational, and environmental risk and protective factors have been found to be related to youth problem gambling behaviors, their causal links have not yet been empirically established.Abbott et al. (2004) suggest that the availability, accessibility, and structural features of specific forms of gambling (e.g., schedules of reinforcement, speed of the game, colors and sounds associated with arousal levels) most likely combine with an individual’s psychosocial characteristics in various ways to create rather complex patterns of risk. Our current knowledge remains limited as to the combinations of risk and protective factors which interact to increase the likelihood of specific individuals engaging in gambling excessively. Similarly, our understanding of those protective factors which may minimize and reduce the risk of excessive gambling remains limited. Longitudinal and prospective studies are needed to help articulate where the lines of risk and resilience intersect for specific adolescents and their interactions with different forms of gambling. Given the short-term and long-term pervasiveness of the problems associated with youth gambling problems and the concomitant mental health, social,
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economic, educational, and legal problems, more research is needed to clearly identify the risk and protective factors associated with problem gambling. A better understanding is needed of the effects of accessibility and availability of gaming venues, the structural characteristics of games, and the changing forms of gambling (e.g., Internet, mobile gambling) on future gambling behaviors.
TREATMENT The treatment paradigms currently being used for adolescents and young adults have generally been based upon a wide range of theoretical approaches which parallel those used for adults. Such models have been psychoanalytic or psychodynamic (Rosenthal 1987; Rugle and Rosenthal 1994), behavioral (Blaszczynski and McConaghy 1993; Petry and Roll 2001;Walker 1993), cognitive and cognitive-behavioral (Bujold et al. 1994; Ladouceur and Walker 1998;Toneatto and Sobell 1990), pharmacological (Grant, Chambers, and Potenza 2004; Grant, Kim, and Potenza 2003; Hollander et al. 2005; Hollander and Wong 1995), physiological (Carlton and Goldstein 1987), biological/genetic (Comings 1998; DeCaria, Hollander, and Wong 1997; Saiz 1992), addiction based (Lesieur and Blume 1991; McCormick and Taber 1988), and self-help (Brown 1987; Lesieur 1990). (For a more comprehensive overview of these models, the reader is referred to the reviews by Ladouceur and Shaffer 2005; Lesieur 1998; National Research Council 1999; Petry 2005; Potenza 2005; Rugle et al. 2001; and Toneatto and Ladouceur 2003.) Abbott et al. (2004), in reviewing the relatively scarce treatment efficacy literature, concluded that the ability to design and implement effective sciencebased treatment programs for problem gamblers has been hampered by a lack of theoretical understanding and agreement as to the etiology underlying problem gambling. They further contend that while the biomedical model has dominated the treatment community within the United States, the cognitive-behavioral model or social learning theory model has dominated other countries. Currently, there exists only a few randomized psychotherapeutic comparative studies and only a handful of randomized, double-blind, short-term psychopharmacological trials (Blaszczynski 2005; Hollander et al. 2005).This shortage of empirically based studies has resulted in a lack of consensus on what constitutes best practices or empirically validated treatment (EVT) approaches for both adolescents and adults with gambling problems (Nathan 2001, 2005;Toneatto and Ladouceur 2003).Whether or not all individuals with gambling problems should be treated as a homogeneous group has also been seriously questioned (Blaszczynski and Nower 2002; Gupta and Derevensky 2004; Nower and Blaszczynski 2004). There is considerable support suggesting that gambling behavior involves a complex and dynamic interaction among ecological, psychophysiological,
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developmental, cognitive, and behavioral components. As well, there is a growing body of evidence suggesting that problem gamblers are not a homogeneous group but rather have distinct behavioral, personal, and social difficulties. Given these assumptions, in the absence of EVT programs, Gupta and Derevensky (2004) argued for a dynamic interactive approach, assuming that a multiplicity of interacting factors need to be addressed and accounted for in any treatment paradigm for youth experiencing significant gambling problems. Empirical support for Jacobs’ General Theory of Addiction vis-à-vis adolescent problem gamblers (Gupta and Derevensky 1998b) and for Jessor’s (1998) Adolescent Problem Behavior Model further suggests that adolescent problem and pathological gamblers exhibit evidence of abnormal physiological resting states, have significantly greater emotional distress and anxiety and increased levels of dissociation when gambling, demonstrate erroneous cognitions when gambling (e.g., they believe that they can predict the outcome of the game even when the outcome is based purely on randomness, they perceive themselves to have exaggerated levels of skill, they have little understanding of randomness and independence of events), display depressive symptomatology, and are more likely to have higher rates of comorbidity with other addictive behaviors and other mental health problems. We therefore contend that treating gambling problems in isolation from other demanding and pressing social, physiological, developmental, cognitive, and emotional difficulties may lead to limited, short-term success but ultimately increases the risk for relapse. Recent work by Hodgins and colleagues (Hodgins 2005; Hodgins, Currie, and el-Guebaly 2001; Hodgins and el-Guebaly 2000) argued that Prochaska and DiClemente’s Transtheoretical Model of Intentional Behavior, originally designed for adults, may be a useful framework in helping to understand treatment and natural recovery of pathological gamblers. Adapting the transtheoretical model for youth pathological gamblers, DiClemente, Story, and Murray (2000) and DiClemente, Delahanty, and Schlundt (2004) have suggested that the stages-of-change model represents a viable conceptual framework for articulating an effective treatment paradigm for adolescent pathological gamblers.While the model has been useful in working with tobacco, alcohol, and substance users and may have important implications for youth problem gamblers, little empirical support currently exists confirming its usefulness. Another approach adopted to help problem gamblers focuses upon the use of short-term brief motivational enhancement therapy and telephone counseling, with and without manuals.While originally developed by Hodgins et al. (2001) for adults, results suggest that brief telephone counseling and the use of a home-based manual may be effective, especially for those with less severe gambling problems (Hodgins 2005; Hodgins and el-Guebaly 2000; Hodgins et al. 2001). Given that many adolescents fail to seek treatment in traditional therapeutic settings (see Derevensky et al. 2003 for a discussion of why adolescents don’t seek treatment for gambling problems), the use of telephone counseling and manuals which can be
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mailed to an individual’s home may be an important innovation and promising approach to helping adolescents with gambling problems. Another alternative may be the use of real-time interactive online computer chats for youth with gambling problems.The feasibility of this approach is currently being tested. On a more general level, Gupta and Derevensky (2000, 2004) have presented a treatment model predicated upon their research and clinical findings that adolescent pathological gamblers frequently exhibit depressive symptomatology; somatic disorders; anxiety; attention deficits (many with hyperactivity); academic, personal, and familial problems; high risk-taking behaviors; and poor coping skills. These individuals use gambling as a form of stress reduction and escape from daily and long-term problems. While Gupta and Derevensky acknowledge that adolescent pathological gamblers experience numerous erroneous cognitive beliefs and distortions, they contend that clinicians must simultaneously address underlying psychological problems as well as the presenting gambling problem. Gupta and Derevensky (2004), based upon their clinical work, suggest that Nower and Blaszczynski’s (2004) pathways approach to treating youth gamblers shows great promise, as adolescent pathological gamblers exhibit a multifaceted constellation of situational, risk, and protective factors. It is likely that these factors differentially influence adolescents who otherwise display similar phenomenological features and patterns, which in turn result in their following alternative and distinct pathways leading toward a gambling disorder. The field of psychopharmacology may provide a promising complementary strategy for working with adolescents experiencing significant gambling problems in the future.While the current pharmacological strategies for treating pathological gambling in adults suggests the use of serotonin reuptake inhibitors (SRIs), mood stabilizers, and naltrexone for adults (Grant et al. 2003), little is known about their success with adolescents (Grant et al. 2004).While the data suggest positive shortterm effects for adults, such studies have methodological challenges (Grant et al. 2004; Hollander et al. 2005). The use of similar drugs for adolescents with gambling problems has not been tested. It represents a potentially promising treatment regimen but must await completion of controlled treatment studies. Combinations of behavioral and drug therapies have been demonstrated in other addictive disorders to be superior to either type of treatment alone (Carroll 1997). Abbott et al. (2004), reviewing treatment outcome studies for adults, concluded that there is sufficient evidence to suggest that individuals who have received treatment for a myriad of mental health disorders and addictions (including gambling addiction) generally do better than controls who do not receive any formal treatment. Based upon the existing literature, they concluded that “irrespective of the particular type of therapy, most clients who show initial improvement maintain it, albeit that probability of relapse increases with time” (p. 138). Further research is much needed into (a) understanding the barriers to treatment,
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(b) whether or not controlled gambling versus abstinence is a realistic goal, and (c) working toward empirically supported treatments for youth. The issue of natural recovery is increasingly important. If the current adult prevalence rates maintain themselves over time, the percentage of youth with gambling problems is likely to decline (adolescent prevalence rates are two to four times that of adults). If most youth are not seeking professional treatment (as is the case with adults), then the issue of understanding the process of natural recovery remains extremely important. No known studies have examined the path which natural recovery takes among adolescents experiencing significant gambling problems and its potential impact for treatment. Still further, until further research and refinement in matching treatment strategies with gambler typologies is realized, it is likely that best practices for treating adolescents with gambling problems will not be realized. Research on the effective treatment of adolescent pathological gamblers is extremely limited and is in its very early stages. Calls for multisite treatment efficacy studies are becoming more widespread. Much research into the efficacy of alternative treatment models for youth problem gamblers is necessary before best practices can be reliably established. It may well be that some of the previously established treatment models for other mental health disorders and addictive behaviors can be applied to youth with gambling problems, given the significant comorbidity and overlap in risk factors.
PREVENTION INITIATIVES Like our understanding of best practices in the treatment of adolescent problem gamblers, our empirical knowledge for translation into a science-based prevention initiative is similarly scarce (Derevensky et al. 2001). Prevention specialists in the gambling field continue to draw heavily upon the substantial research on comparable prevention of adolescent alcohol and substance abuse and have adapted programs from this research as models. Numerous prevention efforts in the field of substance abuse have focused upon the concepts of risk and protective factors and their interaction (Brounstein et al. 1999). These efforts seek to prevent or limit the effects of risk factors while enhancing resilience through protective factors. Although a growing number of prevention programs are readily available, few have any substantial theoretical framework, and there are even fewer scientifically validated prevention initiatives for problem gambling.The increasingly widespread use of a harm reduction/minimization approach toward alcohol and substance abuse may be a useful strategy in preventing gambling problems (Dickson, Derevensky, and Gupta 2004) (see Abbott et al. 2004 and Derevensky et al. 2001 for a comprehensive review of programs).
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Some of these programs have been based upon current theoretical and empirical evidence of common risk and protective factors across adolescent risky behaviors. It has been suggested that strategies for gambling prevention initiatives should be inclusive and should target multiple risk behaviors (Jessor 1998), including problem gambling (Dickson et al. 2004). The harm reduction/minimization approach includes strategies, policies, or programs that have been designed to promote temperance and responsible gambling without requiring abstinence. This framework includes secondary prevention strategies (predicated upon the assumption that it is unfeasible and highly unlikely that one can prevent individuals from participating in particular risky behaviors) and tertiary prevention strategies (DiClemente 1999) (e.g., providing treatment for those individuals in need). While this approach may be somewhat contentious, pure abstinence may not be a realistic goal, as (a) there is ample evidence (e.g., from self-reports) that most youth have gambled for money at some time, and (b) gambling is viewed as a socially acceptable pastime. Nevertheless, it is important to note that a harm reduction/minimization approach does not in and of itself preclude abstinence, especially for young children. This cannot be overemphasized, since we are not advocating that gambling by children is acceptable. On the contrary, we deem gambling among children and adolescents to be inappropriate. However, we remain aware that many youth will engage in this behavior intermittently, often with the approval or tacit knowledge of their parents. If one accepts it as a health paradigm in lieu of, or as an interim step toward, an abstinence model, the harm reduction/minimization approach remains value neutral and supports strategies aimed at reducing harmful negative consequences incurred through involvement in risky behaviors (Dickson et al. 2004; Messerlian and Derevensky 2005; Messerlian et al. 2005). Consistent with an abstinence model, underage youth (defined variously by jurisdiction) are legally prohibited from accessing regulated gambling venues. Statutes vary and are often dependent upon the specific form of gambling—lottery purchases and fruit-machine playing (in the United Kingdom) are considered less problematic than casino gambling and have lower age restrictions. Existing statutes remain necessary and are in need of stricter enforcement and adherence (Messerlian and Derevensky 2005). As Dickson et al. (2004) noted, there exists a paradox and confusion as to which primary prevention approach to promote: abstinence or harm reduction/minimization. They suggested that abstinence from gambling may not be a realistic goal given its widespread social acceptability, promotion by governments throughout the world, and the fact that upward of 80% of adolescents report having gambled during the past year ( Jacobs 2004; National Research Council 1999). While we remain concerned about the occurrence of serious gambling problems among youth, it is also important to recognize that the vast majority of youth who gamble do so without developing any significant gambling-related problems.
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Independent of the complexities of its use, a risk-protective factor model can be used as the theoretical basis of harm reduction/minimization because of its role in science-based prevention and its empirical validity in adolescent risk behavior theory. A strength of the risk-protective factor model is that it enables prevention specialists and educators to create, evaluate, and refine harm reduction/minimization prevention programs based upon changes in risk and protective factors that have been shown to account for changes in gambling behaviors, attitudes toward gambling, and so on, rather than simply relying on traditional means of measuring effectiveness (e.g., quantitatively measuring change rates of harmful consequences of risky behaviors). Given the need for prevention programs, a number of school-based gambling-specific educational materials, videos, student-developed screenplays and productions, poster and public-service-announcement contests, and CD-ROMs have been developed. Such curricula have been generally aimed at high school–age children, although attention has also been given to the upper elementary school grades, given that many problem gamblers report having started by age 9 or 10 (Derevensky and Gupta 1996; Gupta and Derevensky 1998a).The Youth Gambling International (2006) initiative of McGill University, Montreal, has developed a paper-and-pencil curriculum (Count Me Out), two interactive CD-ROMs (The Amazing Chateau for primary school and Hooked City for secondary school), and a DVD docudrama (Clean Break) as prevention presentations against youth gambling. Many of the early prevention efforts have suggested the need for a general mental health prevention program that addresses a wide range of adolescent risky behaviors simultaneously (e.g., substance abuse, gambling, truancy, risky driving and sexual activity). While adolescent risky behaviors have many factors in common, the activities themselves can differ on several important dimensions. Nevertheless, a harm reduction/minimization approach appears appropriate for targeting those risky activities that lie on a continuum of harm (i.e., when engaged in responsibly and moderately, they yield no significant negative consequences) and are socially acceptable, and this approach has significant educational appeal (Dickson et al. 2002). Research highlights that the age of onset of gambling behavior represents a significant risk factor, being correlated with the development of gambling-related problems. Thus, from a prevention perspective, delaying the age of onset of gambling may be a fundamental goal of any prevention program. While advocating a harm reduction/minimization approach, delay of gambling onset is more consistent with an abstinence model. Permitting “responsible” gambling through enhancement of emotional and cognitive coping skills and by providing cognitive decision-making tools is equally appropriate. One of the central goals in effective harm reduction/minimization curricula for a wide range of adolescent problem behaviors is to foster resilience and problem-solving skills. There is ample evidence to suggest that direct and moderator
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effects of protection can be used to guide the development of prevention and intervention efforts to help minimize adolescent risky behaviors (Dickson et al. 2002; Jessor 1998). Other forms of prevention have been initiated, including selfexclusion contracts for those of legal age to ban themselves from gambling in a casino; raising of the legal age to gamble; extensive training of educators, public health workers, and parents; media and promotional campaigns designed to promote responsible gambling and to raise awareness that adolescent gambling may not be such a benign behavior; training programs for industry representatives; and targeted responsible gambling features on electronic gambling machines. While many of these campaigns have not undergone rigorous empirical testing and are in their early stages, they represent a consensus for the need to raise awareness and reduce problem gambling.
CONCLUDING REMARKS Adolescence is marked by significant physical and psychological changes. It is a developmental period often accompanied by rapid changes, experimentation, and an increase in risk-related behaviors.The initiation and incidence of alcohol and drug use, cigarette smoking, and gambling among adolescents is certainly of concern to parents, educators, and mental health professionals. Adolescents’ propensity for risk-taking behaviors and their perceived invulnerability often propel them toward activities which promote instant gratification (Gillespie et al. 2005, 2007). The field of youth gambling is relatively new and as a result there currently are significant gaps in our knowledge. While a few prospective studies have been initiated, their samples are either extremely limited or highly select (Vitaro et al. 2004; Winters et al. 2002; Winters, Stinchfield, and Kim 1995). While the risk/resiliency model may have significant benefits for our understanding as to why some individuals appear to be at high risk for developing a significant gambling problem, further research is required.As Derevensky and Gupta (2004b) previously noted, needed are (a) further research to identify common and unique risk and protective factors for gambling problems and other addictive behaviors, (b) longitudinal research to examine the natural history of pathological gambling, and (c) molecular, genetic, and neuropsychological research to help account for changes in gambling progression. Still further, we require a better understanding of the effects of accessibility and availability of gaming venues on future gambling behaviors and the impact that gambling advertisements and new forms of gambling (specifically Internet and mobile/wireless) have on the initiation, maintenance, and progression of adolescent gambling and problem gambling. Similarly, there likely is not one general adolescent problem gambler exhibiting the same profile but multiple characteristics and causes which help differentiate pathological gamblers. There is also a growing body of research suggesting
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different gambling patterns and incidence of problem gambling among different cultural groups (see Ellenbogen et al. in press and Raylu and Oie 2002 for a more thorough discussion). Our treatment approaches will likely need to be differentiated and take into account a myriad of factors.While we search for the magic treatment approach, alternative therapeutic interventions will continue to be beneficial. Derevensky and Gupta (2004b) have argued that many other more highly visible adolescent mental health problems have prompted social policy interventions (e.g., cigarette smoking, alcohol and other substance use and abuse, increased rates of suicide, risky sexual behavior). Only recently have health professionals, educators, and public policymakers acknowledged the need for the prevention of problem gambling. While gambling is not viewed by parents or adolescents themselves as a problem, excessive gambling (often referred to as a “hidden addiction”) is similar to other maladaptive behaviors and is accompanied by a multiplicity of negative consequences and short-term and long-term problems. Given that it takes several years from onset of gambling and occasional/recreational gambling to a significant gambling problem, the true social impact and long-term consequences upon youth of gambling will likely take years to realize.The widespread acceptability and proliferation of gambling opportunities and venues on an international level is unprecedented. Today’s youth will most likely spend their entire life in an environment where gambling is prolific, government supported, and regulated, and where gambling is viewed as a socially acceptable form of entertainment.While in most jurisdictions underage youths are prohibited from engaging in regulated gambling, they appear to have little difficulty accessing many forms, both regulated and unregulated.The wide social appeal of gambling suggests that this problem is likely to increase before abating. Given the pervasiveness of the mental health, social, economic, and legal problems associated with youth gambling problems, serious efforts must be made to ensure that youths are made aware of the potential dangers, that parents do not view adolescent gambling as harmless and innocuous behavior, and that governmental legislators view this as a serious problem.
GLOSSARY Adolescence defined by varying age designations in different cultures, but Western use of the term includes teens from 13 to 20 years. Prevention educational and practical interventions designed as harm minimization strategies, and/or those programs used to prevent individuals from initiating gambling. Treatment interventions used to help individuals with gambling problems stop, control, or reduce their gambling behaviors.
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Nower, L., Derevensky, J. L., and Gupta, R. (2004). The relationship of impulsivity, sensation seeking, coping and substance use in youth gamblers. Psychology of Addictive Behaviors, 18, 49–55. Petry, N. M. (2005). Pathological Gambling: Etiology, Comorbidity, and Treatment.Washington, DC:American Psychological Association Press. Petry, N. M., and Roll, J. M. (2001). A behavioral approach to understanding and treating pathological gambling. Seminars in Clinical Neuropsychiatry, 6, 177–183. Potenza, M. (2005).Advancing treatment strategies for pathological gamblers. Journal of Gambling Studies, 21, 91–100. Productivity Commission. (1999). Australia’s Gambling Industries. Canberra: AusInfo. Raylu, N., and Oei, T. P. S. (2002). Pathological gambling: A comprehensive review. Clinical Psychology Review, 22, 1009–1061. Romer, D. (ed.). (2003). Reducing Adolescent Risk:Toward an Integrated Approach. San Francisco: Sage. Rosenthal, R. J. (1987).The psychodynamics of pathological gambling:A review of the literature. In The Handbook of Pathological Gambling (T. Galski, ed.). Springfield, IL: Charles C.Thomas. —— . (1992). Pathological gambling. Psychiatric Annals, 22, 72–78. Rugle, L., Derevensky, J., Gupta, R.,Winters, K., and Stinchfield, R. (2001). The Treatment of Problem and Pathological Gamblers. Report prepared for the National Council for Problem Gambling, Center for Mental Health Services, and the Substance Abuse and Mental Health Services Administration, Washington, DC. Rugle, L. J., and Rosenthal, R. J. (1994).Transference and countertransference in the psychotherapy of pathological gamblers. Journal of Gambling Studies, 10, 43–65. Saiz, J. (1992). No hagen juego, senores [Don’t begin the game]. Interviu, 829, 24–28. Schrans, T., Schellinck, T., and Walsh, G. (2000). Technical Report: 2000 Regular VL Players Follow up: A Comparative Analysis of Problem Development and Resolution. Focal Research Consultants, Ltd. Retrieved January 28, 2007, from http://www.gov.ns.ca/heal/downloads/VLPlayers_ TechnicalReport.pdf Sévigny, S., Cloutier, M., Pelletier, M-F., and Ladouceur, R. (2005). Internet gambling: Misleading payout rates during the “demo” period. Computers in Human Behavior, 21, 153–158. Shaffer, H. J., and Hall, M. N. (1996). Estimating prevalence of adolescent gambling disorders: A quantitative synthesis and guide toward standard gambling nomenclature. Journal of Gambling Studies, 12, 193–214. Shaffer, H., LaBrie, R., Scanlan, K., and Cummings,T. (1994). Pathological gambling among adolescents: Massachusetts Gambling Screen. Journal of Gambling Studies, 10, 339–362. Ste.Marie, Gupta, R., and Derevensky, J. (2002).Anxiety and social stress related to adolescent gambling behavior. International Gambling Studies, 2, 123–141. Stinchfield, R. (2000). Gambling and correlates of gambling among Minnesota public school students. Journal of Gambling Studies, 16, 153–173. Toneatto, T., and Ladouceur, R. (2003). Treatment of pathological gambling: A critical review of the literature. Psychology of Addictive Behaviors, 42, 92–99. Toneatto, T., and Sobell, L. C. (1990). Pathological gambling treated with cognitive behavior therapy: A case report. Addictive Behaviors, 15, 497–501. Vitaro, F., Ferland, F., Jacques, C., and Ladouceur, R. (1998). Gambling, substance use, and impulsivity during adolescence. Psychology of Addictive Behaviors, 12, 185–194. Vitaro, F.Wanner, B., Ladouceur, R., Brendgen, M., and Tremblay, R. E. (2004).Trajectories of gambling during adolescence. Journal of Gambling Studies, 20, 47–69. Volberg, R. (1998). Gambling and Problem Gambling Among Adolescents in New York. Report to the New York Council on Problem Gambling, Albany. Walker, M.B. (1993).Treatment strategies for problem gambling: A review of effectiveness. In Gambling Behavior and Problem Gambling (W. R. Eadington and J. A. Cornelius, eds.), pp. 533–566. Reno: Institute for the Study of Gambling and Commercial Gambling/University of Nevada.
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Wallisch, L. (1993). Gambling in Texas: 1992 Texas Survey of Adolescent Gambling Behavior. Austin: Texas Commission on Drug and Alcohol Abuse. Winters, K. C., and Anderson, N. (2000). Gambling involvement and drug use among adolescents. Journal of Gambling Studies, 16, 175–198. Winters, K. C., Stinchfield, R. D., Botzet, A., and Anderson, N. (2002). A prospective study of youth gambling behaviors. Psychology of Addictive Behaviors, 16, 3–9. Winters, K. C., Stinchfield, R. D., and Fulkerson, J. (1993). Toward the development of an adolescent gambling problem severity scale. Journal of Gambling Studies, 9, 371–386. Winters, K. C., Stinchfield, R., and Kim, L. G. (1995). Monitoring adolescent gambling behavior in Minnesota. Journal of Gambling Studies, 11, 165–183. Wood, R., and Griffiths, M. (1998). The acquisition, development and maintenance of lottery and scratch card gambling in adolescence. Journal of Adolescence, 21, 265–272. Wynne, H. J., Smith, G. J., and Jacobs, D. F. (1996). Adolescent Gambling and Problem Gambling in Alberta. A report prepared for the Alberta Alcohol and Drug Abuse Commission. Edmonton, AB: Wynne Resources Ltd. Youth Gambling International. (2006, website). Prevention projects webpage, from “Prevention” menu link. Retrieved January 28, 2007, from http://www.youthgambling.com
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CHAPTER 18
Cross-Cultural Comparisons Jan McMillen Centre for Gambling Research The Australian National University Canberra, Australian Capital Territory, Australia
Introduction Understanding the Relationship Between Gambling Research and Culture: The Importance of Theory Contending Theoretical Perspectives Normative Theories Behavioral Theories Sociological and Comparative Perspectives Problem Gambling: A Case Study of Research Cultures Theoretical Reflections The Way Forward
INTRODUCTION This chapter presents an overview of key developments in gambling research to illustrate how the field has evolved in recent years.The significance of cultural diversity and its influence on patterns of gambling and the prevalence of problem gambling have attracted research attention only relatively recently.The majority of research and explanations of gambling focuses on the individual, in terms of either the gambler’s preferred activities or their psychological disposition. Gambling research is notable for the absence of cross-cultural comparative studies. Although interest in gambling research has increased dramatically in recent years, there have been surprisingly few cross-cultural studies of gambling or comparative studies of gambling participation in the societal contexts that shape the behavior.The small number of such studies which have been conducted have addressed different issues using a range of different measures such that meaningful cross-national comparison 465
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has not been viable. This raises the question of why cross-cultural research has attracted such little attention and how it might be progressed. The original purpose of this analysis was to provide an assessment of the research strategies and measurement techniques used in various cross-national studies. It is not the intention of this chapter to present a comprehensive summary of theories, issues, or evidence relevant to cross-cultural gambling research.The aim is to provide a brief overview of key developments in gambling research to illustrate how the field has evolved in recent years. It presents an assessment of the main theoretical and methodological issues involved in the cross-cultural study of gambling.This discussion adopts a sociocultural framework to explain and compare contemporary gambling issues and research in various countries. In contrast to gambling literature which concentrates on individual characteristics and behavior, analysis focuses on the individual/society relationship in different cultural and societal settings. From this perspective, explanations of the gambling phenomenon, gambling behavior, and policy responses necessarily consider the various societal, cultural, and structural elements that contribute to the nature and prevalence of gambling in particular communities. Culture, ideology, history, and political economy all play a role in determining the prevalence of certain types of legalized gambling and the outcomes of gambling activity, as well as research priorities and assumptions. A central argument is that a dominant Western school of thought, originating in North American psychiatry and clinical psychology, has shaped and molded thinking about gambling behavior and problem gambling. Limitations of this approach and the complex nature of contemporary gambling emphasize the need for a multitheoretical approach using a plurality of methods. Most other areas of research tend to be interpreted through a number of competing explanations, with theoretical development and policy preferences constructed around a debate between contending perspectives. Research into gambling, however, has been characterized by homogenizing theories and individualistic assumptions that tend to overlook the possibility that culture and social diversity matter. Departing from the dominant individualistic approach, my analysis will emphasize culture and values and the way they influence research as well as gambling preferences and are ultimately reflected in policy outcomes.At issue are moral arguments and definitions of legalized gambling, the popularity of gambling among certain groups, and the sociocultural implications of gambling. For example, cultural politics (power struggles, societal ambivalence, and ideological debates) profoundly affect the form and extent of legalized gambling as well as definitions and responses to problems which may arise. Further, from a sociological orientation, cultural norms and practices affect how the individual’s gambling fits in with societal preconceptions of appropriate behavior. In assembling the material for this chapter, I have been guided by an approach that reflects my view about how cross-cultural gambling research should
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be understood.1 The analytical framework reflects a review and synthesis of several contending paradigms. This approach is sensitive to historical change, recognizing that theories about gambling arise in particular historical and disciplinary contexts and thus need to be linked to those contexts.The evolution of gambling research has been locked into particular patterns because of events and decisions that took place many years ago.This approach also recognizes the salience of ideas and theories in structuring research agendas, methodologies, and outcomes. Norms, values, ideologies, and ideas not only reflect the way we think about and research gambling, But also mold, shape, facilitate, and constrain the activities of industry and governments—and ultimately the experience of gamblers.The following sections will explore these key themes to show how different perspectives have shaped gambling research and its capacity to explain gambling across different nations and in different cultural groups. They will establish a descriptive and theoretical framework to explain cultural similarities and differences in the nature of gambling across different countries, and in the different ways gambling has been researched and explained. For practical purposes, the primary focus is on the growing body of literature in the United Kingdom, the United States, Canada, New Zealand, and Australia. Each of these countries has a relatively liberal gambling environment, as well as a population characterized by significant social and cultural diversity.
UNDERSTANDING THE RELATIONSHIP BETWEEN GAMBLING RESEARCH AND CULTURE: THE IMPORTANCE OF THEORY A central question for students of gambling relates to the relevance of theory in research. All gambling studies are written from a particular theoretical and often a specific disciplinary perspective.The study of gambling, to put it bluntly, is defined by those who do it. Explicitly or otherwise, everyone involved in the study of gambling uses theory to make sense of the world, to determine which information is important and what relationships exist between different issues, and to guide action. Gambling operators, regulators, service providers, and students use a variety of theories for a variety of purposes. Theories about gambling behavior tend to reflect particular values and priorities and to advance the causes or interests of a particular group. Each theory has a different purpose; researchers often examine the same phenomenon but offer
1 The analysis in this chapter extends ideas previously suggested by Jan McMillen (1996a, b), Virginia McGowan (2004), and Erin Gibbs Van Brunschot (2000).
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very different analyses of it. For example, some people interested in the economics of gambling would like to predict what type of conditions and gambling behavior will lead to industry stability and market growth. Others, interested in social issues or gambling psychology, are likely to be more concerned with particular conditions and behavior that could lead to human suffering associated with excessive gambling. At their core, these two perspectives are incompatible because they have different basic assumptions about the units of analysis, the nature of gambling, and the motivations and behaviors of actors. Yet each can point to information and evidence to support its case, and each is useful in explaining some aspect of contemporary gambling. Theoretical assumptions, often acquired during training in academic disciplines or shaped by the researcher’s personal experience or institutional affiliation, guide research choices about which theories and evidence should be included or excluded in a particular study and which should be highlighted. Different theoretical perspectives and disciplines also favor particular research methodologies. They guide different ways of doing gambling research and the method by which evidence should be obtained. Economists, for example, utilize statistical analysis and theoretical modeling to explore relationships and predict future trends in the gambling industry. Psychologists also are trained to use statistical and quantitative techniques to identify, measure, and understand observable patterns of gambling behavior. Historians, sociologists, and anthropologists are more receptive to qualitative methods and analytical techniques. Furthermore, evidence is often disputed and interpreted in different ways within disciplines. Although this chapter will refer to “schools” and “approaches,” those terms create an exaggerated sense of cohesion and order within various subdivisions of each discipline. Personal values and beliefs that researchers hold about cause-and-effect relations also will influence conclusions and actions.
CONTENDING THEORETICAL PERSPECTIVES The evolution of contemporary gambling research can be explained in terms of a number of broad theoretical approaches or schools of thought (McGowan 2004; McMillen 1996a;Van Brunschot 2000).This chapter will focus on normative theories, behavioral theories, and sociological and comparative perspectives.
NORMATIVE THEORIES Although rarely acknowledged by researchers, normative theory is the central pillar of gambling research and policy. Normative theories are concerned with
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basic moral and philosophical questions that affect social and political life. Moral and normative perspectives on gambling have a long lineage in Western nations, going back at least to the emergence of Protestantism and its particular interpretation of Christian doctrine (Miers 2004; Morton 2003; Singer 1989; Skolnick 2003). Historically, gambling in all Western societies has been morally contested. Public and religious debates over the immorality of gambling per se, ethical issues, the potential for malpractice and corruption, and the effect of gambling on the social order have been a defining characteristic of gambling in all societies. Religious and moral discourses about gambling have provided the strongest and most consistent grounds for normative objections to gambling. Particularly in the United States, some researchers have argued that it remains outside and in opposition to dominant values (Devereux 1968; Rosecrance 1988, pp. 58–63). In contrast, Abt, Smith, and Christiansen (1985) argue that the erosion of cultural norms that originally prohibited gambling permitted contemporary commercialized gambling to enter mainstream America. Historically, the impetus for that policy shift occurred at a time of general cultural reevaluation, when it became clear that economic and state resources and opportunities were limited. Even in Australia and New Zealand, where many forms of popular gambling have had official acceptance since the nineteenth century, gambling has been the source of conflict and public debate (Curtis 2002; McMillen, O’Hara, and Woolley 1999; O’Hara 1988). In Australia, however, the general thrust of legal and moral arguments has been not so much about whether gambling should be permitted or not (as it was in Britain and the United States), but whether legalization would induce gambling by social groups which otherwise might not gamble, which forms of gambling should be permitted and which should be restricted, the control of illegal gambling by attempts to remove criminal influence and corruption, and practical issues such as provision of rational and well-ordered public access to approved forms of popular gambling. Despite the global liberalization and legitimization of commercial gambling in recent decades, moral propositions remain central to analysis. Indeed, normative theories have benefited from a considerable revival of interest since the 1970s in response to debates over the legalization of commercial gambling and growing evidence of problem gambling. In all cultures, contemporary gambling relates to issues central to social order in its material aspects and in the values it evokes.While often grounded in moral philosophy and religion, this approach can be defined more broadly to cover theorizing about gambling of a prescriptive kind, that is to say, normative explanations that are concerned with “what ought to be” in gambling policies and practice, as opposed to simply describing “what is.” Academic advocates of a normative approach claim that consideration of fundamental moral issues and values is not only essential for good gambling research, but also offers a rigorous and factually informed way of addressing the options open to people. From this perspective, substantive research questions have
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been concerned with the moral and philosophical grounds for government policies that legalize and regulate gambling (Campbell and Smith 2003; Collins 2003; Doughney 2002; Miers 2004); the morality of gambling enterprises that profit from the financial losses of others (Livingstone 2001); and debates about ethical and “responsible” industry practice (Black and Ramsay 2003; Hing and McMillen 2002; McMillen and Pitt 2005). When this work is done to exacting standards, it performs a valuable service to gambling research and the public interest. Importantly, this approach recognizes that values and normative propositions in themselves are not facts or logically derivable from facts. Rather, research utilizes the evidence and arguments that come from the different disciplines of social science. Normative perspectives on gambling employ a variety of methods and evidence, including analysis of the empirical premises or internal logic of arguments about gambling; historical comparative analysis of institutions and outcomes; critical assessment of the use of social-scientific and historical evidence; and measuring the conclusions of others against their own or the community’s moral standards and principles.Thus research to solve a normative or moral “problem”— such as the accessibility of legalized gambling or the role of governments and private corporations—is a legitimate and valuable component of rigorous research. As with all research and policy perspectives, normative theories of gambling have developed in various directions. Many gambling researchers, either implicitly or overtly, suggest that the morally correct outcomes are those which seek the greatest benefit for the greatest number in society. For example, even though gambling may result in problems for some people, a common defense of casino development has been made on the grounds that it creates employment and generates economic development and urban renewal (Eadington 1986; McMillen 1991; Rubenstein 1984). Some theories insist that individuals should be free to gamble as long as they do not violate the freedom of others (Collins 2003), while others argue that certain constraints must be placed on human behavior or industry practice to satisfy acceptable standards of public morality or social welfare (Miers 2004; Productivity Commission 1999).There are also those who emphasize a collective moral obligation and universal principles of social justice and equality—think of gambling policies which discriminate in favor of some social groups, such as the legalization of casinos run by Indian tribes in the United States, or against others. Examples of the latter include the nineteenth-century bans on traditional gambling by Asian communities in Australia (McMillen et al. 1999; O’Hara 1988) and on working-class gambling in the United Kingdom (Dixon 1989, 1996; Munting 1996). One of the benefits of this approach is that it provides an opportunity for integrating the empirical study of gambling history, law, or policy, for example, with the analysis of social values. In Western gambling research, the normative elements, or values, most commonly espoused are based on an idealized conception of the
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virtues of liberal democracy—individual freedom and choice, social welfare, and so on.The problem with such efforts to argue the universality of particular norms or principles, however, is that there is little agreement across or within cultures about what constitutes the moral or ethical yardsticks for adjudicating contending claims about gambling. Different sociocultural norms and political values shape the context in which governments determine national gambling policies and citizens make decisions about acceptable gambling behavior (McMillen 1996a). I have previously argued that there are many and diverse contextual factors that influence the particular character of gambling and the way it fits into society, impacts on community attitudes, and shapes government policies: ●
●
●
●
Social values and community attitudes can include religious beliefs, debates over public morality, cultural practices and preferences, and class and gender differences. Economic conditions and trends include such factors as economic booms and slumps, economic restructuring, revenue needs of government, and market competition. Political structures and processes include core public policy objectives, political conflicts, and power relations. Technological development, particularly information technology innovation, alters the gambling industry landscape (McMillen et al. 1999).
An increasing number of cross-cultural comparisons of gambling history and experience have identified distinctive gambling cultures and patterns (Binde 2005; McMillen, Marshall, and Lorenzen 2005; Raylu and Oie 2004; Scull, Butler, and Mutzleburg 2003;Wynne and McCready 2004a, b).The significance of traditional forms of gambling, patterns of engagement with commercial gambling, and helpseeking behavior differ from one cultural group to another. Cultural differences influence variations in gambling behavior and impacts, as do the ways gambling is available and marketed in different locations. However, as this chapter will suggest, in today’s globalized world, cultural norms and values about gambling overlap, intermingle, engage with each other, and shift in response to external pressures.
BEHAVIORAL THEORIES The idea that gambling research should be explicitly concerned with normative debates and what “ought to be” has not met with universal acceptance in academic circles. Two large influential schools of gambling research (clinical psychology and economics) embrace a different epistemology, informed by the philosophy of logical positivism (Popper 1959; Wittgenstein 1961). This perspective insists on separating “facts” from “values,” arguing that research is capable of
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telling objective truths about the world. They insist that observable behavior, at either the individual or the aggregate level, should be the focus of “scientific” analysis and that any explanation of gambling behavior should be susceptible to empirical testing and the production of a series of laws or generalizations. These disciplines condemn normative theories, suggesting that they involve subjective assessments that cannot aspire to the status of hard intellectual or scientific research. The behavioral and rational-choice theories advocated by clinical psychology and economics constitute well-established approaches to gambling research. Indeed, the behavioral approach is dominant in gambling studies, especially in the United States, where other disciplines such as sociology are also dominated by a positivist, behavioral paradigm.The influence of behaviorism in Britain, Europe, Canada, Australia, New Zealand, and other Western nations has also been substantial. Arguably this cross-cultural dominance has occurred because of the profusion and publication of well-funded research and treatment programs in the United States during the 1980s and the tendency for all researchers and clinical practitioners to rely on previously published international research to guide their own activities. Prior to the 1980s, gambling research was embryonic and marginalized, restricted to a few isolated scholars working on the fringe of mainstream academia. The rapid expansion of legalized gambling in the United States after a century of prohibition had immediate, profound, and observable social effects, stimulating research interest and institutional support.At that time, psychiatry and clinical psychology in the United States were experiencing substantial growth in the areas of drug and alcohol dependency. Gambling was conveniently incorporated into those research and treatment programs. Hence, the evolution of the now dominant behavioral paradigm concerned with “pathological” gambling was historically and culturally contingent. The behavioral approach to gambling research concentrates on a deceptively simple question: Why do people behave the way they do? Its distinctive character is its focus on individual behavior and a concern to generate causal and falsifiable theory. Clinical psychology and positivist sociology, like all behaviorism, advocate quantitative research that elaborates a tentative classificatory scheme, conceptualizes a problem-orientated approach; formulates an hypothesis or set of hypotheses, and tests these hypotheses against empirical data to eliminate unsound propositions and to formulate new ones. For example, the majority of research by psychologists has sought to explain so-called pathological or problem gambling behavior. Using conventional methods and behavioral criteria derived from other fields of “abnormal” behavior such as drug and alcohol abuse, empirical research has extensively analyzed the clinical manifestations of individual gamblers’ behavior, causal relationships, and the relative merits of treatment programs.
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Gambling researchers working in this tradition have investigated a wide range of substantive problems in diverse contexts.Through empirical research they argue it is possible to establish relationships between variables that are consistent and generalizable across time and space.The most common cross-national research has consisted of summary reviews of aggregate levels of problem gambling prevalence and predictors of problem gambling in various populations (e.g.,Abbott et al. 2004; Shaffer, Hall, and Vander Bilt 1997;Volberg 2003). Increasingly large population surveys are the standard tool of the trade for studies of gambling participation; and cost-benefit analysis dominates studies of socioeconomic impacts. Application of broadly similar methodologies, theories, and concepts allows approximate comparisons of these gambling phenomena between jurisdictions, social groups, and time periods (Table 18.1). The underlying assumption of positivist psychology and sociology is that behavior can be understood as the result of choices made by self-interested individuals. In a similar way rational-choice theories have guided research on the economics of gambling, from studies of gambling expenditure to decisions by gambling corporations and policymakers (see McMillen 1996a). This approach to gambling research favors a deductive style of theory building but then subjects those theories to test and revision through empirical observation. Using methods similar to conventional microeconomics, for example, experiments in which gamblers choose between different games or betting options (e.g., between random events or the probabilities of certain outcomes) have assisted better understanding of the utilities and preferences that guide gamblers’ decision making. Rather than a distinct paradigm, however, the rational-choice approach is best understood as a set of research techniques and heuristics appropriated from a range of theories and methods—as long as they give serious weight to individual decision making and rational action. This approach to economic gambling research, then, is both a behaviorist explanation and a normative enterprise— although few researchers subject their own assumptions or the implications of their research to rigorous, reflective examination. In the United States, the rational-choice approach has informed development of public choice theory, which has a strong, normative, anti-state emphasis (see McMillen 1996a). This theory has been particularly influential in much economic research conducted or funded by the gambling industry. Consequently, with the globalization of commercial gambling, the perspective has spread beyond North America. A central theme of this perspective on gambling is that intervention by governments to address economic issues or rectify industry failures often creates more problems than it solves. For example, some macroeconomic studies have argued that the combination of government’s legal control over gambling and bureaucratic motives to maximize budgets can result in overprovision at the expense of market stability. Others argue that such “rent seeking” can encourage successful lobbying by organized industry interests for concessions or monopoly
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Table 18.1 Problem Gambling Prevalence: Australia, Canada, and The United States. Jurisdiction and Prevalence Screen Used Australia: using SOGS 5+
United States: using SOGS 5+
Canada: using SOGS 5+
Canada: using CPGI 8+
New South Wales Victoria Queensland Western Australia South Australia Tasmania Australian Capital Territory Northern Territory All Australia Mississippi Nevada North Dakota Oregon Washington State Montana Louisiana Manitoba Alberta Quebec New Brunswick Ontario Prince Edward Island Nova Scotia Saskatchewan British Columbia Saskatchewan Manitoba Alberta New Brunswick Ontario Nova Scotia British Columbia
% of All Adults
Year
2.55 2.14 1.88 0.70 2.45 0.44 2.06
1999 1999 1999 1999 1999 1999 1999
1.89 2.07 2.1 3.5 1.4 0.9 0.5 1.6 1.6 2.3 2.0 2.1 2.2 2.0 2.0 1.1 0.8 1.1 1.2 1.1 1.3 1.4 0.7 0.8 0.4
1999 1999 1996 2000 2000 2000 1998 1998 1998 2001 1998 1999 1996 1996 1999 1996 1994 2002 2002 2001 2002 2001 2001 2003 2002
SOGS 5+, South Oaks Gambling Screen for pathological gambling; CPGI 8+, Canadian Problem Gambling Index for problem gambling. Source: Australian Gaming Council. (2006). A Database on Australia’s Gambling Industries. http://www.austgamingcouncil.org.au Note: Prevalence findings may not be directly comparable due to different survey methodologies, the wide variety of types and availability of gambling, the effect of different times and context, among other things.
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licenses, with consequent erosion of market efficiency or retarded economic growth. The principles and assumptions of behaviorism also have underpinned the majority of gambling impact studies that have emerged over the past two decades. Cost–benefit analysis has been the preferred framework for impact assessment (ACIL Consulting 1999; Eadington and Cornelius 1991; Gerstein et al. 1999; Grinols 2003, 2004; Kindt 1994, 2001; National Institute of Economic and Industry Research 1997; Rule and Sibanyoni 2000; Walker and Barnett 1999). However, cost-benefit analysis is subject to many flaws, not least of which is the lack of adequate, gambling-specific data and an undue reliance on quantifiable economic indicators and econometric models (McMillen and Masterman-Smith 2000). The subordination of relevant social science knowledge and method inevitably results in poor quality social assessment. Further, definitions of costs and benefits are themselves highly contentious and value laden. Different researchers have developed competing mathematical models; and assumptions built into those mathematical models determine the research outcome. Differentiating between economic and social costs is also a somewhat arbitrary and fraught process. This observation equally applies to the quantification of intangible costs and benefits. A range of assumptions about these issues informs efforts to place intangible social impacts on a cost-benefit scorecard along with quantified economic calculations and government statistics. In its extreme forms, some gambling researchers proselytize for positivist research methods and quantification.That approach has little time for the qualitative methods of cultural sociology and anthropology or the interpretative research methods and normative values of history, policy analysis, and law.Yet value judgments often lie behind, or seek justification in, ostensibly value-free claims about causal connections between phenomena such as problem gambling and the use of clinical criteria to assess “normal” behavior or in assumptions that all individuals have the rational capacity, resources, and conditions to determine their own actions, intentions, or outcomes. As I will explain, a recent sociological critique has begun to challenge the principles of methodological individualism and reductive behavioral explanations that presently dominate gambling research.
SOCIOLOGICAL
AND
COMPARATIVE PERSPECTIVES
The difference between a behavioral approach to gambling research and sociocultural perspectives relates to the epistemological foundations of social science research more generally. As noted previously, much gambling research is strongly associated with a behavioral tradition which is inherently positivistic and based on quantitative studies of large numbers of cases.Variables and theories generated from that research, such as the psychologically informed clinical criteria to
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measure problem gambling, are assumed to be universal and generalizable to other contexts. However, the positivist approach has been increasingly questioned by researchers from sociology and cultural anthropology, who argue that the world, and gambling behavior, is socially constructed and complex (McGowan 2004; McMillen 1996a;Van Brunschot 2000).That framework rejects both methodological individualism and structure determinism. Focusing on the individual/society relationship in different cultural and societal settings, it questions whether people are calculating agents engaging in free choice or are constrained (or privileged) by overreaching structures such as the economy, state authority, and sociocultural position. McGowan (2004), for example, has argued for “nuanced, politically engaged and culturally informed gambling research grounded in the social, cultural, historical and everyday context in which gambling is embedded” (p. 15). Societal, cultural, and structural elements all play roles in the nature and prevalence of gambling in particular communities. From this perspective, a concept like problem gambling is not a given; problem gambling will have different meanings in different societies and cultures. It does not exist independently of the experience and meanings that different groups and individuals attach to it. So studies which impose an objective, predefined, and generalized definition on a culturally and socially diverse world will have little utility. Using historical and political-legal inductive methods, institutional analysis also contextualizes gambling policy and behavior to explore and describe relationships between the formal requirements of law and policy and the informal practice of governments (Eadington 1996; Miers 2004; Pierce and Miller 2004; Rose 1986; Smith and Wynne 2000). However, these studies are primarily historical and descriptive; they are not causal theories in the behavioral sense. Nor is there necessarily any connection between institutional analysis and particular values or normative prescriptions for reform. Furthermore, with few exceptions (Dixon 1996; McMillen 2002, 2003a; Munting 1996), institutional gambling studies have not sought to explain cross-national differences and their consequences for gambling outcomes.
PROBLEM GAMBLING: A CASE STUDY
OF
RESEARCH CULTURES
This section considers implications of the current conceptual framework on problem gambling for comparative cross-cultural research and policy formulation. It argues that theories behind the definition and measurement of problem gambling need critical reassessment and radical reform, and makes a plea for overriding the debilitating effect of narrow disciplinary specialization based on Western clinical psychology.
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Despite the large and increasing number of problem gambling prevalence surveys (Table 18.1), we know little more about the nature of problem gambling than we did 20 years ago.While most social scientists agree that problem gambling is a significant community concern, the precise definition of problem gambling and how to conceptualize and measure the phenomenon recently have become the subjects of international research and debate. At the risk of oversimplification, the definition of problem gambling can be categorized into two main streams of thought: the medicalized “disorder” model, which sees problem gambling as a clinical pathology, addiction, or maladaptive behavior (Peele 2003; Petry 2005; Shaffer et al. 1997); and a more broadly defined “public health,” or social, view of problem gambling (Productivity Commission 1999).2 Confusion over terminologies (problem vs. pathological gambling) has further compounded the debate. Until recently, guided by the work of psychiatrists and psychologists in the United States, problem gambling has been generally understood as a pathology, addiction, or psychiatric disorder of individual gamblers, measured by clinical criteria such as those in the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (American Psychiatric Association 1994), and the South Oaks Gambling Screen (SOGS) (Lesieur and Blume 1987). Although originally designed for use in clinical settings, these theoretical constructs and clinical modes of measurement have been widely used in several countries, in both population prevalence studies and clinical situations (e.g.,Abbott and Volberg 1996, 2000; Cox et al. 2005; Lesieur 1994; Orford, Spronston, and Erens 2003; Orford et al. 2003b; Productivity Commission 1999; Shaffer 2003; Shaffer et al. 1997; Stinchfield 2002). Researchers and clinicians continue to develop other screens to assess the extent and degree of problem gambling in a range of settings, but most measures tend to be based on psychometric attributes (Abbott and Volberg 2006). Current problem gambling screens are also characterized by an implicit emphasis on time and money spent gambling. However, different theories or societal conceptions of problem gambling can produce different screening tools, thus generating different empirical findings about the prevalence of the problem. For example, some researchers have challenged the conception of problem gambling as an “addiction,” by which problem gamblers are categorically distinct from other gamblers (see e.g., Allcock 2003; Blaszczynski 1985; Blaszczynski and McConaghy 1989; Dickerson et al. 1997; Ferris and Wynne 1999; McMillen and Wenzel 2006; Walker 1996; Wenzel, McMillen, and Marshall 2004). It is notable that these critics come from a range of
2 Against this general trend, South African national prevalence studies use the Gamblers Anonymous 20 Questions instrument to measure problem gambling (Collins and Barr 2006).
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disciplines, including clinical and social psychology, and are mainly from Canada and Australia. Like many countries, Australia and Canada initially borrowed theories and psychometric models of problem gambling from the United States and Britain. But these theories and solutions have proved to be either inadequate or misfits in the Australian and Canadian environments. Underlying the new public health approach has been a shift toward a different understanding of problem gambling.Theories that regard problem gambling as a social problem view the gambling experience along a continuum where gambling can be more or less harmful and a gambler’s social environment may affect her gambling involvement (Blaszczynski and Nower 2002; Productivity Commission 1999). This redefinition has influenced the way problem gambling prevalence is measured in those countries and policy responses to address the issue. Based on this perception, both Canada and Australia have adopted a new problem gambling screen, the Canadian Problem Gambling Index (CPGI) (Ferris and Wynne 2001; McMillen et al. 2004; Neal, Delfabbro, and O’Neill 2004; Queensland Government Treasury 2002, 2005). The CPGI aims to address the social and environmental contexts of problem gambling and claims to define problem gambling in terms of harm, although it continues to incorporate many psychological aspects of problem gambling.While the CPGI cannot claim to involve a complete paradigm shift, it has been shown to be a more valid measure of gambling problems than the SOGS (McMillen and Wenzel 2006;Wenzel et al. 2004). This attempt to consider contextual factors is not merely a matter of semantics or academic debate; it has profound public policy implications. Defined this way, the overall prevalence of gambling problems in any community is likely to be much greater than if measured by the “disorder” approach. Moreover, within the medicalized understanding of problem gambling as an individual psychopathology, disorder, or “addiction,” the policy solution focuses on “treatment” of the individual gambler. A more sociological, public health approach sees the problems as multifactorial, as a complex combination of environmental factors and gambler behavior (Figure 18.1). Under this definition, therefore, solutions should also be multifaceted within a broad public health framework emphasizing an integrated program of prevention strategies involving industry, government, and community, as well as individual treatment and counseling. Universal problem gambling screens are a convenient and practical framework for thinking about public policy, of course. What is disputed is the generalized psychological account of problem gambling currently in use. A number of qualitative studies have questioned the extent to which a homogenizing research culture and the dominant clinical paradigm of problem gambling allow adequate expression of cultural difference and diversity. Firstly, the current individualistic paradigm which defines problem gambling as deriving from standardized psychological characteristics fails to explain the diverse “real-life” experiences of people who experience gambling-related
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Venue features Industry behavior
Help services
Gambler characteristics and behavior
Accessibility
Problem gambling
Game features
Government behavior Information
Figure 18.1. An epidemiological framework for problem gambling. Source: Productivity Commission. (1999). Australia’s Gambling Industries. Report No. 10.AusInfo, p. 6.9.
problems and the different structural, sociocultural, and ideological contexts in which commercial gambling exists and people live their lives. It also discounts the contributions of disciplines other than psychology and psychiatry. Indeed, these two failings are intimately linked: It is because the theory ignores cultural and social diversity that it is unable to adequately explain the phenomenon. Second, problems related to gambling are not universal and given—they appear to be largely culturally and socially determined. Rather than a problem of individual behavior or psychology, emerging evidence suggests that non–AngloCeltic and Indigenous groups may experience gambling problems in different ways (e.g., Curtis 2002; McGowan 2004; McMillen et al. 2005; Raylu and Oie 2004; Wynne and McCready 2004a, b). For example, family kinship and social responsibilities are central to the value systems of many cultural groups (e.g., Chinese and Arabic ethnic communities, Indigenous Australians, Maoris, American Indians and First Nation people in Canada). For those groups, problem gambling, however it is experienced by the gambler, may be defined chiefly by the effects on social obligations, family relationships, and the community as a whole, rather than by Western priorities of time and money. Relationships and interactions between people deeply affect our norms, our aspirations, our sense of what is important, and our experience of whether gambling is threatening or not.We know this from frequent findings from population surveys and other supporting qualitative evidence. The most explicit evidence
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comes from exploratory studies with cultural groups suggesting that many people define their gambling problems not necessarily by their own behavior, but by the negative effects on people close to them. Policymakers should not develop responses without explicitly considering these effects also. Further, cultural politics associated with gambling profoundly affects relationships between socially diverse, ethnic, and Indigenous groups and the dominant cultural order, as well as definitions and responses to problems which may arise.We should also be alert to dilemmas surrounding the uses of culture and difference in our thinking about gambling and problem gambling. Globalization, cultural exchange, the realities of daily life, and intermarriage have all made it difficult to determine where one “culture” ends and another begins (Kymlicka 1996). These findings provide a challenge to the theory and indiscriminate conclusions of conventional prevalence surveys. Lack of understanding and precision in the definition of problem gambling also continue to impede effective policy development. The challenge for gambling research is to incorporate the insights of interdisciplinary and cross-cultural research while retaining the rigor and general practicality of modern psychology. Some might argue that these issues are the province of other social sciences, that cultural diversity can be studied as a separate strand of gambling research. It would be expedient if this would solve the dilemma, but it does not. To do so would risk widening the theoretical gulf between other disciplines and psychometric approaches to the research and measurement of problem gambling.
THEORETICAL REFLECTIONS This chapter is necessarily a preliminary account of various positions and arguments within cross-cultural gambling research. It can, however, provide the basis for a more sophisticated assessment of the various cross-cultural and disciplinary aspects of gambling research. The discussion has emphasized the importance of two issues: (a) the limitations of different epistemological positions that underpin and divide approaches to gambling research and (b) the neglect by gambling researchers of cross-cultural diversity and constraints. The time is ripe for a systematic assessment of recent developments in gambling research as well as an examination of how this field of study could develop in the future. What emerges from this preliminary analysis is how little core approaches such as behaviorism, particularly psychological and economic analysis, have adapted and changed in light of reflection by practitioners and insights from other perspectives. Undoubtedly, the theoretical and methodological contributions of both disciplines have helped us to tackle important research questions. Historical and institutional analyses also have offered insights that are not available through
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other methods. Few innovations in methods can be observed, however, although some gambling researchers have backed away from some of the dated and extreme claims of early advocates. In particular, the simple facts/values distinction has been moderated, and the claim to derive “scientific” laws and unambiguous generalizations about gambling behavior has been diluted. Following recent criticism and challenge, most gambling researchers now acknowledge that ideas about what is important make sense only in the context of a framework of investigation.A growing number also concede that current concepts and methodologies provide only a broad approximation of problem gambling prevalence in any population (Lattimore and Phillips 2000). Modern psychology and economics are now much more sophisticated in their approach and modest in their conclusions. Yet they remain the dominant force within contemporary gambling research. Gambling research has been characterized by the global spread of a relatively narrow positivist approach rather than conceptual development and debate among various traditions. Such perspectives are unable to confront the research challenges associated with globalization and the spread of commercial gambling to different countries and cultures. In a dynamic and increasingly complex world, it seems illadvised to give priority to forms of knowledge production that make use of quantification and universal concepts. For example, the current expansion of legalized gambling in several Asian countries influenced by Hindu and Confucian philosophy and different conceptions of political economy (Singapore, Japan, South Korea, Macau, and Hong Kong) presents a potential challenge to dominant Western values and research paradigms (Siu, forthcoming). We need gambling research that captures the richness and diversity of human experience, not research that has a narrow commitment to particular techniques or forms of knowledge production. This chapter has argued that contemporary, globalized gambling demands a holistic approach cutting across disciplinary boundaries.There are practical obstacles to this objective in all countries. With the exception of a small number of designated centers in North America, few researchers have the opportunity to work in a vital, multidisciplinary research environment specifically established to provide an institutional home and sustained funding for gambling research. Research dependency on industry or government funding also encourages incrementalism and fragmentation of expertise and infrastructure, rather than sustained and coordinated research programs. To minimize costs or to monitor trends, researchers frequently replicate previous research designs and methodologies; research proposals are often based on work done previously or elsewhere. Such studies can provide useful information, but they rarely follow up with theoretical development or methodological improvement. Another issue at stake is the overwhelming emphasis on applied research at the expense of “pure” academic research. In our enthusiasm for gathering information about gambling, much research displays a heavy bias toward projects that
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describe the current state of play. Government- and industry-funded research projects tend to address pragmatic and strategic objectives.Thus there has been an overreliance on studies of gambling participation, problem gambling, and community attitudes using survey methods and economic models. As technology and globalization begin to affect radical changes to gambling everywhere, the research spotlight in Australia and Britain has begun to move to community impacts, regulation, and governance (Gambling Review Report 2001; Independent Pricing and Regulatory Tribunal 2004; Productivity Commission 1999). But few governments are motivated to sponsor comparative research.Their focus is on gathering relevant information to guide localized development of evidence-based policy. Industry-sponsored studies, in contrast, are often reactive responses to government policies and thus tend to be incremental, narrowly defined, and self-interested. In itself that is not a problem. Applied research is essential, of course, and working collaboratively with industry can be instructive for both parties. But it is not sufficient if gambling research is to realize its potential. We seem to have forgotten that the principal task of research is to progress knowledge to better understand and explain (the how and why questions). Academics have an added public obligation to posit theories and raise normative issues, that is, to debate what ought to be. The emerging global proliferation of interest in gambling research is a cause for celebration and optimism. Such diversity is a strength; examining gambling from different theoretical and national perspectives can offer alternative explanations and insights.Yet gambling research often takes place within small networks of researchers who share the same methods and core assumptions and pay little attention to other schools of analysis. Each subdiscipline also has its own journals, conferences, and networks. Such developments can mean that researchers become trapped in their own discipline and are unaware of important debates and innovations in other fields. No one theory can provide all the answers; it can be only more or less instructive. Gambling researchers can learn from critical and reflective assessment of their own theories and from a comparative assessment of multiple theories when they are brought to bear on a single topic. However, the integration of different approaches or theories into a broader framework to explain aspects of gambling needs to be done with care and sophistication. It is important to use the different concepts in a complementary, rather than a contradictory, way. In similar fashion, the point has been made that disciplinary boundaries create a false antithesis between quantitative and qualitative methods, between the positivist inductive approach of behaviorism and the more flexible and deductive methods of sociological and normative research. Although behaviorism brought methodological sophistication to gambling research, this approach has significant limitations; the cross-cultural study of contemporary gambling should draw on the full range of methods and research techniques. The option of combining
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quantitative and qualitative analysis should not be overlooked; and methods should be chosen according to specific research goals.
THE WAY FORWARD Gambling research seems to be in search of a unifying rationale.That rationale lies in a multitheoretical approach using a plurality of methods. There is so much that could readily be studied and has not been.The prime purposes of problem gambling research, for example, should be to better understand the precise nature of gambling-related problems in different societies and cultures and to discover what helps or hinders unproblematic, safe recreational gambling in those communities. This requires collaboration between psychologists and other social scientists. Interdisciplinary teams could play a lead role in promoting this approach; social psychologists, cultural anthropologists, social workers, and sociologists have much to contribute. While problem gambling and impact studies have been the primary focus of recent research, it would be a mistake to neglect other issues of equal importance. Gambling research, whatever the school of thought, is notable for the absence of cross-cultural comparative studies. Given that globalization is an important feature of the developing gambling environment, it is surprising that researchers have not taken the international dimension more seriously. Industry and policy decisions in individual countries are subject to a number of cross-national constraints that affect the availability and conduct of gambling. The globalization of gambling involves several interrelated dimensions. National interdependencies have accentuated economic considerations; the economic, and thus political, power of transnational gambling corporations has increased their influence in individual countries; the standardization of commercial gambling forms has challenged local community-based gambling (Dickerson 1996; McMillen 1996b); advances in telecommunications (computerization, global satellite television, the Internet) have created opportunities for cross-border gambling and made gambling accessible to people everywhere (McMillen 2000, 2003a, b; Rose and Owens 2005); and cross-border gambling and the influence of supranational organizations such as the European Union have limited national autonomy (McMillen 2000). Globalization of gambling presents considerable problems, challenges, and opportunities for gambling research. At the same time, it emphasizes the necessity of comparative analysis. In today’s world, gambling researchers need to broaden their concerns to examine in what way and to what extent globalization processes affect gambling institutions, behavior, and policy in particular countries and cultures. That agenda requires a sound research design which acknowledges the strengths and weaknesses of different types of comparison. In social science
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generally, the comparative method is developing beyond an overemphasis on institutional analysis and generalizations from quantitative studies that are methodologically sophisticated but epistemologically naive. In conclusion, the framework for understanding cross-cultural gambling research proposed in this chapter highlights theoretical context and the importance of different schools of thought. Even this brief account indicates the particularity of different epistemological positions. It encourages gambling research to break from narrow disciplinary boundaries and draw on a range of perspectives to provide more complete explanations of specific problems.Twenty years ago the development of prevalence screens such as the SOGS revolutionized gambling research. There are encouraging signs that a similar paradigm shift is under way with the insights of other disciplines and research cultures at last being combined with Western psychological data. This will lead to better research, better theory, and better policies.
GLOSSARY Comparative analysis comparative analysis enables examination of a number of cases to achieve analytical, not statistical, generalizations. For example, comparisons can be made within a single nation or community (e.g., comparing different social groups), between different countries (global comparisons), or across different time periods (historical comparison). The comparative method allows valid generalizations provided there is a theoretical statement or analytical framework (e.g., based on shared concepts or elements) against which to compare the case studies. Comparisons between cases or case studies are often used to approach the same question—for example, to compare patterns of gambling activity in different places. Cross-cultural studies cross-cultural research focuses on factors that differentiate and distinguish cultural groups and phenomena under study. It begins from the premise that social behavior, institutions, and practices must be understood as embedded in particular cultural and historical contexts—that is, they are socially constructed. From this perspective the meanings and experience of gambling often vary from one society to another, as well as across historical periods and between cultural groups within each society. This approach to the analysis of gambling cultures, for example, challenges ethnocentric assumptions that certain ways of organizing, classifying, and engaging in gambling are more “normal” than others, or that particular research cultures are more correct and “logical” than others. Cultural diversity this term refers to fundamental differences that exist among various nations, populations, or subpopulations. It assumes that human
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society cannot be explained simply by nature, biology, or geography, that there are distinctive ways of life shaped by different beliefs, norms/values, traditions, customs, institutions, and practices. Thus contemporary society comprises multiple cultures; it is not a universal phenomenon.The diversity of cultures can exist at all levels of existence: nation, social group, language, religion, social relations, folklore, material culture, organization, economics, politics . . . and gambling. Globalization globalization is often defined broadly as networks of interdependence that span across nations.As such it incorporates a host of profound changes and patterns of integration in the world: growing political linkages, economic interdependence, new transnational forms of transport and telecommunications media, exposure to ideas and models from other countries, and homogenization of social life through global standards, products, and culture. Conceived this way, globalization is an umbrella term covering a wide variety of linkages among countries.A narrower conceptualization of globalization is used to focus on one analytical issue; for example, the process of economic integration or the spread of commercial gambling around the world. Public health public health refers to government policies and programs designed to maintain or improve the health and well-being of all citizens and communities. A public health approach to problem gambling, for example, would provide publicly funded programs to prevent the development of gamblingrelated problems and to support people with problems. However, different conceptions of the “problem” and of appropriate interventions give rise to different public health policies. Some countries (e.g., the United States) tend to focus on individualized problems and solutions, such as treatment and psychological counseling for gamblers. Other countries (e.g., Australia) have adopted a public health approach which views problem gambling as a complex issue requiring multiple solutions that extend beyond individual gamblers to include “at-risk” families and communities.
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CHAPTER 19
Internet Gambling: Past, Present, and Future Robert T. Wood
Robert J. Williams
Department of Sociology University of Lethbridge Lethbridge, Alberta, Canada
School of Health Sciences University of Lethbridge Lethbridge, Alberta, Canada
Introduction History of Internet Gambling Current Situation Prevalence of Internet Gambling The Comparative Legality of Internet Gambling United Kingdom Other European Countries Australia New Zealand Canada United States Demographic Characteristics of Internet Gamblers Game-Play Patterns Why Do People Gamble on the Internet? Problems with Internet Gambling Unfair or Illegal Business Practices Unfair or Illegal Player Practices Internet Gambling by Prohibited Groups Problem Gambling Lack of Responsible Gambling Practices Future of Internet Gambling Researching Internet Gambling
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INTRODUCTION Beginning in the early to mid-1990s, as Internet access expanded into workplaces and private residences, gamblers in Western societies were introduced to a new realm of Internet-based gambling opportunities. Indeed, all of the traditional forms of gambling, widely available in land-based venues, soon appeared in electronic format over the Internet, and have since been easily accessible to any person with an Internet connection and means of electronically transferring money. Virtually mediated casino games, slot machines, bingos, lotteries, sports wagering, horse race betting, and skill games are all now readily accessible, with new forms of gambling and new sites being added each year. Moreover, the number of peripheral or supporting sites is also growing, including gambling website portals, information pages containing odds and payout figures, and pages for sports handicappers. The proportion of people who actually gamble online remains relatively low but is growing as more jurisdictions regulate and legalize Internet gambling opportunities and as Internet gambling becomes more socially acceptable.While Internet gambling is becoming more and more a normalized activity, the expansion of Internet gambling is outpacing people’s understanding of the phenomenon, as well as many of the laws that are supposed to regulate gambling activity. Consequently, we find ourselves in a situation where we have insufficient knowledge of Internet gambling, including the characteristics of gamblers, the social and psychological dynamics of Internet gambling behavior, the potential link between Internet gambling and problem gambling, and the most appropriate regulatory and legislative stance to take with respect to Internet gambling. In light of continued and rapid expansion, and of existing ambiguities and gaps in current knowledge, this chapter seeks to highlight the major trends and issues associated with Internet gambling today.This is not meant to offer a definitive answer to all questions and issues that are emerging from the current state of Internet gambling. Instead, recognizing that much more research is needed in most areas, this chapter merely seeks to highlight crucial domains of knowledge and research on Internet gambling, as well as any resulting implications.
HISTORY OF INTERNET GAMBLING Three important developments set the stage for the emergence of Internet gambling in the 1990s.The first was the creation of a “free trade zone” in the small Caribbean nation of Antigua and Barbuda in 1994, which effectively allowed U.S. bookmakers (based in Antigua) to accept bets by phone on horse racing and sports, theoretically immune from U.S. gambling prohibition laws. The second was the introduction of gambling software by Microgaming in 1994–1995. The third was
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the encryption of communication protocols by CryptoLogic in 1995 that allowed secure online monetary transactions. In 1995, a few sites (e.g., Gaming Club) began offering casino games online without real money being wagered. Sports books (e.g., Intertops Casino and Sports Book) also started posting odds online as well as toll-free numbers to call to place bets. In 1996, InterCasino, based in Antigua, became the first online casino to accept a real money wager online (4 Online Gambling.com 2006; Schwartz 2006).1 It did not take long for other Caribbean islands (e.g., Turks and Caicos, Netherland Antilles) and other online sportsbooks and casinos to follow suit. To better ensure legal protection, most online gambling companies chose to base their operations in small Caribbean or European jurisdictions with permissive gambling legislation. Prosecution of some prominent online companies with connections to countries having clear online gambling prohibition reinforced this trend (e.g., Starnet Communication in Canada in 1999). By the end of 1996 about 15 online sites accepted wagers. A year later, over 200 sites existed. By 1999 they had increased to 650. By 2002 there were approximately 1800 sites (Schwartz 2006). Revenues had similar increases. Hammer (2001) estimated that Internet gambling generated $2.2 billion in 2000, compared with only $300 million several years earlier. For the most part, this expansion was initiated by new companies not associated with any land-based gambling venues. In 1999, Lasseters in Alice Springs, Northern Territory, Australia, became the first land-based casino to go online. Tentative forays were made by a few other land-based companies (e.g., MGM Mirage, Aspinalls, Kerzner International). However, because of the gray legal status of online gambling, most established companies opted not to do anything that might jeopardize their licenses. The initial online sites were exclusively sports/racing books and online casinos, with sports betting accounting for more than half of Internet gambling revenues in 2001 (American Gaming Association 2006a). The first online poker room (planetpoker.com) appeared in 1998. A major expansion of online poker began in 2003 when the World Series of Poker became a popular televised show in the United States. Many of the entrants in the World Series qualified via online poker tournaments, and both the 2003 and 2004 champions were online poker players. In 2003, the estimated revenue from online poker was $365 million, which increased to approximately $2.4 billion in 2006 (Christiansen Capital Advisors 2005). Another type of gambling that was later introduced was online lotteries, where people could either purchase tickets for land-based lotteries or play virtual 1 The first online stock trade was facilitated by E*Trade Financial in 1983 (E*Trade 2006). However, online trading continued to be uncommon until the Internet became more widely accessible to the general public and some of the major companies began offering online trading (Charles Schwab in 1996).
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lotteries or instant win tickets. Online bingo games were also added, partly to attract more females to online gambling (Henderson 2005). The most recent additions have been “skill game sites” and “betting exchanges.” Skill game sites offer a wide range of word games, puzzle games, strategy games (e.g., mahjong, chess), sports games (e.g., billiards, mini-golf), card games (e.g., solitaire), arcade games (e.g., carnival shootout), video games, and trivia games. Most typically, players pay a fee to enter a tournament, with the winner(s) collecting the majority of the entrance fees. Sometimes the contest can be with another specific individual and sometimes it can be against your own previous “high score.” Betting exchanges are sites that create a marketplace for bettors whereby they post potential wagers on certain events (with accompanying odds and stake size) in the hope that someone will take them up on their offer(s). These wagers are primarily on sporting and horse racing events but also include wagers on politics and reality television events, among other phenomena.
CURRENT SITUATION In October 2006 there were over 2500 Internet gambling websites owned by 465 different companies listed at www.online.casinocity.com.2 A few of these companies are publicly traded on the London Stock Exchange, but most are privately owned.The online sites consist of 1083 online casinos, 592 sports and racebooks, 532 poker rooms, 224 online bingos, 49 skill game sites, 30 betting exchanges, 25 lottery sites, and 17 backgammon sites (Casino City 2006).A unique aspect of online gambling is the availability of “free play” at most of these sites, ostensibly to familiarize people with the game and to improve their skill. However, research suggests that a more nefarious purpose is sometimes to deceive players into thinking that the odds of winning are better than they actually are (Sévigny et al. 2005). These online sites operate in 42 different jurisdictions, with the main ones being Costa Rica (382 sites), Antigua and Barbuda (366 sites), Kahnawake Mohawk Territory in Quebec (344 sites), Netherland Antilles (Curacao) (334 sites), Gibraltar (170 sites), United Kingdom (103 sites), Malta (87 sites), and Belize (55 sites).The jurisdictions with the highest volume of online transactions are, in order, the United Kingdom, Kahnawake Mohawk Territory, Antigua and Barbuda, Gibraltar, Costa Rica, Netherland Antilles, Malta, and the United States
2 The high number of sites relative to owners is due to (a) owners wanting to create a larger presence on the Web and (b) the tendency of some of the larger companies to build sites which are then sold to other companies to run. The first company still retains ownership of the site and takes a percentage of the profits.
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(Casino City 2006). Many companies have developed their own gambling software. Many others use commercial software, with the most popular ones being from Microgaming (161 sites), World Gaming (134 sites), Playtech (119 sites), Tribeca Tables (117 sites), and 24hPoker (92 sites) (Casino City 2006). Some sites require software downloads to play, while others allow playing on instant online software such as Java. Revenues are very difficult to determine but were estimated at about $12 billion in 2005 (Christiansen Capital Advisors 2005).This represents perhaps 4–6% of the worldwide gambling market (Bowsher 2006; London Stock Exchange 2005). There have also been widely different estimates of the proportion of the market accounted for by different types of gambling. Consistent with these estimates, however, is the fact that sports and horse race betting, online casinos, and poker account for about 95% of the total share (London Stock Exchange 2005; RSeConsulting 2006).There are no reliable figures on market share of revenues by country.The United States is believed to be the single largest market, at 26%.The Asia-Pacific region is estimated to be 54%, and Europe is estimated to be 20% (RSeConsulting 2006).
PREVALENCE OF INTERNET GAMBLING The actual number of people who gamble online has been estimated to be between 14 and 23 million, with between 28% and 35% (4 million) of these being U.S. citizens, 49% (7 million) being from the Asia-Pacific region, and 23% (3.3 million) from Europe (with the United Kingdom accounting for one-third) (American Gaming Association 2006b; RSeConsulting 2006). The prevalence of online gambling in the general population tends to be quite low, but growing. The 1999 British Gambling Prevalence Study found that 0.2% of the population had gambled online (Sproston, Erens, and Orford 2000). In 2006 it was estimated that 2% of U.K. adults had gambled online in the past month (Gambling Commission 2006). In 2005, 3.5% of Internet users (ages 18–55) in the Netherlands (who are roughly 60% of the population) stated that they had participated in online gambling (Motivaction International 2005).This was a reduction from 5.3% in the previous year. In New Zealand in 2000, approximately 1.3% of adults had gambled on the Internet in the past year (Amey 2001).A national study of gambling behavior in the United States in 2000 found a past-year Internet gambling prevalence of 0.3% (Welte et al. 2002). More recent surveys of the general U.S. adult population in 2006 have found rates of 3% (Rasmussen Reports 2006) and 4% (American Gaming Association 2006c). Provincial studies in Canada from 1999 to 2003 found past-year Internet gambling prevalence to be between 0.2% and 2.0%, with an average of 0.6% (Canadian Partnership for Responsible
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Gambling 2004).3 The most recent Canada-wide study has found rates of 2.3–3.6%, with the higher estimate including high-risk stocks and day trading, and the lower estimate excluding these (Wood and Williams 2006).
THE COMPARATIVE LEGALITY
OF INTERNET
GAMBLING
The legality of Internet gambling is quite complex, varying from country to country, from jurisdiction to jurisdiction within countries, and from year to year (Cabot 2006; Rose and Owens 2005). There are many countries where no laws exist with respect to gambling or online gambling. Other countries have legalized online gambling, permitting both residents and nonresidents to gamble on all forms of online gambling both within and outside the country. Almost all online sites are currently based in one of these two types of countries. Some countries have legislation making certain online forms legal (most typically lotteries, sports/racebooks, and “skill games”) and other forms illegal (most typically casino games). Some countries prohibit nonresidents from accessing jurisdiction-based online gambling sites (e.g., Finland, Canadian provinces). Some go further in also prohibiting residents from accessing online gambling sites located outside the country (e.g., Netherlands). Other countries prohibit residents from accessing jurisdiction-based online sites (e.g., Australia prohibits Australians from accessing their online casino site). Several Muslim countries prohibit all forms of gambling, including online gambling (e.g., Pakistan, Saudi Arabia). United Kingdom In the United Kingdom, Internet gambling is regulated by the national Gambling Commission. Online sports betting, horse race betting, betting exchanges, and games of skill can be legally operated in the United Kingdom and played by U.K. residents. It is currently illegal to establish and operate Internet casino, bingo, or gaming-machine sites because current legislation (the 1968 Gaming Act and the 1976 Lotteries and Amusements Act) dictates that a customer must be present in the room in which gaming takes place. However, U.K. citizens may place bets at offshore Internet casinos without breaking any British laws (Gambling Commission 2005). Lotteries may not be conducted online, but the
3 This excludes the Centre for Addiction and Mental Health (CAMH) surveys of 2000–2003 which found rates of 3–7% for Ontario adults. The CAMH studies are flawed due to not reading a “never gambled on the Internet” option when asking, “In the past 12 months how often did you bet money over the Internet?” (i.e., people had to go out of their way to indicate “never,” because it was not a provided option).
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purchase of traditional lottery tickets may be aided by Internet and email technologies provided there remains some action by a human operator. A new national Gambling Act takes effect in September 2007. At that time all forms of Internet gambling may potentially operate from U.K. soil, conditional upon regulation and licensing. Other European Countries Online lottery ticket sales are permitted in Sweden, Germany, and Liechtenstein. Finland allows online horse race betting.Austria permits online lottery sales, casino games, skill games, and bookmaking. Holland Casinos was recently granted a license to conduct online gambling in the Netherlands. It is unlawful to facilitate participation in “foreign games of chance” in the Czech Republic, Denmark, Germany, Hungary, the Netherlands, Slovakia, and Sweden. Cyprus, Greece, and Portugal explicitly prohibit the granting of online gambling licenses. Australia Online gambling in Australia is regulated at the federal level by the Interactive Gambling Act of 2001. However, the different Australian states have the ability to formulate state-specific gambling policies and legislation (Woolley 2003). Federal legislation permits online sports and racebooks, poker rooms, and skill game sites to be legally operated in Australia and to be played by Australian residents. Online lotteries are permitted except for keno-style games, scratch tickets, and instant lotteries. Australia does not permit Australian residents to gamble at its government-licensed online casino (Lasseters). New Zealand The New Zealand government has granted exclusive operating rights for online racebooks and sportsbooks to the Racing Board, formerly known as the Totalisator Agency Board (TAB). Online lotteries may be run by the Lotteries Commission. It is illegal to organize, manage, or promote any other source of online gambling in New Zealand. Canada Canadian federal law has been interpreted by provincial governments as allowing them to legally operate Internet gambling websites as long as the patronage is restricted to residents within that province (Jepson 2000; Kelley,Todosichuk, and Azmier 2001; Shap 2002).Thus, the provincially owned gambling operators in
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the Atlantic provinces (Atlantic Lottery Corporation, ALC) and British Columbia (British Columbia Lottery Corporation, BCLC) provide online sports betting, online “interactive” lotteries, and the online sale of land-based lottery tickets to residents of their respective provinces. ALC began providing online services in August 2004 and BCLC in October 2004. Horse racing in Canada is regulated by the Canadian Pari-Mutuel Agency under the federal Department of Agriculture. In 2003 the federal agriculture minister made a rule change permitting horse racing bets to be placed not just by telephone but by “any telecommunication device.” As a consequence, in January 2004,Woodbine Entertainment, a Torontobased horse racing track operator, began accepting online bets from across Canada. The legality of Canadians placing bets with online sites outside of their province is unclear. Thus far, no Canadian resident has been prosecuted for such activity. Certain Aboriginal groups (e.g., most notably the Kahnawake First Nation in Quebec) have taken the position that they are sovereign nations able to enact their own gambling legislation. Although the Quebec government has indicated that they consider this illegal, there has been no prosecution of these operations. Kahnawake has been hosting sites since 1999–2000 and is now one of the world’s largest online gambling hosts, with 344 sites currently offering all forms of online gambling. Other First Nation groups have indicated their intent to also issue online gambling licenses (Six Nations in Ontario, Alexander First Nation in Alberta). United States Most online gambling is prohibited in the United States by means of federal and state laws. In October 2006 the federal Internet Gambling Prohibition and Enforcement Act came into effect, which makes it illegal for all “financial transaction providers” to make fund transfers to online sites that take bets or wagers on “outcomes of a contest, sports event or a game subject to chance.” It also makes it illegal for Internet gambling providers to accept money transfers from potential U.S. online gamblers. Although many analysts would contend that non-U.S.-based companies are not subject to this law, there has been previous successful prosecution of non-U.S.-based sites under the federal Wire Act, using the contention that Internet gambling occurs in both the jurisdiction that takes the bet and the jurisdiction that issues the bet. As a consequence, a significant number of online gambling sites have stopped taking bets from U.S. citizens (about 25% in November 2006) (Casino City 2006). Major gambling software companies (e.g., CryptoLogic, Boss Media) have also announced that their software platforms can no longer be used to provide gambling services to U.S. residents (Vallerius 2006). This new law is not directed at individual bettors, and there have been only rare cases of prosecution of a U.S. citizen for placing an Internet bet (Rose and Owens
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2005). There is speculation that U.S. players will begin using non-U.S. bank accounts for betting or will make more use of offshore financial transaction intermediaries to place bets or transfer money (e.g., NETeller, FirePay, Citadel) (American Gaming Association 2006a). This new legislation exempts online intrastate sales of lottery tickets (via terminals in retail outlets), interstate horse race betting, and other types of intrastate online gambling, as long as the individual state does not prohibit it (several states have explicitly prohibited Internet gambling). California permits online wagers on horse racing and also accepts wagers from other nonprohibited states. It is unclear whether this legislation applies to “skill games.” There are currently 29 online skill gambling sites operating within the United States that have opted to continue taking bets from other states that do not specifically prohibit online gambling.
DEMOGRAPHIC CHARACTERISTICS OF INTERNET GAMBLERS While a number of studies have documented the characteristics and correlates of gambling in land-based venues, there has been far less research on the characteristics of people who gamble on the Internet. Recent research is beginning to shed light on this issue. Studies of Internet gambling conducted in Australia in 2001 and 2002 suggest that rates of Internet gambling are higher among men, younger adults, people with professional or managerial occupations, and people who earn above average incomes (McMillen and Woolley 2003; Woolley 2003). Some suggest that this is indicative of a “digital divide,” with Internet gambling occurring at higher rates among skilled professionals, whose jobs rely upon familiarity with and competent use of the Internet (Howard, Rainie, and Jones 2001; Woolley 2003).These Australian studies, however, tend to focus on sports betting, which makes it difficult to generalize these demographic characteristics to all Internet gamblers. Higher prevalence rates have been found among youth. A study in Nova Scotia, Canada, found that 6% of 15- to 17-year-olds in the province reported playing poker online for money in 2006 (Gillis 2006). LaBrie et al. 2003 obtained a rate of 1.9% among U.S. college students. A recent study found that 9% of Montreal, Quebec, high school students reported having gambled for money on the Internet and 6% of a sample of Canadian and U.S. college and university students reporting having done so (Derevensky, Gupta, and McBride 2006). An online study of 552 Internet gamblers commissioned by the American Gaming Association in 2006 found that 68% were male, 70% were under 40 years old, 61% had at least a college degree, 41% earned more than $75,000 a year, almost
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all of them used the Internet for other activities, and 70% had begun gambling online only in the past two years (American Gaming Association 2006c). In 2004 the present authors recruited a sample of 1920 Internet gamblers via an advertisement at an online gambling portal (Wood and Williams, in press). The findings of this study replicate the above studies in many respects. Slightly more than half (56%) of the Internet gamblers were male. Most were U.S. citizens (87%), and another 10% were Canadian. The average age was 34 years, just over 60% had at least some postsecondary education, and 65% reported being comfortable conducting business and purchase transactions over the Internet. Interestingly, 12.3% of the sample described themselves as “disabled,” thereby implying that issues of access and physical environment might play a role in prompting at least some people to gamble online, as opposed to gambling in land-based venues.
GAME-PLAY PATTERNS One of the more underresearched issues is the actual game-play patterns of Internet gamblers, including frequency, duration, and preferred type of play. Given the characteristics of Internet gamblers and the immersive and convenient nature of the Internet gambling interface, it is reasonable to expect that Internet gambling offers a fairly unique range of experiences and patterns that are worthy of investigation. Woolley (2003) surveyed three samples of Internet gamblers and found that roughly half of them reported placing bets online at least on a weekly basis. He also found that between 44.1% and 65.5% reported routinely using more than one site for Internet gambling.Wood and Williams (in press) found that Internet gamblers, on average, reported gambling online a total of 5 hours per week, although 4% reported gambling online in excess of 20 hours per week. When asked about the location of the computer they used most often to gamble online, 86.6% of the respondents claimed they most often used a computer located in their own home. Only 4.3% claimed that their primary gaming computer was located in their workplace. When asked more specifically about workplace gambling, a total of 16.3% indicated that they gambled from the workplace at least occasionally. When asked which single game they played most often, respondents identified blackjack (28.3%), slot machines (25.2%), video poker (15.7%), bingo (12.1%), and sports betting (6.2%). In the American Gaming Association (2006c) study, the casino games people usually played online were blackjack (78%), video poker (65%), slot machines (60%), roulette (37%), craps (29%), pai gow poker or Let it Ride (24%), and baccarat (18%).Texas Hold ‘em was by far the most popular type of poker game.
Internet Gambling: Past, Present, and Future
WHY DO PEOPLE GAMBLE
501
ON THE INTERNET?
Internet gambling has some attributes that clearly distinguish it from landbased gambling.The most obvious one is much greater convenience, as people can gamble any time of the day from their home. Another one is that online venues tend to offer better payout rates, due to very low overheads and because competition for patronage is much stiffer, as people can switch venues in the few seconds it takes to click a mouse.A third one is that certain forms of online gambling (e.g., betting exchanges) do not have any land-based equivalent. Griffiths (2003, 2006) has also identified multilingual service, faster play speed, and the ability to pretend to be the opposite sex as significant advantages. Females pretend to be the opposite sex in order to be taken more seriously and for a greater sense of security, and males pretend to be females, supposedly to give them a tactical advantage. In the American Gaming Association (2006c) study, the main reasons respondents actually reported for betting online were convenience (48%), fun/exciting/entertaining (24%), more comfortable, don’t have to drive (24%), able to win money (9%), and preference for the anonymity and privacy (6%).“To relieve boredom” and “for excitement” were the most common reasons cited by youths (ages 12–24) in the Derevensky et al. (2006) study. In the Wood and Williams (in press) study, the primary reasons respondents gave for gambling on the Internet were: (1) the relative convenience, comfort, and ease of Internet gambling; (2) an aversion to the atmosphere and clientele of land-based venues; (3) a preference for the pace and nature of online game-play; and (4) the potential for higher wins and lower overall expenditures (Wood,Williams, and Lawton, submitted for publication).
PROBLEMS WITH INTERNET GAMBLING UNFAIR
OR ILLEGAL
BUSINESS PRACTICES
Online gambling sites are not as well regulated as land-based venues. There have been many cases where online sites have apparently not paid winnings, have cheated players with unfair games, or have absconded with player deposits (Games and Casino 2006). The ability of players or governments to seek recourse is limited because of the foreign jurisdiction of these sites and/or lax regulatory enforcement within these jurisdictions. Also, as mentioned earlier, another deceptive practice is providing favorable odds on the “free play” sections of online gambling sites to encourage people to play for real money (Sévigny et al. 2005). It is unclear how widespread these problems currently are. Security concerns (51%) and legitimacy (49%) were the main reasons for not playing online in an
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Ipsos Reid (2005) study of 2167 U.S. poker players. Even among people who play online, 55% believe that online casinos cheat players (American Gaming Association 2006c). The online gambling industry itself has made several attempts to create industry standards.The latest attempt is E-Commerce and Online Gaming Regulation and Assurance (eCOGRA). This is an industry organization launched in 2003 that certifies online sites as having prompt payments, safe storage of information, random games, honest advertising, and responsible gambling practices. Currently, eCOGRA has certified only about 100 sites (eCOGRA 2006). It should also be noted that prior organizations have attempted to ensure player protection and have failed to gain widespread acceptance.
UNFAIR
OR ILLEGAL
PLAYER PRACTICES
Interestingly, the American Gaming Association (2006c) survey also found that 46% of online gamblers believed that players have found ways to cheat. One way is by collusion between online poker players at the same table. Another technique employs computer programs using optimal play (“poker bots”) against other players (e.g., Brunker 2004). Hackers have been known to successfully alter online sites to pay wins (RSeconsulting 2006). However, industry representatives usually report their greatest problem to be individuals and criminal organizations demanding payments so as not to disrupt the site’s online service. Reports indicate that online sites pay out millions of dollars in extortion money each year (Kshetri 2005; RSeconsulting 2006). The lack of clear legislation in many countries about these “denial-of-service” attacks complicates this problem. An additional serious concern is money laundering. There are several ways in which this can be done, either by the player or by the site itself (RSeconsulting 2006).The magnitude of this problem is unknown, but the potential is real, especially considering the lax regulatory structure of most jurisdictions where online gambling occurs.
INTERNET GAMBLING
BY
PROHIBITED GROUPS
Online sites are typically required to bar certain people. These include employees of the site, underage gamblers (most sites ban individuals younger than 18), and people who have banned themselves from playing on the site. The sites’ ability to accomplish this, however, is questionable. It would seem to be a relatively easy matter for employees or banned individuals to set up accounts under different names, although cross-referencing against address and banking details are potentially useful deterrents.
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Underage gambling is of particular concern considering that Internet use tends to be highest among teenagers, who commonly access the free play sections of online gambling sites. For example, approximately 50% of high school and college/university students in a North American sample reported having played on free play online gambling sites (Derevensky et al. 2006). There appears to be reason for concern in light of findings from several studies. A study in 2004 by NCH–Children’s Charity (formerly National Children’s Home), GamCare, and CitizenCard in the United Kingdom found that a 16-year-old with a debit card was able to place bets online on 30 out of 37 sites tested (NCH 2004).A European survey found that 17% of visitors to online gambling sites were aged 17 or under (NetValue 2002).A study in Nova Scotia, Canada, found that 6% of 15- to 17-yearolds in the province reported playing poker online for money in 2006 (Gillis 2006). Derevensky et al. (2006) found that 9% of a sample of Montreal, Quebec, high school students reported having gambled for money on the Internet. It seems clear that underage online gambling is a problem, although its magnitude is uncertain.The present ability of online sites to prevent this appears limited, due to the wide legal availability of credit and debit cards to underage youth, and the fact that banks and credit reference agencies rarely provide reliable details on a person’s age to a third party.Addressing this problem is likely going to require greater cooperation from financial institutions plus efforts by parents to block Internet gambling sites, through either normal browser content controls or specialized software (e.g., “BetStopper”; Canada News Wire 2006).
PROBLEM GAMBLING The ease of access to Internet gambling, coupled with the relative comfort enjoyed by the Internet gambler, may lead to a higher frequency of play compared with a land-based venue (Griffiths and Wood 2000). Furthermore, some researchers argue that the immersive, visual, and aural qualities of the Internet gambling interface may cause Internet gamblers to devote more time to online gambling activity than they might otherwise devote in a land-based venue (Griffiths 1996; Shaffer 1996).Together, more frequent and longer play is likely to create greater gambling losses. In an immediate sense, these losses may be felt less acutely, if, as some observers speculate, the psychological value of electronic cash is less than that of “real” cash (Griffiths and Wood 2000). An exacerbating factor is the ability of online gamblers to play under the acute influence of drugs or alcohol, something that is more difficult to do in a land-based venue and that has a well-established link to excessive and disinhibited play (Baron and Dickerson 1999; Ellery, Stewart, and Loba 2005; Kyngdon and Dickerson 1999). There is, in fact, good evidence that online gamblers are significantly more likely to be problem gamblers. As a reference point, 14 countries have conducted
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Research and Measurement Issues in Gambling Studies
national prevalence surveys of problem gambling between 1998 and 2005. Pastyear prevalence ranges from 1.1% to 5.4%, with an average of 2.5% (Alberta Gaming Research Institute 2006).4 By comparison, in an online study of 422 selfselected online poker players, 18% of the sample was classified as problem gamblers using criteria from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (Griffiths,Wood, and Parke 2006). In a study of disordered gambling among university students, Ladd and Petry (2002) found that the mean South Oaks Gambling Screen (SOGS) score (7.8) among university Internet gamblers was over four times higher than the mean SOGS score (1.8) for non-Internet gamblers. A 2005 study of 12,717 Dutch Internet users between ages 18 and 55 found that 14% of online gamblers were “at risk” of problem behavior but that no one actually evidenced problematic behavior (Motivaction International 2005).5 Among an online sample of 1920 Internet gamblers, the present authors found an astounding 23% to be moderate problem gamblers on the Canadian Problem Gambling Index and another 20% to be severe problem gamblers (Wood and Williams, in press). These researchers used logistic regression to identify characteristics differentiating problem from nonproblem gamblers and found the former to spend more time gambling, to be male, and to be more likely of South or East Asian ancestry or African ancestry. Age, marital status, employment status, religion, and education were not predictive of problem gambling status. While there appears to be a relationship between online gambling and problem gambling, the causal connection has not been established. There is a good argument that Internet gambling may provide a unique interface and an overall experience that facilitates the development of gambling problems (Griffiths 1999, 2003; Griffiths and Parke 2002; Griffiths and Wood 2000; LaRose, Mastro, and Eastin 2001). However, it is also quite plausible that problem gamblers gravitate to this new and more convenient form of gambling. This relationship between problem gambling and online gambling creates a potential ethical problem for jurisdictions contemplating legalization. Research has shown that problem gamblers contribute approximately one-third of revenue from all types of gambling (Productivity Commission 1999;Williams and Wood 2004a, b). It would appear that this is likely to be even higher for online gambling.
4 This would be higher if just looking at the prevalence among gamblers, which typically represent two-thirds to three-quarters of the population. 5 It is not clear how “at-risk” and “problem behaviors” were defined.
Internet Gambling: Past, Present, and Future
LACK
OF
505
RESPONSIBLE GAMBLING PRACTICES
The preceding discussion highlights the general lack of responsible gambling practices and safeguards that are more typically found in land-based venues.A study of “social responsibility” practices among U.K. Internet gambling providers found that only half of the 30 websites investigated made meaningful efforts to verify age of majority, and only 7 made explicit reference to the risks of uncontrolled gambling (Smeaton and Griffiths 2004). A recent review of 60 popular Internet poker, casino, and sports betting sites revealed wide variations in the extent and types of player protection strategies. At one end, some sites simply provided a statement concerning age limits or a link to a Gamblers Anonymous site. At the other end, there were sites that provided self-exclusion options, an on-site counselor, and opportunities for setting time, money, and loss limits (Wiebe 2006). Some of this variation has to do with jurisdictional regulatory differences. Some jurisdictions require that online players be allowed to bar themselves from the site, to set loss or betting limits, or put limits on the size of the deposits they can place into their account. Some jurisidictions (e.g., Alderney in the Channel Islands) allow exclusion of a gambler in response to a petition from a family member (American Gaming Association 2006a). Similar to land-based gambling, the Netherlands has the most proactive responsible gambling measures of any jurisdiction. In addition to bans and spending limits, Holland Casino Digitaal has a maximum play limit of €100 per week for ages 18–23, allows players to impose limitations on visit frequency, and will potentially intervene when players show sudden increases in gambling expenditure or frequency (Holland Casino 2006; Chapter 16, this volume).6 The eCOGRA list of recommended responsible gambling practices is as follows (eCOGRA 2006): ●
●
●
6
Presence of mechanisms to try and ensure that people under 18 do not play A clearly identified self-exclusion program that operates for a minimum of six months, with no promotional materials going to that person during that time period and the option of a third party making an application A link to a player protection and responsible gaming page which provides an accepted and simple self-assessment process to determine problem
One of the significant advantages of online casinos compared with most land-based venues is the automatic identification and tracking of all player activity (giving the potential to proactively intervene).
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Research and Measurement Issues in Gambling Studies
●
● ●
gambling risk and other details about self-exclusion, deposit limits, and other responsible gambling practices offered by the site The ability for players to make limitations on their daily, weekly, or monthly deposits A clock on the screen at all times The denomination of each credit clearly displayed
FUTURE OF INTERNET GAMBLING Future trends are difficult to predict. Nonetheless, the following trends seem well established: Continued strong revenue growth Forecasts are for a compound annual growth rate of about 20% to 2008 as Internet use expands, the Internet interface becomes richer (e.g., live videostreaming), confidence and familiarity with Internet gambling increases, and there is more legalization of online gambling (London Stock Exchange 2005). Particularly strong growth among the Asian market and female gamblers Major increases in the Asian market relative to other markets will occur because of (a) current online sites orienting away from the U.S. market, (b) the increasing use of the Internet in Asia, (c) the illegality of land-based gambling in many Asian countries, and (d) the popularity of gambling in these countries. The Asian market has been slower to develop because of difficulties moving money in and out of certain countries and the lack of reliable telecommunications infrastructure (RSeconsulting 2006). Advertising will pose a challenge due to the illegality of gambling in many of these Asian countries. Strong growth in betting exchanges Continued strong growth of betting exchanges is likely due to the better odds for customers and lower cost structures for operators. Market consolidation As the market matures, it is likely that the larger players will attempt to acquire greater dominance through acquisition. In 2005 there were 32 instances of market consolidation, compared to just 9 in 2004 (RSeconsulting 2006).
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Growth of “mobile gambling” Mobile gambling is done on a wireless connected device such as a laptop, mobile phone, or other device. Some online casinos and online poker cardrooms currently offer mobile options, as do some land-based venues in Nevada (Cabot 2006). Movement toward legalized and regulated markets (with later regrets?) Many people believe that prohibition of online gambling is not feasible because of the difficulty in blocking individual players and in prosecuting offshore companies. They sometimes cite the widespread societal disregard for alcohol prohibition as a model of what would happen with online gambling prohibition. This is part of the reason why many Western jurisdictions are either inching toward (e.g., Canada) or making conscious legal changes for some form of regulated free market (e.g., United Kingdom, Netherlands). Other reasons include the belief that regardless of whether online gambling is good or bad for society, it is better for it to come under some form of legal regulatory control.7 The loss of revenue to offshore jurisdictions and European Union challenges to restrictive gambling laws are other pressures. However, an argument can be made that regardless of how difficult it is to enforce, official prohibition may still be the more appropriate stance considering (a) the unsatisfactory business and responsible gambling practices of many online sites, (b) the difficulty of ensuring that these sites meet minimum standards in these areas, (c) the significant contribution that problem gamblers likely make to online gambling revenues, and (d) the high potential that online gambling has to increase both the rates and the numbers of problem gamblers.This last point merits special consideration.The lesson of land-based gambling is that legalization increases legitimacy and availability, which strongly increase both gambling and problem gambling in the general populace. And, as many jurisdictions are now realizing, it is very difficult to put the genie back in the bottle once it is out. The efforts of the U.S. government in the next couple of years will determine the feasibility and utility of prohibiting online gambling. Alcohol prohibition is not a good model, as prohibiting something that the majority of the populace uses (e.g., alcohol) is much different than prohibiting something that only 2–3% currently use. Furthermore, there are other online activities that pose challenges in terms of control (e.g., child pornography; sites promoting illegal, defamatory, or hateful content).
7 Even if online gambling does prove to be cause problems, there is some evidence that, after some time, populations may adapt (to some extent) to the presence of problematic substances or products (e.g., Shaffer, LaBrie, and LaPlante 2004).
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Nonetheless, legal efforts to block, limit, and prosecute these types of sites are helpful and certainly preferable to permitting unfettered promotion and access. In any case, there is some inevitability to the legal expansion of online gambling in Western jurisdictions, regardless of its positive or negative impact. In the past 30 years, whenever a new form of gambling or regulatory practice has been introduced into one jurisdiction, most other jurisdictions have followed suit. As some of the larger jurisdictions begin legalizing and regulating online gambling, there will be some movement toward basing online operations in these jurisdictions.Although taxes will be higher and regulations more stringent, there are advantages of a stable political environment, capital markets, reliable communication infrastructure, and a large pool of skilled workers (American Gaming Association 2006a). Competition among sites will continue to make profit margins very tight. Competitive advantage will increasingly hinge on reputation, which is related to registration in a reputable jurisdiction requiring more stringent rules around business practices, age verification measures, and other responsible gambling policies. Increasing rates of problem gambling As previously mentioned, the inherent nature of Internet gambling would seem to make it conducive to increasing the rates of problem gambling. Although responsible gambling practices will mitigate this to some extent, there will always be “rogue” sites—without these safeguards—willing to accept any patron with money. The increasing patronage of online gambling sites will also increase the actual numbers of problem gamblers in the general population. Increasing prevalence of online counseling services Some researchers posit that Internet gamblers might be particularly receptive to Internet-based counseling or other online interventions (Horton et al. 2001; Wood and Williams, in press). Online counseling is currently being offered in the United Kingdom on a pilot basis. Supported by the Responsibility in Gambling Trust, GamAid provides “instant, real-time, one-to-one professional guidance for remote gamblers whose gambling activities are out of control or for those who wish to better understand the concepts of responsible gaming” (Wood and Griffiths 2006). Early findings indicate that while only 1% of online gamblers accessed the link button from participating gambling websites, women in particular found the service to be helpful.
RESEARCHING INTERNET GAMBLING Our understanding of Internet gambling is still quite limited. Considerably more research is needed to understand the advantages and disadvantages of different
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regulatory and legal structures, the most effective ways of ensuring fair business practices, the most effective means of minimizing harm, the nature of Internet gambling, and the nature of Internet gamblers. Researching Internet gamblers poses some unique challenges. Traditionally, researchers have used computer-assisted telephone interviewing (CATI) via random digit dialing (RDD) to select and study representative samples of gamblers. Although RDD surveys exclude a small minority of people without telephones, in most Western societies the technique has the potential to generate a large and highly representative sample (Singleton and Straits 2005; Chapter 2, this volume). However, a significant impediment to conducting RDD surveys of Internet gamblers is the relatively low prevalence rate of Internet gambling.With current prevalence rates in the range of 1–3%, tens of thousands of people have to be contacted to generate a few hundred eligible Internet gamblers.Typical RDD refusal rates of 50% then decrease the final sample by at least half. Thus, for many researchers of Internet gambling, RDD techniques might prove to be very inefficient as well as cost prohibitive. In recent years, market research companies (and a few academic researchers) have begun using “online panels,” composed of tens of thousands, hundreds of thousands, or even millions of individuals who have agreed to receive online solicitations from the company to participate in various consumer-oriented Internetbased surveys in return for compensation (e.g., eligibility for a prize draw) (Göritz, Reinhold, and Batinic 2002). Membership in these panels is structured to optimally ensure a representative sample of the population. The main advantages of online panels to gambling researchers are that (a) the “yield” of Internet gamblers will always be higher among Internet users, (b) the results can be obtained in a much shorter period of time compared with RDD surveys, and (c) the automated online administration of the survey is very efficient. The cost efficiencies of automated administration tend to be offset by the programming costs of the survey, as well as the need to provide participant compensation. It should also be noted that this strategy is unsatisfactory for studying other types of gambling because it does not sample non-Internet users.The main problems with online panels are that (a) response rates tend to be lower than in RDD surveys, with a bias toward people interested in that particular topic, and (b) the prevalence rate of Internet gamblers among online users is still quite low. Perhaps the most efficient strategy for recruiting large samples of Internet gamblers is via online advertisements (“banner ads”) or direct email solicitation, with these ads and/or emails providing a direct link to an Internet-based questionnaire administered over a secure server (e.g. Griffiths et al. 2006; Wood and Williams, in press; Woolley 2003). The present authors (Wood and Williams, in press) recruited 1920 people using banner advertisements seeking “Internet gamblers” at online gambling portals, which are websites containing information and links to a variety of Internet gambling venues. This strategy can generate a large sample of appropriate respondents, since potentially millions of Internet gamblers
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are exposed to these ads. An important difference between this technique and RDD or online panel sampling is that the obtained sample will typically consist of Internet gamblers from many different countries, rather than country-specific Internet gamblers. For this reason, having your survey translated into several different languages is likely important (most online gambling sites also offer services in various languages). Unfortunately, the overall response rate with this technique tends to be very low because of its passive nature, and because banners usually represent commercial advertising that people ignore. Response rates may be improved through incentives, the promise of short completion times for questionnaires, and guarantees that participation will not result in future solicitation (Cho and LaRose 1999;Trouteaud 2004). Response rates can also be improved with direct email solicitation to people with a known interest in your area of research (e.g., subscribers to an online gambling newsletter). Nonetheless, the obtained sample still remains relatively self-selected and potentially nonrepresentative of the larger population of Internet gamblers. The other problem is that people who do not use gambling portals (e.g., who always go directly to their favored online gambling venue) or do not subscribe to that particular online gambling listserv (for mass email) are also missed. Unfortunately, it is also usually not possible to weight the sample to correct biases, as nothing is known about the population of Internet gamblers unless a corresponding RDD sample is obtained at the same time. Some could argue that issues of representation are at least partially offset by the potential for respondents to answer more honestly when completing computer-administered online questionnaires.When dealing with sensitive and potential illegal behaviors, such as Internet gambling, respondents may be inclined to distort or mask their responses in order to create a socially desirable presentation of self, especially when completion of the questionnaire requires direct interaction with a researcher. Thus, others who have conducted research into sensitive issues have found that self-administered, impersonal, computer-based questionnaires tend to produce more valid results than researcher-administered questionnaires (Bronn 2001; Lipsitz et al. 2001; Treuer, Fabian, and Furedi 2001;Van der Heijden et al. 2000). It remains to be seen, however, whether this is indeed the case for studies of Internet gambling, and further comparative research is required before any decisive conclusions can be drawn about the validity of Internet-based versus researcher-administered studies of Internet gamblers. One final set of challenges encountered by researchers of Internet gambling stems from the varied legal status of the activity, across and within national jurisdictions. Indeed, as we have explained in the present chapter, what is legal and regulated in one country or state is often strictly prohibited in another, and very few jurisdictions offer legal protection to researchers or their subjects.Thus, researchers may find themselves in the ethically contentious position of asking respondents to
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admit to and describe their participation in illegal behavior.This situation is made all the more problematic when researchers may be offering incentives or honorariums in exchange for participation in the survey.
GLOSSARY Problem gambling gambling to the extent that it causes significant harm to the individual or people within that person’s immediate social network.
REFERENCES 4 Online Gambling.com (2006). Online Casino Industry Development Timeline. http://www.4onlinegambling.com/timeline.htm Alberta Gaming Research Institute. (2006). Reference Sources: Prevalence Studies. Available at http://www.abgaminginstitute.ualberta.ca/library_reference.cfm American Gaming Association. (2006a). An Analysis of Internet Gambling and Its Policy Implications. AGA 10th Anniversary White Paper Series. http://www.americangaming.org/assets/files/studies/ wpaper_internet_0531.pdf —— . (2006b). Industry Information Fact Sheets: Internet Gambling. http://www.americangaming.org/ Industry/factsheets/issues_detail.cfv?id=17 —— . (2006c). Gambling and the Internet. 2006 AGA Survey of Casino Entertainment. Washington, DC: Author. Amey, B. (2001). People’s Participation in and Attitudes to Gaming, 1985–2000.Wellington, New Zealand: Department of Internal Affairs. http://www.dia.govt.nz/Pubforms.nsf/URL/PublicAttitudes towardsGamingpt1.pdf/$file/PublicAttitudestowardsGamingpt1.pdf Baron, E., and Dickerson, M. G. (1999). Alcohol consumption and self-control of gambling behaviour. Journal of Gambling Studies, 15, 3–15. Bowsher, E. (2006). The Online Gaming Party Continues. Fool.co.uk. http://www.fool.co.uk/news/ Comment/2006/c060127b.htm Bronn, C. D. (2001). Attitudes and self-images of male and female bisexuals. Journal of Bisexuality, 1, 5–29. Brunker, M. (2004). Are poker “bots” raking online pots? Internet Roulette. http://www.msnbc.msn. com/id/6002298 Cabot, A. N. (2006). The Internet Gambling Report, 9th ed. Las Vegas:Trace Publications. Canada News Wire. (2006, October 6). Nova Scotia Gaming Corporation Announces Pilot Test of Internet Gambling Blocking Software. Canadian Partnership for Responsible Gambling. (2004). Canadian Gambling Digest. Retrieved September 22, 2005, from http://www.cprg.ca/articles/canadian_gambling_digest_2004.pdf Casino City. (2006). Online Casino City. http://online.casinocity.com Cho, H., and LaRose, R. (1999). Privacy issues in Internet surveys. Social Science and Computer Review, 17, 421–434. Christiansen Capital Advisors. (2005). eGaming Data Report. Author. http://www.cca-i.com Derevensky, J. L., Gupta, R., and McBride, J. (2006). Internet Gambling Among Youth:A Cause for Concern. Presentation at the Global Remote and E-Gambling Research Institute Conference. August 31–September 1, Amsterdam, Netherlands.
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eCOGRA. (2006, October 16). Generally Accepted Practices: Casinos. http://www.ecogra.org/2006eGAP_Casinos.doc Ellery, M., Stewart, S. H., and Loba, P. (2005). Alcohol’s effects on video lottery terminal (VLT) play among probable pathological and non-pathological gamblers. Journal of Gambling Studies, 21, 299–324. E*Trade Financial. (2006).Website, https://us.etrade.com/e/t/home Gambling Commission. (2005). Remote Gambling. United Kingdom Gambling Commission. Available at http://www.gamblingcommission.gov.uk/Client/detail.asp?ContentId=38. —— . (2006). Report of the Gambling Commission 2005/06. London:The Stationery Office.Available at http://www.gamblingcommission.gov.uk/UploadDocs/News/documents/annual_report_ 05_06.pdf Games and Casino. (2006). Blacklisted Casinos. http://www.gamesandcasino.com/blacklist.htm Gillis, J. (2006).Youth gambling online: Survey reveals most N.S. Internet gamblers are teens. Chronicle Herald, October 4. Study conducted by D-Code Inc. Göritz, A., Reinhold, N., and Batinic, B. (2002). Online panels. In Online Social Sciences (B. Batinic, U. D. Reips, and M. Bosnjak, eds.), pp. 27–47. Göttingen: Hogrefe and Huber Publishers. Griffiths, M. (1996). Gambling on the Internet: A brief note. Journal of Gambling Studies, 12, 471–473. —— . (1999). Gambling technologies: Prospects for problem gambling. Journal of Gambling Studies, 15, 265–283. —— . (2003). Internet gambling: Issues, concerns, and recommendations. CyberPsychology and Behavior, 6, 557–568. —— . (2006). Internet Gambling:What Can the Past Tell Us About the Future? Presentation at the Global Remote and E-Gambling Research Institute Conference, August 31–September 1, Amsterdam, Netherlands. Griffiths, M. D., and Parke, J. (2002). The social impact of Internet gambling. Social Science Computer Review, 20, 312–320. Griffiths, M., and Wood, R.T. A. (2000). Risk factors in adolescence:The case of gambling, videogame playing, and the Internet. Journal of Gambling Studies, 16, 199–225. Griffiths, M., Wood, R. T. A., and Parke, J. (2006). A Psychosocial Investigation of Student Online Poker Players. Presentation at the 13th International Conference on Gambling. Lake Tahoe, NV, May 22–26. http://www.unr.edu/gaming/13th_Conference_Web_files/Files/Abstracts/index.htm Hammer, R. D. (2001). Does Internet gambling strengthen the U.S. economy? Don’t bet on it. Federal Communications Law Journal, 54, 103–128. Henderson, I. (2005). Bingo attracts new profile of online gambler. 999 Today. August 2, 2006. Holland Casino. (2006). Holland Casino Digitaal: A New Challenge. Presentation at the Global Remote and E-Gambling Research Institute Conference,August 31–September 1,Amsterdam, Netherlands. Horton, K. D., Harrigan, K. A., Horbay, R., and Turner, N. (2001). The Effectiveness of Interactive Problem Gambling Awareness and Prevention Programs. Final research report prepared for the Ontario Substance Abuse Bureau, Ministry of Health and Long Term Care, July 27. Howard, P. E. N., Rainie, L., and Jones, S. (2001). Days and nights on the Internet:The impact of a diffusing technology. American Behavioral Scientist, 45, 383–404. Ipsos Reid. (2005). Online Poker in North America: A Syndicated Study. www.ipsos–na.com Jepson,V. (2000). Internet gambling and the Canadian conundrum. Appeal: Review of Current Law and Law Reform, 6. Available at http://appeal.law.uvic.ca/vol6/pdf/jepson.pdf Kelley, R., Todosichuk, P., and Azmier, J. (2001). Gambling at Home: Internet Gambling in Canada. Gambling in Canada Research Report No. 15. Calgary: Canada West Foundation. Kyngdon, A., and Dickerson, M. (1999). An experimental study of the effect of prior alcohol consumption on a simulated gambling activity. Addiction, 94, 697–707.
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Kshetri, N. (2005). Hacking the Odds. Article in foreignpolicy.com. http://www.foreignpolicy. com/story/cms.php?story_id=2848 LaBrie, R. A., Shaffer, H. J., LaPlante, D. A., and Wechsler, H. (2003). Correlates of college student gambling in the United States. Journal of American College Health, 52, 53–62. Ladd, G.T., and Petry, N. M. (2002). Disordered gambling among university-based medical and dental patients: A focus on Internet gambling. Psychology of Addictive Behaviors, 16, 76–79. LaRose, R., Mastro, D., and Eastin, M. S. (2001). Understanding Internet usage: A social-cognitive approach to uses and gratifications. Social Science Computer Review, 19, 395–413. Lipsitz, J. D., Fyer, A. J., Paterniti, A., and Klein, D. F. (2001). Emetophobia: Preliminary results of an Internet survey. Depression and Anxiety, 14, 149–152. London Stock Exchange. (2005). Market News, June 1, 2005. Available at http://www.londonstockexchange.com/LSECWS/IFSPages/MarketNewsPopup.aspx?id=1012594&source=RNS McMillen, J., and Woolley, R. (2003). Australian Online Gambling Policy: A Lost Opportunity? Paper presented at Pacific Conference on I-Gaming, 24-06 February, Alice Springs. Motivaction International. (2005). Netherlands Participation in Paid Interactive Internet Gaming. http://www.toezichtkansspelen.nl/information.html NCH. (2004). Children as young as 11 can set up gambling accounts at the click of a button. Press release by NCH, GamCare and CitizenCard, July 27. Available at http://www.nch.org.uk/ information/index.php?i=77&r=288 NetValue. (2002). Europeans take a Gamble Online. NetValue Survey. Available at http://www.nua.ie/ surveys/analysis/weekly_editorial/archives/issue1no307.html Productivity Commission. (1999). Australia’s Gambling Industries (Report No. 10). Canberra: AusInfo. Rasmussen Reports. (2006). A Virtual Role of the Dice. October 3, 2006. Survey of 1,000 adults Sept 16–17, 2006. www.rasmussenreports.com Rose, I. N., and Owens, M. D. (2005). Internet Gaming Law. Larchmont, NY: Mary Ann Liebert, Inc. RSeconsulting. (2006). A Literature Review and Survey of Statistical Sources on Remote Gambling. Final Report. October 2006. Available at www.resconsulting.co.uk Schwartz, D. G. (2006). Roll the Bones:The History of Gambling. New York: Gotham Books. Sévigny, S., Cloutier, M., Pelletier, M., and Ladouceur, R. (2005). Internet gambling: Misleading payout rates during the “demo” period. Computers in Human Behavior, 21, 153–158. Shaffer, H. J. (1996). Understanding the means and objects of addiction: Technology, the Internet and gambling. Journal of Gambling Studies, 12, 461–469. Shaffer, H. J., LaBrie, R. A., and LaPlante, D. (2004). Laying the foundation for quantifying regional exposure to social phenomena: Considering the case of legalized gambling as a public health toxin. Psychology of Addictive Behaviors, 18, 40–48. Shap, D. (2002). Internet gambling law in Canada:An examination of the legality of online gaming from the perspective of the players, providers and the parties in between. Internet and E-Commerce Law in Canada, 3, 65–72. Singleton, R. A., and Straits, B. C. (2005). Approaches to Social Research. 4th ed. New York: Oxford University Press. Smeaton, M., and Griffiths, M. (2004). Internet Gambling and Social Responsibility: An Exploratory Study. CyberPsychology and Behavior, 7, 49–57. Sproston, K., Erens, R., and Orford, J. (2000). Gambling Behaviour in Britain. Results from the British Gambling Prevalence Survey. London: National Centre for Social Research. Treuer, T., Fabian, Z., and Furedi, J. (2001). Internet addiction associated with features of impulse control disorder: Is it a real psychiatric disorder? Journal of Affective Disorders, 66, 283. Trouteaud, A. R. (2004). How you ask counts: A test of Internet-related component of response rates to a Web-based survey. Social Science Computer Review, 22, 385–392.
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Vallerius, B. (2006). Gambling sites will change software to stay in U.S. market. Casino City Times, October 20. Van Der Heijden, P. G. M.,Van Gils, G., Bouts, J., and Hox, J. J. (2000). A comparison of randomized response, computer assisted interview, and face-to-face direct questioning: Eliciting sensitive information in the context of welfare and unemployment benefit. Sociological Methods and Research, 28, 505–537. Welte, J.W., Barnes, G. M.,Wieczorek,W. F.,Tidwell, M., and Parker, J. (2002). Gambling participation in the U.S.––Results from a national survey. Journal of Gambling Studies, 18, 313–337. Wiebe, J. (2006). Internet Gambling: Next Steps for Effective RG Strategies. Research Proposal to the Interactive Gaming Council. Available at http://www.igcouncil.org/press.php?id=330& section_name=Press%20Releases Williams, R. J., and Wood, R. T. (2004a). The proportion of gaming revenues derived from problem gamblers: Examining the issues in a Canadian context. Analysis of Social Problems and Public Policy, 4, 1–13. —— . (2004b). The Demographic of Ontario Gaming Revenue. Final report submitted to the Ontario Problem Gambling Research Centre, June 23. Available at http://www.gamblingresearch.org/ contentdetail.sz?cid=198&pageid=1042&r=s Wood, R. T. A., and Griffiths, M. D. (2006). The GamAid Pilot Evaluation Study. Presentation at the Global Remote and E-Gambling Research Institute Conference, August 31–September 1, Amsterdam, Netherlands. Wood, R. T., and Williams, R. J. (2006). Internet Gambling: Prevalence, Demographics, and Behavior. Study funded by the Ontario Problem Gambling Research Centre. —— . (in press). Problem Gambling on the Internet: Implications for Internet Gambling Policy in North America. New Media and Society. Wood, R.T.,Williams, R. J., and Lawton, P. (submitted for publication). Why Do Internet Gamblers Prefer Online Versus Land-Based Venues? Woolley, R. (2003). Mapping Internet gambling: Emerging modes of online participation in wagering and sports betting. International Gambling Studies, 3, 3–21.
CHAPTER 20
Social and Economic Impacts of Gambling Earl L. Grinols Department of Economics Hankamer School of Business Baylor University Waco,Texas
Introduction Social Harm Economic Development The Eleven Components of Economic Development Cost–Benefit Analysis The Two-Sector Economy Revisited Measurement Summary and Conclusions
INTRODUCTION Sincerely civic-minded citizens and high-minded government leaders are often dismayed to find that they cannot get coherent answers from experts to straightforward questions about the social and economic impacts of gambling. I have documented elsewhere the experience of different commissions and committees that found even their most direct questions ignored or finessed by the economists and experts they engaged to provide information and input to their decision making (Grinols 2004a, pp. 4–7). Frequently government asks for reports without providing money to pay for any original independent research. In contrast, the gambling industry is willing to fund studies to provide to government. The gambling industry and the issues attendant to it are not alone in being associated with an apparent failure of information and analysis; in the past, industries such as tobacco have similarly been at the center of a storm of competing claims, unanswered questions, and controversies. Many of these controversial social 515
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issues are related to economic development and share in common the features that 1. the issue’s central focus is the effect of the activity on the well-being of society’s members and citizens, 2. there is a great deal of money to be made by interested business parties for continuing or expanding the activity in question, and 3. the activity creates or is alleged to create significant harmful consequences and damage that is borne by a group different from the business interests that benefit from the continuation or expansion. Among the reasons for the difficulty of the general public to find clear answers to questions such as, What are the impacts of gambling that we should measure? and How do we measure them? are prior predispositions and the absence of a rigorous, quantitative and mathematically based framework to measure the societal impact of externality-producing industries. Measuring the social and economic impacts of development requires a cost-benefit analysis and analytical framework that goes significantly beyond the listing of pros and cons sometimes encountered for a social experiment. Cost-benefit analysis is a methodology for recognizing gains and losses in a way that the identified elements form exhaustive and mutually exclusive categories that are measured in common units. Casino, or class III, gambling is merely a case in point. (For discussions of a range of social and economic impacts of gambling, see Breen and Zimmerman 2002; Bridwell and Quinn 2002; Buck, Hakim, and Spiegel 1991; Chesney-Lind and Lind 1986; Glaeser, Sacerdote, and Scheihkman 1996; Grinols 1996, 1999; Grinols and Mustard 2001; Grinols and Omorov 1996; Kindt 1994a,b,c, 1996; Schwer and Thompson 2004; Thompson, Gazel, and Rickman 1996a,b; and Thompson and Schwer 2003. See Grinols and Mustard 2001a for a discussion of one such confusion. Grinols and Mustard 2001b and Grinols 2004a discuss the accurate concept of economic development.) With respect to the first point, it is well known that casinos operated nowhere in the United States except Nevada from the early 1930s until 1978. Even after Atlantic City casinos began operation, these two locations were not joined by casinos in a multitude of other locations until the 1990s.The reader can form his own conclusions about why during this time there was no original research forthcoming from Nevada about the social costs of casino gambling or any studies conducted of the prevalence of pathological gambling and problem gambling in Clark County (Las Vegas). Many academics, including myself, adhere to the view that research can be evaluated on its own merit, regardless of its sponsor. It is certainly not improper for an industry to sponsor research or for a researcher to accept industry money. Still, some have questioned the extent of the connections: ●
The Los Angeles Times described the head of the American Gaming Association (the lobbying arm of the gambling industry):“He reaches into
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academia, channeling large sums of money to university professors and private consultants whose studies reflect favorably on gambling” (Ferrell and Gold 1998). “Mr. [S] says he subsequently decided to accept $140,000 of the [National Center for Responsible Gaming]’s money for the study. He has subsequently received another $465,000 to create a data base on problem gambling” (Wheeler 1999).The NCRG is supported by gambling companies. The Los Angeles Times also reported Mr. [S]’s acceptance of “nearly $600,000 in grants from the industry” (Ferrell and Gold 1998). “[Professor E], by the way, makes money off the industry, running training sessions for casino managers and sponsoring an international gambling conference that draws from industry and academia” (Simurda 1994). “Earlier this month, the Nevada Resort Association—the chief lobbying group for Nevada casinos—commissioned a rebuttal report by [Professor W], who said the results of Thompson’s study were unreliable because their analysis is seriously flawed” (Benston 2003; see also Kindt 2003. Names were removed from the preceding references at the request of this volume’s editor.)
Shadow research is funded in the hope or expectation that it will contradict research unfavorable to the sponsoring industry (e.g., published research that reported elevated suicide levels connected to gambling among visitors to Atlantic City and Las Vegas). Shadow research, such as gambling-industry sponsored suicide research, has appeared in the Journal of Gambling Studies, which is supported by the National Council on Gambling, which in turn is funded by the gambling industry. I have noted elsewhere that the tobacco industry sponsored research and that “no one believes that the tobacco industry told its researchers what to say” (Grinols 2004b). If information about sponsorship is made publicly available, it can always be ignored by the reader if the reader chooses. On the other hand, valid inferences might be made over time that could not be on a case-by-case basis. For example, if all industry-sponsored research were favorable to the sponsoring industry or antagonistic to its critics, but non–industry-sponsored research appeared on both sides of the issue, this could be known with statistical significance only over a large number of observations.1 As was the case with tobacco, there is a lot of research that the gambling industry would be happy to continue to fund that does not have the potential to touch on or damage its core interests.“The tobacco industry funded researchers who challenged the validity of research
1 This hypothesis has never been tested by me but might form the basis of an interesting experiment.To date my gambling-related research has brought me into contact with dozens of studies of the economic impact of proposed casino expansions over a number of years for various states and locations. I have not seen a study sponsored by the gambling industry (whether conducted by independent accounting firms such as Deloitte and Touche, by research firms such as the WEFA Group or Evans Associates, or by individual academic consultants) that produced a negative conclusion about the proposed project or industry.
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that linked smoking to cancer. Ultimately, of course, the industry did not prevent the link being made” (Grinols 2004b). With respect to the second point, it is possible to form a mathematically based cost-benefit framework to evaluate economic development questions and provide the description of a methodology for collecting information on problem and pathological gamblers and their social costs to place into it. In the remainder of the paper, therefore, I discuss the nature of harm as it relates to gambling, the uses of analytically grounded cost-benefit analysis, and a particular methodology for determining identified social costs. In the final section I will touch on the equally important matter of evidence on the social costs of pathological gambling.
SOCIAL HARM Let us begin with an example of social costs created by gambling as a starting point for discussion (in the next section) of the issues that arise in the measurement of social costs and benefits. Thus, consider an economy with two sectors, each peopled by a consumer, I and II, respectively. The consumers each own one unit of labor, LI = LII = 1, which is devoted to the production of two goods x and y that are capable of being produced in each of the two sectors. The amount of labor needed to produce a unit of x in sector I is a xI = 1/4 and the amount needed to produce good y is a yI = 3/8 . In sector II the respective numbers are (a xII , a yII ) = (3/8, 1/8). The economy under consideration is small relative to the rest of the world, which determines prices px = 12.5 and py = 6.25.The production possibility frontier for the economy in drawn in Figure 20.1.
y 10 2/3 (4, 8) Equilibrium production
Slope = px/py = 2 (2, 4) U⬘ Initial equilibrium
0
6 2/3 Figure 20.1. Production Possibilities for the Two-Sector Economy.
x
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Longtime familiarity with these types of models allows me to identify the equilibrium for the economy just described. Equilibrium prices are (px, py, wI, wII) = (12.5, 6.25, 50, 50). Gross domestic product (GDP) is maximized at these prices for the choice of production (xSupply, ySupply) = (4, 8). Sector I uses the labor of consumer I to produce 4 units of good x (12.5 = p x = w I a xI = 50 (1/4)), and sector II produces 8 units of good y using the labor of consumer II, (6.25 = p y = w II a yII = 50 (1/8)). It is unprofitable at these prices for sector I to produce good y (6.25 = p y = w I a yI = 50 (3/8) = 18.75) or for sector II to produce good x (12.5 = p x < w II a xII = 50 (3/8) = 18.75). GDP measured through the goods loop equals GDP measured through the income loop, pxx + pyy = $12.5 × 4 + $6.25 × 8 = $100 = $50 × 1 + $50 × 1 = wI LI + wII LII. Concerning the demand side of the model, consumers have identical utility functions u I = k x I y I
and u II = k x II y II , where
k = 2 12.5 # 6.25
I I = 17.67767 . Demand for goods x and y is (x I , y I ) = e 2wp , 2wp o and x y II II (x II , y II ) = e 2wp , 2wp o . Markets clear domestically. In equilibrium xI + xII = x y
xSupply = 4 and yI + yII = ySupply = 8, as the reader can verify. Direct computation shows that each consumer has utility uI = uII = 50. Moreover, the utility functions selected have the special property that utility equals the numerical dollar expenditure needed by the consumer at market prices to achieve the attained utility. For example, if consumption were xI = yI = 1, utility would be uI = 17.68. When the consumer spends $17.68, demand is (x I , y I ) = 17.68 17.68 17.68 17.68 e 2p , 2p o = d 2 (12.5) , 2 (6.25) n = (0.707, 1.414), which provides utility x y 17.68.2 In this economy, we now examine the effects of stealing. After equilibrium has been reached, presume that consumer II steals from consumer I 3.96525 units of good y. The reason for selecting this mysterious number as the quantity stolen will become evident. Direct computation shows that the utility of consumer I falls to $4.66, while the utility of consumer II rises to $70.56. In other words, posttheft consumer I (the victim) is as well off as if $45.34 of stolen income had been lost, and posttheft consumer II (the thief ) is as well off as if $20.56 of stolen income had been received.The economy as a whole is as well off as if its aggregate income were $4.66 + $70.56 = $75.22.The economy as a whole, therefore, experiences $24.78
2 Functions with this property are sometimes referred to as “money metric” utility functions. If u[x, y] is utility, and e[px, py, u] is its corresponding expenditure function, then the money metric utility function is e[px, py, u[x, y]].
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Table 20.1 Externality Social Costs Example: Crime. Variable
Consumer I Consumer II Totals Consumer I Consumer II Pre-Theft
Good x Good y Consumer income Stolen good y Utility (U) Social cost (∆U)
Totals
Post-Theft
2 4 $50
2 4 $50
4 8 $100
$50
$50
$100
2 .035 $50 $24.78 $4.66
2 7.965 $50
4 8 $100
$70.56
$75.22
($45.34)
$20.56
$24.78
Pre- and post-Gross Domestic Product = wILI + wIILII = pxx + py y = $50 + $50 = $100.
of lost utility. This happens in this case to equal the value of the stolen property (py × 3.96525 = 6.25 × 3.96525 = $24.78). Stealing 3.96525 units of good y therefore leads to loss to the economy equal to the amount stolen.Table 20.1 summarizes the information just given. What do we learn about the effects of theft from this example? First, even though the thief received the stolen property lost by the victim, society was worse off by the amount stolen. It is not true, as sometimes claimed, that theft per se produces no social costs (in the next section we provide a breakdown of social costs that can explain the social costs captured in the numerical example). In other cases, of course, the net costs may differ. For example, were the thief to have “fenced” the stolen property, taking the income from it ($24.78) to use on purchases, the net loss to society would have been a different number, but the theft still would have resulted in net social costs. If an enlarged police force and increased apprehension, adjudication, and incarceration costs had been required to deal with the thief, the costs of the resources used for these purposes would be in addition to those documented in the preceding example. Second, this economy suffered no loss in physical production, GDP, or the ability to produce tangible real goods. In fact, GDP remained equal to $100 throughout.Yet real harm was imposed that represents tangible social cost. The fact that the economy suffered a reduction in its capacity to produce utility, the prime directive for economic activity, is the clue that social costs are present. GDP, wealth, and other common conceptions by economists are intended to capture the ability of an economy to produce well-being in its residents. Naively misunderstood or applied, they can result in invalid ideas about social cost and a misplaced emphasis on “tangibleness.” When properly understood and applied, they produce measures that are perfectly consistent with real—and equally tangible—social costs of the type documented in this example. In the next section
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we provide a theoretically based framework for social evaluation that is capable of capturing these effects.
ECONOMIC DEVELOPMENT As already explained, cost-benefit analysis quantifies in common units the helpful and harmful consequences of a prospective change in the economy (this section expands on material first presented in Grinols and Mustard 2001b and Grinols 2004a, pp. 99–107).The object is to measure the increase in well-being of the individuals making up society. Often, however, the approaches that have been used for cost-benefit analysis have not been sufficiently sophisticated to include all of the relevant routes of welfare influence. Badly done, so-called cost-benefit analysis has been little more than an informal listing of “pros and cons” that apply to a particular development. Such informal lists frequently mix apples and oranges, and provide no guidance about units of measurement or how the various components relate to one another. For example, a common mistake is to list “jobs” or “jobs created” as an economic benefit. Jobs are certainly an input into the creation of well-being, but they are the means to an end, not the end itself. Properly executed cost-benefit analyis requires a framework that is comprehensive enough to include all of the considerations relevant to the case at hand so that benefits and costs are exhaustively cataloged in a mutually exclusive fashion that prevents missing elements or double counting and provides guidance about how measurements should be taken. In cases (e.g., gambling) where the activity is asserted to cause crime, an often encountered question is whether to include stolen property or abused dollars (Grinols 2004a, p. 197) among the costs.Why should stolen property be treated as a cost when the thief gets what the victim loses and therefore society as a whole is no worse off? One answer to this question that has already been provided in the example in the previous section is that the crime may have harmful consequences even when the thief gets what the victim loses. A second answer—and this is more significant in many ways—is that some have argued for one treatment or another without realizing that the choice is not methodological but depends on what question the researcher wants to answer.The common but invalid claim is that stolen or abused property must not be counted. In many cases of interest, however, it is perfectly correct as an analytical matter to include abused dollars and property in valuing what effects gambling has on the population of nongamblers. The previous section and these brief paragraphs are not intended to be a thorough review of the issue of what costs should be counted and when. Rather, they are offered to alert the reader at the outset to the unstated and often incorrect assumptions that underlie many of the extreme claims that one sometimes finds. The mathematics speak for themselves. In what follows, we will use it to follow a number of the strands discussed previously, as well as certain others.
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THE ELEVEN COMPONENTS OF ECONOMIC DEVELOPMENT COST-BENEFIT ANALYSIS Economic development refers to changes in the economy that lead to increased well-being per citizen. Economic development—increased well-being per citizen—can occur through several routes. One is through improved technological knowledge, the ability to produce more goods with the same inputs or to produce better or more desirable goods with the same inputs.A second is through increased availability of inputs.And the third is through the removal of impediments to economic organization and efficiency. For example, eBay created billions of dollars of wealth through linking markets nationwide and becoming the country’s “national garage sale.” Primarily, it does not make the goods it sells, but it allows them to be traded more efficiently. Enhancement of distributive efficiency is welfare creating. It follows that economic shrinkage, or “undevelopment,” can occur through the reverse of any of these three processes. Measuring the change in well-being from a change in the economy is the subject of cost-benefit analysis. Consider then the general problem of comparing the social welfare between two situations 0 and 1. Our starting point is the satisfaction level, the level of well-being or utility attained by the residents—as evaluated by themselves—in a common unit of account.The derivation presumes for purposes of discussion, therefore, that individuals can make the necessary judgments about their own well-being, that we have access to these assessments, and that we can ask for the information in the form we choose. In what form should we ask for the information? We answer this question with an analogy to how temperatures are measured and reported in the public press. Even though each of us experiences temperatures differently, and no one can know how another feels compared with himself or herself in a given temperature, we nevertheless select the height of a column of mercury in a thermometer as a measure of temperature and report the number it generates. One scale sets the freezing point of water at 32 degrees, while another sets it at 0 degrees. The first sets the standard-temperature/pressure boiling point of water at 212 degrees and the second at 100 degrees. Either scale is correct. What individuals mentally note when hearing the temperature is how they felt when they experienced a similar temperature. We employ a similar device here: We ask individuals to report how much money they would need to achieve the state of well-being they feel, presuming that the money has to be spent at the prices we select. Choosing one set of prices or another generates a different scale, much as Fahrenheit and Celsius represent temperature in different scales. As we did in the previous section, therefore, we ask for information in the form of money metric utility numbers that represent utility in terms of how much money it would take individuals to reproduce their level of well-being if the money were spent at the specified prices. For the best scale, we select prices that actually prevail in the economy in the final
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situation—easing the interpretation of the numbers we find. All of us, for example, know what $100 of additional income will do for our utility if we spend it at the prices we experience. Current prices, therefore, are a better choice than prices that prevailed in, say, 1843. Let e i 8 a i1 , x ig, 1, p i1 , u i1 B be the expenditure needed to achieve utility u i1 when consumer i faces conditions ` a i1 , x ig, 1 , p i1j = (level of amenity ai that affects the utility of individual i, consumption of public good g by individual i, list of prices facing individual i). In the notation that follows, square brackets refer to functional dependence, rounded brackets refer to mathematical grouping, subscript i refers to individuals, and superscripts 0 and 1 refer to the initial and final situations as already noted. Write the social surplus, that is, the change in social welfare from the change in question, as (1) In this discussion, we will proceed as if there were a single amenity to be considered and a single public good, although the framework can be expanded to deal with more complicated cases.The amenity might be the siting of the nearest casino closer to the individual than previously, the establishment of a national park nearby, or an improvement that makes the environment more pleasant to the individual.At the moment, all that we need to recognize is that the presence of amenities and public goods are components of the environment faced by the individual that affect his or her ability to generate utility and therefore are relevant to how much income would have to be spent at the stated prices to generate the target level of utility u i1 or u i0 . Because we are presenting a mathematical structure, we proceed formally to rewrite equation (1) with minimal explanation, saving our comments for later. It is important to emphasize that the following relations are identities and therefore identically true. The first equation is generated as a telescoping sum where each term cancels part of the preceding term.The usefulness of a telescoping sum is that it breaks down any change in utility due to a social change into clearly defined components that are related to the effect of the change on the amenities, public goods (ai, x ig ), and circumstances of consumption of private goods by individuals, as well as changes in income flows experienced by the individuals. The list of terms sums to the total utility change; hence, it satisfies the conditions stated earlier that it be exhaustive and mutually exclusive in the categories derived. Moreover, the formulas explain precisely how each term is computed.As with any framework, however, the number and degree of fineness of the studied terms depend on the extensiveness of the effects that are desired to be modeled. Here we start with a single amenity and a single public good. As already noted, the framework is expandable to cover more complicated situations.
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(2)
As written in (2), the prices described for the individual consumer allow for far more detailed range of policy than is likely to be encountered in practice; that is, (2) allows for the extreme case for policy, which is that the agents each face taxes and prices specific to them alone. Often in considering regional tax implications, it is found that households and firms face different prices. Household i faces prices pi, firm j faces prices pj, and endowments ~ (goods of the K types available in the economy that are not produced in the current period but are inherited from nature or the past) are traded at prices p ~ . We now add price, budget, and market clearing information. It is convenient to relate all prices to domestic prices p as follows: (3) (4) (5) where ( p, pw, pi, pj, c, t i , x j ) = (domestic prices, world prices, individual i prices, firm j prices, the vector of specific tariffs, consumer i taxes, firm j taxes). Since K is the number of goods, each price vector or tax vector has K components. Here the term “tax” is used to mean both tax and subsidy. If a levy collects positive revenues for the government, it is a tax; if it collects negative revenues, it is a subsidy. From social accounting we have for aggregate quantities, (6) (7) where x / /i x i is the total consumption of private goods, y / / j y j is total production of private goods, and w is economy endowments such as natural resources. In this treatment, we will proceed as if all endowments were owned by
Social and Economic Impacts of Gambling
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firms (firm ownerships ~ j add to the economy total / j ~ j = ~), which in turn are owned by individuals.With minor notational complication, we could allow for direct ownership of endowments by individuals. Consumption of public goods by individuals x ig and use of public goods as inputs by firms y Gj each equal the available economy supply
/ j y jg because of their public good feature.
Production is conducted by firms and government. The production choice of agent j is described by its list of inputs and outputs (y j , y jg , - y Gj ) ! Y j where Yj is the (K + 2L)-dimensional set of feasible production choices.The production set is assumed to satisfy standard assumptions such as being nonempty, closed, and convex. yj is the K-dimensional vector listing private goods. Following the usual convention, an element in yj with a positive sign denotes an output, and an element with a negative sign denotes an input. y jg in 1-dimensional space is the public good produced by agent j. y Gj , also 1-dimensional, lists the public goods available for use as inputs in the production of agent j. The capital letter G denotes the publicness of the public good; all firms can use the same public good inputs.The negative sign before y Gj indicates that y Gj are inputs to the firm. z is the economy vector of excess demands for traded goods.A zero element in z denotes a nontraded good, while a positive entry denotes imports.The vector r of nonnegative numbers in K-dimensional space denotes resources used to deal with harmful externalities or to produce public goods.Were a production activity, for example, to create fires in surrounding neighborhoods and fire trucks and firemen be needed to prevent and treat fires, those resources would be taken away from other uses. Since utility depends on (ai, x ig , xi) and not r, the drain of goods and services implies indirect real utility cost to consumers.The special designation of r at this stage, however, is merely a notational convenience to allow us to identify the place of such resources in our economy.Whether we specially label them or not, the redirection of resources to different uses would have an effect on the utility being generated by the economy and measured in equation (1). We now use our framework to expand the contents of /i p i $ x i by rewriting the money flows contained in it:
(8)
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Research and Measurement Issues in Gambling Studies
In this form, equation (8) relates economy aggregates to one another. Since the economy’s financial flows consist of the summed budgets of individuals and government (we have not elaborated the separate budgets of firms), we can derive the budget of government from (8) if we have the budgets of households. To that end, divide the population into two groups. The first group, A consumers, includes those individuals who are the victims of crime, while the second (B consumers) includes those who commit crime. Again, we take a simple case and assume that amount Si is stolen from individual i in group A. For A consumers we write consumption as x i = a i to distinguish their consumption from group B consumers’ consumpt ion (b i ): (9) (10) In equations (9) and (10), i ij is the share of firm j owned by consumer i. As noted before, ~ j is firm j’s ownership of economy endowments. Ti represents direct money transfers to household i, if any. For example, if direct payment of unemployment compensation were made to individual i, who became unemployed because of his pathological gambling, this would appear in term Ti.The right-hand sides of equations (9) and (10) represent the lump sum income sources for consumers in each of the two groups. Labor income and taxes paid are contained in the left-hand term because hours of labor supply appear in a i or b i .The taxes on labor income reflected in (9) and (10) are proportional, but, as already noted, we are considering a relatively simple case. Summing terms in (9) and (10):
(11)
where S is the total value of stolen money and T is the total of transfers by government to group B. Replacing the left-hand side of (11) with the right-hand side of (8), rearranging, and canceling terms yields
Social and Economic Impacts of Gambling
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(12) which is the form we seek.The left-hand side of equation (12) lists the sources of government budget revenue from taxes on consumers, on producers, and on international trade (tariff revenues),3 while the right-hand side shows the uses of government expenditures as purchases of resources r for government purposes such as production of public goods and direct payments to group B. Our final derivation before summarizing what we have learned from the framework is to write the cost-benefit equations for group A.
(13)
Now substitute the right-hand side of (13) into (2):
(14)
(15)
(16)
3 Presuming balanced trade, p w $ z = 0 and p $ z equals tariff revenues. If trade is unbalanced, the effect of the proposed change in the economy on the balance of trade would become an issue that could be accommodated in the analysis. Since it adds nothing to the present discussion, however, we
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(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
Equations (14)–(24) identify eleven specific welfare components of economic development: ● Transactions constraints in situation 1—Consumption bundle (a i1 , x ig, 1 , x i1 ) achieves utility u1. If bundle x i1—summarizing the choices actually made by the consumer in situation 1—is not the cheapest way to achieve this utility, term (14) is positive and the consumer’s choices must have been constrained. The cost imposed by this limitation, expression (14), is the amount of money the individual would be willing to pay to remove the constraint. ● Change in all firms’ profts—Term p $ y is the profit of firm j.4 Profits j j are commonly considered a benefit because the profits of a new firm
Social and Economic Impacts of Gambling
●
●
●
●
●
529
accrue to the firm’s owners.The term in (15) is different, however because it requires that reductions in other firms’ profits be subtracted from the profit of the new firm to arrive at net profits increase. A typical application with respect to gambling is the need to subtract lost profits of competing businesses when counting the profit of a new casino as a social benefit. Net capital gains on endowments—As already noted, in the economy decribed here, firms own the economy’s endowments and individuals own firms. A change in the economy that makes endowments more valuable represents a capital gain to households.Term (16) captures the value of this benefit to the economy. If the introduction of a new firm causes the value of the housing stock to rise, for example, the capital gain in house values would be a benefit to the area’s residents. Change in all taxes paid—Expression (17) measures the net increase in taxes from all sources, including taxes on consumers, on producers, and on trade. As with profits, it is important that taxes paid by a new firm not be treated as a social benefit until the lost taxes that would have been collected from other firms is subtracted.Tax deadweight loss (loss in social value that exceeds the amount of taxes collected) appears implicitly in (14)–(24) in the form of lost consumer surplus and forgone profits that exceed the tax collection. It is automatically present in the collection of terms. Change in firms’ profits and capital gains going to group B—The change in firms’ profits annd capital gains going to group B gamblers is self-explanatory. If group B are the pathological gamblers, and we are evaluating the effect of gambling on the nonpathological segment of society, then we must account for benefits that may accrue to group B alone. Change in stolen assets and change in direct payments to group B—Assume that group B refers to pathological gamblers who steal from the rest of society and receive payments from government (these could be social service support payments or unemployment insurance payments, for example) and that we are interested in the impact on the rest of society. Then any increase in money stolen from group A or increase in government payments to group B in (19) and (20) are required by the mathematics to be counted among social costs. Transactions constraints in situation 0—Expression (21) measures the impact of transaction constraints in situation 0. Its interpretation is the same as described for constraints in situation 1.
Consider the simple case where firm j produces i units of output using inputs L, K whose are w, r. Then p j = (p i , w, r) and y j = (i, - L, - K) so that p j $ y j = p i i - wL - rK , which is firm j’s profit. 4
prices
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Research and Measurement Issues in Gambling Studies
●
●
●
5
Public good effect—The mathematics require the presence of expression (22), which measures the value to individuals of a change in provision of public goods.5 The government pays for the resources that are used to produce public goods with tax dollars.The cost of public goods, therefore, implicitly appears as reduced consumption of private goods. The benefit received from public goods may be above or below the cost of the inputs needed to make them.Wasteful public spending, or the provision of public goods that benefits group B but not group A would generate lower welfare. Amenity consumer surplus—Amenity consumer surplus, expression (23), refers to changes valued by consumers that do not operate through prices, incomes, taxes, transactions constraints, or public goods. In the case of casino gambling, which has been available for decades, for example, the primary benefit of a new casino may not be its lower price or different product—these can be identical to those offered in Las Vegas—but the nearness of the new service. In that case the value of the improved location can be inferred from observations on how the amount of gambling observed varies with distance from the service location (see Grinols 1999, 2004a). In the case of a national park or other recreation site, the amenity value may be inferred in other ways, just as economists have found ways to value reduced pollution through observations on how people’s behavior is altered when, say, the water quality and the swimming beach is cleaner (see Bockstael and McConnell 1993). Amenity values, of course, can be positive or negative. Negative externalities are examples of harmful amenities. If a new enterprise causes crime, for example, quite apart from the direct social costs of apprehension, adjudication, incarceration, increased police presence, destruction of distributive efficiency, or other costs identified in (14)–(24), the social climate may become fearful or unpleasant to residents, affecting their satisfaction with living in the area and also, therefore, influencing the decision of other firms to locate in the vicinity. Negative amenities can be the source of the need for compensating wage differentials—a topic well discussed in the economic literature— that provide information on the costs involved. Surveys and other direct means may also be suitable to determine amenity value in many cases. Conventional price consumer surplus—Expression (24) is the conventional measure of consumer surplus. It can be interpreted as the amount of money the consumer would be willing to give up in return for
Public goods have the characteristic that consumption by one individual does not diminish the ability of others to consume the same good. For example, radio broadcasts may be consumed by multiple individuals.
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having better prices, just as the amenity benefit measures the amount of money the consumer would be willing to give up in return for having the amenity in question.The only difference between the two terms in (24) is the price vector. If prices p i1 are better for the household than prices p i0 (lower for goods purchased and/or higher for goods sold), then it is positive. Certainly, the introduction of new business that causes the local wage rate to rise and raises the price at which the consumer sells his labor is an economic benefit that should be counted. In conclusion, the itemization of social costs is possible in a rational, internally consistent, theoretically sound way that allows refinement and measurement. It would be a mistake to adhere to the view that some of the costs and benefits in (14)–(24) are somehow more or less “real” than others. Certainly difficulty in measuring some types of social costs should not be construed as reason to dismiss them from view.
THE TWO-SECTOR ECONOMY REVISITED The elements (14)–(24) elaborate on the example in the Social Harms section of a two-sector economy by considering the welfare effects of crime on the noncriminal part of the population. In the example, we considered only the effect of stolen good y on all citizens and found that the social cost equaled the value of the stolen property. Had we considered the innocent or victimized portion of society in the two-sector example, the measured social costs would have exceeded the value of the stolen property. Had real resources been diverted to crime prevention, this would have added yet another social cost. Finally, many of the terms in (14)–(24) would be identically 0 for the example in the Social Harms section of this essay because of the economy’s simplicity, absence of government, and fixed prices. No amenity, public goods, tax, capital gain, consumer surplus, or change in firm profit terms would be present, for example.
MEASUREMENT I have discussed elsewhere methodology for estimating the social benefits of gambling (Grinols 2004a, pp. 111–130). Although both benefits and costs are important, we focus here on social costs. Most of the social costs of gambling fall into the category of harmful externalities (positive externalities are also possible) because they are direct costs imposed on the innocent portion of the population that do not operate through the pecuniary mechanism of prices and markets. If introduction of a new business causes the rental price of office space to rise, for example, this imposes a cost
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Research and Measurement Issues in Gambling Studies
on other businesses that want to rent space but would not be considered a harmful externality because the effect is transmitted through the market. If a new business causes congestion on the road to the downtown working district that imposes higher travel-related costs on employees of other businesses or necessitates the need for more police to be hired to regulate commuters, these would be externality costs—in the former case, because a direct harm is imposed on other agents, and in the latter because physical resources are removed from the stream of other production to remedy the deterioration in traffic conditions. Externalities and related social costs of gambling disorders have been discussed elsewhere in greater detail (Grinols 2004a, pp. 24–26, 136). For reference we provide a list here: 1. Crime (police, apprehension, adjudication, and incarceration costs).Table 20.2 documents costs associated with a single case of embezzlement. 2. Business and employment costs (lost productivity, lost work time, unemployment-related employer costs such as search and retraining) 3. Bankruptcy 4. Suicide 5. Illness (stress-related sickness, cardiovascular disorders, anxiety, depression, and cognitive disorders, which have all been linked to pathological gambling) 6. Social service costs (treatment, unemployment, and other social services and payments related to gambling) 7. Direct regulatory costs 8. Family costs (divorce, separation, domestic violence). From 1978 to 1988, Nevada ranked first in child death from neglect and abuse. 9. Abused dollars (see Glossary) Most social costs have multiple causes, only one of which might be pathological or problem gambling. Measurement attempts therefore make a predictable choice: Either social statistics must be accessed and analyzed to identify causal links between the amount of gambling and the amount of social costs or a more direct route must be taken operating through the investigation of the social costs of problem and pathological gamblers themselves. An example of the former is the paper by Grinols and Mustard (2006). FBI Index I crime statistics were obtained for every county in the United States over a period of twenty years and examined to estimate the effect of casinos on crime in host and neighboring counties. Statistical studies involve all of the usual challenges of large-scale econometric research. One disadvantage is that data are often incomplete. In the case of casinos, American Indian casinos are not required to report their gross revenues, so the most obvious measure of degree of casino presence—revenues— is unavailable and must be proxied by dummy variables indicating when operating Class III (casino-level) gambling is present. A second disadvantage is that it is
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Table 20.2 Crime-Related Social Costs Associated with a Single Case of Embezzlement. Minneapolis Police Investigators and staff
$929.48
FBI Agents and staff Lab expenses Total
$38,668.11 $1,200.00 $39,868.11
U.S. Attorney’s Office Prosecutor and staff Supervisory Total
$11,713.80 $521.92 $12,235.72
Judicial Judge Jury Court expenses Miscellaneous (presentence report) Defendant (Wilkinson) Total
$3,853.80 $6,954.00 $1,866.00 $555.98 ($50.00) $13,179.78
Incarceration Danbury, Connecticut Oklahoma Bryan,Texas Halfway house Transportation Total
$4,257.12 $1,080.87 $19,651.17 $4,578.52 $715.00 $30,282.68
Supervised Release Three years Wilkinson pays $80/mo. Total
$7,030.80 ($2,880.00) $4,150.80
GRAND TOTAL
$100,646.57
Source: Reva Wilkinson case, reported in the Minneapolis Star-Tribune, December 4, 1995, p. A6.
often hard to separate the effect of one cause from the the complexity of other causes, even when a causal link is present. Finally, statistical correlation never proves causality, though carefully and thoroughly done econometrics can provide a virtual proof of causality. The second approach is to survey problem and pathological gamblers and compare their gambling-related social costs with the social costs generated by the general population. One must be careful to select samples that are representative of
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the population that one wants to study, and the effects of multicausality must be taken account of.This approach has the disadvantage that only the social costs that operate through problem and pathological gamblers are measured. Here we provide a brief summary, starting with an ideal methodology and moving to next-best alternatives.We close with a concise review of what some of the current research says about the social costs of gambling. A first-best approach to identifying the social costs of gambling is to obtain a representative sample of the population that includes problem and pathological (P&P) gamblers and gamblers who are not P&P. Each observation would include data on social cost items in the foregoing list of externalities, answers to the South Oaks Gambling Screen (SOGS) (the most widely used screen, and for many reasons the preferred instrument), plus questions to identify alcoholism, drug abuse, and other addictions. (If an instrument other than the SOGS is used to identify P&P gambling, then both it and the SOGS should be used, to norm the results of the two screens against each other.) If other conditions are known to cause social costs on the list, then information about them should also be collected. For each social cost to be explained, a regression should be run that includes as explanatory variables the presence of the relevant causal factors other than P&P gambling and the questions on the gambling screen.Very likely it will be sufficient to include as the explanatory variable for gambling the number of SOGS questions answered in the “wrong” way rather than variables for each question individually. If questions are run individually, interaction terms should be present to capture the fact that social costs may rise only after a sufficient number of SOGS questions are answered in a particular way. Care should be taken to select the appropriate statistical procedure. Instrumental variable techniques should be used if necessary. Also, cost observations are often truncated at zero because a portion of the sample is bunched there. Tobit regressions may therefore be preferred. Predictions of social costs based on the estimated coefficients then form the measure of the contribution of P&P gambling to the social cost in question. Presuming that the sample collected is representative of the population of interest and that other relevant explanatory factors are present, sample selection bias and omitted variable bias (the issue of multicausality is sometimes called comorbidity in the medical literature) are eliminated. Predicted costs from the regression form an unbiased measure of the social cost expected if one additional individual of the type described is present. Apply these social cost numbers to the population of person types and sum to the economy level to get total social costs. This procedure can be modified if the required data gathering is too extensive, as in Ryan et al. (1999) or Schwer and Thompson (2004). Ryan et al. surveyed P&P gamblers in treatment and collected information on how many SOGS questions were answered in a way to indicate problem gambling. A survey of the
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general population was then taken. Social costs were greater among the group in treatment, which also exhibited higher SOGS scores. Predicted social costs were interpolated for individuals whose scores placed them in between based on the number of the South Oaks score.This interpolation, therefore, used a linear function. One can also imagine a Latin square approach where the cells are determined by status of pathological or not pathological gambler on one axis, and alcoholism/ drug addiction present or not on the other. A “difference of differences” approach then explains what the contribution of problem gambling is to social costs, depending on whether other addiction is present or not. Schwer and Thompson (2004), for example, found that the social costs of P&P gamblers without other addictions is 81.4% as high as the average social cost per pathological gambler of those with other addictions. Table 20.3 reports information about the social cost of pathological gambling based on current research. The left-hand column separates social costs according to the ninefold classification discussed previously. For comparison purposes, the next column to the right reports numbers from original studies conducted between 1994 and 1999, adjusted to annual social costs per pathological gambler in 2003 dollars (see Grinols 2004a, Table 7.1, pp. 172–73). The column reports the average of figures from studies for Florida, Wisconsin, Connecticut, South Dakota, South Carolina, and the United States. The next column reports original numbers from Ryan et al. (1999) arranged to fit the categories shown.The right-hand side column reports original data from Schwer and Thompson (2004) but makes a sample selection adjustment, suggested by the authors, based on work by Westphal, Johnson, and Stevens (1999). Notice that none of the studies has information on the social costs of suicide. Direct regulatory costs are generally
Table 20.3 Annual Social Costs per Pathological Gambler. Cost
Studies 1994–99
Ryan et al.
Thompson and Schwer
After Westphal– Adjustment
Crime Business/employment Bankruptcy Suicide Sickness Social service Direct regulatory Family Abused dollars
4823 1810 186 ? 773 474
1392 5936 ?
3809 5037 777 ?
1943 2569 396 ?
456
593
303
62 2333
3175
9493
4842
TOTAL
$10,459
$10,959
$19,711
$10,053
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Research and Measurement Issues in Gambling Studies
found as regional totals and are not tied to the number of P&P gamblers. Some studies are missing estimates of family costs, sickness, and bankruptcy, but for the group as a whole all social costs are estimated other than suicide. Total additional social costs are provided in the bottom row, ranging from $10,459 to $10,053 (after correction).These numbers are remarkably similar and, given the size of estimates for bankruptcy, sickness, and family costs, unlikely to diverge hugely based on the inclusion or absence of some of these costs. Completing the table by inserting the maximum estimated numbers into the empty slots raises the highest estimate to $12,571, while completing it by inserting the minimum estimated numbers into the empty slots leaves the lowest estimate at $10,459.The use of numbers such as those in Table 20.2, as already noted, depends on the connection between increased access to gambling leading to more P&P gamblers. Figure 20.2 shows information linking the availability of casino gambling, measured by proximity, to the degree of problem gambling, measured by number of exclusion requests by gamblers in voluntary exclusion programs (VEPs) concerned about their own gambling.The figure comes from the Policy Analytics (2006) report to the Indiana Gaming Commission. On the horizontal axis is distance to nearest casino in miles. On the vertical axis is number of exclusion
VEP participants per adult population
0.0006
0.0005
0.0004
0.0003
0.0002
0.0001
0.0000 0
20
40
60
80
100
120
140
Distance from nearest casing (in miles) Figure 20.2. Relationship between distance to the nearest casino and rate of participation in a voluntary exclusion program (VEP).
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participants per adult capita.The estimated relation shows a clear decline as distance to the nearest casino rises.
SUMMARY AND CONCLUSIONS This paper makes four points. First, the difficulty in generating valid costbenefit information for citizens and government is both political and theoretical in nature:Those with a vested interest in seeing gambling continue without increased limitations or restrictions do not have an unbiased interest in the outcome of costbenefit studies, and many in the economics profession lack one or more of the necessary analytical tools, data, or understanding. Second, many of the oft-repeated claims about social costs are false (e.g., that stolen property per se does not imply social costs because the thief gets what the victim loses). This is shown by the numerical example in the Social Harms section and the mathematical framework of the Economic Development section.Third, just as every statistic is unbiased for something, what to count in a cost-benefit study is often not a methodological choice, as is often asserted, but a matter of what the researcher wants to study. It is perfectly appropriate to report, for example, what social costs are imposed on nonpathological gamblers by those who are pathological gamblers. Fourth, good methodology exists for empirical research and has been used in relatively recent studies conducted in the casino postexpansion era for filling in the unknown costbenefit pigeonholes with numbers.
GLOSSARY Abused dollars dollars obtained improperly but not reported in terms of a crime. Often this is the case because the money or property is stolen by a relative or friend. Cost-benefit analysis a methodology for recognizing gains and losses in a way that the identified elements form exhaustive and mutually exclusive categories that are measured in common units. Economic development changes in the economy that lead to increased wellbeing for existing residents. Externalities an effect that a firm or household’s choices have on other firms or households that does not operate through market prices. For example, a golf course whose groundskeeping pollutes the ground water creates a negative externality to other users of the ground water, but a golf course whose purchase of water causes the market price of water to rise does not create an externality.
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Research and Measurement Issues in Gambling Studies
Pathological gambling defined in the Diagnostic and Statistical Manual of the American Psychiatric Association as “persistent and recurrent maladaptive gambling behavior as indicated by five (or more)” of ten items.Among these are repeated attempting, unsuccessfully, to stop gambling; returning another day to win back losses (“chasing” one’s losses); lying to family members or others to conceal the extent of one’s gambling; and committing illegal acts such as forgery, fraud, or theft to finance one’s gambling. Problem gambling is a nontechnical term. It refers to the same behavior as pathological gambling but when exhibited to a lesser extent. Social benefits components in a cost-benefit analysis that reflect a rise in the utility of households. Social cost components in a cost-benefit analysis that reflect a decline in the utility of households. Social surplus the value of the change in social welfare resulting from a given change to the economy. Shadow research research sponsored for the purpose of revisiting existing research, often with the hope or expectation that it will contradict conclusions of the original study when such conclusions are disliked by the sponsoring agent.
REFERENCES Benston, L. (2003). Expert: Problem gambling study flawed. Las Vegas Sun, March 31. Bockstael, N. E., and McConnell, K. E. (1993). Public goods as characteristics of non-market commodities. Economic Journal, 103, 1244–57. Breen, R. B., and Zimmerman, M. (2002). Rapid onset of pathological gambling in machine gamblers. Journal of Gambling Studies, 18, 31–43. Bridwell, R. R., and Quinn, F. L. (2002). From mad joy to misfortune:The merger of law and politics in the world of gambling. Mississippi Law Journal, 72, 565–729. Buck, A. J., Hakim, S., and Spiegel, U. (1991). Casinos, crime and real estate values: Do they relate? Journal of Research in Crime and Delinquency, 28, 288–303. Chesney-Lind, M., and Lind, I. Y. (1986). Visitors against victims: Crimes against tourists in Hawaii. Annals of Tourism Research, 13, 167–91. Ferrell, D., and Gold, M. (1998). Casino industry fights an emerging backlash. Los Angeles Times, December 14, A1. Glaeser, E. L., Sacerdote, B., and Scheinkman, J.A. (1996). Crime and social interactions. Quarterly Journal of Economics, 111, 507–548. Gould, E. D.,Weinberg, B. A., and Mustard, D. B. (2002). Crime rates and local labor market opportunities in the United States: 1977–1997. Review of Economics and Statistics, 84, 1, 45–61. Grinols, Earl L. (1996). Incentives explain gambling’s growth. Forum for Applied Research and Public Policy, 11, 119–124. —— . (1999). Distance effects in consumption. Review of Regional Studies, 29, 1, 63–76.
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—— . (2004a). Gambling in America: Costs and Benefits. New York: Cambridge University Press. —— . (2004b).Taxation vs. litigation. Managerial and Decision Economics, 25, 179–183. Grinols, E. L., and Mustard, D. B. (2001a). The economics of casino gambling. Journal of Economic Perspectives, 14, 223–225. —— . (2001b). Business profitability vs. social profitability: Evaluating the social contribution of Industries with externalities and the case of the casino industry. Managerial and Decision Economics, 22, 143–162. —— . (2006). Casinos, crime, and comunity costs. Review of Economics and Statistics, 88, 28–45. Grinols, E. L., and Omorov, J. D. (1996). Development or dreamfield delusions? Assessing casino gambling’s costs and benefits. Journal of Law and Commerce, 16, 49–88. Kindt, John W. (1994a). Increased crime and legalized gambling operations: The impact on the socioeconomics of business and government. Criminal Law Bulletin, 43, 538–539. —— . (1994b).The economic impacts of legalized gambling activities. Drake Law Review, 45, 51–95. —— . (1994c). The negative impacts of legalized gambling on businesses. University of Miami Law Journal, 4, 93–124. —— . (1996).The business-economic impacts of licensed casino gambling in West Virginia. West Virginia University Institute for Public Affairs, 13, 22–26. —— . (2003). The gambling industry and academic research: Have gambling monies tainted the research environment? University of Southern California Interdisciplinary Law Journal, 13, 1–47. Levitt, S. D. (1998).Why do increased arrest rates appear to reduce crime: Deterrence, incapacitation, or measurement error? Economic Inquiry, 36, 353–372. Lott, J. R., and Mustard, D. B. (1997). The right-to-carry concealed handguns and the importance of deterrence, Journal of Legal Studies, 26, 1–68. Miller, T. R., Cohen, M. A., and Rossman S. B. (1993). Victim costs of violent crime and resulting injuries. Health Affairs 12, 186–197. Miller, T. R., Cohen, M. A., and Wiersema, B. (1996). Victim Costs and Consequences: A New Look. Washington, DC: National Institute of Justice. Mustard, D. B. (2003), Reexamining criminal behavior:The importance of omitted variable bias. Review of Economics and Statistics, 85, 205–211. Policy Analytics. (2006). A Benefit-Cost Analysis of Indiana’s Riverboat Casinos for FY 2005:A Report to the Indiana Legislative Council and the Indiana Gaming Commission. pp. 1–89. January 17. Ryan, T. P., and Speyrer, J. F., with Beal, S. T., Curckel, D.V., Cunningham, B. R., Kurth, M. M., Scott, L. C.,Wall, J. L., and Westphal, J. R. (1999). Gambling in Louisiana: A Benefit/Cost Analysis. Prepared for the Louisiana Gaming Control Board, Louisiana State University Medical Center, pp. xxv, 107. Schwer, R. K., and Thompson, W. N. (2004). The Social Costs of Gambling Addiction and the Influence of Comorbidity, pp. 1–26. Las Vegas: Center for Business and Economic Research, University of Nevada. Simurda, S. J. (1994). When gambling comes to town: How to cover a high stakes story. Columbia Journalism Review, 1994, 36–38, 38. Thompson, W. N., Gazel, R., and Rickman, D. (1996a). Casinos and Crime in Wisconsin: Is There a Connection? Wisconsin Policy Research Institute Report, 9, 8. —— . (1996b). The Social Costs of Gambling in Wisconsin.Wisconsin Policy Research Report, 9, 1–44. Thompson,W. N., and Schwer, R. K. (2003). Beyond the limits of recreation: Social costs of gambling in southern Nevada. Proceedings of the 14th International Conference of Gambling and Risk Taking. Vancouver, British Columbia, May 28, 1–34. Westphal, J., Johnson, L. J., and Stevens, L. (1999). Estimating the Social Cost of Gambling for Louisiana. Baton Rouge: Louisiana State University Medical Center. Wheeler, D. L. (1999). A surge of research on gambling is financed in part by the industry itself. Chronicle of Higher Education, March 5, A17–19.
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CHAPTER 21
Gambling and Crime Colin S. Campbell
David Marshall
Department of Criminology Douglas College New Westminster, British Columbia Canada
Research and Community Engagement Division Queensland Office of Gaming Regulation Brisbane, Queensland, Australia
Introduction Categorizing Gambling and Its Relationship(s) to Crime Illegal Gambling Crimes Correlated to Problem Gambling Crimes Associated with Legal Gambling Expansion Crimes Correlated to Gambling Venues Crimes Distinct to Legal Gambling Operations Graft and Corruption Researching Illegal Gambling Researching Crime Correlated to Problem Gambling Researching the Impact of Gambling Facilities Methodological Challenges of Researching Gambling and Crime Data Problems The Social Construction of “Official Statistics” Demarcation of Gambling Concluding Observations
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INTRODUCTION Gambling is an activity which, despite having gained popularity and legitimacy in many parts of the world by the early twenty-first century, still occupies an uneasy place within the parameters of legitimate social and economic activity. In part, this is due to the potential for negative side effects. One of these side effects (both historically and presently) is that gambling is often associated with crime. This is true despite the fact that gambling varies dramatically in extent, format, and acceptance across time and space.That is, the legal status and public acceptance of gambling has varied throughout history. Even today, gambling which is legal in one place may be a crime in another. In Canada, Morton (2003) characterized attitudes toward gambling throughout the twentieth century as largely ambivalent. Most forms of gambling were illegal in Canada for most of the century but were, nevertheless, regularly practiced in one form or another. Essentially official condemnation coexisted alongside unofficial toleration during the first half of the twentieth century. Periodically, however, when public sentiments appeared to object to illegal gambling, it was less often a matter of moral objection per se and more usually a concern with “gambling’s support of organized crime or its corruption of police and politicians” (Morton 2003:165).What is evident is that despite its illegality, gambling flourished in Canada’s major cities prior to the modern era of legalization that began with the 1969 amendment to Canada’s Criminal Code. Elsewhere, similar histories are evident. Australia, New Zealand, and Great Britain all experienced fluctuations in the legal status of gambling during the nineteenth and twentieth centuries before ending the period with unprecedented levels of popular and legal acceptance. As recently as the early 1900s, governments in these three nations were tightening rather than loosening legal restrictions on gambling (O’Hara 1988:149). In Australia at the time, most state governments reformed laws forcing the closure of gaming houses (McMillen 1996:16). In the United States, too, state governments banned gambling during the early 1900s, except for six states that permitted wagering on horse racing (Munting 1996:36). In many parts of the world where gambling was illegal, it nevertheless remained popular, with opportunities to participate never difficult to locate (Munting 1996:36–39). Enforcement of unpopular and unworkable laws was difficult, often sporadic, and ineffectual (McMillen 1996, p. 15; O’Hara 1988, p. 136). Gambling thus retained a popular appeal amongst some sectors of the community despite being formally illegal.The simple fact that gambling was frequently an illegal activity in the past is likely to have contributed to the broad perception that it is intrinsically related to criminal activity. However, in certain jurisdictions this perception has also proven to be true. For example, the United States in particular has experienced a long and often colorful historical association between gambling, organized crime, and unsavory characters. Bookmaking, numbers, and casinos have all been
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controlled and conducted illegally by organized criminal entrepreneurs at some point in American history. Even in Nevada, where state laws were relaxed in the 1930s to permit casinos, promoters who once conducted illegal gambling ventures were the first to operate legal gambling establishments (see Reid and Demaris 1963). Opinion polls in Canada indicate a belief that an inevitable association exists between gambling and crime (Azmier 2000). However, empirical research has not convincingly demonstrated that such a link exists. Nor has research demonstrated just what the link might look like. Despite the growth of legal gambling industries worldwide, there is a surprising shortage of empirical evidence regarding the relationship between gambling and crime or the extent to which gambling places a burden on criminal justice systems. This chapter considers a range of research that has focused on the gambling/crime nexus. However, the particular focus is on research that has sought to explicate the nature of gambling-related crime in Canada.This is because some of the more important scholarly work yet conducted on gambling-related crime has been undertaken by Canadian scholars Garry Smith, Tim Hartnagel, and Harold Wynne (see Smith and Wynne 1999; Smith, Wynne, and Hartnagel 2003). The work of Hartnagel, Smith, and Wynne is distinctive, not only for its substantive findings, but for its methodological approach and its endeavor to offer theoretical analyses of the social control of gambling and gambling-related crime. To date, very few academic discussions of gambling and crime have utilized theoretical perspectives to explicate the social control of gambling. Thus the effort of Hartnagel, Smith, and Wynne to offer theoretical interpretations of the relationship between gambling and crime through the application of criminological perspectives distinguishes their work (see Campbell, Hartnagel, and Smith 2005; Smith, Hartnagel, and Wynne, in press; Smith et al. 2003). In addition to examining existing studies, this chapter identifies and considers the methodological challenges that are inherent to gambling and crime research. These include such problems as the lack of conceptual clarity in categorizing different criminal offenses in relationship to gambling, difficulty in gaining access to law enforcement sources and data, and reliance on “official” crime statistics. Despite Canadian research owing an intellectual debt to studies conducted in other jurisdictions, particularly in the United States, it must be emphasized that crime is widely accepted to be dependent on particular social, cultural, political, economic, and historical contexts (see Christie 2004). Consequently, while particular research methodologies may have applicability irrespective of jurisdiction, it is highly unlikely that research findings are directly transferable or applicable across jurisdictions.With specific regard to the relationship between gambling and crime, the effect of gambling on communities is variable and may depend on local conditions (e.g., general crime rates, policing priorities and resources, employment rates, and other such variables) and is thus not generalizable from jurisdiction to jurisdiction (Stitt, Giacopassi, and Nichols 2000).
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CATEGORIZING GAMBLING AND ITS RELATIONSHIP(S) TO CRIME The relationship between gambling and crime is complex and dynamic. There are likely to be different types of crimes associated with gambling and variations among jurisdictions, across cultures, and over time. Therefore, to refer simply to a single relationship between gambling and crime ignores complexities. A multiplicity of relationships between gambling and crime must be recognized. The following section seeks to catalogue the ways in which gambling and crime can intersect. For those areas in which substantial research has been conducted, relevant findings from the academic literature are discussed. Campbell et al. (2005, pp. 38–48) attempted to clarify the ways in which gambling and crime intersect.They proposed seven categories of gambling-related crime (see also Smith et al., in press). It is to be noted that these categories are not discrete, and particular types of crime may be located within several categories. Nevertheless, they provide a clear framework around which to discuss and thus better understand the complicated nexus between gambling and crime.To reduce overlap, their classifications have been condensed into six categories and are reviewed next.
ILLEGAL GAMBLING Illegal gambling entails those forms of unauthorized gambling that explicitly breach the criminal law in any given jurisdiction. For example, the Criminal Code of Canada prohibits certain gambling business activities such as bookmaking, keeping a common gaming house, and operating unlicensed electronic gambling machines and explicitly forbids specific (and somewhat antiquated) games such as three-card monte, punch boards, and coin tables. Generally speaking, any gambling format that is not explicitly exempted from Criminal Code prohibitions is illegal in Canada. Gambling activities which fall under this category are not static and will vary dramatically across jurisdictions depending on local laws at any given point in history. Indeed, given the highly dynamic nature of modern gambling, what is or is not legal is in flux. For example, perhaps the most obvious area in which the legal status of gambling is in question involves the relatively recent phenomenon of Internet gambling. According to a report prepared for the Canada West Foundation on Internet gambling, it is a breach of the Criminal Code for a private, commercial, Canadianbased gambling site to accept bets from Canadian citizens via the Internet (Kelly, Todosichuk, and Azmier 2001). However, in 2001, a publicly traded corporation,
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Starnet Communications International, was criminally charged and pleaded guilty to providing North American bettors with Internet gambling services (see Lipton 2003; Ryan 2002). The fact remains, however, that Canadians have the ability to gamble at offshore Internet sites with relative impunity (Lipton 2003). While the Criminal Code may prohibit Canadians from participating in gambling on a website located in another country, there is no mechanism to effectively enforce the prohibition (Kelly et al. 2001). A similar situation has emerged in Australia, where an Internet casino gambling license has been granted to the Northern Territory–based Lasseters Casino, which is permitted for use only by non-Australian residents (other than those who reside in Alice Springs, where Lasseters’ land-based casino operates). Australians, however, are not restricted by law from gambling with overseas-based online casinos (see Woolley 2003 and McMillen 2000 for a discussion of this unusual situation). Another aspect which has emerged in relation to Internet gambling involves Canadian First Nation sites. More specifically, it has been alleged that the Kahnawake Mohawks are in violation of the Canadian Criminal Code by licensing operators of Internet gambling sites from physical premises located on their tribal lands. While the Quebec and federal governments, together with the provincial police, have investigated their Internet gambling activities, no action has been taken to halt the operations. Even though Internet gambling is not yet a popular activity for Canadians (less than 0.5% of the adult population report having gambled on Internet sites), regulatory challenges and law enforcement dilemmas are nevertheless posed for Canadian authorities (Kelly et al. 2001).
CRIMES CORRELATED TO PROBLEM GAMBLING Problem gambling has been defined as “gambling behaviour that creates negative consequences for the gambler, [for] others in his or her social network, or for the community” (Ferris,Wynne, and Single 1999, p. 57). One of the recognized social and economic impacts of problem gambling involves illegal acts committed to obtain money to gamble or to pay gambling-related debts (Volberg 2001). Blaszczynski and McConaghy (1994) distinguish these two variations as direct and indirect problem gambling–related crime, the former being crime motivated by the desire to keep gambling or to fund gambling, and the latter being crime committed to cover shortfalls in living expenses due to gambling losses. Since electronic gambling machines have been identified as the format most commonly associated with gambling problems (Breen and Zimmerman 2002; Dickerson 2003; Griffiths 1993; Smith and Wynne 2004), the presence of such machines in a jurisdiction is widely believed to add to the crime rate.
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CRIMES ASSOCIATED WITH LEGAL GAMBLING EXPANSION Crimes associated with legal gambling expansion include minor offenses which occur indirectly due to the presence or expansion of gambling facilities. Examples of these are common assaults, car theft, robberies, and other street crime which may not have occurred had gambling facilities not existed. Ambient crime such as prostitution, robbery, drug trafficking, or theft from vehicles is often thought to increase as a consequence of the establishment of new gambling facilities.This issue is one of the most heavily researched in the crime/gambling nexus. Findings, however, have been equivocal. Such research has usually sought to assess the extent to which particular forms of gambling have affected crime rates in specific jurisdictions. Generally, this research has been focused on the impact of casino gambling (e.g., Grinols 2004; Lynch 1999).
CRIMES CORRELATED TO GAMBLING VENUES This type of gambling-related crime entails those criminal acts which occur in gambling venues and which sometimes involve the operations of the venues. Essentially, gambling formats and ambiences draw a variety of participants and onlookers. Some gambling venues are thus rendered more susceptible to criminal activity than others. For example, casinos are more susceptible to crime than are bingo halls (see Smith et al. 2003), and this is very likely due to the nature of their respective clienteles. Organized crime penetration of casinos for the purposes of “skimming” profits and controlling the labor unions that supply and operate casinos was once a commonplace form of gambling venue criminal activity. Reports from both the United States and the United Kingdom indicate that hidden ownership of casinos (by criminal interests) has been an issue of long-standing concern (Pinto and Wilson 1990, p. 3). Tax evasion through hidden ownership can occur whereby criminals “disguise their interests through the use of nominee shareholders holding shares on trust” (ibid.). Some on-site criminal activity, such as money laundering, is tangential to the gambling action per se and is more a result of opportunistic criminal types being attracted by the free-flowing cash, throngs of customers, and relative ease with which the proceeds of crime can be legitimated (Smith and Wynne 1999). Racetracks and casinos are cited as popular venues for money laundering schemes (Beare and Schneider 1990). In Canada, despite a law requiring an official report for cash transactions over $10,000, casino money launderers avoid detection by making several smaller cash exchanges so as not to arouse suspicion (Smith and Wynne 1999). Casinos are also focal points for crimes such as passing counterfeit currency, loan-sharking, prostitution, pandering, and drug trafficking (Calgary
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Police Service 1996). Similarly, video lottery terminals (VLTs) have been associated with an increase in criminal activity. For example, break-ins at facilities housing VLT machines, where monies in the VLTs are the target, are commonplace (see McDonald 1998). Employee theft is claimed to be common in gambling venues and can be attributed to the problematic circumstances of low-paid workers tempted by large amounts of rapidly circulating cash (Smith et al. 2003, p. 13).
CRIMES DISTINCT TO LEGAL GAMBLING OPERATIONS Some criminal behavior in gambling venues is a by-product of the games themselves, such as cheating. For example, tampering with the instruments of gambling is one form of a venue crime (e.g., marking cards, using loaded dice, recalibrating gaming machines, unbalancing roulette wheels, drugging horses), along with gambler/employee collusion (e.g., signaling a blackjack dealer’s hole card, introducing an unshuffled deck, race fixing by jockey/trainer conspiracy) and miscellaneous scams such as altering bets after the outcome is known, using a computer or mechanical device to keep track of cards played, and overpaying winners (again, a prearranged ploy between player and dealer). Ethnographic work conducted by Prus and Sharper (1977) successfully depicted the underworld life and criminal activity of “road hustlers”—confidence men who specialize in cheating at play through the techniques of manipulating dice and card games. Working in small groups, road hustlers actively seek to penetrate private card or dice games held usually as an adjunct to such events as annual fraternal organization meetings, golf tournaments, sports dinners, sales conferences, and other typically all-male social functions. Unlike interpersonal violent crime and property crimes, card and dice cheating that occurs in informal private gambling games (legal in Canada) goes relatively unnoticed and therefore “unpoliced.”Although their research was conducted almost thirty years ago and predates the massive expansion of legal gambling formats, the work of Prus and Sharper stands today as an important exposition of the social organization of criminal activity and the gaffs associated with gambling conducted in private settings. More recently, McMullan and Perrier (2003) examined the social organization and criminal technologies involved in a relatively sophisticated fraud perpetrated against the Atlantic Lottery Corporation (ALC). Specifically, McMullan and Perrier were able to gain access to investigative files that documented the extensive looting of electronic gambling machines in the province of Nova Scotia. Supplementing archived data provided by ALC with interviews of corporate managers, criminal investigators, and computer specialists, the authors undertook a case study of how a partnership between a small group of computer-savvy friends and
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Research and Measurement Issues in Gambling Studies
associates systematically manipulated payouts to their advantage.The study, distinct if not unique, reveals the nature and technical complexity of both committing and combating crime against modern computer-based gambling formats.
GRAFT
AND
CORRUPTION
Finally, graft and corruption are crimes which are associated with governmental licensing and regulation of legal gambling premises. This type of crime could involve bribes to secure licenses or to relax regulatory checks and usually involves government officials. Several Canadian provinces have had their share of gambling-related political scandals in the past decade. These scandals have been documented by Campbell (2000), Campbell et al. (2005), Hutchinson (1999), Norris (2002), and Smith et al. (2003).
RESEARCHING ILLEGAL GAMBLING There have been relatively few studies that have specifically examined illegal gambling. One study by Canadian researchers Smith and Wynne (1999) concluded that there was extensive illegal gambling in the four largest Western Canadian cities—Vancouver, Calgary, Edmonton, and Winnipeg. They identified sports betting with a bookmaker, unauthorized card clubs, unlicensed VLTs, and offshore lottery sales as the most prominent illegal gambling formats in western Canada. Somewhat ironically, these are versions of government-offered gambling formats. Illegal gambling formats generally compete well with their legal counterparts because they offer more attractive betting propositions and services such as credit, better odds, higher stakes, and telephone betting (Small 1999). Since illegal gambling operators are willing to extend credit, there is thus an increased likelihood of gamblers becoming vulnerable to loan sharks, blackmailers, or extortionists. Outside of North America, illegal gambling has also been widespread. In Australia, for example, Sydney is recognized as having had a long history of illegal casinos, many of which operated through loopholes in the law (Lynch 1999:244). Government inquiries into these casinos often identified substantial associated criminal activity, including corruption and drug trafficking. Of note in Sydney is that after the opening of a legal casino operation in the 1990s, all of the illegal premises in nearby areas were reported to have closed (Lynch 1999:244). While identifying and documenting illegal gambling activity can be challenging for researchers given its “hidden” nature, estimating its extent is even more difficult. However, one estimate of illegal gambling in a Canadian region was provided by Ontario’s Illegal Gaming Enforcement Unit. From its inception in 1997 to 2001 the unit recorded 1370 illegal gambling occurrences, 2069 persons
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charged, 3517 charges laid, and 2034 machines seized at a value of $6,016,505. In addition, $1,233,763 in cash was seized and $2,839,533 worth of fines and forfeitures was imposed (Moodie 2002). Moodie’s (2002) observations on illegal gambling in Ontario also include: ● ●
●
●
●
a definite link between organized crime and illegal gambling; an estimated three murders and 25 armed robberies annually in Toronto associated with illegal gambling houses; an estimated 4000 to 5000 illegal gambling machines in Ontario owned and operated by organized crime groups; a belief that profits from illegal gambling are used to fund other unlawful activities, thereby entrenching organized crime in the community; and the view that “proceeds-of-crime” legislation has been the most effective tool in combating illicit gambling, because it allows the dismantling of operations through seizures of assets and profits.
More recently, summary statistics released by the Investigations Branch of the Gaming Policy and Enforcement Branch in the province of British Columbia point to a significant number of criminal breaches pertaining to illegal gambling in the province. For example, in 2004–2005, the Integrated Illegal Gaming Enforcement Team (IIGET) consisting of Royal Canadian Mounted Police (RCMP) officers who work collaboratively with the Investigations Branch, opened 328 files dealing primarily with illegal lotteries, illegal video gaming machines, common gaming houses, and Internet gambling allegations (Gaming Policy and Enforcement Branch 2005).
RESEARCHING CRIME CORRELATED TO PROBLEM GAMBLING Of particular interest to this issue is the extent to which gamblers turn to crime because of their gambling, as opposed to illegal activities by criminals who happen to be gamblers. Although attention has focused on this issue due to recent increases in the availability of legal gambling, crime committed by gamblers is not a new phenomenon. It was raised as far back as 1947 as the factor most frequently present in cases of embezzlement (Peterson 1947). In attempting to ascertain the extent of criminal activity which is committed by problem gamblers as a result of their gambling activity, research has revealed a variety of results. While there has long been anecdotal evidence from clinical, welfare, and judicial sources linking problem gambling to criminal behavior (O’Connor and Jones 1998), more accurate data have not been consistent or available. One of the likely reasons for this is that a number of different approaches have been taken.These have included:
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●
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general population surveys (e.g., Smith and Wynne 2002; Wynne Resources 1998); studies of persons in problem gambling treatment programs (e.g., Blaszczynski and McConaghy 1992, 1994; Blaszczynski, McConaghy, and Frankova 1989; Brown 1987; Ladouceur et al. 1994; Lorenz and Politzer 1990; Meyer and Fabian 1992; Polzin et al. 1998); studies of prison and corrective services populations (e.g., Blaszczynski and Silove 1996; Lahn and Grabosky 2003; Lesieur and Klein 1985; Productivity Commission 1999;Walters 1997); and examination of police or court records (e.g., Australian Institute of Criminology and PricewaterhouseCoopers 2003; Crofts 2002; Gaming Policy and Enforcement Branch 2005; Smith et al. 2003).
Prevalence rates of offending amongst populations have tended to differ depending upon whether the sample is recruited from prisons, counseling, or general populations and whether the methodology is self-report or based on official data. Generally, the first approach—data from general population surveys—has shown only a modest association between problem gambling severity and the commission of criminal acts. For example, in two Alberta general population surveys, respondents were asked if they had ever had trouble with the law because of their gambling activities. Of the problem gamblers, only 2% (Wynne Resources 1998) and 5.6% (Smith and Wynne 2002) admitted to having committed illegal acts to support their gambling participation. In contrast, data gathered from populations of problem gamblers have consistently revealed a much higher rate of offending. In one such study, 68% of a sample of Quebec Gamblers Anonymous (GA) members reported committing illegal acts to finance their gambling (Ladouceur et al. 1994). In what has subsequently proven to be a groundbreaking qualitative study of problem gambling, Henry Lesieur (1984) gained considerable knowledge of the array of crimes perpetrated by compulsive gamblers. While employed parttime at a gas station located near a major horse race track, Lesieur established relationships with jockeys, trainers, bookmakers, and race track gamblers to undertake extensive field work in the study of problem gamblers and the various crimes they committed in order to meet financial obligations and to stay in action. Crimes that came to Lesieur’s attention consisted of mostly property crimes: burglaries, theft from employers, check forgery, and trafficking in stolen goods (Lesieur 1984:170–99).
RESEARCHING THE IMPACT
OF
GAMBLING FACILITIES
Gauging the extent to which particular forms of gambling have affected crime rates in specific jurisdictions has thus been a heavily studied topic.
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Most notably, many studies have sought to ascertain whether “ambient crime” increases as a consequence of the establishment of a new casino facility (e.g., Grinols 2004; Lynch 1999). Findings, however, have been equivocal, with results often heavily dependent on the research method, time period, and location studied. In one of the earliest examinations of the impact of newly opened casinos on crime rates, a study by Friedman, Hakim, and Weinblatt (1989) in and around Atlantic City that compared crime rates in the post-casino years (1978–83) with those in the pre-casino years (1974–1977) found that all types of crime except for larceny had risen. Their conclusion was that crime rates were higher in Atlantic City than would otherwise have been the case in the absence of casinos.This study also suggested that there was a spillover effect into adjacent counties, where higher rates of crime were detected than in counties not bordering a casino county. Similar findings have also been reported from other localities in the United States which have detected increases in specific types of crime but not in others. One study conducted by Stokowski (1996) in rural Colorado reported that in those counties in which casinos had opened, rates for all types of property crime as well as aggravated assault had increased since gaming development. However, as Stokowski (1996) observed, tourists to those counties had also increased—at a rate faster than crime rates—and thus the per capita rate of crime had decreased.A similar study by Wilson (2001) in the Indiana communities of Hammond and Rising Sun found that the introduction of casinos did not have an impact on overall crime rates but that some types of crime did increase, which seemed likely to be due to the casinos’ presence. Notable was a statistically significant rise in the number of thefts and assaults in Rising Sun. Such findings have also been detected in Wisconsin, where Gazel, Rickman, and Thompson (2001) reported that the broad categories of violent and property crime both increased in counties when casinos opened—notably nonrobbery violence and motor vehicle theft. In contrast to these findings, Miller and Schwartz (1998), who reviewed a number of studies, including some conducted in Atlantic City, found no compelling evidence that casinos were unique in generating increases in crime in surrounding areas. As they argued, due to the increased pedestrian and tourist traffic, and perhaps coupled with increased alcohol consumption, it is likely that crime rates will rise when a casino is opened.This outcome, they point out, is no different than any other new tourist destination. In Canada and Australia, studies into crime rates after the opening of casinos have tended to report minimal or no impact. One such study was a one-year review of Casino Windsor prepared by KPMG Management Consulting (1995) for the Ontario Casino Corporation. In that study, the opening of Casino Windsor and the impact on crime in the local community was considered to be minimal. Similar findings have been reported following the opening of casinos in Sydney (see Lynch 1999) and Brisbane (see McMillen and Rolfe 1997). In those studies, factors such as the level of local area policing, casino security, and local pedestrian traffic were
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all identified as factors likely to keep crime rates low. As Wilson (2001) observed, the presence of casino security staff might deter criminal activity, as might the presence of heightened pedestrian activity in the vicinity as levels of “natural surveillance” increase the likelihood of criminal activity being detected by passersby. In a slightly different approach to studying the impact of new casinos on criminal activity, Piscitelli and Albanese (2000) examined trends in the number of persons who were denied entry to Canada at western New York State border crossings after the opening of Casino Niagara.
METHODOLOGICAL CHALLENGES OF RESEARCHING GAMBLING AND CRIME Despite the wide-ranging studies touched on previously, substantial challenges exist in generating valid and reliable data in order to understand the relationships between gambling and crime. Indeed, given the politically sensitive nature of both gambling and crime, research into these areas is problematic. Reasons for the difficulties in determining the extent of gambling-related crime include: ●
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Objective data are lacking. Data are often self-reported and of questionable validity. Gambling-related offenses, particularly against family members, are often not reported, and if reported, charges are not always filed (see “abused dollars” in Chapter 20). Convictions are not always obtained, thus the use of court records or prison populations in research does not provide true measures of the extent of gambling-related crime (Sakurai and Smith 2003:3).
Almost 40 years ago, criminologist Donald Cressey (1967) identified methodological problems faced by social scientists who sought to study organized crime, such as the secrecy of participants, the confidentiality of the information collected by law enforcement and investigative agencies, and the perceptive filters of informants and investigators. Cressey’s observations hold true today for social scientists seeking to study the more general nexus of gambling and crime. Two major issues, however, result in particular difficulty for researchers, namely, the reliability of data and problems in demarcating gambling from other criminogenic factors. Not unlike illicit drug transactions, much of illegal gambling is cloaked in relative secrecy.This presents serious problems not only for law enforcement personnel but for social scientists seeking to understand the phenomenon. Cressey (1967) noted that social science researchers must have “connections” to police in order to gain insight into organized crime. Given the confidentiality and secrecy of criminal
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intelligence data, law enforcement officials are generally not willing to disclose or share information with non-police personnel. Access and cooperation of police agencies are not easily obtained by civilian researchers. Furthermore, criminal intelligence data (if and when they are shared with civilian researchers) are not oriented toward assisting social scientists, and matters that may be of interest to researchers “simply do not occur to law enforcement personnel” (Cressey 1967:109). It is also inherently difficult, ethically problematic, potentially dangerous and conceivably even illegal for researchers to observe illegal gambling directly. Cressey (1967, p. 109) points out in discussing organized crime specifically, “there is no known way to observe everyday interactions of organized criminals with each other, with other criminals, or with non-criminals.These facts of life pose serious methodological problems for the social scientist.” Cressey’s claims have applicability to gambling-related crime research and will be considered subsequently. Data derived from interviews with law enforcement personnel are fraught with problems related to subjective filtering. Similarly, secondary sources such as official crime statistics compiled by police or other criminal justice agencies are problematic given the significant amount of crime that remains unnoticed and unreported.
DATA PROBLEMS At the very root of the problems of investigating the crime/gambling nexus is the simple absence of objective data. In Canada, Smith and Wynne (1999) sought to examine the relationship between crime and gambling in Western Canadian provinces.A major hindrance to the study was that neither law enforcement agencies nor courts maintained comprehensive data files on the incidence of gambling-related crime. As Smith and Wynne (1999, p. 94) reported: ●
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It was virtually impossible to assess the magnitude of gambling-related crime in Western Canada due to the inadequacy of official police and court records; and Due to limited human and financial resources and other priorities, police agencies could not generally give gambling-related crime the attention it might have deserved.
Such observations have been affirmed in Australia. A study in the state of Victoria found that despite considerable anecdotal evidence linking gambling and crime, official statistics—compiled by police, courts, and correctional facilities— could not, for a variety of reasons, be used to identify crimes as being gambling related (Centre for Criminology and Criminal Justice 2000). Simply including gambling as a category on a checklist for all offenders would provide valuable, albeit self-report, data (ibid.).
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THE SOCIAL CONSTRUCTION
OF
“OFFICIAL STATISTICS”
Even if data were routinely collected regarding whether gambling had been a factor in a criminal event, it is unlikely that accurate estimates of gambling’s influence on crime could be calculated. Due to the fact that official statistics are socially constructed, there are multiple points at which the reliability and validity of data collected through official sources can be compromised. Given that official crime statistics, including Uniform Crime Reports, are wholly dependent on “reported crime,” criminologists have typically been well aware of the deficiencies of crime statistics as valid and reliable measures of crime. For example, considerable discretion is exercised in whether or not a particular incident is officially recorded as a “crime.” The exercise of such discretion was particularly evident in the examination of police records conducted by Smith et al. (2003). Criminologists are also aware that some victims of crime simply may not perceive their victimization as a crime. Hence, they will not think to report it to authorities. For example, a stolen wallet may be construed by its owner as simply “lost.” Other victims may perceive their victimization as a crime but will not deem the matter sufficiently serious to report it to the police. Indeed, some victims may even fear reprisals and thus not report it at all.Yet other victims will report their victimization to the police, only to find that the police do not deem the matter serious enough to record it formally as a criminal event. The considerable discretion exercised by victims, the police, and other criminal justice personnel results in omissions from official records. The most obvious, and quite possibly the most insurmountable, obstacle to understanding the crime/gambling nexus thus derives from what criminologists refer to as the “dark figure of crime,” which refers to that portion of criminal activity that goes undetected or unreported to police (Schmalleger and Volk 2005, p. 46). It is a figure that is unknown and, indeed, unknowable. Even once a crime is reported and recorded as such, there remain multiple points at which it can be “lost” as an official statistic. To make sense of criminal justice statistics, Palys (1997) suggested that it is helpful to introduce the notion from test theory that every observed score (O) is a function of the “true” score (T) plus some degree of error (e), that is: O =T + e In the best of all worlds, it is desirable to reduce the error to zero so that O = T. That is, with respect to criminal behavior, observed scores about the incidence of crime ideally would reflect nothing but the true incidence of crime (the amount of crime that exists in reality). However, in reality there is inevitably a high degree of error involved (e.g., due to underreporting and underrecording). Figure 21.1 illustrates how crime statistics are constructed and how the exercise of discretion confounds the observation process.
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A criminal event occurs
Perceived as a crime by someone
A ‘‘True’’ score
Witnessed by police
No
Decision to call police
Event perceived as crime
No
No
Police decide to respond: determine crime
Decision to write report
No
No
Decision to write report
No
No
Coding of event
A crime statistic
An ‘‘Observed’’ score
Figure 21.1. The process by which a crime statistic is constructed (adapted from Palys 1997; Skogan 1975).
When and if a “criminal event” occurs is largely a matter of discretion and is frequently based on ideological choice. For example, certain behaviors related to gambling may be legal or illegal depending on who is operating the game. VLTs are legal if managed and conducted under the auspices of provincial government authority; illegal if they are not. If, however, we can observe a “true” score (an actual criminal act occurring), it must pass through a series of “filters” before it becomes a crime “statistic” (an “observed” score) (Palys 1997, pp. 219–220). It is in the passing through the filters that crime statistics are socially constructed. That is, particular behaviors are judged to be crimes and are dependent on human actors making human decisions about what is to be considered criminal behavior and formally processed as such. The process starts in one of two ways: Either the crime is perceived by a victim/witness or it is directly observed by the police. The left side of Figure 21.1 shows that the first step must be taken by the victim/witness: Someone must perceive the event as a crime. If no crime is perceived, nothing will be done. Similarly, if for whatever reason someone decides not
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to call the police, nothing will be done.Alternatively, police may or may not attend the scene in the event that they are called. Likewise, if they do attend the scene, officers may deal with the matter informally and provide no written report at all. The right side of Figure 21.1 shows a similar process wherein police, independently of victims or witnesses, observe particular acts or incidents and make a series of decisions as to whether or not the incidents are going to be responded to formally as crimes.The overall perceived effect is that there is significant “leakage” or “case attrition” due to the exercise of discretion at the various junctures illustrated in Figure 21.1. As a result of these human decisions, criminologists argue that crime reports and crime statistics have little to do with the amount of actual crime and everything to do with what police consider to be important. In this respect, Brannigan (1984) metaphorically compares policing to fishing. Fishermen do not randomly cast their nets. Nor do the police.The size of the net, the size of the holes in the mesh, and the depth and location of where the net is dropped determine the type and size of the fish that are caught. So it is with policing, where the policies and priorities of law enforcement agencies determine the crimes and criminals that are caught and formally processed. Police reports and crime statistics are thus principally indicators of police priorities and activities more than of crime per se (see Brannigan 1984; Palys 1997, pp. 223–24). By comparing the activity of the Ontario Illegal Gambling Enforcement Unit and the amount of gambling-related crime it detected with that detected by law enforcement personnel in Edmonton, the impact of varying policies and priorities is dramatically revealed.That is, what appears to be a significant amount of gambling-related crime in Ontario may simply be an artifact of a particularly strident enforcement policy rather than a high amount of criminal activity. Conversely, the relatively low gambling-related crime rate in Edmonton may be the result of a lower police priority. The police—as gatekeepers of the criminal justice system—are nevertheless in the best position to collect such data (Centre for Criminology and Criminal Justice 2000). To try to generate more consistent data from police records, Smith et al. (2003) with the cooperation of Edmonton Police Service (EPS), developed a “gambling occurrence report” (GOR) which police officers were asked to complete in the course of conducting routine investigations. However, as the researchers conceded, the results were less than expected, as only 26 gamblingrelated incidents were reported from February 1 to August 31, 2002. In short, despite seeking cooperation from patrol officers, compliance was problematic. However, diligent EPS file clerks were able to identify an additional 93 gamblingrelated occurrences, although the researchers could not be certain that all gambling-related occurrences were identified. Of the total of 119 files identified from February to August 2002, the most frequently noted crimes related to gambling were 72 incidents of passing counterfeit currency, 10 thefts, 8 frauds, and
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5 domestic disputes.The remaining incidents involved a variety of other criminal activities (Smith et al. 2003, pp. 53–56). A potential source of further error in the compilation of data from such sources as court records is the possibility of self-serving claims by defendants.This problem may arise if problem gambling is introduced as a defense during the adjudication phase of a criminal trial or at the sentencing phase as a mitigating circumstance (Rosenthal and Lorenz 1992; Sakurai and Smith 2003). Examples of this are rare, with gambling usually viewed as an explanation rather than an excuse or sometimes as a symptom of some other problem (Sakurai and Smith 2003). However, when the criminal justice system of a jurisdiction is supportive of rehabilitation, it may be possible for an offender to secure some form of treatment for gambling problems or a community service order instead of a prison sentence (Rosenthal and Lorenz 1992). Of course, this prospect could also conceivably encourage defendants who do not actually experience any gambling problems to falsely invoke this as a mitigating circumstance.
DEMARCATION OF GAMBLING Another problem in understanding the nexus between crime and gambling is that it is often difficult to demarcate gambling from other precipitating factors influencing crime or crime rates. This applies when assessing the impact of new facilities or the influence of problem gambling on crime. New gambling facilities may introduce a range of factors that influence the crime rate. For example, a new casino could generate an increase in pedestrian and vehicular traffic, large numbers of tourists, as well as a range of new local businesses, among other things. Such developments would likely result in the general deployment of greater policing resources. Thus, in combination, all of these factors may contribute to changes in both the incidence and the detection of criminal activity. If increases in crime rates are detected in the vicinity of a new gambling venue, caution is needed when assessing the causes of increases due to the probability that casinos and the like attract people to the area.This has a twofold effect in that there are more people to commit offenses and to be victimized; thus the crime rate can rise naturally. However, tourists might also be more vulnerable to victimization due to their likelihood of having cash and valuables on them (Andrew et al. 1997, p. 270). Given that the literature clearly establishes a link between tourism and crime (Wilson 2001, p. 612), it is important to recognize that changes in crime rates in the vicinity of a new casino may be due to the heightened presence of people in the area rather than to gambling per se. As Stokowski (1996) noted, even if more crime is detected in the vicinity of a new casino than in the past, it may simply be that police activity and greater vigilance is influencing the data. A critical issue in examining crime in relationship to gambling venues is whether the venues cater to a
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tourist clientele or to local gamblers. Depending on this, there are likely to be different types of crime, different types of victims, and subsequently different outcomes and implications—such as enforcement and detection methods. Even when attempting to examine the effect of existing gambling facilities on local crime rates, similar methodological problems occur. For example, Smith et al. (2003) sought to plot the geographic location of reported crime between January 1 and August 31, 2002, in relation to existing casino, bingo, and VLT venues in Edmonton. Only those venues that had discrete street addresses could be plotted. As a result, gambling venues that were, for example, located in malls were omitted, since they could not be distinctly identified. Similarly, since VLTs in Alberta are commonly located in bars and cocktail lounges, it was impossible to determine accurately whether police attendance at these venues was related to gambling or to disturbances triggered by alcohol consumption (Smith et al. 2003, p. 59). The geographic plotting of gambling-related criminal incidents nevertheless revealed a minimal account of crime at Edmonton casinos.Those that did come to the attention of the police were considered to be minor. Given that the academic literature and reports from security personnel indicate that loan sharking and money laundering are generally believed to be routine at casinos, the researchers were surprised to note that the mapping exercise provided no incidents of such activity.With respect to crime occurrences at bingo halls in the City of Edmonton, the mapping exercise showed crime occurrences to be “infinitesimal” (Smith et al. 2003, p. 59).
CONCLUDING OBSERVATIONS It is evident from the preceding discussions that the relationship between gambling and crime is particularly complex and not particularly well understood. There are multiple factors to investigate and considerable difficulty in obtaining reliable data. While there have been important efforts made in many parts of the world toward greater understanding of the issues surrounding gambling and crime, substantial challenges remain. The work of Smith et al. (2003) represents a distinct and significant achievement in the sphere of gambling studies with respect to gaining the cooperation and trust of the EPS, the RCMP, the Alberta Gaming and Liquor Commission, and private security personnel. Through their “connections” and on the basis of their strong and positive academic reputations, Smith, Wynne, and Hartnagel were able to win and sustain the trust of law enforcement agencies that facilitated their access to otherwise confidential records. In a similar manner, McMullan and Perrier were also able to establish considerable trust with ALC and policing authorities. However, not all researchers are able to acquire access to generally classified data.
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Notwithstanding the access granted by law enforcement agencies, Smith, Wynne and Hartnagel concede that their findings are limited by their reliance on perceptual data provided by their sources. They also acknowledge the underreporting of gambling-related crimes by policing personnel as a major obstacle to being able to assess gambling’s impact on the broader community. Indeed, in analyzing the crime occurrence files of the EPS and the AGLC, their work illustrates how gambling-related crimes are (or, more accurately, are not) constructed.That is, their analysis reveals the highly discretionary processes which underpin how gambling-related crime is treated. Simply stated, law enforcement records underreport the amount of gambling-related crime and thus such records are deficient with respect to reliability and validity. Police accounts and/or official statistics should therefore be viewed with appropriate caution. To be sure, this is not a methodological problem distinct to the study of the social and economic effects of gambling. Rather, it is a problem that confounds research into crime and its effects in general. Even despite the researchers’ best efforts to solicit the cooperation of patrol officers in documenting ordinary crimes (via the completion of GORs) that might be correlated to gambling, systemic obstacles (overworked, understaffed patrols) impeded an accurate gathering of data. Indeed, Cressey’s (1967) observation that the investigative needs of police officers do not correspond with the needs of social scientists is borne out.The needs and priorities of police are principally and practically focused on gathering evidence sufficient to sustain criminal charges, not on examining background factors (such as problem gambling) that may have played a precipitating role.Thus, in the routine of daily police work, police have priorities other than being attentive to a possible relationship between a crime and gambling. Smith et al.’s (2003) study clearly demonstrates that in the context of gambling’s widespread legal availability at government-licensed, -operated, and -regulated venues, police generally do not give gambling-related crime a high priority. Generally speaking, it is evident that the relationship between crime and gambling remains obscure.The extent to which gambling places a burden on criminal justice system resources has so far been unanswered by research. Given the deficiencies of existing crime measures, particularly those reliant on official police reports and on crime rates generated by the justice system, efforts to assess the social and economic effects of gambling-related crime are handicapped by methodological weaknesses and, in turn, by the questionable reliability and validity of those data that are available. As noted earlier, Cressey (1967) contended that there are severe limitations on the extent to which social scientists can observe the interactions of organized criminals with each other, with other criminals, and with noncriminals. However, there are qualitative studies that tend to disprove Cressey’s contention.With respect to gambling-related crime (some of which is undoubtedly linked to organized crime interests), there have been important qualitative studies that have provided
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insights into the interactions of “underworld” actors and their criminal activities. Prus and Sharper (1977), for example, were able successfully to penetrate the world of confidence men and professional card and dice hustlers and thus reveal the inner workings of career criminals who make their living by cheating. In a similar vein, Lesieur’s 1984 qualitative study was able to gain considerable knowledge of the array of crimes perpetrated by compulsive gamblers. Likewise, the case study by McMullan and Perrier (2003) provided extensive insights into cybercrimes committed by loosely organized actors bent on defrauding legal electronic gambling machines. The studies by Lesieur (1984), Prus and Sharper (1977), and McMullan and Perrier (2003) thus suggest viable alternative methodologies for researching the crime/gambling nexus that circumvent the problems presented by reliance on police records and official crime statistics. While participant observation methodologies have a long and vibrant history in social science research, they have had a more limited application in the study of crime and deviance, and raise particular legal and ethical considerations. However, as alternative approaches to researching the nature, extent, and problems associated with such illegal gambling formats as sports betting, card rooms, and bookmaking, as well as tangential criminal activities (e.g., loan-sharking) associated with illicit gambling formats, they hold considerable promise for innovative and motivated researchers. A diverse range of both quantitative and qualitative data is needed in order to accurately assess the extent to which gambling places a burden on criminal justice system resources. Given the limitations inherent in existing measures of crime, particularly those reliant on official police reports and on justice system–generated crime rates, efforts to assess the social and economic effects of gambling-related crime are stalemated by methodological weaknesses and by serious problems regarding the reliability and validity of available data.
GLOSSARY Ambient crime street crime that occurs in proximity to a particular venue— for example, a casino. CPGI Canadian Problem Gambling Index—a measurement tool, developed by Canadian researchers, used in prevalence studies to determine the extent of problem gambling in the broader community. Criminal Code of Canada federal legislation enacted in 1892 and periodically amended that specifies criminal offenses and penalties. Gaff a trick, hoax, or confidence game intended to defraud someone. Loan sharking the lending of money at illegal or exorbitant rates of interest, usually associated with coercion to ensure repayment.
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Money laundering any attempt to conceal or mask the origin or source of money obtained through crime. Problem gambling gambling that creates negative consequences for the gambler, for others in his/her social network, or for the community. SOGS South Oaks Gambling Screen—a clinical checklist used to assess the level of severity of problem gambling. Street crime sometimes referred to as “ordinary crime” because it is relatively common; street crime typically entails criminal acts such as mugging or robbery, theft, and assault; so called because such crimes tend to occur in public locations.
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Cressey, D. (1967). Methodological problems in the study of organized crime as a social problem. Annals of the American Academy of Political and Social Science, 374, 101–112. Crofts, P. (2002). Gambling and Criminal Behaviour: An Analysis of Local and District Court Files. Report prepared for the Casino Community Benefit Fund, Sydney. Dickerson, M. (2003). Exploring the Limits of “Responsible Gambling”: Harm Minimization or Consumer Protection? Proceedings of the 12th Annual Conference of the National Association for Gambling Studies, Melbourne. Ferris, J.,Wynne, H., and Single, E. (1999, April). Measuring Problem Gambling in Canada. Phase I, Final Report to the Inter-Provincial Task Force on Problem Gambling. Friedman, J., Hakim, S., and Weinblatt, J. (1989). Casino gambling as a “growth pole” strategy and its effect on crime. Journal of Regional Science, 29, 615–623. Gaming Policy and Enforcement Branch [GPEB]. (2005). 2004–05 Investigation Summary Statistics. Prepared by Investigations Division, GPEB, Ministry of Public Safety and Solicitor General, British Columbia. Gazel, R., Rickman, D., and Thompson, W. (2001). Casino gambling and crime: A panel study of Wisconsin counties. Managerial and Decision Economics, 22, 65–75. Griffiths, M. (1993). Fruit machine gambling: The importance of structural characteristics. Journal of Gambling Studies. 9, 101–120. Grinols, E. L. (2004). Gambling in America: Costs and Benefits. Cambridge, UK: Cambridge University Press. Hutchinson, B. (1999). Betting the House:Winners, Losers, and the Politics of Canada’s Gambling Obsession. Toronto:Viking. Jones, P., and Hillier, D. (1995). From out of the shadows: Legalized gambling in the U.K. Geographical Magazine, 67, 30–34. Kelly, R.,Todosichuk, P., and Azmier, J. (2001). Gambling@Home: Internet Gambling in Canada. Calgary, AB: Canada West Foundation. KPMG Management and Ontario Casino Corporation. (1995). One-year Review of Casino Windsor. Toronto: KPMG Management Consulting. Ladouceur, R., Boisvert, J., Pepin, M., Loranger, M., and Sylvain, C. (1994). Social cost of pathological gambling. Journal of Gambling Studies, 10, 399–409. Lahn, J., and Grabosky, P. (2003). Gambling and Clients of ACT Corrections. Canberra: Centre for Gambling Research, Australian National University. Lesieur, H. (1984). The Chase: Career of the Compulsive Gambler. Cambridge, MA: Schenkman Publishing. Lesieur, H., and Klein, R. (1985). Prisoners Gambling and Crime. Paper presented at the Academy of Criminal Justice Sciences annual conference. Lipton, M. (2003, September 17). Internet Gaming In Canada. Presentation to the Global Gaming Exposition, Las Vegas. Retrieved April 29, 2006, from http://www.elkindlipton.com/pub_internet_ gaming.html Lorenz, V., and Politzer, R. (1990). Final Report on the Task Force on Gambling Addiction in Maryland. Report prepared for the Maryland Department of Health and Hygiene, Baltimore. Lynch, R. (1999). Crime in relation to the Sydney Harbour Casino. Current Issues in Criminal Justice, 10, 237–258. McDonald, R. (1998). Legalized Gambling and Its Potential Impact on Police Services. Paper presented to the 9th annual Canadian Association of Police Boards conference, Edmonton, AB. McMillen, J. (1996). Two-up from 1788 to the 1990s. In Gambler’s Paradise (J. McMillen, J. O’Hara, W. Selby, and K. Cohen, eds.). Brisbane: Royal Historical Society of Queensland. —— .(2000). Online gambling: Challenges to national sovereignty and regulation. Prometheus, 18, 391–401. McMillen, J., and Rolfe, R. (1997). Casino Impact Study: Crime Impacts (Brisbane)–Progress Report. Queensland University of Technology.
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McMullan, J. L., and Perrier, D. (2003).Technologies of crime:The cyber-attacks on electronic gambling machines. Canadian Journal of Criminology and Criminal Justice, 45, 159–186. Meyer, G., and Fabian,T. (1992). Delinquency among pathological gamblers: A causal approach. Journal of Gambling Studies, 8, 11–20. Miller,W., and Schwartz, M. (1998). Casino gambling and street crime. Annals of the American Academy of Political and Social Science, 556, 124–138. Moodie, L. (2002, March). Ontario’s Organized Crime Section–Illegal Gambling Unit: Its Evolution and Accomplishments. Paper presented at the Gambling, Law Enforcement and Justice System Issues conference, University of Alberta, Edmonton. Morton, S. (2003). At Odds: Gambling and Canadians, 1919–69.Toronto: University of Toronto Press. Munting, R. (1996). An Economic and Social History of Gambling in Britain and the USA. Manchester, UK: Manchester University Press. Norris, A. (2002).VLT kings were reluctant to discuss profits. Montreal Gazette, September 28. (From a six-article series on the Quebec VLT industry.) O’Connor, J., and Jones, G. (1998). Problem Gambling Related Crime: Where Is the Policy Response to a Structural Problem? Paper presented at the Partnerships in Crime Prevention conference, Hobart, Australia. O’Hara, J. (1988). A Mug’s Game: A History of Gaming and Betting in Australia. Kensington: New South Wales University Press. Palys,T. (1997). Research Decisions: Quantitative and Qualitative Perspectives.Toronto: Harcourt Brace. Peterson,V. (1947).Why honest people steal. Journal of Criminal Law and Criminology, 38, 94–103. Pinto, S., and Wilson, P. (1990). Gambling in Australia. Australian Institute of Criminology,Trends and Issues in Crime and Criminal Justice, 24. Canberra: Australian Institute of Criminology. Piscitelli, F., and Albanese, J. (2000). Do casinos attract criminals? A study at the Canadian–U.S. border. Journal of Contemporary Criminal Justice, 16, 445–456. Polzin, P., Baldridge, J., Doyle, D., Sylvester, J.,Volberg, R., and Moore, W. (1998). Final Report to the Montana Gambling Study Commission. In The 1998 Montana Gambling Study: A Report to the Governor and the 56th Legislature. Helena: Montana Legislative Services Division. Productivity Commission. (1999). Australia’s Gambling Industries.Vols. 1–2. Canberra: AusInfo. Prus, R. C., and Sharper, C. R. D. (1977). Road Hustler:The Career Contingencies of Professional Card and Dice Hustlers.Toronto: Gage Publishing. Reid, E., and Demaris, O. (1963). The Green Felt Jungle. New York: Pocket Books. Rosenthal, R., and Lorenz,V. (1992).The pathological gambler as criminal offender. Psychiatric Clinics of North America, 15, 647–660. Ryan, M. (2002). Organized Crimes and Internet Investigations. Paper presented at the Gambling, Law Enforcement and Justice System Issues conference. Edmonton: University of Alberta. Sakurai, Y., and Smith, R. (2003). Gambling as a motivation for the commission of financial crime. Trends and Issues in Crime and Criminal Justice, 256. Canberra: Australian Institute of Criminology. Schmalleger, R., and Volk, R. (2005). Canadian Criminology Today: Theories and Applications. 2nd ed. Toronto: Pearson. Skogan,W. G. (1975). Measurement problems in official and survey crime rates. Journal of Criminal Justice, 3, 17–32. Small, C. (1999). Gambling and the Harms We Choose to Have. Paper presented at the 2nd National Gambling Regulation Conference, Sydney. Smith, G., Hartnagel, T., and Wynne, H. (in press). Gambling-related crime in a major Canadian city: A case study. In Gambling in the 21st Century (J. F. Cosgrave and T. R. Klassen, eds.). Montreal: McGill University Press. Smith, G., and Wynne, H. (1999). Gambling and Crime in Western Canada: Exploring Myth and Reality. Calgary, AB: Canada West Foundation.
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—— . (2002). Measuring Gambling and Problem Gambling in Alberta Using the Canadian Problem Gambling Index. Report prepared for the Alberta Gaming Research Institute, Edmonton. —— . (2004). VLT Gambling in Alberta. Edmonton: Alberta Gaming Research Institute. Smith, G., Wynne, H., and Hartnagel, T. (2003). Examining Police Records to Assess Gambling Impacts: A Study of Gambling-Related Crime in the City of Edmonton.A report prepared for the Alberta Gaming Research Institute, Edmonton. Stitt, B. G., Giacopassi, G., and Nichols, M. (2000). The effects of casino gambling on crime in new casino jurisdictions. Journal of Crime and Justice, 23, 1–23. Stokowski, P. (1996). Crime patterns and gaming development in rural Colorado. Journal of Travel Research, 63–69. Volberg, R. (2001). When the Chips Are Down. New York:The Century Foundation Press. Walters, G. (1997). Problem gambling in a federal prison population: Results from the South Oaks Gambling Screen. Journal of Gambling Studies, 13, 7–24. Wilson, J. (2001). Riverboat gambling and crime in Indiana: An empirical investigation. Crime and Delinquency, 47, 610–640. Woolley, R. (2003). Mapping Internet gambling: Emerging modes of online participation in wagering and sports betting. International Gambling Studies, 3, 3–21. Wynne Resources. (1998). Adult Gambling and Problem Gambling in Alberta, 1998. Report prepared for the Alberta Alcohol and Drug Abuse Commission, Edmonton.
PART IV
Policy Implications of Gambling Research
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CHAPTER 22
Values, Objectivity, and Bias in Gambling Research Jennifer Borrell
Jacques Boulet
Borderlands Cooperative Melbourne,Victoria, Australia
Borderlands Cooperative Melbourne,Victoria, Australia
Introduction Ontological and Epistemological Premises of Various Research Approaches Positivist (Empiricist) Approaches Interpretationist (Phenomenology) Approaches Critical-Dialectical (Structural/Structuralist) Approaches Critical-Participatory (Action-Oriented) Approaches Postmodernist (Postrelativist) Approaches Transpersonal-Ecological Approaches The Use of the Various Approaches in Existing Gambling Research Context and Corruption of Research: How Values Come to Influence Research Activities Some Contemporary Systemic Influences on Research Processes Organizational and Institutional Influences Neo-Liberal Colonization of Universities and Research Institutions and the Primacy of Commercial Imperatives Government/Industry/Research Institution Collusion in Protecting Positions of Privilege Corruption of Science by Corporate Interests Within a Neo-Liberal Culture Governmental Influences
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Industry/Research-Institute Partnerships and Influence on Research Programs Values in Gambling Research and Their Relationship with Ideological, Cultural, and Systemic Influences Individualism Individual Pathology and Marginalization of the Problem Neo-Liberal Ideology, Individuals as Freely Choosing Consumers, and Utilitarianism Hiding Behind Putative Neutrality and Objectivity The Way Forward Multilevel Framework Understanding Harm Production Researcher Embeddedness Precautionary Principle
INTRODUCTION In this chapter, we investigate the linkages between epistemology, methodology, values, and the place of ethics and norms in (gambling) research. In particular, we discuss the role of the researcher in relation to his/her research according to different philosophical approaches. We then explore the social and systemic processes that contextualize, influence, and bias research endeavors. Key values that permeate gambling research are highlighted, with particular reference to the broad social, political, and economic contexts within which the research takes place. Finally, a few ideas for future research directions are suggested.
ONTOLOGICAL AND EPISTEMOLOGICAL PREMISES OF VARIOUS RESEARCH APPROACHES Reviewing the recent history of gambling research,Virginia McGowan (2004) draws on a comparison of positivist and naturalistic inquiry by Egon Guba and Yvonna Lincoln (1994), which provides a good starting point for this chapter (adapted from a fuller account in Borrell and Boulet 2005), with special reference to the relationship between the research and the world under study.As McGowan highlights: One assumption (underpinning differences in paradigms) concerns ontology, or what is understood as “reality,” and what can be known about it. Are only measurable
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phenomena “knowable” for example? Yet another assumption concerns epistemology, or the relationship between the knower and what is known. This assumption indexes the objective or subjective understanding of “reality,” where knowledge is understood to be either value-free, bounded, and distinct from the observer (objective) or deeply contextualised, multileveled, nuanced, and influenced by the observer (subjective).
Taking a cue from McGowan and moving beyond the positivist/naturalist divide, we look at the basic premises of several major research approaches, highlighting the assumed relationships between researchers and their “subject matter” and “subjects.”This provides a foundation for our later argument, that values form an intrinsic component of contemporary gambling research—as they do in all research.
POSITIVIST (EMPIRICIST) APPROACHES Knowledge is assumed to be based on observation of empirical phenomena (or verbal-cognitive renditions of such) made by objective, value-neutral scientists standing above or apart from the phenomena under study. The aim is to uncover universal laws, describing and predicting causal or associative relationships between the phenomena under study, construed primarily as mechanistic and linear. The effect of the researcher’s own presence is assumed to be minimal or nonexistent, so that “pure reality” can be studied and that whatever the researcher is told or observes would actually be happening, whether or not she is present to observe it (Mark 1996 as cited by Alston and Bowles 2003). James Doughney (2005, p. 10) notes that economics and behaviorist psychology are the most “positivistic” of the social sciences, even while an acknowledged “entanglement” of facts and human values could be said to characterize classical political economic theory, such as was originally proposed by Adam Smith. Research into gambling within this “paradigm” certainly continues to dominate the agenda, notably in many of the American studies. There is a strong focus on assumed cognitive, neural, and/or behavioral dysfunction in the form of psychological/experimental research and in surveys using pathological gambling screens (such as the South Oaks Gambling Screen). Characteristic of this orientation is the view of “the problem” as residing and being generated within the individual as a “pathology” or a “disorder,” an assumption taken on faith, even as evidence of such pathology is being sought.The chief proponent of this approach has been Harvard Medical School’s Division on Addictions, funded by the gambling industry through the National Center for Responsible Gaming (NCRG): The [NCRG] is committed to funding research that someday will identify the risk factors for gambling disorders and determine methods for not only treating the disorder [sic] but preventing it, much like physicians can identify patients at risk from
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cardiovascular disease long before a heart attack. In a field that is just emerging, continued research is critical to the advancement of scientific breakthroughs that will aid in developing tools for prevention and treatment. (NCRG 2007) … As argued by the Harvard Medical School study funded by NCRG in 1996, research that identifies biological markers for the disorder can provide a much-needed “gold standard” against which the accuracy of screening instruments can be measured. (NCRG 1999)
A recent NCRG conference typically focused on a putative “underlying syndrome” that would explain individual substance and behavioral disorders, a worldview supported by Howard Shaffer, director of the Division on Addictions: “Looking at the shared causes of addictive behaviors prompts us to consider new ways of studying and treating pathological gambling and related addictive disorders” (NCRG 2004).
INTERPRETATIONIST (PHENOMENOLOGY) APPROACHES Looking at nonpositivist approaches, within an interpretationist framework, the conceptual “stuff ” used for description and analysis is the everyday life-world of people and their associated states of consciousness through which they give “meaning” to what happens to them and around them. Interpretationists do not consider people’s life-world to constitute a separate (or “separable”) realm or that meticulous and methodical research can uncover universal laws. Rather, the life-world is seen as socially constituted or “constructed” based on (inter-) subjective “meanings about facts.” As the researcher is equally a meaning-giving social being (in the midst of other meaning-giving social beings), he should be situated in the process of life in general and of research about life in particular if he is to be “authentic” to the “object” of investigation.There is a long tradition associated with this view, as discussed in Guba and Lincoln (1994) and reflected in much of the methodological literature associated with qualitative research (Denzin and Lincoln 2001). In other words, interpretationists recognize that humans will always “deal with reality” in a “human” way, by necessity subjectively and intersubjectively and, through the latter, offering chances for “degrees of objectivity.” According to this line of reasoning, this is the only way to “make sense” of and “give meaning” to the world and to understand the meaning given to “their” world by research participants. Phenomenological researchers seek to enter the life-world of the gamblers and, through more or less participatory dialogue and observation, to figure out what it means to “be” and “act as” a problem gambler. Observations of the socialinteractive situations and contexts within which gambling occurs and in which the various consequences of problematic gambling are experienced and lived out may become part of the research.
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CRITICAL-DIALECTICAL (STRUCTURAL/STRUCTURALIST) APPROACHES For the critical-dialectical, or structural(-ist), researcher, the social world (including social theories, their concepts, and the individuals and researchers who “make” them) is constituted through structures (or structuring processes) and social forces of which human agents are (mostly or partly) unaware. Some Marxists, for example, maintain that social phenomena emerge from underlying structures and processes based on the modes and relationships of (economic) production, the related political and institutional processes, and the associated ideological “states of consciousness.”As the dominant ideology of (or hegemony within) a society cocreates the prevailing power structure or order, with experts and knowledge professionals acting as agents of transmission, the assumed neutrality of theory and of its “producers”—scientists and researchers—is fundamentally questioned. Certain feminist approaches as well as writings about the workings of race and caste in given societies could be included in this approach (Smith 1999). Gambling research would, therefore, start from the theoretical premise that the capitalist (or patriarchal, racial) interests and the structural arrangements expressing and reproducing them offer “products” that (under the guise of recreation) further alienate those who use them and are already in less powerful social positions. Profit accumulation and a regressive taxation system invariably affirm the existing inequities and exploitative relationships. Problem gambling research would not see the “problem” as located only in the “subject” of the gambler; rather, it would be seen to be “caused” by the existing structural arrangements (and dialectically related through social-psychological and class-, gender-, or race-specific processes) impinging on the subjective “response” of the more or less consciously aware gambler. We should distinguish between critical-structural and critical-structuralist; the first signifying the real-life and personal impact of existing “structural arrangements,” the second attributing a reified and deterministic quality to these arrangements, leaving human agents no “choice” but to “implement” what is wanted of them by the demands of the structure (Beach 2003). Space prohibits further discussion of the similarities in the conception of the “problem gambler” within such structuralist and positivist approaches, both assuming the existence of potentially immutable social and, especially, economic environmental (societal) elements, which are then understood as providing the “stimuli” for the “behavioral responses” of the gambler.
CRITICAL-PARTICIPATORY (ACTION-ORIENTED) APPROACHES Knowledge, human agency, and the constantly changing and to-be-changedworld “under study” are intrinsically connected; acting and thinking form
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a conceptual and “practical” unity, and all human actions are seen as necessarily meaning-giving, socially constitutive/constituted, and indicative of changes in the life-world of people. The implications for research are fundamental and important; while the positivist paradigm maintains that theory is formed independently of the world under study (with the world submitted to rigorous, objective, and methodical examination), within the critical-participative (action-oriented) approach a constant interchange between knowing, intending, and acting is acknowledged.Thus, theory is seen to be developed and is legitimated through action. Furthermore, the “objects/subjects” of research are, together with the researcher(s), fully involved participants in the research, which itself becomes part of the ongoing dynamic processes inherent in their social life-world. Therefore, the research orientation is constantly changing in a mutually affecting, dialectical relationship with that world. Problem gamblers and those involved with them in supportive or other roles would be seen as (potential) co-researchers in the initial investigations of their situation and their relationships.They would constantly reflect together on the meaning of what they progressively discover through their joint research efforts and, eventually, plan and execute actions or activities to productively deal with the predicament. This is conceived as a “joint” predicament and as intrinsically related to the way in which humans as “actors in relationships” are implicated in the “outside” world and in the structuring processes which constantly “create” them and their world. This philosophical and ethical orientation toward conducting research shares with the critical-dialectical approach a central ethical focus on the social responsibility of the researcher, based on the premise that intellectuals/researchers cannot be neutral in their knowledge development and intellectual activities. The researcher necessarily has an impact on the research process and, through that praxis, on the world (for better or worse or whether acknowledged or unacknowledged).
POSTMODERNIST (POSTRELATIVIST) APPROACHES Postmodernism entails “the rejection of ‘grand narratives’ in theory and the replacement of a search for truth with a celebration of the multiplicity of (equally valid) perspectives” (Burr 1995, p. 185), which can be drawn on to make sense of the world in which we live.The positivist idea of (a) central truth(s) to be uncovered through rigorous research using approved methods (based on “technical rationality”) is rejected for a more decentered conception of reality and knowledge. Structuralism and the idea that social phenomena can be explained by hidden structures (only) are rejected, together with all grand narratives intending to explain the world and all it contains. Individual, collective, or intellectual/professional practice or action is seen to be discursive (as a “text”) and relativistic, diluting the role of social agency as well as our potential for changing the “fate” of humankind, thus
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ignoring the context in which such potential for change can be sustained, dismissing or negating it as part of the rejected “meta-narrative.” Postmodern approaches are, therefore, often seen as nihilistic, because they seem to identify adherence to values and principles as mere forms of selfcenteredness. Since principles are “negated” or subjected to a process of “deference,” the self loses its “anchor” entirely and becomes flotsam on someone/something else’s tide.The ethical responsibilities of the researcher—or the intellectual—dissipate and a “de facto” alliance between positivism and postmodernism tends to emerge which, to a considerable degree, may explain some of the “backlash” against “alternative methodologies” and epistemologies (e.g., Lincoln and Cannella 2004).1 Notions of “problem” or “problematic” gambling or gamblers become themselves problematic, as they are intrinsically linked to the assumption of grand narratives of how one ought to live (or gamble) and how a good society should look and function. Postmodernism as a putative research paradigm represents fundamental problems when attempting to link ontological and epistemological premises of research with the teleological (purpose-oriented) impetus inherent in methodological/methodical inquiry.
TRANSPERSONAL-ECOLOGICAL APPROACHES With its emphasis on wholism and the dynamic interconnectedness inherent in all biological/social/human reality, the transpersonal-ecological approach presents an antithesis to the atomization and reductionism of traditional positivist (or empiricist) frameworks. The analysis of living systems is placed within four interconnected perspectives—form, matter, process, and meaning—making it possible to apply a unified understanding of life to phenomena in the realm of matter and of meaning:“A central insight of this unified, systemic understanding of life is that its basic pattern of organisation is the network.At all levels of life—from the metabolic networks inside cells to the food webs of eco-systems and the networks of communications in human societies—the components of living systems are interlinked in network fashion” (Capra 2003, pp. 228–29). The nature of the self (both of the “subjects” of research and of the researcher him/herself) is seen as relational, decentered, and socially, historically, and contextually contingent. The “self ” of the “human person” is seen to be embodied and a sensuous being, intricately connected with the social and physical world in a web of defining relationships. Knowledge and action, then, are aligned with psychological evolution and development, increasingly concomitant with the experience of ourselves as 1 Interestingly, in an interview with one of us, a gambling executive invoked postmodernism as a sort of apologia and justification for the gaming machine industry in that it provides an outlet for people “living for the day,” in line with what “those French philosophers are saying.”
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“carnal” beings, that is, conceiving of our own personal living reality as psychosocio-biological beings, relationally situated and reciprocally enmeshed in the process of responsibly sustaining life in all its dimensions—personal, social, ecological, and spiritual. Reflecting on the “nature” of human beings within this paradigmatic shift restores the fact of their ontologically social and embodied character to the center of epistemological attention, moving away from the conception of subjects as essentially “greedy” and “selfish” atomized beings, entering purely rational contractual relationships with one another (e.g., as portrayed within neo-liberal, especially “public choice” discourses), toward one which restores the existential duality of our personal-social (and ecological) ontology (or state of being). Elements of the problem gambling/gambler phenomenon being considered within this approach might include: ●
●
●
interrogation of the holistically understood impact of gambling on the social and ecological contexts within which it occurs, including impacts on individual, community, and other social contexts; the “physicality” of the situation within which gambling occurs, including the socio-psychological effect of the gambler’s interaction with electronic gaming machines (EGMs); and questions that would have to be added about the self-understanding of (problem) gamblers within the socio-ecological life-world in which they live, including their understanding of the “manufactured risks” (Beck, Giddens, and Lash 1994) of gambling opportunities they consciously or unconsciously are exposed to, if gamblers are not just understood as behavioral “dopes” reacting to an environmental stimulus they “obviously” are unable to cope with.
THE USE
VARIOUS APPROACHES GAMBLING RESEARCH
OF THE
IN
EXISTING
So much for a rudimentary overview of six ontological-epistemological “clusters” supporting potential approaches to (gambling) research. Obviously, more specific details about the variations within each of these approaches would be necessary, but we trust that, as a general introduction to the landscape of possibilities, they represent a useful background for a better understanding of what follows. Except for the committed positivist position, rarely will any of these approaches be implemented in pure form, with studies leaning toward the application of one or two of them, often attempting triangulation between data collected within each. (A good example of a triangulation approach combining
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phenomenological information with technological, economic, market, and regulatory data can be found in Livingstone et al. 2006). Positivist methodologies have dominated gambling research, but recently there has been a greater interest in phenomenological studies, exploring the lived experience, with personal accounts and stories forming the “data” of investigation (Dow Schull 2002; Rodda and Cowie 2005; Surgey and Seibert 2000), adding to our understanding of (problem) gambling phenomena and their meaning in people’s lives. Dow Schull’s (2002) research, in particular, is interesting for its concomitant analytical integration of structural factors, namely gender-role expectations, economic and market pressures, and a dominant neo-liberal ideology. Research endeavors emphasizing critical participation and action have included public performances and plays, published writings, self-help programs, gambler activism, and organized conferences and conference presentations. In the main, they have aimed at raising community (and professional) awareness and creating change; for example, greater product safety in the face of supply-driven harm (Anonymous 2006; Langworthy and Howard 2005;The Centre–Wangaratta and Benalla and Rural City of Wangaratta 2006;Women’s Health in the North and Brunswick Women’s Theatre 2005). A postmodern leaning may also privilege personal accounts and stories, on the one hand, to the detriment of a more holistic social and systemic analysis, while on the other hand, offering an invaluable “narrative” contribution to insights and knowledge about (problem) gambling and giving “ordinary” people a voice in public life, along with, hopefully, both the feeling and the reality of enhanced social power (Brown et al. 2000). Structural(-ist) analyses in gambling research are evidenced in numerous economic studies (Doughney 2004; Smith 2000).Again, such studies may also explore everyday existential, psychological, and social meaning-giving dimensions (Livingstone 2003), dialectically relating them to the structural and contextual aspects. Finally, transpersonal-ecological treatments of gambling phenomena seem to be the most scarce—although some have explored the interactive physical relationship between the gambler and the gaming machine ( Woolley 2003), and many studies (often government commissioned) have made useful analysis of gambling phenomena at various systemic levels.
CONTEXT AND CORRUPTION OF RESEARCH: HOW VALUES COME TO INFLUENCE RESEARCH ACTIVITIES Returning to the values that researchers hold and inject into their research enterprises, we now briefly outline some of the contextual and systemic ways and processes that values come to be part of human and social inquiry, while the
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section on Values in Gambling Research and Their Relationship will explore some of the specific values permeating gambling research. The five nonpositivist approaches detailed previously provide a compelling case that research necessarily forms an integral part of the world under investigation and that “pure data” do not roam the life-world uninfluenced by the presence of observers or investigators; indeed, values permeate the life-world in which research processes take part. Furthermore, acknowledged or not, researchers are active agents in the world and carry considerable ethical responsibility in their research activities and representations. Interrogating the contextual influences on and implications of research approaches is thus imperative, especially since they are “enacted” in organizational and political contexts which disallow their application in a “pure” form. More often than not, researchers are expected to produce research “results” couched in terms associated with mainstream positivist assumptions, even while using methods embedded in other, often paradoxical, epistemological assumptions. Fundamentally, normative, ideological concepts and ideas (or “values”) form the bedrock of our research undertakings; they include assumptions constituting the “world taken for granted,” as it were. While uninterrogated, they may be the most powerful formative influence shaping the inquiry process, and they inevitably underpin our ideas of what is selected for study and what is observed, what is normal and what is deviant, what is desirable and what is in need of remedy, what needs to change and at what point(s) of the system, who should be listened to and who should be ignored, which discourses or accounts of reality carry legitimacy and which do not, what interests should be represented (in view of the “marginal” and the “respectable”), and even, following the last points, which researchers or academics are worthy of tenure and promotion.All research is in some way about “speaking truth to power” (Wildavsky 1979), whether power comes in the form of funding regimes or as the researcher selecting only certain questions to be asked of a “subject” sampled from a population using “objective” selection criteria. On an operational level, the following are some of the questions we might ask in interrogating research programs, in order to highlight influences and relationships of power and the possible implications for research imperatives and methodologies: ●
●
Who decides what is to be researched, why, and how? Who are the power brokers? Who holds the “purse strings”? For what purpose is it being funded? What interests are involved? What are the implications for the research concepts, values, and methods? Specifically, who frames the subject matter that is to be investigated and what are the implications? (e.g., faulty genes, social disadvantage, government policies, exploitive industries, and/or fragile personalities)
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●
●
What types of researchers and institutions are “rewarded”? (e.g., in status or financial terms).Who is punished, censored, and/or ignored? What are the goals of the key players (in addition to gaining information and understanding)? (e.g., positive social and/or discipline contribution, revenue protection for governments/industries/organizations, casting current policies in a favorable light, organizational protection and survival, career enhancement)
And, to paraphrase Else Øyen (2002, p. 1), Who claims the right to define what is good and bad behavior, what is right and wrong in terms of social norms, and what is best for society at large? In analyzing any research program, reflection on such questions may lead to fruitful and profound insights about the intricate relationship between research and its social/systemic context.
SOME CONTEMPORARY SYSTEMIC INFLUENCES PROCESSES
ON
RESEARCH
Organizational, institutional, governmental, and industrial influences affect to varying degrees the form that research projects take, their investigative directions, and the interpretation of findings. We highlight some examples of how such influences are played out in the “real world” that researchers are living in and dynamically constituting in interaction with others. Organizational and Institutional Influences Within the institutional cultures that “house” research, certain ways of conceptualizing the subject matter and carrying out investigations will inevitably be normative, habitual, institutionalized, and differentially endorsed. McGowan invokes the term epistemic cultures to characterize the organizational and social contexts of expert systems, particularly as they occur in the fragmentation of university departments: “Each discipline is an encapsulated epistemic culture, separated from others by distinct ways of knowing, objectives, expert practices, and symbolic structures” (McGowan 2004, pp. 2–3). Neo-Liberal Colonization of Universities and Research Institutions and the Primacy of Commercial Imperatives A general neo-liberal ideology has come to permeate university culture and operations, giving (largely unquestioned) primacy to commercial imperatives and privileging a view of human beings as individual agents trading with each other in the economic “marketplace” (Gare 2006; Giroux 2005). In particular,
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academics and their departments have come under heavy pressure to bring in outside funding from industries, selling their research expertise and/or entering into joint ventures. This pressure to generate revenue in the face of ongoing funding cuts has become all too evident, as academics increasingly apply for research projects of which the goals, intentions, and conditions are set by outside bodies.As McGowan (2004) notes, in relation to the explosion of gambling studies over the last decade: New funding opportunities established by governments in response to public concern undoubtedly gave some impetus to scholarly interest in gambling—testimony to the notion that “if you fund it, they will come.” (p. 2)2
Adams (2004) illustrates some of the subtle and diffuse distortions in the democratic process that occur due to commercial pressures and/or incremental “temptations,” “when a broad variety of individuals, working in isolation and reacting to pressures from gambling providers, incrementally compromise their roles and responsibilities.” This is said to occur across the media, community agencies, politics, government agencies, and, of special importance here, universitybased research. Giving a fictional example of the latter, Adams (2004) comments that the stronger the relationship that “Jason” develops with gambling providers, the more his work will be seen to be “tainted” or biased by industry interests: “(H)is research output will come to rely more and more on their continued support, and he will over time adapt his focus in ways that are unlikely to jeopardise future funding.”
GOVERNMENT/INDUSTRY/RESEARCH INSTITUTION COLLUSION IN PROTECTING POSITIONS OF PRIVILEGE Governments are often complicit in such processes, as they protect or further the interests of powerful economic players; recent attempts at silencing climate change researchers in the United States and in Australia demonstrate eloquently the collusion between industry, capital interests and the government, thereby also illustrating that government funding of research does not promise independence. In general, those in positions of power may have an interest in ensuring that the production of certain environmental or social problems is not investigated, especially if the findings implicate positions of privilege (Øyen 2002). Certainly in Victoria, Australia, we have seen a parade of harm minimization initiatives in relation to the
2 McGowan continues: “Regardless of the reason for the interest in gambling as an object of study among scholars, the latter part of the 20th century remains remarkable in the vigorous demand for and production of expert knowledge about this phenomenon” (ibid.).
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EGM industry over the last decade, with little demonstrable effect in reducing gambling problems or adverse social impacts. Invariably, research, policy, or program initiatives are diluted as to their potential to change the status quo (inclusive of the lucrative revenue stream for industries and governments), with state budgets consistently forecasting increases in gambling taxation revenue. Corruption of Science by Corporate Interests Within a Neo-Liberal Culture There is not space here to discuss the many documented examples of the systemic corruption of scientific research by corporate interests in this context (Egilman and Rankin Bohme 2005; Horowitz 1967; Jones 1981; Murray 2006; Union of Concerned Scientists 2004). Nevertheless, related to the diagnosis of pathological gambling and other “mental disorders” with putatively individual etiologies, one study found that 56% of the 170 psychiatric experts who worked on the most recent edition of the Diagnostic and Statistical Manual of Mental Disorders (of the American Psychiatric Association) had at least one financial link to a drug maker at some point from 1989 to 2004. The researcher concluded that “transparency is especially important when there are multiple and continuous financial relationships between panel members and the pharmaceutical industry, because of the greater likelihood that the drug industry may be exerting an undue influence” (Richwine 2006). Industry influence over directions and findings in gambling research can be easily illustrated; for example, in Victoria, Australia, the independent Gambling Research Panel was recently disbanded and a new Ministerial Advisory Committee, with heavy industry participation, is overseeing the publicly funded gambling research program. Not only are the industry representatives equal “stakeholders” along with community and welfare groups, according to the Minister for Gambling they must also feel ownership over the future directions of our publicly funded gambling research program (ABC Radio National 2004). Governmental Influences Governments’ legitimation of new forms of gambling has constituted a powerful form of industry endorsement and promotion in many jurisdictions, with concomitant constrictions in the types of research that are resourced and with the ramifications of gambling-related policy stances invariably avoided as suitable subject matter for investigation. Furthermore, (implicitly) policy-critical reports may be withheld, or requests for fundamental changes may be made, with noncompliance having direct consequences for the obtaining of future research and consultancy contracts. Finally, the selection of appointees to regulatory boards and policy and research panels may act as a powerful tool in ensuring “business as
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usual” for governments (with industry appointments serving to protect commercial interests). In the gambling research field, governments and their political and governance imperatives are immensely influential over research programs in general, as they are prime funders of such projects (from the public purse) as put out for public tender. Indeed,“taking the queen’s shilling” has long been regarded as problematic in many respects (Trotman and Robertson, 1991). Industry/Research-Institute Partnerships and Influence on Research Programs Mutually beneficial partnerships between gambling industries, researchers/ research institutions, and sometimes governments have certainly emerged, all sharing the new lucrative revenue stream. As corporations seek to marginalize and (perceptually) minimize the gambling problem—presenting it as “exceptional” and a result of some inner disorder—some researchers working within the positivistempiricist mold have been comfortable and accommodating in such “joint ventures.” One example is the NCRG, the National Center for Responsible Gaming, bankrolled by the U.S. gaming industry, which has commissioned the Harvard Medical School Division on Addictions to research gambling problems through a “personal pathology” lens to the tune of millions of dollars. A key player in this partnership is Frank Fahrenkopf, Jr., president and CEO of the American Gaming Association and gambling industry advocate. In testimony before the U.K. Parliament’s (2004) Joint Committee on the Draft Gambling Bill in January 2004, he gave considerable weight to the notion of personalized gambling pathology and openly endorsed the role of the Division on Addictions by saying (in response to Q906): [I]t is very, very clear that most experts in the US believe today, on research done by the National Research Council of the National Academy of Sciences, and Harvard Medical School’s Division on Addiction [sic], that the rate of pathological gambling in the United States is about one per cent of the adult population.That is pretty consistent actually around the world with other studies that have been done.The important thing to realise is that research also shows that the majority of that one per cent are people who suffer from what is called co-morbidity; gambling is not their only difficulty.
And responding to a request to “give us a view about problem gambling as opposed to pathological” (Q910), Fahrenkopf responded: [W]e suggested that the person they really should consult, and you should get the benefit of his wisdom, is Professor Howard Shaffer of the Division on Addictions of Harvard Medical School who has done more work in this area than anyone in the world, and I think it is recognised now by even the anti-gaming people that the work and research they have done is the best that there is. There may be another 2% to 3% whom you would categorise as problem [gamblers]. These are people who may have a
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number of traits that could possibly lead them to become pathological gamblers. Dr. Shaffer and his research people of Harvard would tell you there is just as much likelihood that they will go the other way and will not have a problem.
VALUES IN GAMBLING RESEARCH AND THEIR RELATIONSHIP WITH IDEOLOGICAL, CULTURAL, AND SYSTEMIC INFLUENCES In this section, we use more examples from gambling research to illustrate some of the points already made, more specifically focusing on the way in which values and ideological concepts underpin the research enterprise. With the earlier formulated questions in mind, we discuss central values associated with individualism, neo-liberal/free-market ideology, and utilitarianism and critically examine the practice of researchers who espouse assumed value neutrality or objectivity when engaging in their (often industry- or government-funded) research efforts.
INDIVIDUALISM Prominently underpinning gambling research within the mainstream positivist approach are various forms and permutations of individualism. First, there is an assumption (especially in economic and cognitive-behavioral research) that social or human phenomena can—and should—be broken down into their constituent “elemental” parts; in this case, individual human subjects. It then “logically” follows that, “the whole being the sum of its parts,” the sum of individual positive and negative impacts of gambling on individuals equals its impact on the community constituted by those individuals.This omits social, relational, and other “intersubjective” and structural meaning-giving processes as related but autonomous fields and foci of investigation, with their own variables and theoretical interpretations. Such individualist assumptions also complement well the idea of the objective, neutral, and detached researcher, able to stand apart from the (singular) phenomena and “objects” under study, in order to observe, interrogate, manipulate, and measure according to predetermined and deduced “variables.” Thus, researchers are prevented, for the sake of “objectivity,” from entering the messy world where “things happen” on an ongoing basis and in the spaces between the assumed autonomous phenomena. Inherent in this conceptual system is a summative or additive approach to apprehending social phenomena, with problem/pathological gambling prevalence studies sometimes being construed as social impact studies (e.g., Cultural Partners Australia Consortium 2000).
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INDIVIDUAL PATHOLOGY AND MARGINALIZATION OF THE PROBLEM Intimately linked with individualism, the most commonly critiqued concept within gambling research is that of individual pathology.We have added our voices to the widely discussed political implications of this, criticizing the implicit and explicit ways in which the “problem” is located within the individual gambler and, less often, in his/her immediate social microcontext—understanding it as an inner pathology based on medical/psychological conceptualizations. This removes the focus from etiological (or causal) factors at other systemic levels, be they economic, political, cultural, or social, thus serving to place both responsibility and remedy for the gambling “addiction” on the individual gambler. At the same time, attention is removed from other agency forces, such as government regulation and the provision and promotion of hazardous products into the community by profitmotivated industries—to say nothing of the various forms of social distress and marginalization that may provide motivation for people to escape into the “zone” of EGM disassociation. There is ample reason for governments and associated interested parties to leave the phenomenon of problematic gambling largely uninterrogated; newer forms of gambling that have proliferated over the last fifteen years, in particular EGMs, have provided governments with substantial taxation revenue windfalls, which were desperately needed in the context of a general tax revolt within Anglo-Saxon countries (making direct taxation less politically palatable) and recurrent worldwide economic downturns.The gambling net constantly spreads to new international jurisdictions, as well as to demographic segments not traditionally prone to great gambling activity. It has suited many governments to take a managerial approach to the effects resulting from new forms of gambling, through “quarantining” the putative problem to be addressed. Committed, even forced, to preserve the lucrative revenue stream, it becomes prudent to marginalize the gambling problem as minimal and attributable to the defects (pathological or moral weakness) of a few individuals. Governments can simultaneously demonstrate responsible governance through setting aside a small portion of the gambling revenue for counseling, education, or “community building” projects. In fact, many governments legislating new forms of gambling include financial and operational provisions for “problem/pathological gamblers”—knowing full well that gambling problems and adverse social impacts are inevitable with certain types of gambling products, such as EGMs (Dickerson, Haw, and Shepherd 2003; Productivity Commission 1999, pp. 6.1, 6.54), while compartmentalizing and ostensibly anticipating them through managerial planning.
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NEO-LIBERAL IDEOLOGY, INDIVIDUALS AS FREELY CHOOSING CONSUMERS, AND UTILITARIANISM A particular form of individualism emerging in “objective” or “neutral” gambling research is that of the individual consumer exercising his/her democratic rights through freely chosen consumption activities, enabled by a free-market economy (Borrell 2005). John L. McMullan (2005, p. 17) states the case well: Typically, gambling is conceived as a “right” to freely consume and purchase products and . . . consuming behaviour is interpreted as a tacit agreement-in-action impervious to “external organisations” [that] may have a duty of care. . . .The rationality of the market and the rights of autonomous subjects and their behaviours are the de facto and de jure backdrop against which public policy is often made.Thus consumer choice is valorized and harm reduction programs are strategically framed within an “individual responsibility” paradigm that pathologizes people, promotes profits to shareholders and invisibilizes the broader social, cultural and political contexts that supply deleterious products.
Representing the human “subject” as a freely choosing consumer, research findings about social impacts and harm minimization options frequently take on a utilitarian cast—with the costs for the putative “majority” of happy or satisfied recreational gamblers weighed against the benefits for the “minority” of problem/pathological gamblers (Blaszczynski, Sharpe, and Walker 2001, pp. 42, 47, 51, 65). Apart from the obvious ideological bias, there are several fundamental problems with this analysis; first, there is the inherent impossibility of putting a cost figure on human suffering associated with gambling problems. The same might be said for happiness, even if economists feel they can measure community well-being through the calculus of purchasing behavior. More fundamentally, the moral underpinning of such utilitarian equations by governments, weighing costs and benefits for different sections of the population, is problematic (Wiseman 2000). In addition, the “sums” themselves are of questionable merit, invariably serving to minimize the gambling problem; very often, whole population prevalence figures are invoked, with, for example, the interests of 2% of problem/pathological gamblers weighed against those of the (putative) 98% of recreational gamblers—even while it seems obvious that to test the safety of a practice or product, we would need to look at the population engaged in such and that all of those without the problem are not necessarily using that product anyway. In fact, studies of EGM gamblers routinely reveal that very high proportions are problematic gamblers (Blaszczynski et al. 2001, p. 55; Rodda and Cowie 2005, p. 81; Schellinck and Schrans 1998, p. 3).
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This mode of reasoning often converges with a neo-liberal or economicrationalist bias that weighs the costs to the industry (and, conjointly, consumers who ostensibly “demonstrate” their satisfaction or well-being through their purchasing behavior) against the good it is likely to do for the assumed marginal few with gambling problems. As already mentioned, it is suggested that we must not impede the enjoyment of the majority of freely choosing gamblers in support of the interests of the few who have problems. However, as loss of control plays a central role in pathological or “addictive” gambling, the idea of choice becomes highly problematic. Furthermore, the democratic “free choice” giving gamblers the right to gamble and risk their money converges with the financial (also “speculative”) interests of the shareholders in gambling industries and governments (the latter either directly or via taxation), depending on the jurisdiction. Such thinking converges with a particular harm minimization approach to “problem gambling” in that the focus of attention—the harm to be minimized— is the individual gambler. Possibly, the hidden policy objective is rather the minimization of the appearance of gambling-related harms.We are, indeed, far removed from traditional concerns in social and other policymaking arenas predicated on eliminating the causes of harm, which, in the case of “problem” gambling, clearly could be identified as lying in the living circumstances of certain groups of gamblers, the widespread availability of demonstrably unsafe products, and the unbridled (demonstrably underregulated) profit motive of corporations and, associated with this, revenue for governments. Ubiquitous “responsible gambling” policies thus individualize the locus of agency and eliminate from language and from public consciousness the complicity of industries and governments in the production of gambling problems.They also provide the conceptual vehicle for a research and therapy industry, for those who want to work under its umbrella or who care to negotiate a line between ethical and social responsibility and organizational and professional survival. Speaking about “responsible gambling” policy initiatives, Campbell and Smith (2003) stated: The “responsible gambling paradigm” is only the latest strategic partnership of “government officials, the gambling industry and experts and professionals” who have transposed the “social problems affiliated with excessive gambling” into individual, moral and therapeutic discourses and in the process depoliticised the very supply of gambling and the conditions under which it is supplied and consumed. Its emphasis on individualism, choice and economic rationalism make it insensitive to genuine community prevention and precautionary politics.
HIDING BEHIND PUTATIVE NEUTRALITY
AND
OBJECTIVITY
The positivist belief in the “objectivity” of the research enterprise at its most naive is probably best elucidated by Howard Shaffer and Debi LaPlante, researchers
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at the Harvard Medical School’s Division on Addictions. As noted earlier, researchers in receipt of large industry funds have good reason to argue for the fact/value dichotomy, or that “technique” or methodical or technical rationality can ensure “neutrality” or “objectivity”—otherwise, if social context is to be taken into account, such funding and the several research/reporting modalities it engenders would have to be included in the evaluative parameters.While not criticizing individual motivations, we highlight the social, systemic, and relational aspects of knowledge production, specifically the relationship between systemic power and what counts as knowledge, including the role of knowledge “gatekeepers.”3 Pertinent here is James Doughney’s (2005) explanation of how the fallacious fact/value dichotomy is used as a formidable political weapon by gambling industry figures to discredit the ethical stances of critical researchers and to undermine those who bring to light research findings about adverse and inequitable impacts of gambling: In gambling research, we have another reason to reject a sharp division between ethics and so-called objective research. . . . [R]epresentatives of the gambling industry and other commentators often label many of us who research in the field moralisers. The Australian term is wowser . . . a finger-wagging, didactic issuer of moral commands . . . implicitly . . . being unconcerned with the real lives people lead. Often enough governments and policy makers also demand value-free gambling research that sticks to the facts. . . . [T]he fact–value dichotomy . . . opens the way to ethical or value relativism. Once values are relativized it becomes all too easy for policymakers to avoid making ethical decisions, especially when they are desperate to avoid such decisions for pragmatic reasons. This is clearly the case with poker machines in Australia. Governments reap huge revenues by partnering with the gambling corporations in this enterprise. (p. 6)
THE WAY FORWARD In this concluding section we briefly identify four points, merely suggestive of possible ways to render gambling research more reflective of the real complexity of gambling phenomena, more socially relevant, and more ethically aware and responsible.
MULTILEVEL FRAMEWORK Research needs to contribute to a multilevel understanding of the gambling phenomenon. Reductionisms of all kind—psychological/psychiatric/medical,
3
Perhaps illustrating one of our central points, we were not able to have our own critique of gambling industry–funded research published in the Division on Addictions’ publication, The Wager, and subsequently had it published as part of an article in another journal (Borrell and Boulet 2005).
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political-economic, structural—should be avoided and replaced with an overall interpretive framework to include: ●
●
●
●
global/national/societal levels of analysis, integrating the economic and the political; corporate/organizational analyses, especially related to the overlap of interests between private-economic interests and public-governance interests; analyses of micro or proximate situated contexts, including locality, community, gambling spaces, interactional and relational settings; and (social-)psychological and sociological/anthropological insights whereby subjects/individuals are properly conceived of as competent actors in situated contexts rather than as mixtures of dark Freudian drives and monads merely reactive/responsive to external impulses.
This requires methodologies in gambling research which use multiple research methods and techniques, appropriate to the research contexts/ levels/“actors” and to the relationships operating within and between them. Requirements include: (i) triangulation of methods and the complementary data generated by them; (ii) whole systems and process analysis, understandings of the relational and real-life situations within which gambling occurs; and (iii) exploration of the meanings attached to human relational contexts, including a reflective stance about the interests and position of the researcher. We concur with McGowan’s (2004) assertion that “[w]hat is desperately needed is nuanced, politically engaged, and culturally informed research that is grounded in the social, cultural, historical, and everyday contexts in which gambling is embedded.”
UNDERSTANDING HARM PRODUCTION Contextualized designs of prevention, treatment, planning, and policy should move away from a focus on harm reduction; similar to the shift in poverty research suggested by Else Øyen, it is imperative to focus on harm production and on reducing and mitigating the production of harm to individuals, to the community, and to society at large. Rather than understanding and singling out harm production as part of the ostensibly neutral and taken for granted world—that is, with negative fallout only to be addressed through its “minimization”—we quote Øyen (2002) as follows: The stage is now set for a new phase, that of understanding the processes that produce poverty (harm) and continue to produce poverty (harm) at a rate no present poverty (harm) reducing measures can possibly win over or even compete with. The challenge ahead is to make poverty (harm) production visible and place it firmly on the research agenda. (p. 1)
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Øyen (2002, p. 7) notes the importance of identifying the actors in (harm) poverty production and remarks on the futility of its minimization when (harm) poverty production is not recognized or counteracted and when the hierarchy of perpetration in (harm) poverty production—its actors and processes and the institutions that fortify the position of dominant group members—is left unanalysed. Indeed,“it can be argued that those responsible for not preventing the disaster, if at all preventable, and those responsible for not providing adequate help afterwards can be seen as second and third line perpetrators” (p. 8). She suggests questions reminiscent of our own at the start of this chapter: There is an urgent need for a new discourse on poverty (harm) understanding that challenges the dominating discourses of the last decades …. Discourses do not just happen. They can be seen as an expression of certain vested interests, whether they are political, material or intellectual.To understand them better, it is necessary to raise questions such as: Why was a certain discourse opened up in the first place, who introduced it and what kind of impact does it have on the questions researchers pose? What kinds of interests lie behind it and why did a certain set of arguments become so powerful that they dominate our way of thinking and the choice of analytical approaches? Why are certain concepts and strategies pushed up front and others made invisible. . . ? Who adopts a certain discourse and why? What is the impact of a certain discourse on actual policymaking? How much power is invested in keeping the discourse alive? Who are the benefactors of the outcome of a certain discourse, and who are those excluded through such a discourse? (Øyen 2002, p. 10)
RESEARCHER EMBEDDEDNESS We now return to key points made in earlier sections. In espousing an epistemological approach to examine the gambling phenomenon and its impact on the identified levels and within specific contexts, the relationship between the researcher, the researched issues, and the “researched” (subjects)—whether persons, organizations, agencies, government bodies, or industry and corporations—needs thorough examination. Just positing the putative possibility of “independence” and “neutrality” of the researcher will not do. Given earlier and present-day abuses of that relationship, the governmental funding of social and policy-relevant research areas, the privileging of certain methodologies, and the virtual defunding others (especially those of a qualitative and participatory nature) (Lincoln and Canella 2004), it should be clear that the ethical responsibility of researchers, academics, and institutions has never been more important than at present—especially when confidence in governments and in the corporate world is at its lowest.There is no space “outside” reality—political, personal, or interpersonal—where researchers can hide and conduct “objective” research.Their acknowledged embeddedness in the world being researched and recognition of the need for control by those whose lives are impacted by the research are necessary preconditions for research being beneficial to individuals and the social body; as it is for the environment sustaining our lives on this planet, there is no escaping responsibility.
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PRECAUTIONARY PRINCIPLE In another context, we argued for the insertion of a precautionary principle in all deliberations about human affairs, especially those related to human involvement in potentially harmful activities and undertakings (Borrell and Boulet 2001). This principle explicitly prioritizes community well-being by advocating for precaution or “erring on the side of caution”—even in the absence of definitive evidence—in the face of possible threats to human and community health.While it is true that the future cannot be predicted, there are useful things to be learned from our past endeavors, mistakes, and victories. Such things are detectable in the stories of people and the careful reflection on action in context and on the linkages between macro- and microprocesses. The direction of research, what is to be studied, and how it will be presented and used, should at all times be oriented to community and personal safety and wellbeing, based on a conception of the common good and oriented to ecological and social sustainability. Given our present understanding of the dangers threatening both the social and ecological spheres, doing otherwise would be immoral and would deny many of the ethical foundations inherent in our understanding of what it takes to be human.While risk avoidance is not a viable option—our global and planetary environments being essentially uncontrollable—precaution in human and ecological affairs, rather than deliberately ignoring evidence or uncertainty about potential harm and danger, is the preferable moral attitude.We should translate this attitude into our approaches to research: what is researched, how it is done, and to what purpose. This will take political will and cultural change, but not engaging with it would make us as researchers accomplices in the harm thus produced.
GLOSSARY Dialectical a process wherein converging ideal/social/material systemic contradictions lead to the generation of a (hoped for) more advanced synthesis. Discursive part of a social discourse, with “discourse” referring to a body of language or a way of talking/acting that carries common assumptions about the world or social life. EGM electronic gaming machine. Epistemology theory/philosophy about the nature or basis of knowledge and knowledge systems. Hegemony domination of a class over others in a society through ideological and political processes. Ideology a set of prevailing ideas, beliefs, and/or attitudes in a society, most often inferring a set that is dominant in and maintaining of the society in important ways.
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Neo-liberal ideology has emerged since the late 1970s as a reaction to the crisis in globalizing capitalism after the so-called oil shock (often associated with economic philosophers such as Hayek); intends to justify attempts at restructuring the political economy toward increasing the power of those in control of capital flows and away from attempts at regulating it by state intervention, by emphasizing the values of enterprise, freedom, individualism, and competition. Nihilistic negating or rejecting of principles or moral beliefs. Ontology theory/philosophy about the nature of being and existence. Postmodernism the rejection of grand accounts or theories about the way the (social) world operates and the replacement of a search for truth with a celebration of the multiplicity of different and (potentially) equally valid perspectives (Burr 1995, p. 185). Relativistic associated with postmodernism, a disbelief in universal truths or principles that apply across different social contexts and times, such that a theory cannot provide criteria for truth beyond the context in which it is developed. Reified when particular social relations are assumed to be “real” or “thing-like,” as part of the necessary, “taken for granted” world (i.e., not contingent on particular circumstances or ways of thinking or operating). Teleological directed toward an aim or purpose. Triangulation the procedures employed by researchers to integrate data and results deriving from different but complementary social research methods. Combining research approaches and “triangulating” the variously obtained data and findings strengthens the validity, reliability, and analytical coherence of the overall findings. Utilitarian a social philosophy that judges the moral quality of acts, systems, and processes according to their usefulness for certain people or all people; it weighs the benefit or “happiness” of people in a population against the “disbenefit” or “suffering” for others (for discussion of the various versions, see Doughney 2002). (W)holism rejection of “atomistic” or “from-parts-to-whole” approaches to understanding, “making sense” of and intervening in (social as well as overall) reality, instead attempting to detect relationships, patterns and entire configurations.
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Adams, P. J. (2004). Minimising the impact of gambling in the subtle degradation of democratic systems. Journal of Gambling Issues, 11. Alston, M., and Bowles, W. (2003). Research for Social Workers: An Introduction to Methods. Crows Nest: Allen & Unwin. Anonymous. (2006). Critical commentary by an EGM gambler, October 2004: Introduction by Jennifer Borrell. International Journal of Mental Health and Addiction, 4, 181–188. Beach, D. (2003). A problem of validity in education research. Qualitative Inquiry, 9, 859–873. Beck, U., Giddens, A., and Lash, S. (1994). Reflexive Modernization. Palo Alto, CA: Stanford University Press. Blaszczynski, A., Sharpe, L., and Walker, M. (2001). The Assessment of the Impact of the Reconfiguration on Electronic Gaming Machines as Harm Minimisation Strategies for Problem Gambling: A Report for the Gaming Industry Operator’s Group. University of Sydney [Australia] Gambling Research Unit. Borrell, J. (2005). The Implications of Values in Gambling Research. Paper presented at the 4th annual Public Policy Implications of Gambling Research conference, Alberta Gaming Research Institute. Borrell, J., and Boulet, J. (2001).VCGA hearings, the onus of proof and the Precautionary Principle. Victorian Local Governance Association Gambling Research Newsletter, 2, 6–8. —— . (2005). A critical exploration of objectivity and bias in gambling (and other research). eCOMMUNITY International Journal of Mental Health and Addiction, 2, 25–39. Brown, S., Johnson, K., Jackson,A. C., Fook, J.,Wynn, J. and Rooke, C. (2000). Healthy,Wealthy and Wise Women: The Health impacts of Gambling on Women in Melbourne’s Western Metropolitan Region. Melbourne:Vichealth. Burr,V. (1995). An Introduction to Social Constructionism. London: Routledge. Campbell, C., and Smith, G. (2003). Gambling in Canada—from vice to disease to responsibility: A negotiated history. Canadian Bulletin of Medical History, 20, 121–149. (Cited by McMullan 2005, p. 35) Capra, F. (2003). The Hidden Connections: A Science for Sustainable Living. London: Flaming. Cultural Partners Australia Consortium. (2000). The Impact of Gaming on Specific Cultural Groups. Melbourne:Victorian Casino and Gaming Authority. Denzin, N. K., and Lincoln,Y. S. (eds). (2001). Handbook of Qualitative Research. Thousand Oaks, CA: Sage. Dickerson, M., Haw, J., and Shepherd, L. (2003). The Psychological Causes of Problem Gambling: A Longitudinal Study of At Risk Recreational EGM players. University of Western Sydney School of Psychology. Retrieved June 4, 2006, from http://www.dgr.nsw.gov.au/pdfs/rr_dickerson_haw.pdf Doughney, J. (2002). The Poker Machine State: Dilemmas in Ethics, Economics and Governance. Melbourne: Common Ground Publishing. —— . (2004). New Data on Poker Machines:The Low-Income Areas Slugged Again. Melbourne:Workplace Studies Centre,Victoria University. —— . (2005). Moral description: Overcoming the fact–value dichotomy in social research. eCOMMUNITY International Journal of Mental Health and Addiction, 1, 6–12. Dow Schull, N. (2002). Escape Mechanism:Women, Caretaking and Compulsive Gambling. Working Paper No. 41, Centre for Working Families, University of California. Retrieved June 6, 2006, from http://wfnetwork.bc.edu/berkeley/papers/41.pdf Egilman, D. S., and Rankin Bohme, S. (2005). Over a barrel: Corporate corruption of science and its effects on workers and the environment. International Journal of Occupational and Environmental Health, 11, 331–337. Gare, A. (2006). The Neo-liberal Assault on Australian Universities and the Future of Democracy. Melbourne: Swinburne University. Giroux, H. A. (2005). Cultural Studies in Dark Times: Public Pedagogy and the Challenge of Neo-liberalism. Retrieved June 6, 2006, from http://www.henryagiroux.com/online_articles/DarkTimes.htm
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Guba, E., and Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In Handbook of Qualitative Research (N. K. Densin and Y. S. Lincoln, eds.).Thousand Oaks, CA: Sage. Horowitz, I. L. (ed.). (1967). The Rise and Fall of Project Camelot: Studies in the Relationship Between Social Science and Practical Politics. Cambridge, MA: MIT Press. Jones, J. (1981). Bad Blood:Tuskegee Syphilis Experiment. New York: Free Press. (Rev. ed. 1993) Langworthy, A., and Howard, J. (2005). Where Are the Third Places? Recreational Alternatives to Gambling. Melbourne: Centre for Regional Development/Swinburne University. Lincoln,Y. S., and Cannella, G. S. (2004). Dangerous discourses: Methodological conservatism and governmental regimes of truth. Qualitative Inquiry, 10, 5–14. Livingstone, C. (2003). Consumption and Its Discontents: “Addiction” and the Problems of Freedom in Late Modernity. Paper presented at the Dangerous Consumptions Colloquium, Melbourne. Livingstone, C., Woolley, R., Borrell, J., Bakacs, L., and Jordan, L. (2006). The Changing Electronic Gaming Machine (EGM) Industry and Technology: Final Report. Prepared by the Australian Institute for Primary Care (AIPC), La Trobe University for the Department of Justice, Gambling Research Program,Victoria. McGowan,V. M. (2004). How do we know what we know? Epistemic tensions in social and cultural research on gambling, 1980–2000. Journal of Gambling Issues, 11. McMullan, J. L. (2005). The Gambling Problem and Problem Gambling: Research Public Policy and Citizenry. Paper presented at the 4th Annual Alberta Conference on Gambling Research: Public Policy Implications of Gambling Research, University of Alberta. Murray, S. (2006).Australian diet plan slammed. Canadian Medical Association Journal, 174. Retrieved June 4 from: http://www.cmaj.ca/cgi/content/full/174/5/606 NCRG [National Center for Responsible Gaming]. (1999, February 8). National Center for Responsible Gaming Grant Awards Exceed One Million Dollars with Support for New Scientific Studies on Problem and Underage Gambling. Retrieved February 5, 2007, from http://www.ncrg.org/newsroom/detail.cfm? ID=18&type=archive —— . (2004, October 20). 5th Annual NCRG Conference to Examine Disordered Gambling, Potential Relationship Among All Addictions. Retrieved February 5, 2007, from http://www.ncrg.org/ newsroom/detail.cfm?ID=38&type=archive —— . (2007). Opening statement at website homepage. Retrieved February 5, 2007, from http://www.ncrg.org Øyen, E. (2002). Poverty Production: A Different Approach to Poverty Understanding. Paper presented at the International Conference on Social Science and Social Policy in the 21st Century, Vienna. Retrieved June 4, 2006, from http://www.crop.org/publications/reports.cfm Productivity Commission. (1999). Australia’s Gambling Industries. Canberra: AusInfo. Richwine, L. (2006). Mental Illness Writers Had Industry Ties: Study. Reuters, April 20. Retrieved June 2, 2006, from: http://ca.news.yahoo.com/s/20042006/6/n-usa-mental-illness-writers-industry-tiesstudy.html Rodda, S., and Cowie, M. (2005). Evaluation of Electronic Gaming Machine Measures in Victoria. Final Report. Melbourne: Caraniche Pty Ltd./Victorian Government Department of Justice. Schellinck, T., and Schrans, T. (1998). Nova Scotia Video Lottery Player’s Survey 1997/1998: Highlights. Focal Research Consultants Ltd. for Nova Scotia (Canada) Department of Health. Smith, J. (2000). Gambling taxation: Public equity in the gambling business. Australian Economic Review, 33, 120–144. Smith, L.T. (1999). Decolonizing Methodologies: Research and Indigenous Peoples. London: Zed Books. Surgey, D., and Seibert, A. (2000). Playing for Time: Exploring the Impacts of Gambling on Women. Melbourne:Women’s Health in the North/Victorian Department of Human Services. The Centre–Wangaratta and Benalla, and Rural City of Wangaratta. (2006). Gambler’s Tales: Stories from People Who Experienced Problems with Gambling in North East Victoria. Victoria: Wangaratta and Benalla.
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Trotman, J., and Robertson, S. (1991). Taking the Queen’s Shilling: Public Policy, Research and Academics in the 1990s. Paper presented at the National Social Policy Conference, University of New South Wales, Australia. U.K. Parliament. (2004). Uncorrected Transcript of Oral Evidence To Be Published as HC 139 ix. United Kingdom Joint Committee on Draft Gambling Bill. Retrieved February 5, 2007, from http://www.publications.parliament.uk/pa/jt200304/jtselect/jtgamb/uc139-ix/uc13902.htm Union of Concerned Scientists. (2004). Scientific Integrity in Policymaking: Investigation of the Bush Administration’s Abuse of Science. Retrieved February 5, 2007, from http://www.ucsusa.org/scientific_ integrity/interference/reports-scientific-integrity-in-policy-making.html Wildavsky, A. (1979). Speaking Truth to Power:The Art and Craft of Policy Analysis. Boston: Little, Brown and Co. Wiseman, M. J. (2000). Gambling and Virtue in Government.Winona, MN:Winona State University. Women’s Health in the North and Brunswick Women’s Theatre. (2005). Just Around the Corner (theatre performance), Melbourne. Woolley, R. (2003). Playing Machines: Commodification, Technics and the Economic Imaginary. Paper presented at the Dangerous Consumptions Colloquium, Melbourne.
CHAPTER 23
Legalized Gambling:The Diffusion of a Morality Policy Patrick A. Pierce
Donald E. Miller
Center for Academic Innovation Saint Mary’s College Notre Dame, Indiana
Department of Mathematics Saint Mary’s College Notre Dame, Indiana
Introduction History of Legalized Gambling Theoretical Issues Data and Methodological Issues The Diffusion of Lotteries and Casinos The Diffusion of Innovations and Temporal Diffusion of Gambling Policies The Diffusion of Innovations and External Diffusion of Gambling Policies Lotteries Casinos Internal Diffusion of Gambling Policies The Changing Symbolic Weight of the “Sinfulness of Gambling” The Puzzle of Indian Casinos The Future of Legalized Gambling Future Research
INTRODUCTION Legalized gambling has swept across North America. Every Canadian province, 42 U.S. states, and the District of Columbia are running lotteries. In addition, 10 Canadian provinces (including Yukon Territory) and 11 American states allow casino gambling. Scholars have explained the spread of legalized gambling through diffusion of innovations theory, where a new policy spreads from innovator states to their neighbors over time. Similarly, states with one form of legalized 593
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gambling are likely to adopt another form.We present an example of this research and note directions for future research.
HISTORY OF LEGALIZED GAMBLING Legalized gambling has become an important part of economic, social, and political life in the United States, Canada, and other nations. Hurricane Katrina revealed vividly the economic importance of casinos in the Gulf Coast region.The social impact of legalized gambling, particularly in the area of gambling addiction (often leading to bankruptcy or crime), has also been of constant concern. Finally, legalized gambling involves issues of morality politics that focus on fundamental principles over which intense conflict can develop (Haider-Markel and Meier 1996; Meier 1999; Pierce and Miller 1999, 2004). This chapter will analyze the political and policy dynamics of legalized gambling in the United States. Legalized U.S. gambling has a long history (Findlay 1986; Munting 1996). During the Revolutionary War, most states had lotteries, and even the new nation instituted a Federal Lottery to fund the war effort. Lotteries continued to flourish and spread across the nation, in part because they constituted a way to raise funds for substantial public works. Before the development of a banking system, states found it difficult to raise the large sums of money needed for the development of their infrastructures and relied heavily on lotteries. Thus, lotteries contributed to some of the nation’s formative economic development. Following a massive scandal with the Louisiana lottery during the 1880s–1890s, however, state and federal governments acted to effectively eliminate lotteries. After a long hiatus, legalized gambling returned in the form of casinos in Las Vegas in 1931 (Lehne 1986; Pierce and Miller 1997; von Herrmann 2002).These casinos were more of an aberration than the beginning of a trend, as it was over 30 years before the next legalization of gambling by an American state. New Hampshire’s Sweepstakes Bill was signed into law in 1963 and the lottery began operation in 1964.After a break of approximately 70 years, New Hampshire initiated the second wave of lotteries, which accelerated during the 1970s–80s (McGowan 1994). Lotteries have now virtually saturated the states, with only eight states eschewing the significant revenue available through their operation. Legalized casino gambling has also enjoyed expansive growth, albeit only recently and on limited bases. Although Nevada led the way with the nation’s first legalized casino gambling, states remained hesitant to embrace an activity that had become linked to organized crime. New Jersey broke the dry spell in 1976 (Lehne 1986), legalizing casino gambling in Atlantic City to revitalize the blighted former resort city. The 1990s witnessed substantial expansion of casinos across the states, both riverboat and land based.A much more complicated (legal and political) phenomenon has been the particularly dramatic expansion of Indian casinos across the states (Anders 1998; Mason 2000).These casinos have created enormous profits for
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the various Indian tribes and sparked conflict and controversy with the states in which they are located.
THEORETICAL ISSUES The politics of lotteries and casinos have generally been understood through the theoretical framework of the diffusion of innovations (Berry and Berry 1990; Filer, Moak, and Uze 1988; Foster 1978; Gray 1973; Katz, Levin, and Hamilton 1963; Pierce and Miller 2004; Rogers 1983; Walker 1969, 1973). Diffusion of innovations theory posits decision makers—in this case, state governments—as risk averse, reluctant to hazard the potential political dangers and unforeseen consequences of a new policy (Lindblom and Woodhouse 1993).A small number of innovators (perhaps only one) will take the risk of adopting the new policy, while most other states will await the consequences experienced by the innovators. If the consequences seem to benefit the innovators, other decision makers will become more receptive to entertaining the innovative policy. As social, economic, and political life are complex, the innovation will spread first to those states that most resemble the innovating states (Mooney 2000). Put simply, if a policy “succeeds” in a state similar to my state, it is likely to succeed in my state. Further, as states receive mounting evidence of a policy’s success, they are more likely to adopt it.The authors have referred to this process of state policies spreading from state to similar, neighboring states as external diffusion. Once an innovation begins to spread, its diffusion should occur across space and time in relatively predictable fashion. The spatial pattern of diffusion should resemble an ink blot, spreading out from the initial innovator(s) over time (Walker 1973). Neighboring states should resemble each other (politically, socially, economically), hence they will copy each other’s successful policies, thus minimizing risk.The temporal pattern of diffusion should resemble an S-curve (Gray 1973). A small number of innovating states will initially adopt the innovation, followed by a certain proportion of their neighbors. This stage of diffusion will be followed by one in which the policy is adopted by neighbors of the previous set of states. In other words, the rate of diffusion will accelerate. At some point in time, the rate of diffusion will decelerate until few or no neighbors without the policy remain. Additionally, certain internal characteristics of a state will influence the likelihood of its adopting the innovation (Berry and Berry 1990; Filer et al. 1988; Mooney 2000; Pierce and Miller 1997, 2004; von Herrmann 2002).With respect to legalized gambling, three sets of factors stand out: (1) fiscal health of the state, (2) partisan control of government, and (3) influence of Protestant Fundamentalists. Poor fiscal health seems to encourage states to explore the possibility of lottery and casino tax revenue.The voluntary nature of this taxation increases its political attractiveness. In partisan terms, Democrats seem to support legalized gambling; Republicans generally oppose it.This partisan difference stems largely from the cultural bases of the two political parties (Pierce 1984). The Republican Party has become the home for
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(particularly white) Protestant Fundamentalists, who are adamantly opposed to the sinfulness of gambling.1 Of these three contextual factors, the influence of Protestant Fundamentalists has been the most important in inhibiting the legalization of gambling. Previously, the authors have argued that studying a particular form of legalized gambling in isolation from other forms is misleading (Pierce and Miller 1997, 2004). The political, fiscal, and social experiences resulting from one form of legalized gambling will affect the political context faced by states considering another kind of legalized gambling. Antigambling advocates have long claimed that acceptance of one form of legalized gambling leads to the acceptance of other forms of legalized gambling. They warned citizens that agreeing to establish a seemingly innocuous state lottery would lead to casino legalization. They echoed Senator Barry Goldwater’s warning about Medicare and Medicaid leading to socialized medicine, that once the camel’s nose was in the tent, the rest of the camel couldn’t be far behind. In the eyes of gambling opponents, state lotteries were the camel’s nose and casinos were the rest of the camel. Living with the “sin” of a state lottery would weaken citizens’ moral sensitivity to casino gambling, paving the way for its passage.The authors have referred to the process of one form of legalized gambling leading to another form in a given state as internal diffusion. To summarize, lottery and casino policymaking have been understood in terms of the diffusion of innovative policies that are constrained (and perhaps encouraged) by certain internal characteristics of the state. The spread of lotteries and casinos across the nation has been hastened or slowed by the “receptiveness” of the particular state. Further, once a state legalizes a “milder” form of gambling, it becomes more receptive to other forms of gambling. A key theoretical issue remains, however, concerning the stability of the risk factor. The diffusion-of-innovations model identifies political units that are “at risk” of adopting the innovation at any given time, and a standard assumption is that the degree of risk remains stable over time. Mooney (2000) has argued that the assumption may be unwarranted; the risk factor may vary systematically over time. In previous research, the authors have claimed that the “weight” of the symbol,“sinful gambling,” has decreased over time, particularly once a state has established a state lottery (Pierce and Miller 2004). (For a discussion of the use of symbols in politics, see Elder and Cobb 1983.) This change in “weight” may result in an increased risk factor over time.
DATA AND METHODOLOGICAL ISSUES Our data include all 50 states in the United States, covering the years from 1966 to 2004. It is important to identify some key issues in data collection concerning 1 The one-party Democratic South constituted an exception prior to the national Democratic Party embracing the cause of civil rights in the 1960s.
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legalized gambling. In all cases, we defined the date of adoption (of state lotteries or legalized casino gambling) as the time of the final decision authorizing the policy. Some investigators have incorrectly identified the time of policy adoption, not realizing that the legislation had not yet been signed by the state’s governor.Various websites have different dates of adoption; we consistently verified these dates with the relevant state agency or commission charged with regulating lotteries and/or casinos.We also relied on these state agencies and commissions for data on revenue generated by lotteries and casinos. We have employed a stricter definition of casinos than some investigators (von Herrmann 2002). Casinos include only those gambling establishments that have a full range of games. Some states allow card rooms, for example, but we have not treated these as states with legalized casino gambling. Card rooms are fairly limited operations and do not have the enormous political, social, and economic impact of full-scale casino operations.The error of including card rooms and other types of gambling operations when measuring casino legalization would be revealed when evaluating the construct validity of the measure. Full-scale casinos have a far more substantial impact on communities and the state than do card rooms, etc. As a consequence, the politics of the two types of gambling differ greatly. Regarding all of these disparate forms of legalized gambling as “casinos” would thus result in problems with measurement validity. For contextual data, the Book of the States (Council of State Governments, various years) is an invaluable source of data on the American states.The Book of the States contains data on partisan control of government, state fiscal operations, and aggregate demographic variables. Finally, we have noted that Fundamentalist Protestants constitute a crucial social and political force opposing any form of legalized gambling. A series of volumes, Churches and Church Membership (Bradley et al. 1992; Johnson, Picard, and Quinn 1974; Quinn et al. 1982), contain data on numbers of individuals adhering to various religious sects. Although there is general agreement concerning this categorization, the careful student should note which sects were included.2 2 Included in Fundamentalist sects for this study: Advent Christian Church, American Baptist Association,American Baptist Churches in the U.S.A.,Apostolic Christian Church,Apostolic Lutheran Church,Assemblies of God, Baptist General Conference, Baptist Missionary Association, Beachy Amish Mennonite Churches, Bible Church of Christ, Christian Catholic Church, Christian Churches and Churches of Christ, Christian Reformed Church, Church of God (various), Church of God in Christ, Church of Jesus Christ, Latter-day Saints (Mormons), Church of the Nazarene, Churches of Christ, Conservative Baptist Association of America, Estonian Evangelical Lutheran Church, Evangelical Church of North America, Evangelical Congregational Church, Evangelical Covenant Church of America, Evangelical Free Church of America, Evangelical Lutheran Churches, Evangelical Lutheran Synod, Evangelical Mennonite Brethren Conference, Evangelical Mennonite Church, Evangelical Methodist Church, Fire Baptized Holiness Church, Free Methodist Church of North America, Grace Brethren Churches, Holiness Church of God, International Church of the Foursquare Gospel, Latvian Evangelical Lutheran Church, Lutheran Church–Missouri Synod, Mennonite Church, Mennonite Church–the General Conference, Missionary Church, North American Baptist Conference, Old Order Amish Church, Open Bible Standard Churches, Pentecostal Free Will Baptist Church, Pentecostal Holiness Church,
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Following other investigators (Berry and Berry 1990), we found that the percentages of Fundamentalist Protestants did not vary significantly over time. Therefore, we treated the variable as one that would vary across space (states), but not time. An important methodological or statistical issue inheres in the use of time series cross-section analysis to study the politics of legalized gambling (Allison 1984; Beck and Tucker 1996; Box-Steffensmeier and Jones 1997).Although the seminal study on lottery adoption by Berry and Berry (1990) makes a good case for beginning the time series after the date of the first state adopting a lottery, “left censoring” is not always a straightforward matter. In the terms of event history analysis, this is the first time at which other states are “at risk” of adopting the innovative policy. However, the aberrant case of Nevada’s casinos makes the problem clearer. Forty-five years passed before the next state legalized casino gambling. Beginning the time series in the early 1930s would severely attenuate any estimated coefficients and effectively distort one’s findings. As a result, we need to “left-censor” the time series, eliminating some early years. Calculating the starting date can be problematic and arguable. Similarly, investigators must consider the issue of “right censoring” the time series, that is, identifying a date that ends the series. Any time series is necessarily “right censored” because we lack data on the future. However, it may be appropriate to right-censor the data further, eliminating observations that have occurred. Scholarship on the diffusion of the innovations notes that once the innovation has saturated the system (e.g., almost every state has a lottery), virtually no further diffusion should occur and the time series should end. Another methodological or statistical problem related to the use of time series data is autocorrelation. Autocorrelation will certainly result from repeated observations of a given state over time. Failing to account for such autocorrelation can result in biased coefficients and reduced standard errors of these coefficients. To remedy the problem, investigators should calculate Huber-White robust standard errors, clustering by the unit of analysis that is being observed over time (e.g., the state).
THE DIFFUSION OF LOTTERIES AND CASINOS THE DIFFUSION OF INNOVATIONS AND TEMPORAL DIFFUSION OF GAMBLING POLICIES First, we should display the contours of policy adoption over time and across space for lotteries and casino gambling.Tables 23.1 and 23.2 present the states and Christian Brethren, Primitive Advent Christian Church, Primitive Methodist Church, Salvation Army, Separate Baptists in Christ, Seventh-day Adventists, Seventh Day Baptist General Conference, Social Brethren, Southern Baptist Convention, Southern Methodist Church, and Wisconsin Evangelical Lutheran Synod.
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Table 23.1 Lottery Adoptions. State
Year of Adoption
Year of Start-up
Purpose
Method of Adoption
New Hampshire New York New Jersey Pennsylvania Connecticut Massachusetts Michigan Maryland Ohio Illinois Rhode Island Maine Delaware Vermont Arizona Washington Washington, D.C. Colorado California Oregon
1963 1967 1969 1971 1971 1971 1972 1972 1973 1973 1974 1974 1974 1978 1981 1982 1982 1982 1984 1984
1964 1967 1970 1972 1972 1972 1972 1973 1974 1974 1974 1974 1975 1978 1981 1982 1982 1983 1985 1985
Legislation Referendum Referendum Legislation Legislation Legislation Referendum Referendum Legislation Legislation Referendum Referendum Legislation Referendum Initiative Legislation Initiative Initiative Initiative Initiative
West Virginia Missouri Iowa South Dakota Kansas Montana Florida Virginia Wisconsin Idaho Indiana Kentucky Minnesota Louisiana Texas Nebraska Georgia New Mexico South Carolina
1984 1985 1985 1986 1986 1986 1986 1987 1987 1988 1988 1988 1989 1991 1992 1992 1993 1994 2001
1986 1986 1985 1987 1987 1987 1988 1988 1988 1989 1989 1989 1990 1991 1992 1993 1993 1996 2002
Education Education Education Elderly General fund Municipalities Education General fund Education General fund1 General fund General fund General fund General fund2 General fund General fund3 General fund Environment Education Education/economic development Education Education General fund General fund Economic development Education Education4 General fund Tax relief Education Miscellaneous General fund/education General fund General fund General fund/education Education/environment Education Education Education
Referendum Referendum Legislation Referendum Referendum Referendum Initiative Referendum Referendum Referendum Referendum Referendum Referendum Referendum Referendum Referendum Referendum Legislation Referendum (Continues)
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Table 23.1 (Continued ) State
Year of Adoption
Year of Start-up
Purpose
Method of Adoption
Tennessee North Dakota Oklahoma North Carolina
2003 2004 2004 2005
2004 2004 2005 2006
Education General fund Education Education
Legislative Legislative Referendum Legislative
1
In 1985, Illinois passed legislation to earmark lottery revenue for education. Vermont’s lottery revenue now goes to education. 3 Washington’s legislature earmarked lottery revenue for education in July 2001. 4 Florida’s initiative did not specify a purpose for the lottery; enabling legislation earmarking funds for education was passed in 1988. 2
the dates on which they adopted lotteries and casino gambling, respectively. Figures 23.1 and 23.2 present line graphs plotting the adoption of state lotteries and legalization of casino gambling (respectively) over time. Figure 23.1, displaying the cumulative number of states with state lotteries over time, exhibits a pattern slightly different from the familiar S-curve (the cumulative normal curve) predicted by Gray (1973). Policy adoptions begin slowly, with a small number of risk-taking, innovative states. The rate of adoption begins to accelerate, then decelerates as the policy has “saturated” the states. From 1994 until 2001, it appeared that saturation had occurred, as seven years passed without a lottery adoption.The remaining states resisting the innovation presumably constituted cases with insurmountable obstacles. For example, one of the few states without a lottery is Utah, with an extraordinarily high percentage of Fundamentalist Protestants (Mormons). However, from 2002 to 2005, five more states (a not inconsiderable Table 23.2 Casino Adoption Dates. State Nevada New Jersey Iowa South Dakota Colorado Mississippi Illinois Louisiana Indiana Missouri Michigan
Year Adopted 1931 1976 1989 1989 1990 1990 1990 1991 1992 1992 1996
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Number of states with lottery
40
30
20
10
0 1960
1970
1980
1990
2000
2010
Year Figure 23.1. Cumulative number of states with lotteries.
number) established state lotteries.The S-curve in Figure 23.2—for casino legalization—is less obvious and a bit truncated as the process is much more recent. It thus appears that with respect to lottery states, we can discern a group of innovators and early adopters, a group adopting lotteries immediately thereafter, and a final group that seemed to require a more significant “push” to adopt. Gray (1973) clearly believes that the basis for the final group of states resisting the innovation is political. In this case, the political obstacle standing in the way of lottery
Number of states with casinos
12 10 8 6 4 2 0 1975
1980
1985
1990 Year
Figure 23.2. Cumulative number of states with casinos.
1995
2000
2005
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Table 23.3 Fundamentalist Percentage by Period of Lottery Adoption. Period Early (1966–1978) Middle (1979–1994) Late (1995–2004) No lottery
States (n) 14 23 5 8
Mean %
SD
3.071 13.55 25.88 26.01
2.412 7.63 11.05 20.87
adoption is Protestant Fundamentalists.With respect to the lottery, we can thus profitably explore the notion that the states constitute four discrete groups—innovators and early adopters, middle adopters, later adopters, and those states still without a lottery—by examining the size of their Fundamentalist populations. Table 23.3 provides descriptive statistics on the states’ Fundamentalist populations broken down by these four groups. The average state percentage of Fundamentalists among the innovators and early adopters is only slightly above 3%, indicating the low level of political resistance to legalized gambling. Among middle adopters, the average increases to 13.55%, and then almost doubles to nearly 26% among late adopters. For these data, t-Tests reveal that all of these differences are statistically significant ( p < .01). The states without lotteries, however, do not differ from the late adopters. This lack of difference suggests that the states without lotteries may be vulnerable to lottery passage. All of these states except Utah have seriously entertained the possibility of a lottery during the last few years.The notion that “insurmountable obstacles” stand in the way of lotteries in the remaining eight states is therefore probably mistaken. It is worth noting that Utah is a clear outlier, with a Fundamentalist percentage more than twice as large as any other state. Thus, lottery adoptions present a problem for Gray’s understanding of the temporal diffusion of innovations. Seemingly, the process can resemble a cumulative normal distribution (the S-curve) with a group of states continuing to resist the innovation. However, that resistance is not necessarily definitive. In the case of legalized gambling, the effective opposition of Fundamentalists seems to have eventually virtually disappeared. Perhaps only the massive Mormon population renders Utah immune from lotteries and casinos.
THE DIFFUSION OF INNOVATIONS AND EXTERNAL DIFFUSION OF GAMBLING POLICIES The primary claim made by students of gambling politics is that legalized gambling policies spread from state to neighboring state. As states resemble each
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other and learn more easily from more proximate examples, state policymakers reason that policies “succeeding” with their neighbors should “succeed” at home (Foster 1978). Importantly, legalized gambling consistently “succeeds” in the terms most important to state policymakers: Lotteries and casinos generate significant revenue for state budgets. We can test the claim initially by examining time-lapse figures of a map of the United States indicating which states have adopted lotteries or legalized casino gambling (Pierce and Miller 2004, pp. 140–143). As numerous scholars have noted, one should observe an ink-blot pattern, as innovators in particular regions of the country have the policy diffuse or spread geographically to neighboring states. A number of studies have conducted this exercise, but only a statistical analysis using time series cross-section analysis can definitively resolve the issue (Allison 1984; Beck and Tucker 1996; Box-Steffensmeier and Jones 1997; Hagle and Mitchell 1992). Most studies have attempted to develop comprehensive rather than parsimonious models to test the external diffusion of lotteries and casinos. However, for the sake of clarity (and acknowledging the value of parsimony), we present results for models that seek to explain the largest amount of variance with the smallest number of explanatory variables.3 We focus on the impact of neighbor adoptions of the relevant policy; for instance, the effect of having neighbors with lotteries on the odds that a given state will adopt a state lottery. As it is the most important constraint on legalizing gambling, we also focus on the impact of Fundamentalist Protestants.
LOTTERIES Before developing a model for testing the existence of external diffusion, we must identify the appropriate data to use. Keeping in mind the nature and extent of temporal diffusion displayed in Figure 23.1, the states were virtually saturated with lotteries by 1994. Extending the time frame of our data beyond that point will simply attenuate the estimated coefficients (and their significance), distorting the dynamics of external diffusion. Therefore, we use observations only prior to 1995. We begin our time series in 1966, shortly after the first (New Hampshire) lottery (see the previous discussion on “left and right censoring”). Table 23.4 presents the results of a model attempting to explain the likelihood of establishing a state lottery.The importance of external diffusion (the number of neighboring states with a state lottery) and Fundamentalist Protestants is undeniable. As a state’s neighbors increasingly turn to state lotteries to generate revenue, that state becomes significantly more likely to join them (coefficient = 0.518, p < .001).The path of external diffusion is slowed, however, by larger percentages 3 Testing more comprehensive models (see Pierce and Miller 2004) reveals that the estimated coefficients presented later are not biased; that is, the models used later are adequately specified.
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Table 23.4 Predicting Lottery Adoption from States with Neighboring States with Lotteries and Fundamentalists. Variable
Coefficient
t-ratio
p-value
Number of neighbors with lottery Percentage of Fundamentalists
0.518 −0.076
7.51 −5.54
0.000 0.000
Chi-square = 90.50, p < .001.
of Fundamentalist Protestants in the state’s population (coefficient = −0.076, p < .001). We can also investigate our claim that the symbolic weight of “sinful gambling” declined over time. Analyzing the impact of external diffusion and Fundamentalists on lottery adoption within each of the two key periods of lottery politics—the innovation and early adoption period (1966–1978) and the secondary adoption period (1979–1994)—implies that the symbolic weight of gambling had declined over time.Table 23.5 displays the results of this exercise. Although the estimated coefficients for Fundamentalists are significant during both time periods, the impact (size of the coefficient) differs markedly. Coefficients from probit models, unlike those in regression models, estimate probabilities rather than the linear impact of the predictor variable on the dependent variable.A positive coefficient indicates that the variable increases the probability of the event occurring; a negative coefficient indicates that the variable decreases the probability of the event occurring.The results in Table 23.5 reveal that the coefficient for Fundamentalists during the early period is −0.163 and declines markedly to −0.01337 during the later period. In other words, Fundamentalists were more effective in opposing lottery adoption during the earlier period. If we arbitrarily set the number of Table 23.5 The Declining Impact of Fundamentalists on Lottery Adoption. 1966–1978 Variable Number of neighbors with lottery Percentage of Fundamentalists
Coefficient
t-ratio
p-value
1.55 −4.54
0.122 0.000
Coefficient
t-ratio
p-value
0.149 −0.01337
2.60 −3.55
0.009 0.000
0.194 −0.163
Chi-square = 32.23, p < .001. 1979–1994 Variable Number of neighbors with lottery Percentage of Fundamentalists Chi-square = 21.92, p < .001.
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neighbors with lotteries at 1 and the Fundamentalist percentage at 5.5% and 21% (first to third quartiles), we can get an idea of the impact of Fundamentalists across the two periods. With one lottery neighbor and 5.5% Fundamentalists during 1966–78, a given state had less than a 4% (.038) chance of adopting a lottery in a given year; increasing the Fundamentalist percentage to 21% drastically reduced the likelihood to practically zero.Although Fundamentalist percentage affected the likelihood of lottery adoption during 1979–94, that probability dropped only from 10% to 4% for states with 5.5% and 21% Fundamentalists, respectively. Conversely, the coefficient for neighbors fails to reach statistical significance during the early period (p = .122) but becomes significant during the later period (p = .009). The coefficients are roughly equal, but their standard errors declined markedly from the early period to the later period. We could plausibly interpret these results as indicating that external diffusion proceeded more consistently and reliably over time, as policymakers saw spreading lotteries as a smaller and more predictable risk. Contributing to this greater predictability, Fundamentalists (whose lobbying efforts were inconsistent) became a less effective obstacle to lottery adoption as the symbolic weight of “sinful gambling” declined.
CASINOS In terms of selecting an appropriate point to right-censor our data (identifying an endpoint beyond which we will collect no observations) (Box-Steffensmeier and Jones 1997), casino legalization does not pose the problem we encountered with our analysis of lottery adoption. Casino legalization has spread to only eleven states and shows signs of being entertained by additional states throughout the nation. However, Nevada’s legalization of casino gambling in 1931 and the long hiatus until New Jersey’s in 1976 leaves us with a different problem, one of left censoring.We would normally consider states “at risk” once the first innovator has adopted the innovative policy. Including the years 1932 to 1976, however, would warp our results. Hence, we will use the time frame from 1976 to 2005 as the most reasonable and appropriate. Table 23.6 presents the results of testing a model of casino legalization. Again, external diffusion operates strongly on the likelihood that a state will legalize casino gambling. As a state’s neighbors allow casinos, that state becomes significantly more likely to join them. However, the type of casino run by one’s neighbors makes a difference. Land-based casinos inhibit the spread of casinos between states; riverboat casinos encourage their external diffusion. Surveying the initiative campaigns and lobbying efforts in states considering casinos reveals that savvy policy entrepreneurs stressed the value of riverboat casinos in generating a more wholesome and romantic tourism economy, recalling the days of the riverboat gamblers (Pierce and Miller 2004, Chapter 5). The association of organized
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Table 23.6 The Impact of Neighbors with Land-Based Casinos and with Riverboat Casinos, and Fundamentalists (1976 to 2004). Variable Neighbors with land casino Neighbors with riverboat casino Percentage of Fundamentalists
Coefficient
t-ratio
p-value
−0.553 0.689 −0.0191
−2.58 3.112 −1.104
0.010 0.002 0.270
Chi-square = 10.84, p < .015.
crime with the two earliest sets of land-based casinos (Nevada and New Jersey) continues to plague casino legalization proponents (Lehne 1986). Inspecting a map reveals that Nevada and New Jersey are isolated casino states, with no neighbors legalizing casino gambling.Tellingly, Illinois’ Riverboat Gambling Act forbade siting riverboat casinos in Cook County, to avoid the perception that they would be controlled by organized crime. Again, Fundamentalist Protestants constitute the bulwark against legalized gambling, but Table 23.6 suggests that the bulwark is weakening. Larger percentages of Fundamentalists lower somewhat the likelihood that a state will legalize casino gambling. However, their impact on casino legalization no longer reaches conventional levels of statistical significance.
INTERNAL DIFFUSION
OF
GAMBLING POLICIES
Most scholars study lotteries or casinos in isolation, but participants in these policy struggles rarely do so. State legislators worry that one form of legalized gambling may siphon business away from existing sources of gambling revenue; Fundamentalists worry that opening the door to one form of legalized gambling will make it easier to turn to other forms of legalized gambling. If participants in the process believe that the fates of various forms of gambling are intertwined, we should at least explore that possibility. As noted previously, we refer to this set of propositions as internal diffusion. The key forms of legalized gambling to consider include lotteries, casinos, and horse racing. Many states have allowed gambling on horse racing for more than a hundred years, with an important industry developing around the sport. Lotteries generally entered into the gambling market later, often during the 1970s–1980s. Finally, casinos have only recently begun their expansion across the states.Although we could explore how each of these forms of legalized gambling affected the diffusion of each of the other forms, this temporal ordering suggests that we explore how horse racing and lotteries changed the likelihood that a given state would legalize casino gambling.
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Importantly, the two forms of gambling legalized before casinos differ in their nature. Horse racing is an industry operating in the private sector and regulated by racing and gaming commissions. On the other hand, lotteries are creatures of the public sector; state governments run state lotteries, usually through lottery commissions. This important difference carries political consequences. The horse racing industry, declining in most states over the last two decades, could see casino gambling as drawing away its ever-shrinking market. Casino gambling provides numerous stimuli for gambling enthusiasts that horse racing lacks: frequent opportunities to bet, nearly immediate feedback, and variable-ratio reinforcement. Faced with this economic threat, one could expect the horse racing industry to organize politically and lobby against casino gambling. If the policy initiative proceeds legislatively, the industry could contact legislators; if it moves through the initiative process, the industry could fund a campaign opposing the initiative. On the other hand, state lottery commissions cannot easily lobby legislators and can certainly not fund campaigns opposing casino legalization.These commissions often communicate their concerns that casinos may reduce lottery revenue to legislators, but such lobbying must be muted. State legislators understand that casinos can generate greater revenue than lotteries, so they will recognize lobbying from the state’s lottery commission as narrowly self-interested. Further, the lottery commission will need to maintain relatively cordial relations with the state legislature to minimize the risk of investigations and oversight. Lotteries affect casino legalization in a different way, as part of the process that we have termed internal diffusion. Extending the theoretical argument underpinning external diffusion, state policymakers may consider a particular policy if a similar policy in their own state has proven successful. As lotteries provided politically painless revenue to a state, policymakers could reasonably surmise that casinos would also generate revenue without arousing the anti-tax sentiment that “coercive” taxes (e.g., sales taxes) generate. We do not believe that this informational heuristic—if a policy has succeeded, make similar policies—constitutes the primary reason that states would legalize casino gambling once they had established state lotteries. No state legislator could possibly doubt that casinos would generate substantial revenue for the state coffers. Instead, doubts about casino legalization would arise from their political fallout. Gambling foes—primarily Fundamentalist Protestants—could mount effective campaigns to “throw out the bums” who supported casino legalization. In other words, a political or electoral incentive might stand in the way of casino legalization.Wary policymakers would weigh the likelihood of such electoral retribution against the benefit of additional state revenue. If the likelihood of electoral retribution was lower, policymakers would become increasingly likely to endorse legalized casino gambling. The successful passage of lottery legislation or a lottery initiative signals policymakers that the risk of electoral punishment for supporting casino gambling
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Table 23.7 Internal Diffusion of Lotteries to Casinos. Variable Horses Lottery Fundamentalism
Coefficient
t-ratio
p-value
−0.836 1.395 0.00633
−2.98 3.78 0.77
0.003 0.000 0.441
Chi-square = 15.65, p < .001.
is low. The failure of Fundamentalist Protestants (and those sympathetic to their rhetoric and arguments) to keep the lottery out of a state indicates their weakness. Hence, the existence of a state lottery tells state policymakers that the political risks of casino legalization are low. If the revenue is assured and the political risks are low, the choice for many state policymakers is clear.Thus, state lotteries constitute the “camel’s nose,” signaling that the rest of the camel (casinos) is not far behind. There is evidence that lotteries drive another political dynamic leading to casino legalization. As a prominent anti-gambling activist told us in an interview, anti-gambling forces must no longer rely on moral arguments against legalized gambling.To be effective, they must make economic arguments about the failure of casinos to help drive economic development (Goodman 1994, 1995). Once a state has established a lottery, it becomes very difficult to argue effectively that legalized gambling is immoral. Fundamentalist Protestants may continue in that belief, but previously sympathetic others will note that the state is already in the business of gambling. What is the moral difference between the state running a gambling operation and the private sector running a gambling operation? So, to lay out the hypothesized relationships, we expect the existence of horse racing in a state to depress the likelihood of casino legalization and the existence of a state lottery to increase the chances for casino legalization.The results are displayed in Table 23.7.4 The coefficient for horse racing (−0.836) and its t-ratio (−2.98) indicate that the presence of horse racing in a state significantly decreases the probability of casino legalization. Conversely, a state with a lottery is significantly more likely to legalize casino gambling (coefficient = +1.395; t-ratio = 3.78). More surprisingly,
4 Table 23.7 presents a severely reduced or simplified model predicting casino legalization. We have omitted the dynamic of external diffusion (number of neighbors with riverboat casinos) as well as other factors reported in Pierce and Miller (2004).Additionally, percentage of Fundamentalists is included despite its statistical insignificance. We have chosen this model for the sake of clarity and appropriateness for the purpose of this chapter. A danger, of course, is that biased coefficients have resulted from this procedure.The results, however, do not differ noticeably from a more fully specified model (Pierce and Miller 2004, pp. 172–73).
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Fundamentalists have no impact on the likelihood of casino legalization (coefficient = 0.006; t-ratio = 0.44).
THE CHANGING SYMBOLIC WEIGHT OF THE “SINFULNESS OF GAMBLING” We have asserted elsewhere that the Fundamentalist Protestants’ symbolic argument against the sinfulness of gambling suffered declining potency over time. A similar assertion has already been made in explaining the dynamics of the internal diffusion, with lotteries paving the way for casino legalization. In different terms, Mooney (2000) has suggested that the risk factor for diffusion of innovations may not be constant over time.We are simply identifying the symbolic weight (or potency) of the sinfulness-of-gambling perception as the engine driving change in the risk factor. The findings in Table 23.5 (lottery adoption) and Table 23.7 (casino legalization) provide evidence suggesting strongly that the symbolic weight of the sinfulness-of-gambling argument has declined over time. Policy entrepreneurs using “sin” to frame the issue of legalized gambling have generally come from Fundamentalist churches and have found a receptive audience among Fundamentalist church members. Hence, the impact of Fundamentalists on the politics of legalized gambling should parallel the symbolic weight of the sinfulness of gambling. The results for both lottery adoption and casino legalization suggest that the weight of that symbol has declined. The impact of Fundamentalists on lottery adoption dropped noticeably between 1966–78 and 1979–94 (Table 5), and Fundamentalists had no impact on casino legalization once lottery adoption was taken into account (Table 23.7). As the impact of Fundamentalists on legalized gambling politics declined, the risk factor for adopting a lottery or legalizing casino gambling increased. Fundamentalists constituted the primary political force opposing legalized gambling; as they became politically weaker, legalized gambling became more likely. The comment made by the prominent anti-gambling activist previously noted illustrates the point: Moral arguments against legalized gambling no longer work; antigambling forces must now make economic arguments against lotteries and casinos.
THE PUZZLE OF INDIAN CASINOS In the United States, one can hardly study casinos without recognizing the booming business of Indian casinos. The Indian Gaming Regulatory Act of 1988 opened the way for a dramatic expansion in the number of Indian casinos throughout the country. Basically, the legislation enabled Indian tribes to conduct any form of gambling not expressly prohibited by the state on which their tribal property
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was located.Tribes were directed to negotiate compacts with the state government, governing the distribution of revenue and regulation of the casino. The policy’s vagueness has resulted in tribes taking their cases to the U.S. Department of Interior and to trial in numerous court cases, as the state’s prohibition against a particular form of gambling may be unclear, parties may disagree over who has authority to negotiate on behalf of the state, and the terms of the compact may be contentious. As a result of this unpredictability, few studies of the politics of Indian casinos exist. Most research focuses on the legal/constitutional issues involved in deciding litigation over proposed Indian casinos. For our purposes, we might believe that the prospect of Indian casinos might have an impact on casino legalization efforts. Our analysis (reported in Pierce and Miller 2004, p. 172), however, indicates that Indian casinos do not affect the likelihood of casino legalization.The lack of any statistical relationship suggests that the uncertainties concerning Indian casinos make political and economic calculations about casino legalization remarkably unclear.
THE FUTURE OF LEGALIZED GAMBLING Explaining the spread of legalized gambling helps us to understand the present, but studies of the politics of lotteries and casinos ought to afford us some insight into the future, too. If the past is any guide, one should not blithely assume that change—even dramatic change—is impossible. Although lotteries sprang up in colonial America and various forms of gambling were allowed and encouraged, lotteries were effectively ended across the states following a massive scandal with the Louisiana Lottery Company in the late nineteenth century. By the turn of the century, no states allowed lotteries.This universal prohibition lasted until the New Hampshire Lottery was established in 1963. So, in the face of lotteries saturating the states and casinos continuing to spread, what should we expect? First, the declining symbolic weight of the sinfulness of gambling (for which we have already provided evidence) argues for the continued spread of casino legalization across the states. States in need of revenue and economic development should find relatively little effective opposition, particularly if casino legislation includes local option provisions, allowing cities and/or counties to decide whether they want to allow casinos in their jurisdictions. Once policy entrepreneurs advocating casino legalization effectively make their case that economic development in the state requires decisive action, casinos become more likely than in the past. The matter of economic development should help us understand two political issues that will arise in casino legalization. First, casino legalization will be particularly likely in those states with badly decaying urban areas. Urban blight helps policy entrepreneurs identify a clear problem which can be solved by casinos, but that point addresses only part of the politics. Some studies of the economics of casinos have
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noted that casinos can “cannibalize” their competition (Kindt 1994, 1995). In order to attract customers, the casino offers various services (hotels, restaurants) at lower prices, undercutting local businesses.5 However, urban blight results in local businesses that have nothing to lose. Put bluntly, they have no business to undercut. Local businesses in a blighted urban area may welcome casinos, believing that they will bring in customers from outside the community. Businesses in Atlantic City and Deadwood, South Dakota, embraced casinos as a vehicle for reviving the local economy. Second, there is one type of declining business that cannot benefit from casino legalization: the horse racing industry. In some states, such as Kentucky, the power of the horse racing industry may be out of proportion to its contribution to the economy and to its voting strength. How might state policymakers assuage the fears of the horse racing industry? Racinos. The term was coined in 1995 by Richard L. Duchossois (who owned Arlington Park Race Track) to describe the siting of slot machines at race tracks. The past few years have seen a flurry of attempts to introduce racinos in numerous states.6 Slots at the tracks provide the horse racing industry with new customers, generating additional revenue for their declining businesses. Locating casino gambling at the track removes the political opposition of the horse racing industry, which had joined the anti-gambling ranks of Fundamentalist Protestants. Given the power of the horse racing industry in many states, this constitutes a significant blow to the anti-gambling cause. Thus far, the prospects for legalized gambling look fairly promising. Antigambling forces have become weaker (Fundamentalists) or co-opted (horse racing industry), lotteries have spread across the United States, and gambling proponents have become increasingly savvy politically.Three illustrations may offer cautionary tales. First, the nineteenth-century scandal at the Louisiana Lottery Corporation involving bribery payments to public officials led to shutting down state lotteries at the turn of the last century. The scandal revived citizens’ and elites’ sense that gambling was a suspicious activity, of questionable moral quality. As long as legalized gambling remained free from scandal, only the social ills associated with addictive gambling could challenge its legitimacy (Kindt 1998; Lesieur 1998; Miller and Schwartz 1998; Stinchfield and Winters 1998).Those ills, however, are generally relatively invisible. Further, individualistic cultural norms will locate the problem with the individual gambler rather than with the system of legalized gambling.
5 Interestingly, this practice of “comping” meals is on the wane, as competing restaurants have been driven out of business. So, once the free meals have eliminated competition, the casino can charge for meals without losing customers or business. 6 Recently, Kentucky has been considering instituting racinos. Its proponents’ particular proposed solution is designed not only to assuage the potential opposition of the horse racing industry, but also to increase public support. State revenue from racinos is supposed to be targeted to education, thus providing a more symbolically appealing purpose for the gambling revenue.
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The frequent funneling of gambling revenue to addictions programs allows progambling forces to address the issue of addictive gambling and define the problem as pertaining to the individual. Similarly, challenges to the regressivity of this sin tax fail to generate significant opposition to legalized gambling; the problem is identified as poor individuals lacking good judgment in spending their money. Second, the link between gambling and corruption informs our finding that riverboat casinos but not land-based casinos contribute to the external diffusion of casinos (Lehne 1986). Although riverboat casinos connote the romance (however misguided) of gambling on the Mississippi, land-based casinos will be associated with the role of organized crime in establishing the casinos of Las Vegas. How else to explain the insistence in some state legislation that riverboat casinos use vessels that resemble riverboats of the mid-1800s? Anticasino campaigns in Florida seized upon the association of land-based casinos with corruption by arguing that Florida’s “familyfriendly” tourist economy would be harmed by allowing casinos to enter the state. Finally, the current scandal involving lobbyist Jack Abramoff provides the potential for antigambling backlash.The allegations that Abramoff both bribed U.S. public officials on behalf of Indian casinos and defrauded the operators of the casinos recall the Louisiana Lottery Corporation scandal. It reminds citizens and elites of the association between gambling and unethical behavior.Various state lotteries and casinos have experienced scandals as well. In all of the current cases, however, state governments have treated the criminal conduct as individual rather than systemic.That is, legalized gambling has no inherent moral problems; the scandals result from individual actions that must be punished. Antigambling activists have failed to cast the issue in more emotional and symbolic terms that could challenge the institution of legalized gambling. We have argued elsewhere that gambling commissions established in the modern bureaucratic state provide venues and procedures that remove such scandals from the public eye and protect the institution of gambling from political attack. Consequently, we anticipate that only corruption and scandal on a massive scale—exploited by antigambling activists in a symbolic campaign—will reverse the spread of legalized gambling.
FUTURE RESEARCH Although the topic presents significant data-gathering difficulties, the administration and implementation of legalized gambling policies deserves attention. Obviously, as lotteries saturate the nation, policy adoption becomes a less important area of research. However, lottery commissions cope with myriad difficulties: occasional scandals, controversies pertaining to advertising the lottery, competition with other forms of legalized gambling, etc. (Smith 1997).The resolution of these matters affects the future viability (fiscal and political) of the lottery. State lottery commissions bear a certain resemblance to federal-level independent regulatory
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commissions, so theory and research design can be drawn from studies on independent regulatory commissions (Krasnow, Longley, and Terry 1982; Reagan 1987; Ringquist 1993, 1995). The growing interest in racinos suggests that research on casino legalization will need to examine the relationship between casinos and racino politics. In some states, racinos may constitute a more palatable version of legalized gambling than casinos. As lotteries have helped pave the way for casinos, however, racinos may play a similar role. In other words, policy entrepreneurs may propose racinos as a way to assist the horse racing industry (with its glamorous image), and then move from racinos to full-blown casino proposals. Another area of future research concerns the fiscal politics of legalized gambling. Although the revenue provided by lotteries and casinos does not constitute more than a small portion of total revenue for most states (less than 5% for lotteries), it is better than nothing.7 Particularly during periods of fiscal stress, lottery and casino revenue can be crucial and receive additional attention from policymakers. Policymakers could view lotteries as another kind of sin tax and could attempt to raise the rate of that taxation. Finally, policy entrepreneurs continue to link legalized gambling initiatives to more attractive symbols by claiming that gambling revenue will fund education (or programs for the elderly, the environment, etc.).The literature on lottery revenue raises doubts about the validity of these targeted programs (Miller and Pierce 1997), but states have developed new ways to attempt to ensure that the funds go to their supposed targets (e.g., Georgia’s HOPE [Helping Outstanding Pupils Educationally] Scholarships). Finally, more work must be done on the very complicated politics of Indian casinos. Mason (2000) has conducted a study on these politics, but it constitutes simply a valuable beginning. At least one observer has referred to Indian casinos as the biggest growth industry in the United States (Frontline/PBS 1998). Given current and ongoing scandals concerning their lobbying efforts at the state and national levels, scholars would be remiss if they failed to improve their understanding of the legal and policy controversies surrounding Indian casinos.
GLOSSARY Diffusion of innovations the spread of a policy from an initial jurisdiction to other jurisdictions. External diffusion the process by which a political jurisdiction passes a policy that already exists in a neighboring jurisdiction.
7 Of course, Nevada depends fairly heavily on revenue from casino gambling. Nonetheless, casino gaming revenue constitutes only roughly 15% of state revenue, even in Nevada.
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Fundamentalists Protestants who adhere to a literal interpretation of the Bible. Internal diffusion the process by which a political jurisdiction passes a policy that is related to some existing policy in the same general policy area. Risk factor the probability that a second jurisdiction might adopt an innovative policy once a first jurisdiction has adopted it. Symbolic weight the strength of the emotional response to a given symbol.
REFERENCES Allison, P. D. (1984). Event History Analysis.Thousand Oaks: Sage. Anders, G. C. (1998). Indian gaming: Financial and regulatory issues. Annals of the American Academy of Political and Social Science, 556, 98–108. Beck, N., and Tucker, R. (1996, September). Conflict in Space and Time:Time-Series-Cross-Section Analysis with a Binary Dependent Variable. Paper presented at the annual meeting of the American Political Science Association, San Francisco. Berry, F. S., and Berry,W. (1990). State lottery adoptions as policy innovations: An event history analysis. American Political Science Review, 84, 395–415. Box-Steffensmeier, J. M., and Jones, B. S. (1997).Time is of the essence: Event history models in political science. American Journal of Political Science, 41, 1414–1461. Bradley, M., Green, N., Jr., Jones, D. E., Lynn, M., and McNeil, L. (1992). Churches and Church Membership in the United States, 1990.Washington, DC: Glenmary Research Center. Council of State Governments (various years). The Book of the States. Lexington, KY: Author. Elder, C. D., and Cobb, R.W. (1983). The Political Uses of Symbols. New York: Longman. Filer, J. E., Moak, D. L., and Uze, B. (1988). Why some states adopt lotteries and others don’t. Public Finance Quarterly, 16, 259–283. Findlay, J. M. (1986). People of Chance: Gambling in American Society from Jamestown to Las Vegas. New York: Oxford University Press. Foster, J. L. (1978). Regionalism and innovation in the American state. Journal of Politics, 40, 179–187. Frontline/PBS. (1998). Gambling Facts & Stats. Retrieved July 21, 2003, from http://www.pbs.org/ wgbh/pages/frontline/shows/gamble/etc/facts.html Goodman, R. (1994). Legalized Gambling as a Strategy for Economic Development: United States Gambling Study. Northampton, MA: Broadside Books. —— . (1995). The Luck Business. New York: Free Press. Gray,V. (1973). Innovation in the states:A diffusion study. American Political Science Review, 67, 1174–1185. Hagle, T. M., and Mitchell, G. E., II. (1992). Goodness-of-fit measures for probit and logit. American Journal of Political Science, 36, 762–784. Haider-Markel, D. P., and Meier, K. J. (1996).The politics of gay and lesbian rights: Expanding the scope of conflict. Journal of Politics, 58, 332–349. Johnson, D., Picard, P. R., and Quinn, B. (1974). Churches and Church Membership in the United States. Washington, DC: Glenmary Research Center. Katz, E., Levin, M. L., and Hamilton, H. (1963).Traditions of research on the diffusion of innovations. American Sociological Review, 28, 237–252. Kindt, J.W. (1994).The negative impacts of legalized gambling on business. Business Law Journal, 4, 93–124. —— . (1995). Legalized gambling activities: The issues involving market saturation. Northern Illinois University Law Review, 15, 271–305.
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—— . (1998). Follow the money: Gambling, ethics, and subpoenas. Annals of the American Academy of Political and Social Science, 556, 85–97. Krasnow, E. G., Longley, L. D., and Terry, H. A. (1982). The Politics of Broadcast Regulation. 3rd ed. New York: St. Martin’s Press. Lehne, R. (1986). Casino Policy. New Brunswick, NJ: Rutgers University Press. Lesieur, H. R. (1998). Costs and treatment of pathological gambling. Annals of the American Academy of Political and Social Science, 556, 153–171. Lindblom, C. E., and Woodhouse, E. J. (1993). The Policy-Making Process, 3rd ed. Englewood Cliffs, NJ: Prentice Hall. Mason, W. D. (2000). Indian Gaming: Tribal Sovereignty and American Politics. Norman: University of Oklahoma Press. McGowan, R. (1994). State Lotteries and Legalized Gambling: Painless Revenue or Painful Mirage? Westport, CT: Praeger. Meier, K. J. (1999). Drugs, sex, rock and roll: A theory of morality politics. Policy Studies Journal, 27, 681–695. Miller, D. E., and Pierce, P. A. (1997). Lotteries for education: Windfall or hoax? State and Local Government Review, 29, 34–42. Miller, W. J., and Schwartz, M. D. (1998). Casino gambling and street crime. Annals of the American Academy of Political and Social Science, 556, 124–137. Mooney, C. Z. (2000, November). Policy Development, Social Learning, and Gambling Policy in the American States. Paper presented at the annual meeting of the Southern Political Science Association, Atlanta. Munting, R. (1996). An Economic and Social History of Gambling in Britain and the USA. Manchester and New York: Manchester University Press/St. Martin’s Press. Pierce, P. A. (1984). Partisan Realignment and Political Change: A Study of Four American States. Ph.D. dissertation, Department of Political Science, Rutgers University. Pierce, P.A., and Miller, D. E. (1997, October). Roll the Dice:The Diffusion of Casinos in the American States. Paper presented at the West Virginia Political Science Association, Morgantown. —— . (1999).Variations in the diffusion of state lottery adoptions: How revenue dedication changes morality politics. Policy Studies Journal, 27, 696–706. —— . (2004). Gambling Politics: State Government and the Business of Betting. Boulder, CO: Lynne Rienner Publishers. Quinn, B., Anderson, H., Bradley, M., Goetting, P., and Shriver, P. (1982). Churches and Church Membership in the United States, 1980.Washington, DC: Glenmary Research Center. Reagan, M. D. (1987). Regulation:The Politics of Policy. Boston: Little, Brown and Co. Ringquist, E. J. (1993). Does regulation matter? Evaluating the effects of state air pollution control programs. Journal of Politics, 55, 1022–1045. —— . (1995). Political control and policy impact in EPA’s Office of Water Quality. American Journal of Political Science, 39, 336–363. Rogers, E. (1983). Diffusion of Innovations. 3rd ed. New York: Free Press. Smith, M. (1997). Fewer jackpots, boats blamed for lottery decline. South Bend (IN) Tribune, July 25, C1. Stinchfield, R., and Winters, K. C. (1998). Gambling and problem gambling among youths. Annals of the American Academy of Political and Social Science, 556, 172–185. von Herrmann, D. K. (2002). The Big Gamble:The Politics of Lottery and Casino Expansion.Westport, CT: Praeger. Walker, J. (1969). The diffusion of innovations among the American states. American Political Science Review, 63, 880–899. —— . (1973). Comment: Problems in research on the diffusion of policy innovations. American Political Science Review, 67, 1186–1191.
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CHAPTER 24
Gambling and Governance Peter Collins School of Accounting Economics and Management Science University of Salford Salford, Greater Manchester, United Kingdom
Introduction A Free Market in Gambling? Two Types of Arrangement for Regulating Gambling Government Objectives in Relation to Gambling Policy Vice Crime and the Gambling Industry Organized Crime and the Gambling Industry Defrauding Customers Money Laundering and Gambling Increases in Gambling Opportunities and in General Levels of Crime Problem Gambling Gambling Opportunities and Problem Gambling Numbers Economic Benefits Taxation Conclusion: Achieving Democratic Consensus About Gambling Policy
INTRODUCTION Gambling refers simply to the activity of making bets on future events whose outcome is uncertain—the turn of a card, the spin of a wheel, the result of races and other sporting and nonsporting events, the numbers which will be thrown up by some sort of random number generator. Usually this activity is undertaken because it affords people a good deal of excitement and fun. Commercial gambling 617
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is simply the business of organizing opportunities for customers to make bets of various kinds and charging them for this service, usually by ensuring that the organizers of these opportunities (the “house,” the casino, bingo club, bookmaker, or lottery organizer) have a statistical advantage which ensures that in the long run they will win a specific percentage of all money staked. As such, commercial gambling is clearly a part of the wider entertainment business. However, gambling is nowhere (yet) in fact treated by government as simply another service industry which sells people the opportunity to enjoy themselves. Instead governments prohibit, restrict, or subject commercial gambling to special regulations. Consequently, even when (at least some forms of ) commercial gambling services are allowed to operate, selling gambling opportunities is much more extensively and strictly regulated by government than selling chocolates or magazines or movie tickets or the chance to ride a roller-coaster or go bungeejumping. In particular, where governments do not prohibit commercial gambling, they typically make special provisions regulating some or all of the following: ● ● ● ● ● ● ●
who may own and operate gambling businesses where they may be located what size their premises must be what products they may sell what prices they can charge when they may open how old their customers have to be
Governments also specify what exceptional license fees and other special (“gambling privilege”) taxes gambling companies must pay. The purpose of this chapter is to explain the different ways in which governments regulate commercial gambling more extensively and more strictly than they do other businesses and to identify and evaluate the different reasons that are commonly given to justify these exceptional regulations.
A FREE MARKET IN GAMBLING? It is helpful to begin by seeing what would happen if governments did treat gambling as just another part of the entertainment industry and subjected trade in gambling products only to the constraints which apply in free markets. In free markets, governments intervene in commercial transactions only to resolve problems which the market itself cannot address—what economists call “externalities,” that is, consequences of business activity which adversely affect third parties. Pollution is the classic example of such an externality from which the affected third parties have a right to be protected or for which they are entitled to be compensated by the polluters.
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If commercial gambling were subject only to the regulations which govern other commercial activities in a free market, then the government would confine itself to ensuring that: ●
● ●
● ●
Commercial gambling services are provided to willing buyers by willing sellers. There is no recourse to force or fraud in these transactions. Anyone is free to start up or buy shares in a gambling business or be employed in it, subject only to the legal provisions which apply to other businesses. Consumers enjoy normal protections in respect to health, safety, and the like. Normal taxes are paid.
There are, of course, strong arguments in favor of free markets.The strongest of these arguments is probably that only a system of free markets conforms to the moral principle that individuals should not be prevented by anyone from living their lives as seems best to them, provided only that they do not wrongfully harm others. This principle entails, among other things, that individuals should be free (provided others are not wrongfully harmed) to use their talents and energies as seems best to them in trying to sell goods and services to their fellows. It also entails that they should be free (again, subject to the harm proviso) to decide for themselves how to spend their own time and their own money, even if others think that their choices are foolish or immoral. Another strong argument for free markets is that they result in the greatest amount of consumer satisfaction as suppliers compete to offer consumers the best possible deal. Similarly (though this point is less well recognized), free markets might be thought to be a fairer system, since they guarantee, as a system of government direction cannot, that the greatest material rewards go to those who best satisfy the preferences of their customers. It might also be thought of as fairer to consumers, since the best deals will be obtained by the shrewdest buyers and—a point which, as we shall see, is of particular importance when considering the gambling industry—no one is discriminated against by the tax system on the basis of his/her preferences. None of these arguments for free markets lose their force simply because the market in question involves gambling.We therefore need to understand why, hitherto, governments have never (except by inadvertence) permitted the gambling industry to operate according to the ordinary principles of free markets. Here it is useful to distinguish three broad types of policy which governments have historically adopted and continue to adopt toward gambling: ● ● ●
Condemnation and prohibition Discouragement and restriction Acceptance but with the imposition of special safeguards
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It should be pointed out that governments may take a different attitude and adopt a different policy in respect to different gambling activities: Quite commonly, they permit betting on horse races but prohibit casinos, or they permit lotteries for good causes but outlaw electronic gambling machines in venues supplying nongambling services, such as bars, restaurants, bowling alleys, gas stations, etc. In the United States, they permit a great deal of gambling at land-based facilities but prohibit gambling using the Internet. For much of history in most societies, gambling has simply been illegal or at least severely restricted. In recent times, especially over the last thirty to fifty years, there has been a general move from prohibition and restriction to permissiveness. This has been comparatively rapid in the West with the widespread introduction of state lotteries and of electronic gambling machines, especially in casinos. In the East the move toward liberalization has been slower and more cautious but may be on the verge of gathering great momentum as jurisdictions such as Thailand, China, Japan, and India consider joining Macao, Malaysia, South Korea, and Singapore in authorizing large, international-style casinos. Most jurisdictions in the English-speaking world—North America, Australasia, South Africa—and in Europe, in fact, currently support versions of the third alternative: Permit gambling but impose special regulations, while the Eastern jurisdictions mentioned previously are moving toward this position from one of prohibition or severe restriction. To this extent, there is probably now majority support amongst governments around the world for some variant of the third alternative, which allows a limited amount of commercial gambling but requires that it be subject to much stricter regulation than other businesses. It is important to recognize, however, that there are substantial and significant dissenters from this view amongst, for example, Muslim countries that retain a principled commitment to prohibition. There are also significant lobbies, in all but the most permissive jurisdictions (e.g., Nevada), which believe that gambling is morally objectionable and that if governments cannot prohibit it completely, they should tolerate as little of it as possible and discourage it as much as possible. Moreover, the main reason why governments historically have moved away from prohibition has not been out of concern for freedom of choice but rather because of the overwhelming difficulties and costs involved in trying to suppress illegal gambling. As we shall see, the feeling that there is something unsavory or unwholesome about commercial gambling continues to shape the policies of governments, even if only implicitly. Before going on to describe the principal ways in which governments seek to regulate and control gambling, I need to notice an area where there may be something very close to a free market in gambling services, namely cyberspace. Although no government has authorized a free market in gambling services, it is arguable that the technology of the Internet has already created a de facto free market. Moreover, governments have not yet devised a way of successfully regulating
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Internet gambling, whether they wish to prohibit it like the United States or to permit it and regulate it like the United Kingdom.The fact is that it is, in practice (not so much technologically as politically) impossible to prevent people from gambling on the Internet with companies whose operations are based offshore. Nor is it possible, under international law, to prevent people from setting up Internet gambling operations in any jurisdiction which is willing to host them. Certainly, as Internet gambling grows, this will present the greatest challenge to governments that wish to continue to treat gambling and the gambling business as being in need of abnormally strict regulation. The result of this anticipated spread of Internet gambling might be much greater and more effective international cooperation to enforce commonly agreed upon regulatory standards.Alternatively, the result might be to undermine the case for trying to impose abnormally strict regulations (and abnormally high taxes) on land-based forms of gambling. Much will depend on how strong the arguments for treating gambling differently from other parts of the entertainment industry turn out to be.
TWO TYPES OF ARRANGEMENT FOR REGULATING GAMBLING Before we consider the question of why governments think that gambling needs to be regulated differently from other industries, we need to note that different jurisdictions that wish to authorize at least some gambling but only with special safeguards adopt one of two main types of institutional strategy in order to achieve this: ● ●
Direct or indirect state ownership and administration Licensing and regulation of private companies by an independent, specialist (but state-appointed) body such as a gambling commission
Thus, lotteries everywhere are most commonly owned and operated by national or regional governments. The same effect is achieved in the United Kingdom by having a competition amongst private consortia every seven years— soon to rise to ten years—to operate a national monopoly, over 80% of whose earnings are paid over to the state in the form of a lottery tax or a mandatory contribution to state-specified “good causes.” In Canada, Sweden, Holland, and Austria, most other forms of gambling, including casinos, are also state owned, even when independently operated, with the result that the profits accrue to the public purse. However, internationally, the commonest arrangement in respect to casinos is to license private companies and ensure that they promote the public interest in other ways than those that flow from being state ownership—for example, by promoting tourism, as in Las Vegas and Macao; by paying abnormally high “gambling privilege taxes,” as in most of
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Europe and now in Illinois; by paying a large onetime licensing fee often determined by auction, as in Australia; or by contributing to economic development, especially the regeneration of disadvantaged areas, through substantial investment in nongambling, job-generating facilities and infrastructure, as in Melbourne, Cape Town, France to some extent, and most recently the United Kingdom. Each type of system has both strengths and weaknesses.The principal arguments in favor of having what is effectively a nationalized gambling industry or a close approximation to it are that under such a system: ●
●
It is easier for governments to take the necessary steps to limit potentially negative social impacts, such as an increase in problem gambling, without being deterred by the fear of losing profits. All the profits go to government and are, therefore, available (in theory, at least) to pay for goods and services which benefit the public as a whole.
The principal arguments against a system of government ownership and control of the gambling industry are that: ●
●
●
Governments are likely to experience a conflict of interest between their desire to protect the public and their desire to raise funds (in a relatively popular manner) for public interest projects. Government monopolies, like any other monopolies, are undesirable because they result in worse services at higher prices for the consumer and thus erode “consumer surplus.” Governments expose themselves to the charge (which may be electorally damaging) that they ought not to be involved in an essentially immoral business and should not be living off what, in some people’s view at least, are immoral earnings.
The alternative to having a nationalized or quasi-nationalized gambling industry is a system in which the development and operation of gambling businesses is left to the private sector. Under such a system the private sector is required to comply with special regulations laid down and enforced by government and government-appointed regulatory authorities, typically known as gambling boards or commissions. These boards or commissions are responsible for protecting the public interest mainly in respect to preventing increases in negative social impacts such as fraud and other forms of crime or increases in problem gambling, which, it is feared, may result from the legalization or liberalization of commercial gambling.They may also, as in the case of some national and state lotteries, be charged with maximizing contributions to “good causes.” Where the number of licenses for gambling businesses is limited, these become a valuable resource, and the only fair way of distributing them becomes through a tendering process. Governments then need to set up a system for adjudicating
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among competing tenders on the basis of which project is, all things considered, most likely to minimize social costs and maximize public benefits. The advantages of such a system are that: ●
●
●
It harnesses the creativity of the private sector in developing projects which offer substantial benefits to the public as a whole (and not just that section of it who will become customers for gambling services). It promotes competition which benefits the customers in various ways— even if the successful bidder for the license is granted a monopoly for a limited period. It enables regulators to concentrate single-mindedly on enforcing measures to protect the public without having to worry about the impact on profits.
The principal disadvantages are that: ●
●
●
Regulators may be constantly thwarted in achieving their objectives by the combination of advances in technology and the ingenuity of operators in finding loopholes in the law. Conversely, regulators may seek to justify their position by imposing unnecessary and expensive regulations which damage tax revenues and shareholder earnings without securing any real benefits for anyone other than the regulators themselves. The whole process means that gambling companies have to devote substantial resources to lobbying governments both to allow them to do what they want to do and to prohibit actual and especially potential competitors from doing what they want to do. This is an economically unproductive use of resources and may be socially unhealthy in other ways by distorting or corrupting the political process.
There is no clear reason for deciding in favor of one system over another. Whichever system is preferred, the obvious strategy is to seek to minimize the disadvantages and capitalize on the advantages. In practice, however, the two systems differ much less than may appear on the surface.The reality is that even in state-owned systems, gambling-specific professional expertise has to be developed or employed to make the businesses successful, and this usually means incentivizing people from the private sector by offering bonuses or other schemes which permit individuals to share in the gross gambling revenues, that is, the earnings of the business measured as money staked less money paid out in winnings. Conversely, the abnormally high “gambling privilege” tax rates which gambling companies are required to pay (59% in France, 70% in Illinois, 92% in Berlin) or the vital contribution they make as a tourism attraction to the economy of the jurisdiction as a whole (as in Nevada and Macao) means that the government has an important economic stake in the profitability of the businesses.
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Furthermore, both systems will typically strive to ensure that gambling is not associated with criminal activity, that consumers are properly protected, and that the harm caused by problem gambling is minimized. Both will also typically give some say to local communities in determining how much gambling and of what kind they wish to see permitted in their area. In general, the organization of commercial gambling is best seen as being always some kind of partnership between the public and private sectors, where each party has a common interest in ensuring that the business is both profitable and popular. Specifically, this means that both government and private companies need to ensure that the gambling industry is perceived by at least most of the public as providing harmless entertainment and contributing to the general economic well-being of the jurisdiction. Both also need to do—and be seen to be doing—everything they reasonably can to ensure that lives are not ruined by excessive gambling and that the presence of gambling businesses leads to an enhancement rather than to a degeneration of the quality of life in the communities where they are located.
GOVERNMENT OBJECTIVES IN RELATION TO GAMBLING POLICY At this point we need to recognize frankly that all governments have as their first priority to retain power and remain in office. At least in relatively democratic societies (and democracy is always a matter of degree), governments are constrained by public opinion in what they are able and disposed to do about the regulation of gambling, as they are about all other issues of public policy. In particular, in democracies governments are compelled, on pain of losing office, to conform their conduct to the broad wishes of the electorate so that they at least remain more popular with the voting public than their opponents. Government policy regarding gambling will, therefore, be ultimately determined in democracies by the relative weight which electorates accord to the following objectives: ● ●
●
the suppression of (what they believe to be) vice the prevention of harm to individuals, to particular groups, and/or to society as a whole the securing of economic policy objectives such as increasing national prosperity, equitably distributing resources, and commissioning public interest projects, which are relatively difficult to pay for out of more conventional forms of taxation
If governments attach overriding importance to the first consideration, they will seek to implement a policy of prohibition in respect to gambling. This has been the traditional policy everywhere and remains the policy in Muslim and Communist states.
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If governments are concerned mainly with the second objective, they will seek a policy which permits only limited gambling opportunities, ensures that they are very tightly controlled, and is careful not to encourage people to participate in gambling.This was the thinking which informed the U.K. Gambling Act of 1968, although the concern then was with the dangers of commercial gambling becoming dominated by organized crime rather by a concern for problem gambling. It is also probably the present policy of China, which forbids all forms of gambling except lottery gambling and casino gambling in Macao. If governments are concerned mainly with economic benefits which can be secured for the public purse, they will do one of two things. Either they will seek to maximize tourism earnings, as has been done with spectacular success in Las Vegas and Macao, or they will impose high gambling privilege taxes or otherwise secure for themselves a share of gambling revenues over and above what they would get through normal income, consumption, and property taxes.This is what happens with all lotteries and is the model which dominates all forms of gambling regulation in most other jurisdictions, such as in some U.S. states, in Canada, in most of Europe, in Australasia, and in parts of Africa. In either case, they will favor a liberal gambling regime which is minimally onerous in regulation to minimize potential negative social impacts such as, most notably, problem gambling. Many of the jurisdictions where governments are concerned primarily with the revenues they can secure from gambling have rather unimpressive records for addressing problem gambling issues. This is true of Las Vegas (at least until very recently), Macao, and most of Europe, though not of Canada, Australia, New Zealand, and South Africa. In the sections that follow, I shall identify and seek to assess the cogency of the arguments used to support each of these policy objectives and thereby to justify government in treating the gambling industry differently from other industries which supply entertainment and other forms of pleasure. It should be noted that although different emphases on different objectives tend to favor greater or lesser restrictions on what gamblers and gambling companies may do, most gambling policies are hybrids which contain elements drawn from consideration of all three objectives.
VICE The prohibition of gambling has been supported in the past because most people in most societies—or at least most people belonging to their ruling classes—have believed two propositions which many people in all societies continue to believe: ● ●
Gambling is a vice. It is the business of government to prevent people from engaging in vice.
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The view that gambling is immoral and ought to be prohibited has been widespread throughout recorded history and is still dominant in many societies today, continuing to be held by significant numbers of people even in liberal, democratic societies which authorize extensive commercial gambling industries. A vice may be defined as any activity which affords people pleasure—and for which they are consequently willing to pay—but which is considered by others to be immoral. Examples of what have historically been accounted vices and which still are by most people (including many of those who indulge in them) are various forms of commercial sex, such as prostitution and pornography; drug taking; and gluttony. It should be noted, however, that what counts as a vice varies greatly among societies and changes over time. Thus, drinking alcohol, which has sometimes been widely regarded as a vice comparable to taking other drugs such as cannabis or various forms of morphine, is no longer usually regarded as a vice in the West (though it still is in Muslim countries), whereas smoking cigarettes is now much more deeply frowned upon as a vice than it used to be. More surprisingly, going to the theatre and, worse, being an actor used to be thought of as comparably immoral to visiting a brothel or being a prostitute. Being a bookmaker in the United States is still thought of as not much better than being a pimp, although in the United Kingdom it is a perfectly “normal” and respectable occupation and the principal bookmaking companies are typically listed on the stock exchange and are run by highly competent, sophisticated, and well-respected managers. Sexual relations between homosexuals used to be regarded as constituting an unquestionably vicious activity that deserved severe punishment by the law. Now, however, it is typically illegal in democratic countries to discriminate against homosexuals, and indeed, to express the view that homosexual relationships are vicious is today regarded, in many circles, as being itself immoral. The fact that what counts as a vice is to a large extent culture relative is one reason why it is difficult to argue successfully for the prohibition of gambling on moral or religious grounds in pluralist democratic societies where there is simply unlikely to be a consensus on what pleasures are inherently sinful or immoral and what are not. Another reason why the prohibition of alleged vices on moral grounds has been difficult to sustain is that vices do not normally or directly harm other people, even if the abnormal minority of those who gamble to excess do harm to those close to them as well as to themselves. In general, in pluralist societies we are brought up to think that we are justified in condemning other people’s conduct only if it adversely and unjustifiably affects other people.We are not justified in condemning their choices simply because we disagree with their convictions or do not share their tastes.We accept that there is an area of private life in which individuals each have the right to choose for themselves what to believe and how to live, provided only that they don’t illegitimately harm others in the process. In short, we are brought up to be tolerant of conduct in others which we personally find distasteful and of views which we deplore.
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A fortiori, we do not accept that it is the business of government to do what has usually been assumed to be an important duty of government, namely to ensure “the removal of wickedness and vice”—the phrase used in the old prayer for virtue and effectiveness amongst those in government. Instead, we follow, by now almost instinctively, the political theory articulated by John Stuart Mill (1859) in his masterpiece On Liberty.This theory asserts that the sole end for which mankind are warranted, individually or collectively, in interfering with the liberty of any of their number is self-protection . . . . The only purpose for which power can rightfully be exercised over any member of a civilised community, against his will, is to prevent harm to others. His own good, either physical or moral, is not a sufficient warrant. He cannot rightfully be compelled to do or forbear because it will be better for him to do so, because it will make him happier, because, in the opinion of others to do so would be wise or right. . . .The only part of the conduct of anyone for which he is amenable to society is that which concerns others. Over himself and his own body the individual is sovereign.
Applied to vice, we may and indeed have a duty to do whatever we can to dissuade our fellows from engaging in self-destructive behaviors, but we have no business demanding that the law be used to prohibit and prevent them. It is worth noting that Mill’s distinction between what affects others and what affects only myself is not only difficult to apply in practice, it is also much contested in theory, mainly by those, on both the left and the right of politics, who stress that “no man is an island” and that we are “all members one of another” in a seamless human community. More surprisingly, perhaps, it needs to be noted that Mill himself was ambivalent about how his principle would apply to, and therefore what public policy should be concerning, commercial gambling, or what he called the “keeping of a gambling house” (Mill 1859).1
CRIME AND THE GAMBLING INDUSTRY It is because modern democracies typically share both a commitment to Mill’s principle of liberty and his uncertainty about whether it does harm to others if people are allowed to trade in goods and services deemed to be in various ways bad for those who indulge in them that laws to permit or extend legalized commercial gambling and other alleged vices are always controversial.The controversy focuses, however, not—or, at least, not explicitly—on whether the activities in
1 In the course of his discussion of gambling, Mill (1859) writes: “Fornication . . . must be tolerated, and so must gambling: but should a person be free to pimp or to keep a gambling house? The case is one of those which lie on the exact boundary between two principles” [i.e., of the duty of government to protect individual liberty and its duty to protect the rest of society from harm] (p. 169).
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question are immoral but on whether they have negative consequences for either innocent third parties or society as a whole. In the case of gambling, the principal worry has historically been related to crime. Obviously, if legalizing any product is likely to lead to an increase in criminal activity, this would be generally accounted a strong reason against legalizing it. It is also well known to the general public that historically the provision of gambling services has been provided and controlled by organized crime or individual criminals. This was so most famously in Nevada, but it has also been true in many other jurisdictions where gambling was or still is originally illegal. There are four principal issues involved regarding the potential of legalized gambling to promote criminal activity: ●
● ●
●
involvement by organized and other criminals in the operation of gambling businesses the potential for defrauding customers the risks that gambling operations will be used for money-laundering purposes the alleged correlation between the arrival of commercial gambling operations, especially of casinos, and an increase in the crime rate
We shall discuss these briefly in turn.
ORGANIZED CRIME
AND THE
GAMBLING INDUSTRY
The first thing to say about the alleged relationship between the gambling industry and the involvement of professional criminals is that it misconstrues the flow of causality. Specifically, illegal gambling industries do not attract professional criminals because they involve gambling: Illegal gambling industries attract professional criminals because they are illegal—or at least because the availability of legal gambling is inadequate to meet consumer demand. One of the most powerful reasons why governments legalize gambling is precisely to prevent future penetration by, or eliminate the current presence of, criminals in the operation of commercial gambling businesses. This was the principal motivation for the U.K. Acts of 1963 and 1968, the South African Act of 1996, the liberalization of the casino market in Macao, and the introduction of strict regulation of the gambling industry into Las Vegas and elsewhere in the United States. On this issue, these jurisdictions and others have been extremely successful: The gambling industry is typically owned by shareholders in public companies and run by professional managers who are rather less likely to behave improperly than their colleagues in other industries because they are more closely scrutinized and have very valuable licenses to lose. Moreover, when the provision of commercial
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gambling is made legal, consumers cease overnight to be criminals. In two very obvious ways, therefore, legalization or liberalization of gambling laws reduces crime and criminal involvement substantially.
DEFRAUDING CUSTOMERS Similar considerations should serve to allay the second kind of worry, namely that gambling companies will defraud their customers. The combination of market forces and tight regulation are usually quite sufficient to guard against this. If customers get the impression that the games they are playing are rigged, they will rapidly withdraw their custom or take it elsewhere, and if a case of defrauding customers is proved against game operators, they will forfeit their ability to operate not only in the jurisdiction where the fraud occured but probably everywhere else in the world where gambling is regulated. Again, regulators are typically extremely careful in specifying what games may be played and stipulating how they must be operated, and again, they also carry out regular checks for compliance. The one area where fraud may yet prove a headache is the online gambling industry, especially poker, where in the nature of the case there is no surveillance equipment to prevent collusion between players and thus to prevent customers from defrauding one another. Much of online gambling is also very weakly regulated, so that there is an ongoing risk that one bad apple may undermine confidence in the entire barrel.
MONEY LAUNDERING
AND
GAMBLING
The third issue in regard to which gambling is often associated with crime is money laundering. Here it is enough to say that money laundering is essentially a problem for the banking industry, not for the gambling industry. Moreover, there are strict codes of conduct about money laundering which govern the gambling industry and involve reporting all transactions over a certain minimum amount as well as any other transactions deemed suspicious. Compliance with these codes is regularly tested by regulators, who arrange for anonymous individuals to pretend to try to launder money in casinos and elsewhere. Furthermore, to the extent that people seek to use gambling facilities to evade taxation by regularly reporting substantial gambling winnings to the tax authorities, the solution is to classify them as professional gamblers and tax them accordingly. Besides all this, there are much easier and cheaper ways to launder money, especially relatively small amounts of it—for example, by exchanging it for foreign currency and then reexchanging it.
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INCREASES IN
GAMBLING OPPORTUNITIES GENERAL LEVELS OF CRIME IN
AND
Finally, there is the question of whether the presence of gambling facilities, especially of casinos, leads to an increase in crime generally. In an important study, Grinols and Mustard (2006) compared crime rates over an extended period in counties with casinos and counties without them.They concluded that the presence of a casino was correlated with a fall in the incidence of crime in the short term but with an increase in the longer term. These findings have not yet been replicated, and the methodology of the study has been persuasively criticized by Walker (under review).The real difficulty is that, as Hume has taught us, mere correlation is not enough to justify a verdict of causation. Until we have formulated and tested a hypothesis about how the presence of gambling opportunities might affect crime rates, we have no means of knowing whether the casinos cause the change in the crime rate or whether it is due to some other factor which differentiates counties with casinos from those which have no casinos. For example, it might be that because casino operators have a huge interest in ensuring that their customers feel safe, they invest substantially in supplementary security and law-enforcement personnel, and this initially reduces crime. However, it may also be that people in noncasino counties tend to be more God fearing and law abiding than those in counties that have casinos, so the incidence of crime in these latter counties might be expected, in any case, to rise more rapidly than in noncasino counties, regardless of the presence or absence of a casino. One form of crime which may increase with the increased availability of gambling opportunities is crime embarked on to feed the habit of excessive gambling. Whether or not this is so clearly depends on the issue of what causes increases in problem gambling.To this question, therefore, I now turn.
PROBLEM GAMBLING It is true that what are commonly regarded as vices do seem especially prone to tempt at least some people into excessive consumption to the point where they are plausibly described as being addicted, find it extremely difficult to control their consumption, and do serious damage to themselves and those close to them in consequence. It should be noted, however, that this is true not only of alleged vices but also of almost any activity which people enjoy. For virtually every object of human enjoyment, there will be some people who spend substantially more time and money indulging themselves than is good for them. This is certainly true of eating and shopping, which, because they are necessary activities for everyone, cannot possibly be accounted as inherently immoral. Thus even if we cannot confidently make out a case for banning vices on the grounds that they constitute
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wicked pleasures, we may perhaps justifiably account them such dangerous pleasures that we should either outlaw them or permit them in only very limited quantities, in the mildest possible forms, and subject to stringent regulations which will minimize their dangerousness. There is, of course a libertarian argument to be made that people should be allowed to indulge in any form of pleasure they choose, provided that they do not illegitimately (which usually means nonconsensually) harm others, regardless of how much harm they do themselves and those voluntarily associated with them or how intense and widespread the disapprobation their conduct inspires. Moreover, in this view, as we saw earlier, governments should do nothing to inhibit trade in such pleasures which they do not also do in respect to trade in other goods and services which people buy for the pleasure they afford. Governments might be justified in ensuring that consumers are made aware of the dangers of what they are doing and in general ensure that no one involved in the trade, whether as supplier or consumer, is coerced or defrauded. They have no right, however, to prevent people from exercising free (which, however, entails informed) choice in order to conform to the moral convictions and preferences of others about what people ought and ought not to enjoy where no harm to others is involved. A corollary of this libertarian position may be that the state (i.e., the taxpayer) should do nothing to alleviate the sufferings of those who become ill or are financially ruined as a result of their self-indulgence—there should be no taxpayerfunded health care for patients with diseases caused by smoking and no welfare payments or food stamps for those who reduce themselves to penury by gambling. As we have seen, no government bases its policy on this view in respect to gambling and other alleged vices; that is, no government has a normal free market in the provision and consumption of commercial gambling services where exchanges are made between willing buyers and willing sellers, just as they do not have free markets in drugs and commercial sex. On the other hand, in pluralist democracies—in contrast to societies which are governed according to the principles of a single religion or ideology such as communism—opponents of gambling do not typically make the argument for prohibiting gambling or limiting the availability of commercial gambling as much as possible on the grounds that according to the religious and/or moral beliefs which they hold, gambling is wicked. The reasons for this are that, in the first case, governments do not wish to court the unpopularity which would follow by first recklessly authorizing a free market in “vice” and then heartlessly leaving the victims of self-indulgence to die untreated and uncared for in the streets. In the second case, opponents of gambling realize that in pluralist and democratic societies, appeals to base public policy on religious or ideological principles are typically ineffective because there is rarely sufficient consensus on religious and ideological principles to secure a majority in this area. (Conceivably, the prohibition of gay marriages in the United States might prove an exception to this generalization.)
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For these reasons, public policy about gambling in such societies typically emerges out of (usually quite heated) debate about whether the legalization of gambling or the liberalization of commercial gambling law will or will not lead to significant harm to the “innocent.” Unfortunately, this debate is rarely carried out dispassionately, and the evidence is rarely collected and evaluated with the required scientific and scholarly objectivity.This is partly because people who are ideologically opposed to gambling seek to promote a prohibitionist or restrictionist political agenda by trying to persuade the public that liberal gambling laws will lead to a much greater incidence of problem gambling than the current state of our knowledge warrants. More importantly, it is because those who already have a license to supply gambling services have a massive commercial interest in joining forces with the opponents of gambling to prevent the legalization of new forms of gambling which would compete with them. Thus an unholy alliance is formed between protectionists in the existing industry and prohibitionists typically from religious groupings to create the impression that liberalization will lead to huge increases in problem gambling. On the other hand, would-be new entrants into the gambling market who hope to profit from the liberalization of gambling laws have an equally powerful interest in persuading the public that the liberalization will deliver substantial economic benefits and lead to no adverse social consequences, including no increase in problem gambling. Both sides are aided and abetted by commercial research firms that are happy, for a sufficiently substantial fee, to produce reports which tell their paymasters that the “objective” evidence supports the conclusion favoring their clients’ economic or ideological interests. The media, moreover, tend to favor the more sensational predictions of problem gambling epidemics because scare stories sell newspapers and attract viewers, and politicians behave opportunistically depending on the relative electoral importance to them of the antigamblers, the protectionists, or the potential beneficiaries of liberalization. For these reasons it is understandable that the great controversies about gambling and public policy tend to center around the issue of how much individual and social damage is caused by excessive gambling. However, as Collins (2003) argues at some length, this leads to judgments which are not warranted by evidence. This is mainly because surveys of general populations everywhere in the world use instruments which are at present too blunt to give an accurate number of the problem gamblers in any population. All they can really do is measure how many people answered how many questions affirmatively when presented with any particular problem gambling screen. This not only leads to the problem that cut-off points are arbitrary, but also means in practice that all screens identify large numbers of false positives (people identified as problem gamblers who are not problem gamblers) and false negatives (people not identified as problem gamblers who in fact are problem gamblers). McMillen and Wenzel (2006) of the Australian National University presented the
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results of a systematic review of three screens: the South Oaks Gambling Screen (SOGS), the Victoria Gambling Screen (VGS), and the Canadian Problem Gambling Index (CPGI).They found that even the best screen (the CPGI) had a 49.7% false positive rate and 34.1% false negative rate.The comparable figures were 69.3% and 46.4% for the SOGS and 66.3% and 63.1%. for the VGS.2 These results confirm that we can have only a very approximate idea of how many people in any community are “problem gamblers.” On the other hand, what can be said with some confidence is that if administration of the same screen is repeated under relevantly similar circumstances to similar population samples at different periods of time, then it should be possible to measure the trend in the incidence of problem gambling.This has now been done in a number of jurisdictions in North America as well as in New Zealand and South Africa and enables us to give at least a reasonably informed and plausible answer to the question, Does increased availability of gambling opportunities lead to an increase in the prevalence of problem gambling?
GAMBLING OPPORTUNITIES AND PROBLEM GAMBLING NUMBERS On the face of it, it seems obvious that if you make more gambling more easily available, you are going to find more people succumbing to the temptation to gamble to excess.To claim, in particular, that you can substantially increase the availability of rapid-action, high-stakes, and high- or frequent-prize gambling without having an increase in problem gambling sounds implausible to the point of perversity. Indeed, the best early research in this area conducted in the 1990s, and in particular the U.S. National Gambling Impact Commission’s Study and the Australian Productivity Commission Report, did indeed find a correlation between availability and problem gambling. The Americans found that increased problem gambling correlated with proximity to casinos, and the Australians (who, interestingly, largely exonerated their casinos) linked high rates of problem gambling to the large number of electronic gaming devices (poker machines, or “pokies”) located outside casinos in bars, hotels, and clubs. Subsequent research, however, suggests that this picture of a straightforward causal connection between increases in the availability of gambling opportunities and increases in the number of problem gamblers is an oversimplification. One puzzle relates to the relative stability of the numbers which different studies in different jurisdictions come up with for people with serious (addiction-like)
2 An even more alarming finding is reported in Ladouceur et al. 2005:“82% of the gamblers initially identified as probable pathological gamblers by the SOGS or the CPGI were not confirmed by clinical interview.”
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problems and with less serious problems.The number for the former seems to be around 1% in all jurisdictions and for the latter around 5% in all jurisdictions where prevalence studies have been conducted, regardless of variations in the type and amount of available gambling. It may, at this point, be worth noting in this connection that we don’t expect the incidence of compulsive shopping to be more than very slightly correlated with the number of shops in a neighborhood, and since problem gambling is essentially a spending disorder, researchers should perhaps explore this analogy further. Another puzzle relates to the fact that Nevadans, who have access to seven times as much gambling as the average for the rest of America, have only somewhat higher problem gambling rates (just under double), and this may be explained by the fact that people likely to become addicted to gambling are especially disposed to go and live in Nevada. However, the really startling challenge to the conventional view that increases in the availability of gambling are closely correlated with the legalization of new forms of gambling comes from research which expected to find this result but in fact found something close to the opposite. Rachel A. Volberg (2004) reviewed the main evidence which suggests that there is indeed a general correlation between increased availability of gambling opportunities and increased problem gambling and goes on to discuss the fact that “a number of replication studies … have identified prevalence rates of past-year pathological gambling that were stable or declined over periods ranging from two to eight years.” (She then cites eight such studies.) She comments on the possible reason for this (surprising) result in studies, many of which she conducted herself: “It is worth noting that despite increased legal opportunities to gamble … comprehensive services for problem gamblers—including public awareness campaigns, helplines and professional counseling programs—were introduced in all of these jurisdictions. The relationship between heightened opportunities to gamble and the prevalence of problem gambling may increasingly be moderated by declines in regular gambling participation and growth in the availability of problem gambling services” (Volberg 2004). Significantly, in jurisdictions where problem gambling numbers increased after new casinos were introduced, such as in Montana and North Dakota, public awareness campaigns and other services to prevent and mitigate the harm caused by excessive gambling were not introduced. What all this evidence seems to show, though not conclusively, may be summarized as follows. If a jurisdiction introduces new forms of gambling and does nothing else, it will most likely experience an increase in the incidence of problem gambling. However, if the jurisdiction combines the introduction of new forms of gambling especially with an effective public awareness campaign about the dangers of gambling and how to avoid them, it is likely to experience a decrease in problem gambling numbers and even in the numbers of people who gamble regularly as well. (For a fuller discussion of the evidence, see Collins and Barr 2006.)
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In short, the answer to the question of whether increased availability of gambling opportunities leads to an increase in the prevalence of problem gambling is that it depends on how a society regulates the increase and what else it does when it authorizes the increase. It should finally be noted that even if there were a clear correlation between increases in legal gambling opportunities and increases in problem gambling numbers, this would still not settle the question of what public policy ought to be.The reason for this is that the interests of problem gamblers and the duty of government to protect those vulnerable to becoming problem gamblers are not the only or necessarily the most important issues of which gambling policy needs to take account. There are also the interests and rights of the vast majority of gamblers who greatly enjoy gambling, do so within their means, and inflict no damage on themselves or anyone else. There are also the rights and interests of those who might benefit economically from liberalization.
ECONOMIC BENEFITS The majority of jurisdictions that liberalize gambling laws, especially in respect to international lotteries, casinos, and big-prize machine gambling, have a clear idea of the economic benefits they hope to secure from increasing access to commercial gambling, as well as of the negative social impacts they hope to avoid or contain. The principal and least disputable economic benefit that liberalizing gambling laws brings is an increase in what economists call “consumer surplus.”This means that liberalizing gambling laws makes consumers better off because they can now get more pleasure for their leisure dollar than they could before. Either the costs of what they currently enjoy come down or the increase in choice means they can get more satisfaction from spending the same amount of money as previously. Securing the interests of gamblers, however, is rarely what drives liberalization. More commonly, it is driven by one of two other economic motives. First, gambling may be liberalized in order to attract new money into a jurisdiction from outside and to retain money within a jurisdiction which would otherwise be spent outside it. The former happens if gambling is legalized in one jurisdiction while remaining illegal in neighboring jurisdictions. Gambling thus becomes an export. The latter occurs when the neighboring jurisdictions legalize in order to prevent their citizens from having to “go abroad” to gamble, thus encouraging them to spend their gambling dollars in their home jurisdiction.This is a form of import substitution. Using gambling as a magnet for tourists is becoming a less and less credible policy as more and more jurisdictions legalize gambling.This brings us to the second kind of economic benefit that liberalization can bring and which nowadays probably dominates the economic thinking of policymakers concerned with gambling.This is the opportunity to use gambling to fund public interest projects, that is, to generate additional tax revenues in various forms.
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TAXATION All taxation involves compelling producers and consumers to contribute to public interest projects. Commonly, special consumption taxes (usually called gambling privilege taxes) are levied on gambling as they are on drinking alcohol. Companies may also be made to pay additional taxes in the form of license fees of various sorts and sizes before they can operate a legal gambling business. The mandatory contribution by lotteries and other forms of gambling to charities or “good causes” and the requirement that would-be casino operators contribute economic benefits to the jurisdiction (e.g., by investing in nongambling and nonprofitable amenities and infrastructure) are both really forms of taxation. This is because, in both cases, governments compel gambling companies to make provision out of anticipated earnings for spending money which they would not spend on purely commercial, profit-maximizing grounds. Governments are able to secure these contributions to public interest projects because gambling companies are willing to pay a premium to government in exchange either for a degree of reduced competition or for the consequent ability to charge higher prices. These higher prices, which are passed on to customers, not only pay the abnormal taxes but typically leave something over which contributes to abnormal profits. The most obvious reason why governments impose high taxes on gamblers is that it is politically easy to do so. Such taxes are comparatively unresented by those who pay them and approved of by those who do not. It is not, however, obvious why those who enjoy gambling should be discriminated against by the tax system by comparison with those who prefer going to movies or eating in restaurants. It is also arguable that since lower-income people are the ones who most enjoy gambling, such taxes are unfair because they are regressive. A case for high gambling taxes is also sometimes made on the grounds that they will reduce the risks of problem gambling, and especially in the hope that high prices will deter the poor from gambling.This, however, raises questions not only of empirical justification (does such taxation in fact reduce problem gambling amongst the poor or anyone else?), but also of fairness (is it defensible to use the tax system to discriminate against the poor?). A more credible economic case for high gambling taxes is that gambling has undesirable consequences (“externalities”) for nongamblers, who consequently need to be compensated through the taxation system. This case is unarguable to the extent that without some sort of additional taxation, the gambling industry would create costs—for example, in terms of extra public administration, law enforcement or health and welfare costs, which nongambling taxpayers would otherwise have to fund out of increases in other forms of taxation. Gambling privilege taxes, however, almost invariably more than cover the costs of the administration of gambling, of the rather small additional law enforcement costs which legalizing more gambling might be thought to bring, and of whatever additional
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costs there might be in supplying treatment and prevention programs for problem gamblers. One might argue that gambling has externalities as a form of moral or, perhaps, aesthetic pollution and that nongamblers and antigamblers, therefore, need to be compensated for having to live in communities where such pollution is rife. This is clearly a difficult argument to make in societies which value individual freedom and mutual tolerance, but most people would probably agree that it would be wrong to authorize the installation of electronic gambling machines wherever there might be a market for them: in hospital waiting rooms, old age homes, bus and train stations, shopping malls, in the vicinity of schools and places of worship. What this shows is that people are right to be concerned about the overall quality of life in their communities, and that consideration may be relevant to gambling policy. What this consideration points to is not a justification for high gambling taxes but for local democratic decision making about how much gambling of what sort ought to be authorized in any particular community or neighborhood. The real reason for high gambling taxes that carries the day in the vast majority of jurisdictions where gambling is legal depends on an economic by-product of limiting the number of gambling operators’ licenses issued. To the extent that the opportunity to supply gambling services is restricted because of the law (and it always is), suppliers will be able to make abnormal profits by charging higher prices than they would in a fully competitive market. Clearly, however, these abnormal, monopoly, or oligopoly profits ought not to accrue as superprofits (i.e., profits over and above the cost of capital for the sector) to gambling companies. Perhaps, the opportunity to make abnormal profits should be eliminated by regulating the price of gambling (i.e., prescribing the maximum house advantage in terms of the odds), thus protecting the interests of consumers. This is what governments do with other monopolies, such as utilities. However, public opinion favors not making gambling as cheap as it could be. Rather, it favors capturing the abnormal profits for the public as a whole by the governments who create the opportunity to generate them.Thus, the real reason for high gambling taxes is political: As taxes go, gambling taxes are relatively popular, not much noticed by those who pay them and unsurprisingly supported by those who don’t pay them.
CONCLUSION: ACHIEVING DEMOCRATIC CONSENSUS ABOUT GAMBLING POLICY All democratic governments need to ensure that the legislation they pass is sufficiently acceptable to public opinion that at least they are not badly damaged electorally. In the case of gambling policy, this means that governments typically have to seek accommodation and compromise between competing principles and
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interests. Among those that compete are not only the principles of minimizing negative social impacts and maximizing economic benefits, but also such principles as respecting individual choice and the democratic rights of local communities, ensuring that taxation is fair and not regressive, avoiding disturbing legitimate existing interests except for good causes shown, and not undermining general respect for the law by passing laws which are unenforceable. Among the competing interests which governments must take account of are those of gamblers, antigamblers, and nongamblers, of existing suppliers of gambling services and of would-be new entrants into the market, of those who pay taxes, and of those who benefit from taxation. All this means that gambling law in a democracy is never a matter simply of applying general principles to a particular case or of making dispassionate costbenefit analyses, though both these activities are vital to making good policy. In democracies, gambling law emerges from what the government judges will be supported by a broad democratic consensus, and in the circumstances of contemporary democracies that consensus typically emerges in the following way and takes the following form. Whenever a government is contemplating legalizing (more) commercial gambling, there will be a significant number of people who think that there are no good reasons for such legalization and many good reasons for continuing prohibition. On the other hand, there will also be many people who take the view that there are no good reasons for banning this kind of commercial gambling and many good reasons for subjecting its availability (with only a few provisos) to the normal competitive disciplines of the free market, as is done, for example, with alcohol. In between, there is typically a moderate majority which, with Mill, mainly believes that provided they do not wrongfully harm others, adults should be free to spend their own time and money on whatever forms of entertainment they choose, regardless of whether others think their choices foolish or wicked. On the other hand, there is also something about gambling which causes this moderate majority to feel uneasy and to feel that too much of it would be a bad thing— perhaps because it would lead too many people to gamble too much or because it would impact negatively on the attractiveness and the quality of life of the region or neighborhood where it is located. Consequently the democratic consensus which emerges stipulates that we will permit some commercial gambling but not too much of it, and what we do permit must be tightly regulated so as to prevent negative social impacts and so that it will contribute positively to the economic well-being of the community as a whole. This “some-but-not-too-much” solution seems to most people and to their governments to accord with the dictates of prudence, fairness, and common sense. But such consensus as there is about gambling policy is always fragile and unstable, not least because of the difficulty of ascertaining how much people think would be too much.
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The consequence of all this is that gambling policy is always complex, requiring substantial knowledge, ingenuity, and skill in reconciling conflicting principles and interests and in avoiding unforeseen, unintended, and unwanted consequences. It is also always controversial, arousing strong and conflicting passions amongst electorates. Moreover, there are typically powerful economic interests which cannot all be accommodated, and the “facts” about gambling will usually be misreported by journalists concerned with pleasing readerships and viewerships by publishing scare stories. Unfortunately, it is also true that gambling is not ultimately very important in the greater scheme of things. Consequently, ministers and civil servants commonly do not devote the necessary time and intellectual energy to mastering the complexities of the subject.This is highly regrettable to the extent that it leads to gambling policy being made in a slipshod and incoherent fashion, which results in the authorization of a commercial gambling environment which most people subsequently come to regret. Moreover, purely from the point of view of getting reelected, politicians do well to attend carefully to the complexity of the issues when formulating gambling policy.This is because if they appear reckless, unprincipled, and incompetent on gambling policy, this will undermine public confidence in their ability to deal wisely and effectively with matters of much greater public importance. Moreover, given the intensity of the passions which gambling policy arouses and the consequent publicity which it attracts, any intellectual weakness in their defense of their policy will be ruthlessly exposed by their political opponents and those who disagree with their policy.
REFERENCES Collins, P. (2003). Gambling and the Public Interest.Westport, CT: Praeger Books. Collins, P., and Barr, G. (2006). Gambling and Problem Gambling in South Africa: The National Prevalence Study 2006. National Centre for the Study of Gambling at the University of Cape Town. Grinols, L., and Mustard, David B. (2006). Casinos, crime and community costs. Review of Economics and Statistics, 88, 28–45. Ladouceur, R., Jacques, C., Chevalier, S., Sévigny, S., and Hamel, D. (2005). Prevalence of problem gambling in Quebec 2002. Canadian Journal of Psychiatry, 50, 451–456. McMillen, J., and Wenzel, M. (2006). Measuring problem gambling: Assessment of three prevalence screens. International Gambling Studies, 6, 147–174. Mill, J. S. (1859) On Liberty. London: Penguin Books, 1974, pp. 68–69. Volberg, R.A. (2004). Fifteen years of problem gambling prevalence research.What do we know? Where do we go? eGambling: Electronic Journal of Gambling Studies, 10. Retrieved February 6, 2007, from http://www.camh.net/egambling/issue10/ejgi_10_volberg.html Walker, D. (2007). Problems with quantifying the social benefits and costs of gambling. American Journal of Economics and Sociology. Available at: published at www.walker-research.gcsu.edu
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INDEX
A AA (Alcoholics Anonymous), 374 ABAB design. See Withdrawal Abramoff, Jack, 612 Abstinence, 392, 454 Abused dollars, 521, 537 Accessibility, dimensions of, 253–254t Acquiescent responding, 61 Adaptation, 253, 273 Adaptive logic, 241 Addiction(s), 23, 324, 326 etiology of, 400 gambling, 335, 477, 584 general theory of, 337–338, 451 paradigms, 333 pathological gambling as, 335 problem gambling as, 477 remedy for, 582 symptoms of, 387 Addiction models, 337 Addiction Severity Index. See ASI Addictions Foundation of Manitoba, 419 Addictive behavior, 159–160 Administration computer-assisted, 73 language of, 65 Adolescence, 457. See also Adolescent(s) Adolescent(s) common characteristics of, 452 policy initiatives aimed at, 457 problem behavior model, 451 risk-taking behaviors of, 456 vulnerability v. resilience in, 449 Adolescent gambling, 259, 264–265, 439–440 alcohol/substance abuse relating to, 448 arousal/excitation related to, 446 behavior, 266, 438–439, 442, 451, 456 correlates/ risk factors associated with, 444 measurement issues related to, 440 with money v. without money, 447 parents relating to, 445 prevalence variability for, 441–442 problems incurred from, 443 protective factors for reducing, 448–449 treatment for, 442, 450–453 Advertising, 271–272, 423–424
AGA (American Gaming Association), 516 Age, 280–281 Age-graded behavior, 70–71 Alberta Gaming and Liquor Commission, 558 ALC (Atlantic Lottery Corporation), 547, 558–559 Alcohol, 269–270, 306–309, 313 adolescent gambling influenced by, 448 child exposure to, 414 consumption while gambling, 422–423 outlets/consumption, 412 servers, 418 Alcoholics Anonymous. See AA Ambient characteristics, 236–239 Ambient crime, 546, 551, 560 Ambiguity, 65 American Gaming Association. See AGA American Psychiatric Association. See APA Amphetamine, 349 Analysis, 120 comparative, 470, 484 cost-benefit, 14–16, 475, 516, 521–531, 537 qualitative/quantitative, 71–72 Antisocial gamblers, 285 Anxiety, 290, 338 Anxiety disorders, 310–311, 317 APA (American Psychiatric Association), 180 Arousal/excitation, 19, 284, 287–288, 297, 446 ASI (Addiction Severity Index), 316, 318 Atlantic Lottery Corporation. See ALC ATMs (Automatic Teller Machines), 268–269, 421 Attitude, 78–79, 170, 542 Australian gaming, 254–255 Automatic Teller Machines. See ATMs Autonomic arousal, 338, 376 Autoplay, 234 Awareness campaigns, 401–404 B Background music, 238–239 BCM (behavioral completion mechanism), 376 BDI (Beck Depression Inventory), 315–316 Beck Depression Inventory. See BDI
641
642
Behavior. See also CBT; Gambling behavior addictive, 159–160 adolescent gambling/problem, 266, 438–439, 442, 451, 456 age-graded, 70–71 belief v., 101–102 pathological/problem, 10, 279 problem gambling, 279 Behavior completion mechanism model, 332 Behavioral approach, 18, 87–88 Behavioral change, 404, 428 Behavioral completion mechanism. See BCM Behavioral models, 331–333 Behavioral observation, 98–101 Behavioral therapies, 392 changing antecedent conditions, 376 efficacy research on, 377–378 rationale/therapeutic model for, 375–377 unresolved issues in, 378–379 Belief behavior v., 101–102 gambling related to irrational, 122, 295 in luck, 20 superstitious, 292–293 Bet frequency, 232, 247 Betting, 198–199 frequency, 232, 247 in-running, 233 sports, 493, 498 Bias. See also Privacy effects; Unbiased information interpretive, 293–294 researcher, 115–116 Bill acceptors, 221, 247 Biobehavioral functioning, 335 Biochemical functioning, 358 Biochemistry, 360. See also Biochemical functioning Biomedical approaches, 16–17 Biomedical research, 18 Bipolar disorder, 309, 354, 387 Blackjack, 352 Book of States, 597 Brief treatments, 392 efficacy research on, 377–378, 383–385 rationale/therapeutic model for, 382–384 unresolved issues for, 385 C Calendar aids, 69 CAMH (Centre for Addiction and Mental Health), 496n3
Index
Canada gambling-related crime in, 543 problem gambling in, 407, 408t–409t Canadian Community Health Survey. See CCHS Canadian gambling statistics, 158t Canadian Problem Gambling Index. See CPGI Capital gains, 527–529 Cash, 225–226 Cashless gambling, 223–224 Cashout, 242 Casino(s). See also Crown Casino; Holland Casino adoption dates, 600t being sued by gamblers, 25 crime rates related to, 551 cumulative number of states with, 601t diffusion of, 598–602 domination by, 256 EGMs outside of, 416 employees of, 135–136 expansions, 517n1 horse racing v. gambling in, 607–608 Indian, 609–610, 613 land-based v. riverboat, 605, 606, 606t Las Vegas, 594, 612 legalization, 605–606, 609–610, 613 money laundering in, 629 restricting number of, 407–410 revenue generated by, 603 sale of alcohol in, 422 self-exclusion contracts, 415–417, 420 smoking bans at, 270–271 varied definitions of, 597 VEP participation/distance to, 536t CASRO (Council of American Survey Research Organization), 46 Categorical model, 325 CATI (computer-assisted telephone interviewing), 509 Causality, 69–70, 140–142 CBT (cognitive-behavioral therapy) efficacy research for, 380–382 rationale/therapeutic model for, 379–380 unresolved issues of, 382 CCHS (Canadian Community Health Survey), 41 Centre for Addiction and Mental Health. See CAMH CGI (Clinical Global Impression Scale), 202–203
Index
Chasing, 294 Child, 70–71, 283, 414, 503. See also Early childhood experience;Youth gambling CIDI (Composite International Diagnostic Interview), 312 Class III gambling, 532. See also Casino(s) Classical conditioning. See Pavlovian classical conditioning Classification accuracy, 210 Clinical education, 220 Clinical judgment, 203 Clinical psychological research, 17–19 Clocks, 235 CLPC (cross-lagged panel correlation), 141–142 Cocaine, 348–349, 355 Cognition, 170, 284, 296 explicit/implicit, 101–105 Cognitive defect, 20 Cognitive effect, 67 Cognitive processes, 446 Cognitive psychological research, 19–21 Cognitive regret, 237 Cognitive testing, 75 Cognitive therapy, 379–380, 392 Cognitive variables, 291–295 Cognitive-behavioral therapy. See CBT Cohort effect, 148, 151 Color/light effect, 236 Commercial gambling, 617–619 free market relating to, 631 liberalization of, 632 organization of, 624 public policy regarding, 627 Community Reinforcement and Family Therapy. See CRAFT Comorbidity, 22 anxiety disorders relating to, 310–311 measurement issues, 311, 318 pathological gambling relating to, 305–322, 346 research concerns, 318–319 in treatment-seeking samples, 316–319 Comparative analysis, 470, 484 Comparative treatment research, 93 Composite International Diagnostic Interview. See CIDI Computer aided telephone survey, 48 assisted administration, 73 simulation, 101
643
Computer-assisted telephone interviewing. See CATI Confirmatory qualitative research, 113–115 one-shot approach to, 116 Consumer surplus, 530, 622, 635 Consumerism, 524–525, 528, 583–584 Content validity, 210 Control, 334 illusion of, 292–295, 297 Control groups, 92–93 Cost-benefit analysis, 475, 537 economic development relating to, 521–531 inconclusive nature of, 14 significance of, 16 social v. economic issues in, 15 social/economic impact requiring, 516 Council of American Survey Research Organization. See CASRO CPGI (Canadian Problem Gambling Index), 168–169, 184t, 200–201, 560, 633 CRAFT (Community Reinforcement and Family Therapy), 389–390, 393 Credibility. See Validity Credit card, 268–269 Credit display, 225–226 Cressey, Donald, 552–553, 559–560 Crime, 532 ambient/street, 546, 551, 560 casinos related to rates of, 551 gambling industry and, 627–629 gambling-related, 543–553, 559 graft/corruption, 548 increases in levels of, 630 official statistics on, 554, 555t, 556–557 rates, 551, 558 as social cost, 520t social costs relating to, 533t Criminal Code of Canada, 542, 544–545, 560 Criterion-related validity, 210 Critical developmental period, 133, 151 Critical-Dialectical approach, 571 Critical-Participatory approach, 571–572 Cross-cultural studies. See Cultural diversity Cross-lagged panel correlation. See CLPC Crown Casino, 419 Cultural context, 66 Cultural diversity, 445, 465, 480, 484 Culture, 7, 445, 466, 476–480
644
D DA (Dopamine), 335–336, 348–350, 359–360 blockage, 385 peripheral, 351 therapy, 350 Data analytic technique, 142 availability, 319 censorship, 598, 605–606 clinical use of quantitative gambling, 173 error in compilation of, 557 on legalized gambling issues, 596–598 longitudinal studies missing, 137–138 problem-centered, 118 problems, 553 quantitative/qualitative, 560 source variation of, 161–163 Data collection, 114 ensuring quality of, 48 methods of, 117–120 Decision making, 356–357, 380 DEFF (design effect), 50, 52 Defrauding customers, 629 Delayed treatment control group. See Wait-list control group Democracy, 637–639 Demographic internet gambling, 499–500 variables, 280–286 Demography, 296 Dependent variable, 107 Depression, 348 assessment of symptoms of, 315–316 in pathological gamblers, 309 as relaxation, 338 symptoms, 312 Design effect. See DEFF Developmental trajectories, 134 Diagnosis, 79–80 Diagnostic and Statistical Manual of Mental Disorders. See DSM Diagnostic and Statistical Manual of Mental Disorders, fourth edition. See DSM-IV Diagnostic Interview for Gambling Severity. See DIGS Diagnostic Interview Schedule. See DIS Diagnostic Measurement, 311 Dialectic, 572, 588 Diffusion of innovations, 593, 595, 598–605, 613
Index
DIGS (Diagnostic Interview for Gambling Severity), 194 DIS (Diagnostic Interview Schedule), 312–313 Discourse, 5, 26 Discursiveness, 572, 588 Disorder(s) alcohol, 313 anxiety, 310–311, 317 bipolar, 309, 354, 387 gambling, 372, 378, 385, 532 heritability in gambling, 335–336 mood, 309–310 other, 311 pathological gambling as enduring, 443 psychiatric, 308t, 312–315 substance use, 306–309 Disordered gambling, 372 Dissociation, 284, 290, 297 Dopamine. See DA Dostoevsky, Fyodor, 326 Doughney, James, 569, 585 Draft Gambling Bill, 580 Drive reduction, 289–290 Drugs, 387 DSM (Diagnostic and Statistical Manual of Mental Disorders), 10, 11, 17, 79–80, 135, 139, 172, 180, 182t, 196–197, 265–266, 346, 360, 579 measuring diagnostic criteria of, 193–194 DSM-IV (Diagnostic and Statistical Manual of Mental Disorders), 312–313, 379, 440, 477, 504 Duchossois, Richard L., 611 Duration, 169, 255 Dysthymia, 309 E Early childhood experience, 283 ECA (Epidemiologic Catchment Area), 307, 309 eCOGRA (E-Commerce and Online Gaming Regulation and Assurance), 502, 505 Ecological validity, 89–91 external validity increased by, 90 laboratory v. natural setting, 243 E-Commerce and Online Gaming Regulation and Assurance. See eCOGRA Economic development, 537, 635 cost-benefit analysis relating to, 521–531 eleven components of, 522–531 lottery contributing to, 594 social issues related to, 516
Index
Economy equations representing, 519–521 horse racing contributing to, 611 impact on, 14–15 production possibilities for Two-sector, 518t revisiting Two-sector, 531 Education. See also Educational initiatives clinical, 220 gambling, 23 Educational initiatives, 426t policy initiatives coordinated with, 428 preventing problem gambling, 401–404 sustained/directed, 404–406 Educational structure, 234–235 Effectiveness evaluations, 419 Efficacy of GA, 374–375 literature, 450 of psychodynamic approach, 373 research, 377–378, 380–388 trials, 392 EGMs (electronic gaming machines), 219, 246, 334, 410, 588 adaptive logic run, 241 age limits, 414 characteristics of particular, 447 density, 258–259, 261f disassociation, 582 gamblers’ interaction with, 574 idiot skill based, 226–227 including bill acceptors, 225 light/color effect of, 236 limiting total number of, 411 modifying parameters of, 420–425 multiplier enabled, 239 numbers v. expenditure, 256 outside of casinos, 416 participation, 269 payment characteristics, 223 payout ratio, 240–241 problem gambling more rapidly developed on, 252 problem gambling related to, 412 RNG used on, 228f stop buttons on, 227–229 Electronic gaming machines. See EGMs Email survey, 42–43 Embezzlement, 533t EMDR (eye movement desensitization and reprocessing therapy), 388
645
Empirically validated treatment. See EVT Empiricist approach. See Positivist research Epidemiologic Catchment Area. See ECA Epidemiological studies, 306–309, 313–314 data availability, 319 investigating psychiatric symptoms, 316 public health model relating to, 328 Epidemiology data on gambling, 157–158 research, 21–24 surveys, 138 Episode enumeration, 68 Epistemology, 3, 18, 26, 568, 577, 587–588 ERP (exposure with response prevention), 97–98, 377 Ethics gambling experiment, 245 gambling study, 96, 99 Ethnography, 118, 123 Etiological models, 324–335, 339 Etiology, 325, 340, 400 Event duration, 232–233 Event frequency, 231–232, 247 EVT (empirically validated treatment), 450–451 Excitation/arousal, 19, 284, 287–288, 297, 446 Expenditure, 156, 169, 174 transparency, 235 Experimental methodologies in gambling studies, 87 sample, 98 Experimental research study, 88–89, 91–92 Explicit cognition, 101 Exploratory concatenation, 123 Exploratory research, 112, 117, 123 Exposure with response prevention. See ERP External diffusion, 595, 602–605, 614 External validity, 89–91, 108 Externalities, 525, 537 Extreme responding, 61 Eye movement desensitization and reprocessing therapy. See EMDR Eysenck Impulsivity Questionnaire, 352 Eysenck Personality Questionnaire, 350 F Familiarity psychology, 231 Family, 389–390 Feature game, 226–227, 247 Field testing, 197 Focus group, 119
646
Index
Foucault, Michael, 11 Fraud. See Defrauding customers Free market, 618–621, 631 Freud, Sigmund, 5, 326 Frustration theory, 230, 237 Fundamentalist(s), 595–596, 614 anti-gambling, 611–612 declining impact on lottery adoption of, 604t as obstacle to lottery adoption, 602 percentage by period of lottery adoption, 602t sects, 597n2 Future trends, 4, 24, 506–508, 610–613 G GA (Gamblers Anonymous), 11, 17, 82, 121, 310–311, 392 attendance, 381 external/validity of, 190 internal consistency of, 181 prevalence, 407 rationale/efficacy of, 374–375 referral to, 389 unresolved issues regarding, 375 Gaffs, 547, 560 GAM (Gambling Assessment Module), 199 Gamble button, 229–230 Gamble, motivation to, 284–286 Gamblers. See also Pathological gambler(s); Problem gambler(s) antisocial, 285 anxiety experienced by, 290 at-risk, 417 binge, 443 fallacy, 294 feeling successful, 338 health, 288 high taxes imposed on, 636–637 interacting with EGMs, 574 intrapsychic conflicts of, 325 motivation to take risk, 240 recreational v. problem, 333–334 responsibility, 24 seeking help, 161 self-reports by, 162 substance abuse among, 36 suing casinos, 25 support for distressed, 403 win/loss multiplicity rate chosen by, 239 Gamblers Anonymous. See GA
Gambling. See also Adolescent gambling; Cashless gambling; Internet gambling; Legalized gambling; Online gambling sites; Problem gambling; U.K. gambling;Youth gambling abstinence v. moderated, 392 activity, 290–291 addiction, 335, 477, 584 adolescent, 259 advertising, 271–272 alcohol use relating to, 422–423, 448 attitudes towards, 78–79, 542 availability, 273 bimodal patterns of, 35 Canadian statistics on, 158t Class III, 532 continuous, 234 correlates, 140–142 crime related to, 543–548, 552–553, 559 cross-cultural research on, 484 dangers of, 443–444 data, 173 definition of, 77 demarcation of, 557–558 developmental groups of, 132 disease control paradigm related to, 328 distorted cognitions about, 280 economic benefits of, 635 economic development relating to, 521 economic impact of, 14, 15, 81 education, 23 epidemiological data on, 157–158 ethnicity/cultural factors of, 80, 466 euphoric effects of, 336 expansion, 546 experiments, 245 exposure, 253, 255, 273 externalities, 637 as form of stress-reduction, 452 free market in, 618–621 frequency of, 168–169, 174, 201–202 games, 222–223t globalization of, 473–475, 483–484 growing markets for, 506 helpline, 403, 411 “high-action” forms of, 357 historical context of, 4–6 historical overview of, 3 historical perspectives, 156 history, 261
Index
illegal, 548–549 income spent on, 169–170 influence of parental, 283–284 influenced by cultural diversity, 465 introduction to, 272 irrational belief related to, 295 issues, 120–122 legal ramifications of, 171 legality of, 6 levels of, 324 liberalization, 629 linked with alcohol disorders, 313 literature, 291–292, 466 male popularity v. female popularity, 445 mobile, 447 moderation of, 267–268 moral/religious grounds for prohibiting, 626 motivation for, 501 multilevel understanding of, 585–586 nomenclature, 13 nonpathological, 316 on-site information/counseling centers, 418–420 opportunities, 411–412, 447, 630, 633–635 parental, 283–284 participation, 128, 252–253, 273 pathological, 305–322 pathology, 140 pathways, 22 population surveys, 38 portals, 510 positive/negative effects of, 376 predictors of outcomes in, 135 predisposition to, 323 prevalence in U.K., 147 privilege taxes, 621, 625, 636 problems arising from, 20, 314, 478–479 prohibiting youth from, 414–415 public health approach to, 478 quantification, 160–161 regulations, 620–621 regulator v. provider, 424–425 reinforcement patterns of, 377 related harms, 172, 175 responsible, 220, 455, 505, 584 restricting advertising for, 423–424 restricting more harmful types of, 410–411 restrictions on general availability of, 406 restrictions/alterations in provision of, 417–420
647
revenue, 162 setting, 164 sexualization of, 326–327 shares in business of, 619 “sinfulness of,” 609 social adaptation to, 329 social benefits/costs, 531–532 social phenomena of, 159–160 social/environmental indicators of, 200 socioeconomic deprivation linked to, 5 sociological/comparative perspectives on, 475–476 state ownership v. licensing, 621–624 stimulus, 287–288 structural characteristics in, 217–218 time/money spent on, 175, 391 tobacco use associated with, 422–423 tourism increased by, 635 in traditional societies, 9 U.K., 12–13 urges relating to cocaine, 355 as vice, 625 websites, 402 widespread popularity of, 438 Gambling Assessment Module, GAM Gambling behavior adolescent, 266, 438–439 cognitive processes relating to, 446 conceptual model of quantification of, 164f cultural differences relating to, 445 development of, 87–88 dimensionalization of, 165–167t gaps in knowledge of, 150–151 initiating from trigger, 338 longitudinal studies of, 127 marketing affecting, 272 motivations for, 330 national survey of, 257 qualification/dimensionalization of, 155 quantitative dimensions, 163–173 research, 206 situational factors affecting, 251 social contexts of, 9 stability of, 142–145 theories, 467–468 Gambling Behavior Interview. See GBI Gambling disorders relapse rate of, 378 social costs of, 532 treatment approaches to, 372, 385
648
Gambling facilities impact of, 550–551 local crime-rates influenced by, 558 Gambling industry, 220 crime related to, 627–629 government ownership/control of, 622 public perception of, 624 research bias in, 15 research community relation to, 244 Gambling involvement changes v. effects of, 148 sequential/stage theories of, 146–147 Gambling occurrence report. See GOR Gambling policies controversial nature of, 639 democratic consensus on, 637–639 diffusion of, 602–605 government objectives in relation to, 624–625 internal diffusion of, 606–609 Gambling researchers, 37, 475 bias from, 115–116 embeddedness, 587–588 learning from multiple theories, 482 neglecting cross-cultural diversity, 480 questions posed by, 587 values of, 575–576 Gambling studies, 99. See also Research; Survey(s) development of field of, 3 emergence of, 7–8 ethics in, 96 experimental methodologies, 87 measurement issues in, 146f purposes of population surveys in, 34–35 Gambling Symptom Assessment Scale. See G-SAS Gambling Treatment Outcome Monitoring System. See GAMTOMS Gambling venues, 268–269 ATMs in, 421 crimes correlated to, 546–547 design of, 424 entry to nonresidents, 415 hours of operation, 413–414 intervention at, 418 limiting gambling opportunities to, 411–412 restricting location of, 412–413 restricting number of, 406–410 training for employees of, 417
Index
Gambling-related crime assessing magnitude of, 543–553 construction/treatment of, 559 Game feature, 226–227 structure, 219–220 type, 168, 174 Gaming. See Gambling Australian, 254–255 preferences, 80 GAMTOMS (Gambling Treatment Outcome Monitoring System), 183t, 195–196 GBI (Gambling Behavior Interview), 184t, 201–202 Gender, 281–282 General population, 208 Genetic(s), 361 factors, 346 model, 335 molecular, 358 population, 357 predisposition, 221 Globalization, 483–484, 485 GOR (gambling occurrence report), 556 Government budget, 526 influences, 579–580 legalization decisions, 638–639 objectives in relation to gambling policy, 624–625 ownership/control of gambling industry, 622 production conducted by, 525 regulation, 424, 620–621 treatment of gambling, 618 Gratification, 289 Grounded theory, 111, 123 arrested development of, 120 Group experimental design, 91–93 randomization in, 93 single-case experimental design v., 94 Growth modeling, 136–137 G-SAS (Gambling Symptom Assessment Scale), 204 GT (Iowa Gambling Task), 356–357, 360 Guba, Egon, 568 H Hallebone, Erica, 121 Hamilton Rating Scale for Anxiety and Depression, 348 Harm production, 586–587
Index
Harm reduction/minimization approach, 267, 273, 454–455, 578, 583 Health gamblers, 288 public, 21 research, 265 Heart rate, 351–352 Hegemony, 571, 588 Heritability, 335–336 Hierarchical linear modeling. See HLM Historical perspectives, 156 HLM (Hierarchical linear modeling), 136–137 Holland Casino, 418 Horse racing, 499 casino gambling v., 607–608 economic contribution of, 611 House edge, 323 5-HT (Serotonin), 347–348, 359 Hypothesis, 107 I IAT (Implicit Association Test), 103–104 IBS (Informational Bias Scale), 101, 102 ICD-10 (International Classification of Diseases, tenth revision), 312 Ideology, 571, 588 IIGET (Integrated Illegal Gaming Enforcement Team), 549 Illegal business practices, 501 Illegal gambling, 548–549 Illegal player practices, 502 Illinois Riverboat Gambling Act, 606 Illusion of control, 292–295, 297 Image, 329–330 Imaginal desensitization, 378 Immorality. See Vice Implicit Association Test. See IAT Implicit cognition, 102 Impulsivity, 280, 288–289, 297 Incidence, 260, 273 Independent variable, 107 Indian Casinos, 609–610, 613 Indian Gaming Regulatory Act, 609 Indices, 74 Individual pathology, 582 Information campaigns. See Awareness campaigns Informational Bias Scale. See IBS Informed consent, 76 Inpatient programs, 388–389 In-running betting, 233
649
Instruments accepted foible of screening, 192 to assess youth gambling, 181, 440–441 for assessing pathological/problem gambling, 179 descriptions of, 182–189t modification of, 191–192 needed to detect/measure problem gambling, 205 used alone, 208 Integrated Illegal Gaming Enforcement Team. See IIGET Inter-individual stability, 143–145 Internal consistency, 210 Internal diffusion, 596, 606–609, 613 Internal validity, 89–91, 107–108 International Centre for Youth Gambling Problems and High-Risk Behaviors, 405 International Classification of Diseases, tenth revision. See ICD-10 Internet gambling, 410, 447 current situation for, 494–495 demographic, 499–500 fraud within, 629 future of, 506–508 game-play patterns, 500 government regulation of, 621 history of, 492–494 legalization, 496–499, 507, 510 prevalence, 495–496 problem gambling related to, 492, 501–504 by prohibited groups, 502–503 researching, 508–511 surveys of, 509 Internet Gambling Prohibition and Enforcement Act, 498 Internet survey, 42–43 Interobserver reliability, 100 Interpretationist approach, 570 Interpreters, 66–67 Interpretive biases, 293–294 Interpretivist perspective, 7–10, 26 Intervention, 319 at gambling venues, 418 imaginal desensitization as, 332 on-site, 417 studies, 317 treatment compliance-improving, 382 treatment, for problem gamblers, 333–334
650
Interview. See also PAPI administered questionnaire, 78 cognitive, 75 context, 56–57 modality, 40 motivational, 383–384 pathing, 72–73 question/answer format of, 57 semi-structured, 119 structured clinical, 194 telephone, 131 Interviewer training, 48 Intra-individual stability, 145, 151 Intrapsychic conflicts, 325, 327 Iowa Gambling Task. See GT Irrationality, 122, 295 Item response frames, 73–74 J Jackpot research, 241–242 Journal of Gambling Studies, 517 L Laboratory experiments, 243–244 Lady Luck, 327 Las Vegas, 594, 612 regulation of gambling industry in, 628–629 tourism earnings, 625 Latent variable growth curve modeling. See LGM Legality, 171 Legalized gambling, 481, 542 casinos, 605–606, 609–610, 613 data/methodological issues for, 596–598 future of, 610–612 future research on, 612–613 history of, 594 internet, 496–499, 507, 510 across North America, 593 promoting criminal activity, 628 theoretical issues regarding, 595–596 Lesieur, Henry, 121 Lexical Salience Task. See LST LGM (Latent variable growth curve modeling), 137 Licensing, 621–624 Lie/bet questionnaire, 198–199 Light/color effect, 236 Likert scale rating, 101, 203 Lincoln,Yvonna, 568
Index
Literature, gambling, 291–292 Livingston, Jay, 121 Loan-sharking, 558, 560 Local survey, 263 Longitudinal studies, 143, 145, 151 challenges in, 136 cross-sectional v., 315 of gambling behavior, 127 missing data in, 137–138 natural experiments in, 149 summery of, 130t youth gambling, 129, 133 Loss, 324, 420–421 Lottery cumulative number of states with, 601t diffusion of, 598–602, 603 economic development relating to, 594 online tickets sales, 497–499 revenue generated by, 603 Lottery adoption, 599–600t, 609 declining impact of fundamentalists on, 604t fundamentalist percentage by period of, 602t prediction of, 604t studies on, 598 Louisiana Lottery Company, 610, 612 LST (Lexical Salience Task), 103–104 Luck belief in, 20 illusory control over, 294–295 M Machine density, 258–259 MAGS (Massachusetts Adolescent Gambling Screen), 193, 209, 440 Mandatory cashout, 242 MAO (Monoamine oxidase), 347, 351 Marital status, 282–283 Market consolidation, 506 Marketing, 271–272 Massachusetts Adolescent Gambling Screen. See MAGS McGowan,Virginia, 568–569, 578n2 Mean level stability, 143, 151 Medication benefits of, 392 to treat disordered gambling, 385 Metabolic rate, 355 Methodological studies with children, 70–71 event frequency calculation in, 68
Index
651
Methodology, 21, 26, 46, 51, 87–108, 111, 596–598 MI (Motivational interviewing) efficacy, 384 goals of, 383 Mill, John Stuart, 627 Mobile gambling, 447, 507 Monetary stakes, 351–352 Money flows, 525–526 laundering, 546–547, 561, 629 restricting access to, 421 Monoamine oxidase. See MAO Mood, 387 disorders, 309–310 regulation, 289–290 Motivation, 349 Motivational interviewing. See MI Multimodal sampling, 43–44 Multiple baseline design, 96–98 Music, background, 238–239
Neurobiological model, 335 Neurobiology, 357, 360 Neuroimaging, 354–356, 359 Neurotransmitter, 345, 353–354, 360 Neutrality, 64 Nevada. See Las Vegas New Hampshire Lottery, 610 New Zealand, 262 NGISC (National Gambling Study Commission), 406 Nihilism, 573, 589 NODS (National Opinion Research Center DSM-IV Screen for Gambling Problems), 196–198, 209 Nomenclature, 13 Nonpathological gamblers, 90, 316 Nonproblem gambler, 13, 47, 295 Nonresponse, 63 Norepinephrine. See NE Normative theories, 468–471 No-treatment control group, 92
N Narratives, 119–120 National Center for Responsible Gambling. See NCRG National Epidemiologic Survey of Alcohol and Related Conditions. See NESARC National Gambling Impact Study Commission. See NGISC National Opinion Research Center, 281, 306 National Opinion Research Center DSM-IV Screen for Gambling Problems. See NODS National survey, 36 gambling behavior, 257 New Zealand, 262 Natural experiments, 149, 151 Naturalistic observation, 99–100 NCRG (National Center for Responsible Gambling), 569–570 NE (Norepinephrine), 347, 350–351, 360, 385 Neal, Mark, 121–122 Near miss, 230, 247, 293 Neo-liberal ideology, 577–579, 583–584, 589 NESARC (National Epidemiologic Survey of Alcohol and Related Conditions), 306, 310–311, 313–315 NESARC (U.S. National Epidemiologic Survey of Alcoholism and Related Conditions), 41
O Objectivity, 584–585 Observation behavioral, 98–101 naturalistic v. simulated, 99 On Liberty (Mill), 627 Online counseling services, 508 Online gambling sites as sports/racing books, 493, 498 underage participants in, 503 Online questionnaires, 510 Online trading, 493n1 Ontario Illegal Gambling Enforcement Unit, 556 Ontology, 574, 589 Operant conditioning model, 331–332 Opioid systems, 346, 359, 360 antagonists, 385 involvement in pathological gambling, 353–354 Øyen, Else, 577, 586–587 P Paper-and-pencil interviewing. See PAPI PAPI (paper-and-pencil interviewing), 58 Paradigm shift, 6–7 Parental gambling, 283–284
652
Index
Participation status, 163–164, 174 Pathing, 72–73 Pathological behavior, 10 Pathological gambler(s). See also Nonpathological gamblers addiction of, 335 adult v. adolescent, 440 clinical treatment of, 305 financial/operational provisions for, 582 genetic factors contributing to, 346, 358–359 grouping of, 442 mood disorders for, 309–310 problem gamblers v., 580–581 psychiatric conditions identified with, 308t remission v. current, 191 slot machine, 381 social cost incurred by, 535t in treatment, 534–535 treatment-seeking, 316–319 VLT gamblers v., 106–107 Pathological gambling, 128–129, 538 biomedical approaches to, 16–17 comorbidities, 305–322, 346 diagnosis of, 79–80 diagnostic screens for, 69 as enduring disorder, 443 opioid systems involvement in, 353–354 pathophysiology of, 347–361 prevalence, 139–140, 261f, 516, 580 psychological treatment for, 372 screening/assessment instruments for, 179 treatment, 348 Pathological Gambling Adaptation of the YaleBrown Obsessive-Compulsive Scale. See PG-YBOCS Pathways model, 338–340, 442 Pavlovian classical conditioning, 331–332 Payment characteristics, 223 Payout, 229–234, 247 Personality factors, 286–290 PET (positron emission tomography), 355 PG-YBOCS (Pathological Gambling Adaptation of the Yale-Brown Obsessive-Compulsive Scale), 185t, 203–204 Pharmacological treatments, 202–203, 372 efficacy research on, 386–388 rationale/therapeutic model for, 385–386 unresolved issues for, 387–388 Phenomenology. See Interpretationist approach Physical context, 58–60 Placebo control group, 92–93
Policy initiatives, 401, 426t–427t, 584, 607 aimed at adolescents, 457 educational initiatives coordinated with, 428 effectiveness of, 425 to prevent problem gambling, 406–425 types of, 619 Population research, 51–52 Population surveys, 33, 157–158, 255, 550 gambling, 38 purpose in gambling studies of, 34–35 response rate calculations for, 47t sampling issues in, 36 special, 259–260 weighting, 49–50 Positivism, 7–8, 10–14, 26. See also Cost-benefit analysis Positivist psychology, 473 Positivist research, 475, 569–570, 581 Positron emission tomography. See PET Postal survey, 59 Postmodernism, 573, 589 Postmodernist approach, 572–573 Postrelativist approach. See Postmodernist approach Precautionary principle, 588 Pretesting, 74–75 Prevalence, 257, 273 adolescent gambling, 441–442 GA, 407 internet gambling, 495–496 pathological gambling, 139–140, 261f, 516, 580 studies, 260–261, 439 survey, 208 U.K. gambling, 147 Prevention, 457 awareness campaigns as primary, 403 effectiveness of initiatives for, 426t–427t harm reduction/minimization approach to, 454–455 policy initiatives for, 406–425 primary v. tertiary, 400–401, 428 relapse, 383–384 school programs for, 401–405, 455 science-based initiative for, 453 secondary, 400–401, 418 Primary prevention, 400–401, 428 Privacy effects, 60 Problem behavior, 10 Problem gambler(s), 314. See also Nonproblem gambler
Index
comorbidity of, 35 credit card use by, 268–269 development of, 22 development of help for, 190 financial/operational provisions for, 582 heart rate in, 351 modifying tendencies of, 106 nonproblem gambler v., 47, 295 pathological gamblers v., 580–581 personality factors of, 286–287 seeking treatment, 391 socioeconomic impacts of, 43–44 subgroups of, 339 tobacco use by, 270–271 in treatment, 534–535, 550 treatment intervention for, 333–334 Problem gambling, 511, 561 as addiction, 477 adolescent, 439–440 alcohol linkage with, 269–270 awareness training, 417 behavior, 279 biopsychosocial model of, 400 Canadian, 407, 408t–409t case study of research cultures on, 476–480 correlates of, 80–81 crime influenced by, 557 crimes correlated to, 545, 549–550 diagnosis of, 207 educational initiatives for prevention of, 401–404 EGMs related to, 252, 412 epidemiological framework for, 479t gambling opportunities influencing, 633–635 general population, 161 help/treatment for, 81–82 imperviousness to research methodology of, 46 incidence, 260 increasing rates of, 508 instruments needed to detect/measure, 205 internet gambling related to, 492, 503–504 lack of precise definition for, 480 lifetime, 135 measure signs/symptoms of, 201 mechanisms linking demography to, 296 onset severity of, 281 personality correlated to, 142 pharmacological treatment of, 202–203 policy initiatives to prevent, 406–425 prevalence, 474t, 504, 516, 550, 634
653
problems directly caused by, 391 public policy relating to, 630–633 screening/assessment instruments for, 179 social/environmental indicators of, 200 symptom development of, 147 tracking, 134–135 underdiagnosis of, 192 variability of, 145 vulnerability to, 413 Production, 586–587 Productivity Commission, 263, 266–267 Profit, 527–529 Protestant Fundamentalists. See Fundamentalist(s) Psychiatric disorders, 308t, 312–315 Psychiatric symptomatology, 149 Psychiatric symptoms assessment of, 315–318 reductions in, 317 time frame of, 318 Psychodynamic approach rationale/efficacy of, 373 unresolved issues in, 373–374 Psychological benefits, 172–173, 174 Psychological models, 18 Psychological variables, 221 Psychology, 231 Psychometric studies, 312, 319 Psychopathology, 133 Psychopharmacology, 452 Public good effect, 530, 530n5 Public health, 485 initiatives, 324 model of gambling, 327–329 Public policy, 630–633 Putative Neutrality, 584–585 Q Qualitative analysis, 71–72 Qualitative methodologies, 26, 111 Qualitative research, 124 on gambling issues, 120–122 open-endedness in, 118 Qualitative sociology, 8 Quantification addictive behavior, 159–160 gambling, 160–161 Quantitative analysis, 71–72 Quantitative dimensions, 163–173 Quantitative methodologies, 21, 26 Quantitative sociology, 8
654
Questionnaire, 82. See also Eysenck Impulsivity Questionnaire; Eysenck Personality Questionnaire construction, 71–72 design, 67, 76 development, 56, 65 Interview-administered, 78 Lie/bet, 198–199 online, 510 question placement in, 72 translation, 66 R Race horse study. See Comparative treatment research Racing Board, 497 Racinos, 407 growing interest in, 613 state revenue from, 611n6 Random digit dialing. See RDD Random number generator. See RNG Randomized controlled trials. See RCT Rationality, 122 RCT (randomized controlled trials), 373–374, 384 RDD (random digit dialing), 39, 509–510 Reaction time tasks, 105–107 Reading level indices, 74 Recall problems, 67 Recovery, 23, 453 Recreational gamblers, 333–334 Regulation government, 156 tight, 6 Reification, 571, 589 Reinforcement patterns of gambling, 377 positive/negative, 331–333, 337 Relapse prevention, 383–384 Relationships, 479, 572 Relativism, 572, 589 Relevance, 64 Reliability, 116–117, 124, 209 Repeated measurement, 94 Replicability, 134 Replication survey, 262–263 Research addiction, 23 adjusting, 44 on behavioral therapies, 377–378, 380–382
Index
biomedical, 18 on brief treatments, 377–378 challenges, 481, 483, 552–553 clinical psychological, 17–19 cognitive interviewing, 75 cognitive psychological, 19–21 comorbidity, 318–319 comparative treatment, 93 compromising, 57 confirmatory qualitative, 113–116 conflicting demands in, 51 contemporary systemic influences, 577 context/corruption of, 575–577 critical-dialectical approach to, 571 critical-participatory approach to, 571–572 cross-cultural, 484 domains, 4 efficacy, 377–378, 380–388 epidemiological, 21–24 evolving, 25–26 exploratory, 112, 117, 123 findings on impact of gambling, 585 future, on legalized gambling, 612–613 gambling behavior, 206 gambling industry, 15, 244 group status, 76 health, 265 identifying current/new cases, 199 impetus for new, 11–12 importance of theory in, 467–468 inconsistency in, 390 individual pathology in, 582 individualism in, 581 internet gambling, 508–511 interpretationist approach to, 570 interpretivist perspective, 8–10 jackpot, 241–242 LST in, 104 nonecologically valid, 243 ontological/epistemological, 568–569 orientation, 588 pharmacological treatment, 386–388 policy-directed, 12 population, 51–52 positivist approach to, 475, 569–570, 581 postmodernist approach to, 572–573 private company, 45 problem gambling, 46, 476–480 programs, 576 psychological, 17–21
Index
qualitative, 118, 120–122, 124 shadow, 517, 538 special considerations for, 77–81 transpersonal-ecological approach to, 573–574 use of various approaches to gambling, 574–575 values in gambling, 581–584 verificational, 113, 124 voluntary, 63 weaknesses, 559 youth, 70–71 Research community, 244 Research institutions Neo-liberal colonization of, 577–578 partnerships, 580–581 protecting positions of privilege, 578 Research methodology imperviousness of problem gambling to, 46 sample, 98 Researcher. See Gambling researchers Response bias, 61–62 Response effects, 62, 82 Response rate, 41, 44–49, 52 balancing, 49 calculations for population surveys, 47t decline of, 45 Responsibility, 24–25 Responsible Gambling Information Centres. See RGICs Revenue growth, 506 from internet gambling, 495 lotteries/casinos generating, 603 optimal v. maximal, 425 Reward characteristics, 239 Reward schedule, 242 RGICs (Responsible Gambling Information Centres), 418–419 Risk, 324, 417, 444, 456 factor, 596, 614 Gamblers motivation to take, 240 generating enjoyment from, 20 identifying factors of, 34–35 political, 608 rewards of, 9 universal factors of, 35 Riverboat casino, 605, 606, 606t RNG (random number generator), 227, 228f, 247 Rosecrance, John D., 122 Rule-based estimation, 68
655
S Safety, 245 Sampling, 37–43, 52 School recruitment, 132–133 SCID (Structured Clinical Interview for DSM-IV), 312–313 SCI-PG (Structured Clinical Interview for Pathological Gambling), 204–205 Secondary prevention, 400, 418 Selective serotonin reuptake inhibitors. See SSRI Self-administration, 40 Self-directed treatments, 382–385 Self-discipline, 446 Self-exclusion, 415–417, 420 Self-presentation theory, 329–330 Self-report, 207 Semi-structured interviewing, 119 Senior gambling, 206 Sensation seeking, 285, 286–287, 297 Sequential/stage theories, 146–147 Serendipity, 113, 124 Serotonin. See 5-HT Setting, 164, 174 Shadow research, 517, 538 Shaffer, Howard, 570 Simulated observation, 99–100 Single-case experimental design, 93–98 Situational factors, 273 Skill games, 499 Skinner, B.F., 93–94 Slot machine(s) introduction of, 218 pathological gamblers, 381 visual presentation of, 334 Smart card, 224 Smith, Adam, 569 Smoking ban, 270–271 Social adaptation, 329 Social benefits, 531–532, 538 Social context, 60–63 Social cost, 538, 632 crime as, 520t crime-related, 533t of gambling disorders, 532 minimizing, 623 pathological gamblers incurring, 535t Social desirability bias, 62–63 Social harm, 518–521 Social learning, 264–265 Social reward, 329–331
656
Index
Social scientific exploration, 112 Social surplus, 523, 538 Social validation, 330–331 Socioeconomics, 3, 5, 141, 149, 282 SOGS (South Oaks Gambling Screen), 104, 130, 139–140, 182t, 190–193, 477–478, 484, 534–535, 561, 633 SOGS-RA (South Oaks Gambling Screen-Revised for Adolescents), 131, 144, 440 Sound effects, 236–238 South Oaks Gambling Screen. See SOGS South Oaks Gambling Screen-Revised for Adolescents. See SOGS-RA Special considerations, 77–81 Special interest groups, 11 Specialist play features, 226–227 Spending limit, 224 Sports betting, 493, 498 SSRI (selective serotonin reuptake inhibitors), 385, 386 Stability in individual differences, 151 STAI (State Trait Anxiety Inventory), 316, 318 State Trait Anxiety Inventory. See STAI Statistical power, 36–37, 138 Statistics Canadian gambling, 158t on crime, 554, 555t, 556–557 error in, 50 social construction of, 554–557 variation in, 162–163 Stimulus, 287–288 Stolen assets, 526–529 Strategy, 267, 273 Street crime, 546, 561 Stress pathways, 352–353 Stroop interference task, 103 performance, 354 Structural approach. See Critical-Dialectical approach Structural characteristics, 217–218, 246 Structural lesions, 356–357 Structured Clinical Interview for DSM-IV. See SCID Structured Clinical Interview for Pathological Gambling. See SCI-PG Studies cross-cultural, 465, 484 cross-sectional v. longitudinal, 315
epidemiological, 306–309, 313–314, 316–317, 319, 328 industry-sponsored, 482 intervention, 317 on lottery adoption, 598 population surveys as, 473 prevalence, 260–261, 439 psychometric, 312, 319 on research cultures for problem gambling, 476–480 substance abuse, 306–309 Subclinical categories, 197 Substance use. See also Alcohol;Tobacco adolescent gambling influenced by, 448 studies on, 306–309 Superstitious belief, 292–293 Survey(s). See also Email survey; Internet survey; Postal survey;Telephone survey complications, 40 data quality, 45 epidemiological, 138 household, 169–170 increasing statistical error of, 50 internet gambling, 509 local, 263 modification of, 191–192 national, 36, 257, 262 online, 42–43 participation in ethically conducted, 63 population, 33–36, 38, 47t, 49–50, 157–158, 255, 259–260, 550 portrait v. landscape, 57 prevalence, 208 replication, 262–263 response, 62–64 Suspension of judgment, 223–224, 247 Symbol matching, 230–231, 247 Symbolic weight, 604, 609, 614 T TA (Think Aloud), 105 Taxes, 524, 529, 582, 613, 618 coercive, 607 gambling privilege, 621, 625, 636–637 Teleology, 573, 589 Telephone interview, 131 Telephone survey, 42, 59–60 anonymity, 44 computer aided, 48 Telescoping effect, 282
Index
Temporal stability, 195, 210 Tertiary prevention, 400–401, 428 Test-retest correlation, 144f Theft, 526–529 Theoretical models, 325, 340 Therapy, 388 alternative approaches/adjuncts to, 388–390 aversion, 377–378 cognitive, 379–382, 392 DA, 350 family approaches to, 389–390 pramipexole, 349–350 telephone/online, 391 Think aloud. See TA Time-geography concept, 258 Timeline Follow-Back. See TLFB TLFB (Timeline Follow-Back), 160 Tobacco, 270–271, 422–423 Tourism, 623 gambling as magnet for, 635 Las Vegas earnings from, 625 Training, 48 Transaction constraints, 527–529 Transpersonal-ecological approach, 573–574 Transtheoretical model of intentional behavior, 451 Treatment, 305, 457, 559 for adolescent gamblers, 442, 450–453 brief/self-directed, 377–378, 382–385, 392 compliance, 382 efficacy literature, 450 follow-up, 384, 391 for gambling disorders, 372, 385 intervention, 333–334 measurement/evaluation issues, 390–391 pathological/problem gambling, 348, 534–535, 550 pharmacological/psychological, 372, 385–388 problem gambling, 81–82 psychodynamic approach to, 373–374 seeking, 316–319, 391 Trends, 24. See also Future trends Triangulation, 116, 574, 589 U U.K. gambling, 12–13, 147, 473, 476, 502 U.K. regulatory body, 244
657
Unbiased information, 33 Uniform Crime Reports, 554 U.S. National Epidemiologic Survey of Alcoholism and Related Conditions. See NESARC Utilitarianism, 583, 589 V VA (Veterans Affairs), 198 Validity, 115, 124, 210 Internal/External, 89–91 laboratory experiments, 243–244 self-report, 207 VEP (voluntary exclusion program), 536, 536t Verbal interaction, 238 Veterans Affairs. See VA Vice, 625–627 Video lottery terminals. See VLTs VLTs (video lottery terminals), 88, 97, 106–108, 229, 547, 555 expectancy of winning at, 288 Volberg, Rachel A., 634 Voluntary exclusion program. See VEP W Wait-list control group, 92 Warning message, 235–236 Websites, 402 Weighting, 38, 49–50, 52 Western society, 6–7 Wholism, 589 Win-loss ratio, 171 Winning, 445 Withdrawal, 95–96, 337 Y Youth gambling, 206. See also Adolescent Gambling; Early childhood experience development of, 264–265 gaps in knowledge of, 456 instruments, 181 instruments used to assess, 440–441 longitudinal studies, 129, 133 prohibition on, 414–415 recruitment for, 132–133 Youth Gambling International, 455
