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Low Self-Control and the Religiosity-Crime Relationship Michael D. Reisig, Scott E. Wolfe and Travis C. Pratt Criminal Justice and Behavior published online 4 June 2012 DOI: 10.1177/0093854812442916 The online version of this article can be found at: http://cjb.sagepub.com/content/early/2012/06/01/0093854812442916

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LOW SELF-CONTROL AND THE RELIGIOSITY-CRIME RELATIONSHIP MICHAEL D. REISIG Arizona State University

SCOTT E. WOLFE University of South Carolina

TRAVIS C. PRATT Arizona State University

Two arguments have been advanced regarding the effect of low self-control on the religiosity-crime relationship. The first holds that self-control explains both religiosity and criminal offending (the confounding hypothesis), whereas the second posits that religiosity promotes self-control and indirectly affects antisocial behavior (the mediation hypothesis). Both hypotheses predict that the observed effect of religiosity on criminal offending is a spurious result of individual variations in self-control. With cross-sectional survey data from a university-based sample of 769 adult participants, the regression models indicate that the effect of religiosity on self-reported criminal offending is no different from zero after controlling for low self-control. This finding is observed when different religiosity measures are used. Religiosity did, however, predict minor crimes characterized by personal indulgence (i.e., ascetic offenses) independent of low self-control. Keywords: self-control; religiosity; offending; religion; crime

R

ecent religiosity-crime research focuses on the role of self-control (see, e.g., Laird, Marks, & Marrero, 2011). This work is driven by arguments that distinguish between two causal pathways. One position holds that the ability to control oneself is established early in the life course, remains stable over time, and not only is a powerful predictor of criminal activity but also influences whether individuals are attracted to religious life (Gottfredson & Hirschi, 1990; Welch, Tittle, & Grasmick, 2006). From this vantage point, the relationship between religiosity (i.e., activity, devotion, and belief) and involvement in illegal behavior is confounded by self-control. A second argument posits that self-control is, in comparative terms, much more dynamic and, like a muscle, will atrophy if not used (Baumeister, Vohs, & Tice, 2007). Religious beliefs and activities can help people exercise and improve their self-control (Geyer & Baumeister, 2005; McCullough & Willoughby, 2009). This argument, referred to here as the mediation hypothesis, holds that the effect of religiosity on criminal offending is indirect, operating through self-control. Although the role of self-control differs, both of these theoretical arguments specify a three-variable system whereby self-control predicts criminal activity. Within this system, the direct effect of religiosity on criminal offending is expected to be nil. Extant research on the effect of self-control on the religiosity-crime relationship has yet to provide consistent, reliable results from which scientific consensus can emerge. This

AUTHORS’ NOTE: An earlier version of this article was presented at the 61st annual meeting of the American Society of Criminology in Philadelphia, Pennsylvania. The authors thank Gary Sweeten for his statistical assistance. Correspondence may be addressed to Michael Reisig, School of Criminology and Criminal Justice, Arizona State University, 411 N. Central Ave., Phoenix, AZ 85004-0685; e-mail: [email protected]. CRIMINAL JUSTICE AND BEHAVIOR, Vol. XX No. X, Month XXXX xx-xx DOI: 10.1177/0093854812442916 © 2012 International Association for Correctional and Forensic Psychology

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2 Criminal Justice and Behavior

state of affairs is not atypical, as the religiosity-crime literature is characterized by several ongoing debates. For example, some scholars argue that religiosity affects only minor crimes characterized by personal indulgence (e.g., marijuana use and alcohol-related offenses; Burkett & White, 1974). This view, however, is not universally accepted (Evans, Cullen, Dunaway, & Burton, 1995). Another point of contention concerns the way religiosity is measured. It is not uncommon for researchers to test the independent effects of specific religious elements (e.g., church attendance and time spent praying) on criminal offending (Benda & Corwyn, 1997). Others advocate the use of multi-item scales that combine a variety of religious behaviors and attitudes (Johnson, Li, Larson, & McCullough, 2000). Suffice it to say, these and other issues remain unsettled. Systematic investigations into whether the observed religiosity-crime relationship is a spurious result of self-control must accommodate these unresolved issues. The present study moves the religiosity-crime research forward by answering the following questions: (a) Does the relationship between religiosity and criminal activity persist after taking into account individual variations in self-control? (b) Are significant relationships between religiosity and offending observed when specific religious elements are assessed separately? and (c) Are religiosity and low self-control significantly related to criminal offending outcomes that reflect different levels of societal disapproval (i.e., ascetic and secular offenses)? Multivariate analyses are carried out using cross-sectional survey data from a university-based sample of 769 adult participants. Importantly, the results will determine whether the observed effect of religiosity on law violating behavior is spurious after controlling for low self-control. Such a finding would provide a firm foundation for future longitudinal research investigating the exact nature of the spuriousness (i.e., confounding or mediation).

THE RELIGIOSITY-CRIME LITERATURE

Early assessments of the influence that religious beliefs and activities have on criminal behavior, delinquency, and deviance were somewhat mixed. For example, in Unraveling Juvenile Delinquency, Sheldon and Eleanor Glueck (1950, p. 167) observed that weekly church attendance was less regular for delinquent boys (39.3%) than for nondelinquent boys (67.1%). Other early investigations were less supportive (Hirschi & Stark, 1969). More recent empirical evaluations of the religiosity-crime link generally indicate that the relationship is inverse in nature and statistically significant at the .05 level, although the magnitudes of reported effect sizes vary from study to study. Baier and Wright’s (2001) meta-analysis of 60 religion-crime studies demonstrated that the mean bivariate effect size is –0.12, with individual coefficients ranging from 0.00 to –0.47. What accounts for this wild fluctuation in observed effects? Recent research focuses on the influence of a key third variable, self-control. THE ROLE OF SELF-CONTROL

Much of the early research examining the role of third variables in the link between religiosity and criminal behavior focused on demographic factors (e.g., socioeconomic status), the results of which were not terribly helpful (see Ellis, 1985, for a review). More contemporary research has assessed whether theoretical variables, such as threat of shame, Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012

Reisig et al. / LOW SELF-CONTROL, RELIGIOSITY, AND CRIME 3

social bonding, peer associations, and legal deterrents, explain the religion-crime link (Benda, 1995; Benda & Corwyn, 2001; Evans et al., 1995; Grasmick, Bursik, & Cochran, 1991; Jang & Johnson, 2001; Johnson, Jang, Larson, & Li, 2001). Some provocative hypotheses and interesting findings have emerged. For example, Ellis (1987) argued that the neurological attributes relating to criminal activity also influence whether people attend religious services, obey religious rules, and find religious life satisfying. Cochran, Wood, and Arneklev’s (1994) test of Ellis’s arousal hypothesis showed that the effects of religious participation and religious salience ceased to be relevant on both assault and property theft after arousal factors were controlled for. Although arousal variables also predicted illicit drug use, the significant effect of Cochran et al.’s religion measures persisted (also see Ellis & Thompson, 1989). One of Cochran et al.’s (1994) strongest arousal measures was impulsivity, which is also considered a key component in self-control scales (see, e.g., Grasmick, Tittle, Bursik, & Arneklev, 1993; Tangney, Baumeister, & Boone, 2004). Prior research has linked selfcontrol to religiosity (see McCullough & Willoughby, 2009) and also to a variety of criminal and antisocial behaviors (Arneklev, Grasmick, Tittle, & Burisk, 1993; Higgins, Wolfe, & Marcum, 2008; Holtfreter, Reisig, Piquero & Piquero, 2010; Pratt & Cullen, 2000; Reisig & Pratt, 2011). These two empirical observations, coupled with the research supporting the religiosity-crime link, satisfy the statistical requirements necessary to detect spuriousness (Baron & Kenny, 1986; MacKinnon, Krull, & Lockwood, 2000). The evidence suggests it is highly plausible that the relationship between religiosity and law violating behavior is spurious, resulting from the failure to account for individual variations in self-control. Existing theory regarding the exact causal mechanisms at work is not clear. Two arguments have been advanced, both of which view the nature of selfcontrol very differently. The confounding hypothesis conceives self-control in a manner consistent with Gottfredson and Hirschi (1990): a product of parental socialization that is fully formed during childhood and remains fairly stable across the life course. Those with poor self-control experience great difficulty delaying gratification and empathizing with the plights of others and are selfish. Welch et al. (2006) contend that high self-control aids in the internalization of religious beliefs and participation in related activities. People with strong self-control find it easier to resist temptation and to follow religious dictates. These same people are also much more likely to regularly attend religious services and other activities that require significant time commitments. In short, this argument holds that the observed correlation between religiosity and crime is explained by self-control. Welch et al. (2006) used survey data from 350 adults in Oklahoma City to test their argument about self-control, religiosity, and crime. Self-projections of future law-breaking behavior (e.g., illegal gambling, assault, and petty theft) served as their dependent variable. The linear regression analysis showed that the inclusion of self-control attenuates the religiosity effect by 28%. However, religiosity remained statistically significant (p < .05), leading Welch et al. to conclude that religiosity and self-control both exert independent effects on criminal behavior. There are some technical issues with the analysis that give us pause. Perhaps most important is the statistical modeling strategy. The dependent variable is a variety score, which reflects the number of the different types of offenses that respondents anticipate committing in the future. Variety offending scores should be modeled using data-analytic procedures designed for count data (e.g., Poisson or negative binomial regression; see, e.g., Apel & Kaukinen, 2008; Burt, Simons, & Simons, 2006; Sweeten, Bushway, & Paternoster, 2009). Using a linear model when the dependent variable is a widely dispersed Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


count measure often results in downwardly biased standard errors (Gardner, Mulvey, & Shaw, 1995; Long, 1997). This kind of situation can mask attenuation because the t ratios are artificially inflated. In sum, we remain skeptical about Welch et al.’s independent effects conclusion and believe more research is necessary. The second theoretical argument views self-control as more dynamic and prone to depletion over time if overworked or not regularly exercised. To convey this point, scholars liken self-control to a muscle that can become stronger with exercise and that can be conserved to deal with impending challenges, and people can manage to exert self-control despite depletion if the incentives are attractive (Baumeister et al., 2007; Muraven & Baumeister, 2000). Behavioral scientists have begun to consider the different ways that religiosity may promote self-control. For example, religious beliefs might motivate people to exercise selfcontrol, even among individuals with depleted levels. Beliefs regarding positive (e.g., eternal salvation) and negative outcomes (e.g., eternal torment) after death can serve as powerful inducements to improve self-control efforts. Various religious activities, such as prayer and meditation, provide opportunities to monitor one’s own behavior, which in turn may facilitate greater self-control efforts in the future. Finally, religion provides believers with moral guidelines to defer to and exemplars to emulate. Such religious prescriptions promote self-control by conserving the energy expended by the irreligious when determining right from wrong (Geyer & Baumeister, 2005; also see McCullough & Willoughby, 2009). These three mechanisms are among the ways in which religiosity might promote self-control and indirectly influence criminal and antisocial behavior. Accordingly, selfcontrol is hypothesized to mediate the religion-crime relationship. The findings from research assessing the argument about religiosity, self-control, and crime are mixed. Using interview data from 166 early adolescents, Laird et al. (2011) found that both of their religiosity measures (i.e., religious importance and attendance) were associated with low self-control and that low self-control was significantly related to antisocial behavior (a measure that includes physical and nonphysical aggression, drug use, and delinquency). The indirect effect of religiosity on antisocial behavior, however, was not statistically significant. Laird et al. interpreted these findings as not supporting the mediation hypothesis. A similar study, which used interview data from middle and high school students, found that the indirect effect of religiosity on substance use (i.e., tobacco, alcohol, and marijuana) via “good self-control” was statistically significant (Walker, Ainette, Wills, & Mendoza, 2007). The authors concluded that their findings support the mediation hypothesis. These two studies use very different dependent variables. On one hand, Laird et al. lump ascetic (e.g., drug use) and secular (e.g., physical aggression) offenses together in their antisocial behavior measures. Walker et al., on the other hand, focused on substance abuse (or anti-ascetic behavior). The use of different outcome measures may explain the discrepant findings. Indeed, religiosity-crime researchers have spent considerable time testing whether the use of criminal behavior outcome measures that reflect different levels of societal disapproval influence research findings. THE SCOPE AND NATURE OF CRIMINAL OFFENDING OUTCOMES

Some researchers argue that the claim that religiosity is inversely related to all types of criminal activity is a gross overgeneralization. Rather, they contend the effect of religiosity is limited to a much narrower set of specific illegal behaviors (Burkett & White, 1974). Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


Long ago, Middleton and Putney (1962) argued that the difference in the offending patterns between the religious and irreligious is partially explained by variation in normative standards. Specifically, religious individuals more frequently follow traditional ascetic standards (i.e., abstinence from self-indulgent behaviors) compared to their secular counterparts. Religious people are more likely to hold the belief that anti-ascetic behaviors, such as smoking marijuana, are spiritually harmful. In short, one should expect religion to affect ascetic offenses not because secularists violate their own ascetic normative standards but rather because they have not internalized such conduct norms. In contrast, according to Middleton and Putney, believers and nonbelievers share a sense of social morality; thus differences across these two groups in secular offending (e.g., theft and assault) should be insignificant. This argument is commonly referred to as the anti-ascetic hypothesis. With but a few exceptions, the research generally supports the anti-ascetic hypothesis. For example, Wills, Yaeger, and Sandy (2003) reported that religiosity is correlated with both marijuana use and alcohol use at the .001 level among 10th graders (r = –.10 and –.15, respectively; also see Cochran & Akers, 1989). Interestingly, some well-known studies refuting the religiosity-crime link have not included anti-ascetic behaviors in their dependent variables (see, e.g., Hirschi & Stark, 1969). A large number of studies supporting the religiosity-crime relationship either include or exclusively feature ascetic forms of offending. In their meta-analysis, Baier and Wright (2001) reported that studies focused on marijuana use and alcohol use (by minors) showed stronger religiosity effects. However, the conclusion that the effect of religiosity is largely limited to anti-ascetic behaviors is not accepted by everyone (see, e.g., Evans et al., 1995). What can be gleaned from this research? Cochran (1988) cautioned that self-report offending scales in religion-crime research are “incomplete to the degree that they fail to include items measuring ascetic deviance” (p. 296). We concur with this assessment and add that disaggregating ascetic and secular offenses from a general criminal offending index to assess the religiosity-crime link is also necessary to avoid masking variations in effect magnitude. OPERATIONALIZING RELIGIOSITY

A good portion of the religiosity-crime research is steeped in the social control tradition. These studies assume that every individual, to a greater or lesser degree, possesses criminal motivation and that religious institutions, in concert with other institutions (e.g., family and school), restrain antisocial behaviors. The different elements of religiosity—activity, devotion, and belief—are believed by many to involve different causal processes. For example, belief in supernatural sanctions for wrongdoing either while living or in the afterlife is hypothesized to deter wayward behavior (Wilson & Herrnstein, 1985; cf. Hirschi & Stark, 1969). Religiously based activity promotes social ties and feelings of belongingness among people with conventional lifestyles (Tittle & Welch, 1983). Finally, devotion to religious principles helps internalize moral commitments and promotes obedience to honor a supernatural entity or religious system (Geyer & Baumeister, 2005). Regardless of the exact causal mechanism specified, each of the three religious elements is hypothesized to promote legal compliance. From a measurement perspective, survey items reflecting the different components that make up religiosity are intercorrelated. Several studies employing factor-analytic techniques report that religious items load on a single underlying construct (Benda & Corwyn, 1997; Evans et al., 1995; Johnson et al., 2001; Welch et al., 2006). Nevertheless, arguments Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


regarding the relative predictive accuracy of individual religious elements are common. Tittle and Welch (1983) note that church attendance among juveniles may reflect parental coercion as opposed to deep religious conviction. Others distinguish between behavioral and attitudinal religiosity measures, arguing that the former are more valid (Evans et al., 1995; cf. Baier & Wright, 2001). Unfortunately, available research provides little in the way of guidance. Whereas some studies using multi-item religiosity scales report significant inverse religiosity-crime relationships (see, e.g., Benda, 1995, 1997), others that use equally sophisticated measures report that the relationship between religiosity and offending is no different from zero (see, e.g., Elifson, Peterson, & Hadaway, 1983; Evans et al., 1995). Debate about the preferred measurement strategy will likely persist for some time. For now, the wise course of action is to test religiosity-crime hypotheses using a variety of religiosity measures to bolster confidence in the observed findings. CURRENT FOCUS

This study contributes to the research literature in several ways. First, we determine whether the effect of religiosity on criminal offending persists after low self-control is accounted for. Next, we examine whether a similar pattern of findings emerges when the religiosity variable is disaggregated into its component parts (i.e., belief, activity, and devotion). Finally, we evaluate the robustness of the empirical observations by disaggregating the dependent variable and assessing the effects of religiosity and low self-control on ascetic (e.g., marijuana use) and secular (e.g., assault) offending. These three research objectives are pursued using cross-sectional survey data from a university-based sample of 769 respondents. Note that the objective is not to determine whether low self-control behaves specifically as a confounder or a mediator. Rather, the purpose is to investigate whether the religiosity-crime relationship is spurious after individual variations in selfcontrol are controlled for in different multivariate contexts. METHOD PARTICIPANTS

This study uses data from pencil-and-paper surveys administered to individuals 18 years and older enrolled at a large university located in the southwestern United States. Data collection took place between the fall 2008 and spring 2010 semesters. Project managers were granted permission to distribute surveys in 12 criminology and criminal justice undergraduate courses. These courses were open to majors and nonmajors alike and provided a good cross-section of the student population. Individuals who were invited to take part in the study were given extra credit regardless of whether they decided to participate. Prior to filling out the questionnaire, participants received instructions on how to complete the survey, they were informed that their participation was voluntary, and everyone was assured that their responses were completely anonymous. The instrument took approximately 15 min to administer. A total of 781 individuals were invited to participate. Only 12 people declined. Similar-response pattern imputation (SRPI) was used to handle missing cases (less than 1% of cell values were missing), which is available in PRELIS Version 2.30 (Scientific Software International, Chicago, IL). After the imputation process was complete, information for 769 respondents was available. Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


The sample consisted of slightly more women (56.6%; n = 435) than men (43.4%; n = 334). Regarding racial-ethnic background, 54.4% of the sample were White (n = 418), 26.8% were Hispanic (n = 206), 8.4% were Black (n = 65), 3.5% were Asian (n = 27), 3.0% were Native American (n = 23), and 3.9% self-identified as “Other” (n = 30). Concerning age (in years), 35.4% were 18 (n = 272), 24.6% were 19 (n = 189), 15.1% were 20 (n = 116), 9.0% were 21 (n = 69), and 16.0% were 22 or older (n = 123). Approximately 93% of participants were full-time students (n = 715). In terms of religious background, 66.1% (n = 508) of the sample self-identified as Christian. Specifically, 32.1% identified Protestant (n = 247), 33.9% identified Catholic (n = 261), 9.9% identified another religion (n = 76; e.g., Buddhist, Muslim, and Jewish), and 24.1% self-reported “no religion” (n = 185). Interestingly, conventional religious beliefs are not uncommon among this group. For example, 54.1% (n = 100) of people who selected no religion as their preference also report that they believe in life after death. These individuals are sometimes referred to as “unchurched believers” (Hout & Fischer, 2002). POTENTIAL DATA LIMITATIONS

It is important to acknowledge two ways in which the data used in the current study are potentially limited. One concern is range restriction in key theoretical variables (e.g., low self-control and criminal offending) that can downwardly bias observed relationships. This type of problem is generally more common when samples are drawn from institutions of higher education with homogeneous populations (e.g., White and upper middle class) and low acceptance rates (Payne & Chappell, 2008). The sample in the present study is culturally, racially-ethnically, and intellectually diverse. The university from which the sample was drawn prides itself on being accessible to people from all walks of life, especially individuals who traditionally lack educational opportunity. The theoretical variables possess sufficient variation to conduct multivariate analyses; thus bias caused by restriction in range is not a concern. Another potential concern is generalizability. Similar to behavioral and social scientists who employ experimental designs and investigators who use listwise deletion to deal with large amounts of nonrandomly missing information, researchers who use university-based samples cannot easily generalize their findings to broader populations. The ability to generalize the results to a larger population of interest was forfeited for two reasons. First, prior reviews emphasize the importance of constructing multi-item religiosity scales (see, e.g., Johnson et al., 2000). Second, Baier and Wright’s (2001) meta-analysis found that the use of a university-based sample had no significant impact on the religiosity-crime effect size. Accordingly, the development of psychometrically sound religiosity and criminal offending scales took precedence. Nevertheless, care should be exercised when considering the broader implications of the reported findings until replication studies using more representative samples are available. MEASURES

Religiosity. Ten survey items that reflect the important domains of religiosity—activity, devotion, and belief—are used in the present study. Religious Activity is a two-item scale: “How often do you pray?” and “How often do you attend religious services?” (r = .658). The response scale ranged from 1 (never) to 4 (frequently). The second domain, Religious Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012

8 Criminal Justice and Behavior TABLE 1: Religiosity Scale Psychometric Properties Factor Loadingsa

Survey Item 1. Do you believe in a life after death?c 2. How often do you pray?d 3. How often do you attend religious services?d 4. I try hard to carry religion over to all my other dealings in life.e 5. I’ve often been keenly aware of the presence of a divine being.e 6. My religious beliefs lie behind my whole approach to life.e 7. Prayers said alone are as meaningful as when said during religious service.e 8. Religion is important for answering questions about life’s meaning.e 9. I read literature about my faith.e 10. Private religious thought and meditation is important to me.e Eigenvalue Marginal reliability

Item Discriminationb (SE)

0.46 0.82 0.71 0.85 0.74 0.75 0.63

1.43 3.08 2.30 4.21 2.31 2.67 1.63

(0.17) (0.19) (0.17) (0.27) (0.15) (0.16) (0.13)

0.81 0.65 0.70 5.19 —

3.00 (0.20) 1.95 (0.14) 2.03 (0.14) — 0.92

a. Pattern loadings from principal-axis factor model. b. Item discrimination coefficients and standard errors from graded response model. c. Response set ranging from 0 = no to 1 = yes. d. Response set ranging from 1 = never to 4 = frequently. e. Response set ranging from 1 = strongly disagree to 4 = strongly agree.

Devotion, is captured using Allport and Ross’s (1967) intrinsic motivation scale (e.g., “My religious beliefs lie behind my whole approach to life” and “I try hard to carry religion over to all my other dealings in life”; Cronbach’s α = .891; mean interitem r = .541). Prior research has shown that this scale is psychometrically sound (see, e.g., Brewczynski & MacDonald, 2006; Genia, 1993; Leong & Zachar, 1990). The closed-ended response set for these items ranged from 1 (strongly disagree) to 4 (strongly agree). Finally, Religious Belief is a single survey item that asked respondents, “Do you believe in a life after death?” (1 = yes, 0 = no). Several steps were taken to construct the 10-item Religiosity Scale. First, principal-axis factoring was used to evaluate dimensionality. The results showed that the items are represented by a unitary latent factor (eigenvalue = 5.19, pattern loadings > 0.40; see Table 1). These items are also characterized by a high degree of internal consistency (Cronbach’s α = .911; mean inter-item r = .506). Because the response categories varied (i.e., binary and ordinal), a graded response model was estimated using MULTILOG Version 7.02 (Scientific Software International, Chicago, IL). The discrimination parameters range from 1.43 (“Do you believe in a life after death?”) to 4.21 (“I try hard to carry religion over to all my other dealings in life.”), and the average estimate is 2.461. In relative terms, the standard errors are very low (range from 0.13 to 0.20), so all of the estimates are statistically significant. In short, each of the 10 survey items is related to the underlying construct (i.e., religiosity), although some items more strongly than others. The marginal reliability coefficient is 0.92, indicating that the average reliability across trait levels is very high. Finally, the 10 survey items are used to compute item response theory (IRT) scale scores for each participant (Osgood, McMorris, & Potenza, 2002; Piquero, MacIntosh, & Hickman, 2002). The scale is coded so that higher scores reflect higher levels of religiosity.



Low self-control. This study tests whether any observed effect of religiosity on selfreported criminal offending is a spurious result of not controlling for individual variations in the ability to regulate one’s own behavior. Low self-control is operationalized using the Brief Self-Control Scale (Tangney et al., 2004). This 13-item scale was originally developed using university-based samples and captures four domains of self-control: nonimpulsive action (“I say inappropriate things”), self-discipline (“I wish I had more self-discipline”), work ethic (“I am able to work effectively toward long-term goals”), and healthy habits (“I refuse things that are bad for me”) (see the appendix for a complete list of scale items and corresponding summary statistics). Closed-ended responses to the self-control items are anchored from 1 (not at all like me) to 5 (very much like me). The scale meets conventional criteria for internal consistency (Cronbach’s α = .808, mean inter-item r = .245), and the distribution of scores is well-behaved. The scale is coded so that higher scores reflect lower levels of self-control. Criminal offending. The primary criterion variable, criminal offending, is measured by a 12-item additive scale. Respondents self-reported their involvement in a wide range of criminal activity, including petty offenses thought to be common among college students (e.g., “Made a lot of noise at night”), relatively minor crimes (e.g., “Stole something worth less than $50”), and more serious law violations (e.g., “Got into a fight with another person with the idea of physically harming them”; see appendix). The reference period for the offending items was the 6 months prior to the survey. Each item featured a closed-ended response set that ranged from 1 (never) to 4 (frequently). The level of internal consistency exhibited by the scale exceeded conventional thresholds (Cronbach’s α = .781; mean interitem r = .243). Because the raw distribution of scores exhibited a slight positive skew, the natural logarithmic transformation was used to induce normality. The scale was coded so that higher scores reflect more frequent criminal offending.1 Because prior research has demonstrated that the effect of religiosity varies across different types of criminal behavior, two additional offending outcomes were constructed using items from the Criminal Offending Scale. Ascetic Offending is a two-item summated scale that reflects personal indulgences that are prohibited by most religious traditions and the criminal law but not universally shunned by the rest of society. Items regarding marijuana use, such as the one included in the present study (i.e., “Used marijuana or some other drug”), are common in the religion-crime research. Alcohol items are also common. However, there are two problems with using alcohol-related measures. First, it is not a crime for adults to consume alcohol, and second, alcohol (e.g., sacramental wine) is regularly used in religious services, especially in liturgical Christian churches (e.g., Roman Catholic, Episcopal, and Lutheran). Accordingly, we use a measure that reflects an alcoholrelated legal infraction (i.e., “Drank alcohol in a place where you are not supposed to”). The two ascetic offending items are correlated at .416 (p < .001). Secular Offending is a five-item additive scale that captures law-breaking behavior for which strong norms against such conduct are generally felt among religious and nonreligious individuals alike (e.g., “Broke into a motor vehicle without the owner’s permission” and “Stole something worth more than $50; Cronbach’s α = .639; mean inter-item r = .276). To better approximate normal distributions, the natural logs were taken and the transformed Ascetic and Secular Offending scales were used in the analyses.


10 Criminal Justice and Behavior TABLE 2: Summary Statistics and Pearson’s Correlation Coefficients Variable

1

1. Criminal offendinga 2. Ascetic offendinga 3. Secular offendinga 4. Religiosity 5. Religious belief 6. Religious activity 7. Religious devotion 8. Low selfcontrol 9. Male 10. Latino 11. Racial minority 12. Age M or percentage SD

—

4

5

–0.113 –0.149 –0.049 –0.067 –0.092 –0.038

— 0.494

—

–0.124 –0.172 –0.052

0.865

0.389

—

–0.102 –0.131 –0.048

0.971

0.433

0.776

0.769 0.730

0.469

2

3

6

7

8

9

10

12

— 0.377

0.361

—

—

0.347 –0.101 –0.035 –0.132 –0.085

—

0.152 0.059 0.152 –0.052 –0.017 –0.050 –0.041 0.082 — –0.042 –0.025 –0.011 0.095 0.057 0.062 0.084 –0.015 –0.080 — 0.025 –0.022 0.061 0.158 0.044 0.136 0.151 0.021 –0.067 –0.292 –0.084 –0.080 –0.041 –0.019 –0.071 –0.014 –0.013 –0.086 2.96 1.26 1.81 0.01 81% 2.76 10.59 34.99 0.25

11

0.46

0.25

0.93

—

1.89

5.54

8.34

—

0.043 –0.151 –0.011 — 44% 27% 19% 2.80 —

—

—

2.23

a. Natural logarithmic transformation.

Demographic variables. Several demographic variables are included in the analyses so that unbiased estimates of the independent effects of religiosity and low self-control on criminal offending can be established. Two dummy variables are used to reflect the racialethnic background of participants: Latino (1 = yes) and racial minority (1 = yes) (nonHispanic Whites serve as the omitted category). Male is a dichotomous measure (1 = yes). Age is coded using nine categories (1 = 18 years to 9 = 26 years or older). Summary statistics for the variables used in the present study are provided in Table 2. ANALYTIC STRATEGY AND MODEL DIAGNOSTICS

The analyses proceed in three stages. First, the effects of religiosity and low self-control on criminal offending are tested. This stage involves estimating two ordinary least squares (OLS) models. The direct effect of religiosity, net of demographic characteristics, is evaluated in the first equation. Low self-control is introduced in the second equation. Both the confounding and mediating hypotheses predict that the effect of religiosity and criminal offending will be no different from zero after low self-control is controlled for. The analyses continue by estimating a series of OLS models that assess whether low self-control explains the effects of specific religious domains (i.e., belief, activity, and devotion) on criminal offending. Finally, attention is directed toward whether the effect of religiosity is reduced below statistical significance after low self-control is controlled for when considering specific types of offending. Here, separate regression models for ascetic and secular offending are estimated. Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012

Reisig et al. / LOW SELF-CONTROL, RELIGIOSITY, AND CRIME 11 TABLE 3: The Effects of Religiosity and Low Self-Control on Criminal Offending Criminal Offending Model 1a Variable Religiosity Low self-control Male Latino Racial minority Age Intercept F test R²

Model 2a

b (SE) [β]

t ratio

–0.030 (0.012) [–0.112] — 0.075 (0.018) –0.011 (0.029) 0.029 (0.029) –0.010 (0.004) 2.953 (0.017) 16.13** .046

–2.45* — 4.20** –0.64 0.99 –2.84* 177.53**

b (SE) [β] –0.017 0.013 0.056 –0.011 0.017 –0.006 2.490

(0.011) [–0.065] (0.001) [0.447] (0.016) (0.020) (0.024) (0.003) (0.029) 111.39** .241

t ratio –1.62 15.56** 3.49** –0.56 0.71 –1.85 85.20**

Note. Entries are unstandardized partial regression coefficients (b), standardized partial regression coefficients in brackets [β], and robust standard errors that adjust for clustering on classrooms in parentheses. a. Ordinary least squares regression equation. *p < .05. **p < .01 (two-tailed test).

Preliminary analyses indicate the presence of heteroscedastic errors. Additionally, the observations are not independent. Both of these data features may bias the reported findings. To guard against these threats, Huber-White robust standard errors corrected for clustering on classrooms are used in multivariate models. This procedure is available in Stata 10 (StataCorp, College Station, TX). Several diagnostic tests were performed to determine whether harmful levels of collinearity would bias the multivariate parameter estimates in the regression models presented below. None of the bivariate correlations between religiosity, low self-control, and the demographic variables exceed an absolute value of .30 (see Table 2), which is well below the traditional .70 threshold. The variance inflation factors were below 1.5, and condition indices failed to approach the commonly used threshold of 30 (Tabachnick & Fidell, 2007). These diagnostics indicate that harmful collinearity is not a concern. RESULTS

Three empirical conditions must be established before testing whether the religiositycrime relationship is spurious and explained by low self-control (see MacKinnon et al., 2000). The first two requirements are that the independent variables of interest covary with the outcome measure. The Pearson’s r coefficients in Table 2 indicate, as expected, that low self-control (r = .469, p < .001) and religiosity (r = –.113, p < .01) are significantly correlated with criminal offending. The relatively weak religiosity-offending relationship is consistent with prior research (Baier & Wright, 2001). The final requirement is that the two independent variables are related. A weak but statistically significant correlation between low self-control and religiosity is observed (r = –.101, p < .01). In sum, the necessary empirical conditions for detecting spuriousness are satisfied. In Table 3, the Criminal Offending Scale is regressed onto the 10-item Religiosity Scale and four demographic variables (see Model 1). Although the joint association test indicates Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


that the model provides more explanatory power than would be expected by chance alone (F = 16.13, p < .01), the coefficient of multiple determination is very low (R2 = .046). Additional analyses (not shown) revealed that the Religiosity Scale accounts for a small proportion (approximately 28%) of the explained variation. In Model 1, the standardized partial regression coefficient (β) indicates that the strength of the religiosity estimate is small but not inconsequential (β = –0.112, p < .05). In short, this relatively lenient test shows that individuals in the sample with higher religiosity scores report less law-breaking relative to their more secular counterparts. Finally, two demographic variables exert significant effects: males and younger participants report violating the law with more regularity. These results are consistent with a large number of prior investigations on criminal and delinquent behavior (see, e.g., Andrews & Bonta, 2003) and thus increase confidence in the validity of the data. Model 1 is underspecified, however, to the extent that religiosity is correlated with individual levels of self-control. Low self-control is added to the equation in Model 2. The data reveal three specific findings that warrant attention. First, the explanatory power of the model improves considerably when compared to Model 1 (i.e., a 424% increase in the R2). Second, the relationship between low self-control and criminal offending is in the hypothesized direction. As evidenced by the relative magnitude of the low self-control estimate (β = 0.447) and the amount of explained variation that the scale accounts for (91.4%), it is clear that low selfcontrol dominates the model. These findings square with prior research demonstrating the explanatory power and predictive accuracy of low self-control on offending and crimeanalogous outcomes (see Pratt & Cullen, 2000). Last, the unstandardized partial regression coefficient (b) for religiosity is reduced by 43% (from –0.030 in Model 1 to –0.017 in Model 2), and the corresponding t ratio is no longer significant at the .05 level (p = .133). At this point, the evidence indicates that the observed effect of religiosity on self-reported criminal activity is spurious and explained by low self-control in the present sample. However, some argue that the causal mechanisms associated with specific elements of religiosity, such as religious activity, are more effective at preventing criminal behavior (Evans et al., 1995). Whether the effects of the different elements of religiosity on illegal behavior are attenuated by low self-control remains an open question. The results presented in Table 4 shed some light on the relative effects of religiosity domains (i.e., belief, activity, and devotion) on law-breaking behavior and whether such effects are attributable to the omission of low self-control in the model. Because potentially harmful collinearity was detected when religious devotion and activity are included in the same regression equation (tolerance estimates < 0.40), the effect of each religiosity subscale is assessed separately. Notice first that all three religious domain variables are significantly related to offending in the trimmed models (i.e., Models 1, 3, and 5). And, much like the religiosity effect observed in Table 3, these estimates are relatively weak. In terms of explanatory power, two demographic characteristics, male and age, account for the lion’s share of explained variation (i.e., 79% in Model 1, 65% in Model 3, and 70% in Model 5). The religious domain effects are rendered statistically nonsignificant in the more saturated equations that include the low-self-control scale (see Models 2, 4, and 6). The average reduction in the unstandardized regression estimates is 34%. In contrast, low self-control is statistically significant at the .01 level in all three models (average β = 0.449). Consistent with the analyses from Table 3, the level of explained variation increases dramatically with the inclusion of low self-control. To sum up, the OLS models in Table 4 clearly demonstrate Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


13

—

—

—

4.28** –1.00 0.59

—

—

––

0.076 (0.018) –0.018 (0.018) 0.018 (0.030)

–0.006 (0.003) 2.515 (0.032) 148.64** .239

0.013 (0.021) [0.452] 0.057 (0.016) –0.015 (0.020) 0.011 (0.024)

—

—

–0.032 (0.021)

b (SE) [β]

Model 2a

–2.10 77.52**

3.49** –0.74 0.47

15.26**

—

—

–1.58

t ratio

4.09** –0.75 0.92

—

—

–2.72*

—

–0.011 (0.004) –2.90* 2.997 (0.0251) 119.34** 17.79** .048

0.074 (0.018) –0.013 (0.018) 0.028 (0.004)

—

–0.016 (0.006) [–0.123] —

—

b (SE) [β]

t ratio —

b (SE) [β]

Model 4a

3.49** –0.66 0.64

15.60**

—

–1.63

—

t ratio

–0.006 (0.003) 1.88 2.515 (0.033) 76.13** 111.39** .240

0.013 (0.001) [0.446] 0.056 (0.016) –0.013 (0.020) 0.015 (0.024)

–0.008 (0.005) [–0.063] —

Criminal Offending Model 3a

4.29** –0.74 0.92

—

–2.25*

—

—

t ratio

–0.011 (0.004) –2.88* 3.000 (0.028) 107.85** 16.57** .044

0.075 (0.018) –0.013 (0.017) 0.027 (0.029)

–0.004 (0.018) [–0.102] —

—

—

b (SE) [β]

Model 5a

3.51** –0.60 0.68

15.92**

–1.61

—

—

t ratio

–0.006 (0.003) –1.85 2.518 (0.033) 75.84** 114.66** .240

–0.003 (0.002) [–0.062] 0.013 (0.001) [0.449] 0.057 (0.016) –0.012 (0.020) 0.016 (0.024)

—

—

b (SE) [β]

Model 6a

Note. Entries are unstandardized partial regression coefficients (b), standardized partial regression coefficients in brackets [β], and robust standard errors that adjust for clustering on classrooms in parentheses. a. Ordinary least squares regression equation. *p < .05. **p < .01 (two-tailed test). †p < .05 (one-tailed test).

–0.011 (0.003) –3.25** 2.993 (0.017) 171.86** 16.42** .039

–2.05†

–0.044 (0.022)

Religious belief Religious activity Religious devotion Low selfcontrol Male Latino Racial minority Age Intercept F test R2

t ratio

b (SE) [β]

Variable

Model 1a

TABLE 4: The Effects of Religious Elements and Low Self-Control on Criminal Offending


that the observed inverse effects of specific religious domains on self-reported criminal offending are spurious and are explained by variations in self-control among the study participants. Up to this point, the data vindicate the prediction that the effect of religiosity (and different religious domains) on criminal offending is spurious, resulting from the failure to account for low self-control. The unknown is whether this spuriousness generalizes to more specific forms of criminal activity. This is not a trivial matter. Indeed, religiosity-crime researchers have long argued that the explanatory power of religious beliefs, activity, and devotion will be nil for offenses that are condemned by religious and nonreligious people alike (e.g., assault and destruction of property). Criminal activity that is widely tolerated by secular society but for which religious prohibitions exist (i.e., ascetic offenses, such as marijuana use) should covary inversely with religiosity (Middleton & Putney, 1962). Accordingly, the analyses move on to investigate whether the religiosity effects are invariant across two types of self-reported offending and what role (if any) self-control plays in these variable systems. The left-hand side of Table 5 presents a series of OLS models where secular offending is regressed onto different operational forms of religiosity, the low-self-control scale, and demographic variables. Two interesting patterns require emphasis. The four religion variables fail to achieve statistical significance. In other words, individual religious proclivities have no effect on criminal activity that is widely condemned by society. In contrast, the low-self-control scale is highly significant in all four models. Not only is low self-control the strongest correlate in the model, it also accounts for the largest portion of explained variation. Additional analyses (not shown) revealed that the effects of the religion variables on offending are tenuous even when low self-control is omitted from the model. Overall, then, the data reveal that the effect of religiosity, in all of its operational forms, on secular offending is no different from zero regardless of whether low self-control is part of the model specification. It is a different story altogether when it comes to ascetic offending. As shown in the four OLS models on the right-hand side of Table 5, low self-control is a strong and significant correlate of ascetic offending. We see, too, that all four of the religiosity measures are impervious to low self-control. In other words, religiosity, religious beliefs, religious activity, and religious devotion depress the frequency of ascetic offending, even after taking low self-control into account. Note that in terms of absolute magnitude, the religiosity estimates are much weaker than those observed for low self-control. Nevertheless, the data in Table 5 present a regression context in which the religiosity-crime link is not a spurious result of failing to control for low self-control. These findings also provide support for early religiositycrime researchers, such as Middleton and Putney (1962), who argued that the effects of religious-oriented propensities and actions are associated with criminal activities for which religious prohibitions exist and that secular society tends to tolerate. DISCUSSION

In the past several decades, social and behavioral scientists have assessed the link between religiosity and a wide array of factors, including stress (Ano & Vasconcelles, 2005), depression (Miller, Warner, Wickramaratne, & Weissman, 1997), and psychological well-being (Leondari & Gialamas, 2009). Criminal and other types of antisocial behavior Downloaded from cjb.sagepub.com at ARIZONA STATE UNIV on June 7, 2012


15

t ratio

–0.006 (0.009) –0.71 [–0.023] — —

b (SE) [β]

—

b (SE) [β]

Model 2

a

—

t ratio —

b (SE) [β]

Model 3 a

—

—

—

t ratio

–0.45 –0.001 (0.003) –0.44 31.29** 1.446 (0.046) 31.36** 26.31** .141

–0.001 (0.002) –0.82 [–0.029] 8.26** 0.010 (0.001) 8.41** [0.331] 3.91** 0.065 (0.017) 3.89** 0.73 0.015 (0.018) 0.81 2.24* 0.047 (0.021) 2.26*

—

–0.47 —

—

—

b (SE) [β]

—

—

t ratio

Model 4 a

14.48**

0.019 (0.001) [0.343] 0.023 (0.020) –0.021 (0.042) –0.021 (0.043)

0.019 (0.001) [0.351] 0.025 (0.020) –0.033 (0.043) –0.040 (0.044)

—

—

–0.094 (0.045)

—

b (SE) [β]

Model 2

1.21 –0.78 –0.92

14.62**

—

—

–2.10†

—

t ratio

Model 3a

0.019 (0.001) [0.338] 0.023 (0.021) –0.025 (0.043) –0.022 (0.045)

–0.030 (0.009) [–0.123] —

—

—

b (SE) [β]

Ascetic Offending a

—

—

—

b (SE) [β]

–0.008 (0.004) [–0.096] 14.28** 0.019 (0.001) [0.346] 1.09 0.024 (0.019) –0.59 –0.025 (0.043) –0.49 –0.025 (0.043)

—

–3.33**

—

—

t ratio

Model 4a

1.23 –0.59 –0.58

14.70**

–2.23*

—

—

—

t ratio

–0.012 (0.008) –1.56 –0.013 (0.007) –1.76 –0.012 (0.007) –1.60 –0.012 (0.007) –1.57 0.636 (0.053) 12.11** 0.706 (0.071) 9.98** 0.730 (0.071) 10.25** 0.715 (0.075) 9.50** 154.60** 125.56** 379.92** 125.06** .147 .143 .151 .145

1.16 –0.52 –0.48

—

—

—

–2.30*

t ratio

—

—

–0.054 (0.023) [–0.109] —

b (SE) [β]

Model 1 a

Note. Entries are unstandardized partial regression coefficients (b), standardized partial regression coefficients in brackets [β], and robust standard errors that adjust for clustering on classrooms in parentheses. a. Ordinary least squares regression equation. *p < .05. **p < .01 (two-tailed test). †p < .05 (one-tailed test).

Religious –0.019 (0.032) –0.59 — belief Religious — — — — –0.002 (0.004) activity [–0.013] Religious — — — — — devotion Low self0.010 (0.001) 8.32** 0.010 (0.001) 8.47** 0.010 (0.001) control [0.331] [0.332] [0.332] Male 0.065 (0.017) 3.88** 0.065 (0.017) 3.91** 0.065 (0.017) Latino 0.015 (0.019) 0.78 0.014 (0.019) 0.73 0.013 (0.018) Racial 0.046 (0.021) 2.22* 0.045 (0.020) 2.19† 0.045 (0.020) minority Age –0.002 (0.003) –0.45 –0.002 (0.003) –0.52 –0.002 (0.003) Intercept 1.433 (0.042) 34.26** 1.448 (0.049) 29.35** 1.437 (0.046) F test 27.05** 27.38** 26.56** .141 .141 .140 R2

Religiosity

Variable

Model 1

a

Secular Offending

TABLE 5: Secular and Ascetic Offending Regression Models


are included among the outcomes of interest. In general, the empirical evidence demonstrates that religiosity has a fairly weak inverse effect on criminal behavior (Baier & Wright, 2001). Much of the recent scholarly attention has focused on whether the religiositycrime link is a spurious result of not taking into account individual variations in selfcontrol. Two competing arguments regarding the role of self-control in the causal process have been advanced, referred to here as the confounding and mediation hypotheses. The evidence is inconclusive. Extant research is characterized by methodological limitations that make detecting spuriousness difficult. Among the limitations are problematic multivariate modeling strategies (Welch et al., 2006) and reliance on dependent variables that lump together ascetic and secular offenses (Laird et al., 2011). The current study sought to overcome these and other challenges. With survey data from a university-based sample of 769 participants, the regression analyses showed that low self-control explains the religiositycrime link in many situations, regardless of which religiosity measure was used. This observation was, however, contingent on the operationalization of the dependent variable. In particular, the effect of religiosity on ascetic offending persisted even after accounting for self-control, and a meaningful relationship between secular offending and religiosity was not observed. These findings bear directly on three issues that require further attention. The first issue concerns the spurious nature of the relationship between religiosity and self-reported criminal offending. Consistent with previous studies investigating the matter with cross-sectional survey data, the current study is limited in that it is unable to determine whether low self-control acts as a confounding or a mediating variable. Future research employing sophisticated panel designs with repeated measures (e.g., religiosity and low self-control) may help shed light on how this causal process unfolds. However, research using panel data often suffers from sample attrition and other limitations that can weaken causal arguments. Therefore, other research methodologies should also be considered. Experimental research programs designed to deplete levels of self-control among participants and evaluate the buffering effect of religiosity (or lack thereof) could prove fruitful in this regard (see, e.g., Muraven, Tice, & Baumeister, 1998). A second point relates to the ongoing debate on how best to operationalize religiosity. Some advocate the use of multi-item scales that capture the different elements of religiosity. Others prefer behavioral (e.g., religious service attendance) and/or attitudinal (e.g., religious salience) measures. Many researchers are forced to rely on the limited number of religion-oriented measures that are available in large data sets. The extent to which there is cause for concern rests a great deal on the demonstrated predictive accuracy of different religiosity measures across a variety of criminal offending outcomes. The current study showed that measurement had little influence on the results. For example, the different religiosity subscales (i.e., devotion, activity, and belief) were all related to self-reported criminal offending, though religious belief did not perform as well when considering the relative magnitude of the bivariate correlations and the observed p values in the regression tables. We attribute this variation in predictive efficacy to the quality of the belief measure used in the present study. This measure could be drastically improved if additional belief items were included to create a summated scale (e.g., belief in divine intervention) and if the survey item used made reference to supernatural sanctions and/or rewards. Doing so should help distinguish secular from religious views on the afterlife, the latter of which frequently entails consequences for sinful behavior (e.g., criminal offending).



Finally, the present study finds support for a theoretical argument made five decades ago: The effect of religiosity on offending varies by the scope and nature of the dependent variable (Middleton & Putney, 1962). Put simply, religiosity influences ascetic but not secular offending. Echoing Cochran’s (1988) sentiments, future work investigating the religiosity-crime link should exercise care to construct self-report criminal offending scales that include both ascetic and secular offenses. Additionally, more nuanced analyses featuring disaggregated offending outcomes should be presented. Care should also be exercised when constructing ascetic offending scales. Well over a decade ago, Benda (1997) lamented that the operational definition of ascetic offending leaves much to be desired. Some have wrongly conceived victimless crimes as ascetic offenses (see Cochran, 1988, for a discussion). Others lump drug-use items together, but public condemnation varies depending on drug type. For example, a recent national survey of U.S. adults found that a majority of respondents (55%) supported legalizing marijuana, but only a very small percentage supported legalizing powder cocaine (9%), heroin (8%), and methamphetamine (7%) (Angus Reid Public Opinion, 2011). Although the use of hard drugs, such as heroin, may technically be classified as an ascetic offense, public opinion data indicate it is a secular offense as well. We suspect that lumping drugs such as marijuana and heroin together in an ascetic offending measure may result in attenuated religiosity effects. The present study demonstrated that low self-control is a strong and robust correlate of criminal activity, regardless of how the self-reported offending measure is operationalized. It was also shown that the effect of religiosity on criminal offending is largely a spurious result of not taking into account individual variations in self-control. Religiosity did, however, predict ascetic offenses (i.e., minor crimes characterized by personal indulgence) independent of low self-control. Little is known about the nature of the spurious religiositycrime relationship. Does self-control mediate the effect of religiosity on legal compliance? Is self-control confounded with religiosity? Longitudinal research that addresses these and related questions is a necessary next step. Appendix Summary Statistics for Criminal Offending and Low-Self-Control Survey Items Scale and Item Criminal offending 1. Illegally disposed of trash and littera 2. Made a lot of noise at nighta 3. Broke traffic lawsa 4. Bought something that might be stolena 5. Drank alcohol in a place where you are not supposed toa,b 6. Stole something worth less than $50a,c 7. Stole something worth more than $50a,c 8. Used marijuana or some other druga,b 9. Sold marijuana or some other druga 10. Purposely damaged property without the owner’s permissiona,c 11. Got into a fight with another person with the idea of physically harming thema,c 12. Broke into a motor vehicle without the owner’s permissiona,c

M

SD

2.146 2.363 2.605 1.330 2.269 1.403 1.131 1.652 1.172 1.289 1.463 1.034

0.858 1.006 0.995 0.689 1.138 0.708 0.453 0.971 0.542 0.584 0.744 0.231 (continued)



Appendix (continued) Low self-control 1. I am good at resisting temptationd,e 2. I have a hard time breaking bad habitsd 3. I am lazyd 4. I say inappropriate thingsd 5. I do certain things that are bad for me, if they are fund 6. I refuse things that are bad for med,e 7. I wish I had more self-disciplined 8. People would say that I have iron self-disciplined,e 9. Pleasure and fun sometimes keep me from getting work doned 10. I have trouble concentratingd 11. I am able to work effectively toward long-term goalsd,e 12. Sometimes I can’t stop myself from doing something, even if I know it is wrongd 13. I often act without thinking through all the alternativesd

2.414 2.887 2.606 2.921 2.700 2.742 2.869 2.967 3.229 2.875 2.064 2.340 2.379

1.005 1.159 1.115 1.257 1.221 1.220 1.296 1.094 1.233 1.205 0.966 1.191 1.139

a. Response set ranging from 1 = never to 4 = frequently. b. Ascetic offending. c. Secular offending. d. Response set ranging from 1 = not at all like me to 5 = very much like me. e. Reverse coded.

NOTE 1. A four-factor solution explaining 42.54% of the variance is derived when the 12 offending items are used to estimate a principal-axis factor model with promax rotation. The multidimensional latent structure indicates that these data are not well suited for item response theory scaling (see Osgood, McMorris, & Potenza, 2002, p. 282).

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Michael D. Reisig is a professor in the School of Criminology and Criminal Justice at Arizona State University. He received his PhD from Washington State University in 1996. His research has appeared in a variety of scholarly journals, including Criminology, Journal of Research in Crime and Delinquency, and Crime and Justice: A Review of Research. Scott E. Wolfe is an assistant professor in the Department of Criminology and Criminal Justice at the University of South Carolina. He received his PhD from Arizona State University in 2012. His research has appeared in a variety of scholarly journals, including Criminal Justice and Behavior, Journal of Criminal Justice, and Social Science Research. Travis C. Pratt is a professor in the School of Criminology and Criminal Justice at Arizona State University. He received his PhD from the University of Cincinnati in 2001. His research has appeared in a variety of scholarly journals, including Criminology, Journal of Research in Crime and Delinquency, and Crime and Justice: A Review of Research.
