Considering the efficacy of situational crime prevention in schools

e 35 (2007) 511–523
Journal of Criminal Justic
Considering the efficacy of situational crime prevention in schools

Lauren O'Neill, Jean Marie McGloin ⁎

Department of Criminology and Criminal Justice, University of Maryland, 2200 LeFrak Hall, College Park, MD 20742, United States

Abstract

Situational crime prevention (SCP) techniques have proven successful in a variety of spheres. One setting that has increasinglyrelied on such tactics to control crime, namely schools, has not been subject to much evaluation, however. Much of the schoolcrime research has focused on distal risk factors such as individual propensity and ignored more proximal factors, specificallyopportunities for crime. Additionally, research that does evaluate opportunities for crime in school settings does so with limitedtheoretical bases and methodologies. Given the pervasiveness of SCP tactics within schools, as well as the associated costs, this is aclear void in need of research. Using a cross-sectional, nationally representative sample of schools, this study investigated theefficacy of a variety of SCP tactics with regard to violent and property crimes, net of statistical controls. The findings revealed thatmost SCP techniques did not have a relationship with school crime, with the exception of closing campus for lunch and the numberof classroom changes. The discussion considers the robustness of these results, focusing on the methodological and substantivemechanisms that may underlie them.© 2007 Elsevier Ltd. All rights reserved.

Introduction

Among the national education goals of the 1990s wasthe objective that, “all schools in America will be free ofdrugs and violence and the unauthorized presence offirearms and alcohol, and offer a disciplined environ-ment that is conducive to learning” by the year 2000(Heaviside, Rowand, Williams, & Farris, 1998, p. 1).Although recent trends in school crime suggest thatviolent crime is on the decline (DeVoe et al., 2004), it isclear that school crime has not been eliminated, andmoreover, schools with high crime rates still exist. Atthe same time, the perception of risk continues to subsistamong students and public concern remains widespread(Mawson, Lapsley, Hoffman, & Guignard, 2002).

⁎ Corresponding author. Tel.: +1 301 405 3007.E-mail address: [email protected] (J.M. McGloin).

0047-2352/$ - see front matter © 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.jcrimjus.2007.07.004

Coincident with the growth in attention to schoolcrime, school crime prevention research has alsoexpanded (Astor, Meyer, Benbenishty, Marachi, &Rosemond, 2005). Most evaluation research has focusedon programs that address delinquent behavior amongyouth and happens to occur in a school-based setting.Schools are certainly logical venues for such programs,providing access to: the population of interest, relevantcriminogenic risk factors, and staff resources (D. C.Gottfredson, 1997). This sphere of research was notfully reflective of the range of techniques in use,however. As a consequence of the aforementionedissues, along with high profile violent incidents, schoolsacross the nation have shown a staggering array of crimeprevention techniques. The National Study of Delin-quency Prevention in Schools (G. D. Gottfredson &Gottfredson, 2001; G. D. Gottfredson et al., 2000)showed that the majority of schools in the nation relyon a multitude of tactics which reflect larger categories

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http://dx.doi.org/10.1016/j.jcrimjus.2007.07.004

512 L. O'Neill, J.M. McGloin / Journal of Criminal Justice 35 (2007) 511–523

of: direct services to students, staff, or families (e.g.,behavior modification); organizational/environmentalarrangements (e.g., classroom management practices);and disciplinary tactics (e.g., security).

There is an established literature on the efficacy ofschool-based delinquency prevention programs, but itdoes not speak to the broad range of techniques or to thefact that many schools have adopted tactics with adecidedly different focus and goal. For example, in 1993the National School Board Association reported that of720 school districts surveyed, “39 percent of urbanschool districts use metal detectors, 64% conduct lockersearches, and 65% employ security personnel” (W. N.Welsh, 2001, p. 911). The purpose with such situationalcrime prevention tactics is not to impact the criminalbehavior of the students, per se, but rather to affect theprobability of a criminal event occurring on schoolgrounds by altering opportunity structures. The goal inthe latter case is to create and sustain a safe environment(the unit of focus is the place rather than the individual).

Although some evaluations of such tactics exist, thisliterature is minimal and suffers from methodologicalflaws. This is not only challenging from an empiricalperspective, but also from a policy and practical viewpointsince significant financial resources have been invested.In 2000 alone, President Clinton allotted sixty milliondollars to hire over four hundred school resource officersnationwide (Juvonen, 2001). To simply assume suchtactics are effective is problematic and potentiallydamaging, both financially and socially. For example,some have criticized situational crime prevention tacticson the basis that they violate individual rights. Subjectinglarge numbers of juveniles to these tactics if they areineffective, or if they do not produce a benefit substantialenough to “outweigh” such concerns, would surely beunethical and inappropriate. Simply, despite the breadthand history of such tactics (indeed, the National Instituteof Education recommended security tactics to combatviolent and property crime in schools nearly thirty yearsago), “at present, there is a limited base of dependableinformation to guide schools in selecting approaches tothe prevention of problem behavior” (G.D.Gottfredson&Gottfredson, 2001, p. 337).

This inquiry therefore investigated the relationshipbetween several situational crime prevention techniquesand school crime. In doing so, it not only offered acommentary on relevant policies, but also on the generalutility of situational crime prevention (SCP). Mostevaluations of SCP tactics focused on public spaces,such as the impact of closed circuit televisions (CCTV)on parking garage theft and improved lighting on streetcrime. Thus, this research also spoke to the extent to

which SCP tactics can generalize, that is, how well they“held up” under a new context (e.g., schools).

Situational crime prevention

The 1970s witnessed a shift in criminology as policy,theory, and empirical work acknowledged the potentialimpact that contextual or environmental factors mayhave on criminal activity (Cornish & Clarke, 2003;Mawson et al., 2002). Researchers began to see thesituation as a dynamic participant in the criminal event,arguing it had the capacity to prompt, permit, andprovoke offending (G. Newman, 1997). As work ondefensible space (O. Newman, 1972) and crimeprevention through environmental design (CPTED)(Jeffery, 1971) emerged in the United States, theHome Office coincidentally shifted its interest awayfrom correctional treatment to the potential utility ofreducing opportunities for crime (see Cornish & Clarke,2003). In the criminological sphere, empirical work wassuggesting that offenders evidenced a level of rationalitywhen selecting targets (Brantingham & Brantingham,1975; Repetto, 1974) and a new theoretical premise,routine activity (Cohen & Felson, 1979), focusedattention on situational variables and the criminalevent. The culmination of this shift is perhaps bestcharacterized by the development of situational crimeprevention (SCP), which emerged in the followingdecade.

In articulating SCP, Clarke (1983, p. 225) originallystated one should view it as: “comprising measuresdirected at highly specific forms of crime that involve themanagement, design, or manipulation of the immediateenvironment in as systematic and permanent a way aspossible so as to reduce the opportunities for crimeand increase its risks as perceived by a wide range ofoffenders.” Later, Clarke (1995) offered twelve specifictechniques aimed at impacting criminal opportunitystructures by increasing the effort and risks, and bydecreasing the rewards associated with committing aparticular crime in a specific context. Over time, thesetechniques expanded to account for such considerationsas guilt and/or shame (Clarke, 1997) and the role ofsituational precipitators (Cornish & Clarke, 2003),resulting in the most recent formulation, which specifiestwenty-five techniques.

As these techniques have expanded over time, theyhave consistently reflected the primary theoreticalframework of rational choice (Clarke & Cornish,1985) and opportunity theories, namely routine activitytheory (Cohen & Felson, 1979). With regard to theformer, rational choice posits that individuals utilize a

513L. O'Neill, J.M. McGloin / Journal of Criminal Justice 35 (2007) 511–523

calculus when deciding whether to (1) generally commitcrime, and (2) commit a particular crime at a specifictime and place. Situational crime prevention speaksprimarily to the latter decision, which is predicated onthe notion that the costs, risks, and benefits associatedwith committing an offense under a given context willimpact and structure an offender's decision-makingprocess. As such, the tendency for an individual tooffend (though perhaps not his general propensity to doso) can be manipulated via situational characteristics(Clarke & Cornish, 1985; Cornish & Clarke, 1986).Opportunity theories more clearly articulated whatsituational factors, in particular, should be the focus onthis manipulation. Routine activity theory (Cohen &Felson, 1979) proposed that three elements must con-verge in time and space for a criminal event to occur: amotivated offender, a suitable target, and the lack of acapable guardian. In short, the basic premise of someSCP techniques is that one can impact opportunitystructures by manipulating such elements. For example,one may make a target less “suitable” and therebyincrease risks/effort and reduce rewards, or one maychoose to improve guardianship, which would also im-pact an offender's decision-making process. By relyingon these theories, SCP techniques essentially advocatefor affecting the most proximal causes of crime ratherthan the more distal risk factors of a general drive orpropensity for offending behavior.

Research on the effectiveness of SCP techniques hassuggested that they have promise. For example, B. C.Welsh and Farrington (2004a) conducted a meta-analysis on the ability of CCTVs to reduce crime. Thestudies, most of which reflected research done in theUnited Kingdom, showed that “CCTV had a significantdesirable effect on crime…[and] was most effectivewhen combined with improved lighting” (p. 28). Addi-tional research by B. C. Welsh and Farrington (2004b)further underscored the potential efficacy of both CCTVsand street lighting. Other investigations have highlightedthe utility of SCP tactics across an array of crimes,including obscene phone calls (Clarke, 1997), robberynear ATMs (Guerette & Clarke, 2003), motor vehiclecrime (Webb, 1997; Webb & Laycock, 1992), parkinggarage crime (Tseng, Duane, & Hadipriono, 2004), andprostitution (Matthews, 1997), among others.

It is worth noting there are criticisms of SCP tactics.Perhaps the best known critique is the “displacementhypothesis,” which argues that decreasing opportunityfor crime in certain areas will only force those who havethe propensity to commit crime to find opportunityelsewhere (Painter & Farrington, 2001). Thus, thiscriticism does not suggest that SCP tactics are

ineffective, per se, but rather that they do not controlfor crime merely shifting location. As Gabor (1994,p. 477) explained: “preventing crime without tacklingits social or psychological roots will merely result in thedisplacement of crime, whereby the perpetrator selectstypes of crime, tactics, or targets not affected by themeasures.”

Total displacement, and in some cases any displace-ment, has yet to be observed across evaluations ofsituational crime prevention techniques (see for exam-ple, Matthews, 1997). In fact, a “diffusion of benefits”has resulted on some occasions (see Clarke &Weisburd,1994). For example, improved surveillance and limitedaccess to a parking garage to reduce theft also led to adecline in theft offenses in two adjacent lots that had notaltered surveillance (Poyner, 1991). Poyner (1988)additionally found that when CCTVs were installed infive public buses, damage decreased not only in the“treatment” vehicles, but across the entirety of the fleet,which contained eighty buses.

Second, some researchers have voiced concernsabout the cost-effectiveness of SCP techniques. Sinceoffenders may adjust to these environmental strategies,SCP tactics must be evaluated and upgraded in order tosustain their utility, which can lead to additional costs(Clarke, 1997). This may be true for some strategies, butpreliminary cost-benefit analyses of SCP show promis-ing results. B. C. Welsh and Farrington (1999) foundthat eight of the thirteen situational crime preventionstudies had a “desirable benefit-cost ratio.” Althoughconsidered studies relied on different methodologicalspecifications, the review was rather conservative: whenuncertainty arose, researchers overestimated the costsand underestimated the benefits. Accordingly, situation-al crime prevention can be an “economically efficientstrategy for the reduction of crime” (B. C. Welsh &Farrington, 1999, p. 366).

Thus, evidence suggests that situational crimeprevention is a reasonable approach for reducingparticular crimes under certain contexts. Even so, theliterature hardly offers an exhaustive consideration ofall venues of its effectiveness. For example, researchhas shown that the effect size of SCP tactics is reducedwith regard to investigations from the United States (B.C. Welsh & Farrington, 2004a). B. C. Welsh andFarrington (2004a) hypothesized this may be due to thegeneral public support for such tactics in the UnitedKingdom as opposed to the American aversion to “bigbrother.” In addition, many evaluations of SCP tech-niques were essentially case studies. Such rich inquirieswere helpful, but the field's ability to “take a step back”and state how well SCP tactics operate in aggregate


contexts was limited. Recent work had attempted toaddress this void: Smith and Clarke (2000) spoke to theutility of SCP strategies for affecting public transportcrimes. Other contexts had used SCP techniques,however, under the assumption of utility. One particulararea of research that had coupled these two limitationswas the reliance on SCP techniques in the UnitedStates' school system. Indeed, as the next sectionunderscores, this is a clear void in the research thatdeserves attention, not only from the perspective ofthose with an interest in SCP, but also for researchersand policymakers with an interest in school-basedcrime prevention.

School crime prevention

School crime research tends to operate in three planes.First, the best developed area in terms of breadth anddepth focused on ameliorating criminal behavior amongyouth. In this domain, school is a base and/or a partner inprevention and intervention strategies (see D. C.Gottfredson, 1997; D. C. Gottfredson, Gottfredson, &Weisman, 2001; Wilson, Gottfredson, & Najaka, 2001).In her review of “what works” in the realm of school-based crime prevention, D. C. Gottfredson (1997)suggested that programs that build school capacity forinnovation, communicate behavioral norms clearly, anduse a comprehensive approach by addressing a range oflife skills and needs best impact delinquency andsubstance use among the student population.

The second and third spheres of research shiftedfocus from criminality within the student body tocriminal incidents on school grounds. First, onecategory of research evaluated reactions (e.g., suspen-sions and expulsions) to criminal activities, especiallythrough zero-tolerance programs (see Skiba, 2000;Skiba et al., 2000; Skiba, Peterson, & Williams, 1997;Snell, Bailey, Carona, & Mebane, 2002). This body ofresearch revealed three primary findings: (1) there wereunintended consequences from these tactics, namely theremoval of students from the school through suspensionand expulsion, which negatively impacted social bondsand often led to an increase in the dropout rate (Skiba,2000), (2) there was little evidence that these programshad an impact on overall school safety (Skiba, 2000;Skiba et al., 2000; Skiba et al., 1997), and (3) there wasevidence that suspensions and expulsions were notadministered consistently across race, SES, and gender(Skiba, 2000; Skiba et al., 1997).

The third sphere of research was most germane tothis investigation since it shifted focus from strategies ofreaction to strategies of prevention by investigating the

predictors of crime and disorder in school settings. Thisdomain of research existed in two distinct streams. Thefirst focused on predictors of school disorder, oftendefined as student misconduct, victimization, and/oroffending (see W. N. Welsh, 2001). Typically, suchinvestigations focused on individual level variables(e.g., age of the students), community variables (e.g., thelocal crime rate), and school-level variables. Theseschool variables tend to be social in nature, reflectingcommunal organization (Payne, Gottfredson, & Gott-fredson, 2003) and school climate (G. D. Gottfredson,Gottfredson, & Payne, 2005; W. N. Welsh, 2001; W. N.Welsh, Stokes, & Greene, 2000) (e.g., the perceivedclarity and fairness of rules). The second stream placedattention on attempts to reduce school crime by im-pacting opportunities for criminal events. These strate-gies often reflected a situational crime preventionframework, though existing research rarely used theSCP lexicon, nor did it explicitly rely on a theoreticalcontext. As the forthcoming sections illustrate, themethodological specifications of these investigationsoften prevented the field from properly commenting onthe efficacy of this sphere of school crime prevention.

Perhaps the best illustration of the lack of develop-ment of research in this area emerged from a recentreview by Skiba (2000). A search of four major researchabstract data bases (ERIC, PsychInfo, SociologicalAbstracts, and Criminal Justice Abstracts) across tenyears of implementation yielded only six empiricalevaluations using the key words: metal detector, lockersearch, surveillance or video camera, school uniforms,zero tolerance, and school security. Furthermore, manyof these evaluations failed to use sophisticated analysesand had serious methodological problems, collectivelyproducing no consensus regarding the effectiveness ofschool security measures (Skiba, 2000; Snell et al.,2002). The forthcoming paragraphs describe two of therelatively more rigorous evaluations conducted after theemergence of Skiba's conclusions.

Cheurprakobkit and Bartsch (2005) investigated anarray of school crime prevention tactics across middle,junior high, and high schools in Texas. This evaluationincluded such variables as metal detectors, schooluniforms, and closing campus for lunch, as well asitems such as mentoring, formal program training forstaff, family management strategy, community service,and others. The dependent variables of interest weredrug crime and interpersonal violence. They found apositive relationship between school uniforms and drugcrime and between closed campus and interpersonalcrime (Cheurprakobkit & Bartsch, 2005). The only SCPtactic that was associated with lower crime rates and


reached significance was metal detectors. Unfortunately,the: lack of controls, failure to progress past bivariateanalysis, and low response rate (18 percent of surveyswere returned) raises caution about the internal andexternal validity of this evaluation.

Schreck, Miller, and Gibson (2003) used a morerigorous methodology to evaluate the impact ofguardianship on an individual's victimization withinschools. They evaluated exposure to likely offenders(e.g., school population size) and guardianship (e.g., thepresence of security guards) while controlling fordemographic and community context variables. Al-though many variables emerged as significant, the onlyrelationship that can be viewed within the realm of SCPwas a positive relationship between victimization inschools and using locker checks. Thus, this studyindicated that this particular SCP tactic was noteffective. This relationship emerged in the overallvictimization and theft victimization models, but notin the violent victimization models (Schreck et al.,2003). A major limitation to this study was the failure tocontrol for school level identifiers (e.g., percent maleand percent minority), which are known to influenceschool crime and victimization.

Even so, this study was one of the most method-ologically advanced in this research area since itprogressed to multivariate analyses and containedsome statistical controls. As such, it further under-scored the most widespread sentiment among thisexisting literature— more rigorous research is requiredin order to assess the utility of these security tacticsand justify their expense (Barrios, 2000; Cheurpra-kobkit & Bartsch, 2005; Juvonen, 2001; Schreck et al.,2003; Skiba, 2000). Only after such evaluations canschool officials make informed decisions about themost effective approaches to address their crimeproblems.

The current research

The current research intended to respond to this needusing a nationally representative sample to investigatethe relationship between various school-level SCPstrategies and school crime, net of statistical controls.By examining a wide array of tactics operating in manydifferent school environments, this research took a“first step” toward more rigorous evaluations of schoolcrime prevention programs. At the same time, thisinquiry was important from a theoretical perspectivebecause it spoke to how well the SCP frameworkgeneralized to a school setting and impacted a varietyof behaviors.

Methods

Data source

Data for this project, in which the unit of analysis wasprimary and secondary schools, were obtained from theInter-University Consortium for Political and SocialResearch (ICPSR). In particular, the data came from the2000 School Survey on Crime and Safety, administeredby the National Center for Education Statistics (NCES).At the time of data collection, this was the only surveycollecting detailed information on crime and safety fromthe schools' perspective. A stratified sample design wasused to target 3,366 public schools for data collection.Schools were sorted into sampling strata using level(elementary, middle, or high school), type of locale (city,urban fringe, town, or rural), and enrollment size.Within each stratum, schools were also groupedaccording to minority status and region to ensure thatvarious minority groups and regions were appropriatelyrepresented in the sample. Data were collected via a mailsurvey to the school principal with a telephone follow-up. The response rate was 70 percent, with 2,270surveys completed.

As a result of the sample design, schools wereselected into the sample with unequal probabilities,which necessitated the use of sample weights in allanalyses.1 The weights provided were created in threesteps. First, base weights were used to account for theunequal probability of selection into the sample andinflate the survey responses to population levels. Next,adjustments were made to the base weights to accountfor unit non-response to address schools in the samplethat did not return the survey. The final step of theweight construction was post-stratification adjustments.The sampling frame and stratification for the currentsample was based on all of the schools in the 1997–98Common Core Data (CCD). Data collection occurreda year later, however, and school closures and/orreorganization may have altered which schools wereavailable for data collection. Therefore, these adjust-ments were made in order to make the sample repre-sentative of the actual 1998–99 population. Together,these steps created final sample weights that helpedalleviate non-response bias and made the samplenationally representative (Chaney, Chowdhury, Chu,Lee, & Wobus, 2003b).

Dependent variables

As part of the School Survey on Crime and Safety,respondents reported any crime that occurred on school


grounds, regardless of whether the offender was astudent, staff member, outsider, or unknown. The surveyasked about many individual crime types, but thefrequency of certain crimes was minimal. Therefore,this investigation collapsed the frequencies into mea-sures of violent crime and property crime. Violent crimeincluded: homicide, shootings, rape, sexual battery,attack or threat of attack with or without a weapon,robbery with or without a weapon, sexual harassment,and possession of a gun or a knife/sharp object.2 Prop-erty crime included theft and vandalism. Both measureswere continuous and consisted of a raw count of allcriminal events included in the aforementioned descrip-tion. These criminal events were not necessarily reportedto the police, but reflected those events that wererecorded by the school (i.e., these measures were likelyto be more inclusive than official records would be).

Independent variables

The primary independent variables measured crimeprevention techniques employed by schools, which hadthe goal of altering the opportunity for criminal activitywithin the school. Although most of these variablesreflected the situational crime prevention (SCP) frame-work, this investigation did not engage in data reductionto create measures of the “categories” of techniques, butinstead kept these variables in their original form. Arange of situational crime prevention tactics had yet tobe evaluated within a school setting. Therefore, itseemed prudent to evaluate the techniques individuallysince reducing them to factors or summed scores mighthave obscured the potential differential impact ofvarious tactics on the two dependent variables.3

Most variables were dichotomous, for which 1indicated the presence of the technique in the school,and 0 indicated its absence. These variables included:access controlled by locked/monitored doors, groundshave locked/monitored gates, close campus for lunch,require clear book bags or ban book bags, requirestudents to wear a badge or photo ID, students passthrough metal detectors, have random metal detectorchecks, security cameras monitor the school, and re-quire students to wear uniforms. One could argue thatthe first two measures reflected an attempt to increaseeffort through controlled access. The next sevenmeasures captured an intention to increase the risksassociated with committing crime by reducing anonym-ity, utilizing place managers, and strengthening surveil-lance (Cornish & Clarke, 2003).

There were also two continuous variables included inthe models: the ratio of students to teachers, and the

typical number of classroom changes during the schoolday. The first measure was another tactic within thecategory of increasing risks, since it spoke to guardian-ship and surveillance. The latter measure was anexample of reducing provocations in the violent crimemodel since fewer classroom changes might have been ameans of avoiding disputes among students (Cornish &Clarke, 2003). In the property model, this measuretapped into the idea of increasing effort since fewerclassroom changes may reduce the likelihood ofstudents leaving personal items (e.g., a purse) behind,requiring theft of an item to be lifted from an individualas opposed to from a classroom. While all of thesevariables were included in the violent crime model, theproperty crime model did not include students passingthrough metal detectors or random metal detectorchecks since they were tactics specifically aimed atviolent crime.

Control variables

Previous research had revealed a relationship be-tween school characteristics (e.g., grade level, urbani-city of location) and crime prevention tactics in use(G. D. Gottfredson & Gottfredson, 2001), as well asschool crime (Chaney, Chowdhury, Chu, Lee, &Wobus, 2003a; G. D. Gottfredson & Gottfredson,1985; Toby, 1983; W. N. Welsh, Greene, & Jenkins,1999). Accordingly, these characteristics were includedin both the violent and property models: in order tocontrol for any influence they might have had on anindividual school's crime rate, and so that therobustness of the independent variables was notoverstated. All control variables were categorical innature. Total number of students was measured as: lessthan 300 students, 300–499 students, 500–999 stu-dents, and 1,000 or more students. Percent receivingfree lunch was coded as: less than or equal to 20percent, 21–50 percent, and greater than or equal to 51percent. Percent minority was coded as: 0–5 percent,6–20 percent, 21–50 percent, and 51–100 percent.Percent male students in the school had threecategories: 0–44 percent, 45–55 percent, and 56–100percent. School grade offered had four categories(elementary, middle, secondary, or combined grades).This variable also served as a proxy for the age ofstudents, which was not included in the data set.

Urbanicity, a categorical variable measured as city,urban fringe, town, or rural, was also included as acontrol variable to represent the type of communitysurrounding the school. It would have been ideal tocontrol for crime levels in the communities surrounding


the schools (see W. N. Welsh, Stokes, & Greene, 2000),but that information was not available. A crime wherestudents live (high crime rate, moderate crime rate, lowcrime rate)4 variable was available in the data set. Thisinquiry was unable to assess whether this wasrepresentative of the neighborhoods directly surround-ing the schools or some distance away (i.e., from howlarge a distance school children commuted), but this wasan important control variable, particularly given thecross-sectional nature of the data.

Although most of these variables were reportedby respondents of the School Survey on Crime andSafety, the “school grade offered” and “urbanicity”controls were supplemented by the 1998–99 CommonCore of Data (CCD) because they were not includedon the questionnaire. The CCD is a comprehensive,annual, national statistical data base maintained by theU.S. Department of Education's National Center forEducation Statistics. It includes information on allpublic elementary and secondary schools and schooldistricts (Chaney et al., 2003a).5

The violence model also included a control mea-sure of whether or not the school had a formal violenceprogram, which could include such items as the fol-lowing: prevention curriculum, instruction, or trainingfor students; behavioral or behavior modification in-tervention, counseling; mentoring; peer mediation;and programs to promote sense of community. To theextent that such programs might impact the criminalbehavior of students, it was important to include itin the models for better specification. At the sametime, this allowed an opportunity to gauge the relativerobustness of SCP tactics in comparison to this othersphere of crime prevention in schools.

Analytic strategy

The dependent variables of interest were countvariables. Relying on OLS with such outcomes canresult in biased and inefficient estimates. Instead,many researchers have turned to the Poisson regres-sion model (Long, 1997). This model assumes thatthe dependent variable's conditional mean and condi-tional variance are equal, however, which was notthe case here. Instead, the distributions of both theproperty crime and violent crime outcomes indicatedoverdispersion. Using the Poisson model under suchcircumstances runs the risk of generating inflated z-values and overestimating the significance of thepredictors (see Cameron & Trivedi, 1986). Accord-ingly, the analyses for this article relied on negativebinomial regression.

Results

Table 1 presents descriptive statistics for eachvariable used in the multivariate models. The range ofuse for the SCP tactics of interest varied from less than 1percent of the schools (daily metal detectors) to 74percent (access monitored by locked doors). Themajority of schools (78 percent) had a similarpercentage of male and female students. The distribu-tions of many control variables were fairly equal acrosscategories, which reflected the National Center forEducation Statistics' decision to create the categoricalbreakdowns of the control variables by looking atnatural breaks in the distributions of the raw data. Aswith the National Study of Delinquency Prevention inSchools (G. D. Gottfredson & Gottfredson, 2001; G. D.Gottfredson et al., 2000), the descriptive statistics spoketo the fact that schools engaged in an array of suchtactics.

Table 2 presents the results from the negativebinomial regression model predicting the amount ofproperty crime in schools.6 Of the nine tactics specified,three emerged as significantly associated with schoolcrime: locked doors, closing campus for lunch, and thenumber of classroom changes. The results indicated thatschools that had locked doors were likely to report lessproperty crime, net of statistical controls. In particular,the incidence rate ratio suggested that having lockeddoors in a school decreased the expected count ofproperty crime by a factor of .77 (a 23 percent decrease).Schools in which students stayed on campus for lunchwere likely to report more property crime. For suchschools, the expected count of property crime increasedby a factor of 1.35 (net of statistical controls). Althoughone may have expected closing a campus for lunch todecrease crime by making guardianship easier, closingcampus for lunch essentially puts a large number ofstudents and their valuables in a confined space. Inessence, from a routine activity viewpoint, it bringsmotivated offenders and suitable targets into the sametime and space (a theoretical explanation that alsoapplies to the classroom changes variable). According tothe model, for every additional number of classroomchanges reported, the expected count of property crimeincreased by a factor of 1.09. Having classroom changesmay have also resulted in students leaving theirbelongings unattended (i.e., without guardianship) inclassrooms.

It was worth noting that some control variables weresignificant in the predicted direction, as well. Consistentwith previous research (Chaney et al., 2003a; D. C.Gottfredson, 2001; Heaviside et al., 1998; W. N. Welsh

Table 1Descriptive statistics for variables included in the multivariate models

Variable Mean Std. Dev.

SCP tacticsLocked doors (0 = no, 1 = yes) .746 .435Gates (0 = no, 1 = yes) .337 .472Closed lunch (0 = no, 1 = yes) .646 .478Clear bookbag (0 = no, 1 = yes) .059 .235Student ids (0 = no, 1 = yes) .039 .195Metal detectors (0 = no, 1 = yes) .009 .095Random metal detector checks (0 = no, 1 = yes) .072 .258Security cameras (0 = no, 1 = yes) .194 .395Uniforms (0 = no, 1 = yes) .118 .323Student-teacher ratiob12% .352 .47812–16% .299 .458N16% .349 .477

Classroom changes 4.840 2.570

ControlsCrime where students liveLow .732 .443Moderate .193 .395High .075 .264

Violence program (0 = no, 1 = yes) .728 .445Total studentsb300 .245 .430300–499 .278 .448500–999 .370 .4831,000 or more .106 .308

% minority0–5% .304 .4606–20% .226 .41821–50% .204 .403N50% .267 .442

School level .145 .352High school (reference category) .609 .488Elementary school .188 .391Middle school .059 .235Combined grades

% male0–44% .122 .32845–55% .758 .42856–100% .120 .324

% free lunchb21% .281 .44921–50% .352 .47851% or more .367 .482

School locationUrban fringe (reference category) .322 .467City .236 .425Town .127 .333Rural .315 .464

Dependent variablesProperty crime 5.210 11.935Violent crime 20.570 44.671


et al., 2000), smaller schools and schools with youngerpopulations were associated with lower levels ofproperty crime. In addition, the crime level where thestudent lives had a relationship with property crimeat school: in reference to a low crime level, both a

Table 2Negative binomial regression model of SCP tactics predicting propertycrime (n = 1,678)

Variable B Robust SE IRRa

SCP tacticLocked doors − .266 ⁎ .124 .767Gates − .123 .109 .884Closed lunch .301 ⁎⁎ .109 1.351Clear bookbag − .069 .167 .933Student ids .010 .159 .990Security cameras − .070 .100 .932Uniforms .084 .172 1.087Student-teacher ratio .112 .074 1.118# of classroom changes .092 ⁎⁎⁎ .025 1.096

ControlsCrime where students live(reference= low)Moderate .572 ⁎⁎ .179 1.773High 1.016 ⁎⁎⁎ .228 2.764

Total students(reference=b300)300–499 .459 ⁎⁎ .170 1.582500–999 .687 ⁎⁎⁎ .171 1.9891,000 or more 1.145 ⁎⁎⁎ .192 3.142

% minority (reference=b6%)6%–20% .098 .140 1.10321%–50% .005 .172 1.006N50% − .157 .208 .855

School level(reference=high school)Elementary −1.218 ⁎⁎⁎ .134 .296Middle − .464 ⁎⁎⁎ .111 .629Combined grades − .333 ⁎ .169 .717

% male (reference=45–55%)b45% − .275 .151 .759N55% − .149 .161 .862

% free lunch(reference=b21%)21–50% .129 .136 1.138N50% − .054 .211 .947

School location(reference=urban fringe)City .416 ⁎⁎ .136 1.516Town .247 .160 1.280Rural .006 .177 1.007

Constant .684 ⁎ .285Log likelihood −147998.92 ⁎⁎⁎

a IRR = incidence rate ratio, which is the exponentiated coefficient.⁎ = p b .05.⁎⁎ = p b .01.

⁎⁎⁎ = p b .001.

Table 3Negative binomial regression model of SCP tactics predicting violentcrime (n = 1,659)

Variable B Robust SE IRRa

SCP tacticsLocked doors − .009 .124 .991Gates − .100 .129 .905Closed lunch .205 .124 1.227Clear bookbag .206 .194 1.229Student ids .050 .182 1.052Metal detectors − .206 .334 .814Random metal detector checks .214 .183 1.238Security cameras .119 .114 1.127Uniforms .077 .179 1.080Student-teacher ratio .059 .067 1.061# of classroom changes .066 ⁎⁎ .023 1.068

ControlsCrime where students live

(reference= low)Moderate .834 ⁎⁎⁎ .190 2.303High 1.625 ⁎⁎⁎ .220 5.079

Violence program .578 ⁎⁎⁎ .113 1.783Total students

(reference=b300)300–499 .570 ⁎⁎ .166 1.768500–999 1.108 ⁎⁎⁎ .173 3.0271,000 or more 1.397 ⁎⁎⁎ .189 4.044

% minority (reference=b6%)6–20% .207 .148 1.23021–50% .208 .185 1.231N50% − .171 .186 .843

School level (reference=highschool)Elementary − .355 ⁎⁎ .131 .701Middle .101 .121 1.106Combined grades − .188 ⁎⁎ .171 .829

% male (reference=45–55%)b45% − .287 .154 .750N55% − .367 ⁎⁎ .140 .693

% free lunch(reference=b21%)21–50% .128 .145 1.137N50% .144 .192 1.155

School location(reference=urban fringe)City .141 .136 1.151Town .654 ⁎⁎ .235 1.924Rural .396 ⁎ .176 1.486

Constant .526 .314Log likelihood −224403.65 ⁎⁎⁎

a IRR = incidence rate ratio, which is the exponentiated coefficient.⁎ = p b .05.⁎⁎ = p b .01.

⁎⁎⁎ = p b .001.


moderate and a high crime residential area wasassociated with higher amounts of property crime atschool.

Table 3 presents the negative binomial regressionresults for the second dependent variable of interest:violent crime. This model included two additional SCPtactics: metal detectors, and random metal detectorchecks, as well as an additional control, formal violenceprevention program. With this dependent variable, onlyone SCP tactic emerged as a significant predictor:the number of classroom changes. In particular, theexpected count of violent crime for schools increased bya factor of 1.06 for every additional reported classroomchange. Theoretically, having more classroom changesincreases the probability of having a large number ofstudents in a confined space, which may also increasethe likelihood of provocations and disputes amongstudents. With regard to the control variables, schoolsthat had a violence program were likely to report moreviolent crime, as were larger schools, schools that had amajority of male students, and schools that were inmoderate or high crime areas (in reference to low crimeareas). Schools in relatively less urban areas were morelikely to report violent crime, an intriguing findingthat suggests future research should investigate thepotentially complicated relationships that may existamong the control variables.7 Finally, not surprisingly,elementary schools were likely to report less violentcrime in reference to high schools.8

Some researchers may argue that it was the numberof SCP tactics employed that was important. Perhapssome combination of techniques was powerful, orconversely, perhaps this measure would capture thetendency for a school to “react” to crime with an array ofsecurity measures. In supplemental analyses, thenumber of SCP tactics did not have a relationship withproperty crime, nor with the amount of violent crime,net of statistical controls.9

Discussion

A variety of situational crime prevention tactics arein use across the nation's schools, but the extantresearch literature could only make minimal statementsabout their efficacy. Indeed, compared to the literatureon school-based programs that aim to impact thedelinquent behavior of students, this sphere of researchdemonstrated a considerable lack of depth and breadth.Even so, schools continued to implement thesestrategies, often times in response to media portrayalsof school crime (Snell et al., 2002). The researchpresented here was a “first step” towards addressingthis void. In particular, it investigated whether an arrayof SCP techniques impacted the amount of propertyand violent crime in schools across the United States,


and in turn, whether the framework of situational crimeprevention could extend to the general context ofschools.

The findings suggested that, at the national level, ahandful of SCP tactics showed a significant relationshipwith school crime. Schools that had locked doors, thatdid not close campus for lunch, and had fewer classroomchanges tended to report less property crime. Inaddition, schools with fewer classroom changes alsotended to report less violent crime. Both closing campusfor lunch and having a number of classroom changes sothat students did not remain in one room throughout theday may have served to corral motivated offenders andsuitable targets in confined spaces at the same time,essentially creating criminal opportunities that requiredminimal effort. Conversely, controlling access to theschool via locked or monitored doors likely increasedthe effort a motivated offender would need to expend tocommit property crime. In the end, however, there wererelatively few tactics in the array utilized across schoolsthat emerged as significant.

These findings provided a number of points forconsideration. Some may view the results as suggestingthat situational crime prevention tactics simply do notwork within school settings. There are a number of dataand methodological concerns that lend caution to thisinterpretation, however. First, research by G. D.Gottfredson et al. (2000) suggested that principals hada tendency to over-report the use of crime preventiontactics within their schools and underreport the amountof crime. Principal surveys might not be the ideal, butthis concern would suggest a systematic bias towardsfinding that SCP tactics were successful. Although therewere no data on how “biased” the principals were in thisdata set, it was important to note that this trend did notemerge. Second, some might suspect that the imple-mentation of these tactics was purely a reaction to highlevels of crime in schools. If this were true, the use ofSCP tactics would have evidenced a significantlypositive relationship with violent and property crime,even if it were theoretically inconsistent. This was notclearly evident, either with individual tactics or with theaforementioned count variables.

The data under use were cross-sectional and it istherefore valid to question the causal order among thevariables of interest. It would not be illogical topresume that the initial adoption of tactics was inresponse to school crime. The larger issue, however, iswhether the adopted measures actually preceded thereported crime for the year 2000 (the measurement timeof the dependent variables). The data included adichotomous variable which represented whether the

school completed any architectural or environmentalmodifications to reduce opportunities for crime andviolence within the past three years (from 1997 to2000).10 These modifications could have includedsome of the SCP tactics being evaluated. Therefore,those schools that reported changes were those thatwould be the most suspect with regard to problems incausal order. Supplemental analyses were thereforeconducted, in which this subsample of schools wasremoved in order to determine if the pattern of resultswas consistent. This was an attempt to address theconcern about causal order and further “chip away” atpotential confounds of the relationship under consid-eration. Of course, this did not fully ameliorate the datalimitation, but it was worthwhile to note that thesubstantive findings remained similar.11

Another methodological concern of interest was thatthe aggregate focus might cloud the efficacy of SCPtechniques. Many case studies of SCP tactics suggestedthey are useful (Clarke, 1997; Guerette & Clarke, 2003;B. C. Welsh & Farrington, 2004a, 2004b). Perhaps thisaggregate analysis was obscuring the utility of individualtactics to address crime problems in particular schools.Some researchers have highlighted the utility of SCP inbroad situations, however, namely public transport(Smith & Clarke, 2000). Granted, Smith and Clarke(2000) used other, more specific, studies to “build up”the status of SCP in an aggregate context. Nonetheless,the tone and thrust of their discussion was that SCP cangeneralize to larger situations— the research presentedhere simply tested that premise more directly. With thatin mind, it is important to mention that the findings forthis investigation coincided with the few empiricalstudies that used data from multiple schools to evaluatenumerous policies through aggregate analyses. Despitevariant methodological specifications, the pattern ofinsignificance of these tactics was widespread (Cheur-prakobkit & Bartsch, 2005; Schreck et al., 2003).

Still, one should not be satisfied with simplyconcluding that these tactics do not work. There are po-tential substantive reasons as to why these tactics did notemerge as significant. First, schools might have relied onSCP techniques that did not “match” the actual problem(s)that was prevailing in their respective environments. Inessence, this returns to the core of SCP, which reliesheavily on problem analysis and evaluation. The basictenet of SCP is that altering opportunity structures impactsthe risks, benefits, and effort associated with specificcrimes in particular settings. Research by Snell et al.(2002), however, illustrated that schools implementedsecurity measures more often as a response to mediareports and current events rather than to incidents within

1. For a detailed description of the weighting procedure,including the chi-square automatic interaction detector (CHAID)analysis, see the 2000 School Survey on Crime and Safety detaileddata documentation.

2. Not surprisingly, the majority of violent crimes reported acrossschools included relatively less minor acts, such as sexual harassment,possession of a weapon, and attack or threat of attack. It is also worthnoting that these crime categories capture all reported property and violentcrimes (i.e., they are exhaustive, not a limited selection from the data set).

3. Factor analysis further underscored the appropriateness of notreducing and/or combining these measures into a single score or scale.Principal components analysis extracted four factors with no clearconceptual pattern to the individual factor loadings.

4. This variable also had a value coded as “mixed.” Thisexplanation was unclear and therefore was recoded as “missing.” Thisaffected approximately 275 cases, or just over 12 percent of the sample.

5. The following control variables were maintained as categoricalrather than treated as continuous for two reasons: total number ofstudents, percent receiving free lunch, percent minority, and percentmale students. First, although there was a natural order to the categoriesof the variables, the size of the difference between the categories wasinconsistent (i.e., these were ordinal, not interval, variables). Second,although the Common Core Data collected the raw counts of thesevariables, they were transferred into ordinal form prior to being addedto the SSOCS data and the public-use data did not include identifiersthat would make linking the two data sets possible.

Notes


their individual schools. Though one cannot know forcertain, it may therefore be the case that schools weresimply failing to implement the appropriate tactics fortheir particular problems.

Second, even if the techniques do “match” theproblem at hand in theory, the schools may not beimplementing them correctly in practice. D. C.Gottfredson et al. (2001) suggested that crime preven-tion techniques in schools rarely are implemented wellor consistently applied. With this in mind, it wasinteresting to note that the tactics that did emerge assignificant for this study were the more simple tasks thatwere less susceptible to implementation problems, suchas closing campus for lunch and having locked doors, asopposed to metal detectors.

Even if these two “process” problems are the primaryreasons for the lack of emergent significant relation-ships, however, it does not change the findings. It wouldbe ideal to always match the problem at hand throughproblem analysis and for tactics to always be imple-mented correctly and consistently. Yet, that ideal isunlikely to be the reality in most schools. This researchrepresented an evaluation of situational crime preven-tion tactics as they were currently used and executed inschools. The research question of interest was whetherSCP tactics, as employed, appeared to have utility ormay be a waste of resources. Thus, the findings did notnecessarily show that SCP tactics failed to work in aschool setting, but rather that they appeared not to workas presently implemented and captured by extant datasets and measures. Certainly, future research shouldadopt a finer, yet still rigorous, lens to truly determine ifSCP tactics that both match the problem at hand and areproperly executed significantly reduce school crime.

Lastly, there is an additional policy implication. Itwould be remiss for people to ignore the fact that thesignificant predictors, both the controls and few SCPtactics, spoke to crowd control and were not stringentsecurity measures, which is important for two reasons.First, some have suggested that SCP is unpopularwithin the United States because people are wary of“big brother” and unsure about the balance betweencommunity safety and individual civil rights (see B. C.Welsh & Farrington, 2004a). The current analysissupported the utility of security measures that do notinfringe greatly upon privacy, certainly when compared toother items such as metal detectors and surveillancecameras. Second, it is important to note that the significantpredictors are relatively less costly. Cost effectiveness isunlikely to be a concern for keeping students in oneclassroom throughout a day or allowing students to leavecampus for lunch. Some may find it troublesome that one

of the most robust predictors, namely, total number ofstudents, suggests the need to build more schools in orderto keep enrollment low. Financial resources may be toolimited to build more schools in some districts, but thereare alternative policies, such as split sessions, thatnecessitate fewer resources but are conceptually similarto small schools. The utility of such policies should beevaluated in order to assess alternative avenues ofimpacting school crime.

In the end, the current research highlighted the needfor more evaluations. It also suggested that perhapsschools should begin with simple SCP policies that arenot invasive or costly and engage in sequentialinterventions if and when they become necessary. Ateach stage, schools need to conduct detailed problemanalysis and only implement the strategies that matchthe specific problem and school environment. Researchshould complement this process, so that findings may“build up” and speak to the robustness and generaliz-ability of what was revealed in this investigation.

Acknowledgements

The authors wish to thank Dr. Denise Gottfredsonand Dr. Alex Piquero for their comments on an earlierversion of this article. They also thank the anonymousreviewers for their helpful suggestions.

6. Correlations among the independent variables did not indicatethe presence of multicollinearity for either model.

7. For example, some research suggests povertymediates the relation-ship between race and school disorder (see Stretesky & Hogan, 2005).

8. It is possible that conducting the analyses across all schoollevels obscured certain patterns. In particular, elementary schoolswere not likely to report a large amount of property or violent crime.In addition, though comparing the percentage of schools using certainSCP tactics between the entire sample and elementary schools aloneshowed similar patterns, there were some differences with regard toclosing campus for lunch, having random metal detector checks andsecurity cameras, as well as the typical number of classroom changes.With this in mind, supplementary analyses excluded the elementaryschools. With this restricted sample, the results were substantivelysimilar except for one change. Closing campus for lunch was nolonger significant in the property model, but emerged as a significantpredictor in the violent model. Specifically, the expected count ofviolent crime among non-elementary schools increased by a factor of1.32 in schools that closed campus for lunch. The authors thank ananonymous reviewer for recommending this supplemental analysis.

9. The SCP count variables were not included in the mainregression models since their inclusion resulted in problems withmulticollinearity.

10. Although it is not explicitly stated that these modificationsinvolved instituting changes as opposed to removing tactics, it seemsunlikely that a school would remove a prevention tactic in an attemptto reduce opportunities for crime.

11. The only notable change in the results was that closing campusfor lunch just crosses the statistical significance threshold (p b .048).


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Considering the efficacy of situational crime prevention in schools