Theories of Delinquency A Contextual Analysis of...

Theories of Delinquency / 753

© The University of North Carolina Press Social Forces, March 2002, 81(3):753-785

A Contextual Analysis of DifferentialAssociation, Social Control, and StrainTheories of Delinquency*

JOHN P. HOFFMANN, Brigham Young University

Abstract

The history of criminological thought has seen several theories that attempt to linkcommunity conditions and individual-level processes. However, a comparative analysisof contextual effects has not been undertaken. This article estimates a multilevel modelthat examines the effects of variables derived from three delinquency theories. The resultsindicate that youths residing in areas of high male joblessness who experience stressfullife events or little parental supervision are especially likely to be involved in delinquentbehavior. The attenuating impact of school involvement on delinquency is morepronounced in urban environments low in male joblessness. These results suggest thatexamining the contextual implications of delinquency theories is important, but theoriesneed to be developed with more attention to specific contextual processes.

The search for macro-micro linkages and how they affect deviant and crimi-nal behavior has a substantial and notable history (Coleman 1990; Durkheim1951 [1897]; Stark 1987). The history of criminological thought has seen Shawand McKay’s seminal work on how social disorganization affects behavior atthe individual level, especially with reference to the qualitative life histories

* Support for this research was provided by National Institute on Drug Abuse grant11293. An earlier version of this article was presented at the 2000 annual meeting ofthe American Society of Criminology, San Francisco, Calif. I thank Bob Bursik, FrankCullen, Bob Agnew, David Greenberg, and an anonymous Social Forces reviewer forhelpful suggestions on earlier drafts. I also appreciate the assistance and advice providedby Bob Johnson, Harvey Goldstein, Jon Rasbash, Ken Rasinski, Shaun Koch, and JingZhou. Please address all correspondence to John P. Hoffmann, Department of Sociology,844 SWKT, Brigham Young University, Provo, UT 84602. E-mail: [email protected].

754 / Social Forces 81:3, March 2003

that they collected (Bursik & Grasmick 1993; Shaw & McKay 1931, 1969);Merton’s discussions of opportunity structures and strain (Merton 1968, 1995);and Sutherland’s discourse on the links between differential association anddifferential social organization (Reinarman & Fagan 1988; Sutherland 1939,1973 [1942]). Although attention to these processes suffered a period of theo-retical and empirical dormancy, the last ten to fifteen years or so has seen aresurgence of interest in how macroprocesses affect microlevel social relation-ships.

At least two motivating factors underlie this resurgence. First, Shaw andMcKay’s (1969) social disorganization theory has been revisited and found tohave merit. A number of studies indicate that aspects of social or communitydisorganization, a macrolevel construct, either affect individual behavior indirectlythrough micro relations or condition the impact of individual-level factors ondelinquent and criminal behavior (Bursik & Grasmick 1993; Elliott et al. 1996;Sampson & Groves 1989; Taylor 1997; Veysey & Messner 1999; Yang & Hoffmann1998). A key theoretical proposition is that socially disorganized communities areless able to control the general behavior of residents, thus affecting delinquent andcriminal behavior via attenuated social control processes (Kornhauser 1978; Shaw &McKay 1931).

The resurgence of social disorganization theory has prompted others to describepotential macro-micro linkages that elaborate several important theories ofdelinquency. These include elaborations of conflict and control processes in thedevelopment of delinquent behavior (Colvin & Pauly 1983; Hagan 1989),differential association and social learning theory to account for structuralinfluences on learning and peer affiliations (Akers 1998; Reinarman & Fagan 1988),and the variable distribution of strains across types of communities (Agnew 1999).

Second, recently developed statistical models, drawn primarily from educationalresearch, now allow precise empirical attention to how macrolevel (contextual)variables condition the impact of explanatory variables on a variety of outcomesof interest to the criminological community. Recent studies have examined whetherschool- and community-level factors affect the relationship between demographic,family, and peer factors and various measures of delinquent behavior, drug use,violence, victimization, and fear of crime (Elliott et al. 1996; Hoffmann 2002;Perkins & Taylor 1996; Rountree, Land & Miethe 1994; Sampson, Raudenbush &Earls 1997). For instance, research suggests that community disorganizationattenuates informal social control, which is then negatively related to adolescentdeviant behavior (Elliott et al. 1997). Community disorganization may also have adirect impact on individual-level deviant behavior, even net of the effects ofindividual-level control mechanisms (Gottfredson, McNeil & Gottfredson 1991;Simcha-Fagan & Schwartz 1986; Taylor 1997).

A limitation of this research has been its conceptual focus on linking socialdisorganization at the contextual level and social control or bonding mechanisms


at the individual level (Bursik & Grasmick 1993; Elliott et al. 1997; Sampson,Raudenbush & Earls 1997; Yang & Hoffmann 1998). Although the links betweensocial disorganization and individual-level bonds are appealing andtheoretically elegant, recent discussions of how other delinquency theories maybe elaborated to include macro-micro connections offer a promising avenuefor research (cf. Agnew 1999; Akers 1998; Reinarman & Fagan 1988; Simcha-Fagan & Schwartz 1986).

In this article, I draw upon three major theories of delinquent behavior —social control, strain, and differential association/social learning — to elabo-rate the community context of adolescent involvement in delinquency.1 Thegoal is to determine whether some of the key individual-level relationshipsexpressed by these theories vary across U.S. communities and, if so, whethercommunity characteristics condition these relationships. To provide motiva-tion for this goal, the following section reviews these three theories with a cleareye toward discussing how their implied relationships might be conditionedby community characteristics. This discussion is followed by an empirical analy-sis designed to test hypotheses concerning the contextual effects of delinquencytheories.

Macro-Micro Context of Delinquency Theories

A key goal of the sociological enterprise, and the criminological initiatives thatit engendered, has been to describe how group processes and environmentalconditions affect individual-level behavior (Durkheim 1982 [1895]; Hechter 1987).Important criminological inquiries drawn from this interest include the following:Why do residents of certain urban regions tend to engage in more delinquent andcriminal behavior than residents of other areas? (Shaw & McKay 1931, 1969; Stark1987). What ecological characteristics affect the probability of gang formation orindividual delinquent behavior? (Short 1997). What community factors affect thefear of victimization or actual victimization? (Perkins & Taylor 1996; Rountree,Land & Miethe 1994). A variety of explanations have been proposed to answerquestions such as these. The following discussion addresses three of theseexplanations: social control (bonding) theory, strain theory, and differentialassociation theory. Although these theories focus primarily on individual-levelprocesses, all are amenable to contextual elaboration.

SOCIAL CONTROL THEORY

Although its individual-level processes are well known due to the work ofHirschi (1969), several observers argue that social control theory’s macro-microlinkages are demonstrated in early criminological work (Kornhauser 1978;


Sampson & Groves 1989). Community disorganization, for instance, is thoughtto attenuate bonding mechanisms by making supervision and interpersonalattachments more tenuous (Elliott et al. 1997; Shaw & McKay 1931; Simcha-Fagan & Schwartz 1986). One might also ask whether communitydisorganization weakens the ability of social bonds to circumscribe delinquentbehavior:

In communities characterized by residential instability and heterogeneity anda high proportion of broken and/or single parent families [i.e., communitydisorganization], the likelihood of effective socialization and supervision is reducedand it becomes difficult to link youths to the wider society through institutionalmeans. (Bursik & Grasmick 1983:37)

Empirical research supports the notion that the impact of social bondsvaries by type of community and that disorganized communities negativelyaffect the ability of social bonds to reduce delinquent behavior. Attachmentto parents and peers, for instance, has a differential impact on delinquent be-havior that depends on the type of community within which it occurs (Krohn,Lanza-Kaduce & Akers 1984; see, however, Reinarman & Fagan 1988). More-over, community disorganization reduces social support structures and thusattenuates effective parenting, an important source of successful socializationand conventional bonding (Peeples & Loeber 1994; Sampson & Laub 1994;Simons et al. 1997; Yang & Hoffmann 1998). In general, social bonds such asattachment and involvement in conventional activities may have significantcountervailing forces in disorganized communities characterized by poor com-munity supervision and control (Sampson 1987); hence their effectiveness atpreventing delinquency is diminished.

STRAIN THEORY

The initial development of strain theory had both macro and micro roots(Agnew 1987; Bernard 1987; Bernard & Snipes 1996; Merton 1995). Merton(1968) posited that opportunity structures affect the ability to realize commoncultural goals, such as the quest for monetary gain. This has primarily astructural component that affects deviant behavior in the aggregate. But it alsohas an individual-level component: The strain of pursuing goals within diverseopportunity structures may lead to adaptations such as crime, delinquency, andother deviant behavior (Cullen 1984). However, assuming that opportunitystructures vary by community (Cloward & Ohlin 1960), it is reasonable to positthat the effects of strains caused by the disjunction between goals and meanson deviant behavior will vary by community. One might hypothesize, forinstance, that strained youths in disorganized communities have a more realisticpicture of their plight, so deviant adaptations become more likely.


Agnew’s (1992) recent elaboration of this theoretical tradition broadens thenotion of strain considerably by conceptualizing it as coming from a variety ofsources, including families, schools, and cognitive skills. Moreover, he hasrecently proposed an elaboration of general strain theory to encompasscommunity effects (Agnew 1999). In general, Agnew posits that “deprived”communities are more likely to be populated by “strained” individuals and thatthese communities will suffer from more blocked opportunity structures.Hence these communities tend to create an atmosphere conducive to angerand frustration, key antecedents to delinquent behavior. Communitycharacteristics produce environments that “condition the effect of strain on . . .crime” (Agnew 1999:128). Since Agnew’s definition of a deprived communityincludes many of the same characteristics that delineate disorganizedcommunities (e.g., economic deprivation, percent minority), it seems clear thathe is proposing that community disorganization either indirectly orconditionally affects deviant behavior via straining mechanisms (for a reviewof the empirical support for these points, see Agnew 1999:130-45).

Similarly, recent studies suggest that stressful life events, an importantstraining mechanism under Agnew’s scheme (cf. Hoffmann & Cerbone 1999),vary by communities. Community disadvantage (an aggregate of poverty,unemployment, and low education) is associated directly with more stressfullife events (Simons et al. 1997), and the impact of life events on variousoutcomes is conditioned by community contexts (Aneshensel & Sucoff 1996;Takeuchi & Adair 1992).

DIFFERENTIAL ASSOCIATION/SOCIAL LEARNING THEORY

Early versions of Sutherland’s differential association theory addressedexplicitly its broader structural implications. Under the term “differential socialorganization” (Akers 1998; Cressey 1960; Matsueda 1988; Reinarman & Fagan1988; Sutherland 1973 [1942]), this macro analogue to differential associationproposes that criminal associations and normative conflict vary acrosscommunity types; it is this variation that explains the distribution of crimerates (Cressey 1960; Reinarman & Fagan 1988). Individuals embedded withinstructural units are differentially exposed to definitions in favor of or opposedto delinquent and criminal behavior; these definitions directly affect one’s owndelinquent behavior (Krohn, Lanza-Kaduce & Akers 1984; Matsueda 1988).This macro-micro link has been described, albeit rather vaguely, but it has beenignored in most empirical examinations (Reinarman & Fagan 1988).

Akers (1998) has recently elaborated his social learning theory to expresslylink macrolevel processes with individual-level learning structures. A key issuefor this elaboration is describing the source of prodeviant definitions andeffectiveness of differential reinforcement across social groups. Akers (1998)sees the source of these differences in whether or not a social system is organizedor cohesive: “The less solidarity, cohesion, or integration there is within a


group . . . the higher will be the rate of crime and deviance” (334). Thismacrostructure then determines whether an individual will be exposed tovarious associations and definitions conducive to delinquency. Akers proposesthat social structural influences on delinquency and other deviant behaviorsare mediated fully by social learning processes.

A social learning model of structural influences has not been testedexplicitly, although several studies support its basic precepts. For example, sociallearning variables such as deviant peer relations and differential reinforcementmay mediate community influences on deviant behavior (Krohn, Lanza-Kaduce & Akers 1984; Simcha-Fagan & Schwartz 1986), although some studiesindicate little variation of social learning’s effects on delinquency (Reinarman &Fagan 1988).

Each of these theories of delinquency offers avenues that link communitycharacteristics and individual-level behavior. Each assumes that there is significantvariation in individual-level correlates of delinquent behavior: bonds, strain, anddifferential associations and reinforcements depend, in part, on macro contexts.Nevertheless, if one is to adopt a social or community disorganization framework(cf. Agnew 1999; Akers 1998; Sampson & Groves 1989), then, in addition tosearching for mediating effects, it is also essential that we ask how communitycharacteristics condition the impact of various individual-level attributes ondelinquent behavior. If various straining mechanisms lead to delinquentadaptations, then areas that allow fewer opportunities to escape strain should see astronger link between strain and delinquent behavior (Agnew 1999). Similarly,community disorganization makes the social bonds that restrain delinquentbehavior less effective, especially since such communities are less able to providesufficiently broad control over residents’ behaviors. Differential associations andreinforcements conducive to delinquent behavior are more likely in certain socialenvironments, and they may be more effective in disorganized environments sinceprosocial definitions and reinforcements are concomitantly less frequent.

Unfortunately, these propositions remain largely untested except byinappropriate statistical models. Whether attention has focused on mediating effectsor conditional effects, studies have relied primarily on single-level regressionmodels. These models are inappropriate since observations are not independentwithin social contextual units; hence variance estimates from these models arebiased (Goldstein 1995).2

The following analysis improves upon previous research by (1) using amultilevel model that allows for the correct specification of the error structure whenexamining macro-micro links, (2) employing nationally representative data froma large sample of adolescents from the U.S., (3) incorporating key variables fromthree common theories of delinquency, and (4) addressing directly the question ofwhether community characteristics condition the impact of these variables ondelinquent behavior. Furthermore, it explores potential indirect effects that areimplied by these three theories.


Data and Methods

The data used to examine the contextual variation of delinquency theories aredrawn from the National Educational Longitudinal Study (NELS), a longitudinalstudy designed to explore the impact of families and schools on a variety ofeducational, vocational, and behavioral outcomes. The initial wave of NELS drewa representative sample of 24,599 eighth-grade students from U.S. schools in 1988.A subsample of this original group was also interviewed in 1990, when most of thestudents were in tenth grade. The sample was also “refreshed” by drawing asupplemental sample of tenth-grade students. Therefore, the tenth-grade sample isrepresentative of tenth-grade students in the U.S. in 1990 (N = 20,706) (NCES1992). Details of the sample selection procedures, interview format, and sampleattrition are provided in NCES (1992). The analysis relies on the tenth-grade samplefor two reasons. First, a larger number of questions about delinquent behavior wereadministered to the tenth-grade participants than to participants in other years.Second, the analysis uses a special NELS data file that has been linked to decennialcensus data at the zip code level. These census data are most appropriate for thetenth-grade data since they were collected in 1990. Thus, the communitycharacteristics that may condition the impact of relevant variables on deviantbehavior are contemporary in the lives of the adolescents.

NELS used a randomly rotating panel of questions, so that some sets were askedonly of a subset of the sample. This reduces the sample size used in the analysis to10,860 adolescents who were in tenth grade in 1990 and, assuming a typical lifecourse trajectory, were scheduled to graduate from high school in 1992.

A special supplemental file was prepared for the National Center of EducationStatistics (NCES) that matches the students’ residential addresses to census tractidentifiers. It was recognized early in the file preparation stage that the typical censustract did not contain a sufficient number of subjects to permit statistical analyses.Therefore, census tract data were aggregated to the zip code level. Census tracts areoften used in studies that examine the impact of neighborhoods on variousoutcomes (Sucoff & Upchurch 1998). Zip codes generally cover a geographic areathat is two to three times the size of a census tract,3 so I do not claim to be examiningneighborhood effects; rather, I use the zip code area as a proxy for a geographicallybounded “community” (cf. Arora & Cason 1998; Corcoran et al. 1992; Hoffmann2002). In the following analysis, the 10,860 adolescents are nested in 1,612communities identified by zip code. Hence, there is an average of about 6.7adolescents per zip code in the applicable NELS data.4

MEASURES

The key explanatory variables in this analysis are conventional definitions, peerexpectations, stressful life events, monetary strain, parental attachment, parentalsupervision, and school involvement. The first two variables are drawn from


differential association/social learning; the next two are used to examine straintheory; and the final three are common measures from social control theory.

Conventional definitions are constructed from a set of nine questions that askedrespondents whether it is “OK” to engage in a variety of deviant activities such asfighting, belonging to a gang, destroying school property, bringing weapons to school,or using illegal drugs. The response categories are (1) often OK, (2) sometimes OK,(3) rarely OK, and (4) never OK. Each variable was standardized prior to computingan additive score, higher values of which indicate that it is rarely acceptable to engagein these types of activities.5 The alpha reliability for this scale is .81.

A limitation of the NELS data set is that it does not ask any direct questionsabout peer behavior, a staple of differential association and social learning theory(Akers 1998; Akers et al. 1979; Matsueda 1982; Mears, Ploeger & Warr 1998; Warr2002). However, there are a set of questions that inquire about one’s friends’expectations concerning behavior and life goals. Hence the measurement of oneaspect of differential reinforcement is feasible (Akers 1998; Akers et al. 1979).Interactions with peers who see the importance of conventional behaviors and goalsprovide reinforcement for those behaviors and goals. The questions that gauge thesereinforcement patterns ask respondents whether, among their friends, the followingactivities are (1) not important, (2) somewhat important, or (3) very important:getting good grades, finishing high school, continuing one’s education past highschool, and studying. After standardizing each item, an additive scale was computed.The alpha reliability for this scale is .81.

To measure strain theory, I draw upon two sets of items. First, continuing atrend that began about ten years ago (Burton et al. 1994; Farnworth & Lieber 1989),traditional individual-level strain is operationalized as the disjunction between thefollowing two items: “How important is it to you to have a lot of money?” and“What are the chances that you will graduate from high school?” Monetary strainis a binary indicator coded 1 if money is very important yet the respondent saidthere is a “low” chance that he or she would graduate from high school, and 0otherwise.6

Second, a scale of stressful life events is included to gauge one important aspectof Agnew’s general strain theory: the presentation of noxious stimuli (Agnew 1992;Hoffmann & Cerbone 1999). Previous studies indicate that stressful life events area consistent predictor of various delinquent and other deviant activities (for a review,see Hoffmann & Su 1998). The scale is conceptualized as a count variable of thenumber of activities experienced over the past year. These fourteen activities includefamily moves, parental divorce or remarriage, job loss among parents, and seriousillness or death among family members. The alpha reliability for this scale is .44,reflecting, not surprisingly, some independence among the items. Since stressprovides cumulative stimuli, however, it is reasonable to represent it as a countvariable (Agnew 1992; Hoffmann & Cerbone 1999).

Social control theory is assessed by three commonly used scales: attachmentto parents, parental supervision, and involvement in school activities. Attach-


ment to parents is measured by four questions that ask respondents about “lik-ing” parents, “getting along” with parents, being “understood” by parents, and“disappointing” parents. The items were coded so that higher values indicateda better relationship with one’s parents. The items were standardized and usedto create a summated scale. The alpha reliability for this scale is .80.

Parental supervision is based on a set of five questions that asked if therespondents’ parents know their friends, know where they go at night and afterschool, know how they spend money, and know what they do with their free time.The alpha coefficient for this standardized additive scale is .84.

School involvement is gauged by questions that asked about participationin seven different types of activities, including honor society, cheerleading,music/theater, hobby clubs, academic clubs, yearbook or school newspaper, andstudent council (cf. Hoffmann & Xu 2002). The variable is coded to count thenumber of activities respondents are involved in, so it ranges from 0 to 7. Thealpha coefficient is .42, thus reflecting some independence in school activities.As with stressful life events, the key is the cumulative impact of schoolinvolvement as a mechanism for attenuating delinquent behavior.

Several additional variables are included in the model as control variables. Sincethere are clearly differences demonstrated in the literature between males andfemales in general delinquency involvement (Mears, Ploeger & Warr 1998) andrace/ethnicity affects involvement in delinquent behavior, I include variablesindexing these demographic characteristics. A set of dummy variables gauges race/ethnicity, with white adolescents representing the omitted reference group. I alsoinclude a dummy variable that measures family structure (0 = living without twobiological parents; 1 = living with two biological parents). Finally, family incomewas included in the model as a set of three dummy variables, with the highestquartile serving as the omitted reference category. Although a number of othercontrol variables were considered, a preliminary analysis examining the impact ofurban/suburban/rural residence and region (North, South, Midwest, West) showedno significant effects. However, as shown in the analysis section, urban residenceemerged as an important consideration.

There are numerous community-level characteristics that might be exam-ined. The analysis is restricted, however, to four variables that previous researchsuggests are important for understanding delinquent and other deviant behav-iors (Chase-Lansdale & Gordon 1996; Hoffmann 2002; Sampson & Groves1989). The variables are often used as indicators of community disorganiza-tion, disadvantage, or economic viability (Elliott et al. 1997; Sampson,Raudenbush & Earls 1997). They are based on data from the 1990 decennialcensus aggregated to the zip code level. Percent female-headed households inthe community ranges from 0% to 24.3%, with a mean of 5.9%; percent un-employed or out-of-workforce males ranges from 0% to 67.8%, with a meanof 10.8%; and percent below the poverty threshold ranges from 0% to 68.3%,with a mean of 12.7%. These variables are assumed to regulate macroprocesses


that make the impact of individual-level characteristics on delinquent behav-ior more or less probable.

The fourth community-level variable used in the analysis is a racial segregationindex. Several studies consider percent black, percent white, or some index ofdissimilarity to gauge the effects of community segregation on behavioral outcomes(Brooks-Gunn et al. 1993; Krivo & Peterson 1996). A common finding is thatpercent black has a curvilinear relationship with community problems, with thelowest prevalence of problems occurring when blacks are a small proportion or alarge proportion of the population (e.g., Messner & South 1992). These measuressuffer from at least two drawbacks for the present study. First, if percent black hasa curvilinear effect on crime and delinquency, then it forces one to introducenonlinear effects in the model. Second, percent black or percent white fails toaddress the role of Hispanics, a large and rapidly growing minority group. In orderto overcome these deficiencies, I considered three alternatives for a racialsegregation index: an entropy-based measure (Theil 1972), a proportion-basedheterogeneity measure (Blau 1977), and a log-linear index derived from work onoccupational sex segregation (Weeden 1998). These measures are free of marginaldependencies and allow one to consider the distribution of three or more groups.They also assess the segregation-integration continuum in a linear fashion. Althoughthe three measures are highly correlated in the NELS zip code–level data (Pearson’sr ≥ .80), I use the log-linear-based index because a series of simulations indicatedthat it was less skewed than the entropy-based or the heterogeneity measures. Thelog-linear-based segregation index is given as follows:

Segregation index =

12 2

3

1 1 1

1 1ln ln

n ni i

j i ii i

p p

n q n q= = =

ÿ þ� �� × − ×� �� (1)

The ratios of pi /qi indicate the three racial/ethnic comparisons within each zipcode.7 The letter i indexes the numbers in the subsamples, and the summation ofj = 1 to 3 indicates that the equation sums the three difference measures to theright (cf. Weeden 1998). The index has a minimum value of 0 that implies thatnon-Hispanic whites, non-Hispanic blacks, and Hispanics are equally representedin the community. The maximum value of about .30 is attained in thosecommunities that are almost fully racially segregated.

The outcome variable, delinquency, is based on six questions that ask aboutpast-year involvement in fighting, getting suspended or expelled from school, andbeing arrested by the police. The response categories for these questions arenever (0), 1-2 times (1), 3-6 times (2), 7-9 times (3), and 10 or more times (4).As is common for this type of variable, a raw additive frequency measure basedon these questions results in a highly skewed outcome variable. Hence thenatural logarithm of this scale (+1) is used as the endogenous variable in the


models. Mean involvement in delinquency is 1.16, with a standard deviationof .86, a minimum of 0, and a maximum of 3.18. The alpha coefficient for thedelinquency scale is .78.

METHODS

Since the data consist of a two-level hierarchy with respondents nested withingeographically bounded communities, a multilevel statistical model is used toestimate the direct and conditional effects of the key explanatory variables ondelinquency. Unlike traditional single-level models, multilevel models allow oneto estimate the variance of some outcome at the individual level and the communitylevel (Goldstein 1995). This is important since we wish to determine whether thepresumed effects drawn from theories of delinquent behavior vary by community.These models also allow the unbiased estimation of cross-level effects, such as thoseexamined between the individual-level variables and community characteristics.

Since the outcome variable is a continuous measure of involvement indelinquency, the model is estimated with a linear regression approach. A Q-Q plotdemonstrates that the logged version of delinquency follows a normal distribution.

Multilevel modeling normally follows a two-step process (Bryk & Raudenbush1992). First, a variance components model is estimated to determine whether thevariance in the outcome of interest differs by the level-2 unit of analysis. If we letyij denote the delinquency score reported by respondent i in community j, thenthe variance components model may be expressed as

Level 1 (respondents): yij = β0j + eij(2)

Level 2 (community): 0 0j juβ γ= +

The second level of equation 2 consists of a single equation: The community-specific intercept of the j-th community is set equal to the sum of an overallintercept and a level-2 random error term.

The presence of two random error terms, eij and u0j, distinguishes the multilevelmodel from the standard linear regression model. The level-1 error term, eij, variesamong respondents, while the level-2 error term, u0j, varies across communities.The presence of level-2 error implies that there are unmeasured community-levelcharacteristics that affect β0j. Thus, β0j varies depending upon the community, ratherthan remaining constant across all communities.

Second, a random coefficients model extends the variance componentsmodel by adding individual-level variables at level 1 and community-levelvariables at level 2. Assuming there are p level-1 and q level-2 explanatoryvariables, the random coefficients model may be written as

Level 1 (respondents): 0 1 1ij j j ij pj pij ijy x x eβ β β= + + + +ÿ


Level 2 (community): 0 00 01 1 0 0j j q qj jw w uβ γ γ γ= + + + +ÿ

1 10 11 1 1 1j j q qj jw w uβ γ γ γ= + + + +ÿ(3)

ÿ

0 1 1Pj P P j Pq qj Pjw w uβ γ γ γ= + + + +ÿ

The first level of equation 3 is the same as in equation 2, except that yij dependsnot only on the community-level intercept, β0j, but also on the community-specificregression slopes denoted by β1j through βPj . Each of the regression parametershas a subscript j that denotes that each of these parameters varies acrosscommunities. When these parameters are specified as random, they are treated asresponse variables in the model. Each may be regressed on the community-levelexplanatory variables. An alternative specification that yields the same results is toestimate a series of cross-level interactions, such as

( )00 10 1 01 1 11 1 1 0 1 1ij ij j ij j j ij j ijy x w x w u x u eγ γ γ γ= + + + + + + (4)

This model specification is useful for determining whether the community-levelvariables amplify or dampen the effects of the individual-level explanatory variableson the outcome variable (Goldstein 1995).

Although the most general formulation of equation 4 could include a largenumber of parameters, we specify only the level-1 intercept and the key explanatoryvariables drawn from the theories of interest as random at level 2. This is a practicalconstraint for two reasons: the first is that one of our goals is to determine whetherthese effects on the outcome vary across communities; the second is that the sparsecommunity subsamples limit the number of random coefficients that may beestimated in the model (Goldstein 1995). Hence we specify the level-1demographic variables (sex, race/ethnicity, family structure, family income) as fixedeffects in the model.8

The models shown below were estimated using a restricted interactivegeneralized least squares (RIGLS) approach and validated using a Monte CarloMarkov Chain (MCMC)–Gibbs sampling estimation method (Browne & Drapern.d.; Gilks, Richardson & Spiegelhalter 1996) available in the software packageMLwiN (Goldstein et al. 1998).9 In order to guard against capitalizing on chanceto obtain significant results when examining the models with cross-level interactionterms, model fit is determined by the AIC statistic. The AIC statistic is sensitive tosample size and penalizes models that simply include additional parameters yetprovide no additional statistical information about the outcome variable (Heck &Thomas 2000). An R2 measure, based on the proportional reduction in errorfor predicting the individual-level delinquency measure, is also used todetermine model fit (Snijders & Bosker 1999).


Results

Table 1 provides a crude assessment of the cross-level conditional effects thatare examined in this study. Along with the means and standard deviationsoverall, the table presents the mean values of the level-1 variables at threecategories of each level-2 variable based on their quartiles. Post hoc multiplecomparison tests are used to determine whether there are significant differencesacross the categories (Westfall et al. 1999). Assuming that recent cross-leveltheorizing is correct, one might expect youth from disorganized communitiesto experience more stress, fewer positive roles and relationships, and moreinvolvement in delinquent activities. The crude results shown in Table 1 do notconsistently support such hypotheses. As generally expected, there is slightlymore delinquency in areas with a higher proportion of jobless males orresidents living below the poverty threshold. The other results provide noconsistent picture, however. Conventional definitions and peer expectationsvary little across communities, except in high poverty areas. There is slightlyless parental supervision in high poverty areas, and there is less schoolinvolvement in areas high in poverty or female-headed households.

Table 2 shows the initial multilevel models. Model 1 exhibits the variancecomponents model. Exponentiating the fixed effects intercept term provides theexpected value of delinquency among the adolescents (e1.16 – 1 = 2.19). Moreimportant for this analysis, though, is the random effects intercept. This termindicates that the frequency of delinquency varies significantly across the level-2communities. Average expected delinquency varies across communities from alow of about 1.9 to a high of about 2.5 (95% confidence intervals). This significanteffect coupled with an intraclass correlation of .05 suggests that modeling theproposed effects with a single-level regression model would lead to biased estimates.

Model 2 includes the control variables and random intercept only. The randomeffect for the intercept remains significant. The coefficients for the control variablesindicate that males and adolescents who do not live with both biological parentsare more likely to be involved in delinquency. Moreover, blacks and Asian/PacificIslanders are less likely than whites to be involved in delinquent behavior.

Model 3 provides an assessment of the fixed and random effects of the keyindividual-level explanatory variables. Most of the variables demonstrate theirexpected fixed effects: Adolescents who report more stressful life events, fewerconventional definitions, lower peer expectations, poor parental attachment, lessparental supervision, or involvement in fewer school activities are more likely thanother adolescents to be involved in delinquent activities. Monetary strain doesnot significantly affect delinquency in general (cf. Farnworth & Lieber 1989).A further exploration of the effects of monetary strain suggests that itssignificant effects dissipate once parental attachment and supervision are addedto the equation.


TABLE 1: Distribution of Individual-Level Variables, by Community-Level Characteristics, National EducationalLongitudinal Study, 1990

Percent Female- Percent Unemployed orTotal Segregation Index Headed Households Out-of-Workforce Males Percent Poverty

Variable Mean S.D. Low Medium High Low Medium High Low Medium High Low Medium High

Individual-level variablesPercent male 47.5 45.7 48.1 48.1 49.1 47.8 45.3* 50.0 47.8 44.3* 51.3 46.8 45.1*

Percent Asian/Pacific Islander 7.5 13.2 7.2 2.5* 6.2 8.5 7.0* 11.2 7.5 4.0* 10.1 8.0 4.1*

Percent black 9.5 12.9 10.4 4.3* 2.0 5.8 24.5* 5.5 8.4 15.9* 3.6 6.9 20.8*Percent Hispanic 12.2 24.5 8.3 7.6* 5.9 10.4 22.0* 9.9 12.9 13.2* 5.2 10.2 23.0*Percent white 70.8 49.4 74.1 85.6* 85.9 75.3 46.5* 74.0 71.2 66.9* 81.1 74.9 52.1*

Percent living w/biological motherand father 66.6 63.1 67.5 68.4* 69.4 68.1 60.8* 70.4 66.3 63.5* 73.9 66.0 60.7*

Conventionaldefinitions 34.2 2.9 34.2 34.2 34.1 34.1 34.2 34.2 34.2 34.2 34.2 34.0 34.1 34.4*

Peer expectations 9.9 1.9 9.9 10.0 9.8* 9.8 9.9 10.0 10.0 9.9 9.9 10.0 9.8 9.9*

Stressful life events 1.0 1.2 1.1 1.0 1.0 1.0 1.0 1.1 1.0 1.0 1.0 .9 1.0 1.1*Monetary strain

(percent yes) .6 .7 .5 .5 .3 .4 1.0* .3 .6 .5 .6 .5 .6

Parental attachment 19.1 4.5 18.9 19.0 19.3* 19.2 19.1 18.8 19.1 19.0 19.1 19.1 19.0 19.1Parental supervision 11.3 3.4 11.3 11.4 11.1* 11.3 11.4 11.2 11.4 11.3 11.1 11.4 11.3 11.1*School involvement 1.0 1.1 .9 1.0 1.0* 1.1 1.0 .9* .9 1.0 1.0 1.1 .9 .9*

Theories of D

elinqu

ency / 767

TABLE 1: Distribution of Individual-Level Variables, by Community-Level Characteristics, National EducationalLongitudinal Study, 1990 (Continued)

Percent Female- Percent Unemployed orTotal Segregation Index Headed Households Out-of-Workforce Males Percent Poverty

Variable Mean S.D. Low Medium High Low Medium High Low Medium High Low Medium High

Community-level variablesSegregation index .13 .5Percent female-headed

households 5.92 3.3Percent unemployed

or out-of-workforcemales 10.84 3.5

Percent below povertythreshold 12.68 9.3

Outcome variablePast-year delinquency

(0-3.18) (naturallogarithm) 1.16 .9 1.14 1.17 1.15 1.14 1.15 1.20 1.10 1.16 1.18* 1.14 1.17 1.18*

(N = 10,860 observations and 1,612 communities)

Note: “Low” refers to the lowest quartile, “medium” to the second and third quartiles, and “high” to the highest quartile of the distribution. Kruskal-Wallis testswith Dunn’s multiple comparison adjustments (Daniel 1990) and a step-down bootstrap adjustment for multiple mean comparisons (Westfall et al. 1999)were used to determine significant differences across community types. The numbers shown are means based primarily on additive scales; standardized scalesare used in subsequent analyses.

* p < .05 (two-tailed)


TABLE 2: Multilevel Linear Regression Models of Delinquent Behavior,National Educational Longitudinal Study, 1990

Parameter Model 1 Model 2 Model 3 Model 4

Fixed effectsIntercept 1.16 (.01)* 1.23 (.02)* 1.37 (.04)* 1.29 (.05)*

Individual-level variablesMale .21 (.02)* .04 (.01)* .04 (.02)*Asian/Pacific Islandera –.33 (.03)* –.26 (.03)* –.27 (.03)*Blacka –.23 (.03)* –.13 (.03)* –.15 (.03)*Hispanica .01 (.03) .03 (.03) .03 (.03)Biological mother and father –.23 (.02)* –.14 (.02)* –.14 (.02)*Stressful life events .05 (.01)* .05 (.01)*Monetary strain .13 (.11) .13 (.11)Conventional definitions –.07 (.00)* –.05 (.00)*Peer expectations –.03 (.00)* –.04 (.00)*Parental attachment –.04 (.00)* –.04 (.00)*Parental supervision –.01 (.00)* –.01 (.00)*School involvement –.05 (.01)* –.05 (.01)*

Community-level variablesSegregation index –.14 (.18)Percent female head .78 (.31)*Percent jobless males .93 (.27)*Percent poverty .41 (.12)*

Random effectsIntercept .04 (.01)* .03 (.01)* .02 (.01)* .02 (.01)*Stressful life events .01 (.00)* .01 (.00)*Monetary strain .13 (.12) .12 (.12)Conventional definitions .00 (.00) .00 (.00)Peer expectations .00 (.00) .00 (.00)Parental attachment .00 (.00) .00 (.00)Parental supervision .00 (.00) .00 (.00)School involvement .00 (.00) .00 (.00)

Level-1 error .78 (.01)* .68 (.01)* .49 (.01)* .48 (.01)*AIC 2.53 2.49 2.23 2.21R2 (level 1) .13 .38 .39

(N = 10,860)

Note: The outcome variable is a logged frequency measure that gauges involvement in six types ofdelinquent behavior in the past year. The random effects were estimated in piecemeal fashion byestimating a random intercept and then adding the relevant groups of variables in three separatemodels. The final models were validated with an MCMC-Gibbs sampling approach using (Baye-sian) diffuse Γ–1 priors. The table shows coefficients with standard errors in parentheses. Familyincome effects are not shown.

a The comparison group is white adolescents.



Model 3 also indicates that the notion that the effects of the key explanatoryvariables vary across communities is not supported. With but one exception, theeffects of variables drawn from differential association/social learning, strain, andsocial control theory are invariant across a range of communities (cf. Krohn,Lanza-Kaduce & Akers 1984; Reinarman & Fagan 1988). The one exceptioninvolves stressful life events: Their effects vary significantly, yet quite modestly,across communities. The random effect suggests that in certain communitiesthey have a stronger impact on delinquency than in other communities. Theexpected range of this effect is from .03 to .07 (95% confidence intervals), thusindicating a modest significant difference across the set of communities.

Model 4 adds the community-level characteristics to the multilevel equa-tion. The inclusion of these variables has little effect on the other coefficientsin the model. However, three out of the four community-level variables areassociated significantly with delinquency. Adolescents living in communitieswith more male joblessness, a higher percentage of female-headed households,and more poverty are more likely than adolescents living elsewhere to be in-volved in delinquent behavior, even after controlling for the effects of a hostof individual-level variables, including several drawn from important theoriesof delinquency.

As a final modeling exercise, I computed a series of cross-level interaction termsto determine whether, even in the absence of significant random coefficients, theremight be some conditional effects based on community characteristics. Mostrelevant for this exercise are the interactions between the community-level variablesand stressful life events. The results of this model (see Table 3) indicate that therandom effects of stressful life events on delinquency are not conditioned bycommunity characteristics. The only cross-level interaction that approachedsignificance was stressful life events × percent jobless males (β = .46, p ≅ .11). Itsuggests that in communities with a high proportion of jobless males the impactof stressful life events on delinquency is particularly consequential. Nevertheless,the p-value must make one suspicious of this interpretation. Moreover, the AIC(2.21) indicates that including the interaction terms does not improve the model(cf. Table 2, model 4). No other cross-level interaction approached significance.10

ARE CONDITIONING EFFECTS OF COMMUNITY VARIABLES SPECIFIC TO URBAN AREAS?

Although the lack of varying effects of the individual-level variables ondelinquency may seem disheartening to those who advocate a contextualapproach for delinquency theories, one should recall that many of the seminalarguments that informed criminological theory emerged from studies of urbanareas (e.g., Cloward & Ohlin 1960; Shaw & McKay 1931, 1969; Stark 1987;Sutherland 1973 [1942]). Hence it is not unreasonable to ask whether theimpacts of strain, definitions, social reinforcement, or social bonds ondelinquent behavior are variable within urban areas. To examine this issue, I


TABLE 3: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National EducationalLongitudinal Study, 1990

Parameter Coefficient

Intercept 1.28 (.09)*

Individual-level variablesStressful life events .02 (.02)Monetary strain .33 (.43)

Conventional definitions –.05 (.01)*Peer expectations –.04 (.00)*

Parental attachment –.04 (.00)*Parental supervision .01 (.01)School involvement –.10 (.02)

Community-level variablesSegregation index –.14 (.18)Percent female head .65 (.35)Percent jobless males 1.04 (.81)Percent poverty .79 (.35)*

Interaction termsStressful life events × percent female head –.23 (.22)Monetary strain × percent female head .25 (.23)Conventional definitions × percent female head .03 (.06)Peer expectations × percent female head .13 (.09)Parental attachment × percent female head –.12 (.09)Parental supervision × percent female head –.09 (.08)School involvement × percent female head .45 (.38)

Stressful life events × percent jobless males .46 (.28)Monetary strain × percent jobless males .74 (.73)Conventional definitions × percent jobless males .04 (.05)Peer expectations × percent jobless males –.08 (.10)Parental attachment × percent jobless males –.00 (.07)Parental supervision × percent jobless males –.08 (.07)School involvement × percent jobless males .30 (.24)

Stressful life events × percent poverty –.07 (.09)Monetary strain × percent poverty –.61 (.98)Conventional definitions × percent poverty –.08 (.08)Peer expectations × percent poverty .03 (.04)Parental attachment × percent poverty .05 (.04)Parental supervision × percent poverty .04 (.03)School involvement × percent poverty –.03 (.10)


restricted the sample to adolescents residing in urban areas only. The NELSsample contains 2,061 adolescents residing in urban areas nested within 266geographically bounded communities. The results of fitting identical multilevelmodels are presented in Table 4.

The initial two models, model 1 and model 2, were quite similar to thoseshown in Table 2. In other words, males were more involved, and blacks, Asian/Pacific Islanders, and those living with both biological parents were less involvedin delinquency. Moreover, the mean level of delinquency varied significantlyacross urban communities by approximately the same degree as in theunrestricted sample.

Model 3 includes the effects of the key explanatory variables. It appears thatin urban communities, stressful life events do not affect delinquency whereasmonetary strain does. This supports the notion that a traditional measure ofstrain has its most consequential impact on urban environments (cf.Farnworth & Lieber 1989). However, it should be noted that while the meaneffect of stressful life events on delinquency is not significant, their effect doesvary across urban communities. Hence they may affect delinquency in sometypes of urban areas.

It is also interesting to compare the impact of items drawn from differentialassociation/social learning and social control theory. Those who report moreconventional definitions, peer expectations, parental attachment, and schoolinvolvement are less likely to be involved in delinquent behavior, but the impact

TABLE 3: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National EducationalLongitudinal Study, 1990 (Continued)


Level-1 error .49 (.02)*AIC 2.21R2 (level 1) .39

(N = 10,860)

Note: The outcome variable is a logged frequency measure that gauges involvement in six types ofdelinquent behavior in the past year. Although the full model was included (see model 4 ofTable 2), only the fixed effects interaction terms and their constituent variables are shown for easeof presentation. The interactions that involved the segregation index were omitted from the finalmodel since none approached significance. The final model was validated with an MCMC-Gibbssampling approach using (Bayesian) diffuse Γ–1 priors. The table shows coefficients with standarderrors in parentheses.

* p < .05 (2-tailed)


TABLE 4: Multilevel Linear Regression Models of Delinquent Behavior,National Educational Longitudinal Study, 1990 (Urban Areas Only)

Parameter Model 1 Model 2 Model 3 Model 4

Fixed effectsIntercept 1.12 (.02)* 1.18 (.05)* 1.31 (.08)* 1.22 (.11)*

Individual-level variablesMale .19 (.04)* .04 (.03) .05 (.03)Asian/Pacific Islandera –.30 (.06)* –.27 (.06)* –.27 (.06)*Blacka –.19 (.06)* –.09 (.06) –.07 (.06)Hispanica .01 (.05) .01 (.04) .04 (.05)Biological mother-father –.18 (.04)* –.12 (.04)* –.11 (.04)*Stressful life events .02 (.01) .03 (.02)Monetary strain .45 (.21)* .43 (.21)*Conventional definitions –.05 (.00)* –.04 (.00)*Peer expectations –.04 (.01)* –.04 (.01)*Parental attachment –.04 (.01)* –.04 (.01)*Parental supervision –.01 (.01) –.01 (.01)School involvement –.04 (.02)* –.04 (.02)*

Community-level variablesSegregation index –.32 (.44)Percent female head .32 (.69)Percent jobless males 1.60 (.73)*Percent poverty .68 (.29)*

Random effectsIntercept .04 (.01)* .03 (.01)* .02 (.01)* .03 (.01)*Stressful life events .01 (.00)* .01 (.00)*Monetary strain .00 (.00) .00 (.00)Conventional definitions .001 (.000)* .001 (.000)*Peer expectations .00 (.00) .00 (.00)Parental attachment .001 (.000)* .001 (.000)*Parental supervision .00 (.00) .00 (.00)School involvement .00 (.00) .00 (.00)

Level-1 error .67 (.02)* .64 (.02)* .54 (.03)* .51 (.04)*AIC 2.49 2.46 2.25 2.23R2 (level 1) .05 .25 .27

(N = 2,061)

Note: The outcome variable is a logged frequency measure that gauges involvement in six types ofdelinquent behavior in the past year. The random effects were estimated in piecemeal fashion byestimating a random intercept and then adding the relevant groups of variables in three separatemodels. The final models were validated with an MCMC-Gibbs sampling approach using (Baye-sian) diffuse Γ–1 priors. The table shows coefficients with standard errors in parentheses. Familyincome effects are not shown.

a The comparison group is white adolescents.



of definitions and parental attachment vary across the urban communitiessampled in NELS. Hence conclusions drawn from the entire NELS sample —which include many diverse communities from throughout the U.S. — maybe hasty. Consistent with the seminal descriptions of two of these theories, thereis variability across urban communities.

The next step is to determine whether the community characteristicsassessed in this study condition the variable impact of the individual-levelconstructs. Model 4 provides the first model designed to examine this issue.Note first that, among the community-level variables, both percent jobless malesand percent poverty are significantly associated with delinquency. These resultssuggest that involvement in delinquent behavior is especially likely in urbanareas with a large proportion of unemployed or out-of-workforce males or ahigh percentage of residents living below the poverty threshold.

A series of cross-level interaction terms (see Table 5) indicate that thepercent of jobless males in a community interacts significantly with stressfullife events (β = 1.12, p < .05), school involvement (β = 1.11, p < .05), andparental supervision (β = –.48, p < .05) to affect delinquency. These interactionterms indicate that the positive impact of stressful life events and the negativeimpact of parental supervision on delinquent behavior is much more substantialin communities that include a high proportion of males who are unemployedor out of the workforce. For example, the expected delinquency among youthswith a high degree of stressful life events (one standard deviation above themean) in high jobless areas (15%) is 6.4, whereas the expected delinquency inthese areas among youths with a low degree of stressful life events (onestandard deviation below the mean) is 1.2. In low jobless areas this differenceis much less dramatic (3.2 vs. 1.6). Similarly, in low jobless areas (4%) thedifference in expected delinquency between youths with high parentalsupervision and those with low parental supervision is quite modest(2.0 vs. 2.5); in high jobless areas (15%) the expected difference is much moresubstantial (1.2 vs. 6.6). Hence, in areas with low rates of joblessness, theexpected effects of stressful life events and parental supervision are much moremodest. On the other hand, the attenuating impact of school involvement ondelinquency is more substantial in urban environments that have low rates ofmale joblessness. In fact, using the results to estimate the expected value ofdelinquency suggests that there is a positive relationship between schoolinvolvement and delinquency in areas of high joblessness; the anticipatednegative relationship occurs only in areas of low joblessness. School activitiesmay offer conventional alternatives for youth only in areas that are able tosupport complementary activities and involvement (Hoffmann & Xu 2002).


TABLE 5: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National EducationalLongitudinal Study, 1990 (Urban Areas Only)


Intercept .86 (.24)*

Individual-level variablesStressful life events .11 (.06)Monetary strain .92 (.81)

Conventional definitions –.05 (.02)*Peer expectations –.02 (.01)

Parental attachment –.05 (.01)*Parental supervision –.01 (.01)School involvement –.19 (.06)*

Community variablesSegregation index –.28 (.45)Percent female head .58 (.94)Percent jobless males 1.72 (.77)*Percent poverty .38 (.19)*

Interaction termsStressful life events × percent female head –.00 (.61)Monetary strain × percent female head .89 (.76)Conventional definitions × percent female head .21 (.19)Peer expectations × percent female head .38 (.29)Parental attachment × percent female head –.51 (.29)Parental supervision × percent female head –.04 (.08)School involvement × percent female head –.13 (.51)

Stressful life events × percent jobless males 1.12 (.54)*Monetary strain × percent jobless males .71 (.77)Conventional definitions × percent jobless males –.10 (.21)Peer expectations × percent jobless males –.30 (.24)Parental attachment × percent jobless males .37 (.23)Parental supervision × percent jobless males –.48 (.18)*School involvement × percent jobless males 1.11 (.54)*

Stressful life events × percent poverty –.21 (.23)Monetary strain × percent poverty –.70 (.93)Conventional definitions × percent poverty –.13 (.08)Peer expectations × percent poverty –.08 (.08)Parental attachment × percent poverty .12 (.08)Parental supervision × percent poverty .08 (.07)School involvement × percent poverty .29 (.23)


Discussion

Recent theoretical activity in criminology has adopted the notion that macroconditions affect the relationship between individual-level variables and delinquentbehavior. The history of sociological thought, in fact, almost requires the existenceof these indirect or conditional relationships. Social control theory, strain theory,and differential association/social learning theory have each been elaborated to positthat community characteristics — a key macrolevel construct — affectimportant aspects of their theoretical structure. For instance, disorganizedcommunities are thought to weaken social bonds, expose residents to morestressful environments which offer little chance of escape and reinforceperceived blocks to opportunity, and provide deviant learning opportunitiesand reinforcements (Agnew 1999; Akers 1998; Elliott et al. 1996; Fischer 1984;Sampson & Groves 1989). Each of these conditional characteristics is deemedto increase the risk of individual-level involvement in delinquent behavior.

Using data from a large, nationally representative survey of U.S. adolescents,there is little evidence, in general, that these indirect or conditional relationshipsexist. Rather, if one uses models that observe a range of diverse communities acrossthe United States, key variables drawn from three major theories of delinquencyare equally predictive of delinquent behavior. Moreover, the results supportrecent work that indicates that poverty and joblessness at the community levelare associated with more delinquency (Sampson 1987; Short 1997). The value

TABLE 5: Multilevel Linear Regression Model of Delinquent Behavior,Interaction and Constituent Effects Only, National EducationalLongitudinal Study, 1990 (Urban Areas Only) (Continued)


Level-1 error .48 (.02)*AIC 2.22R2 (level 1) .29

(N = 2,061)

Note: The outcome variable is a logged frequency measure that gauges involvement in six types ofdelinquent behavior in the past year. Although the full model was included (see model 4 ofTable 4), only the fixed effects interaction terms and their constituent variables are shown for easeof presentation. The interactions that involved the segregation index were omitted from the finalmodel since none approached significance. The final model was validated with an MCMC-Gibbssampling approach using (Bayesian) diffuse Γ–1 priors. The table shows coefficients with standarderrors in parentheses.



of the current study is that it shows, in one sense, the unique impact of theseindividual-level and macrolevel variables on delinquency.

At first glance, one might contend that the results cast serious doubt onthe utility of recent macro-micro theorizing in criminology. Taking a moreoptimistic view, one might argue that these three theories of delinquency (orat least key variables drawn from each) offer general explanations of adoles-cent behavior that transcend broader structural conditions. Hence, when oneconsiders attempts by various criminologists to develop general theories ofcriminal and delinquent behavior, the results of this study are promising. Theysuggest that definitions that oppose delinquent behavior, peer reinforcementof prosocial activities, absence of stress, solid attachment to parents, sufficientparental supervision, and involvement in conventional activities all serve todiminish the likelihood of delinquent behavior, regardless of where they oc-cur (Akers 1998; Reinarman & Fagan 1988).

Moreover, the results using the full sample indicate that, consistent withprevious studies, the percentage of unemployed or out-of-workforce males, theproportion of female-headed households, and the percent living below the povertyline significantly affect delinquent behavior. These relationships are not mediatedor moderated by individual-level variables (cf. Akers 1998; Chase-Lansdale &Gordon 1996). Therefore, the explanation for these effects is elusive, although severalobservers have pointed out the pernicious role that male joblessness and otherneighborhood characteristics play in communities (Sampson 1987; Wilson 1996).As Shaw and McKay (1931) described several decades ago, communities that areimpoverished economically and socially may have particular difficulties controllingthe behavior of residents. Community supervision is inadequate, organizations thatoffer alternative resources and activities find it difficult to thrive, and residents donot perceive that they have the ability or support to affect community change(Bursik & Grasmick 1993; Sampson, Raudenbush & Earls 1997; Simcha-Fagan &Schwartz 1986). These communities may also provide substantial opportunitiesfor delinquent and criminal behavior (Cloward 1959; Felson 1998; Stark 1987).Without additional information not available in this study, however, anyinterpretation of these direct community-level effects must be tentative.

Nevertheless, a key drawback of such a broad macro-micro test is that itignores an important issue. That is, the major sociological theories ofdelinquency emerged from research on urban areas. Shaw and McKay’s (1969)seminal work on social disorganization theory, for example, developed fromobservations restricted to Chicago’s inner-city areas, which they subsequentlybroadened by examining other urban areas in the U.S. (Shaw & McKay 1931).Sutherland’s macrolevel notions about differential social organization weremotivated by a concern about why so much crime and deviance seemed tooccur in urban areas, especially among urban minorities (Sutherland1973 [1942]). Similarly, Fischer’s (1984) ideas about how urbanism affects


deviant behavior draws partly from Sutherland by stressing the opportunitiesand social supports for these behaviors (Stark 1987). And while Merton’s (1968,1995) proposed links between anomie and deviant behavior were concernedprimarily with broad cultural and social processes (Bernard 1987; Bernard &Snipes 1996), the main uses of his theory have concerned the etiology of seriousoffending among inner-city youth (e.g., Cloward & Ohlin 1960). It is thusreasonable to ask whether the most popular theories of delinquency areactually theories of urban adolescent behavior.

In response to this line of reasoning, the multilevel models were reestimatedusing a subsample restricted to adolescents residing in urban areas. With respectto the main effects of the individual-level explanatory variables, the results ofthe models using the full and urban samples were roughly similar. The onlydifference involved the role of strain: Stressful life events significantly affectdelinquency in the general population, while monetary strain significantlyaffects delinquency in urban communities. In addition, the rates of malejoblessness and poverty have similar positive relationships with delinquencyin both models (although the size of these relationships is larger in the urbanmodel). Consistent with the ideas that motivated this study, however, the impactof several of the individual-level explanatory variables on delinquent behaviorvaries significantly across urban communities. In particular, the effects ofstressful life events, conventional definitions, and parental attachment dependupon the types of urban communities in which they are observed. Although itis difficult with these limited data on community characteristics to pinpointthe types of communities in which these variables had stronger or weakereffects, one important cross-level interaction emerges. This interactionindicates that stressful life events are more consequential in communitiessuffering from high rates of male joblessness. In these communities, adolescentswho are exposed to more stressful life events are highly likely to reportinvolvement in delinquent behavior, perhaps because they are more likely toassociate with other “strained” individuals and perceive fewer opportunitiesto escape their plight (Agnew 1999). Hence, as hypothesized by Agnew (1992,1999), they are likely to react to strain with anger and thus engage in delinquentbehavior. Moreover, although there is no evidence that the impact of schoolparticipation or parental supervision on delinquency varies randomly, theeffects of both of these individual-level variables on delinquency depends, inpart, on community-level rates of male joblessness. It seems that parentalsupervision has a more important effect on delinquency in areas where malejoblessness is high.

Although these results appear inconsistent with recent theorizing thatposits that “disorganized” communities are less able to take advantage of familyresources to control adolescent behavior (Furstenberg 1993; Peeples & Loeber1994; Sampson & Laub 1994; Simons et al. 1997; Yang & Hoffmann 1998), they


are compatible with recent research on the pernicious role that male joblessnessplays in communities (Almgren et al. 1998; Short 1997; Wilson 1996). Wilson(1996) argues, for example, that joblessness is a key ingredient to socialdisorganization in a community, along with crime and drug abuse. Poverty isless likely to result in disorganization if residents hold jobs, although, as we seeabove, poverty is positively related to delinquency even after controlling formale joblessness. Following this line of reasoning, adolescents from moredisorganized communities benefit substantially more than adolescents fromorganized communities when they are supervised by parents. Althoughsupervision may be difficult in these communities as parents are pulled awayfrom their families by other financial and social concerns (Furstenberg 1993),it clearly serves as an important mechanism through which the likelihood ofinvolvement in delinquency is diminished.

Similarly, recent research on the disintegration of community resources inmany urban areas indicates that this trend has affected disorganizedcommunities more than others (Furstenberg 1993; Furstenberg et al. 1999).Hence parents in these communities have few extrafamilial resources to drawupon in raising children. The families that successfully dissuade adolescentsfrom participating in delinquent activities, therefore, are those that depend onclosely supervising and restricting activities (Furstenberg et al. 1999). In areaswhere raising children is more of a collective enterprise, there is less need forparental supervision to affect involvement in delinquency.

Moreover, the finding that areas of high joblessness have more delinquency,even after controlling for individual-level processes and other communitycharacteristics, helps elaborate criminological theorizing about opportunities androutine activities (Cook 1986; Felson 1998). A debate in the criminology literatureis that unemployment has countervailing effects on crime and delinquency: It mayincrease the motivation to commit crime (Kohfeld & Sprague 1988) or it maydecrease crime because of increased guardianship (Cantor & Land 1985; Cook1986). The results of the present study suggest that, if there is a guardianship effectthat is linked to unemployment patterns, it is outweighed substantially by otherfactors (e.g., community stress due to high poverty or joblessness; lack of access tolegitimate opportunities; lack of collective supervision of adolescent activities).11

Although the results support at least two conditional effects of variablesdrawn from major theories of delinquent behavior, there is an importantlimitation that recommends further research on this topic. That is, the outcomemeasure admittedly focuses on relatively minor forms of delinquency. TheNELS data set is limited in the number of questions that address delinquentbehavior. It does not include measures of more serious forms of delinquency(e.g., robbery, sexual assault, or other forms of violent behavior), yet it is thesebehaviors that may be affected most by community characteristics (Sampson1987; Sampson, Raudenbush & Earls 1997; Short 1997).


In sum, although much recent effort has been expended to describe thecontextual effects of some common theories of delinquency, the results of thisresearch suggest that these efforts may be slightly misdirected. Variables drawn fromsocial control, general strain, and social learning theory might actually offercompelling and quite general predictions of delinquent behavior in broadlyinclusive general samples. In a practical sense, this should serve as a positiveoutcome. If one goal of research on delinquency is to prevent its negativeconsequences, then an understanding of the general individual-level processes thataffect it is needed. However, the implicit grounding of these theories in urbanenvironments should also be considered and examined carefully. The evidencepresented here indicates that the effects of at least two variables drawn from socialcontrol theory and strain theory — namely, parental supervision and stressful lifeevents — on delinquency are conditioned by the rate of male joblessness in thesurrounding urban area. However, contrary to the suggestions of some, thesevariables are more consequential in communities that appear less organized;communities embedded in urban areas that garnered most of the attention of theoriginators of criminological thought.

Notes

1. These three theories were not chosen simply for convenience. Rather, as demonstratedin the next section, they were chosen because each has been discussed in the context ofhow community factors might condition the implied relationships of these theories. Thereare certainly other delinquency theories that might be broadened to focus on contextualfactors (e.g., labeling, various integrated theories, rational choice; Braithwaite 1989;Hechter 1987); there are a number of theories designed explicitly to address broaderstructural processes (e.g., conflict, radical; Lynch & Groves 1991); and several conceptualmodels have been introduced that expressly link macro-micro processes (power-control,integrated Marxist; Colvin & Pauly 1983; Hagan 1989). Nevertheless, since social control,strain, and differential association represent the most widely tested microlevel delinquencytheories and each has affected policies designed to prevent delinquency and other deviantbehavior (Akers 1998; Vold, Bernard & Snipes 1998), concentrating on their tacitcontextual variation is warranted.

2. Assuming a positive correlation of observations within contextual units, the directionof the bias is typically downward. Thus, standard errors from these single-level modelstend to be too small, and significant findings are more likely to emerge.

3. There are about 51,000 census tracts in the U.S. and about 20,000 zip codes used. Thezip code–level file was constructed by the National Opinion Research Center undercontract to the National Center for Education Statistics.

4. Although one would prefer to have more respondents sampled per community unit,power analyses of multilevel models suggest that having a large number of level-2(community) units is more important than the number of level-1 units (respondents)(Cohen 1998).


5. There is some controversy over whether questions such as these measure an aspectof differential association (i.e., definitions) or a component of social bonding theory(beliefs). In this article, I take the position that these are a direct measure of negative orantidelinquent definitions (Akers 1998; Matsueda 1998). Of course, one might reverse-code this variable to compute a measure of positive definitions of delinquency, or attemptsome within-unit ratio measure.

6. I also computed a monetary strain measure that used a variable that asked about therespondent’s “chances of having a job that will pay well.” There was considerable overlapbetween these two indicators of monetary strain, so I used the Farnworth and Lieberapproach.

7. The supplemental file from which the census measures were drawn did not includethe number of Asian and Pacific Islanders in the communities. Hence they could not beconsidered in the construction of the segregation index.

8. Another practical constraint resulting from the sparse within-unit sample sizes is theinability to include all the random coefficients in one model. As an alternative, I examineda series of piecemeal models that included three sets of random coefficients denotingdifferential association/social learning, strain, and social control theory, respectively. Asshown in the results section, few of the parameters significantly varied acrosscommunities. This strongly suggests that even if all the random parameters could beestimated in a single model, the results would not differ from those presented.

9. A substantial amount of research has been conducted in the past few years to determinethe best approaches for analyzing multilevel data. An MCMC-Gibbs sampler approachwith diffuse priors is recommended to validate models (Browne & Draper n.d.). MCMCtakes a Bayesian approach to estimating parameters by way of a resampling procedure.Hence it reduces the potential biases in standard errors (similar to a bootstrap) andmakes chance findings less likely. Mathematical details are provided in Gilks, Richardson,and Spiegelhalter (1996). I allowed 10,000 iterations of the Gibbs sampler to validate themodels (Goldstein et al. 1998).

10. Although community characteristics do not condition the individual-level relationshipsin the model, it is feasible that there may be some indirect effects of communitycharacteristics on delinquency that are routed through differential association/sociallearning, strain, or social control variables (cf. Akers 1998; Sampson & Groves 1989;Veysey & Messner 1999). In order to explore this possibility, I estimated a series ofstructural equation models designed to assess potential indirect effects (Hox 2000; Krull &MacKinnon 2001; Raudenbush & Sampson 1999). The results are not promising for thosewho would advocate such an approach. The community characteristics do not indirectlyexplain the variability in delinquency via the individual-level explanatory variables.Moreover, the direct effects of the community-level variables on delinquency areunchanged when one adds the individual-level variables to the model. Taken together,these results strongly suggest that any potential indirect effects of communitycharacteristics on delinquency are not routed through key variables drawn from theoriesof delinquency.

11. It is noteworthy that the zero-order correlation between community characteristics,in particular male joblessness, and parental supervision is negative, but minimal


(r = –.02). One may infer that this questions the assumption of routine activities theorythat unemployment increases guardianship. Nevertheless, without substantially moreinformation about the urban communities in question or longitudinal data that aredesigned to examine changes in the macro and micro characteristics of communities, itis overly speculative at this point to draw inferences from this analysis that are germaneto the debate about unemployment, routine activities, and crime. I thank David F.Greenberg and an anonymous Social Forces reviewer for helping me see the connectionbetween the effects of joblessness found in the analysis and research on unemploymentand crime.

References

Agnew, Robert. 1987. “On ‘Testing Structural Strain Theories.’” Journal of Research in Crime andDelinquency 24:281-86.

———. 1992. “Foundation for a General Strain Theory of Crime and Delinquency.” Criminology30:47-87.

———. 1999. “A General Strain Theory of Community Differences in Crime Rates.” Journal ofResearch in Crime and Delinquency 36:123-55.

Akers, Ronald L. 1998. Social Learning and Social Structure: A General Theory of Crime andDeviance. Northeastern University Press.

Akers, Ronald L., Marvin D. Krohn, Lonn Lanza-Kaduce, and Marcia Radosevich. 1979. “SocialLearning and Deviant Behavior: A Specific Test of a General Theory.” American SociologicalReview 44:635-55.

Almgren, Gunnar, Avery Guest, George Immerwahr, and Michael Spittel. 1998. “Joblessness,Family Disruption, and Violent Death in Chicago, 1970-90.” Social Forces 76:1465-93.

Aneshensel, Carol S., and Clea A. Sucoff. 1996. “The Neighborhood Context of AdolescentMental Health.” Journal of Health and Social Behavior 37:293-310.

Arora, Seema, and Timothy N. Cason. 1998. “Do Community Characteristics InfluenceEnvironmental Outcomes? Evidence from the Toxics Release Inventory.” Journal of AppliedEconomics 1:413-53.

Bernard, Thomas J. 1987. “Testing Structural Strain Theories.” Journal of Research in Crime andDelinquency 24:262-80.

Bernard, Thomas J., and Jeffrey B. Snipes. 1996. “Theoretical Integration in Criminology.”Pp. 301-48 in Crime and Justice: An Annual Review of Research. Vol. 20, edited by MichaelTonry. University of Chicago Press.

Blau, Peter. 1977. Inequality and Heterogeneity. Free Press.

Braithwaite, John. 1989. Crime, Shame and Reintegration. Cambridge University Press.

Brooks-Gunn, Jeanne, Greg J. Duncan, Pamela Kato Klebanov, and Naomi Sealand. 1993. “DoNeighborhoods Influence Child and Adolescent Development?” American Journal ofSociology 99:353-95.

Browne, William J., and David Draper. n.d. “A Comparison of Bayesian and Likelihood Methodsfor Fitting Multilevel Models.” Journal of the Royal Statistical Society. Forthcoming.

Bryk, Anthony S., and Stephen W. Raudenbush. 1992. Hierarchical Linear Models. Sage.


Bursik, Robert J., and Harold G. Grasmick. 1993. Neighborhoods and Crime: The Dimensions ofEffective Community Control. Lexington Books.

Burton, Velmer S., Francis T. Cullen, T. David Evans, and R. Gregory Dunaway. 1994.“Reconsidering Strain Theory: Operationalization, Rival Theories and Adult Criminality.”Journal of Quantitative Criminology 10:213-39.

Cantor, David, and Kenneth Land. 1985. “Unemployment and Crime Rates in the Post–WorldWar II United States: A Theoretical and Empirical Analysis.” American Sociological Review50:317-32.

Chase-Lansdale, P. Lindsay, and Rachel A. Gordon. 1996. “Economic Hardship and theDevelopment of Five- and Six-Year-Olds: Neighborhood and Regional Perspectives.” ChildDevelopment 67:3338-67.

Cloward, Richard A. 1959. “Illegitimate Means, Anomie, and Deviant Behavior.” AmericanSociological Review 24:164-76.

Cloward, Richard A., and Lloyd E. Ohlin. 1960. Delinquency and Opportunity: A Theory ofDelinquent Gangs. Free Press.

Cohen, Michael P. 1998. “Determining Sample Sizes for Surveys with Data Analyzed byHierarchical Linear Models.” Journal of Official Statistics 14:267-76.

Coleman, James. 1990. Foundations of Social Theory. Harvard University Press.

Colvin, Mark, and John Pauly. 1983. “A Critique of Criminology: Toward an Integrated StructuralMarxist Theory of Delinquency Production.” American Journal of Sociology 89:513-51.

Cook, Philip J. 1986. “The Demand and Supply of Criminal Opportunities.” Pp. 1-27 in Crimeand Justice: An Annual Review of Research. Vol. 7, edited by Michael Tonry and NorvalMorris. University of Chicago Press.

Corcoran, Mary, Roger Gordon, Deborah Laren, and Gary Solon. 1992. “The Association betweenMen’s Economic Status and Their Family and Community Origins.” Journal of HumanResources 27:575-601.

Cressey, Donald R. 1960. “Epidemiology and Individual Conduct: A Case from Criminology.”Pacific Sociological Review 3:47-58.

Cullen, Francis T. 1984. Rethinking Crime and Deviance Theory. Rowman and Allanheld.

Daniel, Wayne W. 1990. Applied Nonparametric Statistics. PWS-Kent.

Durkheim, Emile. 1951 [1895]. Suicide: A Study in Sociology. Free Press.

———. 1982 [1898]. Rules of the Sociological Method. Free Press.

Elliott, Delbert S., William Julius Wilson, David Huizinga, Robert J. Sampson, Amanda Elliott,and Bruce Rankin. 1996. “The Effects of Neighborhood Disadvantage on AdolescentDevelopment.” Journal of Research in Crime and Delinquency 33:389-426.

Farnworth, Margaret, and Michael Lieber. 1989. “Strain Theory Revisited: Economic Goals,Educational Means, and Delinquency.” American Sociological Review 54:263-74.

Felson, Marcus. 1998. Crime and Everyday Life. Pine Forge Press.

Fischer, Claude. 1984. The Urban Experience. 2d ed. Harcourt, Brace, Jovanovich.

Furstenberg, Frank F. 1993. “How Families Manage Risk and Opportunity in DangerousNeighborhoods.” Pp. 231-57 in Sociology and the Public Agenda, edited by William JuliusWilson. Sage.


Furstenberg, Frank F., Thomas D. Cook, Jacquelynne Eccles, Glen H. Elder, and Arnold Sameroff.1999. Managing to Make It: Urban Families and Adolescent Success. University of ChicagoPress.

Gilks, Walter R., Sylvia Richardson, and David J. Spiegelhalter. 1996. Monte Carlo Markov Chainin Practice. Chapman and Hall.

Goldstein, Harvey. 1995. Multilevel Statistical Models. Edward Arnold.

Goldstein, Harvey, Jon Rasbash, Ian Plewis, David Draper, William Browne, Min Yang, GeoffWoodhouse, and Michael Healy. 1998. A User’s Guide to MLwiN. Institute of Education,University of London.

Gottfredson, Denise C., Robert J. McNeil, and Gary D. Gottfredson. 1991. “Social Area Influenceson Delinquency: A Multilevel Analysis.” Journal of Research in Crime and Delinquency 28:197-226.

Hagan, John. 1989. Structural Criminology. Rutgers University Press.

Hechter, Michael. 1987. Principles of Group Solidarity. University of California Press.

Heck, Ronald H., and Scott L. Thomas. 2000. An Introduction to Multilevel Modeling Techniques.Lawrence Erlbaum Associates.

Hirschi, Travis. 1969. Causes of Delinquency. University of California Press.

Hoffmann, John P. 2002. “The Community Context of Family Structure and Adolescent DrugUse.” Journal of Marriage and the Family 64:314-30.

Hoffmann, John P., and Felicia Gray Cerbone. 1999. “Stressful Life Events and DelinquencyEscalation in Early Adolescence.” Criminology 37:343-74.

Hoffmann, John P., and S. Susan Su. 1998. “Stressful Life Events and Adolescent SubstanceUse and Depression: Conditional and Gender Differentiated Effects.” Substance Use andMisuse 33:2219-62.

Hoffmann, John P., and Jaingmin Xu. 2002. “School Activities, Community Service, andDelinquency.” Crime and Delinquency 48:568-91.

Hox, Joop. 2000. “Multilevel Analyses of Grouped and Longitudinal Data.” Pp. 15-32 in ModelingLongitudinal and Multilevel Data, edited by Todd D. Little, Kai U. Schnabel, and JurgenBaumert. Lawrence Erlbaum Associates.

Kohfeld, Carol W., and John Sprague. 1988. “Urban Unemployment Drives Urban Crime.” UrbanAffairs Quarterly 24:215-41.

Kornhauser, Ruth Rosner. 1978. Social Sources of Delinquency. University of Chicago Press.

Kreft, Ita, and Jan De Leeuw. 1998. Introducing Multilevel Models. Sage.

Krivo, Lauren J., and Ruth D. Peterson. 1996. “Extremely Disadvantaged Neighborhoods andUrban Crime.” Social Forces 75:619-50.

Krohn, Marvin D., Lonn Lanza-Kaduce, and Ronald L. Akers. 1984. “Community Context andTheories of Deviant Behavior: An Examination of Social Learning and Social BondingTheories.” Sociological Quarterly 25:353-71.

Krull, Jennifer L., and David P. MacKinnon. 2001. “Multilevel Modeling of Individual and GroupLevel Mediated Effects.” Multivariate Behavioral Research 36:249-77.

Lynch, Michael, and W. Byron Groves. 1991. Radical Criminology. Rev. ed. Harrow and Heston.


Matsueda, Ross L. 1982. “Testing Control Theory and Differential Association: A Causal ModelingApproach.” American Sociological Review 47:489-504.

———. 1988. “The Current State of Differential Association Theory.” Crime and Delinquency34:277-306.

———. 1998. “Cultural Deviance Theory: The Remarkable Persistence of a Flawed Term.”Theoretical Criminology 1:429-52.

Mears, Daniel P., Matthew Ploeger, and Mark Warr. 1998. “Explaining the Gender Gap inDelinquency: Peer Influence and Moral Evaluations of Behavior.” Journal of Research inCrime and Delinquency 35:251-66.

Merton, Robert K. 1968. Social Theory and Social Structure. 3d ed. Free Press.

———. 1995. “Opportunity Structure: The Emergence, Diffusion, and Differentiation of aSociological Concept, 1930s-1950s.” Pp. 3-78 in The Legacy of Anomie Theory, edited byFreda Adler and William S. Laufer. Transaction.

Messner, Steven F., and Scott J. South. 1992. “Interracial Homicide: A Macrostructural-Opportunity Perspective.” Sociological Forum 7:517-36.

National Center for Education Statistics (NCES). 1992. NELS 1988 User’s Manual: First Follow-up Student Component, NCES 1992-030. U.S. Department of Education.

Peeples, Faith, and Rolf Loeber. 1994. “Do Individual Factors and Neighborhood Context ExplainEthnic Differences in Juvenile Delinquency?” Journal of Quantitative Criminology 10:141-57.

Perkins, Douglas D., and Ralph B. Taylor. 1996. “Ecological Assessments of Community Disorder:Their Relationship to Fear of Crime and Theoretical Implications.” American Journal ofCommunity Psychology 24:63-107.

Raudenbush, Stephen W., and Robert Sampson. 1999. “Assessing Direct and Indirect Effects inMultilevel Designs with Latent Variables.” Sociological Methods and Research 28:123-53.

Reinarman, Craig, and Jeffrey Fagan. 1988. “Social Organization and Differential Association:A Research Note from a Longitudinal Study of Violent Juvenile Offenders.” Crime andDelinquency 34:307-27.

Rountree, Pamela Wilcox, Kenneth C. Land, and Terance D. Miethe. 1994. “Macro-MicroIntegration in the Study of Victimization: A Hierarchical Logistic Model Analysis acrossSeattle Neighborhoods.” Criminology 32:387-414.

Sampson, Robert J. 1987. “Urban Black Violence: The Effect of Male Joblessness and FamilyDisruption.” American Journal of Sociology 93:348-82.

Sampson, Robert J., and W. Byron Groves. 1989. “Community Structure and Crime: TestingSocial Disorganization Theory.” American Journal of Sociology 94:774-802.

Sampson, Robert J., and John H. Laub. 1994. “Urban Poverty and the Family Context ofDelinquency: A New Look at Structure and Process in a Classic Study.” Child Development65:523-40.

Sampson, Robert J., Stephen W. Raudenbush, and Felton Earls. 1997. “Neighborhoods andViolent Crime: A Multilevel Study of Collective Efficacy.” Science 277:918-24.

Shaw, Clifford R., and Henry D. McKay. 1931. Social Factors in Juvenile Delinquency: A Study ofthe Community, the Family, and the Gang in Relation to Delinquent Behavior. Prepared forthe National Commission on Law Observance and Enforcement. U.S. Government PrintingOffice.


———. 1969. Juvenile Delinquency and Urban Areas. Rev. ed. University of Chicago Press.

Short, James F. 1997. Poverty, Ethnicity and Violent Crime. Westview Press.

Simcha-Fagan, Ora, and Joseph E. Schwartz. 1986. “Neighborhood and Delinquency: AnAssessment of Contextual Effects.” Criminology 24:667-99.

Simons, Ronald L., Christine Johnson, Rand D. Conger, and Frederick O. Lorenz. 1997. “LinkingCommunity Context to Quality of Parenting: A Study of Rural Families.” Rural Sociology62:207-30.

Snijders, Tom, and Roel Bosker. 1999. Multilevel Analysis. Sage.

Stark, Rodney. 1987. “Deviant Places: A Theory of the Ecology of Crime.” Criminology 25:893-909.

Sucoff, Clea A., and Dawn M. Upchurch. 1998. “Neighborhood Context and the Risk ofChildbearing among Metropolitan-Area Black Adolescents.” American Sociological Review63:571-85.

Sutherland, Edwin H. 1939. Principles of Criminology. Lippincott.

———. 1973 [1942]. “Development of the Theory.” Pp. 13-29 in On Analyzing Crime, editedby Karl Schuessler. University of Chicago Press.

Takeuchi, David T., and Russell K. Adair. 1992. “The Exposure and Vulnerability of EthnicMinorities to Life Events.” Research in Community and Mental Health 7:111-24.

Taylor, Ralph B. 1997. “Social Order and Disorder on Street Blocks and Neighborhoods: Ecology,Microecology, and the Systemic Model of Social Disorganization.” Journal of Research inCrime and Delinquency 34:113-55.

Theil, Henri. 1972. Statistical Decomposition Analysis: With Applications in the Social andAdministrative Sciences. North-Holland.

Veysey, Bonita M., and Steven F. Messner. 1999. “Further Testing of Social DisorganizationTheory: An Elaboration of Sampson and Groves’s ‘Community Structure and Crime.’” Journalof Research in Crime and Delinquency 36:156-74.

Vold, George B., Thomas J. Bernard, and Jeffrey B. Snipes. 1998. Theoretical Criminology. 4thed. Oxford University Press.

Warr, Mark. 2002. Companions in Crime: The Social Aspects of Criminal Conduct. CambridgeUniversity Press.

Weeden, Kim A. 1998. “Revisiting Occupational Sex Segregation in the United States, 1910-1990: Results from a Log-Linear Approach.” Demography 35:475-87.

Westfall, Paul H., Randall D. Tobias, Dror Rom, Russell D. Wolfinger, and Yosef Hochberg. 1999.Multiple Comparisons and Multiple Tests Using the SAS System. SAS Institute.

Wilson, William Julius. 1996. When Work Disappears: The World of the New Urban Poor. Vintage.

Yang, Shu-Lung, and John P. Hoffmann. 1998. “A Multilevel Assessment of Social DisorganizationTheory in Taipei, Taiwan.” Journal of Contemporary Criminal Justice 14:222-47.

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