Security Technology in U.S. Public Schools (Criminal Justice) 1593322003, 9781593322007, 9781593323165

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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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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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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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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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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

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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.

Studying School Security

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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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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