Security Technology in U.S. Public Schools (Criminal Justice)

Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

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Security Technology in U.S. Public Schools

Julie Kiernan Coon

LFB Scholarly Publishing LLC New York 2007

Copyright © 2007 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Coon, Julie Kiernan, 1970Security technology in U.S. public schools / Julie Kiernan Coon. p. cm. -- (Criminal justice recent scholarship) Includes bibliographical references and index. ISBN 978-1-59332-200-7 (alk. paper) 1. Public schools--Security measures--United States. 2. Security systems--United States. I. Title. LB2866.C67 2007 371.7'82--dc22 2007023803

ISBN 9781593322007 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

TABLE OF CONTENTS CHAPTER 1: SCHOOL SAFETY.....................................1 CHAPTER 2: TECHNOLOGY USE AND INNOVATION....................................................................9 CHAPTER 3: STUDYING SCHOOL SECURITY.........49 CHAPTER 4: SCHOOLS’ USE OF SECURITY TECHNOLOGY................................................................71 CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS FOR SCHOOL SECURITY...................137 REFERENCES................................................................155 INDEX.............................................................................161

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ACKNOWLEDGEMENTS First, I would like to thank all of the schools and police departments that participated in the research study on which this work is based. I am grateful to the faculty at the University of Cincinnati, especially Lawrence F. Travis III, James Frank, and Edward Latessa, and to Steven Lab from Bowling Green State University. I am also thankful to my sister, Amy Conrad, my parents, Christine and Wayne Kiernan, and my grandparents for their unending support. Finally, I dedicate this book to my husband, Jonathan Coon and our son, Theodore. You both made this book possible.

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CHAPTER 1

School Safety INTRODUCTION The prevention of crime and violence in schools continues to be a national priority. High profile, multiple fatality school shootings such as those that occurred in Nickel Mines, Pennsylvania; Red Lake, Minnesota; Littleton, Colorado; Springfield, Oregon; Jonesboro, Arkansas; West Paducah, Kentucky; and Pearl, Mississippi captured the attention of the public. These types of violent events put pressure on legislators, law enforcement, and school administrators to take action (Garcia, 2003). In response to school tragedies, Congress passed the Gun-Free Schools Act (20 USC 8921) in 1994. This act included a requirement that all states receiving federal funds under the Elementary and Secondary Education Act (ESEA) enact laws to expel, for a minimum of one year, any student caught bringing a firearm to school (Gray and Sinclair, 2000). Further, federal agencies sponsored projects such as Indicators of School Crime and Safety (National Center for Education Statistics, U.S. Department of Education and Bureau of Justice Statistics, 2005; 2002); School and Staffing Survey 2003-04 (National Center for Education Statistics, U.S. Department of Education); Safe School Initiative (U.S. Secret Service and U.S. Department of Education, 2002); Annual Report on School Safety 2000 (U.S. Department of Education and U.S. Department of Justice); and the Principal/School Disciplinarian Survey on School Violence 1996-97 (National Center for Education Statistics). These projects examined school problems with an aim to prevent future school violence. Some police departments have also dramatically changed officer training 1

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Security Technology in U.S. Public Schools

and policies for dealing with potential school shooters, in the hope that these changes would reduce the number of victims during such an event (Harper, 2000). In addition to new laws and different police approaches to school violence, many schools have changed how they operate and how they attempt to protect students (Garcia, 2003). Schools may work closely with law enforcement, develop written plans of action for crisis situations, and adopt zero-tolerance policies toward weapons, drugs, and violence (Arnette and Walsleben, 1998; Dwyer and Osher, 2000; Dwyer, Osher, and Warger, 1998; Heaviside, Rowand, Williams, and Farris, 1998). Some schools have even instructed students to fight with an intruder if a weapon is present (Associated Press, 2006). In addition to policy changes, schools may attempt to address a variety of potential serious threats and less severe problems through the use of security technologies, such as metal detectors, security cameras, and alarm systems. Federal grants such as the Safe Schools/Healthy Students Initiative (APSS, 2000) have provided funds to increase school safety through a broad range of programs and security equipment. Federally sponsored publications, especially The Appropriate and Effective Use of Security Technologies in U.S. Schools (Green, 1999) and Surveillance Tools for Safer Schools (Blitzer, 2002), have described what security technologies are available to schools, how these technologies should be used, and possible advantages and disadvantages of technology use in schools. What is lacking in the literature is information concerning what security technologies are being used and by what types of schools. We do not know if schools that tend to use security technology have serious crime problems, or if they adopted technology for other reasons. While the data do not allow for conclusive explanations as to why schools chose security technology, this book will describe what technologies are being used, and identify school characteristics and contextual factors that tend to be associated with a broad range of security technology use.

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Lavrakas, Normoyle, Skogan, Herz, Salem, and Lewis (1981:2) state: “Without a full understanding of the extent to which citizens (on their own) engage in crime prevention, public policy to promote citizen crime prevention will be formulated in somewhat of a vacuum.” A similar argument can be made for the use of security technologies in schools. It is too soon to promote policies regarding schools’ use of security products. We must first understand the extent to which schools have chosen security technology as a crime prevention tool in order to ultimately help schools make informed security choices. This book will add to our knowledge about the use of security technologies in schools by addressing several questions. First, what technologies do schools most commonly use? Second, what school (organizational) and contextual factors are associated with use of security technologies (both total amount of technology use and amount within categories of technology will be explored)? Third, is security technology use in schools better explained by school problems (crime, disorder) or other factors (e.g. school characteristics such as size, level, formalization, percent minority, percent eligible for free lunch, wealth, school crime; and contextual factors such as urbanism, region, neighborhood crime level)? In other words, does it appear that schools use security technologies to address known problems, or does it appear that certain types of schools (e.g. large; southern; urban schools) adopt security technologies, regardless of major problems? This book will identify the types of security technologies that are being used by schools, but more importantly, will provide information about what types of schools are using security technologies. Importance of Security Technologies and Potential Correlates of Technology Adoption There are many reasons why security technologies may be useful in school safety efforts. Factors such as size of the

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Security Technology in U.S. Public Schools

school, condition of the building, lighting, and acoustics may negatively impact some students and create safety problems (Duke, 2002). Further, the architectural design of the school may be an issue. While natural surveillance, access control, and territoriality may be adequately considered in the design of the ideal school, most schools are far from idyllic (Schneider, 2001). Security technologies can be used to compensate for physical design flaws, and therefore enhance student safety (Schneider, 2001). Additionally, while there are good programs that address issues such as bullying, anger, hate, drugs, and vandalism, these programs are not yet in all schools, and cannot be successful overnight (Green, 1999). Green (1999) argues that there are incidents at schools that must be dealt with immediately and the use of security technologies is one way to deal with such problems. Security technologies may reduce crime and violence in schools by decreasing or eliminating opportunities for violations, and increasing the likelihood of apprehending perpetrators if violations do occur (Green, 1999). It is expected that there will be variation in the level of technology use among schools. Further, it is expected that schools will use certain technologies more than other types of technology. For example, prior research indicates that the use of security cameras is more common than metal detection systems in schools (DeVoe, Peter, Kaufman, Ruddy, Miller, Planty, Snyder, Duhart, and Rand, 2002; Garcia, 2003). It is also hypothesized that the use of security technology is not solely a factor of crime problems in schools. The limited research about the use of security products in schools suggests that several factors may be correlated with the use of at least certain types of security technology. The NCES sponsored both the School and Staffing Survey 2003-04 and Principal/School Disciplinarian Survey on School Violence (PSDSSV) in 1996-1997 (Heaviside et al., 1998). These studies examined school and contextual characteristics such as size; school

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level; urbanism; region; percentage of minority students; and percentage of students eligible for free or reduced-price lunch, to assess whether particular types of schools were more likely to use security technology. PSDSSV examined the use of controlling access to school grounds, controlling access to school building, use of metal detectors, and drug sweeps; SASS examined the use of video surveillance, drug sweeps, and metal detectors. The PSDSSV and SASS studies had some similar findings. For example, both studies found that schools that were large; urban; southern; with a high percentage of minority students; and a high percentage of students eligible for free or reduced-price lunch, appeared more likely to use metal detectors on a random basis (DeVoe et al., 2002; Heaviside et al., 1998). One of the limitations of prior research, however, is that it is often purely descriptive, typically lacking any correlation analyses, and almost always lacking multivariate analyses with statistical controls. It is unknown which school and contextual characteristics are significantly related to the use of security technologies, and multivariate models need to be explored. Further, previous studies describe the use of only a small number of security products. The study described in this book will explore whether the same school characteristics examined in previous studies are correlated with a broad range of security technology use, or if other characteristics are associated with different types of technology, and/or level of technology use. Given the limited amount of research on security product use in schools, the literature on how other organizations (e.g. businesses, residential complexes, and government agencies) use security technologies, and what appear to be the correlates of security technology use was also reviewed. Characteristics of organizations and their environments such as size, region of the country, and urbanism have been examined as possible correlates of security technology adoption. For example, Blakely and Snyder (1998) examined the use of gates and fences to

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Security Technology in U.S. Public Schools

control access to residential communities. They found high concentrations of gated communities in the suburbs of large cities including Los Angeles; Phoenix; Houston; Chicago; Miami; and New York, and claimed that gated communities are primarily a suburban phenomenon (Blakely and Snyder, 1998). More broadly, the literature about the adoption of innovations in organizations was reviewed since security technologies are a type of innovation, and greater level of use is an indication of innovativeness. Prior research identifies individual, structural (organizational), and contextual factors that may be related to organizational innovativeness. Similar variables may be correlates of the adoption of security technologies in schools. Research that focuses on what types of individuals/households are the most likely to use security products was also examined. Demographic characteristics of individuals and households, including race, location of residence (urbanism), and family income appear to be correlated with security technology adoption. For example, some research indicates that wealthier individuals are more likely than poorer individuals to use security products to protect their property, despite relatively low risk (National Crime Prevention Council, 2001). Similarly, it may be that wealthier schools with few problems are more likely to use technologies to protect property. While schools and individuals are not the same unit of analysis, this literature is suggestive in establishing that there are correlates of security technology use whether at the individual or organizational level. Throughout the book, the importance of the relationship between school, other organizational, individual/household characteristics, and the use of security technology (as a type of innovation) will be discussed. Findings from the research in all of these areas guided the development of several hypotheses about what variables might help explain the use of security technologies in public schools. If the literature suggests that particular traits tend to be important

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correlates of the adoption of innovation in organizations, these characteristics may also be related to security technology use in schools. This book will add to the existing research in several ways. First, a larger number of security technologies (26) will be examined than in previous research (typically 3-4). Second, this work will be using data from a national sample of schools, unlike some previous research based on a small number of organizations/schools or limited number of states represented. Third, a broader set of possible correlates of technology use will be included than in prior studies. Fourth, this book will include an examination of whether the correlates of security technology use in schools are consistent with previous research about security use and the adoption of innovation among organizations. Fifth, multivariate models will be presented that provide a better estimation than previous studies of which characteristics of schools and contextual factors are related to the adoption of security technologies. Finally, this book will extend the limited literature about the use of security technology in schools in three major ways. First, this research will describe what technologies schools most commonly use. Second, correlates of the level of security technology use (both total amount of technology use and amount within categories of technology) will be identified. Third, this research will examine whether security technology use in schools is better explained by school problems (e.g. crime and disorder) or by school and contextual characteristics (e.g. school level and urbanism).

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CHAPTER 2

Technology Use and Innovation INTRODUCTION This chapter addresses several areas important to understanding the adoption of security technologies in schools. First, security technology is defined and the purposes of its use in schools are described. Second, the limited literature regarding school and contextual characteristics that may be related to security technology use is summarized. Third, individuals’ use of security technologies as a form of protective behavior is discussed. Specifically, this review of the research examines what characteristics of individuals are associated with the use of security products. Fourth, literature addressing the use of security technologies in organizations is included. Fifth, research that identifies correlates of the adoption of innovations in organizations is reviewed. It is argued that security technologies are a type of innovation, and greater use of such products is a measure of an organization’s innovativeness. The chapter concludes with a summary of the literature regarding potential correlates of the adoption of security technologies in schools. There are two major purposes for reviewing research from different areas. First, this review illustrates that there is variation in the use of security technologies across individuals and organizations, including schools. It is demonstrated that individuals and organizations react to protect themselves from crime, whether real or perceived. 9

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Security Technology in U.S. Public Schools

One way that individuals and organizations react to perceived threats is through the use of a variety of security products. Second, this review describes what variables appear to be correlated with the adoption of innovations/use of security technologies. It is evident that certain characteristics, whether at the individual or organizational level, are important in explaining security technology use. Findings from the research in all of these areas aid in identifying possible correlates of security technology use in schools, and guide the development of hypotheses regarding how these variables might be related to the use of security technologies. What are Security Technologies? According to Trump (1998), security refers to the response and prevention of criminal acts and serious misbehavior. Security technologies will be defined as products or tools that are designed to deter, detect, or delay (Green, 1999) intentional acts against people or property (Trump, 1998). The use of security technologies can also be considered a type of protective behavior. Though much of the literature regarding protective behavior includes citizen participation in crime prevention programs (e.g. neighborhood watch), the focus of this review will be the use of security technology (products) rather than programs among individuals and organizations. Goals of Security Technology Use in Schools One of the weaknesses of some prior research is that crime prevention measures are often considered a onedimensional concept (Lab and Stanich, 1993). Green

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(1999) expands the literature by examining the use of security technologies in schools based on the possible goals of such products. She argues that deterrence, detection, and delay are the three main goals of security technology use in schools. Schools want to deter violence, weapons, drugs, vandalism, theft, and trespassers on campus. Security technologies may help with discouraging a wide range of undesirable behavior. Video cameras may help to prevent inappropriate conduct in schools, such as fights and thefts. Products such as x-ray devices or metal detectors may deter some individuals from bringing weapons into the school. A school’s use of alcohol detection devices, drug testing, and drug sniffing dogs may reduce the presence of illegal substances on campus. Anti-graffiti sealers may discourage vandalism by denying satisfaction to vandals. Signs that indicate unauthorized trespassers are subject to arrest, and having a well-lit campus may help discourage strangers on the grounds or from entering the school (Green, 1999). It is clear that schools would prefer to prevent all undesirable behavior. Since this is not possible, another goal in the use of security technologies is detection (Green, 1999). Duress alarms or telephones may be used when fights or other dangerous situations arise. Devices such as x-rays and metal detectors may be used to screen for firearms, knives, and other weapons. Detecting the presence or use of illegal substances may be achieved through the use of drug sniffing dogs, or drug and alcohol testing of students (Green, 1999). Once a problem has been detected, schools may choose to use security technologies to delay perpetrators, so that responders have time to arrive at the scene (Green, 1999). For example, locks may delay intruders from entering the school and from stealing equipment or supplies kept in

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Security Technology in U.S. Public Schools

locked classrooms or cabinets. Fences may help to delay break-ins or vandalism from occurring on campus (Green, 1999). In addition to Green’s classification, other studies have also attempted to examine the use of crime prevention technologies along different dimensions. For example, Travis and Coon (2005a) propose that categorizing technologies by the level of complexity is another way of examining the use of such products. They argue that since technologies differ in the amount of knowledge, expertise, and training required by personnel in order to use products effectively, this may influence what types of technologies are adopted in schools. Travis and Coon (2005a) found that low complexity products such as lighting and marking/identifying property were the most commonly used technologies and suggest that these technologies may be more easily adopted by schools since they do not require significant investment of personnel. Other researchers have also examined the use of crime prevention technologies and the purpose of such products. Lavrakas et al. (1981) suggest that actions to prevent property loss could be categorized as primarily either physical barriers (e.g. locks) meant to deny access to potential offenders or psychological barriers (e.g. lights or radio on while not at home). While there are some overlapping purposes of the technologies included in this study (e.g. cameras can be used for detection and deterrence), it makes sense to examine different dimensions of security products. Schools may adopt security technology to address potential external or internal threats; therefore the level of use within categories of technologies based on the possible goals of products will be explored. Specifically, two categories will be examined: 1) outward,

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13

directed toward keeping unauthorized people out of the school/school property; and 2) inward, directed toward student behavior. While this is only one way of distinguishing among the security technologies, it may provide some insight as to which of these two situations is the primary focus of schools. There is clearly a wide range of security technologies that may be adopted by schools. As previously noted, many technologies such as cameras, lighting, metal detectors, and drug sniffing dogs may serve multiple purposes such as deterrence and detection (Green, 1999). Further, not all security products are technologically advanced. Gottfredson, Gottfredson, Czeh, Cantor, Crosse, and Hantman (2000) observe that some schools use gates, fences, walls, or other barricades as security measures. Schools’ Use of Security Technology The limited research indicates that there appears to be variation in the use of security technologies in schools. The National Center for Education Statistics (NCES) commissioned a survey entitled, “The Principal/School Disciplinarian Survey on School Violence 1996-97.” The survey was administered to a nationally representative sample of 1,234 regular public elementary, middle, and high schools. As part of a larger effort to examine crime, violence, principals’ perceptions of disciplinary problems and how schools handle these problems, the survey also included questions about security technology (Heaviside et al., 1998). The researchers examined the use of metal detectors (both requiring students to pass through detectors daily and random checks), drug sweeps (including locker searches and dog searches), and controlling access to the

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Security Technology in U.S. Public Schools

school grounds and building. They found that while most schools reported low levels of security measures, 2% had stringent security (defined as presence of full-time guard and use of random or daily metal detectors), 11% reported moderate security (full-time guard with no metal detectors and no restricted access, or part-time guard with or without metal detectors and restricted access), and 3% of schools had no security (Heaviside et al., 1998). DeVoe et al.’s (2002) analysis of the U.S. Department of Education, NCES, School and Staffing Survey (SASS), (“Public and Public Charter School Surveys, 1999-2000”) also indicates that there is variation in the use of security technology among schools. Twenty-one percent of schools conducted drug sweeps, 15% used video surveillance, and 8% used random metal detector checks on students, with only 2% of schools using metal detectors on a daily basis. More recent research has also found variation in security technology use. Using a convenience sample of 41 interviews with school safety administrators in 15 states, Garcia (2003) examined types of security technologies used and perceived effectiveness of these technologies. She examined the use of five types of technologies in school districts: cameras; recording systems; weapon detection systems; entry control devices; and duress alarms. She found that video cameras were the most common technology used (90%), and 85% had recording technologies (most used videocassette recorders). Fifty-five percent of school districts reported having some type of weapon detection system, with metal detector wands being the most common. Entry control devices (e.g. turnstiles, passwords, or biometric identifying technology) were the least common of all technologies, with only 7 districts (18%) using such products. Finally, duress alarms were less

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common than most other security technologies, with only 40% reporting some form of duress alarm use (Garcia, 2003). Other research has found differences by city in the use of security staff and technologies. The study entitled, “Approaches to School Safety in America’s Largest Cities” (Vera Institute of Justice, 1999) found that all public schools in Houston were connected to a burglar/fire alarm system. Further, all Los Angeles public secondary schools had metal detectors, burglar alarms, window grilles, and security doors. Further, these schools had locks that were used on all gates and exterior doors (except the main entrance) during school hours (Vera Institute of Justice, 1999). Some research indicates that most schools do not rely heavily on security technology. According to Snyder and Sickmund (1999), 84% of schools surveyed during 19961997 controlled access to school grounds, but had no other security measures. Thirteen percent of schools had a combination of law enforcement presence and/or metal detector use. Three percent did not have any security measures in place. Further, as part of a study on school violence, Sheley and Wright (1998) reported responses from 48 school administrators about the use of security products. They found that video monitoring of hallways was relatively rare (10%) and metal detector use at school entrances was even more rare (2%). As previously stated, the literature regarding security technology use in schools is incomplete. The research specifically addressing possible correlates of security technology use in schools is even more limited. For example, Garcia (2003) identifies characteristics of her sample such as region; urbanism; student population;

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Security Technology in U.S. Public Schools

number of schools in each district; and expenditures on technology, but does not provide a description of what types of schools are using these technologies. Fortunately, there are two studies that may shed some light on possible structural and contextual correlates of security technology use. There is some research suggesting school level may be an important factor in explaining variation in security technology use. One study found that high schools were the most likely to perform random metal detector checks on students, have daily metal detector screening, and drug sweeps (Heaviside et al., 1998). DeVoe et al. (2002), found relationships between school level and the use of video surveillance; drugs sweeps; random metal detector checks; and daily pass-through metal detector use. Secondary schools were more likely (26%) than elementary schools (11%) and combined elementary/secondary schools (20%) to use video surveillance. Secondary schools were also more likely to report conducting one or more drug sweeps (49% vs. 10% elementary schools and 40% combined elementary/secondary schools). Combined elementary/ secondary schools were the most likely to conduct random metal detector checks on students (19%) and use passthrough metal detectors on a daily basis (8%). Fourteen percent of secondary schools reported conducting random metal detector checks on students, with only 3% of secondary schools using a daily pass-through metal detector. Finally, elementary schools were the least likely use metal detector, with only 5% of elementary schools reporting random checks, and 1% using pass-through metal detectors on a daily basis (DeVoe et al., 2002). Other school characteristics examined in the literature include percentage of minority students and percentage of

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students who are eligible for free or reduced-price lunch. Specifically, Heaviside et al. (1998) found that schools with a high percentage of minority students (defined as 50% or higher) and high level of poverty (defined as 75% of students eligible for free or reduced-price lunch) were more likely than other schools to control access to school grounds; control access to school building; conduct random metal detector checks; and use metal detectors on a daily basis. DeVoe et al. (2002) had similar findings. They found that schools with a high percentage of minority students (also defined as 50% or higher) and a high level of poverty (also defined as 75% of students eligible for free or reduced-price lunch) were more likely than other schools to use video surveillance, random metal detector use, and daily metal detector use (DeVoe et al., 2002). Interestingly, both studies found that drug sweeps were most common in schools with a low or low-moderate percentage of minority students and students eligible for free or reduced-price lunch (DeVoe et al., 2002; Heaviside et al., 1998). It may be that drugs are more of a concern than weapons for certain types of schools. The size of a school may be an important factor in explaining security technology use. According to Schneider (2002:19), there are a number of issues related to larger schools. First, larger schools may be more challenging for controlling access, because there tend to be a larger number of entry points. Further, some students may feel lost in a school with many students and be at an increased risk. Additionally, if there are a large number of students, it may be difficult for students and staff to know who belongs on the campus and who does not, which in turn reduces a sense a territoriality (Schneider, 2002:19).

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Security Technology in U.S. Public Schools

The research has found that school size may influence the adoption of security technologies. Heaviside et al. (1998) found that large schools (defined as 1,000 students or more) were most likely to control access to school grounds; conduct random metal detector checks; use metal detectors daily; and conduct drug sweeps. DeVoe et al. (2002:139) also found that large schools were consistently more likely to report using security products. For example, 32% of large schools (defined as 1,000 students or more) reported using video surveillance, with only 14% of midsized schools (300-999 students), and 10% of small schools (fewer than 300 students) reporting video surveillance use. The researchers also found differences in the use of drug sweeps and metal detector checks. Thirty-seven percent of large schools reported having one or more drugs sweeps, with 18% of mid-sized schools and 22% of small schools reporting one or more drug sweeps. Further, 20% of large schools conducted random metal detector checks on students and 4% of large schools required students to passthrough metal detectors each day. Seven percent of midsized schools and 5% of small schools conducted random metal detector checks on students, with daily metal detector use even more rare (1% mid-sized schools and 2% small schools) (DeVoe et al., 2002). While only descriptives were presented, this research provides some evidence that school size may be related to at least some types of security technology. Urbanism is another factor that may be related to the use of security products in schools. Heaviside et al. (1998) found that suburban schools were the most likely to control access to school buildings, but urban schools were the most likely to control access to school grounds, use random metal detectors, and use metal detectors on a daily basis.

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DeVoe et al. (2002) also found that urban schools were the most likely to use both random and daily use of metal detectors. Further, drug sweeps were most common among rural schools according to both studies. Additionally, Gottfredson et al. (2000) found that urban schools were more likely than rural schools to use gates, fences, walls, or other barricades as security measures. Region is another contextual factor that may be important in explaining the use of security technologies. It may be that support/available funds for security technologies are more common in certain parts of the country. DeVoe et al. (2002) found that Southern schools were the most likely to adopt all of the security technologies examined (video surveillance, random and daily use of metal detectors, and drug sweeps). These findings contrast those of Heaviside et al. (1998) who found differences in security technologies used by region, yet there was no region that was consistently more likely to use security technologies. For example, they found that controlling access to school grounds and drug sweeps were most common in the West; controlling access to school building most common in the Northeast; use of random metal detectors most common in the Southeast; and no major differences for daily metal detector use (see Tables 2.1-2.3 for a summary of findings).

TABLE 2.1. SCHOOLS MOST LIKELY TO USE SECURITY TECHNOLOGIES BY SCHOOL CHARACTERISTICS (1996-97) (ADAPTED FROM HEAVISIDE ET AL., 1998) School Level Technology Control Access to School Grounds Control Access to School Building Random Metal Detectors Daily Metal Detectors Drug Sweeps

% Free or Reduced $ Lunch**

Size***

Urbanism

Region

% Minority*

High School and Elementary Elementary

Urban

West

High

High

Large

Suburban

Northeast

High

High

Med.

High

Urban

Southeast

High

High

Large

High

Urban

High

High

Large

High

Rural

No major differences West

LowModerate

Low-Mod. and Mod.

Large

Note the following categorizations: *percent minority: less than 5%=low; 5-19%=low-moderate; 20-49%=moderate; 50% or higher=high **percent of students eligible for free or reduced-price lunch: less than 20%=low; 20-34%=low-moderate; 35-49%=moderate; 5074%=moderate-high; 75% or more=high ***size: fewer than 300 students=small; 300-999=medium; 1,000 or more=large

TABLE 2.2. SCHOOLS MOST LIKELY TO USE SECURITY TECHNOLOGIES BY SCHOOL CHARACTERISTICS (1999-2000) (ADAPTED FROM DEVOE ET AL., 2002)

Technology Video Surveillance

School Level

Urbanism

Region

% Minority*

% Free or Reduced $ Lunch**

Secondary

Urban and Suburban Urban

South

High

Low

Size*** Large

Random Metal Combined elem/ South High High Large Detectors secondary schools Daily Metal Combined elem/ Urban South High High Large Detectors secondary schools Drug Sweeps Secondary Rural South Low Moderate Large Note the following categorizations: *percent minority: less than 5%=low; 5-19%=low-moderate; 20-49%=moderate; 50% or higher=high **percent of students eligible for free or reduced-price lunch: less than 15%=low; 15-29%=low-moderate; 3049%=moderate; 50-74%=moderate-high; 75% or more=high (note: this categorization differs slightly from Heaviside et al., 1998 study) ***size: fewer than 300 students=small; 300-999=medium; 1,000 or more=large

TABLE 2.3. SCHOOLS MOST LIKELY TO USE SECURITY TECHNOLOGIES BY SCHOOL CHARACTERISTICS AND LEVEL OF AGREEMENT (COMMON FINDINGS FROM HEAVISIDE ET AL., 1998 AND DEVOE ET AL., 2002)

Technology Random Metal Detectors Daily Metal Detectors Drug Sweeps

Urbanism

Region

% Minority

% Free or Reduced $ Lunch

Size

Urban

South

High

High

Large

High

High

Large

Urban Rural

Large

*School level is not included in this table since schools were defined differently in these studies.

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Individual/Households’ Use of Security Technology In addition to the literature regarding the use of security technologies in schools, there is a body of research that examines variation in the use of security technology among individuals. As stated by Lavrakas et al. (1981:6), “As found here, and in earlier surveys of the urban populace, there is great variation among American households in the extent to which they employ home protection measures.” Lavrakas et al. (1981) conducted a survey of households in the metropolitan Chicago area and found that particular households were more likely to use many or all household protection devices asked about, while other households used few or none. Approximately 33% of households had installed outdoor lights and 40% reported using timers on indoor electrical devices such as lamps and radios. Further, approximately 33% of households had engraved their valuables with identifying marks (Lavrakas et al., 1981). Other research supports Lavrakas et al.’s (1981) findings that there is variation in the use of security technologies among households. One example includes findings from the 1984 Victimization Risk Survey (VRS). The VRS was administered to 21,016 people in 11,198 households as a supplement to the National Crime Survey of the Bureau of Justice Statistics. The purpose of this survey was to collect information about perceptions of safety in homes, neighborhoods, and workplaces, as well as crime prevention strategies used in these places (Whitaker, 1986). When examining the use of home crime prevention measures, Whitaker (1986) examined several strategies including the use of two security technologies. Specifically, she found that 7% of respondents reported using a burglar alarm system and 25% reported engraving property with an

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identification number. In another study, Marshall (1991) examined fear of crime, community satisfaction, home protection, and personal protection strategies among greater Omaha, Nebraska residents. In terms of security devices used, the vast majority of respondents locked their homes at night (97.6%), 52% had special types of locks installed, and 12.6% reported they had a burglar alarm system (Marshall, 1991). Recent research continues to find variation in the use of security products among citizens. For example, in a National Crime Prevention Council (2001) survey, 51% of respondents had deadbolt locks on all entrance doors of their home; 22% reported deadbolt use on main entrances only; 9% reported deadbolt use on most entrances; and 18% stated that they did not use deadbolt locks at all. Further, 81% reported having and using exterior lighting around their homes and 14% used a home security system. The research suggests that there is a wide range of security technology use among individuals and households. In addition to research describing variation in security technology use, another set of literature examines possible correlates of its use among individuals. Lab and Stanich (1993) used Lab’s (1990) five categories of crime prevention measures to examine protective behavior among individuals: 1) target hardening (e.g. burglar alarms, property marking); 2) personal access control (e.g. multiple door locks, door peepholes); 3) personal security (e.g. owning and carrying firearms for protection, owning a dog for protection); 4) surveillance (e.g. participating in neighborhood watch, watching neighbors’ homes); and 5) avoidance (e.g. staying home). Their preliminary analyses revealed several trends. The independent variables examined (education; race; income; sex; age; marital status;

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home ownership; victimization; and fear) did not explain much of the variation in personal access control or avoidance behavior. Most of the variables did however, significantly impact target hardening, personal security, and surveillance. Further, the variables generally had a consistent impact, and the direction of this impact was generally the same across crime prevention categories (with exceptions in the avoidance and personal access control categories) for both rural/small town and large city residents (Lab and Stanich, 1993). Lab and Stanich (1993) also found that for personal security measures, education; income; sex; age; marital status; home ownership; and prior victimization all had a significant impact. Specifically, higher educated; lower income; female; older; non-married; non-home owner; and previous victims were more likely to engage in personal security behavior. The same factors influenced target hardening and surveillance, with the exception that marital status was not significantly related to target hardening among small town/rural respondents (Lab and Stanich, 1993). One limitation with many studies of citizens’ use of security technology is that researchers have focused primarily on urban residents. Lab and Stanich (1993) build on the literature by comparing possible correlates of citizen crime prevention behavior across levels of urbanism (small town/rural referred to an area with a population of less than 25,000, large urban area was a population greater than 250,000). Lab and Stanich (1993) examined the strength of relationships to determine whether the significant variables had a greater impact among residents in small town/rural areas or large urban areas. They found several noteworthy differences. For example, with personal security and target

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hardening, education had a greater impact among urban residents than rural residents. Overall, Lab and Stanich (1993) found that many of the independent variables were more influential on crime prevention measures in rural areas, indicating that not all variables had a greater impact in urban settings as had been expected. Some research indicates that wealth may be an important correlate of security technology adoption. According to, “Are We Safe?” (National Crime Prevention Council, 2001:6) the wealth of citizens makes a difference in their security efforts. Specifically, their findings suggest that poorer citizens were more likely to take measures toward personal protection than wealthier citizens, but wealthier citizens were more likely to secure their property than the poor. Not surprisingly, other research has also found that wealthier households may be more likely to use security products to protect property (Whitaker, 1986). This may be related to the fact that wealthier households are more likely to be able to afford such products. Not all research has found that wealth is correlated with security product use. For example, Lavrakas et al. (1981) examined a variety of crime prevention measures citizens may take, including the use of several security products such as burglar alarms; window bars; special locks; indoor timers; outdoor lights; and engraving property. They categorized crime prevention measures as: 1) protecting oneself; 2) protecting household (family and property); and 3) protecting neighborhood/community. Among their findings were that home owners were much more likely than renters to protect their households. The researchers state that this was a result of greater control over their property and greater financial and psychological investment in their home. Further, the authors argue that this was not

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directly due to higher incomes. Once home ownership was controlled for, household income was not significantly related to most property protection measures. The researchers also note that one-person households, regardless of gender, were consistently less likely than multi-person households to use protection measures (Lavrakas et al., 1981). There is some evidence that race/ethnicity may be correlated with certain types of security technology use. In Whitaker’s (1986) examination of crime prevention strategies (e.g. burglar alarms and engraving valuables) there was evidence that citizens’ ethnicity might be correlated with use of security products. Specifically, Hispanics were less likely than non-Hispanics to engrave valuables, but Hispanics were about as likely as nonHispanics to use burglar alarm systems. There were no major differences between Black and White households in their use of burglar alarms or engraving property (Whitaker, 1986). Lavrakas et al. (1981) found that Blacks, Latinos, and other non-Asian minorities were the most likely to use target hardening devices (specifically examined were alarms, window bars, and special locks). Security Technology Use in Organizations In addition to security technology use among individuals, there is research about security technology use among organizations. Zaltman, Duncan, and Holbeck (1984: 106) define an organization as “a social system created for attaining some specific goals through the collective efforts of its members.” There appears to be variation in security technology use across a variety of organizations and settings. For example, the use of security technologies was

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examined as part of a survey of over 200 members of the Urban Land Institute and Building Owners and Managers Association, entitled, “National Survey of Security Concerns Within the Real Estate Industry” (BOMA International and Urban Land Institute, 2002). The survey included questions about security measures in residential buildings before and after September 11, 2001. The most commonly used security measures were building alarm monitors (80.2%); lobby security controls (74.3%); surveillance cameras (64.9%); and employee background checks (60.9%). The least common security measure was perimeter barriers (14.9%) (BOMA International and Urban Land Institute, 2002). Examining market trends provides additional evidence of variation in security technology use. Cunningham and Strauchs (1992) argue that one of the fastest growing areas in private security revolves around manufacturing, distributing, and installing security technology. They describe the percentage market share as: manufacturing and distributing (29.4%); proprietary security (21.7%); guard and patrol services (19.8%); alarm services (9.6%); private investigations (4.8%); armored car services (1.3%); locksmiths (5.7%); security consultants and engineers (0.7%); and other (6.9%).1 The authors also note that the total annual spending for security products and services far surpasses expenditures for law enforcement (Cunningham and Strauchs, 1992). This provides evidence that there is variation in the use of security technologies and suggests that its use may be growing.

1

Other category includes over 20 market components such as guard dogs, drug testing, and honesty testing.

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Some research indicates that the location of an organization/entity may be related to security product use. One example is Blakely and Snyder’s (1998) examination of the growing use of fences, walls, and gates to control access to residential areas. They argue that the use of gates has increased dramatically since the early 1980s. The researchers contend that while gated communities can be found across the United States, they are most common in metropolitan areas, and are rarely found in predominantly rural places such as much of the South and New England. Further, high concentrations of gated communities were found in the suburbs of Los Angeles, Phoenix, Houston, Chicago, Miami, and New York (Blakely and Snyder, 1998). There is also evidence suggesting that the use of gates as a type of security technology may not be related to actual risk of victimization. According to Blakely and Snyder (1998), urban residents are more likely to experience household and violent crime than suburban residents, yet gated communities tend to be found in suburban areas. They examine the importance of gates and security to the residents in different types of communities. The authors identify three types of communities: 1) lifestyle communities where gates are used for security and to separate amenities/activities such as retirement communities, golf, and country clubs; 2) prestige communities where gates symbolize prestige, and are used to create and protect social status such as communities for the rich and famous and successful professionals; and 3) security zones where gates are used as a tool to achieve the primary goal of community safety. This type of gated area may be found in urban or suburban areas, as well as in poor or rich neighborhoods. Gates are considered protection

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from some threat, real or simply believed. Further, it is not developers who build gates, it is residents who put up fencing/gates (Blakely and Snyder, 1998). This research suggests that fencing may not be related to actual risk. Similarly, it may be that school crime/disorder problems and neighborhood crime levels are not related to the use of security technologies in schools. The size of an organization is discussed in some of the research about the use of security technologies. One such example is Cunningham, Strauchs, and Van Meter’s (1990) description of the results of a 1989 Department of Labor study and a Gallup Poll. Both surveys indicated that larger companies (measured as number of employees) were more likely to have drug programs. The Gallup survey found that 28% of large companies (more than 5,000 employees); 13% of medium-large companies (1501-5000 employees); 10% of medium-sized companies (500 to 1500 employees); and 2% of small companies (fewer than 50 employees) had drug programs2 (Cunningham, Strauchs, and VanMeter, 1990). One issue with the definition of drug programs in these studies is that it includes drug testing (which can be considered a security technology), but may also include drug prevention programs such as counseling. This book includes the use of drug testing in schools as a technology, and it may be that larger schools, like larger companies, are more likely to use this technology.

2

The authors do not report findings regarding companies with 50 to 499 employees.

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Adoption of Innovations by Organizations The purpose of examining the adoption of innovations in organizations is to identify possible correlates of the adoption of security technology. Researchers define innovation in several different ways. For example, Corwin (1975:16) defines innovation as, “a variable pertaining primarily to a deliberate change in structural relationships and procedures in a particular organization that could lead secondarily to changed outputs.” Altshuler and Zegans (1997:73) propose that, “an innovation consists of at least two elements: a fresh idea, and its expression in a practical course of action.” They further state that innovation is “novelty in action” (Altshuler and Zegans, 1997:73). Damanpour (1991:557) defines innovation as the “adoption of an internally generated or purchased device, system, policy, program, process, product, or service that is new to the adopting organization.” He argues that organizations adopt innovations as a reaction to environmental (internal and external) changes or as a proactive measure aimed at influencing their surroundings. While the data used in the current study are cross-sectional and therefore it cannot be established when security technologies were adopted, it is reasonable to assume that a school’s adoption of security technology is a response to its environment, or an action to prevent future problems. While the multitude of reasons why schools might adopt security technology are beyond the scope of this book, the adoption of security technologies is consistent with previous definitions and concepts of innovation. In addition to defining innovations, some research has defined innovativeness. Rogers (1995:22) for example, defines innovativeness as, “the degree to which an

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individual or other unit of adoption is relatively earlier in adopting new ideas than other members of a system.” Innovativeness has also been operationalized in different ways. A count of the number of innovations is a fairly common measure of innovativeness (Baldridge and Burnham, 1975; Kimberly and Evanikso, 1981). Corwin (1975) adapted this somewhat by operationalizing innovativeness of a school as the number of innovations and weighted these innovations by how extensively they had been adopted and created scores based on these two factors. Since the current research will examine the number of security technologies adopted by schools, innovativeness of a school can be operationalized as the number of technologies adopted by a school relative to the number of technologies adopted by other schools in the sample of respondents. The organizational innovation literature includes a broad range of categories of innovation, including adoption and diffusion of innovations, initiation vs. implementation stages, and radical vs. incremental innovations (Damanpour, 1991). Additionally, Damanpour (1991) contends that it is important to consider the type of organization when examining innovativeness. He asserts that the structure of an organization and contextual factors may influence innovativeness differently. Specifically, Damanpour distinguishes between manufacturing vs. service and non-profit vs. for-profit organizations, and argues that type of organization may act as a moderator of the relationship between innovation and other variables. Additional research supports the importance of distinguishing between the private and public sector when examining innovation. Elmore (1997) contends that in the private sector, innovation is a determinant of how firms

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make, sell, and protect their market share for a product. He states that innovation in the public sector serves more complex functions such as, “clarifying relationships with key publics, strengthening accountability links with those publics, and explaining the value of what public agencies produce” (Elmore, 1997: 248). Altshuler (1997) argues that innovation is often considered a criterion for organizational success in the private sector, but is not considered as critical for the public sector. He identifies three major reasons for this: 1) government agencies have little direct competition; 2) there is not the same focus on profitability as there is in the private sector; and 3) there is a great fear of negative publicity resulting from failure in the public sector (Altshuler, 1997: 73). These major differences between private and public organizations indicate that the innovation process may not work the same in these two sectors. Following Damanpour’s (1991) and Altshuler’s (1997) arguments, and King’s (1998) logic of examining previous research about organizations most similar to the police, it seems that public service organizations, rather than manufacturing or private organizations are the most similar to schools. While some studies regarding other types of organizations are briefly discussed, the focus is on schools and other public service organizations, since these are most pertinent to the adoption of security technologies in schools. Specifically, studies that focus on the implementation stage are most relevant since the current study examines the security technologies schools are using, rather than what schools are planning to use in the future. Additionally, since cross-sectional data are used in this study, it is appropriate to focus the review of the literature

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on organizational innovativeness rather than the diffusion of innovations, which requires longitudinal data. In addition to variables having different influences on innovation depending on the type of organization (e.g. as discussed by Zaltman, Duncan, and Holbeck, 1984), other researchers have examined predictors of different types of innovation. For example, Kimberly and Evanisko (1981) examined the effects of individual, organizational, and contextual factors on the adoption of both administrative and technological innovations. Administrative innovation was based on the electronic data processing in eight different areas (e.g. accounting and medical records). Technological innovation was based on the use of 12 items (7 new pieces of equipment, 2 new drugs, 1 surgical procedure, 2 new techniques). Innovativeness referred to the sum of adopted innovations (Kimberly and Evanisko, 1981). This section of the review also focuses on the importance of organizational (structural) and contextual characteristics, rather than individual characteristics of organizational members, in explaining the adoption of innovations. There is evidence that suggests it is inappropriate to depend on individual data to explain organizational behavior. Robertson and Wind (1983) state that the organizational research has demonstrated that attitudes, perceptions, and values differ among organizational members, and therefore research should not depend on one member (even a leader) to explain organizational decisions. Some research also suggests that individual characteristics are important in explaining individual innovation adoption, but do not explain much of the variation in the adoption of innovations among organizations (Baldridge and Burnham, 1975; Hage and

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Aiken, 1970; Kimberly and Evanisko, 1981). Since the current study is exploring the use of security technologies in an organization, it is reasonable to focus on the innovation literature that includes structural and contextual characteristics. It should be noted that the purpose of reviewing a portion of the organizational literature is not to compare the consistency of measures across these studies. The purpose of this review, rather, is to first demonstrate that organizational level variables have been found to be important correlates of innovation adoption and identify which variables are most often related to the adoption of innovations in organizations. Before discussing in greater depth the correlates of innovativeness, findings from an important contribution to the organizational innovation literature should be mentioned. Dampanpour’s (1991) meta-analysis of innovation research found that several factors were significantly related to organizational innovativeness and the rate of adoption of innovations. He examined the influence of four moderators: 1) type of innovation; 2) stage of adoption; 3) type of organization; and 4) scope of innovation. Damanpour contends that it is important to consider these moderators since they may differentially influence the adoption of innovation. Among his findings were that specialization; functional differentiation; slack resources; professionalism; managerial attitude toward change; technical knowledge resources; administrative intensity; and external and internal communication were all positively associated with innovation. In addition, Damanpour found a negative association between innovation and centralization and non-significant associations between vertical differentiation, managerial

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tenure, and formalization. Damanpour had hypothesized that formalization would have a negative relationship with innovativeness, and that tenure of managers would have a positive relationship with innovativeness. Further, slack resources had only a weak positive relationship with innovation (Damanpour, 1991). Much of the research on innovative behavior in organizations includes structural (organizational) characteristics. For example, Kimberly and Evanisko (1981) examined the influence of structural characteristics, including centralization; specialization (number of medical specialties); size of organization; functional differentiation (number of subunits); and external integration (extensiveness of mechanisms which increase likelihood that information about innovations will be available in the organization). Hage and Aiken (1967) also examined structural characteristics and the rate of adoption of new programs and services among sixteen social welfare organizations (social casework agencies, hospitals, rehabilitation centers, homes for the emotionally disturbed, and a special education department in a public school). Specifically, they examined the relationships between formalization, complexity, centralization, job satisfaction/attitude toward change (classified as a performance variable), and rate of new program adoption (they contend that there is a distinction between structural variables and performance variables but consider both to be organizational variables). There is evidence in the organizational literature that the size of an organization may be related to innovativeness. Baldridge and Burnham (1975) found that an organization’s size and administrative complexity are two characteristics that influence an organization’s

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innovativeness. Further, they argue that these two characteristics are closely associated. Baldridge and Burnham contend that larger organizations are more complex (measured as number of hierarchical levels) and such organizations create situations that require innovative practices purely because of their size. They cite the example that small school districts may not have enough special needs students to have programs for these students, but that large districts are likely to have enough students to warrant special programs. Baldridge and Burnham also argue that larger-sized districts have a greater number of clients and this results in a greater number of interested parties who may make particular demands of the school district. Similarly, it may be that larger schools create situations that warrant greater use of security technologies. Kimberly and Evanisko (1981) make arguments similar to those made by Baldridge and Burnham (1975). They state that larger organizations may be more likely to adopt innovations because of a “critical mass” which facilitates adoptive behavior. Kimberly and Evanisko argue that for certain types of innovation, however, the size of an organization necessitates innovative behavior. They state that these two situations are theoretically different, since in one situation an organization may not have much of a choice about adopting innovations. Kaluzny, Veney, and Gentry (1974) examined the influence of size on adoption of innovations in 59 hospitals and 23 health departments. Their findings suggest that size may be more important in explaining certain types of program innovation in some organizations, but is less important in other situations. Specifically, they found that size was an important variable in explaining program innovation in high-risk services within hospitals and low-

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risk services provided by health departments, but was not an important factor for program innovation in high-risk services in health departments and low-risk services in hospitals (Kaluzny, Veney, and Gentry, 1974). Further, Hage and Aiken (1967) found that size was positively related to rate of program change. Not all of the research about an organization’s size and innovativeness suggests a linear relationship. Corwin (1975) found that school size (measured as number of fulltime teachers) was significantly related to innovativeness, but his findings suggest there may be a curvilinear relationship. Specifically, he found that medium-sized schools were the most likely to be innovative, while most of the large schools (52%) were moderately innovative, and almost half of the small schools (45%) were in the least innovative category (Corwin, 1975). Among other variables, Damanpour (1987) examined the influence of organizational size (measured as the average yearly budget over a 5 year period) on the adoption of technological, administrative, and ancillary innovations among 75 public libraries in the Northeast. He contends that previous research generally indicates that the relationship between innovation and organizational size is not curvilinear. Other research by Damanpour has also examined the effect of organizational size on the adoption of innovations. Damanpour (1991) cites his 1989 metaanalysis in which he found that organizational size was positively associated with innovation. In general, the research suggests that size is likely to be positively correlated with the adoption of innovations. This hypothesis may also be applied to the use of security technologies in schools. Larger schools may typically have a broader range of problems than smaller schools, which

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may result in more innovative practices. Further, following Baldridge and Burnham’s argument, larger schools will not only have a larger number of clients, but will also tend to have a greater number of interested parties who may make demands regarding security issues in schools. The literature that includes measures of formalization generally finds that greater formalization in organizations seems to discourage innovativeness. Hage and Aiken (1967) for example, examined formalization in several different ways, including both the number of rules/regulations (regarding which employees are supposed to do what, where, and when) and a measure of the commitment in enforcing such rules. One of these measures of formalization (rules/regulations about jobs) was significantly and negatively related to the rate of adoption of innovations. Further, Damanpour’s (1991) findings suggest that type of organization acts as a moderator for the relationship between formalization and innovation. For example, in a manufacturing organization, formalization may facilitate innovation, but in a service organization, formalization may inhibit innovation (Damanpour, 1991). Since schools more closely resemble service organizations, it is likely that formalization inhibits innovations in schools. For example, Corwin (1975) included standardization (consisted of agreement with eight statements about school rules and procedures) as a measure of formalization. He hypothesized, (as previously stated by Aiken and Hage in 1971), that innovation may conflict with existing procedures, and therefore more rules hinder innovativeness. Corwin found that standardization in schools was negatively and significantly related to innovativeness.

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Other research indicates that the effect of formalization may depend on the stage of innovation. Zaltman, Duncan, and Holbeck (1984) contend that the impact of formalization on organizational innovation depends on whether the innovation is at the initiation or implementation stage. They argue that greater formalization seems to hinder innovativeness at the initiation stage, but facilitates change at the implementation stage. The researchers contend that more rules and procedures can help members better understand their roles, which in turn allows the innovation to be used. They also note that stimulating initiation of innovations is facilitated by organizations that have a higher degree of complexity, lower formalization, and lower centralization. At the implementation stage, a lower level of complexity and higher levels of formalization and centralization reduce ambiguity and therefore enhance innovative behavior, suggesting that organizations should shift their structure through the innovation process (Zaltman, Duncan, and Holbeck, 1984). These assertions about the influence of formalization on the implementation stage are inconsistent with much of the previous research that suggests formalization is generally a hinderance to innovative behavior. The influence of organizational slack on innovativeness is also discussed in some of the organizational literature. It is generally hypothesized that greater slack resources are positively related to innovation. Damanpour (1987) for example, found that slack resources were positively related to innovations, with slack having a stronger effect on technological innovations than administrative or ancillary innovations.

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It is often problematic, however, to determine how much organizational slack exists, particularly with public service organizations that may claim not to have any slack. Some research has therefore found other ways to measure slack. For example, Kaluzny, Veney, and Gentry (1974) measured slack in health departments as ratio of dollars to the population they were covering. In a similar vein, measurement of wealth in schools may be measured as the number of dollars spent per pupil. While this is not an exact measure of slack resources, it seems reasonable to suggest that more funds available per student may indicate that there are funds available beyond those needed to provide basic needs in schools. Some of the literature has examined the role of centralization on innovation. For example, Kimberly and Evanisko (1981) found that innovative hospitals tended to be large, specialized, decentralized, and highly differentiated. Hage and Aiken (1967) found that decentralization (in terms of agency-wide decision-making) was positively associated with program change. In general, the research seems to find that greater centralization is negatively correlated with innovative behavior in organizations. There is some research that has focused on variables specific to schools. For example, Corwin (1975) examined the relationship between a typology of schools and innovativeness. He divided his sample into two groups (labeled low-income problem schools and middle-class schools) based on the following variables: percent of students at least one year behind in reading achievement; percent of students from poverty level homes; percent of students involved in serious disciplinary situations; and the average daily absence rate. Corwin found that overall, the

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low-income schools appeared to be slightly more innovative than wealthier schools. Further, Corwin (1975) found a statistically non-significant relationship between percent minority students and school innovativeness. He notes that individual characteristics of the teachers, and contextual factors such as size of the city and level of outside support are the most important variables (all positively related to innovativeness), but also size of the school (positively related); standardization (negatively related); and centralization (negatively related) were significantly related to innovativeness. Corwin also argues that principal characteristics seem to play more of an important role in innovativeness for the middle-class schools. Not all research has found that structural characteristics were important predictors of organizational innovativeness. Hage and Dewar (1973) examined the influence of organizational structure and elite values on innovation in sixteen health and welfare organizations (all provided rehabilitation services). Structural variables included centralization, complexity, and formalization. While elite values were generally better predictors of the rate of program innovation, greater complexity (measured both as number of occupational specialties which was significant at .05, and professional activity which was significant at .10) was also positively and significantly related to innovation (Hage and Dewar, 1973). It should be noted that there were relatively few organizations examined in this study, and that all were located in the same city. Their findings therefore, may not be generalizable to other types of organizations, in different locations, or even to the same type of organization given the small number of cases.

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There is evidence that contextual factors may be important correlates of innovative organizations. Kimberly and Evanisko (1981) examined the relationship between contextual variables and innovativeness. They included a measure of competition (whether there were other hospitals in the area); size of the city (categorized as urban or rural); and age of the hospital. The separate effects of individual, structural, and contextual factors on technological innovation were first examined. In their first model, they found that the individual variables accounted for 21% of the variation in innovation. In their second model, they found that structural variables accounted for 62% of the variation, and in their third model, contextual variables accounted for 30% of the variation in innovation. In terms of the contextual characteristics, Kimberly and Evanisko (1981) found that innovative hospitals tended to be older, urban, and faced competition from another hospital or hospitals. Additionally, when they examined all three levels of variables in a single regression model, none of the individual variables was a significant predictor of innovative behavior. All of the structural variables were significantly related to technological innovation except for one variable (a measure of the extent of mechanisms which facilitate information about innovations). When contextual variables are examined in this model, competition and size of city are no longer significant predictors of innovativeness. Age is still significant, but becomes negatively related to innovation. Kimberly and Evanisko offer the explanation that once organizational size and specialization are controlled for, age becomes negatively related because young hospitals are more likely to innovate in an attempt to establish themselves. In this multi-level

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model, 65% of the variance in technological innovation was explained (Kimberly and Evanikso, 1981). Urbanism is another variable that has been considered as a possible correlate of innovation in some of the literature. Corwin (1975) for example, examined 30 possible correlates of innovativeness in schools. Included among these variables were individual characteristics of teachers and principals, such as education level, experience, and gender. Corwin also included contextual level factors such as level of outside support (e.g. support from community groups, teacher associations, and the federal government); size of the city; and a measure of how modernized the state was that the school was located in. Corwin (1975) found that three contextual variables (number of federal programs, support for change from the community, and size of the city) were positively and significantly related to innovativeness. Specifically, most of the schools (56%) that had four or more federal programs were considered highly innovative and schools with high levels of community support were twice as likely to be highly innovative than low on innovation (41% vs. 22%). Size of the city appeared to have a curvilinear relationship with innovativeness. While highly innovative schools tended not to be located in small cities (17%), it was also found that the least innovative schools were found to be common in both the smallest and the largest cities and that the most innovative schools were most likely to be located in mid-sized cities. Altogether, Corwin found that eight contextual variables accounted for 17% of the variance in innovativeness. Baldridge and Burnham (1975) did not find a curvilinear relationship between innovativeness in schools and urbanism. They found that school districts that

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were the most innovative tended to rank about 50% higher on urbanism. There is evidence that percent minority population may be related to innovativeness. Baldridge and Burnham (1975) considered contextual factors and argue that a more heterogeneous environment is positively associated with innovativeness. Among other variables, they examined whether innovativeness appeared to be related to the percentage of nonwhites (in the school district). They found that school districts that were the most innovative tended to rank about 75% higher on the percentage of nonwhites (Baldridge and Burnham, 1975). Since the current study examines schools and not school districts, it should be noted that percentage of minority students in a school is often considered an organizational (structural) level variable. As stated previously, Corwin (1975) included percentage of minority students and found that it had a statistically non-significant relationship with school innovativeness. Summary The studies reviewed in this chapter have some limitations. For example, many of the studies on organizational innovation have small sample sizes. One limitation of the research on the use of security technology in schools is that this literature is largely descriptive. The significance of relationships is not tested, or at least not reported. Clearly, bivariate relationships should be explored, followed by multivariate models in order to establish which structural and contextual characteristics remain related to the use of security products in schools. Further, much of this research only examines the use of a few types of security

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technologies, as part of examining school safety issues more broadly. The use of three or four products, even among large samples, does not give a complete picture of the range of technologies schools may use. The research reviewed in this chapter indicates that there is variation in security technology use among individuals and organizations. The literature also suggests that certain characteristics of people, organizations, and contexts may be correlated with the adoption of innovations and security technology. Since the unit of analysis in this study is schools, the correlates examined in Chapter 3 are structural and contextual, rather than at the individual level. Prior research generally suggests that organizations that are large; less formalized; younger; decentralized; with competition; greater slack resources; and located in an urban/suburban setting tend to be more innovative. The innovation literature specifically examining schools also suggests that absence rate and lower school achievement may be positively associated with innovativeness. The extant research regarding schools’ use of security technologies suggests that schools that are large; secondary; have a high percentage of minority students; have a high percent of students eligible for free or reducedprice lunch; urban; and located in the South may be more likely to use security technologies. While the school literature did not include measures of school and neighborhood crime/disorder, it is hypothesized that greater levels of crime and disorder in schools and the neighborhoods in which they are located will be associated with greater levels of security technology use. In sum, it is hypothesized that several structural factors will be associated with greater level of security technology use. Specifically, schools that are large; at the secondary

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level; with a high percentage of minority students; high percentage of students eligible for free lunch; wealthy; less formalized; that have greater crime/disorder problems in the school; high absence rate; and low school achievement level will be more likely to use security technologies than other schools. It is also hypothesized that several contextual factors will be associated with security technology use. In particular, schools that are urban, located in the South, located in high crime neighborhoods, and have greater community presence will be more likely than other types of schools to adopt security technologies.

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CHAPTER 3

Studying School Security INTRODUCTION This chapter first identifies the two data sources that were used in this study. Second, the sample selection process and the methods followed as part of a national survey of schools are explained. Third, the major research questions, independent and dependent variables, and hypotheses are described. Fourth, limitations and strengths of the study are identified. Data Sources and Sample Selection There were two sources of data used in the current study. First, this study primarily used data from a national mail survey of schools (for the complete survey see Travis and Coon’s (2005b) “Final Report: The Role of Law Enforcement in Public School Safety: A National Survey.” Prepared for the U.S. Department of Justice, National Institute of Justice. Washington, DC: National Criminal Justice Reference Service, NCJ 211676). Second, demographic data about schools were obtained from the Common Core of Data (National Center for Education Statistics, U.S. Department of Education, 1999-2000), a dataset which contains information on approximately 90,000 schools. The unit of analysis for the current study is schools. 49

50

Security Technology in U.S. Public Schools

The Common Core of Data (CCD) consists of descriptive information about all public elementary and secondary schools in the United States (National Center for Education Statistics, U.S. Department of Education, 19992000). The CCD was used to select a sample of schools (n=3,156) and a sample proportionate to the population of public schools (N=88,511) was selected based on the following variables: state (all 50 states and Washington, DC); type of school (regular, special education, vocational, other/alternative); location (large city, mid-sized city, urban fringe large city, urban fringe mid-sized city, large town, small town, rural-outside MSA, rural-inside MSA); Title- I eligible, school wide Title-I programs1; whether the school was a magnet or charter school; grade span of the school; and number of grades in the school. As part of a larger effort to learn about school safety efforts, a nine page survey was mailed to 3,156 schools. The school questionnaire was designed primarily to examine the role of law enforcement in school safety, and incorporated items from previous surveys, particularly the School Survey on Crime and Safety (National Center for Education Statistics, 2000) and the National Assessment of School Resource Officer Programs Survey of School Principals (Finn and Hayeslip, 2001). This survey also included questions about the use of security technologies. The school surveys were mailed between January 2002 and May 2002. Dillman’s 2000 “Mail and Internet Survey: The Tailored Design Method” was used (a pre-survey notification letter informing the recipient that a questionnaire would be arriving, followed by the 1

Title I is a federal program that provides school districts with additional funds to help students meet expected standards. Funds are allocated based on the percentage of students living in poverty.

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questionnaire mailing, reminder postcard, and two subsequent mailings). One of Dillman’s suggestions is to personalize survey letters (Dillman, 2000:156). Since the Common Core of Data did not contain the names of school principals, names were searched for in Patterson’s Elementary Education (2002) guidebook to schools and whenever known, names were included on correspondence. Since the expected response rate had not been achieved, a fourth survey mailing was completed (deviating slightly from Dillman’s survey design). In addition, 100 nonresponding schools were selected and telephone calls were made to the principals. The telephone calls served several purposes: 1) to find out if the principals had received the questionnaire; 2) to ask if they had any questions or concerns about the survey; and 3) to stress the importance of the study. It was difficult to assess the impact of these telephone calls since most principals could not be reached directly. The vast majority of calls required leaving messages for principals, and of the one hundred, only two principals returned the phone calls. Since principals were not returning telephone calls, additional calls were not made. Ultimately, 19 completed surveys were received from the 100 schools contacted. It was not determined if the phone calls caused these principals to complete the questionnaire, but it seemed likely that the calls served as a reminder for those principals who were considering completing the survey. Originally, 3,156 surveys were sent to schools. Fifty schools were ultimately removed from the sample for various reasons, including school closings and surveys that were repeatedly returned by the post office as undeliverable. For surveys that were returned, attempts were made to find the correct addresses and resend these

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Security Technology in U.S. Public Schools

surveys. Many of these questionnaires continued to be returned as undeliverable. Since these surveys never reached the schools, these cases were removed from the sample. A total of 1,387 completed surveys were received out of 3,106 surveys successfully sent, which indicates a response rate of 44.7%. Tables 3.1 and 3.2 describe how the responding schools differ from the population of U.S. public schools. The responding schools were not significantly different from the population of schools in terms of school size (number of students), number of full-time teachers, and pupil:teacher ratio. Responding schools did however, have a significantly higher percentage of White students, lower percentage of students eligible for free lunch, and fewer number of grades per school. TABLE 3.1. CHARACTERISTICS OF RESPONDING SCHOOLS VS. POPULATION OF SCHOOLS (INTERVAL/RATIO LEVEL VARIABLES) School Characteristics Number of students Number of full-time classroom teachers Proportion white students** Proportion of students eligible for free lunch** Pupil:teacher ratio Number of grades in school* *p<.05, **p<.01

Responders (mean) 528.11 32.23

Population (mean) 542.73 32.51

Sig. .196 .704

.73

.66

.000

.29

.33

.000

14.83 5.42

15.18 5.56

.062 .020

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Additionally, the responding sample consisted of a lower than expected percentage of elementary schools and a greater than expected percentage of other types of schools. In terms of region, there was less than the expected response from schools located in the North and West, and higher than the expected response from Southern and Midwestern schools. Finally, urban and suburban schools were underrepresented in the sample of respondents, while rural schools were overrepresented. The unit of analysis for the present study was schools. TABLE 3.2. CHARACTERISTICS OF RESPONDING SCHOOLS VS. POPULATION OF SCHOOLS (NOMINAL AND ORDINAL LEVEL VARIABLES) School Characteristics School Level** Elementary Middle Junior High Jr/Sr High High Other Region** North South Midwest West Location** Urban Suburban Rural *p<.05, **p<.01

Responders (%)

Population (%)

56.5 15.4 1.5 4.5 19.0 3.1

63.0 15.2 1.2 3.7 14.3 2.7

13.6 33.3 33.0 20.0

16.6 33.2 29.0 21.3

18.5 30.4 51.1

24.0 32.0 44.0

ChiSquare 34.231

Sig. .000

15.541

.001

34.085

.000

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Security Technology in U.S. Public Schools

The survey included a series of 26 yes/no questions regarding security technology use (see Table 3.3). A sum of these technologies provided a measure of a school’s innovativeness. There were fourteen school survey respondents who did not respond to the technology section of the survey. Since these respondents did not answer this section of the survey, it is unknown whether their schools use security technologies. These cases were therefore removed from the analysis. There were other surveys for which the respondents did not respond to some of the technology questions. In order not to lose valid information, it seemed reasonable to include these cases. Following this method, there were 1,373 cases. Additionally, a few respondents answered questions in a manner other than yes/no. For example, to the question, “do you use security cameras on school buses,” a response might be, “some buses”. These types of “sometimes” responses were coded as yes, since this study does not address frequency of use, but simply whether the school used the technology at all. Research Questions The research questions were as follows: 1) What security technologies do schools most commonly use? 2) Assuming there is variation in technology use, what are the correlates of the level of security technology use (both total number of technologies used and number within categories of technologies)?

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3) Is security technology use in schools better explained by school problems (e.g. crime and disorder) or by other school and contextual characteristics (e.g. school level and urbanism)? Dependent Variables The first dependent variable was the total level of security technology use (measured as the total number of technologies used). As discussed in Chapter 2, organizational innovativeness has been measured in several ways. One of the most common ways is to use a count of the total number of innovations adopted by an organization (Baldridge and Burnham, 1975; Kimberly and Evanisko, 1981), so it seemed reasonable to use the total number of security technologies as the first dependent variable. One of the weaknesses of some previous research is that crime prevention measures are often considered a onedimensional concept (Lab and Stanich, 1993). There are, however, studies that have attempted to examine the use of crime prevention technologies along different dimensions. For example, Travis and Coon (2005a) propose that categorizing technologies by the level of complexity is another way of examining the use of such products. They argue that technologies differ in the amount of knowledge, skill, and training required by personnel in order for products to be used effectively, and this may affect what types of technologies are adopted in schools (Travis and Coon, 2005a). Further, Lavrakas et al. (1981) suggest that actions to prevent property loss could be categorized as primarily either physical barriers (e.g. locks) meant to deny access to potential offenders or psychological barriers (e.g. lights or radio on while not at home).

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Security Technology in U.S. Public Schools

While there are some overlapping purposes of the technologies included in this study (e.g. cameras can be used for detection and deterrence), it made sense to examine different dimensions of security products. Schools may adopt security technology to address potential external or internal threats; therefore the level of use within categories of technologies based on the goals of products was explored. Specifically, two categories were examined: 1) outward, directed toward keeping unauthorized people out of the school/school property; and 2) inward, directed toward student behavior (see Table 3.3). TABLE 3.3. SECURITY TECHNOLOGY USED IN SCHOOLS BY GOALS Technology Inward technologies Mark/identify school property Provide telephones or duress alarms in most classrooms Use security cameras on school buses Have student “hotline” or crimestopper program Use one or more random dog sniffs to check for drugs Monitor inside of school using one or more security cameras Perform one or more random sweeps for drugs (not including dog sniffs) Use one or more random dog sniffs to check for weapons Perform one or more random sweeps for weapons (not including dog sniffs) Use alcohol detection devices Use anti-graffiti sealers on exterior or interior walls Require drug testing for any students (e.g. athletes) Perform one or more random metal detector checks on students Require students to pass through metal detectors each day View contents of school bags with x-ray devices

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TABLE 3.3. SECURITY TECHNOLOGY USED IN SCHOOLS BY GOALS (CONTINUED) Technology Outward technologies Have well-lit campus at night Have a burglar alarm system for the school Control access to school building during school hours through use of locked or monitored doors Have posted signs regarding trespassing (e.g. unauthorized trespassers are subject to arrest) Have exterior doors automatically lock from the outside Have fencing surrounding the school Have caller ID on phone system Control access to school grounds during school hours through use of locked or monitored gates Monitor outdoor areas with one or more security cameras Have entry/exit alarms on exterior doors Require visitors to pass through metal detectors

While the categorization of inward and outward was only one way of distinguishing among the security technologies, it may provide some insight as to which of these two situations is the primary focus of schools. Additionally, a confirmatory factor analysis was used to empirically determine whether the categories of inward and outward technology were two different dimensions, and whether the variables assumed to belong to each of these dimensions appeared to belong in these categories (Kim and Mueller, 1978). Independent Variables The independent variables included various school characteristics such as school size; percentage of students eligible for free or reduced-price lunch; expenditure per

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Security Technology in U.S. Public Schools

student; school instruction level; school crime (actual and reported to the police); percent minority students; absence rate; school achievement level; and formalization. In addition to organizational characteristics, the following contextual factors were examined: region, urbanism, community presence, and crime level in the neighborhood in which the school was located. The size of an organization is one of the most commonly examined correlates of innovativeness, and is also described in the school security technology literature. School size has been measured in different ways, including the number of full-time teachers (Corwin, 1975) and the number of students (DeVoe et al., 2002; Heaviside, et al., 1998). The current study utilized the number of students as a measure of school size, since this is most commonly used and makes intuitive sense. Generally, size is considered positively related to both innovativeness in organizations and more specifically, to the use of security technologies in schools. One area of disagreement is whether the relationship between size and innovation is linear or curvilinear in nature (Corwin, 1975; Damanpour, 1991). In general, the literature indicates a linear relationship, but it may be worthwhile to explore the possibility of a curvilinear relationship. Kimberly and Evanisko discuss this possibility and state, “Curvilinearity exists when the correlation between a variable and the log of size exceeds the correlation between the raw size measure and that variable” (1981:701). It was hypothesized that there would be a positive and linear relationship between the amount of security technologies used in schools and school size, but the possibility of a curvilinear relationship was also explored.

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School level is a factor that has been considered in the school safety research. It is difficult to compare the findings from the studies since schools were categorized in different ways (e.g. elementary, middle, high or elementary, secondary). Generally, secondary and high schools were found to use a greater number of security technologies. School level was classified as elementary; middle; junior high; junior/senior high; high; and “other” (typically referred to K-12 schools). One limitation with this categorization is that the “other” category is somewhat ambiguous. Since there were only 43 cases in this category, however, it should not significantly affect the results of the analyses. It was hypothesized that school level would be positively associated with security technology use. In addition to school level, another school characteristic that is important to examine is the number of grade levels. Schools that have many grade levels will have a student body with a broad range of ages. Schools that have a broad range of ages may be more likely to have problems such as bullying than schools with only a few grade levels. It was therefore hypothesized that the number of grade levels would be positively associated with security technology use. Crime level at the school was examined in several ways. Previous research identifies several limitations of measuring crime/disorder by what schools report to the police. Cantor and Wright (2001) argue that reported school crime is not simply a function of crime/disorder in the school, but is also influenced by the school’s policies and/or informal practices for reporting incidents to the police. Since reported crimes were likely to underestimate

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Security Technology in U.S. Public Schools

actual incidents, actual events2 and reported events were both examined. Further, it seemed important to examine crime as either violent or non-violent (Cantor and Wright, 2001). Crime was first included as a raw score, but was also categorized as violent (e.g. homicide, rape, robbery), or nonviolent (e.g. theft, vandalism). By examining crime in these various ways, the relationship between school crime and the adoption of security technology may be more apparent. It was hypothesized that overall crime level would be positively correlated with the use of security technologies. Another variable that may be related to the use of security technologies in schools is the level of police presence. Police presence was measured as the number of hours per week that a school resource officer is at the school. This was not a perfect measure since in some locations police may not be labeled “school resource officers” but still provide a police presence at the school. Further, some respondents did not state a set number of hours per week but said for example, 10 hours/year or “as needed.” These types of responses were recoded as zero hours per week since the school resource officer is at the school so infrequently it essentially is zero, or there is simply no way of knowing how many hours the officer is present at the school. Additionally, some respondents stated a range of hours the officer worked or responded “40+.” These responses were recoded to reflect the lowest estimated number of hours. While recoding such responses may have underestimated the level of police presence, a conservative measure seemed more desirable than 2

Actual events refer to events known to school officials, but not necessarily reported to the police.

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overestimating police involvement. Further, asking respondents to indicate how many hours the school resource officer worked at the school also probably avoided overestimating police presence. For example, some schools may have had a school resource officer, but if that officer spent most of his/her time at other schools, it would not be a reasonable measure of how much time the officer is present at the sample school. While some schools may have both high levels of police presence and security technology, many schools have very limited resources and may face the choice of “personnel or products” for security needs. It was hypothesized that police presence would be negatively correlated with the use of security technologies. Percentage of minority students has been considered both in the school safety literature and in some of the school-organizational research as a possible factor in the adoption of innovations/security technologies. While Corwin (1975) found a statistically non-significant relationship between percentage of minority students and school innovativeness, most of the literature indicates that a higher percentage of minority students tends to be associated with greater levels of innovativeness (Baldridge and Burnham, 1975) and the adoption of security technologies in schools (DeVoe et al., 2002, Heaviside et al., 1998). Given prior research, it was hypothesized that a higher percentage of minority students would be positively correlated with level of security technology use. The limited literature about the use of technologies in schools suggests that a higher percentage of students eligible for free or reduced-price lunch may be associated with greater use of security technologies. While there are numerous issues with this research (e.g. no statistical tests were employed and only a few technologies were

62

Security Technology in U.S. Public Schools

examined), it describes the current state of our knowledge about security technologies in schools. It was therefore hypothesized that a higher percentage of students eligible for free lunch would be positively associated with the use of security technologies. In general, the organizational literature indicates that slack resources are positively associated with innovation in organizations. Measuring organizational slack was not an easy task, especially with public service organizations that may report zero slack. Following Kaluzny, Veney and Gentry’s (1974) measurement of slack in health departments as the ratio of dollars to the population covered, measurement of wealth in schools was measured as the number of dollars spent per pupil. As stated previously, this was not an exact measure of slack resources, but it seems reasonable to suggest that more funds per student may indicate that there were funds available beyond those needed to provide the basic necessities. While not all research has found a positive correlation (e.g. Corwin, 1975 found that low-income schools were more innovative), it was hypothesized that expenditure per pupil would be positively associated with the use of security technologies in schools. One variable considered by Corwin (1975) was the average daily absence rate in a school. He found that schools with problems such as a higher daily absence rate tended to be slightly more innovative than schools with fewer problems. While it is unknown precisely why these schools were more innovative, it is possible that problematic schools may be more accustomed to finding innovative solutions to problems than other types of schools. It was therefore hypothesized that absence rate

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would be positively correlated with level of security technology use. School achievement level was another variable considered by Corwin (1975). He found that the percent of students at least one year behind in reading achievement was positively associated with more innovative schools. The current study used three measures of school achievement: 1) students’ reading level compared to other schools in the state; 2) students’ math ability compared to other schools in the state; and 3) overall school achievement level (how other educators in the state would rank the school’s achievement). It was hypothesized that school achievement level would be negatively correlated with the level of security technology use. The organizational innovation literature often refers to level of formalization in terms of the number of rules and procedures about job roles. For example, one way that Hage and Aiken (1967) examined formalization included the number of rules/regulations regarding which employees were supposed to do what, where, and when. Additionally, Hage and Aiken (1967) measured the commitment in enforcing such rules. One limitation of this study was the lack of data regarding job roles, therefore the measure of formalization may not accurately reflect how formalization has typically been measured in prior organizational research. Despite this weakness, the data do allow a measure of formalization, in terms of rules, procedures, and meetings in schools. These rules and procedures are typically safety related, but should also provide an indication of the level of formalization in a school. It seemed reasonable to assume that many of these rules and procedures were developed by

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Security Technology in U.S. Public Schools

a group of people (e.g. school board), indicating some formality had to occur to create these rules. TABLE 3.4. SCHOOL RULES, PROCEDURES, AND MEETINGS School Rules, Procedures, Meetings Require visitors to sign or check in Close the campus for most students during lunch Provide a printed code of student conduct to students Provide a printed code of student conduct to parents Require students to wear badges or picture IDs Require faculty and staff to wear badges or picture IDs Have restricted parking areas or require parking decals Prohibit tobacco use on school grounds Require students to wear uniforms Have a student dress code Confiscate a student’s ID when that student is expelled or suspended Make pictures of expelled and suspended students available to security staff Have an emergency plan agreement with law enforcement Written plan that describes procedures to be performed in case of a shooting Written plan that describes procedures to be performed in case of a riot or large-scale fight Written plan that describes procedures to be performed in case of a bomb scare or comparable school-wide threat (not including fire) Written plan that describes procedures to be performed in case of a hostage situation School had a school safety committee School had regularly scheduled meetings to discuss general school issues with law enforcement School had regularly scheduled meetings to discuss specific incidents with law enforcement School reviewed school-wide discipline practices and procedures with law enforcement

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School rules, procedures, and meetings are listed in Table 3.4. In general, the research indicates that formalization is negatively associated with innovation in organizations (Corwin, 1975; Damanpour, 1991; Hage and Aiken, 1967). It was therefore hypothesized that formalization would be negatively related to security technology use in schools. Some research indicates that innovation in organizations may have a curvilinear relationship with urbanism, suggesting that suburban organizations may be the most innovative (Corwin, 1975). Other organizational literature indicates that organizations in urban areas are more innovative (Baldridge and Burnham, 1975). The school safety literature indicates that urban schools appear more likely to use security technologies. It was therefore hypothesized that urban schools would be most likely to use security technologies. The possibility of a curvilinear relationship was also explored using the methods described previously when testing for a curvilinear relationship between innovativeness and size of the school. Adequate information about the relationship between organizational innovativeness and region is lacking in much of the literature. Many studies examine organizations within a single city, so region does not vary. The school literature is largely in disagreement, with the exception that Southern schools appear more likely to use random metal detectors (DeVoe et al., 2002; Heaviside et al., 1998). DeVoe et al. (1998) consistently found that Southern schools were the most likely to use security technologies than schools in other regions. It was therefore hypothesized that Southern schools would have greater levels of security technology use than schools in other regions.

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Security Technology in U.S. Public Schools

In addition to level of crime/disorder within schools, a factor that may influence schools in their decisions to adopt security technologies is the neighborhood crime levels surrounding the schools. The data used in this study included a question asking respondents to describe the crime level in the area where the school was located as either: high crime; moderate level of crime; low level of crime; and mixed levels of crime. It made sense to collapse moderate and mixed levels of crime into one category. Based on research regarding protective behavior discussed in Chapter 2, it seemed reasonable to suggest that schools may react to real or perceived crime levels in the surrounding area. It was hypothesized that higher levels of perceived crime in the area surrounding the school would be positively correlated with use of security technologies. While community presence is not specifically addressed in the literature, there may be a relationship between community presence at the school and the adoption of security technologies. Community members may act as “interested parties” who may make demands on the school district (Baldridge and Burnham, 1975). This may include requests for additional security measures. The question that was used as a measure of community presence was the level of agreement or disagreement with the statement that the school served as a community center. It may be that community members were at the school during evening hours and therefore security issues were more pressing. While the exact nature of this relationship cannot be precisely determined, it was hypothesized that schools with greater levels of community presence would be more likely to adopt security technologies.

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Limitations of the Study This study has several limitations. First, given the somewhat low response rate (44.7%), it is quite possible that important information about schools’ use of security technologies was missed. Despite this overall response rate, an expected, or higher than expected response from certain types of schools was received (see Tables 3.1 and 3.2). The findings therefore, may be more generalizable for certain categories of schools. Further, the survey was only administered to public schools, so it is likely these findings may not be generalizable to private schools, nor to certain types of public schools that are underrepresented among the respondents. Other limitations of the study involve issues with the dependent variables that were examined (see Table 3.3 for a list of the technologies included in this study). One issue was that several of the variables have two components within a single statement. For example, “control access to school building during school hours through use of locked or monitored doors” did not specify whether the access is controlled by people, cameras, locks, or is otherwise monitored. While previous research (e.g. Heaviside et al., 1998) has similar issues with “double barreled” questions, it is nevertheless a limitation that could be easily rectified in future research. Another issue with the dependent variable (level of security technology use) is that the survey data lacked detail about the specifics of technology use. For example, the survey included a question about schools’ use of security cameras inside the school, outside the school, and on school buses. While this provided some detail about camera use, it did not address issues such as whether the

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cameras were part of a cctv system, whether events were recorded, and whether the cameras were controllable remotely to pan, tilt, or zoom. Further, since the security technology section consisted of a series of yes/no questions it was unknown precisely how it was used or how frequently. There are also several limitations with the independent variables included in this work. For example, there were several issues related to the measurement of crime based on principal surveys. Cantor and Wright (2001) identify several of these issues. First, it is unknown if the information on the number of crime/disorder incidents exists in school records. Not all schools may keep these types of records, or the records may not be organized in a way that is consistent with survey questions. Further, when examining crime reported to the police, it is based on the assumption that the principal was aware of the event and decided to make an official report to the police (Cantor and Wright, 2001). Another limitation with measuring crime based on surveys of school administrators is that they may be concerned that crime/disorder will reflect poorly on them. Kingery and Coggeshall (2001:8) state that, “many school officials may feel that reporting the occurrence of incidents/acts will be used to negatively judge their abilities to administer a school.” While it was made clear in the correspondence with schools what the purpose of the survey was and that their responses were confidential, it was possible that principals still did not want to answer crime/disorder questions for the aforementioned reasons. Many studies of organizational innovation examine the influence of factors that are not included in this study. For example, individual characteristics such as sex, age, length of time in organization, and level of education are factors

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often included in the literature. The current study is limited since the data did not include individual level measures. As previously mentioned however, some researchers contend that innovative behavior in complex organizations is better explained by structural and contextual factors than individual characteristics (Baldridge and Burnham, 1975; Hage and Aiken, 1970; Kimberly and Evanisko, 1981). In addition, some studies include a broad range of structural characteristics such as centralization and specialization, for which this study did not have measures. Much of the literature also discusses the association between organizational complexity (also referred to as hierarchical levels) and innovative behavior, and the datasets used in this dissertation do not include such a measure. It can be argued, however, that schools were likely to differ from many organizations in this respect. The typical structure of a school includes administration, faculty, staff, and students. It seems reasonable to suggest that schools had less variation in hierarchical levels than many other types of organizations. Further, some studies examine several different types of innovations, such as programs and products. The present study was therefore not as broad, since it only examined one type of innovative behavior. While the current work was not a comprehensive examination of innovative behavior in schools, it did attempt to examine the relationship between structural and contextual level factors and innovativeness (number of security technologies adopted) in a large number of schools. Since this study used cross-sectional data (reflecting technology use during the 2000-2001 school year) and the survey did not ask when security technologies were adopted, it was not possible to determine whether security

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technologies were new to the school. It was also not possible to establish that any of the independent variables caused schools to adopt security technologies. It was possible however, to assess level of innovativeness by comparing the number of technologies a school has relative to other schools. Another limitation of this work is that since it only examined the adoption of one type of innovation in one type of organization, the findings may not be generalizable to other types of organizations, nor be similar to other types of innovations that may be adopted by schools. Most of the literature is limited to examining one type of organization, so this situation is not uncommon. While there are limitations to this study, this work is an important contribution to the research for several reasons. First, a larger number of security technologies are examined than in previous school safety research. Second, this work uses data from a national sample of schools, unlike much of the organizational innovation literature. Third, a broader set of possible structural and contextual correlates of technology use are included than in prior studies. Fourth, this study includes an examination of whether the correlates of security technology use in schools are consistent with previous research regarding school safety and the adoption of innovations among organizations. Fifth, multivariate models using ordinary least squares regression are presented that provide a better estimation than previous studies of which structural and contextual factors are related to the adoption of security technologies in schools.

CHAPTER 4

Schools’ Use of Security Technology This chapter presents the findings from the analyses outlined in Chapter 3. First, this chapter examines security technologies most commonly used by schools. Second, the level of security technology use among schools is described. Overall level of use and amount of use based on a classification of the likely goals of technologies is explored. Third, this chapter examines bivariate relationships between characteristics of schools and communities with the level of security technology use. Fourth, correlations among individual types of security products are presented followed by a discussion of whether a categorization of technologies by their goals is supported empirically. Fifth, multivariate models using ordinary least squares regression are examined in order to describe which variables appear to best predict level of security technology use in schools. Type of Security Technology Use The first research question is, “What security technologies do schools most commonly use?” Table 4.1 shows the reported use of security technologies in schools during 2000-2001 school year. The first column shows the raw percentage of schools that reported having the products listed in the questionnaire. As can be seen in this column, marking/identifying school property was the most common 71

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crime prevention technology used by schools (81.4%), followed by having a well-lit campus at night (77.1%), and providing telephones or duress alarms in most classrooms (68.7%). The third column of this table also indicates that most schools completed the security technology section in the survey (total sample size=1387). Fourteen cases were removed from the sample since these schools skipped all of the security technology questions. The second column in Table 4.1 describes the reported use of security technologies in schools during 2000-2001 with missing or ambiguous responses counted as “no.” Specifically, if a respondent circled both “yes” and “no” regarding the use of a particular technology, this response was counted as no. Similarly, if some of the questions in the technology section were skipped or if respondents stated that they didn’t know if the technology was used, these responses also counted as no. This method may underestimate the presence of security technology in schools, but a conservative estimate seemed more appropriate than potentially overestimating its use. When comparing the percentage of schools that reported having each type of technology, there was virtually no difference in the raw percentage and with the missing values included. In the interest of not losing information about schools’ use of security technologies, and since there were no significant differences between the raw values and missing values included, 1373 cases were used for all of the analyses in this chapter. When examining these 1373 cases, marking/identifying property (80.8%); having well-lit campus at night (76.1%); and providing telephones or duress alarms in most classrooms (68.1%) were the most common forms of security technology use. Most schools also reported using

Schools’ Use of Security Technology

73

burglar alarm systems (63.3%); controlling access to school building during school hours through the use of locked or monitored doors (60.1%); and using security cameras on school buses (52.9%). Some schools reported having a student “hotline” or crimestopper program (35.5%); caller ID on phone system (29.3%); using one or more random dog sniffs to check for drugs (28.1%); monitoring inside of the school using one or more security cameras (21.6%); and controlling access to school grounds during school hours through the use of locked or monitored gates (21.1%). Less than 1% of schools reported requiring visitors to pass through metal detectors (0.7%) or viewing contents of school bags with x-ray devices (0.7%). There is clearly variation in security technology use among schools, and only 1.4% of responding schools (n=19) reported that they did not use any technologies. TABLE 4.1. REPORTED USE OF SECURITY TECHNOLOGY IN SCHOOLS DURING 2000-2001

Technology

Raw Yes (%)

Mark/identify school 81.4 property Have well-lit campus at 77.1 night Provide telephones or 68.7 duress alarms in most classrooms Have a burglar alarm system 63.8 for the school Control access to school 60.7 building during school hours through use of locked or monitored doors Percent is rounded to the nearest tenth

Missing Included Yes (%)

Raw N

Missing Included N

80.8

1362

1373

76.1

1356

1373

68.1

1361

1373

63.3

1363

1373

60.1

1359

1373

TABLE 4.1. REPORTED USE OF SECURITY TECHNOLOGY IN SCHOOLS DURING 2000-2001 (CONTINUED)

Technology

Raw Yes (%)

Use security cameras on 53.9 school buses Have posted signs regarding 52.7 trespassing (e.g. unauthorized trespassers are subject to arrest) Have exterior doors 40.3 automatically lock from the outside Have fencing surrounding 36.0 the school Have student “hotline” or 34.6 crimestopper program Have caller ID on phone 29.8 system Use one or more random 28.6 dog sniffs to check for drugs Monitor inside of school 21.9 using one or more security cameras 21.4 Control access to school grounds during school hours through use of locked or monitored gates Monitor outdoor areas with 19.5 one or more security cameras Have entry/exit alarms on 18.7 exterior doors Percent is rounded to the nearest tenth

74

Missing Included Yes (%)

Raw N

Missing Included N

52.9

1347

1373

51.7

1347

1373

39.8

1357

1373

34.1

1353

1373

35.5

1352

1373

29.3

1349

1373

28.1

1352

1373

21.6

1355

1373

21.1

1356

1373

19.2

1356

1373

18.4

1345

1373

TABLE 4.1. REPORTED USE OF SECURITY TECHNOLOGY IN SCHOOLS DURING 2000-2001 (CONTINUED)

Technology

Raw Yes (%)

Perform one or more 15.9 random sweeps for drugs (not including dog sniffs) Use one or more random 11.1 dog sniffs to check for weapons Perform one or more 11.0 random sweeps for weapons (not including dog sniffs) Use alcohol detection 9.6 devices Use anti-graffiti sealers 7.7 on exterior or interior walls Require drug testing for 7.0 any students (e.g. athletes) Perform one or more 4.9 random metal detector checks on students Require students to pass 1.5 through metal detectors each day Require visitors to pass 0.7 through metal detectors View contents of school 0.7 bags with x-ray devices Percent is rounded to the nearest tenth

75

Missing Included Yes (%)

Raw N

Missing Included N

15.7

1354

1373

10.9

1351

1373

10.9

1353

1373

9.4

1348

1373

7.5

1343

1373

6.8

1346

1373

4.9

1357

1373

1.5

1360

1373

0.7

1354

1373

0.7

1351

1373

76

Security Technology in U.S. Public Schools

Level of Security Technology Use The level of security technology use refers to the total number of technologies adopted and is one of the dependent variables that will be examined for the second and third research questions. Table 4.2 shows the number of security products used by schools. The majority of schools reported that they used at least some of the technologies included in the survey. The modal number of technologies used was 6, with a mean of 7.69, and a median of 7. Only one school reported having all of the technologies listed. This may lend some support for the validity of the findings, since it indicates that respondents did not simply circle all of the options without reading them. As stated previously, only 1.4% of responding schools (n=19), reported that they did not use any security products. These cases were included in the category of “low technology use.” Since they answered the technology section these cases should not be excluded, yet there are so few of these cases that it makes sense to include them with the schools that use only a few security technologies. TABLE 4.2. LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS DURING 2000-2001 Number of Technologies Used Low Use 0 1 2 3 4 5 6

Valid (%)

Cumulative (%)

N

1.4 1.3 3.6 5.0 6.0 9.3 13.0

1.4 2.7 6.3 11.3 17.3 26.6 39.5

19 18 49 69 82 128 178

Schools’ Use of Security Technology

77

TABLE 4.2. LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS DURING 2000-2001 (CONTINUED) Number of Valid Technologies Used (%) Moderate Use 7 11.4 8 10.9 9 9.8 High Use 10 7.9 11 6.6 12 5.0 13 3.4 14 2.2 15 1.5 16 0.7 17 0.3 18 0.3 19 0.4 21 0.1 23 0.1 26 0.1 Percent is rounded to the nearest tenth Mean=7.69 Std. error of mean=.096 Median=7

Cumulative (%)

N

50.9 61.8 71.6

156 150 134

79.5 108 86.0 90 91.0 68 94.4 47 96.6 30 98.0 20 98.8 10 99.1 4 99.3 4 99.8 6 99.9 1 99.9 1 100.0 1 N=1373 Mode=6 Std. deviation=3.55 Variance=12.605

In addition to examining the total number of security technologies used by schools, it also seems important to recognize that schools may choose technologies to address different issues. Specifically, schools may select technologies that are aimed primarily: 1) outward, directed toward keeping unauthorized people out of the school/school property; or 2) inward, directed toward student behavior. Table 4.3 describes the reported use of security technologies according to this classification.

78

Security Technology in U.S. Public Schools

Burglar alarms, requiring visitors to pass through metal detectors, and fencing surrounding the school are examples of outward technologies. Inward technologies include security cameras on school buses, drug sniffs to check for drugs, and x-ray devices to view contents of school bags. TABLE 4.3. REPORTED USE OF SECURITY TECHNOLOGY IN SCHOOLS DURING 2000-2001 Inward Technology Mark/identify school property Provide telephones or duress alarms in most classrooms Use security cameras on school buses Have student “hotline” or crimestopper program Use one or more random dog sniffs to check for drugs Monitor inside of school using one or more security cameras Perform one or more random sweeps for drugs (not including dog sniffs) Use one or more random dog sniffs to check for weapons Perform one or more random sweeps for weapons (not including dog sniffs) Use alcohol detection devices Use anti-graffiti sealers on exterior or interior walls Require drug testing for any students (e.g. athletes) Perform one or more random metal detector checks on students Require students to pass through metal detectors each day View contents of school bags with x-ray devices Percent is rounded to the nearest tenth

Yes (%) 80.8 68.1

No (%) 19.2 31.9

52.9 35.5

47.1 64.5

28.1

71.9

21.6

78.4

15.7

84.3

10.9

88.9

10.9

89.1

9.4 7.5

90.6 92.5

6.8

93.2

4.9

95.1

1.5

98.5

0.7

99.3 N=1373

Schools’ Use of Security Technology

79

TABLE 4.3. REPORTED USE OF SECURITY TECHNOLOGY IN SCHOOLS DURING 2000-2001 (CONTINUED) Outward Technology Have well-lit campus at night Have a burglar alarm system for the school Control access to school building during school hours through use of locked or monitored doors Have posted signs regarding trespassing (e.g. unauthorized trespassers are subject to arrest) Have exterior doors automatically lock from the outside Have fencing surrounding the school Have caller ID on phone system Control access to school ground during school hours through use of locked or monitored gates Monitor outdoor areas with one or more security cameras Have entry/exit alarms on exterior doors Require visitors to pass through metal detectors Percent is rounded to the nearest tenth

Yes (%) 76.1 63.3 60.1

No (%) 23.9 36.7 39.9

51.7

48.3

39.8

60.2

34.1 29.3 21.1

65.9 70.7 78.9

19.2

80.8

18.4 0.7

81.6 99.3 N=1373

Categorizing technologies in this way has some limitations, since several of the technologies may serve dual purposes. For example, marking/identifying school property may help to reduce thefts regardless of whether or not the potential thief is a student. Additionally, monitoring outdoor areas with one or more security cameras could be used to observe students during breaks, but may also be used to deter or detect unauthorized visitors on campus. Knowing the total number of technologies adopted by schools is important, but it is also informative to examine use across different dimensions of security products since schools may select specific security technologies to address potential external or internal threats.

80

Security Technology in U.S. Public Schools

In an attempt to better understand the adoption of security technologies in schools, the amount of use within categories of technologies based on the likely goals of products was explored. As can be seen in Tables 4.4 and 4.5, the level of use within these categories is presented along with descriptive statistics. TABLE 4.4. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS DURING 2000-2001 Number of Outward Technologies Used 0 1 2 3 4 5 6 7 8 9 10 11 N=1373 Mean=4.14 Std Error of Mean=.057 Median=4

Schools (%) N 3.6 50 7.2 99 11.7 161 17.6 241 18.9 260 13.6 187 12.6 173 8.7 120 4.2 57 1.7 23 0.1 1 0.1 1 Mode=4 Std. Deviation=2.105 Variance=4.432

There was a larger possible range for inward technologies (0-15), yet on average schools used more outward technologies. In some sense, this is not surprising since many of the inward technologies include drug, alcohol, and weapon detection systems which would typically be limited to schools with an older student body. It may also indicate that school officials are more willing to

Schools’ Use of Security Technology

81

use products that are designed to protect the school from outsiders than they are to use technologies focused on students. While this is only one way of characterizing security technologies, it suggests that adopting outward rather than inward technologies may be less controversial and therefore more common among schools. Using this classification also aids in identifying the types of schools that appear most likely to use greater levels of technologies to address possible internal and external threats. TABLE 4.5. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS DURING 2000-2001 Number of Inward Technologies Used 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 N=1373 Mean=3.55 Std Error of Mean=.059 Median=3

Schools (%) N 3.9 54 10.8 148 18.9 259 25.0 343 15.2 209 8.0 110 7.8 107 5.2 72 2.1 29 1.5 21 0.8 11 0.4 6 0.1 2 0 0 0.1 1 0.1 1 Mode=3 Std. Deviation=2.179 Variance=4.747

82

Security Technology in U.S. Public Schools

In order to identify possible correlates of security technology use in schools, the relationships between level of technology use and nominal/ordinal level variables were first explored. Chi-square was used to test for significance for the bivariate relationships between these types of variables and level of security technology use. Since the chi-square statistic does not specify the direction of relationships (Vito and Latessa, 1989), contingency tables (Tables 4.6 through 4.29) are presented which allow a visual inspection of each bivariate relationship. School level is a possible correlate of level of security technology use in schools. Based on the extant literature, it was hypothesized that secondary schools would generally have greater levels of technology use than elementary schools. Schools were classified as elementary; middle; junior high; junior/senior high; high; and “other” (typically referred to K-12 schools). As stated in Chapter 3, the sample of respondents differs from the population of schools in several ways, including a lower than expected response from elementary schools and a higher than expected response from high schools. When examining this bivariate relationship it appears that level of security technology use in schools is significantly related to school level (Table 4.6). Among all types of schools, elementary schools were the most likely to report low use of security technologies. Junior high schools, other schools, and high schools were the most likely to report higher levels of technology use. It should also be noted that due to the small number of junior high schools in the sample and population (according to an examination of the U.S. Department of Education’s Common Core of Data, junior high schools only comprise 1% of all public schools), these results should be interpreted with caution.

TABLE 4.6. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY SCHOOL LEVEL** Element. % (n)

Jr High % (n)

Jr/Sr High % (n)

Other % (n)

Total % (N)

49.4 30.7 9.5 8.7 22.6 5.6 (382) (65) (2) (24) (59) (11) Moderate 33.2 32.1 38.1 30.6 29.5 25.6 Use (257) (68) (8) (19) (77) (11) High Use 17.4 37.3 52.4 30.6 47.9 48.8 (135) (79) (11) (19) (125) (21) 100.0 100.0 100.0 100.0 100.0 100.0 Total % (774) (212) (21) (62) (261) (43) (N) **level of security technology use and school level are significantly related at the .01 level Chi-Square=134.788

9.5 (543) 32.0 (440) 28.4 (390) 100.0 (1373)

Level of Technology Use Low Use

Middle % (n)

High % (n)

TABLE 4.7. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY SCHOOL LEVEL

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Element. % (n)

Middle % (n)

Jr High % (n)

Jr/Sr High % (n)

High % (n)

Other % (n)

Total % (N)

42.0 (325) 32.3 (250) 25.7 (199) 100.0 (774)

39.2 (83) 33.0 (70) 27.8 (59) 100.0 (212)

19.0 (4) 42.9 (9) 38.1 (8) 100.0 (21)

45.2 (28) 38.7 (24) 16.1 (10) 100.0 (62)

36.8 (96) 31.8 (83) 31.4 (82) 100.0 (261)

34.9 (15) 25.6 (11) 39.5 (17) 100.0 (43)

40.1 (551) 32.6 (447) 27.3 (375) 100.0 (1373)

TABLE 4.8. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY SCHOOL LEVEL** Element. % (n)

Jr High % (n)

Jr/Sr High % (n)

Other % (n)

Total % (N)

47.0 19.3 4.8 27.4 10.7 23.3 (364) (41) (1) (17) (28) (10) Moderate 44.1 40.1 38.1 33.9 33.3 23.3 Use (341) (85) (8) (21) (87) (10) High Use 8.9 40.6 57.1 38.7 55.9 53.5 (69) (86) (12) (24) (146) (23) 100.0 100.0 100.0 100.0 100.0 100.0 Total % (774) (212) (21) (62) (261) (43) (N) **level of security technology use and school level are significantly related at the .01 level Chi-Square=328.488

33.6 (461) 40.2 (552) 26.2 (360) 100.0 (1373)

Level of Technology Use Low Use

Middle % (n)

High % (n)

86

Security Technology in U.S. Public Schools

Interestingly, when examining only outward security technology use (Table 4.7), there do not appear to be significant differences by school level. One possible explanation is that schools may have a more universal concern about unauthorized people entering their schools. When examining the level of inward security technology use by school level (Table 4.8), there are significant differences. For example, only 8.9% of elementary schools reported high use of inward security technologies while more than half of high schools (55.9%) reported high levels of use. This may in part be due to the fact that several of the inward technologies include drug testing and weapons searches which are likely to be more common in upper level schools. Urbanism is another possible correlate of level of security technology use in schools. Based on the extant research, it was hypothesized that urban schools would generally have greater levels of technology use. As can be seen in Table 4.9, the total level of security technology use appears to be significantly related to urbanism. Half of rural schools (50.1%) reported low technology use, while only 28.6% of suburban schools, and 28.5% of urban schools reported low use. Urban schools were also the most likely to report high levels of security product use. Table 4.10 shows the level of outward security technology use by urbanism. It appears that urbanism is a significant correlate of the level of outward technology use. Almost half of urban schools (46.6%) reported high levels of use, while only 16.4% of rural schools reported high use. About a third of suburban schools (33.8%) reported high levels of security product use.

TABLE 4.9. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY URBANISM** Rural % (n)

Suburban % (n)

Urban % (n)

Total % (N)

Level of Technology Use Low Use

50.1 28.6 28.5 39.5 (351) (120) (72) (543) Moderate Use 27.6 40.0 31.2 32.0 (193) (168) (79) (440) High Use 22.3 31.4 40.3 28.4 (156) (132) (102) (390) 100.0 100.0 100.0 100.0 Total % (700) (420) (253) (1373) (N) **level of security technology use and urbanism are significantly related at the .01 level, Chi-Square=76.465 TABLE 4.10. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY URBANISM** Rural % (n)

Suburban % (n)

Level of Technology Use Low Use

Urban % (n)

Total % (N)

53.6 28.8 21.7 40.1 (375) (121) (55) (551) Moderate Use 30.0 37.4 31.6 32.6 (210) (157) (80) (447) High Use 16.4 33.8 46.6 27.3 (115) (142) (118) (375) 100.0 100.0 100.0 100.0 Total % (700) (420) (253) (1373) (N) **level of security technology use and urbanism are significantly related at the .01 level, Chi-Square=142.181

87

88

Security Technology in U.S. Public Schools

The relationship between the level of inward security technology use and urbanism was also explored. At least at the bivariate level, there does not appear to be a significant relationship (Table 4.11). Schools appear fairly similar in their use of inward technologies regardless of urbanism. For example, 26.9% of urban schools, 26.7% of suburban schools, and 25.7% of rural schools reported high levels of inward technology use. Finally, given these findings there is no evidence to suggest that the relationship between level of security technology use and urbanism is curvilinear. TABLE 4.11. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY URBANISM

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Rural % (n)

Suburban % (n)

Urban % (n)

Total % (N)

34.1 (239) 40.1 (281) 25.7 (180) 100.0 (70.0)

31.7 (133) 41.7 (175) 26.7 (112) 100.0 (420)

35.2 (89) 37.9 (96) 26.9 (68) 100.0 (253)

33.6 (461) 40.2 (552) 26.2 (360) 100.0 (1373)

While the previous research regarding use of security products in schools is fairly limited, it suggests that Southern schools may be more likely to use at least some

Schools’ Use of Security Technology

89

types of security technology. When examining the total level of security technology use (Table 4.12), schools located in the South were more likely than schools in other regions to report high levels of technology use. For level of outward technology use (Table 4.13), schools located in North (33.0%) were the most likely to report high levels of use, followed closely by schools in the South (32.8%). More dramatic differences can be seen in terms of level of inward technology use (Table 4.14) with schools in the South (37.3%) much more likely than other schools to report using high levels of inward focused security products. TABLE 4.12. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY REGION** North % (n) Level of Technology Use Low Use

South % (n)

Midwest % (n)

West % (n)

Total % (N)

39.5 31.2 45.8 43.3 39.5 (73) (144) (207) (119) (543) Moderate Use 34.6 30.2 31.2 34.9 32.0 (64) (139) (141) (96) (440) High Use 25.9 38.6 23.0 21.8 28.4 (48) (178) (104) (60) (390) 100.0 100.0 100.0 100.0 100.0 Total % (185) (461) (452) (275) (1373) (N) **level of security technology use and region are significantly related at the .01 level Chi-Square=41.312

TABLE 4.13. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY REGION** North % (n)

South % (n)

Midwest % (n)

West % (n)

Total % (N)

Level of Technology Use Low Use

33.0 34.7 48.0 41.1 40.1 (61) (160) (217) (113) (551) Moderate Use 34.1 32.5 32.1 32.4 32.6 (63) (150) (145) (89) (447) High Use 33.0 32.8 19.9 26.5 27.3 (61) (151) (90) (73) (375) 100.0 100.0 100.0 100.0 100.0 Total % (185) (461) (452) (275) (1373) (N) **level of security technology use and region are significantly related at the .01 level, Chi-Square=29.250 TABLE 4.14. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY REGION** North % (n)

South % (n)

Level of Technology Use Low Use

Midwest % (n)

West % (n)

Total % (N)

42.7 23.6 39.2 34.9 33.6 (79) (109) (177) (96) (461) Moderate Use 41.6 39.0 37.4 45.8 40.2 (77) (180) (169) (126) (552) High Use 15.7 37.3 23.5 19.3 26.2 (29) (172) (106) (53) (360) 100.0 100.0 100.0 100.0 100.0 Total % (185) (461) (452) (275) (1373) (N) **level of security technology use and region are significantly related at the .01 level, Chi-Square=61.621

90

Schools’ Use of Security Technology

91

As was described in Chapter 3, community presence was a measure of the level of agreement with the statement, “the school served as a community center.” It was hypothesized that schools with greater community presence might be more likely to use security technologies. While community presence did not appear to be a significant correlate of total (Table 4.15) or outward (Table 4.16) technology use, it was significantly related to level of inward technology use (Table 4.17). Those who strongly agreed that their school served as a community center (31.5%) were significantly more likely to report higher levels of inward technology use than those who agreed (24.9%), disagreed (23.8%), or strongly disagreed (17.6%). While neighborhood crime is not specifically addressed in the existing research regarding the adoption of security technologies in schools, it was hypothesized that perceptions of higher levels of neighborhood crime would be associated with higher levels of security product use. Crime in the neighborhood surrounding the school appears to be significantly related to total level (p<.01), outward (p<.01), and inward (p<.05) security technology use. Schools located in high crime areas were the most likely to report high levels of total security product use (Table 4.18). Sixty-three percent of schools in high crime neighborhoods reported having high levels of security technologies, 20.4% in these areas reported moderate use, and only 16.7% of schools in high crime areas reported low use. Schools that were in moderate/mixed crime locations were almost evenly divided among levels of technology adoption. Among schools in low crime areas, 43.1% reported low use of technology, and only 25% reported high use.

TABLE 4.15. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY COMMUNITY PRESENCE

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Strongly Disagree % (n)

Disagree % (n)

Agree % (n)

Strongly Agree % (n)

Total % (N)

45.6 (31) 35.3 (24) 19.1 (13) 100.0 (68)

38.9 (93) 33.5 (80) 27.6 (66) 100.0 (239)

40.0 (238) 33.9 (202) 26.1 (155) 100.0 (595)

37.5 (168) 28.6 (128) 33.9 (152) 100.0 (448)

39.3 (530) 32.1 (434) 28.6 (386) 100.0 (1350)

TABLE 4.16. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY COMMUNITY PRESENCE

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Strongly Disagree % (n)

Disagree % (n)

Agree % (n)

Strongly Agree % (n)

Total % (N)

45.6 (31) 30.9 (21) 23.5 (16) 100.0 (68)

36.4 (87) 31.0 (74) 32.6 (78) 100.0 (239)

39.7 (236) 36.3 (216) 24.0 (143) 100.0 (595)

40.8 (183) 29.7 (133) 29.5 (132) 100.0 (448)

39.8 (537) 32.9 (444) 27.3 (369) 100.0 (1350)

TABLE 4.17. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY COMMUNITY PRESENCE* Strongly Disagree % (n) Level of Technology Use Low Use

Disagree % (n)

Agree % (n)

Strongly Agree % (n)

42.6 37.7 34.5 29.2 (29) (90) (205) (131) Moderate Use 39.7 38.5 40.7 39.3 (27) (92) (242) (176) High Use 17.6 23.8 24.9 31.5 (12) (57) (148) (141) 100.0 100.0 100.0 100.0 Total % (68) (239) (595) (448) (N) *level of inward security technology and community presence are significantly related at the .05 level Chi-square=13.129

Total % (N) 33.7 (455) 39.8 (537) 26.5 (358) 100.0 (1350)

Schools’ Use of Security Technology

95

TABLE 4.18. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY CRIME LEVEL IN AREA SURROUNDING THE SCHOOL** Low Crime % (n)

Moderate/ Mixed Crime % (n)

High Crime % (n)

Total % (N)

Level of Technology Use Low Use

43.1 30.7 16.7 39.5 (442) (85) (9) (536) Moderate Use 31.9 33.9 20.4 31.8 (327) (94) (11) (432) High Use 25.0 35.4 63.0 28.7 (257) (98) (34) (389) 100.0 100.0 100.0 100.0 Total % (1026) (277) (54) (1357) (N) **level of security technology use and crime level are significantly related at the .01 level, Chi-Square=49.714

Similar patterns can be seen when examining high levels of outward security product use (Table 4.19). Additionally, schools located in low crime neighborhoods were the most likely to report low levels of outward technologies (44.3%), while schools in high crime areas were the least likely to report low levels of outward technologies (13%). Finally, neighborhood crime level also appeared to be a significant correlate of inward security technology use (Table 4.20).

TABLE 4.19. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY CRIME LEVEL IN AREA SURROUNDING THE SCHOOL** Low Crime % (n)

Moderate/ Mixed Crime % (n)

High Crime % (n)

Total % (N)

Level of Technology Use

Low Use

44.3 28.5 13.0 (456) (79) (7) Moderate Use 32.1 35.7 29.6 (329) (99) (16) High Use 23.6 35.7 57.4 (242) (99) (31) 100.0 100.0 100.0 Total % (1026) (277) (54) (N) **level of outward security technology use and crime level are significantly related at the .01 level, Chi-Square=55.187

39.9 (541) 32.7 (444) 27.4 (372) 100.0 (1357)

TABLE 4.20. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY CRIME LEVEL IN AREA SURROUNDING THE SCHOOL* Low Crime % (n)

Moderate/ Mixed Crime % (n)

High Crime % (n)

Total % (N)

Level of Technology Use

Low Use

35.0 29.6 24.1 (359) (82) (13) Moderate Use 40.8 38.6 38.9 (419) (107) (21) High Use 24.2 31.8 37.0 (248) (88) (20) 100.0 100.0 100.0 Total % (1026) (277) (54) (N) *level of inward security technology use and crime level are significantly related at the .05 level, Chi-Square=10.965

96

33.5 (454) 40.3 (547) 26.2 (356) 100.0 (1357)

Schools’ Use of Security Technology

97

School achievement level was considered by Corwin (1975) and he found that the percentage of students at least one year behind in reading achievement was positively associated with more innovative schools. The current study examines three measures of school achievement: 1) students’ reading level compared to other schools in the state; 2) students’ math ability compared to other schools in the state; and 3) overall school achievement level (specifically how other educators in the state would rank the school’s achievement). While these measures have their limitations, particularly since the third measure is speculative in nature, it is nonetheless an attempt to test whether what has been found to be significant in an earlier innovation study will be significant in the current study. It was therefore hypothesized that school achievement level would be negatively correlated with the level of security technology use. The bivariate relationships between level of security technology use and school achievement are presented below in Tables 4.21-4.29. For these tables, achievement is categorized in the following manner: low achievement refers to schools with students in the 0-25th percentile compared to other schools in the state; low-moderate is 2550th percentile; moderate-high is 50-75th percentile; and high is 75-100th percentile. Unlike Corwin’s (1975) findings, there were no significant relationships found between level of security technology use and measures of school achievement. It is interesting to note however, that few schools rated their students’ ability in the lowest percentile (0-25th) for their state. This may indicate that our sample of respondents came from high achievement schools or that principals overestimated the school’s achievement record.

TABLE 4.21. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY STUDENTS’ READING ABILITY

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

30.4 (14) 39.1 (18) 30.4 (14) 100.0 (46)

37.1 (89) 32.9 (79) 30.0 (72) 100.0 (240)

40.8 (243) 33.4 (199) 25.8 (154) 100.0 (596)

39.6 (178) 30.4 (137) 30.0 (135) 100.0 (450)

39.3 (524) 32.5 (433) 28.2 (375) 100.0 (1332)

TABLE 4.22. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY STUDENTS’ READING ABILITY

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

34.8 (16) 34.8 (16) 30.4 (14) 100.0 (46)

35.4 (85) 32.1 (77) 32.5 (78) 100.0 (240)

40.8 (243) 35.4 (211) 23.8 (142) 100.0 (596)

41.3 (186) 30.4 (137) 28.2 (127) 100.0 (450)

39.8 (530) 33.1 (441) 27.1 (361) 100.0 (1332)

TABLE 4.23. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY STUDENTS’ READING ABILITY

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

34.8 (16) 30.4 (14) 34.8 (16) 100.0 (46)

34.2 (82) 42.5 (102) 23.3 (56) 100.0 (240)

33.6 (200) 41.9 (250) 24.5 (146) 100.0 (596)

34.4 (155) 36.7 (165) 28.9 (130) 100.0 (450)

34.0 (453) 39.9 (531) 26.1 (348) 100.0 (1332)

TABLE 4.24. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY STUDENTS’ MATH ABILITY

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

36.5 (19) 40.4 (21) 23.1 (12) 100.0 (52)

35.4 (91) 33.9 (87) 30.7 (79) 100.0 (257)

40.5 (240) 32.7 (194) 26.8 (159) 100.0 (593)

40.3 (172) 30.2 (129) 29.5 (126) 100.0 (427)

39.3 (522) 32.4 (431) 28.3 (376) 100.0 (1329)

TABLE 4.25. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY STUDENTS’ MATH ABILITY

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

36.5 (19) 32.7 (17) 30.8 (16) 100.0 (52)

37.7 (97) 31.5 (81) 30.7 (79) 100.0 (257)

40.1 (238) 35.8 (212) 24.1 (143) 100.0 (593)

40.3 (172) 30.7 (131) 29.0 (124) 100.0 (427)

39.6 (526) 33.2 (441) 27.2 (362) 100.0 (1329)

TABLE 4.26. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY STUDENTS’ MATH ABILITY

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

40.4 (21) 30.8 (16) 28.8 (15) 100.0 (52)

30.7 (79) 41.2 (106) 28.0 (72) 100.0 (257)

33.4 (198) 42.8 (254) 23.8 (141) 100.0 (593)

36.1 (154) 35.8 (153) 28.1 (120) 100.0 (427)

34.0 (452) 39.8 (529) 26.2 (348) 100.0 (1329)

TABLE 4.27. TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS BY HOW OTHER EDUCATORS WOULD RANK YOUR SCHOOL’S ACHIEVEMENT

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

34.5 (19) 40.0 (22) 25.5 (14) 100.0 (55)

38.3 (79) 28.2 (58) 33.5 (69) 100.0 (206)

40.3 (234) 34.7 (201) 25.0 (145) 100.0 (580)

38.6 (183) 30.4 (144) 31.0 (147) 100.0 (474)

39.2 (515) 32.3 (425) 38.5 (375) 100.0 (1315)

TABLE 4.28. LEVEL OF OUTWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY HOW OTHER EDUCATORS WOULD RANK YOUR SCHOOL’S ACHIEVEMENT

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

38.2 (21) 30.9 (17) 30.9 (17) 100.0 (55)

35.4 (73) 31.6 (65) 33.0 (68) 100.0 (206)

40.7 (236) 35.2 (204) 24.1 (140) 100.0 (580)

40.5 (192) 31.0 (147) 28.5 (135) 100.0 (474)

39.7 (522) 32.9 (433) 27.4 (360) 100.0 (1315)

TABLE 4.29. LEVEL OF INWARD SECURITY TECHNOLOGY USE IN SCHOOLS BY HOW OTHER EDUCATORS WOULD RANK YOUR SCHOOL’S ACHIEVEMENT

Level of Technology Use Low Use Moderate Use High Use Total % (N) NS

Low % (n)

LowModerate % (n)

ModerateHigh % (n)

High % (n)

Total % (N)

38.2 (21) 32.7 (18) 29.1 (16) 100.0 (55)

32.5 (67) 40.8 (84) 26.7 (55) 100.0 (206)

34.5 (200) 42.8 (248) 22.8 (132) 100.0 (580)

33.8 (160) 35.4 (168) 30.8 (146) 100.0 (474)

34.1 (448) 39.4 (518) 26.5 (349) 100.0 (1315)

Schools’ Use of Security Technology

107

In addition to nominal/ordinal level correlates of security technology use, interval/ratio level correlates were also examined. Table 4.30 shows the zero order relationships between the independent variables and level of security technology adoption. As can be seen in this table, a number of factors appear to be related to the level of security product use in schools. School size is often considered in the school security literature and generally suggests that larger schools are more likely to have security products. School size (number of students) was significantly and positively related to overall level of security technology use (.354), outward technology use (.262), and inward technology use (.324). Larger schools appear more likely than smaller schools to adopt crime prevention technologies. Additionally, there has been some disagreement as to whether the relationship between organizational size and innovation is linear or curvilinear in nature (Corwin, 1975; Damanpour, 1991). To address this issue, scatterplots of school size and level of security technology use (including inward and outward categories) were examined, and did not suggest the relationships were curvilinear. It was hypothesized that the number of grade levels might be positively correlated with level of technology use, because schools with a broader age-range of students might have more security problems as a result of these age differences. The number of grade levels in the school was significantly related to overall technology adoption and inward technology use, but was not significantly related to the use of outward technologies. Specifically, the number of grade levels was negatively related to technology use, which may be partially attributed to the fact that secondary schools tend to have fewer grade levels. While number of

108

Security Technology in U.S. Public Schools

grade levels and school level were significantly and negatively correlated, they were not highly inter-correlated (see Table 4.32). In sum, schools with fewer grade levels were significantly more likely than schools with a larger number of grade levels to report higher levels of overall and inward technology use, but were not significantly more likely to use outward technologies. The percentage of students eligible for free lunch is a school characteristic that is considered in much of the extant school safety research. The percentage of students eligible for free lunch was a significant correlate of the total level of security technology use and outward technology use. As predicted, schools with a higher percentage of students eligible for free lunch were more likely than schools with a lower percentage of free lunch students to adopt a greater number of security products in general, and specifically outward technologies. Percentage of free lunch students was not, however, a significant predictor of level of inward security use. The percentage of minority students is another school trait that is often considered in the school safety literature. The percentage of minority students was significantly related to the total level of security product use and level of outward security use. Similar to the results for percentage of free lunch students, schools with a higher percentage of minority students were more likely than schools with a lower percentage of minority students to adopt a greater number of security products in general, and specifically outward technologies. Percentage of minority students was not a significant predictor of level of inward security use. The level of formalization is a variable often considered in the organizational innovation literature. At the bivariate level, formalization had the highest correlation among all

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109

of the predictors with total level of security technology use (.493), inward (.456), and outward (.360) technology use. Unlike the findings of much of the innovation research however, schools with a greater number of rules and policies appeared more likely to use security technology. While some schools may have both high levels of police presence and security technology, it is assumed that schools often have very limited resources and may face the choice of “personnel or products” for security purposes. It had been hypothesized that police presence would be negatively correlated with the use of security technologies, yet this hypothesis is not supported at the bivariate level. Examining the zero order relationships between police presence and level of technology use, police presence is significantly and positively associated with the total, outward, and inward levels of technology use. It was hypothesized that absence rate would be positively associated with the adoption of security technologies. In the current study, absence rate was significantly and positively correlated with total level of security product use, level of outward technology use, and level of inward technology use. These results provide some support that absence rate is positively associated with innovative behaviors in schools, including the use of security technologies. In general, slack resources are positively associated with innovation in organizations. It was therefore hypothesized that expenditure per pupil per year would be positively correlated with level of security technology use. Interestingly, wealth was negatively correlated with the three categories of security technology use, but the relationships were not significant. It is possible that security products are purchased with funds that would

110

Security Technology in U.S. Public Schools

otherwise go toward school supplies, field trips, or maintenance needs. At the bivariate level, four measures of school crime were examined. The “number of known incidents at the school” refers to the total number of incidents, even those not reported to the police. Also examined were the number of reported incidents, number of violent incidents, and number of non-violent incidents. All of these crime measures were significantly and positively associated with total level of security technology use, outward, and inward use. This supports the hypothesis that level of technology use is positively correlated with level of school crime and disorder. Some cases had missing data for a few of the variables (school size, percentage of students eligible for free lunch, percentage of minority students, absence rate, wealth). Mean substitution was used for these variables, and is seen in Table 4.30, using this method either did not change the correlation values at all, or changed the values slightly. One of the variables that had missing information was absence rate. The majority of cases had complete information for this variable (n=1254), but the remaining cases had missing data. In order to address this situation, schools with missing information were compared with schools with complete information on this variable to see if there were significant differences on demographic variables. There were no significant differences between the two groups for the following variables: region, urbanism, school level, school crime, percentage of minority students, and percentage of students eligible for free lunch. The only variable that was significantly different at the .05 probability level was total number of students and this value (.049) was approaching non-significance. Schools that had missing data appeared to

Schools’ Use of Security Technology

111

be slightly larger than schools without missing data for the absence rate variable. School wealth (expenditure per student per year) also had missing information. While there were complete data for 995 cases, the remainder of the schools either left this question unanswered or indicated that they did not know this information. In order to address this situation, schools with missing information were compared to schools with complete data on this variable to see if there were significant differences on demographic variables. The results were mixed. Schools with missing data were significantly different from other schools in regard to region; urbanism; percentage minority students; and percentage of students eligible for free lunch. Specifically, urban schools, and schools located in the South and West had higher than expected representation in the group of schools with missing data on wealth. Additionally, schools with missing data on wealth had a higher percentage of minority students and percentage of students eligible for free lunch. Schools that had missing data on wealth were not significantly different from other schools on several of the variables including school level, size, and school crime. Additionally, the bivariate relationships between wealth and level of technology use are not significant. At this stage of the analysis, wealth does not appear to be an important correlate of level of security product use in schools. To avoid losing a large number of cases for the analyses, mean substitution was used for school wealth.

TABLE 4.30. ZERO ORDER RELATIONSHIPS BETWEEN LEVEL OF SECURITY TECHNOLOGY USE AND INTERVAL/RATIO LEVEL VARIABLES Total Level of Security Technology Use School size (number of students) School size (with mean substitution) Number of grade levels in school Percentage of students eligible for free lunch Percentage of students eligible for free lunch (with mean substitution) Percentage of minority students Percentage of minority students (with mean substitution) Level of formalization Level of police involvement (# hours SRO worked) Absence rate Absence rate (with mean substitution) Wealth (expenditure per pupil per year) Wealth (with mean substitution) Number of known crime/disorder incidents at school Reported number of crime/disorder incidents at school Number of violent incidents at school Number of non-violent incidents at school *p<.05 **p<.01

112

Pearson Correlation .354** .354**

Sig. .000 .000

N 1367 1373

-.127** .075*

.000 .011

1373 1134

.069*

.010

1373

.172** .170**

.000 .000

1312 1373

.493** .317**

.000 .000

1373 1373

.120** .115**

.000 .000

1254 1373

-.025

.422

995

-.022 .291**

.414 .000

1373 1373

.291**

.000

1373

.223**

.000

1373

.325**

.000

1373

TABLE 4.30. ZERO ORDER RELATIONSHIPS BETWEEN LEVEL OF SECURITY TECHNOLOGY USE AND INTERVAL/RATIO LEVEL VARIABLES (CONTINUED) Level of Outward Security Technology Use School size (number of students) School size (with mean substitution) Number of grade levels in school Percentage of students eligible for free lunch Percentage of students eligible for free lunch (with mean substitution) Percentage of minority students Percentage of minority students (with mean substitution) Level of formalization Level of police involvement (# hours SRO worked) Absence rate Absence rate (with mean substitution) Wealth (expenditure per pupil per year) Wealth (with mean substitution) Number of known crime/disorder incidents at school Reported number of crime/disorder incidents at school Number of violent incidents at school Number of non-violent incidents at school *p<.05 **p<.01

113

Pearson Correlation .262** .262**

Sig. .000 .000

N 1367 1373

-.016 .163**

.551 .000

1373 1134

.147**

.000

1373

.268** .263**

.000 .000

1312 1373

.360** .171**

.000 .000

1373 1373

.083** .079**

.003 .003

1254 1373

-.037

.247

995

-.031 .172**

.247 .000

1373 1373

.155**

.000

1373

.145**

.000

1373

.167**

.000

1373

TABLE 4.30. ZERO ORDER RELATIONSHIPS BETWEEN LEVEL OF SECURITY TECHNOLOGY USE AND INTERVAL/RATIO LEVEL VARIABLES (CONTINUED) Level of Inward Security Technology Use School size (number of students) School size (with mean substitution) Number of grade levels in school Percentage of students eligible for free lunch Percentage of students eligible for free lunch (with mean substitution) Percentage of minority students Percentage of minority students (with mean substitution) Level of formalization Level of police involvement (# hours SRO worked) Absence rate Absence rate (with mean substitution) Wealth (expenditure per pupil per year) Wealth (with mean substitution) Number of known crime/disorder incidents at school Reported number of crime/disorder incidents at school Number of violent incidents at school Number of non-violent incidents at school *p<.05 **p<.01

114

Pearson Correlation .324** .354**

Sig. .000 .000

N 1367 1373

-.192** -.033

.000 .264

1373 1134

-.030

.271

1373

.022 .024

.435 .383

1312 1373

.456** .351**

.000 .000

1373 1373

.115** .111**

.000 .000

1254 1373

-.007

.834

995

-.006 .308**

.832 .000

1373 1373

.324**

.000

1373

.224**

.000

1373

.368**

.000

1373

Ggates

Smetald

Vmetald

Rsmetald

Block Ggates Smetald Vmetald Rsmetald Xrays Icamera Ocamera Buscam Telephalrm Fencing Lckdoor Almdoor Trspsign Well-lit Hotline Callerid Rdrgdog Rwepdog Drgsweep Wepsweep Drugtest Alcoholdet Antigraffiti Idproperty Burglarm

Block

TABLE 4.31. ZERO ORDER CORRELATIONS OF SECURITY TECHNOLOGIES

.298** .065* .048 .053* -.008 .139** .103** .145** .109** .075** .356** .125** .102** .101** .073** .103** -.003 -.034 .036 .040 -.021 -.033 .057* .138** .148**

.066* .069* .098** .046 .097** .114** .035 .056* .309** .086** .174** .193** .030 .112** .055* .046 .059* .096** .089** .015 .041 .083** .103** .139**

.431** .275** .284** .107** .135** -.037 -.017 .036 .056* .079** .049 -.028 .044 -.015 -.025 .032 .142** .147** .037 .041 .122** .016 .058*

.275** .329** .067* .052 -.032 -.002 -.001 .081** .031 .060* .003 .053* -.013 -.011 .029 .064* .059* .049 .036 .080** .040 .062*

.275** .136** .181** .017 .003 .044 .071** .067* .117** .048 .094** .070** .099** .116** .274** .377** .113** .112** .115** .093** .102**

Phi was used as the measure of association since the data are nominal and dichotomous *significant at the .05 level **significant at the .01 level

115

Icamera

Ocamera

Buscam

Telephalrm

Block Ggates Smetald Vmetald Rsmetald Xrays Icamera Ocamera Buscam Telephalrm Fencing Lckdoor Almdoor Trspsign Well-lit Hotline Callerid Rdrgdog Rwepdog Drgsweep Wepsweep Drugtest Alcoholdet Antigraffiti Idproperty Burglarm

Xrays

TABLE 4.31. ZERO ORDER CORRELATIONS OF SECURITY TECHNOLOGIES (CONTINUED)

.067* .098** -.014 -.002 .037 .045 .031 .078** .024 -.004 .007 .009 .029 .064* .088** .085** .067* .080** .040 .043

.614** .159** .090** -.008 .082** .075** .129** .091** .136** .136** .211** .105** .187** .141** .159** .176** .059* .104** .084**

.116** .104** -.008 .060* .084** .124** .087** .144** .129** .196** .108** .186** .186** .131** .229** .043 .126** .119**

.130** .008 .083** -.016 .089** .097** .136** .101** .152** .078** .086** .081** .129** .089** -.008 .091** .038

.067* .104** .038 .111** .086** .044 .104** .004 -.006 .033 .033 .006 .033 .076** .154** .124**

Phi was used as the measure of association since the data are nominal and dichotomous *significant at the .05 level **significant at the .01 level

116

Lckdoor

Almdoor

Trspsign

Well-lit

Block Ggates Smetald Vmetald Rsmetald Xrays Icamera Ocamera Buscam Telephalrm Fencing Lckdoor Almdoor Trspsign Well-lit Hotline Callerid Rdrgdog Rwepdog Drgsweep Wepsweep Drugtest Alcoholdet Antigraffiti Idproperty Burglarm

Fencing

TABLE 4.31. ZERO ORDER CORRELATIONS OF SECURITY TECHNOLOGIES (CONTINUED)

.049 .119** .188** .050 .051 .040 -.111** -.030 .011 .055* -.025 -.079** .064* .136** .175**

.160** .128** .086** .068* .134** -.026 -.042 .022 .041 .009 -.017 .045 .103** .148**

.164** .063* .061* .046 -.008 -.015 .060* .046 .013 .064* .036 .050 .213**

.190** .186** .090** .076** .086** .140** .159** .048 .076** .137** .198** .226**

.115** .128** .134** .048 .049 .064** .030 .063* .036 .160** .080**

Phi was used as the measure of association since the data are nominal and dichotomous *significant at the .05 level **significant at the .01 level

117

Callerid

Rdrgdog

Rwepdog

Drgsweep

Block Ggates Smetald Vmetald Rsmetald Xrays Icamera Ocamera Buscam Telephalrm Fencing Lckdoor Almdoor Trspsign Well-lit Hotline Callerid Rdrgdog Rwepdog Drgsweep Wepsweep Drugtest Alcoholdet Antigraffiti Idproperty Burglarm

Hotline

TABLE 4.31. ZERO ORDER CORRELATIONS OF SECURITY TECHNOLOGIES (CONTINUED)

.139** .180** .082** .108** .118** .088** .173** .136** .149** .154**

.128** .093** .106** .095** .041 .122** .090** .143** .108**

.518** .435** .297** .260** .254** .068* .104** -.051

.280** .336** .155** .111** .095** .064* -.038

.726** .240** .212** .090** .098** .020

Phi was used as the measure of association since the data are nominal and dichotomous *significant at the .05 level **significant at the .01 level

118

Drugtest

Alcoholdet

Antigraffiti

Idproperty

Block Ggates Smetald Vmetald Rsmetald Xrays Icamera Ocamera Buscam Telephalrm Fencing Lckdoor Almdoor Trspsign Well-lit Hotline Callerid Rdrgdog Rwepdog Drgsweep Wepsweep Drugtest Alcoholdet Antigraffiti Idproperty Burglarm

Wepsweep

TABLE 4.31. ZERO ORDER CORRELATIONS OF SECURITY TECHNOLOGIES (CONTINUED)

.221** .136** .141** .099** .047

.219** .032 .081** -.015

.117** .087** -.040

.125** .102**

.127**

Phi was used as the measure of association since the data are nominal and dichotomous *significant at the .05 level **significant at the .01 level

119

Key: Block Ggates Smetald Vmetald Rsmetald Xrays Icamera Ocamera Buscam Telephalrm Fencing Lckdoor Almdoor Trspsign Well-lit Hotline Callerid Rdrgdog Rwepdog Drgsweep Wepsweep Drugtest Alcoholdet Antigraffiti Idproperty Burglarm

Control access to school building during school hours through use of locked or monitored doors Control access to school grounds during school hours through use of locked or monitored gates Require students to pass through metal detectors each day Require visitors to pass through metal detectors Perform one or more random metal detector checks on students View contents of school bags with x-ray devices Monitor inside of school using one or more security cameras Monitor outdoor areas with one or more security cameras Use security cameras on school buses Provide telephones or duress alarms in most classrooms Have fencing surrounding the school Have exterior doors automatically lock from the outside Have entry/exit alarms on exterior doors Have posted signs regarding trespassing (e.g. unauthorized trespassers are subject to arrest) Have well-lit campus at night Have student “hotline” or crimestopper program Have caller ID on phone system Use one or more random dog sniffs to check for drugs Use one or more random dog sniffs to check for weapons Perform one or more random sweeps for drugs (not including dog sniffs) Perform one or more random sweeps for weapons (not including dog sniffs) Require drug testing for any students (e.g. athletes) Use alcohol detection devices Use anti-graffiti sealers on exterior or interior walls Mark/identify school property Have a burglar alarm system for the school

120

Schools’ Use of Security Technology

121

In addition to examining the zero order correlations of school and contextual factors and level of security technology use, this study also included an examination of correlations between pairs of individual security technologies. As can be seen in Table 4.31, many of the correlations were significant, but only “monitoring the inside and outside of the school using one or more security cameras” (.614) and “performing random sweeps for drugs and weapons” (.726) approached high inter-correlations. A confirmatory factor analysis was used to empirically test whether the categories of “outward” and “inward” technology were two different dimensions, and whether the variables assumed to belong to each of these dimensions appeared to belong in these categories (Kim and Mueller, 1978). The only security technologies that loaded high (using a standard of .5 or higher as used by Kim and Mueller, 1978) on the first component were: monitoring inside of the school using one or more security cameras; monitoring outdoor areas with one or more security cameras; using one or more random drug dog sniffs; performing one or more random non-dog sweeps for drugs; and performing one or more random non-dog sweeps for weapons. The analysis attempted to force the technologies into two components, but there were no technologies that loaded high on the second component. The infrequently used technologies (those used by less than 10% of respondents) were removed to see if conducting another confirmatory factor analysis would yield different outcomes, but the results were essentially the same. This factor analysis largely failed to support classifying security products as outward and inward, though four of the five variables were considered inward and three of the five variables specifically included either drug or weapons

122

Security Technology in U.S. Public Schools

detection. Nevertheless, there was reason to believe that these five items might be tapping the same construct, so an index of these items was created to see if this dimension of technology use was related to school characteristics. The findings from this analysis revealed that several school characteristics were significantly related to level of technology use within this dimension. For example, school level, formalization score, and police presence were all significantly related to this factor. The highest correlation was school level (.447), which is not surprising given that secondary schools are presumably more likely than primary schools to be concerned with weapons and drugs on campus. It is unknown precisely how cameras fit into this dimension, but it may be that one of the goals of monitoring inside and outdoor areas with security cameras was detection of drugs and weapons. While these results suggest there are other explanations why schools choose security technologies, these reasons are unknown given the exploratory nature of this study. Additionally, factor analysis may not be ideal for dichotomous data. Given these limitations, total technology use and the theoretical categories of outward and inward technology use continued to be further explored in this study. Ordinary least squares regression analysis was used to examine multivariate models. One of the advantages of multiple regression is that it allows an estimation of the independent influence of each of the predictor variables on the dependent variable, while controlling for the other predictors in the model (Blaikie, 2003). Since it is possible for variables that were not significant at the bivariate level to become significant in a multivariate model, these variables were included in the regression analyses. Some

Schools’ Use of Security Technology

123

adjustments were made however, including the removal of percentage of minority students to address potential problems with multicollinearity, which is discussed later in more detail. Additionally, since reported crime/disorder, violent, and non-violent crime should all be part of the total number of known incidents, only the total number of known incidents was included in the multivariate models. Similarly, only the most comprehensive measure of school achievement was examined in these models. The only nominal level independent variable is “region.” In order to include this variable in the following regression analyses, region was dummy coded in the following manner: North=1, “not North”=0; Midwest=1, “not Midwest”=0; West=1, “not West”=0. The variable of South (yes/no) was excluded and serves as a reference group. This allows use of coefficients to estimate the difference in level of security technology use among schools located in each of the three regions and the reference group (Schroeder, Sjoquist, and Stephan, 1986). It is argued that the other variables are either at the ordinal level or interval/ratio level. For example, urbanism is a proximate measure of population size, therefore categorizing schools as urban, suburban, and rural creates an ordinal level measure. Similarly, with the exception of the “other” category, school level is also an ordinal level measure. For example, elementary schools have younger students than middle schools, and middle schools have younger students than high schools. While there may be some overlap between middle and junior high schools, junior high schools tend to have older students than middle schools. Further, while there is the issue of lack of precision for use of ordinal variables in the regression

124

Security Technology in U.S. Public Schools

equation, OLS regression is robust and since order is known, linearity is approximated (Pedhazur, 1997). In addition to the assumption of linearity, another assumption of OLS regression is the existence of homoscedasticity. Homoscedasticity refers to the situation in which the variance of the error term in the regression model is the same across values of the independent variables, otherwise there is a problem with heteroscedasticity (Berry and Feldman, 1985). Berry (1993) states that heteroscedasticity is a more common issue in cross-sectional research, and suggests it may be problematic in situations where there is systematic error in the dependent variable, such as in cross-national research where nations may differ dramatically in the quality of their government records. Given the nature of the data, there is no reason to suspect a problem with heteroscedasticity. Berry (1993) also suggests visually inspecting plots of the regression residuals with one or more of the independent variables. This process was done for several of the variables and there did not appear to be a problem. Further, while heteroscedasticity does influence tests of significance, it does not bias regression coefficients (Berry, 1993). The absence of autocorrelation is another assumption of OLS regression. Autocorrelation is when error terms are correlated for any two observations and is most common in time series models (Berry, 1993). While the current study uses cross-sectional data, in order to confirm the absence of autocorrelation, the Durbin-Watson statistic was calculated for each model. A Durbin-Watson statistic value of approximately 2 is a good indicator that there is an absence of positive serial correlation (Younger, 1979). Since all of

Schools’ Use of Security Technology

125

the Durbin-Watson values were close to 2, this suggests the absence of autocorrelation.

*p<.05 **p<.01

School size

School level

# Grades in school

% Free lunch

School size School level # Grades in school % Free lunch School crime index Police presence Expenditure per pupil Absence rate Overall school achievement Formalization Urbanism Region Neighborhood crime level Community presence

School crime index

TABLE 4.32. ZERO ORDER RELATIONSHIPS FOR INDEPENDENT VARIABLES

.273** -.220**

-.146**

-.098** .363**

-.166** .236**

.117** -.172**

.062*

.408**

.312**

-.191**

.016

.350**

-.046

.092**

.056*

-.098**

.006

.038 .080**

.156** -.028

-.014 -.039

.146** -.424**

.133** -.122**

.358** .332** -.045 .075**

.273** -.045 .011 .020

-.204** .016 -.004 .047

.008 .160** -.008 .360**

.287** .163** -.031 .209**

.048

.001

-.100**

-.008

.021

Absence rate

Overall school achievement

Formalization

.104** -.010

-.005 .039

-.257**

.325** .127** -.062* .064*

.028 -.030 -.066* -.048

.099** .070** .015 .228**

.018 -.098** -.119** -.304**

.176** -.050 .149**

.014

.043

-.063*

.196**

.077*

Police presence

Expenditure per pupil

TABLE 4.32. ZERO ORDER RELATIONSHIPS FOR INDEPENDENT VARIABLES (CONTINUED)

School size School level # Grades in school % Free lunch School crime index Police presence Expenditure per pupil Absence rate Overall school achievement Formalization Urbanism Region Neighborhood crime level Community presence

.013

*p<.05 **p<.01

126

-.001 .291**

.050

-.153**

-.042

Neighborhood crime level

Region

School size School level # Grades in school % Free lunch School crime index Police presence Expenditure per pupil Absence rate Overall school achievement Formalization Urbanism Region Neighborhood crime level Community presence

Urbanism

TABLE 4.32. ZERO ORDER RELATIONSHIPS FOR INDEPENDENT VARIABLES (CONTINUED)

-.085**

*p<.05 **p<.01

One issue that may arise is collinearity between pairs of variables or multicollinearity among three or more independent variables. Table 4.32 shows the zero order relationships for the independent variables ultimately used in the regression models. Kimberly and Evanisko (1981) argue that there is a lack of consensus about the level of correlation and among how many of the correlates this causes a problem. They follow guidelines used in previous research, specifically stating, “Blau and Schoenherr (1971) 127

128

Security Technology in U.S. Public Schools

used substantive interest as a general criterion for inclusion of independent variables, although as a rule of thumb they did eliminate one independent variable in cases in which correlations exceeded .82” (as cited by Kimberly and Evanisko, 1981: 702). A more conservative test of a correlation of .70 or higher was the criteria for this study. The only pair of variables that approached a correlation of .70 was percentage of minority students and percentage of free lunch students (.685 without mean replaced values, .641 with mean replaced values). Additionally, each of the independent variables was regressed on the other independent variables to test for multicollinearity (LewisBeck, 1980). While Lewis-Beck (1980) suggests that an R2 value approaching 1 indicates multicollinearity, a more conservative test (R2 values of .50 or higher) was used for the current study. Again, the only variables that posed a problem for the models were percentage of minority students and percentage of students eligible for free lunch (R2 values of .612 and .504, respectively). In order to examine this further, percentage of free lunch students was removed which resulted in an R2 value of .355. Percentage of free lunch students was then put back into the model and percentage of minority students was removed, which resulted in an R2 value of .496. Since percentage of minority students was the more problematic variable, it was not included in the multivariate models while percentage of free lunch students remained. The third research question that this book addresses is whether security technology use in schools is better explained by school crime/disorder or by other school and contextual characteristics. When examining total technology use (Table 4.33), formalization had the strongest relationship (beta value of .331) with level of

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technology use. Schools with a greater number of policies and rules also appeared to use a greater number of security technologies, which contradicts much of the organizational innovation research. The second strongest relationship was school level, with upper level schools significantly more likely to report greater use of security products. Region was also a significant predictor of total level of technology use. Consistent with previous research, schools located in the South were more likely to report higher levels of technology use than schools in other regions. Other significant predictors of total level of security technology adoption include number of students; police presence; urbanism; neighborhood crime level; community presence; and school crime. As predicted, larger schools; schools located in urban areas; schools located in higher crime neighborhoods; schools with greater community presence; and schools with higher levels of crime/disorder all had higher levels of security technology use. Surprisingly, police presence was positively related to level of security technology use, which does not support the hypothesis that schools may have to choose either products or personnel to address safety concerns. Additionally, number of grade levels, percentage of free lunch students, and absence rate become non-significant in the multivariate model. School wealth and achievement remain nonsignificant predictors of total level of security technology use. Finally, while school crime is positively and significantly related to use of security technologies, it had a weaker relationship (beta of .071) with technology use than many of the other school and contextual characteristics. The R2 statistic measures the percentage of total variation in the dependent variable explained by the model (Schroeder, Sjoquist, and Stephan, 1986). Therefore the R2 value

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Security Technology in U.S. Public Schools

indicates that approximately 35% of the variation in total level of security technology use was explained by the independent variables in the model. When examining outward technology use (Table 4.34), level of school formalization remained the strongest predictor (beta=.275), followed by urbanism (beta=.204). Schools with a higher level of formalization and schools located in urban areas were more likely than other types of schools to use a greater number of outward security products. As hypothesized, schools with a higher percentage of free lunch students and larger schools were also more likely than other types of schools to report higher levels of technology adoption. Neighborhood crime level was also significantly and positively related to outward security product use, which lends support that these technologies are indeed selected to keep unauthorized people from entering the school building or property. Additionally, there is some evidence that schools in the South were also more likely to have outward security products. School level and number of grade levels were not significantly related to level of outward security technology use. While the original hypotheses are not supported, the findings do make intuitive sense. Trying to keep unauthorized people out of the school is a universal concern among schools, while there is likely to be more disagreement regarding the use of technologies that focus on inward concerns, such as testing students for drugs. Additionally, school wealth, police presence, absence rate, and school achievement were not significantly related to level of outward security product use. Interestingly, the level of school crime was not a significant predictor of the level of outward technology use in the multivariate model.

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School and contextual factors only explain 23% of the variation in outward security product use in this model, yet appear to be more important predictors than school crime. The school and contextual factors explained 38% of the variation in inward security technology use in this model. Formalization remained the strongest predictor (beta=.273) of level of inward technology adoption (Table 4.35). Schools with more rules and procedures appeared more likely to use a greater number of inward focused security products. While school level was not significantly related to level of outward technology use, school level was the second strongest predictor (beta=.268) of amount of inward technologies. Not surprisingly, secondary schools and schools with fewer grade levels appeared more likely to use these types of technologies than primary schools. In addition, region was a significant predictor of inward technology use, with schools in the South more likely to use these types of products than schools in other regions. Number of students, police presence, neighborhood crime level, and community presence were all significantly and positively related to the number of inward security products. Consistent with the other models, school wealth, absence rate, and school achievement were not significantly related to level of technology use. Additionally, percentage of free lunch students was not significantly related to inward technology use. Finally, while school crime was positively and significantly related to the use of inward security products, certain school and contextual factors also appeared to explain the use of security technology.

TABLE 4.33. OLS REGRESSION ANALYSIS OF TOTAL LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS Independent Variables School Characteristics Number of students** School level** Number of grade levels Percent free lunch students School crime index** Police presence* School wealth Absence rate School achievement Formalization** Contextual Characteristics Urbanism** Region North** Midwest** West** Neighborhood crime level** Community presence* *p<.05 **p<.01 Model Summary: F=45.639, sig=.000 N=1373 R2=.350 Adjusted R2=.342

B

Stand. Error

Beta

t

Sig.

.001

.000

.095

3.390

.001

.329 -.004

.052 .036

.158 -.003

6.344 -.116

.000 .908

.846

.467

.050

1.812

.070

.006

.002

.071

2.819

.005

.014 .000 .012 .188

.006 .000 .013 .117

.064 -.026 .021 .042

2.469 -1.158 .881 1.614

.014 .247 .379 .107

.388

.029

.331

13.197

.000

.384

.117

.083

3.280

.001

-.693 -.779 -1.048 .545

.268 .205 .229 .170

-.067 -.103 -.118 .081

-2.586 -3.797 -4.577 3.201

.010 .000 .000 .001

.232

.097

.055

2.387

.017

132

TABLE 4.34. OLS REGRESSION ANALYSIS OF OUTWARD LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS Independent Variables School Characteristics Number of students** School level Number of grade levels Percent free lunch students** School crime index Police presence School wealth Absence rate School achievement Formalization** Contextual Characteristics Urbanism** Region North Midwest* West* Neighborhood crime level** Community presence *p<.05 **p<.01 Model Summary: F=26.175, sig=.000 N=1373 R2=.236 Adjusted R2=.227

B

Stand. Error

Beta

t

Sig.

.000

.000

.090

2.961

.003

-.013 -.042

.033 .023

-.010 .045

-.379 1.793

.704 .073

.926

.300

.092

3.086

.002

.000 .002 .000 .004 .079

.001 .004 .000 .009 .075

.007 .015 -.023 .010 .030

.244 .541 -.956 .406 1.058

.807 .589 .339 .685 .290

.192

.019

.275

10.139

.000

.559

.075

.204

7.440

.000

.232 -.285 -.303 .336

.172 .132 .147 .109

.038 -.064 -.058 .084

1.344 -2.157 -2.061

3.068

.179 .031 .040 .002

.074

.063

.029

1.179

.239

133

TABLE 4.35. OLS REGRESSION ANALYSIS OF INWARD LEVEL OF SECURITY TECHNOLOGY USE IN SCHOOLS Independent Variables School Characteristics Number of students** School level** Number of grade levels* Percent free lunch students School crime index** Police presence** School wealth Absence rate School achievement Formalization** Contextual Characteristics Urbanism* Region North** Midwest** West** Neighborhood crime level* Community presence* *p<.05 **p<.01 Model Summary: F=52.609, sig=.000 N=1373 R2=.383 Adjusted R2=.376

B

Stand. Error

Beta

t

Sig.

.000

.000

.068

2.485

.013

.342 .046 -.080

.031 .022

.268 -.048

11.019 -2.122

.000 .034

.279

-.008

-.287

.774

.006

.001

.110

4.452

.000

.012 .000 .008 .109

.003 .000 .008 .070

.089 -.020 .024 .040

3.547 -.909 1.037 1.561

.000 .363 .300 .119

.196

.018

.273

11.169

.000

-.176

.070

-.062

-2.515

.012

-.925 -.495 -.745 .209

.160 .123 .137 .102

-.145 -.107 -.137 .051

-5.770 -4.032 -5.439 2.054

.000 .000 .000 .040

.158

.058

.061

2.725

.007

134

Schools’ Use of Security Technology

135

This chapter has identified the types of security products that are most commonly found in schools. In addition, the level of security technology adoption among schools was described. Both total level of use and level of use based on the classification of outward and inward focused products were explored. This chapter also examined bivariate relationships between school and contextual factors with the level of security technology use. Additionally, correlations among individual types of security products were presented. Finally, multivariate models using ordinary least squares regression were examined in order to describe which variables appear to best predict level of security technology use in schools.

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CHAPTER 5

Conclusions and Future Directions for School Security Federally sponsored projects have examined school problems in an effort to prevent school violence, yet there was a lack of information concerning what security technologies are being used and by what types of schools. The research reviewed in Chapter 2 included studies of possible school and contextual correlates of security technology use, as well as research that identified factors related to the adoption of innovations in organizations. This book has described the technologies most commonly used by schools, and identified factors associated with both the total amount of technology use and level of use within categories based on possible goals of security products. This study has built both on the limited research regarding the use of security technologies in schools and examined whether the adoption of crime prevention technologies appeared to be a form of innovation in schools. In an effort to examine these issues, data from a national mail survey of public schools were used. The survey included questions about schools’ use of security products, as well as information about the schools, such as amount of school crime and disorder; perceptions about neighborhood crime; measures of school achievement; absence rate; school wealth; police and community presence; and number of formal rules and policies. Additionally, other information regarding these schools was obtained from the U.S. Department of Education’s Common Core of Data and included variables such as 137

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Security Technology in U.S. Public Schools

school level; number of students; number of grade levels; percentage of free lunch students; urbanism; and region. According to Trump (1998), security refers to the response and prevention of criminal acts and serious misbehavior. Security technologies were therefore defined as products or tools that are designed to deter, detect, or delay (Green, 1999) intentional acts against people or property (Trump, 1998). Schools appeared to use a wide range of security products to address safety concerns and only 1.4% (n=19) reported that they did not use any technologies. Not surprisingly, several of the basic forms of technologies were also the most commonly used. For example, marking/identifying school property was reported by more than 80% of schools. While this tool was most likely aimed at preventing theft, it may also aid in the recovery of stolen items. Additionally, the use of lighting at night on school campuses was extremely common (76.1%), which may have been used to discourage burglaries as well as other undesirable activities that could occur on the school grounds at night. Security technologies that were used by the majority of schools included telephones or duress alarms in most classrooms (68.1%); burglar alarm systems (63.3%); controlling access to school building during school hours through the use of locked or monitored doors (60.1%); using security cameras on school buses (52.9%); and having posted signs regarding trespassing (51.7%). In addition to describing the most commonly used technologies, these products were also examined in terms of their possible goals. Specifically, it was suggested that schools may select technologies that are aimed primarily: 1) outward, directed toward keeping unauthorized people out of the school/school property; or 2) inward, directed at student behavior. Four of the most commonly used

Conclusions and Future Directions

139

technologies were considered to have an outward focus, while three were inward. Many schools appeared to be balancing these goals in their use of technology to protect students from unauthorized people on campus, protect students from each other (e.g. prevent or possibly detect fights/problems on school buses through the use of cameras), and prevent theft of school property. Clearly, it is important to identify the most commonly used security products, yet it is also interesting to note the least commonly used types of security technology. The products used least by schools tended to be inward focused, and largely were attempts to keep drugs, alcohol, and weapons out of the school. For example, only 10.9% of schools used random sweeps to check for weapons; 9.4% reported using alcohol detection devices; 6.8% required drug testing for at least certain types of students (e.g. athletes); 1.5% required students to pass through metal detectors each day; and only 0.7% viewed contents of school bags with x-ray devices. It seems likely that the potential threat of intruders is perceived as more serious than drug and alcohol use among students. Additionally, technologies aimed at students may be controversial and hence more difficult to implement. These results further support the assumption that schools have a primary responsibility to protect students, particularly from external threats. Conversely, in most schools the internal threats may be nonexistent, limited, or simply are not considered serious problems by school officials. In addition to describing the types of security products most commonly found in schools, this study identified numerous correlates of security technology use in schools. Both bivariate relationships and multivariate models examining predictors of security technology use were

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Security Technology in U.S. Public Schools

presented in Chapter 4. The results from the multivariate regression models will be the main focus in this chapter (see Table 5.1 for a summary of the findings). It is important to note that the correlates of security technology use vary when the model is predicting the total, outward, or inward level of security technology use. Examining the results for total level of security technology revealed that several school and contextual factors were significant predictors in this model. Level of formalization in the school had the strongest relationship with level of technology use. While formalization is a variable considered in much of the organizational innovation literature, prior research generally suggests that greater formalization in organizations appears to discourage innovativeness (Corwin, 1975; Hage and Aiken, 1967), particularly in service organizations (Damanpour, 1991). The current study found that formalization was positively related to amount of security technologies employed, which is generally contradictory to the organizational literature, yet supports prior organizational research in the sense that formalization was a key variable to consider. It should also be noted that the organizational innovation literature often refers to level of formalization in terms of the number of rules and procedures regarding job roles (e.g. Hage and Aiken, 1967). The lack of information regarding job roles is a limitation of this study, and therefore the measure of formalization may not accurately reflect how formalization has typically been measured in prior organizational research. Absence rate and school achievement level were also considered in previous school innovation research (Corwin, 1975), but these variables do not appear to be related to the amount of security products used in schools. Perhaps most surprisingly, school wealth was not significantly related to

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141

total level of security technology use. Generally, slack resources are positively associated with innovation in organizations (Damanpour, 1991). Since school wealth as defined here was not significantly related to the level of security technology use, this may indicate that schools reduce funding for other programs in order to purchase security equipment. The research regarding the use of security technologies in schools is limited both by the number of studies, and because these studies only examine the use of a few types of security technologies. Despite these limitations, the literature generally suggests that schools that are large; at the secondary level; urban; with a high percentage of students eligible for free lunch; and located in the South may be more likely than other types of schools to use security products. As predicted, larger schools; schools located in urban areas; and secondary schools were more likely to report greater use of security products. Also consistent with some of the previous research, schools located in the South were more likely to report higher total levels of technology adoption. Additional research is needed to explore why Southern schools may be more willing to use security products than schools in other regions. Inconsistent with previous school security research, percentage of free lunch students was not significantly related to total level of security technology use. Further, number of grade levels also became nonsignificant in the multivariate model. While not specifically examined in prior studies, the variables of community and police presence were both significantly related to level of technology use. As predicted, schools with greater community presence were more likely to adopt security products. Since this concept is based on a response to a single survey question however,

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Security Technology in U.S. Public Schools

this finding should be interpreted with caution. Better measures that explore different types of community involvement should be included in future studies. It was somewhat surprising that police presence was positively related to level of security technology use, and this finding does not support the hypothesis that schools may have to choose between products or personnel to address safety concerns. Schools that chose police presence appeared to be the same schools that chose to use security products, while schools with low levels of police presence tended to have fewer security technologies. Although exact decision processes regarding how best to address security issues were beyond the scope of this study, perhaps schools understood the strengths and limitations of police presence and security products, and therefore employed both types of strategies or neither, depending on views of safety needs. It also may be that there are other sources of financial support for police presence in schools such as police agencies themselves or grants. If schools are not funding police officers, these schools may not be forced to choose between personnel or products. Perceptions of neighborhood crime and school crime/disorder were also significantly and positively related to level of technology use. Schools that appeared to have more crime in the surrounding area, and more crime and disorder within the school were more likely to report higher levels of technology use. In sum, while the variables included in the multivariate models explained about 35% of the variation in level of security product use among schools, further research is needed to explore other factors that may help to explain the adoption of technologies in schools. When examining the results for level of outward technology use, several school and contextual factors were

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143

significant predictors in this model. Consistent with the model predicting total level of security technology use, level of formalization had the strongest relationship with level of outward technology use. Schools with more rules and procedures also appeared to use more outward focused security products. Also consistent with the results regarding total level of technology use, absence rate and school achievement level were not significantly related to the level of outward security product use. It had been hypothesized that absence rate would be positively associated, and achievement level would be negatively related to level of technology use, but neither hypothesis was supported. Corwin (1975) found that lower school achievement and higher absence rates were positively associated with more innovative schools. Since absence rate and achievement level were not significantly related to level of technology use, Corwin’s findings were not supported. Additionally, school wealth was also non-significantly related to level of outward technology use. As stated previously, the research about schools’ use of security technologies generally finds that schools that are large; at the secondary level; urban; with a high percentage of students eligible for free lunch; and schools located in the South may be the most likely to adopt certain types of security technologies. As predicted, larger schools; schools located in urban areas; and schools with a high percentage of free lunch students were the most likely to report high levels of outward technology use. The relationship between region and outward security technology use was somewhat less clear than the previous model. There was some support that Southern schools were more likely to use outward security products, though these differences were most pronounced for schools in the Midwest and West, which

144

Security Technology in U.S. Public Schools

were significantly less likely to use technologies than schools in the North and South. Unlike for the total level of technology use however, there did not appear to be significant differences in level of outward technology use by school level. As previously noted, a possible explanation is that all schools may have concerns about unauthorized people entering their schools and campuses, while there may be more disagreement about security products that specifically target students. Interestingly, unlike the model predicting total level of technology use, police presence and community presence were not significant predictors of outward security technology use. It is important to be aware that presence does not necessarily indicate involvement in specific security issues. It may be that school resource officers and community members are more focused on non-security related matters or security issues regarding student behavior, rather than focused on potential external threats. Some particularly interesting findings were in regard to the amount of crime and disorder in schools and neighborhood crime levels. The level of school crime and disorder was not related to level of use of outward technologies, yet neighborhood crime was significantly and positively related to outward technology use. While these findings are partially consistent with the original hypotheses, they also provide support for categorizing certain technologies by their goal of keeping unauthorized people from entering the school. Finally, this model is the weakest of the three main models examined, with school and contextual factors only explaining about 23% of the variation in level of outward technology use. The model predicting the level of inward technology use was the most powerful of all three models, with school and contextual factors explaining 38% of the variation in

Conclusions and Future Directions

145

level of inward security product use. Level of formalization remained the strongest predictor of level of technology use. Schools with more rules and procedures were significantly more likely than other types of schools to use inward focused security products. While the direction of the relationship was inconsistent with much of the organizational innovation literature, formalization was consistently the strongest predictor of level of technology use. Though the precise reasons for this relationship are unknown, technologies and policies were largely addressing similar security concerns. Further, perhaps schools that had formal plans, procedures, and policies were generally more organized, had greater parental involvement, more effective leadership, or closer collaborations with the community and were therefore better able to coordinate the purchase of security products. Some of the literature suggests that absence rate, school achievement, and school wealth might be significant correlates of innovative behavior in schools. None of these variables were significant predictors of any category of technology use, including level of inward technology use. While absence rate and school achievement were not commonly examined in the literature, wealth is a variable that is considered in much of the organizational innovation research. The fact that school wealth was not significantly related to the level of technology use suggests future research efforts are necessary to explain how security products are purchased by schools. The security technology literature implies that certain types of schools may be more likely to use security products than other schools. When examining the level of inward security technology use by school level, it became apparent that secondary schools were much more likely to report using inward security technologies. As noted

146

Security Technology in U.S. Public Schools

previously, this may largely be due to the fact that several of the inward technologies included drug testing and weapons searches which were presumably more common in upper level schools. School size also remained positively and significantly related to level of technology use. It may be that larger schools had additional security concerns and a greater sense of anonymity among students, and therefore had a greater reliance on security technologies to create a safer school environment. Further, urban schools and schools located in the South were significantly more likely than other types of schools to use inward focused security technologies. Additionally, while it had been hypothesized that percentage of free lunch students would be positively associated with the use of security products based on the literature, this variable was not a significant predictor of level of inward technology use. Unlike the results for predicting the level of outward security technology use, police presence and community presence were significantly related to level of inward security product use. Similar to the results for total level of security technology use in schools, schools with greater police and community presence appeared to use greater levels of inward technologies. These results also suggest that examining security technologies as outward or inward focused may be an important distinction. In sum, the results described above were largely consistent with the limited research on the use of security technologies in schools, but largely inconsistent with the findings in the organizational innovation literature. Further, while school crime/disorder was a significant predictor of total level of security technology use and inward technology use, other school and contextual characteristics

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147

were consistently and strongly associated with levels of security technology use in schools. TABLE 5.1. SUMMARY TABLE PREDICTING LEVEL OF TECHNOLOGY USE BY SCHOOL/CONTEXTUAL CHARACTERISTICS Total Outward Inward Independent Technology Technology Technology Variables Use Use Use School Characteristics Number of students + + + School level + 0 + Number of grade 0 0 levels Percent free lunch 0 + 0 students School crime index + 0 + Police presence + 0 + School wealth 0 0 0 Absence rate 0 0 0 School 0 0 0 achievement Formalization + + + Contextual Characteristics Urbanism + + Region North 0 Midwest West Neighborhood + + + crime level Community + 0 + presence + indicates positive significant relationship at .05 or .01 level - indicates negative significant relationship at .05 or .01 level 0 indicates non-significant relationship

148

Security Technology in U.S. Public Schools

Security technologies can be used to compensate for design flaws in school buildings and therefore enhance student safety (Schneider, 2001). Additionally, it is important to recognize that while there are good programs that address issues such as bullying, anger, hate, and drugs, these programs are not in all schools, and cannot be successful overnight (Green, 1999). Clearly, certain school incidents must be dealt with immediately and the use of security technologies is an important strategy to address such problems (Green, 1999). Security technology may decrease crime and violence in schools by reducing or eliminating opportunities for violations, and increasing the likelihood of apprehending perpetrators if violations do occur (Green, 1999). For these reasons, it is hoped that this study helps to make policymakers aware of some of the products that are available and the extent of their use across different types of schools. While the limitations of the data are identified throughout the book, nonetheless, this research represents the first comprehensive study of this issue using multivariate models rather than solely descriptive information. The analyses conducted in this study have allowed a better estimation of the independent effects of a broad range of school and contextual factors on the use of security technologies in schools. Many of these school and contextual factors had not been adequately addressed in prior studies. In addition, the data used in this dissertation are based on a national sample of schools, which allows a greater possibility for generalizations. Further, the use of technologies was examined in an innovative way, specifically, across different dimensions based on the likely goals of security products. It is believed that this study has significantly contributed to the existing research about the use of security technologies in public schools.

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149

One area that is emphasized in much of the school safety literature is the importance of conducting a school security survey or risk assessment (Green, 1999; Schneider, 2002; Trump, 1998). This book has demonstrated that schools may adopt a wide range of security technologies, but it is unknown precisely how schools make security choices. Schools may be selecting products based on factors such as price and availability, but not necessarily after a careful assessment of school problems. This suggests that some schools may be using technologies that do not necessarily match school problems. Policymakers should encourage schools to have risk assessments conducted through independent sources that do not have a vested interest in the adoption of particular security products (Green, 1999; Schneider, 2002; Trump, 1998). While the current study has demonstrated that technologies are clearly being used by many schools, we should not assume we fully understand why security technologies are chosen without further exploration. For example, people may have alarms to deter burglary, but may also purchase an alarm system to reduce homeowner insurance premiums (Whitaker, 1986). Similarly, schools may choose security products for less obvious reasons than strictly crime prevention. One factor that may influence decisions to adopt security technologies in schools is concern about lawsuits. According to Garrison (1996) at the Center for Safe Schools, schools can be sued for inadequate safety/security when injury or death of a student occurs. As schools become more vulnerable to lawsuits (as cited by Garrison, 1996), it seems plausible that more schools may choose security technology as a way of establishing their commitment to safer schools. This is a possible trend that should be explored and may help explain schools’ use of

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security technologies. Additional studies should attempt to examine the decision making processes across different types of schools. The influence of school wealth on the adoption of security technologies is something that needs further exploration. This study found that expenditure per pupil per year was not related to level of security technology use. While lack of wealth may prohibit the use of the most expensive technologies, creating the image that schools are safe may be deemed so important that students endure other hardships. Policymakers need to examine school functions, programs, supplies, and other school needs that may be sacrificed in order to purchase security products, that may not be appropriate or effective. Level of formalization was consistently related to the amount of security products in schools. It is important to note that the formalization index primarily consisted of policies and procedures related to safety. The findings suggest that schools with safety concerns tend to adopt both policy and technology. Conversely, some schools have fewer safety plans and security products than others. More research is needed to explore why some schools seem to participate in a range of efforts toward school safety while other schools appear to do much less. Police presence in schools is another area that should be examined with regard to the use of security technologies. It appears that schools with greater levels of police presence also have more security technologies, while other schools have limited police presence and lack security products. It is unknown if these two strategies are used to address similar or different safety issues. It is also unclear if these differences represent conscious choices among school administrators or if other factors limit schools’ abilities to

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work with police and obtain security products. These areas could be addressed with additional research. School crime and disorder appear to explain some of the variation in level of security technology use, but clearly other factors are more influential. It appears that the use of technologies is not solely related to problems and that school traits have more of an impact. A major question that remains is whether the schools that may benefit the most from technologies are able to acquire these products. Future research efforts should include additional school and contextual factors that may be related to the use of security technology. For example, the measure of region as a four category variable is particularly limited since there is likely to be much variation within each region. Further, the measure of neighborhood crime that was used in this study was the school principal’s perception of neighborhood crime, which may be different from actual crime. More detailed and additional neighborhood factors should be explored which may help to explain trends in schools’ use of security products. Finally, such variables may also allow a better estimation of neighborhood conditions that influence whether schools seek to protect themselves from their surroundings. In addition to exploring more detailed school and contextual factors that may be related to the level of security technology use, the data used in this study could serve as a basis to examine correlates of specific types of technologies. For example, further analysis could show that particular types of schools are likely to use certain technologies such as cameras, alarm systems, metal detectors, and drug detection systems. This type of analysis may reveal important differences regarding the use of security technologies in schools.

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This book has identified the security technologies that are being used by schools, yet the data do not allow for any conclusions regarding the effectiveness of such technologies. It seems likely that the appropriate use of products can improve school safety, but for what type of schools and circumstances is unknown. Longitudinal studies are required in order to examine this issue. There may also be a multitude of reasons why schools choose not to use security technologies. While the survey did not specifically address this issue, some schools may perceive certain disadvantages in the use of security technologies. For example, excessive reliance on technology may create a “big-brother” or prison-like image, which may contribute to a negative social climate (Schneider, 2001:15). Further, technology may not fully compensate for design weaknesses, and may be burdensome, expensive, or even counterproductive (Schneider, 2001). It is important to share information with schools that could aid in making decisions about which products might be the most useful. Schools may also benefit from awareness of alternative sources of funding so that products are not purchased at the expense of educational programs. Future research could better examine the reasons why schools may avoid the use of security technology. The research presented here provides a foundation to further explore several issues. First, there is a need for more detailed information regarding when and why particular schools adopt security technology. While some of the findings may hint at possible reasons, additional data are needed to better develop and confirm such explanations. Second, while this study identified a broad range of products and the extent of their use by schools across the country, it largely remains unknown precisely how these

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products were used. For example, while the location for some of the technologies was known (e.g. security cameras on schools buses), much of the data do not allow for such specificity (e.g. use of cameras inside the school building does not include information regarding the exact location(s) of these cameras). Additionally, the data do not include the frequency of security technology use, such as how often drug sniffing dogs were brought into the school. Further, since the survey only included yes/no questions regarding technology use, the quantity of each type of security product is unknown (e.g. number of cameras used in the school). Longitudinal research is needed to demonstrate whether these tools are effective in improving school safety, and for what types of schools security technology can make the most significant contribution. Future research could examine the circumstances under which the use of security technology may affect fear of crime among students, teachers, staff, parents, and the community. Despite limitations with the data used in this study, this research represents the first comprehensive study of a wide range of security technology use in schools using multivariate models It is hoped that this book has increased our knowledge about the use of security technologies in schools by addressing several issues. First, the use of a larger number of security technologies was explored than what was examined in prior research. Second, data from a national survey of schools were used, unlike previous studies based on a small number of organizations, schools, or states. Third, a broader set of school and contextual variables were explored as possible correlates of the total level, outward, and inward focused security technology. Fourth, the predictive power of school crime and disorder was compared to the predictive power of school and contextual

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factors on the level of security technology use in schools. Fifth, multivariate models were presented that allowed a better estimation than previous studies of the school and contextual factors that were related to the use of security products. Understanding the extent to which certain types of schools have chosen security technology as a crime prevention tool will be a basis for future research to examine more precisely why specific products are selected and the effectiveness of such products in enhancing school safety.

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INDEX controlling access to school grounds, 5, 19, 73. See also control access to school grounds crime prevention programs, 10 crimestopper program, 56, 73, 74, 78 daily absence rate, 41, 62 drug sniffing dogs, 11, 13, 153 drug sweeps, 5, 13, 14, 16-22 drug testing, 11, 30, 56, 75, 78, 86, 139, 146 duress alarms, 11, 14, 56, 72, 73, 78, 138 engraving property, 23, 26, 27 entry control devices, 14 fear of crime, 24, 153 fences, 5, 12, 13, 19, 29 gated communities, 6, 29 gates, 5, 13, 15, 19, 29, 30, 57, 73, 74, 79, 115 grade levels, 107, 108, 112, 113, 114, 129-134, 138, 141 high crime neighborhoods, 47, 91 home protection, 23, 24 household protection devices, 23

access control, 4, 24, 25 alarm systems, 2, 27, 73, 138, 151 alcohol detection devices, 11, 56, 78, 12, 139 anti-graffiti sealers, 11, 56, 75, 78, 120 apprehending perpetrators, 4, 148 architectural design, 4 barricades, 13, 19 bullying, 4, 148 burglar alarms, 15, 24, 26, 27, 78 cctv system, 68 Common Core of Data, 49, 50, 51, 82, 137 community presence, 66, 91, 129, 131, 141, 144 control access to school building, 17, 18, 20, 57, 67, 79. See also controlling access to school building controlling access to school building, 5, 19, 73, 138. See also control access to school building control access to school grounds, 17, 18, 20, 57. See also controlling access to school grounds

161

162 illegal substances, 11 indoor timers, 26 innovation definition, 31 innovativeness definition, 31 lawsuits, 149 lights, 12, 23, 26, 55. See also lighting lighting, 4, 12, 13, 24, 138. See also lights locker searches, 13 locks, 11, 12, 15, 24, 26, 27, 28, 55, 67 marking/identifying property, 12, 72 metal detectors, 2, 5, 11, 13-22, 56, 57, 65, 73, 75, 78, 79, 139, 151 natural surveillance, 4 neighborhood crime level, 3, 30, 66, 95, 127, 129, 130, 131, 144 organizational innovation, 32, 35, 40, 45, 63, 68, 70, 108, 129, 140, 145, 146 organizational slack, 40, 41, 62 percent of students eligible for free or reduced-price lunch, 46. See also percentage of students eligible for free lunch

Index percentage of minority students, 5, 16, 17, 45, 46, 47, 61, 108, 110, 111, 123, 128 percentage of students eligible for free lunch, 5, 47, 52, 57, 61, 62, 108, 110, 111, 128, 141, 143. See also percent of students eligible for free lunch personal protection, 24, 26 physical barriers, 12, 55 population of U.S. public schools, 52 poverty, 17, 41 principal surveys, 68 private security, 28 protective behavior, 9, 10, 24, 66 pupil:teacher ratio, 52 recording systems, 14 risk assessment, 149 risk of victimization, 29 school achievement, 46, 47, 58, 63, 97, 123, 130, 131, 137, 140, 143, 145 school crime/disorder, 30, 128, 142, 146 school problems, 1, 3, 7, 55, 137, 149 school shootings, 1 school size, 18, 38, 52, 57, 58, 107, 110, 146

Index school violence, 1, 2, 4, 13, 15, 137. See also violence security cameras, 2, 4, 54, 67, 73, 78, 79, 121, 122, 138, 153. See also surveillance cameras and video surveillance security cameras on school buses, 54, 73, 78, 138 security survey, 149 security technologies definition, 10 surveillance cameras, 28. See also security cameras and video surveillance target hardening, 24-27

163 territoriality, 4, 17 theft, 11, 60, 79, 138, 139 vandalism, 4, 11, 12, 60 video surveillance, 5, 14, 16-19. See also security cameras and surveillance cameras violence, 1, 2, 4, 11, 13, 15, 137, 148. See also school violence weapons, 2, 11, 17, 86, 121, 122, 139, 146 window bars, 26, 27 x-ray devices, 11, 73, 78, 139