Life domains and crime: A test of Agnew's general theory of crime and delinquency

Journal of Criminal Justice 39 (2011) 302–311

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Journal of Criminal Justice

Life domains and crime: A test of Agnew's general theory of crime and delinquency Fawn T. Ngo a,⁎, Raymond Paternoster b, Francis T. Cullen c, Doris Layton Mackenzie d a

University of South Florida-Sarasota/Manatee, United States University of Maryland, United States c University of Cincinnati, United States d Pennsylvania State University, United States b

a r t i c l e

i n f o

Available online 14 April 2011

a b s t r a c t Purpose: This study presents a preliminary test of Agnew's general theory of crime and delinquency. This study examines whether each of the five life domain variables at the core of Agnew's theory is related to recidivism, whether there is a non-linear relationship between the life domains and recidivism, and whether the five life domains interact in causing recidivism. Methods: Data were derived from the baseline survey of the Maryland Boot Camp Experiment and through a criminal records check conducted by the Maryland Department of Public Safety. Results: Overall, the results lend weak support for Agnew's general theory. In particular, only two of the five life domains, having a bad job and being a high school dropout, are significantly correlated with recidivism. Further, with the exception of the peers domain, there is neither a linear nor a non-linear relationship between the life domains and recidivism. The results also reveal that none of the two-way bivariate interactions are significant in a multivariate linear probability model. Conclusions: Although our findings are not supportive of Agnew's (2005) general theory of crime, the theory contains many other implications that we simply did not have the data to address. Published by Elsevier Ltd.

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . Agnew's general theory of crime and delinquency . . . . The five life domains: self, family, work, school, peers Prior research . . . . . . . . . . . . . . . . . . . Research hypotheses . . . . . . . . . . . . . . . . Data and methods . . . . . . . . . . . . . . . . . . . Data collection . . . . . . . . . . . . . . . . . . . Sample. . . . . . . . . . . . . . . . . . . . . . . Measurement of variables . . . . . . . . . . . . . . Dependent variable . . . . . . . . . . . . . Independent variables . . . . . . . . . . . . Control variables and interaction terms . . . . Analytic strategy . . . . . . . . . . . . . . . . . . Results. . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . Appendix A . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . .

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⁎ Corresponding author at: College of Arts and Sciences, University of South Florida-Sarasota/Manatee, 8350 N. Tamiami Trail, C263, Sarasota, FL 34243, United States. Tel.: + 1 941 359 4727; fax: + 1 941 359 4489. E-mail address: [email protected] (F.T. Ngo). 0047-2352/$ – see front matter. Published by Elsevier Ltd. doi:10.1016/j.jcrimjus.2011.03.006

F.T. Ngo et al. / Journal of Criminal Justice 39 (2011) 302–311

Introduction Robert Agnew is arguably best known for his efforts in revitalizing traditional strain theory (Agnew, 1992, 2006a). His General Strain Theory has proven to be one of the more popular theories in criminology and has kept the community of crime scholars busy trying to solve some of its numerous puzzles (for a comprehensive review see Agnew 2006b; Agnew et al., 2008; Kubrin et al. 2009). In 2005 in an attempt to craft a general theory of criminal offending, Agnew's work moved beyond strain theory with the publication of Why Criminals Offend: A General Theory of Crime and Delinquency. Agnew's general theory (to distinguish it from general strain theory) is an integration of several major criminological theories. More specifically, it is an inductive theory built upon a foundation of empirical research findings of the causes of crime with a focus on those variables having direct effects on crime and the relationship among them. At the heart of the theory are five clusters or “life domains” of variables each one he claims has shown to be a proven and robust correlate of crime. These five domains – self, family, peers, school, and work – represent the best known explanations in the existing literature for why some individuals are more likely to engage in crime than others. In other words, one thing the general theory is supposed to account for is between-individual differences in the risk of committing crime. In addition, Agnew's general theory is an even more ambitious theory in that it seeks to account for all types of crime, address the “known facts” of crime, explicate micro and macro patterns of crime and guide policies in preventing and controlling crime (Agnew, 2005). Accordingly, Agnew's general theory represents an important contribution to the field of criminology and knowledge about crime and criminals. However, to date, the theory has not been tested. Subjecting the theory to empirical test is both essential and crucial as Agnew himself recognizes that this will help “shed important light on the status of the general theory and contribute to the crime literature more generally” (Agnew, 2005, p. 185). In this context, we present the first empirical test of Agnew's general theory. Specifically, we examine the theory's ability to explain recidivism within a sample of serious young adult offenders. As a prelude to our methodology and findings, we first briefly review Agnew's general theory and present our hypotheses. Agnew's general theory of crime and delinquency As stated previously, Agnew's general theory is an inductive theory that seeks to integrate the main correlates of crime and the relationship among them (for a discussion of this type of theoretical integration, see Bernard & Snipes, 1996). Agnew's general theory commences with the fundamental idea that crime is most likely to occur when the constraints against crime are low and the motivations for crime are high (2005, pp. 17–33). In particular, capitalizing on the concepts of external and internal controls, stakes in conformity and forces that push or pull individuals into crime (Reckless, 1961; Reiss, 1951; Toby, 1957), Agnew's general theory argues that individuals restrain from committing crime 1) because they fear of getting caught and punished (external control), 2) when they have a lot to lose if they are punished (stake in conformity), and 3) when they believe that crime is wrong or are high in self-control (internal control). Conversely, the theory posits that individuals are motivated toward crime as a result of factors or forces that either pull them (e.g., gangs, delinquent subcultures, living in high crime neighborhoods) or push them toward crime (i.e., inner psychological impulses and drives). It is noteworthy that Agnew also incorporated causal processes from social learning theory (Akers, 1985, 1998) and GST (Agnew, 1992, 2001) in conceptualizing the motivations for crime. Specifically, congruent with social learning theory, Agnew maintains that individuals are taught to break the law when 1) they are reinforced

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for crime, 2) exposed to successful criminal models, and 3) taught beliefs favorable to crime. Agnew's general theory also recognizes that individuals may be pressured into crime when 1) they are prevented from achieving their goals, 2) their valued possessions are taken away from them or are threatened to be taken away, and 3) they are presented with noxious or negative stimuli. Agnew's general theory proceeds with the contention that a host of individual and social variables affect the constraints against and the motivations for crime. However, he argues that it would be futile to attempt to group these variables by the type of constraint or motivation they index because many of these variables affect both the constraints against and the motivations for crime. Accordingly, Agnew (2005, pp. 42–55) resolved to group these variables into clusters organized around five major life domains: self, family, school, peers and work. Agnew (2005) defended his grouping scheme by stressing that “Grouping the causes of crime in this manner allows us to ensure that each cause is part of one and only one category” (p. 40) and “because the variables in each domain are caused by many of the same factors and have large causal effects on one another” (Cullen & Agnew, 2006, p. 594). These five life domains represent the general factors that are most strongly correlated with crime. In assigning variables into the five major life domains, Agnew only focuses on those variables that have been empirically demonstrated to have moderate to large direct effects on crime (for a comprehensive description of these variables, see Agnew, 2005, Chapter 3). Further, while crime is caused by the five life domains, Agnew stresses that their effects on crime will vary over the life course. That is, his theory is an age-graded general theory of crime. For instance, among children, Agnew (2005, pp. 56–59) claims that irritability/low selfcontrol would have the largest effects on crime, while among adolescents he argues that both irritability/low self-control and peer delinquency have the largest effects on crime, and among adults he added poor or no marriage and no or poor employment. The five life domains: self, family, work, school, peers In the domain of “self,” Agnew argues that low self-control and the super trait of “irritability” constitute the key variables that directly reduce the constraints against and the motivations for crime. In the domain of “family,” Agnew claims that poor parental supervision and discipline, weak bonds between the parent(s) and juvenile, family conflict and child abuse, a lack of parental social support, and criminal parents and siblings are the main causes of crime. For adults, however, Agnew replaces these factors with the variables of failing to marry or divorce/separation, negative bonding to spouse/partner, negative bonding to children, family conflict, poor spouse/partner supervision, having a criminal spouse or partner, and low social support. Within the domain of “school,” Agnew recognizes that negative bonding to teachers/school, poor academic performance, negative treatment by teachers, little social support from teachers, little time on homework, low educational and occupational goals, and poor supervision and discipline are the major correlates of crime. However, similar to the family domain, given that most adults are not in school, Agnew theorizes that crime will be higher among adults with limited educations but the effect of limited education on crime will be indirect (Agnew, 2005). That is, Agnew hypothesizes that limited education will affect crime “primarily through its effect on the individual's work, marital life, and peer associations” (Agnew, 2005, p. 51). For the domain of “peers,” Agnew maintains that having delinquent peers, having frequent conflicts with and being abused by peers, and spending much time with peers in unstructured and unsupervised activities are the main factors impacting delinquency. Among adults, Agnew suggests that although they are less likely to have criminal friends due to their work and marital commitments, peers will continue to play a major role in the lives of adults who are unmarried, unemployed, or employed in “bad jobs” (Cullen & Agnew,

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2006). Finally, within the domain of “work,” unemployment, poor work performance and working conditions, poor supervision and discipline, negative bonding to work, and criminal co-workers are the significant variables affecting crime. Agnew also recognizes the important role of prior crime. Agnew's general theory delineates that prior crime has both direct and indirect effects on subsequent crime. Prior crime is theorized to indirectly impact subsequent crime when it affects certain of the constraints against and motivations for crime. For instance, engaging in illicit activities such as drugs may lead to monetary strain (i.e., a desperate need for cash), which is a motivation for crime. Agnew also drew from labeling theory (Becker, 1963; Lemert, 1951; Tannenbaum, 1938) in accounting for the indirect effect of prior crime on subsequent crime. In particular, he argues that prior crime increases the likelihood of subsequent crime when individuals who are labeled as “criminals” or “bad people” end up associating with each other as a result of being rejected by conventional others. Associating with criminal others, in turn, decreases the constraints against crime and increases the motivations for crime (Agnew, 2005). Prior crime also indirectly affects subsequent crime through the five life domains. As an example, engaging in delinquent activities may lead to weak bonds between parents and children, increase the likelihood of poor parental supervision, and/or cause parents to treat their children in a harsh manner. These factors in turn contribute to subsequent delinquent behavior. This indirect process is harmonious with the “knifing off” of conventional opportunities described by Sampson and Laub (1993) and the “snares” that lead to persistent offending described by Moffitt (1993). Notably, Agnew's general theory does not posit that engaging in crime always increases the chances of subsequent crime. In fact, the theory argues that in some cases, prior crime has no effect on crime or even may reduce the likelihood of further crime. In these cases, the theory claims that how others react to the individual's crime and the characteristics of the criminal are the determining factors (for a comprehensive description of these factors, see Agnew, 2005, Chapter 5). Agnew's general theory concludes with two important propositions: 1) the five life domains interact with one another in affecting crime, and 2) the five life domains have contemporaneous and nonlinear effects on crime and one another. With regard to the first proposition, Agnew's general theory suggests that the effect of each life domain on crime is influenced or conditioned by the individual's standing on the other life domains and the effect of one life domain on another is influenced or conditioned by the remaining life domains. As an example, individuals experiencing poor parenting are more likely to engage in delinquency when they are also low in self-control, tend to be irritable, have negative school experiences and are high in peer delinquency. Similarly, among adults, those who have both no or a poor marriage and no or a poor/ unsatisfying job are at greater risk that those who have only one or the other risk factor. Pertaining to the second proposition, Agnew's general theory proposes that the life domains have largely contemporaneous effects on crime and one another, and each domain also has a large, lagged effect on itself. Agnew defines “contemporaneous effects” as effects occurring within a short period of time, namely, within a few months’ time. Accordingly, in some cases, a cause may have an immediate effect on crime while in other cases a cause may take somewhat longer to affect crime (contemporaneous effect). Further, some causes are likely to have an accelerated effect on crime after a certain threshold point is reached (a nonlinear effect). For example, the effect of having a poor relationship with one's spouse or partner is expected to have a greater effect on crime when the relationship is particularly disrupted then when it is only minimally disrupted. To summarize, Agnew's age-graded general theory is an amalgamation of the major correlates of crime, with a focus on those variables having moderate to strong direct effects on crime and the relationship among those variables. At the heart of the theory are five

clusters or life domains of variables – self, family, peers, school and work – representing the explanation for why some individuals are more likely to engage in crime than others. Among other things, therefore, the theory was designed to explain between-individual differences in criminal offending. Prior research To the best of our knowledge, Agnew's general theory has not been empirically tested to date, although Agnew insists that there is much indirect support for the theory. That is, Agnew points to the fact that many of the specific variables comprising the life domains in his theory have been demonstrated to be related to crime (for a comprehensive discussion on this topic, see Agnew, 2005, Chapter 10). Nevertheless, assessing the core propositions of Agnew's general theory is warranted and Agnew does provide very clear general guidelines for this undertaking (see Agnew, 2005, Chapter 10). Before presenting our hypotheses, it is important to note that a full test of Agnew's general theory is not viable at this time. As Agnew (2005, p. 185) acknowledges: At present, however, there are few data sets that allow for anything close to a full test. Also, a full test would impose large demands on the data and computational programs, as large number of effects require estimation, including reciprocal, nonlinear, and interactional effects. Given these facts, the general theory is probably best tested in bits and pieces. We follow Agnew's suggestion for modesty by only examining portions of the theory, some of its more central propositions but not all of them. In particular, using a sample of adult offenders, we assess whether 1) crime is caused by each of the life domain variables, 2) whether there is a non-linear relationship between the life domains and crime, and 3) whether the five life domains interact in causing crime. Research hypotheses According to Agnew's general theory, crime is caused by five clusters or life domains of variables, namely, self, family, peers, school and work. Further, the effects of the specific variables on crime will vary over the life course depending on the individual's stage in life (i.e., childhood, adolescence and adulthood). With regard to young adults – who comprise the sample for the present study – the theory suggests that the variables with the greatest impact on crime include irritability/low self-control, peer delinquency, limited education, no marriage/poor relationships, and unemployment/bad jobs. Combining the above proposition with our focus on explaining crime among young adults yields the following hypotheses: H1. Offenders with low self-control are more likely to commit crime relative to offenderswith higher self-control. H2. Offenders who are not married are more likely to commit crime relative to offenders who are married. H3. Offenders with poor relationships with close others are more likely to commit crime relative to offenders with more positive social relationships. H4. Offenders who dropped out of high school are more likely to commit crime relative to offenders who graduated from high school. H5. Offenders with criminal friends are more likely to commit crime relative to offenders with prosocial friends. H6. Offenders who are unemployed are more likely to commit crime relative to offenders who are employed.

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H7. Offenders with bad jobs are more likely to commit crime relative to offenders with good jobs.1 In describing his theory, Agnew (2005, pp. 121–123) also argues that the effect of each life domain is non-linear. The expectation is that the effect of each life domain on crime is an increasing function of the level of the domain. Hence, we hypothesize: H8. There is a non-linear relationship between each life domain and crime.2 The theory also suggests that the life domain variables interact in affecting crime so that, for example, the effect of low self-control is greater when accompanied by negative social relationships. Therefore, we hypothesize: H9. The effect of each life domain variable on crime is conditional on each of the remaining life domain variables. Data and methods To test our hypotheses, we use data from the Maryland Boot Camp Experiment, a randomized experimental evaluation designed to assess the efficacy of the state of Maryland's only correctional boot camp for adult offenders. The main objective of the evaluation was to determine whether a correctional boot camp with a treatment orientation (i.e., includes addictions treatment, a life skills component, and basic education courses), namely the Herman L. Toulson Boot Camp, reduces recidivism in comparison to a standard correctional facility that also had a treatment orientation but had no military component (for a full description of this evaluation, see Mackenzie, Bierie, & Mitchell, 2007). The Herman L. Toulson Boot Camp (hereafter, “TBC”) was established in 1990 in an effort to reduce prison overcrowding and as a means to motivate inmates to become responsible and productive citizens. TBC, like all correctional boot camps, was designed to be similar to military basic training. However, unlike many other correctional boot camps, TBC has a significant treatment component. Inmates are required to participate in adult basic education programming, drug treatment/education programming, and a cognitive-behavioral life skills training program. Thus, TBC integrates a treatment component into a military model. The comparison facility, the Metropolitan Transition Center (hereafter “MTC”), is one of the oldest continually operating correctional facilities in the U.S. Originally, MTC operated as a maximum-security prison. At present, MTC serves as a pre-release facility and as such all inmates are within 18 months of their expected parole release dates. Like TBC, MTC also has adult basic education programming, drug treatment and education programs, and a life skills component. The major difference between TBC and MTC is that MTC does not have a military component. Hence, inmates at the MTC have much less structure and rigor in their confinement than inmates at TBC. Many inmates at MTC spend their time watching television, reading, playing dominoes and other games, and sleeping. Male offenders who volunteered for participation in the boot camp program and who were determined to be eligible for the boot camp program by the Maryland Parole Commission were randomly assigned to either TBC or MTC. All eligible volunteers signed a Mutual Agreement Program (MAP) contract agreeing to avoid rule infractions and participate in adult basic education, addiction treatment, and a life skills course. In return for completing the terms of their MAP contract, inmates were guaranteed an early release date six months after program entry. Accordingly, once anticipated good time is factored in, inmates’ prison terms were reduced by 6 to 18 months. Inmates who failed to meet the provisions of the MAP contract had

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their contracts terminated, their original sentence re-imposed, and were re-assigned to a different correctional facility. Non-compliant inmates also faced similar consequences as inmates who had their contracts revoked. Data collection Each month, a platoon of 8 to 20 eligible inmates was added to the study. One week before inmates learned the results of the random assignment procedure (i.e., whether they will be serving their time at TBC or MTC), trained survey facilitators met with the inmates and solicited their participation in a 45-minute self-report survey. Approximately one week before the inmates were released to the community, the survey facilitators travelled to TBC and MTC and asked each inmate to complete another 45-minute self-report survey. For both surveys, inmates were informed that participation in the surveys was voluntary and their responses would remain confidential. Further, participating inmates were asked to sign informed consent forms and the surveys were administered in a group format. The surveys were also read aloud to help inmates with limited reading skills. A total of 238 offenders were solicited to participate in the first survey and everyone cooperated. This yielded an overall response rate of 100% and these 238 individuals comprise the sample for the current study. Further, data on the independent variables for this study come from the first survey that include measures of demographic features, employment history, self-reported criminal history (both juvenile and adult), perception of employment and family, antisocial attitudes and association with anti-social peers. Data for the dependent variable (described later) were taken subsequent to the inmate's release from their respective institution. Sample Table 1 shows the demographic and other characteristics of the sample. As shown in Table 1, the sample was composed of mostly African Americans and the mean age was 23 years. Most of the offenders were not married (92%) and most had not completed high school (64%). Further, many did not have full-time employment prior to incarceration. While these offenders were supposed to be serving their first extended term of incarceration, they had considerable histories of contact with the criminal justice system. In particular, on average, sample members had approximately 5 prior arrests and 2.6 prior convictions. Also, slightly less than half of the offenders served their time at the boot camp institution, and the number of days these offenders have been in the community during the follow up ranged from 1 month to 40 months (mean time at risk was 26 months).

Table 1 Demographic and other characteristics of sample Variable

N

Mean (or %)

Black Unmarried Unemployed Assigned to Boot Camp High School Dropout Rearrested in Follow-Up Age Time at Risk (months) Number of Prior Arrests Self-Control Bad Relationship Bad Job Peers

238 238 238 238 230 238 238 232 238 227 205 205 229

83% 92% 22% 47% 64% 61% 23.32 26.37 5.18 39.56 9.85 2.40 10.65

SD

Min

Max

4.20 7.44 4.305 15.63 4.18 2.33 5.66

0 0 0 0 0 0 16 1 0 0 4 0 0

1 1 1 1 1 1 35 40 28 79 25 8 24

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Measurement of variables Dependent variable Our outcome variable in this study is crime that was committed after the inmate was released to the community. More specifically, we measure subsequent crime as the occurrence of at least one arrest or recidivism during a post-release period that was a little more than two years on average since release.3 This measure was obtained through a criminal records check conducted by the Maryland Department of Public Safety in November of 2005 and it was coded as a dichotomous variable where 1 = recidivated during the postrelease period and 0 = did not recidivate (see Table 1). Inmates were followed up for an average of a little more than two years with a maximum of 40 months. During that time 61% of the offenders had experienced at least one re-arrest. Independent variables A number of variables were selected to represent the five life domains of Agnew's theory. Further, Cronbach's alpha was computed for each scale in each life domain to assess their internal consistency. Each scale was also standardized to facilitate interpretations and to ease comparisons between scales measured on different metrics. Finally, for respondents with missing data on a scale but who completed at least 80% of the scale, scale scores were based on the items they did complete. Conversely, respondents who completed less than 80% of any scale were coded as missing on that scale. Listwise regression was used throughout and the number of valid cases was never less than 210, 88% of the original number of respondents. Following Agnew's guidance (2005), the self domain is represented by a measure of low self-control. This measure was obtained using the Grasmick, Tittle, Bursik, and Arneklev's (1993) twenty-four item self-control scale,4 with response options ranging on a five-point scale from strongly agree to strongly disagree. Higher scores on this scale indicate lower levels of self-control. A confirmatory factor analysis indicated that four of the original twenty-four items did not load highly in the one-factor model and were subsequently dropped. Each of the twenty items that were retained had a factor loading of at least .46 and the Cronbach's alpha for this twenty-item scale was 0.89. The items for this and all other scales that were constructed are included in Appendix A (the items that were dropped from the scale are italicized in Appendix A). The family domain consists of two measures, no marriage and bad relationship. No marriage was coded as a dichotomous variable where 1 = unmarried and 0 = married. Bad relationship was measured using 5 items that assess the respondent's perception of his relationship and activities with his spouse or partner (for those not married the partner was the respondent's girlfriend). These items were measured on a five-point scale – never, rarely, sometimes, often, and always – and the responses were reversed so higher scores indicate that the respondent perceived his relationship with his spouse or partner as unfavorable. A confirmatory factor analysis indicated that all items had a factor loading of .77 or higher and the Cronbach's alpha for this five item scale was 0.88. The peer domain includes 5 items that assessed the respondent's perceptions of their friends’ anti-social behaviors/attitudes. These items were measured on a five-point scale: never, rarely, sometimes, often, and always. Higher scores indicated that the respondent perceived his friends to have anti-social behaviors or attitudes. A confirmatory factor analysis revealed that all items had a factor loading of .75 or higher and the Cronbach's alpha for this five item scale was 0.89. The work domain encompasses two measures, no job (unemployed) and bad job. Similar to the variable of no marriage, no job was coded as a dichotomous variable where 1 = unemployed and 0 = employed part or full-time prior to being incarcerated. Bad job was measured using 2 items that assess the respondent's perception of his employment (e.g., “In my last job, I really enjoyed working there”).

Similar to the construct of bad relationship, these items were measured on a five-point scale – never, rarely, sometimes, often, and always – and the responses were reversed with higher scores indicating that the respondent's perception of his employment was unfavorable. The Cronbach's alpha for this two-item scale was 0.91. Lastly, the school domain is represented by the variable of limited education. Limited education was coded as a dichotomous variable where 1 = high school dropout and 0 = high school graduate or more (see Table 1). Control variables and interaction terms Age, race, prior offending, type of institution (boot camp vs. traditional), and time at risk for recidivism were also included as control variables in our multivariate models. Prior offending was measured by the self-reported number of offenses experienced by each inmate before their current incarceration (see Table 1). Specifically, respondents were asked the question, “Prior to your admission to this facility, how many times had you been arrested for a crime, including any juvenile arrest.” Two-way interaction terms were created representing all possible combinations among the life domain variables (e.g., low self-control times unmarried, low self-control times unemployed, low self-control times limited education, etc.).5 With seven measures a total of 21 interaction terms were created. Analytic strategy The dependent variable in this paper is binary, whether or not the respondent was rearrested at any time during the follow-up period. When the independent variable is measured as a continuous variable we will examine the bivariate relationship with rearrest with a pointbiserial correlation coefficient. When the independent variable is itself binary (employed/unemployed; married/not married; high school dropout/high school graduate) the bivariate relationship with rearrest will be examined with contingency tables (chi-square and gamma). When we estimate a multivariable model we will employ a linear probability model (OLS with a binary dependent variable). We do this because the coefficients in a linear probability model are easy to interpret, particularly when we estimate non-linear effects. Agnew (2005; Thaxton & Agnew, 2004) argues that the non-linearity comes about because the effect of each life domain on offending increases at almost an exponential rate – the effect of x on y increases with increasing values of x. The effect parameters for such non-linear terms are more ambiguous in censored dependent variable models like logistic regression. We will estimate non-linear effects in a linear probability model two ways. First, we will enter the variable measuring the life domain in question and the square of that variable after centering both variables (a polynomial model). Second, we will estimate non-linearity with the log of the domain variable - a “power” or lin-log model.6 Results Table 1 reports the descriptive statistics for the variables in the study. We begin the substantive analysis by looking at the bivariate associations between each of the life domains (and their squared and logged terms) and whether or not the inmate was re-arrested upon release from their respective institution. These results are reported in Table 2. In terms of their bivariate linear relationship, only having a bad job and being a high school dropout are related to the probability of being rearrested upon release. Thus, the results can be said to provide only minimal support for hypotheses four and seven. For only two of the five life domains discussed by Agnew is there support for his general theory, and while statistically significant, the magnitude of these two correlations are not impressive (r = .146 for having a bad job on the probability of rearrest and γ = .391 for being a high school

F.T. Ngo et al. / Journal of Criminal Justice 39 (2011) 302–311 Table 2 Measure of association (point biserial or gamma) between life domains and re-arrest

Table 3b Linear probability model for effect of bad relationships on re-arrest

Functional Form: Variable

Linear a

Unmarried Bad Relationship Unemployeda Bad Job High School Dropouta Low Self Control Criminal Peers

.245 -.032 .286 .146⁎ .391⁎⁎ -.005 .005

307

Life Domain Variable is: Squared

Logged

-.066

-.107

.122⁎

.071

.022 .045

.008 .010

a Based on chi-square and gamma statistic. Since these variables are binary their squared and logged terms were not used. ⁎ p b .05. ⁎⁎ p b .01.

Age Age Squared Black TBC Time at Risk Prior Arrests Bad Relationships Bad Relationships2 Log of Bad Relationships Constant R2

Linear

Squared

Logged

-.295** .005** .122 -.084 .009* .040** -.007

-.290** .005** .133 -.086 .009* .040** -.007 .033

-.305** .005** .112 -.080 .009* .040**

4.176 .22

4.097 .22

-.029 4.287 .22

dropout).7 Agnew (2005, pp. 121–123) has specifically argued, however, that the relationship between each of these life domains and criminal outcomes is likely to be non-linear with an increasing effect at higher levels of the domain (when the conditions of the domain are worse). At the bivariate level, we tested two particular forms of non-linearity, with a quadratic (variable squared) term and logged term. The results in Table 2 show that all of these non-linear effects are very weak in magnitude and only one is significantly different from zero. In sum, in the bivariate analysis, we find only very weak support for Agnew's general theory of crime based on five life domains. Only the domains of work (having a bad job) and school (being a high school dropout) were significantly correlated with the risk of being arrested after release. Moreover, contrary to Agnew's prediction, the effects are not non-linear in their functional form; only for having a bad job was the non-linear effect on crime significant. We next estimated both linear and non-linear multivariate OLS models for each of four life domains using the constructed scales for self, work, peers, and family.8 In these multivariate models, the following control variables were included with each life domain: age, age squared, Black, Boot Camp assignment, time at risk, and number of prior arrests. The results are reported separately for each domain in Tables 3a–3d. In these models, the effects of the control variables on the probability of being rearrested subsequent to release are consistent across the different life domains and are in the expected direction. Age and age squared are significantly related to the probability of rearrest across all the models, and the probability of rearrest is higher for those with a longer time at risk and for those with more prior arrests. Further, the effects for these control variables are of comparable magnitude and significant in both the linear and two non-linear models. Offenders who are African-American were more likely to be arrested and those incarcerated at the boot camp were less likely to be arrested. However, these relationships were never statistically significant.

Looking first at the linear effects of each of the four life domains on the probability of being arrested in the follow up, there is little in these data to support Agnew's general theory. There is no significant relationship between low self control (self domain; b = −.022), bad relationships (family domain; b = −.007), criminal peers (peers domain; b = −.018), having a bad job (work domain; b = .033) on the probability of being rearrested. Moreover, only for the work domain is the regression coefficient even in the expected direction. However, because Agnew has argued that the relationship between each of these life domains and crime is likely to be non-linear, we estimated two forms of a non-linear probability model. One model includes a quadratic term for each life domain while the other includes the natural log of the domain. We would note at the outset that these non-linear models do not generally provide a better fit to the data than their linear models. The variance explained is the same across the different models and formal F-tests for the difference between the linear and non-linear models were not significant. In each case the linear model is comparable to both the quadratic and logged models in terms of showing no effect for the life domain variable in question on the probability of rearrest. It would appear, at least with these data, that there is neither a linear nor a non-linear relationship between the life domains of self, family, work and peers. There is, however, one minor exception to note. Table 3c shows the effect of criminal peers on the probability of being rearrested during the follow up period. The quadratic model indicates that the squared effect of criminal peers on the probability of rearrest is positive and statistically significant. Although an F-test indicated that the quadratic model did not provide a significantly better fit to the data than the linear model (F = 1.78; p N .05), the results are nonetheless suggestive that the effect of having delinquent peers on one's own offending is greater at higher levels of delinquent peers. Supporting this view is the bivariate finding that the relationship between delinquent peers and the probability of rearrest is positive though not significantly different from zero in the bottom

Table 3a Linear probability model for effect of low self-control on re-arrest

Table 3c Linear probability model for effect of criminal peers on re-arrest

Life Domain Variable is:

Age Age Squared Black TBC Time at Risk Prior Arrests Low Self Control Low Self Control2 Log of Self Control Constant R2

Life Domain Variable is:

Linear

Squared

Logged

-.295** .005** .087 -.092 .010** .037** -.022

-.295** .005** .087 -.092 .010** .037** -.022 .005

-.298** .005** .081 -.088 .011** .037**

4.239 .20

4.197 .20

-.030 4.253 .20

Age Age Squared Black TBC Time at Risk Prior Arrests Criminal Peers Criminal Peers2 Log of Criminal Peers Constant R2

Linear

Squared

Logged

-.306** .005** .086 -.073 .011** .038** -.018

-.297** .005* .077 -.073 .011** .036** -.008 .042*

-.322** .006** .053 -.044 .012** .038**

4.328 .19

4.124 .21

.007 4.485 .20

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F.T. Ngo et al. / Journal of Criminal Justice 39 (2011) 302–311

Table 3d Linear probability model for effect of bad jobs on re-arrest Life Domain Variable is:

Age Age Squared Black TBC Time at Risk Prior Arrests Bad Job Bad Job2 Log of Bad Job Constant R2

Linear

Squared

Logged

-.252⁎⁎ .004⁎⁎

-.260⁎⁎ .004⁎⁎

.063 -.093 .011⁎⁎ .036⁎⁎

.067 -.094 .011⁎⁎ .037⁎⁎ .061⁎

-.238⁎⁎ .004⁎⁎ .047 -.044 .012⁎ .042⁎⁎

.033

-.028 3.539 .19

3.697 .19

-.029 3.463 .23

⁎ p b .05. ⁎⁎ p b .01.

third of the criminal peers distribution (r = .041; p N .05), negative though not significantly different from zero in the middle third of the distribution (r = −.036; p N .05), and both positive and statistically significant at the highest one-third of the distribution of criminal peers (r = .260; p b .01). Again, although the quadratic model (it is actually an interaction model with the effect of an explanatory variable x dependent on the level of x itself) is not statistically significant, the finding of a statistically significant quadratic term for criminal peers and the conditional bivariate correlations would suggest having criminal peers is most criminogenic when one's social networks are more saturated with criminal peers. This is consistent with previous findings that persistence in crime is the likely result of having a social network that is mostly or exclusively made up of criminal peers (Haynie, 2002). Although not definitive, our findings would suggest that at least this part of Agnew's general theory is correct and that additional research should look closer at the possibility of a non-linear relationship between criminal peers and criminal offending. The eighth hypothesis predicted that each life domain will interact with each of the other domains in its effect on criminal offending. Agnew does not specify the terms of the interaction and so without much guidance from the theory we tested all two-way interactions only. With seven indicators of the five life domains, we examined a total of twenty-one two-way interactions. We first estimated simple bivariate correlations between each interaction term and whether the former inmate was rearrested during the follow up period. All of these twenty-one interactions were very modest in magnitude (Pearson's correlation coefficients), with only two of them exceeding .20 (r's = .209 and .201). Only seven of the correlations were statistically significant (but all of them had the correct sign): 1. 2. 3. 4. 5. 6. 7.

Low Self-Control x Bad Job Low Self-Control x High School Dropout Unmarried x Bad Job Unmarried x High School Dropout Criminal Peers x Bad Job Criminal Peers x High School Dropout High School Dropout x Bad Job

For interpretation, the significant interaction effect between low self-control and bad job indicated that having low self-control was related to the risk of being arrested in the follow up period when the person also had a bad job (but not when they had a good job). We then estimated multivariate linear probability models (these results are not reported but are available upon request) for each of these seven interaction terms that were statistically significant at the bivariate level. The same control variables for the previous models (Tables 3a–3d) were entered and both terms in the interaction were first centered before being interacted. In these models the effects of the control variables were virtually identical to what is reported in

Tables 3a–3d and none of the interaction terms was significantly different from zero. We would therefore reject hypothesis 8's suggestion that the effect of the life domains depends upon the level of each of the other life domains.9

Discussion In 2005, Robert Agnew published a general theory of crime that was premised not on general strain theory but on the most important known correlates of crime. Unlike his general strain theory, this general theory seems devoid of any explicit image of the nature of human beings, and both the propositions and empirical constructs of the theory seem only loosely interconnected. The theory simply combines the so-called most robust known correlates of crime culled from already existing criminological theories together into a unified scheme. As such Agnew's theory does not develop new theoretical ground, and is more like the risk-factor approach to theory construction as elucidated by Bernard and Snipes (1996) than traditional inductive or deductive theory. Agnew argues that the most important risk factors for crime are age-graded but can be categorized into five general life domains that coalesce around the areas of: self, family, peers, school, and work. The theory is age-graded in that some life domains may be more important at particular ages than others. For example, the domains of work and family may be more important among adults while school and peers may be more important for adolescents. Nevertheless, it is true that Agnew's general theory is a cobbling together of concepts from other criminological theories. A question for the field to ultimately address is whether such a risk-factor model is the preferred approach to theoretical development since its novelty resides solely in how old variables are linked together rather than the construction of new concepts or even new ideas. In fact, one reviewer of our paper perhaps playing Devil's advocate questioned the novelty and value of the theory, noting that “his theory is simply an amalgam of all criminological theories and therefore really offers nothing new to the field”. Our position is that the issue can for the moment be deferred and the empirical value of Agnew's general theory can be addressed on its own merits. That more restricted goal has animated our research. In spite of its loose formal theoretic structure, much to his credit Agnew does deduce some specific hypotheses or empirical implications of the theory which provide an empirical test of the theory. As King et al. (1994) have noted, the more observable implications that can be generated from a theory the more chances there are to test the theory and the greater our confidence in it. Whatever else may be said about the theory, Agnew is clear as to its empirical implications. He argues that if the theory is true, then each one of the life domains should be related to criminal offending (though again since the causal process is age-graded, some will be more strongly related at different points in the life course), that the relationship between indicators of each life domain and crime is more likely than not to be non-linear with the effect of a life domain an increasing function of its own level, and that the life domains should interact with one another in their effect on crime. We tested this general theory of crime on a sample of offenders released from either a “boot camp” or more traditional prison environment. These offenders were part of a more general study of the effect of a boot camp experience on inmates where the inmates were randomly assigned to either the boot camp or more traditional prison. The outcome variable in our study was whether or not the released offender was rearrested during a follow up period that lasted on average about two years. Measures of the self (self-control), family (being unmarried and having a bad relationship with one's spouse or partner), work (being unemployed and having a job that one was unhappy with, peers (having criminal friends), and school (dropping

F.T. Ngo et al. / Journal of Criminal Justice 39 (2011) 302–311

out of high school) were collected from each inmate before they were released from the institution. Estimating both linear and non-linear regression models, we found that traditional measures of the possible risk factors for crime (age, age squared, time at risk, having a more extensive criminal history) were significantly related to the probability of being rearrested but that none of the measures of the life domains was significantly related to rearrest. There was some indication that having more criminal peers was related to the probability of being arrested. This effect did not exist at lower levels of criminal peers, thus, suggesting the possibility of a non-linear effect. However, this was the extent of the support we were able to find for Agnew's theory. Although some two-way bivariate interactions among the life domain variables were significantly related to rearrest, none of these were significant in multivariate linear probability models. In sum, contrary to his general strain theory which has enjoyed more than a little empirical support (Agnew, 2006a), we found weak support for Agnew's non-strain general theory of crime.10 It is possible that our null findings were partially shaped by the nature of the data. First, we would have to note that we have limited variability in many of our explanatory variables (and this may related to the nature of our sample discussed next). We assessed the possibility that our results could be due to a lack of variability in the independent variables by calculating the coefficient of variation for each of the life domains. The coefficient of variation ranged from 1.90 for the domain of unemployed to a low of 0.29 for the domain of unmarried. A rule of thumb with the coefficient of variation is that measures with coefficients greater than 1 have considerable variation; by this rule, most of the life domains exhibit low variability and thus, it is likely that our results are due at least in part to low variability among the life domains. Our null findings may also be due to the nature of the sample we had which implies something about the scope of Agnew's general theory. Our sample was consisted of non-trivial offenders whose members had been convicted of a crime serious enough to warrant incarceration. The outcome variable, moreover, was the occurrence of an arrest during a follow up period or criminal recidivism. It is possible that the theory is not applicable to such a group of clearly experienced offenders whose life domains may have already been severely compromised by the time they entered the study (more than half had dropped out of high school, and approximately one-fifth were unemployed). There was no a priori reason, however, to suspect that our sample was an inappropriate sample upon which to test the theory. Agnew (2005) clearly regarded the theory as a general theory of crime. It may be, however, that the theory has more merit in explaining the relatively minor offending of more conventional samples than serious and extended participation in crime. In anticipation of future research on other samples, therefore, our null findings may speak more to the scope of the theory than its general validity. In this regard, with a group of offenders possessing rather lengthy criminal records (an average of five arrests), the key issue might not be which of these men continued to engage in crime but rather why some stopped their criminal careers. Recent theories of desistance have begun to emphasize that the key factors leading offenders out of crime might not be the same as those leading them into crime (Veysey, Christian, & Martinez, 2009). In particular, scholars have begun to identify social psychological mechanisms that offenders invoke to resist criminal influences and choose a pathway out of crime. These have been variously termed redemption scripts by Maruna (2001), cognitive transformation by Giordano, Cernkovich, and Rudolph (2002), and human agency by Laub and Sampson (2003). The correctional rehabilitation literature also recognizes the importance of cognitive change as integral to offender reformation (MacKenzie, 2006). Notably, most traditional theories of crime, including Agnew's (2005) general theory do not incorporate these transformative social psychological factors into their models. When seeking to explain persistence as opposed to desistance among

309

committed offenders, however, it might be essential to do so. Future research thus should collect data not only on structural locations and social experiences but also on how offenders interpret their world as a prelude to taking a different road in their lives. It is instructive that the measures we chose seemed to directly correspond with those suggested by Agnew in his discussion of the theory's concepts and possible operationalizations (2005, p. 11). He includes as a factor under the self life domain the individual trait of low self-control, and the measure of self control we used here is taken directly from measures previously used by others in the field (Arneklev et al., 1993; Grasmick et al., 1993; Piquero, 2009). Agnew argued (2005, p. 11) that an indicator of the family life domain is having a bad marriage which we captured with our measure of the extent to which one's relationship with one's spouse or partner was unsatisfactory. He further suggested (2005, p. 11) that an indicator of the school domain was having a limited education such as not having completed high school, an indicator of the peers domain was having criminal peers, and an indicator of the work domain was being unemployed or having a bad job. These are precisely the measures we employed in this study, so it is unlikely that our null findings are due in any large measure to the low correspondence between the theoretical constructs suggested by Agnew and our operationalizations. Further, our measures of the life domains, though unrelated to rearrest, are correlated with other things they should be. For example, our measure of low self-control is related to having poor relationships with others, having criminal peers, and being unemployed, and those who dropped out of school were significantly more likely to be unemployed and be unmarried. It is noteworthy that our study assessed the lagged effect of the life domains on recidivism. Future research may wish to examine both concurrent and contemporaneous effects of the life domains on crime because according to Agnew, a cause may have an immediate effect on crime or it may take a few months’ time (contemporaneous effect) to affect crime. Hence, future research should explore the concurrent and contemporaneous impacts of each of the life domains on crime as well as the concurrent and contemporaneous effects of the each of the life domains on the other domains. Further, given that our study involves an official measure of recidivism, future research should consider evaluating Agnew's theory using self-report data. Finally, since our sample consists of young African American males, we also recommend that future tests of Agnew's theory be conducted using a broader spectrum of samples (college students), other types of offenders (female, white collar), other age groups, as well as other racial and ethnic groups (Asian Americans). Conclusion In conclusion, although our findings are not supportive of Agnew's (2005) general theory of crime, we hasten to add that our research is the first, to our knowledge, that empirically tests some of the observable implications of his theory. The theory contains many other implications that we simply did not have the data to address, and, as we acknowledged above, there are populations other than fairly serious offenders recently released from incarceration, for which the theory may be more relevant. Acknowledgements This paper was supported in part by 2003-DB-BX-0004, awarded by the U.S. Department of Justice. The Assistant Attorney General, Office of Justice Programs, coordinates the activities of the program offices and bureaus. Points of view or opinions contained within this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice. Funding was provided by the State of Maryland, Governor's Office of Crime Control and Prevention, BYRN-2002-1286, to the University of Maryland.

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Appendix A. Life domain variables

Self-control scale* (alpha = 0.89)

Mean SD

Min Max

I often act on the spur of the moment without stopping to think I don't devote much thought and effort to preparing for the crime I often do whatever brings me pleasure here and now, even at the cost of some distant goal I'm more concerned with what happens to me in the short run than in the long run I frequently try to avoid projects that I know will be difficult When things get complicated, I tend to quit or withdraw The things in life that are easiest to do bring me the most pleasure I dislike really hard tasks that stretch my abilities to the limit I like to test myself every now and then by doing something a little risky Sometimes I will take a risk just for the fun of it. I sometimes find it exciting to do things for which I might get in trouble Excitement and adventure are more important to me than security If I had a choice, I would almost always rather do something physical than something mental I almost always feel better when I am on the more than when I am sitting and thinking I like to get out and do things more than I like to read or contemplate ideas I seem to have more energy and a greater need for activity than most other people my age I try to look out for myself first, even if it means making things difficult for other people I'm not very sympathetic to other people when they are having problems If things I do upset people, it's their problem not mine I will try to get the things I want even when I know it's causing problems for other people I lose my temper pretty easily Often, when I'm angry at people I feel more like hurting them than talking to them about why I am angry. When I'm really angry, other people better stay away from me When I have a serious disagreement with someone, it's usually hard for me to talk calmly about it without getting upset.

1.87

1.28 0

4

1.62 1.86

1.28 0 1.23 0

4 4

1.76

1.43 0

4

1.40 1.07 1.76

1.28 0 1.17 0 1.35 0

4 4 4

1.27 2.21

1.23 0 1.33 0

4 4

1.89 1.59

1.35 0 1.24 0

4 4

1.17

1.11 0

4

1.59

1.21 0

4

2.39

1.28 0

4

2.36

1.24 0

4

2.47

1.18 0

4

1.57

1.27 0

4

1.18

1.19 0

4

1.21 1.40

1.13 0 1.14 0

4 4

1.30 1.48

1.21 0 1.29 0

4 4

1.66

1.28 0

4

1.93

1.26 0

4

* 0 = strongly disagree, 1 = disagree, 2 = neither agree nor disagree, 3 = agree, 4 = strongly agree.

Bad relationship* (alpha = 0.88)

Mean SD

In the 12 months before you came to this facility: I was happy with the relationship (reverse) 3.27 We got along together (reverse) 3.21 We really enjoyed being together (reverse) 3.46 We had serious talks about each other's interest and 3.03 needs (reverse) We helped each other with problems (reverse) 3.19

0.90 0.88 0.89 1.05

Min Max 0 0 0 0

4 4 4 4

1.06 0

4

*0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = always.

Peers domain variables Criminal associations (alpha = 0.89)

Mean SD

In the 12 months before you came to this facility, did your friends: Use illegal drugs? 1.65 Trade, sell, or deal drugs? 1.30 Do other things that were against the law? 1.09 Get arrested? 1.76 Do things that could get them into trouble? 2.31 *0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = always.

1.12 1.00 1.04 1.00 1.24

Min Max

0 0 0 0 0

4 4 4 4 4

Bad job (alpha = 0.91)

Mean

SD

Min

Max

In my last job: I was happy with the job (reverse) I really enjoyed working there (reverse)

2.69 2.73

1.21 1.26

0 0

4 4

*0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = always.

Notes 1. Although there are five life domains in Agnew's general theory we have listed seven hypotheses testing main effects because two of our life domains (Family and Job) have two separate measures. Since one of each measure indicates the absence of the life domain (no marriage and no job) and the other indicates the quality of the domain we treat each indicator separately. 2. Our sole measure of the school domain is a binary indicator whether or not the respondent dropped out of school. As a binary measure, we could not determine its non-linear effect on the outcome variable by way of a quadratic or logged term. Only the linear effect is therefore reported for the school domain. Our second measure of the family domain (being unmarried) and second measure of the job domain (being unemployed) are also binary measures and so quadratic and lin-log results are not reported for them either. 3. We recognize the criticisms associated with official data. However, we should also point out that the self-report method is not without criticisms. In particular, there is evidence that self-report measures tend to tap less serious offenses while official data appear to measure the types of offenses that are likely to result in an arrest (Hindelang et al., 1979). Further, the self-report method has also been documented to produce less valid results among groups that tend to have high rates of official delinquency – particularly black males (Hindelang et al., 1981). Accordingly, given the characteristics of our sample – Africian American males with extensive prior criminal records – we feel the inclusion of an official measure of recidivism in our study is justifiable. 4. The Grasmick et al. (1993) self-control scale was designed to measure the six dimensions of the latent trait self-control. Although the Grasmick et al. scale possesses high scale reliability and appears to reflect Gottfredson and Hirschi's construct of selfcontrol, it has been the subject of debates about unidimentionality (Arneklev et al., 1999; Longshore et al., 1996; 1998; Piquero & Rosay, 1998; Piquero et al., 2000; Varzonyi et al., 2001). Further, the adequacy between behaviorally based and cognitive based measures of self-control has also been raised. It is noteworthy that while Gottfredson and Hirschi (1995) favor behaviorally based measure over cognitively based measure, there is evidence that cognitive based scales of self-control perform just as well as behavioral measures (Tittle et al., Grasmick, 2003; see also, Pratt & Cullen, 2000). 5. Although Agnew (2005) was not clear about the nature of the interactions, we estimate the simplest form in this preliminary test of the theory – two way interactions. 6. For comparison we also estimated forms of the logistic regression model and the results are the same as for the reported linear probability models. 7. Most of the correlations among the life domain variables are as theoretically expected even if a given life domain variable was not related to rearrest. For example, although low-self control was not related to being rearrested, it was significantly related to having poor social relationships, having criminal peers, and being unemployed – outcomes you would expect those with low self-control to have. 8. The measure for the school domain, high school dropout, was a binary variable and non-linear models were not estimated. We did estimate linear multivariate OLS models for being a high school dropout, unmarried, and unemployed. In each model the regression coefficient for the domain in question was negligible (b = .025 for HS dropout, b = .062 for being unmarried, and b = −.017 for being unemployed) and in each case was not significantly different from zero. These results are available upon request. 9. Following one reviewer's advice we also created a risk index which was a summed score of the number of risk factors each respondent possessed (having low self-control, a bad job, bad social relationships, and a lot of criminal peers, where each continuous variable was split at the median). We included this variety measure of risk in our models along with the other covariates in place of the individual measures. The model with this index was statistically significant (χ2 = 21.56, p b .001) and the effect of this variety risk index was positive as expected but did not reach statistical significance (though the odds ratio was 1.7). 10. Our finding that a good job and being married are unrelated to recidivism is contrary to some recent and very notable empirical work such as that by Laub and Sampson (2003), Horney et al. (1995), and Piquero et al. (2002). This discrepancy deserves some comment. In their study, Laub and Sampson (2003) found that marital attachment, rather than being married per se, was one of the strongest predictors of adult criminality. Further, their measure of marital attachment encompassed the respondent's assessment of the general marital relationship, his attitude toward marital responsibility, and his perception on family cohesiveness. The measure of bad marriage employed in our study, while also captures the respondent's perception of

F.T. Ngo et al. / Journal of Criminal Justice 39 (2011) 302–311 his relationship with his spouse or partner, is certainly not as extensive as the Laub and Sampson's marital attachment measure. It is possible that the difference in findings between our study and Laub and Sampson's study could be due to this fact. Furthermore, the sample employed in Laub and Sampson's study, white males from the 1950s, is very different from the sample employed in our study, mostly African American males from the 2000 decade. It is possible that Laub and Sampson's theory may itself be of limited historical scope (Giordano et al., 2002) and thus, their proposition relating to marital attachment and recidivism was not supported in our study. With regard to Horney and colleagues’ (1995) study, it is noteworthy that their study did not include the measure of marital attachment. Rather, they examined the effects of living with a wife and living with a girlfriend on the likelihood of offending. We did not include these measures in our study. Instead, in concordance with Agnew's theory, we created a measure called “unmarried” and this measure captured whether the respondent was married or not and therefore was theoretically faithful to Agnew's theory. Further, we could not be certain as to whether our measure reflects the measures employed in Horney and colleagues’ study as it is possible that one could be married but not living with his wife. Additionally, unlike the data utilized in Horney and colleagues’ study, our data do not contain concurrent independent and dependent measures. Accordingly, the difference in findings between our study and Horney et al.'s study may be attributed to either factor listed above (i.e., different measures or a lack of concurrent independent and dependent measures) or both. Pertaining to the study by Piquero et al. (2002), it should be noted that in their study, the authors created an index called “a stakes in conformity” by combining the measures of marriage and employment. In contrast, our study contains separate measures or domains relating to marriage and work. In particular, following Agnew's guidance, our study examines the specific effects of the following domains unmarried, having a bad marriage, unemployed and having a bad job - on recidivism. Further, similar to the study by Horney and her colleagues, the work by Piquero and his colleagues involved concurrent independent and dependent measures. However, the authors did recommend that future studies examine the lagged effect of local life circumstances on crime because they believe that nonrecursive modeling of local life circumstances and arrests may uncover interesting findings (page 162 of their paper).

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