An Application of “Broken‐Windows” and Related … Application of “Broken‐Windows” and...

An Application of “Broken‐Windows” and Related Theories to the Study of Disorder, Fear,and Collective Efficacy in SchoolsAuthor(s): Stephen B. Plank, Catherine P. Bradshaw, and Hollie YoungSource: American Journal of Education, Vol. 115, No. 2 (February 2009), pp. 227-247Published by: The University of Chicago PressStable URL: http://www.jstor.org/stable/10.1086/595669 .

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FEBRUARY 2009 227

American Journal of Education 115 (February 2009)� 2009 by The University of Chicago. All rights reserved.0195-6744/2009/11502-0002$10.00

An Application of “Broken-Windows” andRelated Theories to the Study of Disorder,Fear, and Collective Efficacy in Schools

STEPHEN B. PLANKJohns Hopkins University

CATHERINE P. BRADSHAWJohns Hopkins Center for the Prevention of Youth Violence

HOLLIE YOUNGJohns Hopkins University

This article considers school climate and perceptions of social disorder. Whena school is characterized by disorder or physical risk, basic educational goalsand processes are jeopardized. We use survey data from 33 public schools servinggrades 6–8 in a large mid-Atlantic city to examine relationships among physicaldisorder (e.g., broken windows and poor building conditions), fear, collectiveefficacy, and social disorder. Path analyses reveal a direct association betweenphysical disorder and social disorder even when prior levels of collective efficacyare controlled—a finding consistent with traditional broken-windows theories.Further, there is evidence that the effects of physical disorder may be operatingthrough increased fear and decreased collective efficacy to affect perceptions ofthreatening or violent interactions among people.

Many students, parents, educators, policy makers, and researchers are con-cerned about school climate and the safety of students and teachers (Anderson1998; Arum 2003; Edwards 2001), particularly in urban communities wherethe rates of school violence are the highest. When a school environment ischaracterized by social disorder and physical risk, basic educational goals andprocesses are jeopardized and likely become secondary concerns of studentsand educators (Buckley et al. 2005; Henry 2000). The current article buildsupon theories and insights into social order, disorder, and violence from studiesof neighborhoods and links them with research on school climate and schoolviolence. We use survey data from 33 urban public schools serving grades 6–8

Electronically published December 3, 2008



Disorder, Fear, and Collective Efficacy

228 American Journal of Education

to better understand the relationships among physical disorder (e.g., brokenwindows and poor building conditions), fear, collective efficacy, and socialdisorder. Our primary aim is to shed light on the conditions and social pro-cesses that may lead to high or low levels of perceived social disorder in schools.In fact, prior research suggests a relatively strong association between per-ceptions of the environment and objective ratings, both of which are correlatedwith an increased risk of involvement in deviant and potentially aggressiveacts (Cantillon et al. 2003; Perkins et al. 1992; Sampson and Raudenbush1999; Sampson et al. 1997; Seidman et al. 1995).

Over the past 25 years, there has been dialogue between proponents ofbroken-windows theories of neighborhood orderliness (e.g., Wilson and Kelling1982) and proponents of alternative explanations involving shared expecta-tions, cohesion, and—more broadly—collective efficacy (e.g., Sampson andRaudenbush 1999; Sampson et al. 1997; Xu et al. 2005). Wilson and Kellingargue that “if a window in a building is broken and is left unrepaired, all ofthe rest of the windows will soon be broken” (1982, 31). They claim thatbroken windows and other visible signs of physical disorder (e.g., graffiti, litter,and boarded-up buildings) signal to people that no one cares about the place;thus, people are likely to choose these sites as places to partake in criminalor antisocial activities. Wilson and Kelling further argue that physical disorderleft unchecked affects the extent to which people are willing to attempt toorganize collectively for social control. The fear brought on from physicaldisorder results in a decrease in collective action for social control, which leadsto increased opportunities for—and incidence of—violent crime. One cansummarize Wilson and Kelling as having asserted both an “invitation to evil-doers” hypothesis and a second hypothesis of physical disorder invoking fear,thereby eroding collective efficacy, thereby allowing social disorder and vio-lence. They would expect both direct and indirect causal effects of physicaldisorder upon social disorder or violence.

STEPHEN B. PLANK is an assistant professor in Johns Hopkins University’sDepartment of Sociology and is also affiliated with the Johns Hopkins Centerfor Social Organization of Schools. Much of his past and current researchfocuses on solutions to the high school dropout problem, predictors of suc-cessful transitions to college, and school climate. CATHERINE P. BRADSHAW

is an assistant professor in the Department of Mental Health at the JohnsHopkins Bloomberg School of Public Health and is the associate director ofthe Johns Hopkins Center for the Prevention of Youth Violence. Her researchfocuses on the prevention of aggressive behavior and the design and evaluationof school-based prevention and intervention programs. HOLLIE YOUNG wasa graduate student in sociology at Johns Hopkins University at the time ofthis research and currently teaches in an urban charter school.



Plank, Bradshaw, and Young

FEBRUARY 2009 229

Sampson and Raudenbush (1999) consider the claims of broken-windowstheories and offer an alternative account of causal processes. Examining urbanneighborhoods, they contend that physical disorder (e.g., graffiti on buildings,abandoned cars, broken windows, and garbage in the streets) and social dis-order (e.g., verbal harassment on the street, open solicitation of prostitution,public intoxication, and rowdy groups of youth in public) are not generallycausal antecedents to more serious crimes. Instead, physical disorder and socialdisorder are visual indicators of incivilities that have the same underlying causesas criminal activities.1 Consistent with social disorganization theory (Sampsonand Groves 1989; Shaw and McKay [1942] 1969), Sampson and Raudenbushposit that certain structural factors and neighborhood collective efficacy havea significant influence on deviant outcomes (physical disorder, social disorder,and other criminal activities) in neighborhoods. They define collective efficacyas “cohesion among residents combined with shared expectations for the socialcontrol of public space” (1999, 603).

Sampson and Raudenbush’s empirical results supported their conceptualideas—and differ notably from broken-windows theories—in that physicaldisorder was estimated to have relatively little effect on social disorder onceprior levels of collective efficacy were controlled. Sampson and Raudenbushalso noted a significant feedback loop between social disorder and collectiveefficacy, a finding that has implications for the present analyses.

Xu and colleagues (2005) provide further insights into the roles of physicaldisorder and collective efficacy in explaining social disorder and more seriouscrimes. They also usefully direct attention to fear as a psychological state thatshould not be underestimated for its effects on sociological processes (see alsoDevine 1995, 1996; Reed and Strahan 1995). Xu et al. assert that Sampsonand Raudenbush have misrepresented Wilson and Kelling somewhat. WhenSampson and Raudenbush claim that the relationship between disorder andcrime is spurious and that collective efficacy is the central factor leading todisorder and crime outcomes, it is perhaps implied that Wilson and Kellingpaid little attention to collective efficacy. However, Xu et al. contend thatcollective efficacy was indeed implicated in Wilson and Kelling’s thesis—as amediating link between disorder and crime. Thus, Xu et al. posit that bothconceptual frameworks are talking about the same factors but are disagreeingabout the causal links among the factors. Without presenting a full discussionof Xu and colleagues’ findings and conceptual model, we simply note thatthey found empirical support for direct effects of disorder on crime even aftercontrolling for collective efficacy—a finding notably different from that ofSampson and Raudenbush.





The Context of Schools and Specific Research Questions

At this point we must make clear our intentions in applying theories of neigh-borhood order and disorder to school settings. It is perilous to argue by analogyor to expect structural relationships from one social setting to be perfectlyreplicated in a different social setting. Alert to these perils, we nonethelessbelieve theories from neighborhood studies have considerable promise to shedlight on matters of school climate and order. We are not the first to applythese neighborhood theories to school settings (see Braham 2004; Mijanovichand Weitzman 2003; Schneider et al. 1999), though we think the data wemarshal allow for insights that are new and important.

Whereas neighborhood scholars sometimes lump physical disorder and so-cial disorder into a broad collection of factors that are distinct from “seriouscrimes,” we take a different tack. We keep physical disorder distinct from socialdisorder and direct our attention to physical disorder, collective efficacy, fear,and structural characteristics of schools as possible influences on social disorder(threatening, violent, or disruptive interactions among people within a schoolsetting). An important factor in the causal pathway is fear, for we hypothesizethat the social and physical indicators of disorder serve as signals that the envi-ronment is unsafe, which in turn may contribute to further disorder. Social dis-order holds the position often held by violent crime in neighborhood models.

We estimate a series of path models to explore the broken-windows andsocial disorder hypotheses as applied to the school context. Specifically, thefirst model is designed to test whether a direct association between physicaldisorder and social disorder is observed once prior levels of collective efficacyare controlled. A second model is designed to examine whether fear may beacting as an important mediating variable between (prior) physical disorderand (later) social disorder. Our third model assesses whether the effects of(prior) physical disorder operate through fear and collective efficacy to affect(later) social disorder, as well as whether fear and collective efficacy may haveindependent effects on social disorder. Finally, we explore a fourth model thatassesses whether it is consistent with the data to claim that physical disordermay be prompting fear, which then has direct effects on social disorder as wellas indirect effects on disorder operating through (lowered) collective efficacy.

Data and Method

The data for this study come from anonymous surveys administered to studentsin grades 6–8 in a large mid-Atlantic urban public school district in 2004–5and 2005–6. The survey was developed and administered annually by thedistrict. We limited our analyses to schools serving all grades between 6 and




FEBRUARY 2009 231

8 and not extending beyond grade 9. Concretely, this means we includedelementary/middle schools (i.e., K–8 or pre-K–8), traditional middle schools(i.e., grades 6–8), and any very close “cousins” to these (e.g., grades 6–9).Within these schools, we then used completed surveys from sixth-, seventh-,and eighth-grade students.

There were 36 schools fitting the targeted description that had studentscomplete surveys in both 2004–5 and 2005–6. Of these, we excluded threeschools that had extremely low response rates (i.e., below 20 percent of enrolledstudents completing surveys in one or both years). That left us with 33 schoolsto be used in our analyses. Among these 33, the percentage of enrolled studentscompleting surveys in 2004–5 ranged from 21.8 percent to 93.4 percent, witha median of 56.3 percent. Rates ranged from 30.5 percent to 95.7 percentfor 2005–6, with a median of 74.0 percent. To explore the extent to whichthe available responses are representative of perceptions and experiencesamong all students in the school, we reestimated all of our regression models,limiting ourselves to schools with response rates above 30 percent, and thenabove 40 percent. The findings (in terms of statistical significance and sub-stantive interpretation) did not change with these sensitivity checks. Therefore,we rely on the available responses within these 33 schools as adequate rep-resentations of perceived school climate.

Within the 33 schools, there are no missing data for our analytic variables,as these variables are based on school-level means of student responses; asalready stated, student responses (albeit not from every enrolled student) wereavailable for both academic years for the 33 schools. Because the surveys wereanonymous, we are not able to link responses to individual report card grades,test scores, or other person-level traits. The fact that we could not link astudent’s survey responses from 2004–5 to his or her responses (if available)from 2005–6 had implications for our choice of statistical method, as will bediscussed below. We use the school as the unit of analysis, with school meansof student survey responses as the analytic variables.

We believe it is appropriate to estimate and interpret statistical models basedon school-level averages of student reports collected in two consecutive yearsfor the following reasons. First, we are assuming that the student responsesavailable for either year are a fairly unbiased set of reports representing thecentral tendency and range of perceptions for that school in that year. Second,even though there was only partial overlap in the students being served by agiven school (and thus being surveyed) in the two years, we are assuming thereare mechanisms by which there is some continuity and likely a causal influencebetween aspects of one year’s school climate and the next year’s. Specifically,thinking about students and families who were new to a school for the secondyear of the study (2005–6), we make assumptions about word of mouth orreputation about the school. We are assuming that some information or rep-





utation about conditions such as physical disorder, social disorder, and col-lective efficacy existing in 2004–5 reached these students and families as theybegan the 2005–6 school year or even during the weeks and months leadingup to that school year. By the logic of our models, conditions existing in2004–5 are hypothesized to have various direct or indirect effects on percep-tions and actions during 2005–6.

Third, we are conceptualizing the contextual variables as properties of theschool (e.g., disorder, climate, collective efficacy) rather than individual stu-dents (Hoy and Feldman 1987). From a methodological perspective, otherstudies have noted that similar indicators of school climate have greater var-iation within schools than between schools (Grodsky and Gamoran 2003;Pallas 1988). These past findings may call into question the very notion ofschool climate or schoolwide conditions experienced similarly by all membersof a school. Aware of these conceptual and measurement issues, we conductedANOVAs with each of the analytic variables to be included in our models.Table 1 provides intraclass correlations (ICCs), which reflect the proportionof total variation in these variables residing between schools as opposed towithin schools. The ICCs range from .08 to .19. We must acknowledge, then,that typically only around one-tenth to one-fifth of the variation in thesevariables resides between schools. F-tests, however, confirm in each case thatthe between-school portion of the variation is a significant part of the totalvariation. These findings provide support for our decision to treat school meansas indicators of school-level traits or conditions.

The following variables are all composite measures based on multiple surveyitems or descriptors of school organization: structural characteristics of theschool, physical disorder, social disorder, collective efficacy, and fear. We de-scribe each analytic variable’s construction and internal reliability. Descriptivestatistics for variables are provided in table 1, and intercorrelations are pro-vided in table 2.

The structural characteristics of the school variable is a composite scalecomputed as the mean of Z-scores for six indicators (Birnbaum et al. 2003).Each of these indicators (after reverse coding where appropriate) has beenshown in previous research to have higher levels associated with more orderlyschool climate and higher achievement (Bevans et al. 2007; Birnbaum et al.2003). The indicators are (a) percentage of classes taught by teachers certifiedas highly qualified, (b) student mobility (percentage of students moving in orout of the school each year; reverse coded), (c) student attendance rate, (d ) per-centage of students receiving free or reduced-price lunch (reverse coded), (e) nat-ural log of enrollment in grades 6–8 (reverse coded), and ( f ) percentage ofeighth-grade students scoring advanced or proficient on a state-administeredstandardized reading test. While there may be conceptual reasons to analyzesome of these indicators independently for their associations with physical and



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FEBRUARY 2009 235

social disorder, small sample size and high collinearity required the use of thecomposite scale. Bolstering the decision to use the constructed scale is a rel-atively high Cronbach’s alpha of 0.84.2

Physical disorder is measured by combining five survey items. These areresponses on a four-point scale to questions asking how much the respon-dent agrees or disagrees with each of the following: (a) the school building isclean (reverse coded), (b) the temperature in my school is comfortable all year-round (reverse coded), (c) the bathrooms in my school are clean (reverse coded),(d ) there are a lot of broken windows, doors, or desks at my school, and(e) vandalism of school property is a problem at the school.3 These items werebased on prior research, which also suggests a high level of correspondencebetween perceptions of disorder and objective ratings (Cantillon et al. 2003).Physical disorder was computed for both the 2004–5 and 2005–6 school years.

As reported in table 1, the school-level Cronbach’s alphas are quite high,at 0.90 and 0.93 for the two years, respectively. The reliability indices aremarkedly lower (0.52 and 0.57, respectively) at the student level. We presentCronbach’s alphas computed at both levels to acknowledge that each of ouranalytic variables is measured more reliably at the school-mean level than atthe student level.4 This is not surprising since, within any particular school,students’ outlying responses on any given survey item tend to cancel oneanother out when averaged. Taken together, the ICCs indicating that a sig-nificant proportion of the variation in our analytic variables resides between(as opposed to within) schools, prior research on school climate supportingthe conceptualization of climate as a school-level construct, and the highschool–level reliabilities support our decision to treat the school as the unitof analysis.

Social disorder combines seven survey items. Students were asked, “To whatextent is each of the following a problem at your school?” The seven itemsare (a) outsiders can get into the school, (b) there is fighting among students,(c) there is physical or verbal abuse of teachers, (d ) students possess weaponslike guns and knives, (e) students pick on other students at school, ( f ) thereis student drug/alcohol abuse, and ( g) fires are set at the school. These itemsall probe for the presence or absence of interpersonal aggression or violenceand were derived from previous measures of neighborhood social disorder(Cantillon et al. 2003; Sampson and Raudenbush 1999) that have been shownto be valid and reliable (Cantillon 2006). Cronbach’s alphas for these sevenitems are 0.92 (2004–5) and 0.93 (2005–6) when computed at the school level.

The composite scale for collective efficacy is intended to combine attach-ment and cohesion with shared expectations for social control and is basedon the measure developed by Sampson et al. (1997). As such, the scale usesfive items that primarily probe for cohesion and another five items that pri-marily ask about shared expectations for control. The cohesion items are





(a) most of the teachers at school know me by name, (b) most of the studentsat school know me by name, (c) students get along well with one another,(d ) students get along well with teachers, and (e) teachers care about theirstudents. The shared expectations items are (a) teachers can handle studentswho disrupt class, (b) my school has clear rules about student behavior,(c) students are rewarded for positive behavior, (d ) my school has programs todeal with violence and conflicts between students, and (e) when I do somethingbad at school, my parent or guardian hears about it. Cronbach’s alphas forthese 10 items are 0.91 and 0.95, respectively, for the two years. Finally, thevariable measuring fear is based on two survey items: (a) I feel safe at thisschool (reverse coded) and (b) I feel safe going to and from school (reversecoded). Cronbach’s alphas are 0.84 and 0.83, respectively, for the two years.

Our statistical method is path analysis with ordinary least squares (OLS)as the estimation technique. Path analysis is an extension of the standardregression model used to test the fit of a correlation matrix against hypothesizedmodels (Cohen et al. 2002; Heise 1975). We considered applying hierarchicallinear models (HLM; Raudenbush and Bryk 2002) and structural equationmodeling (SEM; Bollen 1989), but data constraints limited the feasibility orbenefits of these more sophisticated methods. Regarding HLM, the anonymousnature of the questionnaires meant that we could not link student responsesacross the two years. Furthermore, some students exited the sampled schoolsafter 2004–5, while others arrived only for 2005–6. Thus, there is no straight-forward nesting of students within schools that can be modeled if we are toinclude two years of data as our theoretical agenda recommends. RegardingSEM, these models are most useful when multiple-indicator measurementmodels of latent variables are developed, and these latent variables are thenmodeled within a hypothesized causal scheme. With only 33 schools, we lackedthe power to adequately test multiple-indicator measurement models (Bollen1989). We considered using a SEM approach to conduct path analyses withthe available manifest variables; however, we opted for the simpler OLS re-gression path analyses approach to address our primary aims.

Results

A series of path models was estimated to test the hypothesized set of rela-tionships and correlations among exogenous variables. Figures 1, 3, 5, and 7depict these schemes.5 For example, figure 1 depicts structural characteristics,collective efficacy 2004, and social disorder 2004 as exogenous variables, eachcorrelated with the others. Each of these three variables is modeled as po-tentially having a direct effect on social disorder 2005 and also an indirecteffect that operates through physical disorder 2005.




FEBRUARY 2009 237

FIG. 1.—Conceptual diagram of first model. Intended to assess whether a direct associationbetween physical disorder and social disorder is observed once prior levels of collective efficacyare controlled.

FIG. 2.—Estimated first model. . Adjusted R2 for model of Soc_D_5 p 0.78; adjustedN p 33R2 for model of Phys_D_5 p 0.45. *** .p ! .001

Following the standard logic of path analysis, we estimated a regressionmodel predicting the rightmost endogenous variable, social disorder 2005.Nonsignificant independent variables were dropped one at a time, beginningwith whichever one was furthest from obtaining statistical significance.6 Work-ing from right to left, we then modeled mediating endogenous variables asdependent variables, dropping nonsignificant coefficients. Figures 2, 4, 6, and8 summarize these models graphically and also show standardized coefficients,significance levels, and adjusted R2 values.

The model in figure 1 is designed to assess whether a direct associationbetween physical disorder and social disorder is observed once prior levels of





collective efficacy are controlled. If Wilson and Kelling’s account is an accuratedescription of processes within schools, we expect a strong positive associationbetween physical disorder 2005 and social disorder 2005, even after controllingfor structural characteristics and prior levels of collective efficacy and socialdisorder. If Sampson and Raudenbush’s account is more accurate, we expectthe direct association between physical disorder 2005 and social disorder 2005to become weak or nonexistent once we have accounted for structural char-acteristics and prior levels of collective efficacy.

As figure 2 shows, the path analysis yields significant direct associationsbetween structural characteristics and social disorder 2005 (negative) and be-tween physical disorder 2005 and social disorder 2005 (positive). Prior levelsof social disorder (2004) do not have a direct association with social disorder2005 within this broader hypothesized causal scheme but do have an indirectassociation operating through physical disorder 2005. Collective efficacy 2004drops out of the model altogether. It does not have significant associationswith either physical disorder 2005 or social disorder 2005 within this pathmodel. Furthermore, it is important to note that the estimated coefficientsoriginating from structural characteristics, social disorder 2004, and physicaldisorder 2005 do not change appreciably depending on whether collectiveefficacy is retained in the estimations.

We take the estimated model in figure 2 as initial support for the Wilson-Kelling perspective. A significant association between physical disorder andsocial disorder is observed regardless of whether prior levels of collective ef-ficacy (and prior social disorder) are controlled. When a school rates higheron desirable aspects of structural characteristics (e.g., teacher quality, studentstability, attendance rates), significantly less social disorder is predicted. Wherethere is more reported physical disorder, there tends to be more reported socialdisorder (i.e., threatening, violent, or disruptive interpersonal relations). Finally,one year’s level of social disorder has a positive association with the next year’sphysical disorder, which, in turn, is once again associated with social disorderin that second year.

Certainly the path model in figures 1 and 2 does not establish causation.Path analysis with nonexperimental data is simply an exercise in estimatingregression coefficients within a defensible, hypothesized causal scheme. Themost we can say is that the estimated coefficients are consistent with certaincausal claims and that (in the present situation) the temporal ordering amongvariables lends further support to causal interpretations. Nevertheless, thepath modeling helps us adjudicate between the theoretical claims of broken-windows theorists and others who claim that associations between physicaldisorder and interpersonal aggression are spurious and explained by referenceto previously existing levels of collective efficacy. We interpret these findingsas initial support for the broken-windows theories.




FEBRUARY 2009 239

FIG. 3.—Conceptual diagram of second model. Intended to assess whether fear is acting asan important mediating variable between (prior) physical disorder and (later) social disorder.

FIG. 4.—Estimated second model. . Adjusted R2 for model of Soc_D_5 p 0.85; adjustedN p 33R2 for model of Fear_5 p 0.65. * ; ** ; *** .p ! .05 p ! .01 p ! .001

Figures 3 and 4 introduce fear into the hypothesized scheme and explorewhether the association between perceived physical disorder and social dis-order may operate via altered levels of fear. In this path model, physicaldisorder is measured for the earlier school year (2004–5), and we ask whetherdirect or indirect associations between physical disorder 2004 and social dis-order 2005 are observed when structural characteristics and prior social dis-order (2004) are controlled. The estimated model has a higher adjusted R2

(0.85) than did the model in figure 2. Thus, a greater portion of the variationin social disorder 2005 is accounted for by the elements in figure 4 than wasthe case with figure 2.

In figure 4, structural characteristics are once again estimated to have anegative and significant direct association with social disorder 2005. Addi-tionally, more desirable structural characteristics are associated with less fearin 2005, while greater physical disorder in 2004 is associated with more fear





FIG. 5.—Conceptual diagram of third model. Intended to assess whether effects of (prior)physical disorder seem to be operating through fear and collective efficacy to affect (later) socialdisorder, as well as whether fear and collective efficacy may have independent effects on socialdisorder.

in 2005. Fear 2005, in turn, is strongly positively associated with social disorder2005.

While all of the paths estimated as significant are in the expected directions,it is worth noting paths estimated to be nonsignificant. First, social disorder2004 does not have significant direct or indirect associations with social dis-order 2005 once other variables in the model are considered. This impliesthat a mere stability effect between social disorder in two successive years isnot the dominant story within these data. It is not the case that one year’slevel of social disorder is the strongest predictor of the next year’s socialdisorder, trumping other sociological and psychological factors. Rather, struc-tural characteristics, physical disorder, and fear—which have bivariate asso-ciations with social disorder 2004 but are not fully determined by it—jointlyaccount for much of the variation in social disorder 2005.

Another nonsignificant path is the direct path from physical disorder 2004to social disorder 2005. According to the estimates of figure 4, physical disorderfrom one school year has a strong association with elevated fear in the nextschool year and, through this mechanism, extends its reach to perceived socialdisorder in that second school year. This indirect but not direct associationbetween one year’s physical disorder and the next year’s social disorder sug-gests some important aspects of social and psychological process. We willdiscuss these themes further in the article’s concluding section.

We turn next to figures 5 and 6. The analytic variables featured in figures1 and 3 (structural characteristics, physical disorder, social disorder, fear, andcollective efficacy) are brought together in figure 5’s causal scheme to predict




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FIG. 6.—Estimated third model. . Adjusted R2 for model of Soc_D_5 p 0.87; adjustedN p 33R2 for model of Coll_Eff_5 p 0.59; adjusted R2 for model of Fear_5 p 0.65. * ; **p ! .05 p !

; *** ..01 p ! .001

social disorder 2005. Elaborating upon figure 3, figure 5 is designed to assesswhether associations between (earlier) physical disorder and (later) social dis-order seem to be operating through not only fear but also collective efficacy.

We begin by noting that, as we had seen in previous models, the stabilityeffect between social disorder in 2004 and 2005 is not the dominant story ofthis model estimation. Social disorder 2004 drops out of the estimation, show-ing neither direct nor indirect associations with social disorder 2005 onceother variables in the path model are considered.

Structural characteristics have negative and significant estimated direct ef-fects on social disorder: greater structural resources are associated with lessperceived interpersonal aggression or violence. Further, structural resourcesare estimated to diminish fear, and diminished fear is associated with less socialdisorder. Physical disorder at the earlier time (2004) is associated with greaterfear and lower collective efficacy in 2005. Where there is greater fear, theretends to be more social disorder. Where there is more collective efficacy, theretends to be less social disorder.

Finally, a fourth model is summarized in figures 7 and 8. This model simplyrearranges the paths between variables featured in figure 5. Rather than spec-ifying a path from physical disorder 2004 to collective efficacy 2005, we removethat path and replace it with one from fear 2005 to collective efficacy 2005.

We are interested in this fourth scheme for theoretical reasons. We wantedto confirm that one plausible interpretation of the pattern of observed cor-relations among variables would be the following: it may be the case that adilapidated or distressing physical environment in one year (2004) raises levelsof fear and apprehension in the next year. This process would presumablyoccur via the memories and lived experiences of youth and families who areserved by the school in both years and also through reputation and word ofmouth about the school reaching youth who would be new to the school in





FIG. 7.—Conceptual diagram of fourth model. Intended to assess whether the data are con-sistent with the claim that physical disorder may be prompting fear, which then has direct effectson social disorder as well as indirect effects on disorder operating through (lowered) collectiveefficacy.

FIG. 8.—Estimated fourth model. . Adjusted R2 for model of Soc_D_5 p 0.87; adjustedN p 33R2 for model of Coll_Eff_5 p 0.71; adjusted R2 for model of Fear_5 p 0.65. * ; **p ! .05 p !

; *** ..01 p ! .001

the second year. These elevated levels of fear in the second year (2005) wouldhave direct effects on social disorder and also indirect effects operating through(diminished) collective efficacy.

Such a direct (positive) path from fear 2005 to social disorder 2005 is shownin figure 8. A plausible interpretation is that those who are fearful perceiveand act differently than they otherwise would. First, a given action by aclassmate is more likely to be interpreted as hostile or threatening than itwould be if the overall level of anxiety and fear were lower (Plank et al. 2001).Second, in a climate of high anxiety and fear, a particular individual is more




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likely to act aggressively than he otherwise would, feeling the need or desireto show the world a bold face as a means of self-protection (Anderson 1999).The first of these mechanisms would seem to increase perceived social disorder.The second would likely increase actual social disorder (and surely perceiveddisorder as well). Both actual and perceived social disorder are disruptive toteaching, learning, and healthy adolescent development.

The hypothesized indirect path of fear operating through collective efficacyto affect social disorder is also shown in figure 8. A plausible interpretationis that elevated fear erodes people’s willingness or ability to contribute toattempts at fostering cohesion and communal control of shared spaces, therebyincreasing the likelihood of deviant or aggressive interpersonal behaviors (i.e.,social disorder).

Given the mathematics and logic of path analysis, we cannot adjudicatebetween the models of figures 6 and 8, declaring one of them better fittingor more correct than the other. Indeed, they fit the data exactly equally well—as they must. We simply offer the models as two theoretically motivated andempirically supported schemes of the factors that ultimately affect social dis-order in a set of urban schools serving the middle grades.

Discussion and Conclusions

Our estimated models suggest a robust association between physical disorderand social disorder. This association is not explained away by consideringcommon prior conditions or context such as reported levels of collective ef-ficacy. The association between physical disorder and social disorder is detectedboth directly over short spans of time (i.e., within a single school year) andindirectly through fear and collective efficacy over longer spans of time (i.e.,across successive school years).

It is important to bear in mind a few limitations when interpreting thesefindings. The sample included schools serving students in grades 6–8; thus, itis unclear to what extent these findings will generalize to elementary or highschools. The sample included 33 schools in a single urban community, andthus additional research is needed with larger samples in different communities.Although data are available from the schools across two school years, theanonymous data collection procedures preclude us from knowing exactly theextent to which the sample of students was the same across the two years.Furthermore, we focused on the influence of school-level factors; however,there may be classroom-level characteristics such as teachers’ efforts to managestudent discipline problems, teacher characteristics, or the student compositionof the class (e.g., a high concentration of aggressive students) that may influencethe students’ perceptions of disorder (Koth et al. 2008).





There is much more to be explored on these topics. Future studies couldexplore not just the Wilson-Kelling perspective at a general level as it appliesto urban middle schools (and other schools) but also the relative merits of twosubhypotheses embedded within the Wilson-Kelling theory. The first of thesewe have called the invitation to evildoers claim. This hypothesis takes thesomewhat dark view that a social system always has people lurking within it,or at its edges, who are looking for an opportunity to act on their devianttendencies. According to the theory, when there is physical neglect or disorderthese people will conclude that they have found a setting conducive to anti-social behavior without high probability of sanction.

The second subhypothesis is what we refer to as the idea of physical disorderinvoking fear and eroding collective efficacy. This idea suggests something thatmay even be more worrisome. It is a fairly simple extension to think that ayouth who truly desires an orderly setting conducive to good teaching andlearning gets scared by the physical disorder of some schools. Fear is invoked,and then the student’s actions and reactions are fundamentally reoriented.Because of the physical disorder and ethos of fear, the student presumes thecollective group is not there to keep her safe and she had better revert to self-protection. This subhypothesis is consistent with the adaptive, self-defensivebehaviors described by Elijah Anderson (1999) in his ethnographic book Code

of the Streets. Additional empirical research is needed to understand the extentto which youth hold these types of beliefs and how these attitudes may influencetheir perceptions of and interactions with other students at school.

Taken together, the findings of the current study suggest that educators andresearchers should be vigilant about factors that influence student perceptionsof climate and safety. Fixing broken windows and attending to the physicalappearance of a school cannot alone guarantee productive teaching and learn-ing, but ignoring them likely greatly increases the chances of a troublingdownward spiral. Future research should examine how these factors influenceother student outcomes such as attendance, willingness or ability to engagewith academic tasks, and achievement and also how preventive interventionstargeting school climate can alter these associations.

Notes

We acknowledge grants and persons who facilitated completion of this research. Thedata collection was supported in part by a grant to the Johns Hopkins Center forPrevention and Early Intervention, jointly funded by the National Institute of MentalHealth and the National Institute on Drug Abuse (grant number P30 MH06624).Preparation of the manuscript was supported by grants from the Centers for DiseaseControl and Prevention (grant numbers 1U49CE 000728, R49/CCR318627, andK01CE001333-01) to the Johns Hopkins Center for the Prevention of Youth Violence.




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We thank Clifford Melick, Philip Leaf, and Nicholas Ialongo and staff at the (unnamed)school district for their assistance with the data collection and support of this project.

1. In fact, some of these incivilities are criminal activities.2. Cronbach’s alpha is an index of internal consistency, or reliability, often examined

when a set of variables is being considered for grouping to form a composite scale.3. For the vandalism of school property item, a prompt asked, “To what extent is

each of the following a problem at your school?”4. Structural characteristics of the school, of course, are only measured at the school

level, so the comparison is not relevant.5. Actually, figures 1, 3, 5, and 7 show all paths that are plausible under two or

more competing sets of conceptual expectations. Our Results section is focused onsummarizing which of these competing conceptual schemes are most consistent withour estimated models.

6. Because decisions about declaring particular paths insignificant (and thus to bedropped) involve comparing unstandardized coefficients to their standard errors, weexplored potential concerns regarding collinearity. Given the fairly high bivariate cor-relations shown in table 2, we were careful to verify that our final models (featured infigures 2, 4, 6, and 8) were not the result of unstable or unreliable estimates at thevarious stages of modeling. We did this primarily by examining variance inflation factors(VIFs) at every stage of the modeling. While statisticians have developed no formalcriteria for deciding whether a VIF is large enough to affect the predicted values, onerule of thumb is that serious problems arise only if the square root of a VIF is above2.0 (Belsey et al. 1980; Fox 1997). We had no VIFs above this level for any of thestages of modeling accompanying figures 2 and 4. We exceeded this level slightly (withthe square root of a VIF rising to 2.15) as we adjudicated the predictors of socialdisorder 2005 for the models of figures 6 and 8. Because the most central argumentsof our article follow from figure 2, and because even for figures 6 and 8 any problemsof collinearity are just marginal threats to the precision of estimation, we are confidentof the general integrity of our results.

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