© 2015 American Psychological Association 0893-3200/15/$ 12.00 http://dx.doi.org/10.1037/fam0000079

Journal of Family Psychology 2015, Vol. 29, No. 3, 360-370

Different Dimensions, Different Mechanisms? Distinguishing Relationship Status and Quality Effects on Desistance Ashley Brooke Ban-

Ronald L. Simons

University at Buffalo, SUNY

University of Georgia

This study follows from a long line of research aimed at understanding the effects of romantic relationships on desistance from crime. We expanded this work by testing the differential effects of relationship status (i.e., single, dating, cohabiting, married) and relationship quality on crime and the different mechanisms explaining these effects. We drew upon longitudinal data on African American young adults, and utilized a fixed effects approach to examine intraindividual change in relationship status, relationship quality, and offending. Results suggested that, for men, relationship status was directly associated with crime, in that coresidential unions reduced offending independent of their quality. High-quality relationships, however, were found to deter crime for both men and women no matter their form. The effect of relationship status was largely accounted for by social control processes, whereas the relationship quality effect was explained by cognitive transformation, particularly a change in the “criminogenic knowledge structure.” These findings demand greater attention to multiple dimen­ sions of relationships and the unique mechanisms through which they may foster desistance. Keywords: romantic relationships, desistance, transition to adulthood, crime

the link between romantic relationships and crime. First, it is unclear exactly which dimensions of romantic relationships are important for deterring criminal behavior. Much research focuses on relationship status and ignores relationship quality (e.g., mar­ riage v. cohabitation; marriage v. singlehood). When relationship quality is considered, there is little variation in relationship status (e.g., high-quality marriage v. low-quality marriage). This work has left us with an incomplete understanding of the unique con­ tribution of relationship status and relationship quality in deterring crime. That is, how much of the “good relationship” effect is caused by the quality of the relationship and how much by the relationship itself? Such an understanding is vital to building better-specified the­ ories of desistance, as there is some suggestion in the literature that relationship status and quality may affect crime through different mechanisms. In the current study, we explored this possibility using longitudinal data on several hundred African Americans during the transition to adulthood. Using fixed effects models to examine within-individual change, we first tested the effect of relationship status (i.e., single, dating, or coresidential) on criminal behavior. Unlike past work, we were able to make comparisons between such statuses explicit and explore the effect of shifts from singlehood to different relationship statuses and among relation­ ship statuses themselves. Then, using an internal moderator ap­ proach, we examined how relationship quality may condition the effect of romantic involvement on crime. Finally, we tested social psychological (cognitive transformation via a change in crimino­ genic knowledge structure) and social control (affiliation with deviant peers and routine activities) mechanisms whereby relation­ ship status and quality may be differentially linked to crime. Distinguishing between relationship status and quality effects and the different mechanisms through which they operate may reduce

Life course theorists have long been interested in the extent to which adult role transitions, chief among them marriage, predict desistance from criminal behavior (Laub, Nagin, & Sampson, 1998; Sampson & Laub, 1990; Warr, 1998). Even taking into account selection processes (King, Massoglia, & MacMillan, 2007; Sampson, Laub, & Wimer, 2006), this work has shown robust effects across multiple countries (Bersani, Laub, & Nieuwbeerta, 2009), diverse types of crime (Bersani et al., 2009; Maume, Ousey, & Beaver, 2005), and different risk categories of offenders (Horney, Osgood, & Marshall, 1995; Sampson & Laub, 2003). Given that marriage is becoming less common among the general population and increasingly absent among young people (Cherlin, 2010), particularly African Americans (Chambers & Kravitz, 2011), researchers have begun to expand their focus beyond marriage to examine the potential influence of nonmarital relationships on criminal behavior. Although far from conclusive, this work suggests that nonmarital unions are also linked to crim­ inal behavior (Giordano, Lonardo, Manning, & Longmore, 2010; Leverentz, 2006; Simons & Barr, 2014). Despite the growing tendency in the literature to pay attention to relationships outside of marriage, much remains unknown about

This article was published Online First April 27, 2015. Ashley Brooke Barr, Department of Sociology, University at Buffalo, SUNY: Ronald L. Simons, Department of Sociology, University of Geor­ gia. This research was supported by the Grants MH48165 and MH62669 from the National Institute of Mental Health and the Grant 029136-02 from the Centers for Disease Control and Prevention. Correspondence concerning this article should be addressed to Ashley Brooke Barr, University at Buffalo, SUNY, Department of Sociology, 402 Park Hall. Buffalo. NY 14260. E-mail: [email protected] 360

DIFFERENT DIMENSIONS. DIFFERENT MECHANISMS?

the current fragmentation of theory surrounding the link between romantic relationships and desistance.

Relationship Status and Crime Criminologists have devoted more attention to the association between marriage and crime than perhaps any other adult role transition. Research to date suggests that marriage has a robust relationship with desistance. This effect holds for adults (Laub & Sampson, 2003) as well as for young people just entering adult­ hood (Salvatore & Taniguchi, 2012). Further, it continues to be evident after using various techniques to reduce potential selection effects (Barnes & Beaver, 2012; Sampson et al., 2006) and after taking into account relationship quality (Beijers, Bijleveld, & van Poppel, 2012; Sampson et al., 2006). From a classic social control perspective (Flirschi, 1969), this is so because marriage provides a formal tie to a conventional institution, thereby enhancing invest­ ment in conventionality and increasing costs involved in engaging in unconventional behavior (Sampson & Laub, 1993; Sampson et al., 2006). The literature is much less clear regarding the impact of nonmarital relationship statuses on criminal behavior. Sampson et al. (2006) offer evidence that cohabitation is associated with reduced crime and argue that cohabitation can be “marriage-like” in terms of its ability to create “interdependent systems of obligation, mutual support, and restraint” (p. 467). This may be especially the case among African Americans, the focus of the current study, as much research suggests that differences between cohabitation and marriage (in both their character and their implications) are less striking in contexts in which cohabitation is more normative (Lee & Ono, 2012; Wiik, Keizer, & Lappegard, 2012). Several studies, including those drawn from the current data (Barr, Culatta, & Simons, 2013; Barr & Simons, 2014), support the idea that cohab­ itation operates much more similarly to marriage for Blacks than it does for Whites (Liu & Reczek, 2012; Smock, 2000). As for dating relationships, most work (McCarthy & Casey, 2008; Monahan, Dmitrieva, & Cauffman, 2014; Simons & Barr, 2014) finds no association between simply having a romantic relationship and engaging in criminal behavior. Taken together, then, this work suggests that the degree to which having a romantic partner predicts desistance may vary by the type of the relation­ ship. Building upon this research, we expected that, independent of relationship quality, coresidential unions, but not dating unions, would be associated with reduced offending relative to singlehood. Formally stated, this hypothesis was as follows: Hypothesis 1: Relationship status, independent of relationship quality, will predict criminal involvement such that coresiden­ tial unions, but not dating unions, will be associated with a reduction in offending relative to singlehood.

Relationship Quality and Crime Thus far, we have discussed only one aspect of relationships— that of relationship status. Relationship status, however, is not the only important element of relationships relevant to the desistance process. In fact, although many studies examining the “marriage effect” lack measures of marital quality, quality is essential to foundational life course theories of desistance. For instance, Samp­

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son and Laub (1990) emphasized “the quality or strength of social ties more than the occurrence or timing of specific life events” (p. 611). Although much work since then, including that by Sampson, Laub, and their colleagues (Sampson & Laub, 2003, 2009; Samp­ son et al., 2006), has indeed shown that marriage—and to some extent, cohabitation—in and of itself fosters desistance, the quality of the relationship, when it is considered, has been shown to matter above and beyond the effect of relationship status (Giordano, Schroeder, & Cemkovich, 2007; Sampson et al., 2006). This independent effect of relationship quality has proven im­ portant not only for marital relationships but also for dating and cohabiting relationships. For instance, Simons and Barr (2014) found that among unmarried African American young adults, quality of relationship was associated with a reduction in criminal behavior for both men and women. Others studying adolescence and the transition to adulthood have found a similar pattern—that the effect of nonmarital relationships on antisocial behavior was dependent, for better and for worse, upon the characteristics of the relationship and/or the partner (McCarthy & Casey, 2008; Mo­ nahan et al., 2014; Oudekerk, Burgers, & Dickon Reppucci, 2014). Hence, relationship quality appears to be an important aspect of all relationships, with implications for desistance. Hypothesis 2: Relationship quality, independent of relation­ ship status, will be associated with a reduction in offending.

Mechanisms Linking Relationships to Crime Drawing upon a social control (Laub et al., 1998; Sampson & Laub. 1990, 1993) or a symbolic interaction (Giordano, Cernkovich, & Rudolph, 2002; Giordano et al., 2007) perspective, life course scholars highlight several mechanisms whereby romantic relationships might bring about a reduction in criminal behavior. With few exceptions (e.g., Forrest & Hay, 2011; Simons & Barr, 2014), however, these mechanisms tend to be speculated rather than tested. Further, because the effects of relationship status and relationship quality are often blurred theoretically and empirically, it is often unclear which aspect of romantic relationships is being explained by the tested or untested mechanisms. The present study examines the potentially mediating effect of three variables— cognitive transformation, peer affiliations, and routine activities. We know of no study that has assessed all three mechanisms and their differential ability to explain relationship status and relation­ ship quality effects on crime.

Cognitive Transformation: Changes in Criminogenic Knowledge Structure As Sampson et. al. (2006) note, one mechanism thought to account for the robust effect of marriage on desistance, a mecha­ nism that has gained ground in the last decade, is a “change [in] one’s sense of self through cognitive transformation” (p. 468). In fact, Giordano and colleagues (2002, 2007) view cognitive trans­ formation as an essential element in explaining desistance from criminal behavior. They argue that relationships “will serve well as catalysts for lasting change when they energize . . . changes in the meaning and desirability of deviant/criminal behavior itself” (Giordano et al., 2002, p. 992). In an attempt to capture these cognitive changes associated with crime, Simons and Burt (2011)

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have formulated a criminogenic knowledge structure. The crimi­ nogenic knowledge structure is indicated by three cognitive char­ acteristics— concern with immediate rewards, cynicism regarding conventional morality, and a hostile view of people. As they have shown, these three indicators form a latent construct indicative of a general psychological propensity for engaging in criminal be­ havior (Simons & Burt, 2011). Importantly, this criminogenic knowledge structure is predictive of but distinct from relationship characteristics and criminal behavior, and has been shown to be malleable and responsive to social context even in adulthood (Burt & Simons, 2013; Simons & Barr, 2014; Simons, Burt, Barr, Lei, & Stewart, 2014).

Affiliation With Deviant Peers In addition to cognitive transformation, changes in peer affilia­ tions have been posited to explain the link between romantic relationships and crime. It is well known that peer associations help to differentiate those who engage in deviant and criminal behavior from those who do not. Guided by differential association (Burgess & Akers, 1966) and social learning (Akers, 1998) theo­ ries, scholars have examined the degree to which affiliation with deviant peers accounts for the effect of marriage on crime. For instance, Warr (1998) argues that the primary reason marriage reduces crime is because it acts as a form of social control to reduce time spent with friends and exposure to delinquent friends.

Risky Routine Activities A third mechanism through which romantic relationships are thought to influence the desistance process is that of changing involvement in risky routine activities. The types of routine activ­ ities that are conducive to deviant behavior, Osgood, Wilson, Omalley, Bachman, and Johnston (1996) argue, are those that involve unstructured socializing with peers in the absence of authority figures. Such activities, like going to parties or going joy-riding with friends, in contrast to more home-based activities, enhance opportunity for deviance, make deviant behavior more rewarding, and reduce the potential for social control responses to deviant behavior (Osgood et al., 1996). As others have suggested (Sampson et al., 2006), adult role transitions, like marriage or a marriage-like relationship, may potentially inhibit crime by direct­ ing people toward more family- or home-oriented activities and away from those activities linked to criminal opportunity.

Relationship Status, Relationship Quality, and Mechanisms These three mechanisms most often used to explain the link between romantic relationships and desistance from crime follow from two contrasting theoretical perspectives. The latter two mech­ anisms—affiliation with deviant peers and risky routine activi­ ties—take root in a social control perspective (Laub et ah, 1998; Laub & Sampson, 2003; Sampson & Laub, 1990, 1993) that emphasizes the constraints imposed by attachments to conven­ tional institutions or roles (e.g., work and family). The changes in social cognition explanation, however, rest largely upon a sym­ bolic interaction framework in that this mechanism focuses on agentic internal change, although social in origin (Giordano et ah,

2007), rather than the imposition of external constraints. From this perspective, the partner acts not only as an agent of social control “but also as an ever-present emotional role model and source of social support” (Giordano et ah, 2007, p. 1615, italics in original). Over a decade ago, Giordano et ah (2002) wrote that these contrasting theoretical perspectives “can in most respects be inte­ grated” and that “such an integration provides a more complete conceptual tool kit for understanding changes in life direction than either perspective on its own” (p. 992). We argue that parsing out the “good” from the “relationship” in discussions of the “good relationship effect” on desistance may offer one means of integrat­ ing these perspectives. In particular, the social control mechanisms of reducing deviant peer affiliations and altering routine activities may be particularly helpful in explaining the relationship status effect. That is, the “interdependent systems of obligation, mutual support, and restraint” (Sampson et ah, 2006, p. 467) formed by coresidential romantic relationships are likely to reduce affdiation with deviant peers and orient partners toward more conventional activities, like time spent with family or with other committed couples away from risky social settings. The sheer presence of a romantic relationship, however, would likely not be enough to evoke lasting changes in cognition. Rather, the symbolic interac­ tion mechanism of cognitive transformation may be pertinent to explaining the relationship quality effect. That is, it is largely the quality of a romantic union that determines the extent to which the partner is “able to make a contribution to the actor’s emergent view of a different and more ‘worthy’ self’ (Giordano et ah, 2007, p. 1616). This type of cognitive transformation is unlikely to occur as the result of lower quality unions, no matter their form. In fact, although lacking extensive social control mechanisms, Simons and Barr (2014) offered support for this notion by showing that changes in criminogenic knowledge structure fully mediated the effect of relationship quality on crime. Taken together, the cogni­ tive transformation and social control hypotheses were as follows: Hypothesis 3: Changes in the criminogenic knowledge struc­ ture will mediate the effect of relationship quality but not relationship status on offending. Hypothesis 4: Changes in affiliation with deviant peers and routine activities will mediate the effect of relationship status but not relationship quality on offending.

Gender, Relationships, and Desistance Although not central to our arguments, we would be remiss to ignore the debates in the literature concerning the potentially gendered effects of relationships in the desistance process. Laub and Sampson (2003) argue for a gendered effect of relationship status, for instance, when they write that “given the crime differ­ ences between men and women, it is almost invariably the case that men marry ‘up’ and women ‘down’” (p. 45). Such claims have been refuted by some work that has found the negative effect of marriage on crime to be consistent across gender (Beaver, Wright, DeLisi, & Vaughn, 2008). Other work, however, supports Laub and Sampson’s claim. After accounting for the propensity to marry, King et al. (2007) found that the impact of marriage on desistance was stronger and more robust for men (see also Bersani et al., 2009). Likewise, qualitative work by Leverentz (2006) revealed that the marital and cohabiting partners of deviant women

DIFFERENT DIMENSIONS, DIFFERENT MECHANISMS?

sometimes enhanced rather than stifled women’s involvement in criminal activity. Finally, one study suggested that the deterrent effect of marriage is actually more robust for women than for men (van Schellen, Apel, & Nieuwbeerta, 2012). Although there is much less work examining the potential gendered effects of relationship quality on crime, this work, too, offers mixed evidence. In an examination of nonmarital relation­ ships, for instance, Simons and Barr (2014) showed that relation­ ship quality inhibited crime for both men and women. This is so, they argue, because the cognitive transformative processes encour­ aged by warm, supportive partners should operate regardless of gender. On the contrary, Monahan et al. (2014) showed that, under some circumstances, young women’s criminal involvement was more strongly affected by the characteristics of their partners than was young men’s. Given this inconsistent evidence for gendered effects of relationship status and quality, we did not have strong hypotheses regarding gender. We tested for gendered effects, how­ ever, and highlight them when they were found. Our full theoretical model is shown in Figure 1. Importantly, we tested all of our hypotheses derived from this model using a longitudinal sample of young African Americans. Research indi­ cates that, compared with other racial groups, African Americans are less likely to marry (Goldstein & Kenney, 2001) and that their relationships, both marital and nonmarital, are less stable (Copen, Daniels, Vespa, & Mosher, 2012; Guzzo, 2009) and more troubled (Broman, 1993; Kurdek, 2008). Such race differences have been attributed to, in the words of Chambers and Kravitz (2011), an “amalgam of sociological and psychological constraints” (p. 648), yet plenty of research on desistance suggests that romantic rela­ tionships can serve as sources of support and positive transforma­ tion in the lives of African Americans. Hence, it is important that we understand when and how this is the case in order to recognize and to build upon the strengths and capacities within these rela­ tionships.

Method We tested our hypotheses using the most recent two waves, Waves V and VI, of the Family and Community Health Study (FACHS). The FACHS began in 1997 as a longitudinal investi­ gation of African American youth and their families living in Iowa and Georgia. Given space constraints and the fact that sampling procedures have been described in great detail elsewhere (e.g., Simons, Stewart, Gordon, Conger, & Elder, 2002), we offer a

Figure 1.

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much abbreviated version here. All youth were in the fifth-grade public school system at the time of recruitment, and such youth and their families were drawn from a variety of communities, those that differed on racial composition and economic level, within each state in order to capture the heterogeneity of African Amer­ ican experiences. At the first wave of data collection (1997-1998), data were gathered on 889 target children and their families. More than a decade later, 699 target children, now in their early to mid-twenties, participated in the sixth wave of the study (78.6% of the original sample). The sample used to examine changes in criminal behavior during early adulthood is drawn from the 634 target respondents who participated in both Waves V (collected in 2008-2009) and VI (collected in 2010-2011) of the FACHS. Because it makes little theoretical sense to examine desistance among respondents who never engaged in criminal behavior or who have already desisted by Wave V of the study, the analytical sample was made up of the 225 respondents (450 person-years) who committed at least one criminal act in young adulthood. This restriction was not only logical theoretically but also necessary empirically given that the fixed effects approach, explained in the Plan of Analysis section, could be performed only on those whose outcome (criminal involvement) was not zero across both waves. Respondents at the later waves of the FACHS used here com­ pleted surveys in their own homes. The interviews were presented on laptop computers and were audio enhanced and selfadministered to increase anonymity. On average, interviews took roughly 2 hr to complete. There is little evidence of selective attrition over the course of the study. For instance, although Wave VI participants were more likely than Wave I participants to be female, and showed slightly less childhood delinquency than their nonparticipating peers, there were no significant differences be­ tween participants and nonparticipants on factors like family struc­ ture or parenting practices. In addition, as outlined extensively by Barr et al. (2013), the FACHS young adult sample proved similar to a nationally representative sample of African American young adults on measures of marital status, education, and parenthood. Table 1 provides descriptive statistics for all study variables, yet we highlight a few here to situate the study and the sample within the larger literature on crime and young adult romantic relation­ ships. Respondents averaged 21.5 years of age at Wave V and 23.5 years of age at Wave VI. Roughly half at each wave were attending school or were planning to enroll within the next year, and more than half of respondents (56.9%) at each wave had experienced

Theoretical model linking relationship status and quality to criminal involvement.

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Table 1 Descriptive Statistics by Wave fo r Deviant Analytic Sample (N = 225) Wave VI

Wave V Variable

Mean

SD

Female Age (in years) Crime In school Unemployment in past year Parent Single Dating relationship Coresidential relationship Relationship quality Friends’ deviance Criminogenic knowledge structure Risky routine activities

0.547 21.547 1.707 0.498 0.569 0.307 0.387 0.427 0.187 -0.907 23.209 0.966 —

0.499 0.850 1.855 0.501 0.496 0.462 0.488 0.496 0.391 3.061 5.541 2.271 —

Note.

Min

Max

0.000

1.000

20.000

24.000 10.000

0.000 0.000 0.000 0.000 0.000

0.000

1.000 1.000

1.000 1.000

0.000

1.000 1.000

-10.147 14.000 -4.629 —

4.471 40.000 7.466 —

Mean ___

23.520 1.244 0.480 0.569 0.498 0.462 0.360 0.178 -0.451 23.266 1.066 2.766

SD ___

0.866 1.841 0.501 0.496 0.501 0.500 0.481 0.383 3.064 5.656 2.420 0.346

Min ___

22.000 0.000 0.000

0.000 0.000

0.000 0.000 0.000

-10.136 14.000 -4.382 2.079

Max —

26.000 10.000 1.000 1.000 1.000 1.000 1.000

1.000 4.410 54.444 8.902 3.584

Min = minimum; Max = Maximum.

recent unemployment. About one third of respondents (30.7%) were parents by Wave V, and almost half (49.8%) were parents by Wave VI. Further, although not shown in Table 1, about 15% of respondents’ primary caregivers had less than a high school edu­ cation, and about 41% had at least some postsecondary education. Slightly less than one fifth of respondents (19.6%) were living with both of their biological parents when the study began.

Measures Dependent variable: Criminal behavior. At both Waves V and VI, participants responded to a series of questions regarding how often during the preceding year they had engaged in 10 adult criminal acts, including shoplifting, destroying property, breaking and entering, and physical assault with a weapon. A full list of these criminal acts can be found in Simons et al. (2014). If the respondent reported engaging in the behavior, he or she was given a score of 1 for that particular criminal act. The 10 different acts were then summed to create a count indicator of the number of different crimes committed in the past year. This summary index is a standard index of criminal involvement and is commonly used to measure desistance from crime (Hindelang, Hirschi, & Weis, 1981; Simons, Simons, Lei, & Landor, 2012; Sweeten, Bushway, & Paternoster, 2009). At both waves, the number of criminal activities committed ranged from 0 to 10, with nearly one third (31.20%) of the full sample committing at least 1 criminal act at Wave V and one fifth (20.03%) committing at least one criminal act at Wave VI. Among those who reported engaging in any crime over the past year, about half at each wave reported committing two or more different crimes. The most commonly committed crimes were carrying a hidden weapon and physical assault, with about 15% of the sample reporting each at Wave V and 10% reporting each at Wave VI. Primary independent variables. Relationship status. Relationship status was measured at each wave via a question that asked, “What best describes your current relationship status?” Respondents who reported “not dating any­ one right now” or dating but not having “a steady romantic partner” were coded as being single. Those who indicated that they were steadily dating one person but not living with their partner

were coded as dating. Those who reported living with their ro­ mantic partner were coded as cohabiting. Finally, those who indicated being married were coded as such. These relationship statuses were further reduced by combining cohabiting and mar­ ried respondents into a coresidential status. This was done for several reasons. First, only 5% of the sample reported being married at either wave. Second, cohabiting and married respon­ dents did not differ from one another, but did differ from dating respondents, on indicators of relationship quality and commitment. Finally, models were examined excluding married respondents, and results mirrored those presented here. Hence, we distinguish between single, dating, and coresidential relationship statuses in the analyses that follow. Relationship quality. Relationship quality was assessed at each wave for those respondents reporting that they were in a romantic relationship. The index was comprised of two items tapping relationship satisfaction (e.g., “How satisfied are you with your relationship?”), five items tapping partner’s hostility (e.g., “During the past month, how often did your romantic partner insult or swear at you?”), and three items tapping partner’s warmth (e.g., “During the past month, how often did your romantic partner act loving and affectionate toward you?”). Each of these subscales was standardized and summed to form the measure of relationship quality. The reliability of this composite measure using Nunnally’s (1978) formula for the linear combination of measures approached .90 at each wave. In line with the works of Mirowsky (1999), Freeh and Williams (2007), and Simons and Barr (2014), we treated this relationship quality index as an internal moderator. Internal moderators, enable one to test the extent to which “the qualities of a situation deter­ mine the effect of being in it” (Mirowsky, 1999, p. 117). In our case, we wished to understand the degree to which relationship quality conditioned the effect of being in a relationship, a difficult undertaking given that unpartnered respondents have no score on relationship quality. The use of internal moderators, however, allows one to overcome this challenge. To code relationship qual­ ity as an internal moderator, we first standardized it to have a mean of 0 and a standard deviation of 1. Then, as instructed by Mi­ rowsky (1999), we assigned all respondents who were not in a

DIFFERENT DIMENSIONS, DIFFERENT MECHANISMS? relationship (and hence had no measure of relationship quality) a score of zero. This resulted in a relationship quality index that showed variation among partnered respondents and no variation among single respondents. The resulting internal moderator al­ lowed for comparisons not only between high- to low-quality relationships, but also between relationships of varying quality and no relationship. Mediating variables. Affiliation with deviant peers. Deviant peer association was assessed at both waves via respondents’ self-reports of their friend­ ship networks. At each wave, respondents answered 14 questions indicating the proportion of their friends that, in the previous year, participated in a range of deviant activities, such as stealing some­ thing, getting into a fight, or using illegal drugs. Responses ranged from 1 (none o f them) to 4 (all o f them). Items were summed to form an index at both waves. Cronbach’s alpha was .82 at Wave VI and .81 at Wave V. Criminogenic knowledge structure. Following the work of Simons and Burt (2011) and Simons and Barr (2014), cognitive transformation was measured by changes in criminogenic knowl­ edge structure. This measure was comprised of three subscales assessed at each wave and designed to tap the extent to which respondents endorsed a hostile view of people, held a low com­ mitment to conventional norms, and possessed a tendency to discount the future. The 18-item Hostile View of People subscale consisted of two dimensions: a cynical view of others’ intentions (e.g., “Some people oppose you for no good reason”) and a belief that aggres­ sion is often necessary in order to avoid exploitation (e.g., “People will take advantage of you if you don’t let them know how tough you are”). This instrument was designed to tap into Dodge’s (2006) notion of hostile attribution bias and has been shown to be distinct from, yet predictive of, deviant and criminal behavior (Simons & Burt, 2011; Simons et al., 2006). Cronbach’s alpha was .89 at Wave V and .88 at Wave VI. The 10-item Low Commitment to Conventional Norms (i.e., deviant values) subscale assessed the extent to which respondents think it is wrong for someone their age to engage in a range of deviant activities. These activities included things like hitting someone in order to hurt them, cheating a close friend, shoplifting from a store, and selling illegal drugs. Similar to the Wikstrom, Ceccato, Hardie, and Treiber’s (2010) Moral Values scale, this subscale has been shown to be an important predictor of criminal behavior in young adulthood (Simons & Burt, 2011). Cronbach’s alpha was .87 at Wave V and .83 at Wave VI. Lastly, the 16-item Discounting the Future subscale combined items from Kendall and Williams’s (1982) inventory of self­ constraint (e.g., “You have to have everything right away”) and items from Eysenck and Eysenck’s (1977) scale of risk-taking tendencies (e.g., “You would do almost anything for a dare”). Together, these items captured respondents’ impulsivity and short­ sightedness, elements that, as pointed out by Simons and Burt (2011), are foundational to Gottfredson and Hirschi’s (1990) selfcontrol theory of crime. Cronbach’s alpha was .75 at Wave V and .72 at Wave VI. The work of Simons and Burt (2011), which also utilized data from the FACHS, indicated that these three social schemas load on a single factor and formed a cognitive structure that is important for explaining individual variation in criminal behavior. Accord­

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ingly, these three scales were standardized and then summed to form a composite index of criminogenic knowledge structure. The reliability of this composite measure was .90 at Wave V and .88 at Wave VI (Nunnally, 1978). Risky routine activities. At Wave VI, we assessed the extent to which respondents tended to spend their free time engaging in potentially risky activities. No measures of risky activities were available at earlier waves of the FACHS. Respondents were asked to think about how they “spend [their] time on a typical weekend evening or night” and to indicate how often they engage in eight activities, including bar hopping, clubbing, hanging out at pool halls or strip clubs, drinking or getting high at a friends’ place, and staying out until 2:00 or 3:00 in the morning. Each of these activities entails unstructured time outside of the home and poten­ tial exposure to “deviant subcultures” that might enhance oppor­ tunity for crime (Osgood et al., 1996). Responses for each activity ranged from 1 (never) to 5 (several times a week). Cronbach’s alpha for the eight-item index was .77. Because of right skew, we used the logged version of this variable in all models. Control variables. As Guzzo (2006) pointed out, relationship transitions are often accompanied by transitions in other life course domains (like work and school). To better specify the effects of relationship status and quality on criminal behavior, then, several time-varying control variables that signal other life course transi­ tions and that have been linked to relationship status, relationship quality, and crime were also considered. These variables included school enrollment (in school = 1), parental status (parent = 1), and employment difficulties (unemployed in past year = 1). Given the fixed effects, within-individual change approach used here, time invariant factors, like childhood family structure and socio­ economic status, were unnecessary.

Plan of Analysis Because our dependent variable, criminal behavior, was a count variable, we utilized negative binomial fixed effects models via Stata 12 (StataCorp, 2011) to test our hypotheses. These models assessed the association between relationship status, relationship quality, and crime within each individual while accounting for the non-normally distributed outcome variable. Focusing on intraindi­ vidual change—thereby controlling for observed and unobserved time-invariant factors—is one way to reduce the chances of draw­ ing erroneous conclusions caused by omitted variable bias and potential selection mechanisms. Given the emphasis on intraindi­ vidual change, the direct effect of time-invariant factors, like gender, could not be estimated directly. The interaction effect could be tested, however, by entering the main effect of the time-varying predictor and the interaction effect between the timevarying and time-invariant predictors into the model (Allison, 2009). Further, fixed effects of time (i.e., survey wave) were included in all models for two reasons. The first was to account for the reduction in crime that tends to occur with age. Second, one of our mediating variables, risky routine activities, was assessed only at Wave VI, so all respondents were given a score of zero for this item at Wave V. A control for wave allows us to account for this artificial “change” across waves. The fixed effects negative binomial models proceeded in several steps. We first addressed Hypothesis 1 by testing for direct rela­ tionship status effects. We did so by entering our time-varying

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ratio, which indicates the factor change in the expected count of criminal acts given a one-unit increase in the independent variable.

relationship status indicators, with single person-years as the ref­ erence category, into a model with our control variables. We made alternative comparisons between different union types via nonlin­ ear Wald tests of parameter constraints. These tests were useful for comparing the effect of different parameters in the most recent model, and hence tested the extent to which dating and coresidential relationship statuses differed from one another (rather than from singlehood) in their effect on crime (p values are based on the delta method). A significant test statistic provided evidence that the coefficients were different from one another. After establishing relationship status effects (or lack thereof), we assessed the effect of relationship quality to address Hypothesis 2. Because relationship quality was coded as an internal modera­ tor, this variable was statistically comparable to an interaction between romantic involvement and relationship quality. Its coef­ ficient indicates the extent to which relationship quality matters for criminal involvement above and beyond the effect of simply being in a relationship. To test the extent to which relationship quality operated similarly or differently depending on the particular rela­ tionship status, we also interacted relationship quality with each relationship status. Nonlinear Wald tests were used to determine the extent to which the effect of relationship quality on crime was different for those in dating versus coresidential relationships. Following the establishment of relationship status and quality effects, we tested the psychological (Hypothesis 3) and social control (Hypothesis 4) explanations for these effects by entering criminogenic knowledge structure, affiliation with deviant peers, and risky routine activities into the model in a stepwise fashion. After each step, we tested the extent to which the union type and quality effects have been mediated using the multilevel mediation techniques with bootstrapped standard errors (500 repetitions) adapted from Krull and MacKinnon (2001) and available in Stata (,ml_mediation). We present all results using the exponentiated negative binomial regression coefficient, or the incidence rate

Results Table 2 presents a series of negative binomial fixed effects models in which within-individual change in criminal behavior was predicted by changes in relationship status, quality, and the three mediators of interest. We begin with relationship status and quality effects relevant to Hypotheses 1 and 2. Model 1 of Table 2 tested the association between relationship status and criminal behavior. Because the direct effects of relationship status were dependent upon gender, as illustrated in the significant Gender X Relationship Status interaction terms, we start with this interactive model. As shown in Model 1, coresidential, but not dating, unions were significantly and negatively associated with criminal behav­ ior. As indicated by the significant Coresidential X Female inter­ action term, however, the protective effect of coresidential rela­ tionships relative to singlehood was stronger for men than for women. Models separated by gender (not shown) confirm this interpretation, as coresidential relationship status was significantly associated with a reduction in crime for men (eb = .314, p < .01) but not for women (eb = 1.413, ns). Further, Wald tests revealed that coresidential relationships proved more protective that dating relationships for men (Wald x2 = 3.120, p < .05, one-tailed) but not for women (Wald \ 2 = -260, ns). Supportive of Hypothesis 1, this direct effect of relationship status held even after taking into account relationship quality in Model 2. Coresidential relation­ ships, no matter their quality, reduced men’s criminal involvement relative to both dating relationships and singlehood. As Model 2 suggested, however, relationship quality was also relevant, as it conditioned the effect of being romantically in­ volved. Models 3 and 4 assessed the extent to which the effect of relationship quality was dependent upon relationship status and

Table 2 Incidence Rate Ratios From Negative Binomial Fixed Effects Models Predicting Criminal Involvement 1

2

3

0.935 (0.172) 0.940 (0.173) 0.882 (0.161) School Enrollment 1.056 (0.178) 1.056 (0.177) Unemployed during year 1.050 (0.177) 0.641* (0.150) 0.654* (0.154) 0.659* (0.155) Parent 0.663 (0.176) 0.682 (0.176) Dating relationship (ref = single) 0.751 (0.190) Coresidential relationship (ref = 0.380* (0.162) 0.391* (0.167) 0.372* (0.163) single) 1.556 (0.527) 1.539 (0.519) Dating X Female 1.436 (0.480) 3.482* (1.813) 3.131* (1.643) 3.375* (1.834) Coresidential X Female Relationship quality (internal 0.935’ (0.031) moderator) 0.923* (0.039) Dating X Relationship Quality Coresidential X Relationship 0.958 (0.056) Quality Relationship Quality X Female Criminogenic knowledge structure Friends’ deviance Risky activities 0.669**’ (0.077) 0.685*** (0.079) 1.679*** (0.079) Survey wave 2.056** 2.072** 2.048** Intercept

4 0.923 1.059 0.641* 0.736

5 (0.171) (0.178) (0.151) (0.194)

1.014 1.063 0.694 0.711

6 (0.182) (0.177) (0.164) (0.180)

1.034 1.064 0.686 0.725

7 (0.187) (0.177) (0.162) (0.184)

1.013 0.981 0.756 0.827

(0.183) (0.167) (0.181) (0.211)

0.382* (0.163) 1.403 (0.484) 3.100* (1.633)

0.406* (0.174) 1.622 (0.540) 3.534’ (1.883)

0.412* (0.176) 1.614 (0.537) 3.504* (1.862)

0.471* (0.203) 1.526 (0.510) 3.549* (1.914)

0.984 (0.056)

0.943* (0.031)

0.946* (0.031)

0.951 (0.031)

0.924 (0.065) 1.240*** (0.060) 1.219*** (0.063) 1.189*** (0.062) 1.017(0.019) 1.021 (0.020) 2.560** (0.806) 0.688** (0.079) 0.665*** (0.076) 0.667*** (0.076) 0.047*** (0.042) 2.075** 1.531 1.013 .958

Note. N = 450 person-years; exponentiated coefficients, or incidence rate ratios, presented; standard errors in parentheses, ref — reference. > = .10. > < .05. * > < . 0 1 . * * > < .0 0 1 .

DIFFERENT DIMENSIONS, DIFFERENT MECHANISMS?

gender, respectively. Although the coefficient for the Dating X Relationship Quality interaction term in Model 3 reached marginal significance, the reference group was singlehood. Wald tests re­ vealed that the Dating X Relationship Quality term and the Coresidential X Relationship Quality term did not significantly differ from one another (Wald x2 = .270, ns). Hence, constraining the effect of relationship quality to be equal across union types did not significantly worsen the fit of the model. Supportive of Hypothesis 2, then, an increase in relationship quality, no matter the type of relationship, was associated with a reduction in criminal behavior (roughly a 7% decrease in the expected crime count per unit change in quality). As shown in Model 4, this relationship quality effect was not conditioned by gender. That is, although women did not seem to benefit from dating or coresidential relationships in and of themselves, high-quality relationships reduced crime for both men and women. The null results regarding the Relationship Status X Quality and Quality X Gender interactions made Model 2 the best fitting model for understanding the direct and distinct effects of relationship status and relationship quality on criminal involvement. To illustrate these effects by gender, we graphed the predicted percent change in the expected crime count given a transition from singlehood at Wave V to unions that vary by type and quality at Wave VI. Lower quality unions were estimated at one standard deviation below the grand mean, and higher quality unions were estimated at one standard deviation above the grand mean. As shown in Figure 2, relationships became more beneficial for men as they increased in both quality and status. This was not the case for women, however, as cohabiting unions were not universally more beneficial than singlehood. Instead, the effect of relation­ ships was wholly dependent on quality for women. It should also be noted that the magnitude of women’s predicted change in crime was much smaller than men’s. This was likely because women’s involvement in crime was already much lower than men’s by Wave V of the study. In Models 5 through 7 of Table 2, we tested the cognitive transformation (Hypothesis 3) and social control (Hypothesis 4) mechanisms in mediating the relationship status and quality effects found thus far. In Model 5, we incorporated criminogenic knowl­ edge structure into our fixed effects model. In doing so, the

2 0 .0 0 %

SM en

■ Women

LQ Dating

HQ Dating

LQ HQ Coresidential Coresidential

Figure 2. Predicting percent change in expected crime count when tran­ sitioning from singlehood to each relationship category by gender, a = predicted change is significantly different from singlehood; b = significant gender difference in predicted change.

367

coresidential status effect weakened slightly but remained statisti­ cally significant (recall that Model 2 was our baseline model). Multilevel mediation tests revealed that the indirect effect from coresidential status to crime through criminogenic knowledge structure was not significant (bIE = -.005, ns). Criminogenic knowledge structure did, however, significantly mediate the effect of relationship quality on crime. Criminogenic knowledge struc­ ture accounted for 17% of the relationship quality effect on crime (bre = - -025, p < .01) and attenuated the relationship quality coefficient to marginal significance. Such findings provided sup­ port for Hypothesis 3. Although we have largely explained the effect of relationship quality via cognitive transformation, the effect of relationship status (for men) remained significant. As Model 6 shows, affilia­ tion with deviant peers did not mediate this effect, given that there was no significant association between friends’ deviance and crime above and beyond that accounted for by criminogenic knowledge structure. With the introduction of risky routine activ­ ities in Model 7, however, the incidence rate ratio for coresidential status was attenuated (moved closer to 1) and reduced to marginal significance. Partially supportive of Hypothesis 4, mediation tests revealed that the indirect effect of coresidential relationships on crime through risky routine activities was significant (t>m = “ 0.170, p < .01) and that routine activities accounted for about 36% of the coresidential status effect.

Discussion As the age at first marriage continues to climb (Payne, 2012), scholars have begun to move beyond the “good marriage” effect in understanding desistance by turning their attention to cohabiting and dating relationships (Giordano et al., 2010; McCarthy & Casey, 2008; Sampson et al., 2006; Simons & Barr, 2014). Plagu­ ing this expanding literature aimed at understanding “good rela­ tionship” effects, however, is a continued conflation of relation­ ship status effects and relationship quality effects. That is, there are at least two distinct dimensions of romantic relationships—status and quality—that matter in predicting crime. Importantly, sprin­ kled throughout the literature are suggestions that these dimen­ sions predict crime through different processes. Our findings in­ dicate that this nuance should not be taken lightly. We not only showed that relationship status and relationship quality yield in­ dependent, intraindividual effects on crime but also found that these effects can be explained via different mechanisms. Whereas relationship status effects were largely explained by social control processes, particularly routine activities, relationship quality ef­ fects were largely explained by cognitive transformation. These findings suggest that parsing out the “good” from the “relation­ ship” in empirical assessments and theoretical discussions of the “good relationship effect” may be a promising avenue for integrat­ ing these seemingly competing perspectives. Further inquiry into these mechanisms is warranted, as they may enable a better un­ derstanding of what aspects of romantic relationships can be expected to produce situational changes in crime (i.e., via routine activities) and those that may produce more enduring change (i.e., via cognitive changes). As Bersani and Doherty (2013) argue, understanding these processes is an important theoretical en­ deavor, as they take us beyond asking whether, and what types of, relationships matter for crime to asking how relationships matter.

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Importantly, in our sample of African American young adults, some aspects of relationships were gendered in their effects. Coresidential relationships were associated with a reduction in crime, relative to both singlehood and dating, among men but not among women. The deterrent effect of relationship quality on crime, however, was not gender-specific. Both men and women benefitted from higher quality relationships, no matter the type of union. In essence, these findings suggest that distinguishing between relationship status and relationship quality effects in the desistance process might not only aid theoretical integration but also help to clarify inconsistent findings regarding the gendered links between relationships and crime. Although the findings presented here are theoretically intrigu­ ing, they must be considered in light of several limitations. First and foremost, although we argue that research on African Amer­ ican young adults is much needed, it remains unknown the extent to which the findings presented here are unique to this group of young people. The processes tested here need to be tested with more racially (and spatially) diverse samples, especially given evidence that the meaning and motives for cohabitation may vary across groups. Second, given our relatively small sample, we were underpowered to detect potential differences between cohabitation and marriage. It may be possible that these differences are not very profound in the young African American FACHS sample, but it would be fruitful to test more nuanced relationship status effects in larger and more diverse samples. Third, our routine activities measure was only available at the latest wave of data collection. Consequently, the fixed effects of this variable were not true change effects. Further, all of our measures relied upon self-report. This shared method bias may have been particularly problematic for our measure of deviant peer affiliations (Young, Rebellon, Barnes, & Weerman, 2013) and may help to explain the lack of a significant effect of peer deviance on crime above and beyond criminogenic cognitions. Lastly, although fixed effects models examine intraindividual change, and hence account for selection processes by holding constant observed and unobserved time-invariant factors, fixed effects models with only two waves cannot parse out causal ordering. Although our causal ordering is consistent with social theory, our confidence in our interpretation of the results was enhanced for other reasons as well. For instance, the overwhelm­ ing majority of partnered respondents at each wave (84.75% at Wave V and 90.55% at Wave VI) were involved with their romantic partner for at least one year. Given that the crime items measure criminal involvement within the past year, it is unlikely that the causal ordering between relationships and crime is the reverse of what is proposed here. Further, in a previous version of this manuscript, we utilized a between-individual propensity score approach, in which we accounted for the propensity of being in different types of relationships of differing quality at Wave VI (e.g., singe, high-quality coresidential, low-quality coresidential, high-quality dating, low-quality dating). Although this approach was limited because of the small sample size, the results were consonant with the findings from the fixed effects models pre­ sented here. Nonetheless, longitudinal models with more than two waves capable of identifying lagged effects and proper time or­ dering are necessary. Despite these limitations, the present study examined a variety of mechanisms whereby both relationship status and relationship

quality influence criminal behavior in young adulthood. Further, we did so utilizing an all-African-American sample, a population that has been largely neglected in relationships and desistance research, but could perhaps most benefit from this research. Given that marriage among young people is increasingly uncommon, particularly for African Americans, our findings prove promising by suggesting not only that cohabiting relationships provide sim­ ilar social controls to marital relationships but also that highquality relationships, no matter their form, present a deterrent to crime. Understanding the nuanced processes through which rela­ tionships of all forms are able to foster desistance may be increas­ ingly vital for making policy and intervention decisions that not only recognize but also capitalize on the strengths and capacities within young people’s relationships.

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Received September 5, 2014 Revision received February 20, 2015 Accepted February 25, 2015 ■

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