Low Self-Control and the Victim-Offender Overlap: A Gendered Analysis.

572471 research-article2015

JIVXXX10.1177/0886260515572471Journal of Interpersonal ViolenceFlexon et al.

Article

Low Self-Control and the Victim–Offender Overlap: A Gendered Analysis

Journal of Interpersonal Violence 1–25 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0886260515572471 jiv.sagepub.com

Jamie L. Flexon,1 Ryan C. Meldrum,1 and Alex R. Piquero2

Abstract The overlap between victimization and offending is well documented. Yet, there have been fewer investigations of the reasons underlying this relationship. One possible, but understudied, explanation lies with Gottfredson and Hirschi’s arguments regarding self-control. The current study adds to this line of inquiry by assessing whether low self-control accounts for the victim–offender overlap in a sample of young adults and whether self-control accounts for the observed overlap similarly across gender. Results from a series of bivariate probit regression models indicate that low self-control is positively related to both victimization and offending. However, only among males does low self-control account for a substantive portion of the victim–offender overlap. Limitations of the study and implications and directions for future research are discussed. Keywords self-control, victimization, offending, overlap, bivariate probit regression

1Florida

International University, Miami, USA of Texas at Dallas, Richardson, USA

2University

Corresponding Author: Ryan C. Meldrum, Department of Criminal Justice, Florida International University, 11200 S.W. 8th Street, PCA-364B, Miami, FL 33199, USA. Email: [email protected]

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Journal of Interpersonal Violence

Introduction An overlap between criminal offending and victimization has been observed by scholars for some time (Gottfredson, 1981; Jennings, Piquero, & Reingle, 2012; Lauritsen & Laub, 2007; Sampson & Lauritsen, 1990; Singer, 1981; Wolfgang, 1958). However, research identifying factors that account for this overlap is lacking. Several reasons have been noted for this deficit, including a lack of available data and adequate theoretical development (Jennings et al., 2012). To date, we are confident in only a few observations concerning the concordance between offenders and victims. Namely, offenders and victims share a similar demographic profile, are often one and the same (i.e., offenders today, victims tomorrow), and the two appear to have related etiology (Piquero, MacDonald, Dobrin, Daigle, & Cullen, 2005). Several theoretical approaches have been used to explain this overlap including routine activities (Schreck, Stewart, & Osgood, 2008; Smith & Ecob, 2007) and subcultural theories of crime (Anderson, 1999). The general theory of crime—with its focus on self-control (Gottfredson & Hirschi, 1990)—also appears to offer a promising contribution to this line of inquiry. While low self-control has been identified as a significant correlate of both outcomes (Pratt & Cullen, 2000; Pratt, Turanovic, Fox, & Wright, 2014), it is only recently that research has started to address the question of whether and to what extent it accounts for the overlap between victimization and offending (e.g., Piquero et al., 2005). Another undeveloped area concerning this overlap is whether existing theory can adequately account for observed differences between males and females. To date, no studies have specifically addressed this question. The purpose of this study, then, is to address the above-noted deficiencies in prior research examining the victim–offender overlap by (a) vetting the capacities of self-control to explain this victim–offender concordance and (b) assessing the general theory by examining whether it explains this overlap equally well for males and females. Existing research suggests that selfcontrol will add to our understanding of this redundancy in victims and offenders, but there is more uncertainty surrounding whether the theory will be robust across gender. To address these issues, data from a sample of young adults were gathered concerning their self-control and recent experiences as victims and offenders. Data were then used in a series of bivariate probit regression models to ascertain the value of self-control in explaining the victim–offender overlap and whether it explains this redundancy equally well across gender. Prior to this evaluation, the theoretical underpinnings of our approach are discussed along with past research that has examined this overlap.


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Low Self-Control and the Victim/Offender Overlap Gottfredson and Hirschi’s general theory has been used successfully for explaining crime in a large array of studies, and extensive reviews report a consistent relationship between low self-control and crime across a variety of methodologies and research designs (de Ridder, Lensvelt-Mulders, Finkenauer, Stok, & Baumeister, 2012; Pratt & Cullen, 2000). Victimization research has also increasingly relied on self-control owing to Gottfredson and Hirschi’s postulate concerning non-criminal acts linking those having low self-control with risk for victimization. This research has been reasonably successful in uncovering a direct link between attributes of low self-control and victimization (e.g., Baron, Forde, & May, 2007; Forde & Kennedy, 1997; Pratt et al., 2014; Schreck, 1999; Schreck, Stewart, & Fisher, 2006). The intersection of these two lines of research has recently led some to examine the ability of self-control to explain the victim–offender overlap, finding that the general theory is well positioned to account for at least some of the redundancy observed between victim–offenders (Jennings, Park, Tomsich, Gover, & Akers, 2011; Piquero et al., 2005). Summarily, according to the general theory (Gottfredson & Hirschi, 1990), poorly socialized youth are at risk for both offending behavior and other problematic outcomes. Several deficits are counted under the umbrella of low self-control. The six prevailing characteristics that increase the probability for crime (and victimization) that are counted under low self-control include shortsightedness, self-centeredness, anger, low diligence, preference for physical over mental tasks, and risk or sensation-seeking behavior. Gottfredson and Hirschi (1990) asserted that youth failing to develop selfcontrol are more likely to engage in crime as well as criminally equivalent non-criminal acts, such as smoking, skipping school, suffering accidents, and early death. The cause of all behaviors and outcomes, then, is the manifestation of or lack of self-control. Thus, along with explaining the balance of criminal behavior, Gottfredson and Hirschi implied that low self-control contributes to youth engaging in victim precipitating behaviors as well (Gottfredson & Hirschi, 1990; Piquero et al., 2005; Turanovic & Pratt, 2014). While to some, claims made in the general theory are perhaps overstated concerning the symmetry between all offenders and victims (Pratt et al., 2014), self-control is particularly suited for an explanation of the victim– offender link. Gottfredson and Hirschi (1990) observed that “ . . . victims and offenders tend to share all or nearly all social and personal characteristics,” which to them was important for etiological questions as it showed “a pattern of crime consistent with the recreational patterns of youth . . . ” (p. 17). Although youth having low self-control were synonymous with criminality,


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the attributes of having low self-control would place these youth in vulnerable situations to be victimized (see Turanovic & Pratt, 2014). Scholars have offered illustration for how self-control might account for the victim–offender overlap (Piquero et al., 2005; Schreck, 1999). According to Schreck (1999), “It is not in anyone’s self-interest to be a victim of crime, but low self-control behavior produces a vulnerability as a by-product” (p. 635). Schreck then offers a discussion about how the different facets of low self-control work to produce these vulnerabilities. As those lacking in self-control fail to appreciate long-term consequences, they are at risk for victimization via impulsive behavior, because they lack the capacity to appreciate future dangers to themselves or property. Self-centered individuals also might engage in victim precipitating behaviors simply by being insensitive to others, which increases their vulnerability by possibly inciting altercation. In addition, being quick to anger may exaggerate and incite interpersonal responses by provoking an attack or counter measures. Schreck (1999) also noted that persons who lack self-control are less likely to be careful and take precautions, which increases their vulnerability to victimization. A preference for physicality also makes people vulnerable when there is a need to think about problems or conduct a careful assessment of risk. Finally, preferring risk is likely to place individuals in situations where victimization has a higher probability, for example, hitch-hiking. Given the ability of self-control to reasonably account for not only criminal and analogous behavior, but victimization as well, we expect, and the general theory of crime would anticipate, that the construct should account for at least some of the overlap observed between the two parallel groups of victims and offenders. However, the reasonableness of the general theory to account for differences in victimization and offending across gender is also an important, understudied question, even though Gottfredson and Hirschi (1990) argued that low self-control should account for all crime and analogous behaviors across all people, time, and places.

Observations Concerning the Victim–Offender Overlap A large amount of criminological research is devoted to examining the causes and correlates of criminal behavior and victimization. Yet, less is known about those falling within the typology of victim–offenders, although scholars have made observations concerning this high-risk group for decades (e.g., Lauritsen & Laub, 2007; Sampson & Lauritsen, 1990; Singer, 1981; Wolfgang, 1958). For example, Wolfgang’s (1958) seminal work on Philadelphia homicide victims showed that many of the victims in his


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research were likely to have a criminal history. Causes cited for homicidal behavior included victim precipitation, whereas the perpetrator of some crime became the victim. Oftentimes, the perpetrator and victims shared similar characteristics, and the victim in an altercation was often determined by situational forces such as the availability of a weapon. Predominantly, the offenders and victims in these altercations were male. Research also indicates that along with criminal behavior, risk for victimization is increased with younger age (Lauritsen, Sampson, & Laub, 1991). While victims are less likely to have a criminal background than offender groups generally, they are more likely to have criminal histories than the population at large (Kuhlhom, 1990). Based on prior work, it is also known that some criminal behaviors are more likely associated with overlapping victimization than others, such as assault, larceny, robbery, vandalism, violence, theft, and drug use (Jennings et al., 2012). For example, in evaluating the victim–offender overlap in youth, Lauritsen et al. (1991) examined the first five waves of the National Youth Survey (NYS) and found that delinquents were 4 times more likely to be the victims of assault than nondelinquents, with similar risk profiles for robbery and vandalism. The authors note that “victimization patterns among youth cannot be understood apart from criminal and deviant activities” (p. 265).

Sex Differences and the Victim/Offender Overlap It is reasonable to assume the interactions, or overlap, that govern victimization and offending are influenced by gender differences. For example, in many instances, males find themselves the victims of crime through sequences of progression where it would be difficult to sort out the dissimilarities of the perpetrator and the victim, for example, confrontational, honor defense, street status, situated transactions (Anderson, 1999; Fox & Zawitz, 2006; Luckenbill, 1977; Silver, Piquero, Jennings, Piquero, & Leiber, 2011; Wolfgang, 1958). Furthermore, the higher frequency with which males offend is likely to elevate their risk for victimization through broadened opportunities (Jennings et al., 2012; Lauritsen et al., 1991). Consistent explanations may hold for females, but to a much lesser extent (Scott & Davies, 2002). Females are less implicated in criminal behavior than men, though they have historically borne the weight of being a subject class along with the deleterious consequences thereof. Females are also less likely to be exposed to violence than males, a known risk factor for criminal behavior (Reingle, Jennings, & Maldonado-Molina, 2011). Simultaneously, distinctions may also be realized through the power differential experienced between males


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and females, particularly when it comes to victimization, which is partly based on cultural expectations that vary by sex and may be augmented or mitigated by having lower self-control. Apart from the variation that could be explained by individual factors (i.e., low self-control) known to contribute to crime and victimization, its escalation, as well as the overlap between the two, there are likely other variables that may better explain victimization for particularly the female population that rely on social and cultural factors, as well as situational characteristics. Given this, it is reasonable to argue that low self-control accounts for a substantive portion of the victim–offender overlap for males. Males may find themselves in provocative situations combined with social pressures to engage. Such situations may escalate, particularly if they lack self-control (e.g., provocation, retaliation; Silver et al., 2011). Females are arguably under less societal pressure to engage in such interactions. Several studies are relevant to this discussion, if only in an indirect manner. For example, Stewart, Elifson, and Sterk (2004) explored whether the general theory of crime could be incorporated into an explanation of violent victimization among female drug users. Informatively, the authors found that while low self-control was a significant predictor of victimization within the sample, lifestyle variables and both violent and property offending measures also exhibited significant effects on victimization. Thus, this study illustrated that while self-control was a contributing factor to females’ victimization experiences, a robust association between offending and victimization remained. Beyond this study, research examining the overlap between victimization and offending seldom seeks to differentiate by gender, though limited results have emerged incidental to this research. In a recent analysis exploring the violent offending and victimization overlap among discharged psychiatric patients, Silver et al. (2011) found that while males were more likely to be involved in violent offending mirroring the general population (Sampson & Lauritsen, 1990), the same did not hold for predicting violent victimization. The authors concluded that the same demographic correlates (i.e., gender) of violent behavior and victimization do not overlap for their unique sample. Research examining contextual forces, however, finds that those involved with gang activity, whether male or female, heighten the risk for victimization rather than protect against harm (Miller & Decker, 2001). Such findings may be spurious as individual-level factors may lead certain people to join gangs that also place them at risk for crime and victimization (i.e., low selfcontrol; Jennings, Higgins, Tewksbury, Gover, & Piquero, 2010). In another important study, Schreck et al. (2008) did not find sex to shape the contrast in criminal offending and victimization. Longitudinal examination of the nature of victim and offending trajectories, however, reveals that


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among noted risk factors (i.e., school commitment, parental monitoring, and low self-control), sex was important for distinguishing trajectories for delinquency and victimization (Jennings et al., 2010). In particular, females faced a reduced “likelihood of being assigned to a low or moderate delinquency trajectory and sex also significantly distinguished victimization trajectories” (p. 2166). Still, beyond these few studies, research seeking to assess whether the factors accounting for the overlap between victims and offenders are gender-specific is lacking.

Current Study The current study attempts to fill a void in the literature on the victim– offender overlap by examining adult experiences with victimization and offending and by further determining the efficacy of self-control as an explanation of this overlap separately for males and females. To accomplish this, self-report data were collected concerning self-control, criminal behavior, and victimization experiences. Notably, we use a modeling strategy that both estimates the joint association between victimization and offending and enables us to assess the extent to which covariates (i.e., self-control) account for any overlap between the two constructs. This is something which few studies have done, and even fewer have done so in the context of evaluating the influence of self-control on both offending and victimization. As existing research has only started to assess the ability for low selfcontrol to account for the overlap between victimization and offending and whether this may differ for males and females, we hold to theoretical arguments made by Gottfredson and Hirschi and hypothesize that low self-control should account for the observed overlap between victimization and offending. At the same time, given the research reviewed earlier concerning potential sex differences in the association between victimization and offending, we also hypothesize that self-control will account for a substantively greater portion of the overlap for males than for females.

Data Data for this study are obtained from approximately 500 young adults who were enrolled in 12 different courses in a criminal justice department in January 2013 at a large university located in the southeastern United States. After receiving institutional approval to conduct the study, a number of instructors were approached and asked if individuals enrolled in their courses could be recruited to participate. All instructors who were approached consented to allowing the study researchers to invite individuals to participate


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during the first class meeting of the first week of the semester.1 Respondents in each class were told that the topic of the study focused on things that explain deviant behavior and victimization and were informed that the choice of whether to participate or not would in no way affect their course grades. In accordance with the approved research protocol, verbal consent rather than written consent was obtained to maintain the anonymity of the participants. With these considerations in mind, 535 respondents present on the first day of their respective classes were given the opportunity to participate in the study.2 Of these, 42 declined, yielding a participation rate of 92%. Of the 493 subjects who elected to participate, 18 had missing data on one or more of the items used in the analysis, and were excluded. Complete data were available for 475 individuals.

Measures Dependent variables—Victimization and offending. Given that the focus of the analysis is on the joint association between victimization and offending, and whether low self-control explains this association, two dependent variables are considered: victimization and offending. As we discuss shortly, the measures of victimization and offending pertain primarily to interpersonal violence and property offending/victimization. They do not, however, make any reference to whether the offending or victimization was of a sexual nature. Thus, readers may view the focus of our analysis as limited in its scope. We recognize this limitation and address it further at a later point in the article. To measure victimization, respondents were asked, “In the past 12 months, how many times have you [act]?” Six different forms of victimization were assessed: “Had someone threaten to beat you up”; “Had someone steal something from you that was on you or that you were carrying”; “Had someone steal something from your place of residence, such as a home or apartment”; “Been hit, punched, or kicked by someone”; “Been attacked by someone with a weapon”; and “Had someone purposefully damage or destroy something that belonged to you.” For each of the six items, response options were “0 times” (= 0), “1-2 times” (= 1), “3-5 times” (= 2), “5-9 times” (= 3), and “10+ times” (= 4). Each item was transformed into a dichotomized score of 0 (0 times) or 1(at least 1 time). For the analysis, we created a single dichotomized variable indicating whether a respondent had experienced any victimization whatsoever based on the six items within the past 12 months (no = 0; yes = 1). To measure offending, respondents were asked, “In the past 12 months, how many times have you [act]?” A total of 12 items appeared on the survey, but several were related to substance use or behaviors that did not reflect the general focus of the victimization items (property and violent victimization).


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Thus, six offending items are the focus of the current study: “Gotten into a place that charges admission without paying”; “Broken into a building, home, or office”; “Carried a hidden weapon other than a plain pocket knife”; “Purposefully damaged or destroyed property that didn’t belong to you”; “Got in a fight with someone with the intent of seriously injuring them”; and “Stolen money, property, or goods.” The response options mirrored those for the victimization items. As with the victimization items, each offending item was transformed into a dichotomized score of 0 (0 times) or 1 (at least 1 time). For the analysis, we created a single dichotomized variable indicating whether a respondent had committed any offending whatsoever in the past 12 months based on the six items (no = 0; yes = 1). Results based on the measures of victimization and offending before dichotomizing them reinforce the results presented in the article, and we comment on these results in a later portion of the article. Independent variable—Low self-control. There has been considerable debate surrounding the measurement of self-control, particularly with regard to the use of behavioral versus attitudinal measures. Yet, research shows that both are consistent predictors of delinquency, crime, and victimization (Pratt & Cullen, 2000; Pratt et al., 2014). Furthermore, research also indicates that some dimensions of low self-control described by Gottfredson and Hirschi (1990) are more strongly related to antisocial behavior (Piquero, 2008), particularly the dimensions of anger, risk seeking, and impulsivity. In this study, we measure low self-control using eight items from the Grasmick, Tittle, Bursik, and Arneklev (1993) scale, the majority of which tap into the dimensions of anger, risk seeking, and impulsivity: “I often act on the spur of the moment without stopping to think”; “I am more concerned with what happens to me in the short run than the long run”; “I try and avoid projects that I know will be difficult to finish”; “Sometimes I will take a risk just because it’s fun to do so”; “I sometimes find it exciting to do things that may get me into trouble”; “I lose my temper pretty easily”; “When I am angry, people better stay away from me”; and “I try to lookout for myself first, even if it means making things difficult for other people.” For each of the items, responses ranged from “strongly disagree” (=1) to “strongly agree (=5).” The items were averaged together for the analysis (α = .75).3 Demographic and control variables. While the primary focus of the analysis is a strict test of whether low self-control accounts for the joint association between victimization and offending, we also consider how standard demographic characteristics are related to this association. Of primary interest, we had participants self-report their sex (male = 1; female = 0), so that we could


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Table 1. Descriptive Statistics for Full Sample and Split by Gender. Full Sample (N = 475)

Males (n = 204)

Females (n = 271)

Variable

Mean

SD

Min

Max

Mean

SD

Mean

SD

Age Male Hispanic School attachment Low self-control Victimization Offending

22.22 0.43 0.73 3.34 2.39 0.41 0.39

4.07 0.50 0.44 0.80 0.68 0.49 0.49

17 0 0 1.00 1.00 0 0

57 1 1 5.00 5.00 1 1

22.79 — 0.73 3.35 2.41 0.41 0.46

4.66 — 0.45 0.78 0.68 0.49 0.50

21.79 — 0.74 3.34 2.36 0.41 0.33

3.50 — 0.44 0.82 0.67 0.49 0.47

disaggregate the models to test our hypothesis concerning potential sex differences in accounting for the victim/offender overlap. In addition, the selfreported age of each respondent (in years) is included, along with a variable for race/ethnicity. As a high proportion of participants indicated that they were Hispanic (73%, reflective of the study university), a single dichotomous variable (Hispanic = 1; all Others = 0) was used to measure race/ethnicity. In addition to these demographic variables, we also included a five-item measure of school attachment in all analyses given that prior research has established victimization and offending is partially shaped by school-related bonding variables (e.g., Jennings et al., 2010). Items in the current study assessed, for example, how closely attached respondents felt to their school and teachers. Response options for each of the items ranged from “strongly disagree” (=1) to “strongly agree” (=5), with higher values representing greater attachment. The items were averaged together for the analyses (α = .79). Table 1 provides the descriptive statistics for the full sample and by sex; the appendix provides the correlation matrix. Although there was no statistical difference between males and females with regard to school attachment, low self-control, and the proportion of individuals reporting victimization, males were more likely to report having been involved in any offending relative to females (46% vs. 33%).

Analytic Method To examine the overlap between victimization and offending, we use bivariate probit regression, an established method used to assess the victim– offender overlap (Jennings et al., 2011; Silver et al., 2011). Bivariate probit regression fits maximum-likelihood two-equation probit models, which in


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the current study is one for victimization and one for offending, while also expressly considering the joint association between offending and victimization (i.e., the errors of the two models are correlated; Greene, 1997). Initially, constant-only models are estimated to capture the covariance between the error terms for victimization and offending (referred to as rho, ρ). Covariates are then added to the models to examine how they are related to each dependent variable and whether the covariance between the error terms for the dependent variables is reduced to non-significance. We first considered this possibility for the full sample, and then examined the issue separately for male and female participants.

Results Full Sample Analyses The analysis began by estimating four bivariate probit models for the full sample. The results of these models are presented in Table 2. Model 1 (the constant-only model) establishes that a significant association between victimization and offending exists within these data. The value for the covariance between the error terms for the two dependent variables (ρ) is ρ = .35, and the likelihood-ratio test confirms that this association is statistically significant (χ2 = 23.98; p ≤ .001). Model 2 adds the covariates of age, male, Hispanic, and school attachment. While being male (b = 0.34; p ≤ .01) and having stronger school attachments (b = −0.16; p ≤ .05) is significantly related to previous offending, the former positively and the latter negatively, being older (b = −0.03; p ≤ .05), being Hispanic (b = −0.37; p ≤ .01), and having stronger school attachments (b = −0.19; p ≤ .05) are all negatively related to being victimized. It is also instructive to note that the value for ρ is nearly identical in the first two models, indicating that age, sex, race, and school attachment do not account for the joint association between victimization and offending. Turning to Model 3, which removes the demographic and bonding variables as covariates and adds low self-control as the single covariate, the results offer mixed evidence regarding the salience of self-control in explaining the joint association between victimization and offending. On one hand, low self-control is positively related to each outcome and the estimates are statistically significant (b = 0.38; p ≤ .001 for victimization; b = 0.44; p ≤ .001 for offending). However, as shown at the bottom of Table 2, ρ remains relatively large (.30), and the likelihood-ratio test indicates that this value remains statistically significant (χ2 = 15.71; p ≤ .001). In other words, even though low self-control is significantly related to both victimization and


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b

SE

†p

— −0.03* 0.03 −0.37** −0.19* 1.40** — −0.02 .34** −0.26† −0.16* 0.82

— — — — — −4.72

b

— — — — — −3.90

Z

< .10. *p < .05. **p < .01. ***p < .001 (two-tailed).

DV1: Victimization Low self-control — — — — Age — — Male Hispanic — — School attachment — — Constant −0.23*** .06 DV2: Offending Low self-control — — — — Age Male — — Hispanic — — School attachment — — Constant −0.28*** .06 LR −628 ρ .35 23.98*** LR test χ2(1) ρ

Variable

Model 1

— −2.09 0.28 −2.76 −2.52 3.06

Z

— — .02 −1.49 .12 2.86 .13 −1.92 .07 −2.19 .45 1.83 −612 .32 19.15***

— .02 .12 .13 .07 .46

SE

Model 2

Table 2. Bivariate Probit Regression of Victimization and Offending (N = 475).

0.44*** — — — — −1.34***

0.38*** — — — — −1.13***

b

.09 — — — — .22 −609 .30 15.71***

.09 — — — — .22

SE

Model 3

4.98 — — — — −6.02

4.30 — — — — −5.17

Z

.09 .02 .12 .13 .08 .56

SE

3.74 −1.43 0.06 −2.87 −1.79 0.37

Z

.09 4.55 .01 −0.67 .12 2.65 .14 −2.09 .08 −1.33 .55 −1.18 −597 .27 12.92***

0.42*** −0.01 0.32** −0.28* −0.10 −0.65

0.34*** −0.02 0.01 −0.39** −0.13† 0.21

b

Model 4

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offending in theoretically expected ways, it fails to substantively account for the victim–offender overlap, which runs counter to theoretical expectations and our first hypothesis. Model 4 retains the covariate for low self-control and reinserts the demographic and bonding variables. Consistent with Model 3, low self-control is positively related to victimization (b = 0.34; p ≤ .001) and offending (b = 0.42; p ≤ .001). The results also indicate that being Hispanic is negatively related to both victimization (b = −0.39; p ≤ .01) and offending (b = −0.28; p ≤ .05) when low self-control is included in the same model, whereas being male continues to be positively related to offending (b = 0.32; p ≤ .01). Note also that the value of ρ in Model 4 (.27) continues to be statistically significant (χ2 = 12.92; p ≤ .001). Thus, low self-control and the other modeled variables do not account for the victim–offender overlap. Having examined this issue for the full sample, our attention then turned to replicating the analysis separately for males and females to address our second hypothesis.

Split-Gender Analyses Table 3 presents the results of the bivariate probit analysis for males. As with the analysis conducted for the full sample, four separate models were estimated for the male sample. Model 1 provides the constant-only models and indicates that the value of ρ is .29 and that the covariation between the error terms for victimization and offending is statistically significant (χ2 = 7.06; p ≤ .01). Model 2 adds the covariates for age, Hispanic, and school attachment, the results of which show that among males those who have stronger school attachments are less likely to be victimized (b = −0.35; p ≤ .01). Age, race, and school attachment are unrelated to offending among males. It should also be noted that the value of ρ (.26) continues to be statistically significant (χ2 = 5.54, p ≤ .05). Model 3 removes the variables for age, race, and school attachment and adds low self-control as the single covariate. As in the full sample analysis, low self-control is positively related to both victimization (b = 0.54; p ≤ .001) and offending (b = 0.50; p ≤ .001) among males. Informatively, the results for the male sample diverge from the full sample in one important way—The value for ρ declines from .29 in the constant-only model to .19 in the model with low self-control as the single covariate, and the test for the significance of ρ in Model 3 fails to reach statistical significance (χ2 = 2.60; p = .11). This indicates that, among males, low self-control reduces the joint association between victimization and offending to non-significance, providing support for proponents of the general theory and its ability to account for a substantive portion of the overlap between victimization and offending. This position is


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†p

— — — — .09

— — — — .09 −275 .29 7.06**

— — — — −0.10

SE

— — — — −0.22*

b

— — — — −1.12

— — — — −2.52

Z

< 10. *p < .05. **p < .01. ***p < .001 (two-tailed).

DV1: Victimization Low self-control Age Hispanic School attachment Constant DV2: Offending Low self-control Age Hispanic School attachment Constant Log likelihood ρ LR test χ2(1) ρ

Variable

Model 1

— −0.04† −0.16 −0.05 1.13†

— −0.05† −0.26 −0.35** 2.30**

b

— .02 .20 .12 .68 −267 .26 5.54*

— .03 .21 .12 .75

SE

Model 2

— −1.73 −0.77 −0.45 1.66

— −1.90 −1.27 −2.91 3.06

Z

0.50*** — — — −1.30***

0.54*** — — — −1.55***

b

.14 — — — .34 −262 .19 2.60

.14 — — — .35

SE

Model 3

Table 3. Bivariate Probit Regression of Victimization and Offending, Males Only (n = 204).

3.67 — — — −3.84

4.00 — — — −4.49

Z

0.47** −0.03 −0.18 −0.02 −0.47

0.50** −0.03 −0.27 −0.33** 0.63

b

.14 .02 .21 .12 .83 −256 .18 2.31

.14 .03 .21 .12 .87

SE

Model 4

3.37 −1.06 −0.89 −0.14 −0.57

3.47 −1.27 −1.28 −2.69 −0.72

Z

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further supported by the results presented in Model 4. Low self-control remains positively related to both victimization (b = 0.50; p ≤ .01) and offending (b = 0.47; p ≤ .01) after accounting for age, race, and school attachment, and the test for the value of ρ again indicates that the remaining covariation in the error terms between victimization and offending (0.18) is not significantly different from 0 (χ2 = 2.31; p = .13). Table 4 presents the results of the bivariate probit analysis for females. Once again, four separate bivariate probit models were estimated. Model 1 provides the constant-only models and indicates that the value of ρ is .41 and that the covariation between the error terms for victimization and offending is statistically significant (χ2 = 18.04; p ≤ .001). Model 2 adds the covariates for age, Hispanic, and school attachment. The data indicate that among females, those who are Hispanic are less likely to be victimized (b = −0.49; p ≤ .01). As was true for males, age is unrelated to offending among females, while the negative coefficient for race approaches statistical significance (b = −0.33; p ≤ .06). School attachment is also a significant negative predictor of offending (b = −0.22; p ≤ .05) but not victimization among females. The value of ρ (.38) continues to be statistically significant (χ2 = 14.92; p ≤ .001). Model 3 removes the demographic and bonding variables and adds low self-control as the single covariate. As with the full and male samples, low self-control is positively related to both victimization (b = 0.25; p ≤ .05) and offending (b = 0.40; p ≤ .01). However, unlike in the male sample, accounting for low self-control does not render the covariation between the error terms for victimization and offending non-significant (χ2 = 14.89; p ≤ .001). This is again confirmed in Model 4. Low self-control remains positively related to each outcome, net of age, race, and school attachment, but the value of ρ (.35) remains statistically significant (χ2 = 12.04; p ≤ .001). Taken together, the results from the models presented in Tables 3 and 4 support our second hypothesis by demonstrating that low self-control accounts for a substantive portion of the victim–offender overlap among males but to a far lesser extent among females.

Discussion The purpose of the current study was to address two deficits related to the victim–offender overlap. First, studies that have examined how low selfcontrol is jointly related to both victimization and offending offer some promise (Jennings et al., 2010; Jennings et al., 2011; Piquero et al., 2005), but the research base remains thin. In addressing this issue, we sought to extend prior work by using a modeling strategy that enabled us to directly examine whether low self-control accounts for the victim–offender overlap. Second,


16


†p

— — — — .08

SE — — — — −2.97

Z

— — — — — — — — .08 −5.27 −348 .41 18.04***

— — — — −0.41***

— — — — −0.23**

b

< .10. *p < .05. **p < .01. ***p < .001 (two-tailed).

DV1: Victimization Low self-control Age Hispanic School attachment Constant DV2: Offending Low self-control Age Hispanic School attachment Constant Log likelihood ρ LR test χ2(1) ρ

Variable

Model 1

— −0.01 −0.33† −0.22* 0.69

— .02 .18 .10 .61

SE — −0.63 −2.76 −0.66 1.04

Z

— — .02 −0.25 .18 −1.84 .10 −2.29 .63 1.11 −341 .38 14.92***

— −0.01 −0.49** −.06 0.64

b

Model 2

.11 — — — .28

SE 2.20 — — — −2.92

Z

.12 3.33 — — — — — — .30 −4.58 −341 .38 14.89***

0.40** — — — −1.37***

0.25* — — — −0.83**

b

Model 3

Table 4. Bivariate Probit Regression of Victimization and Offending, Females Only (n = 271).

.12 .02 .18 .10 .76

SE

2.16 −0.28 −2.85 −0.08 −0.42

Z

.13 2.97 .02 0.22 .18 −1.95 .10 −1.48 .78 −0.85 −335 .35 12.04***

0.37** 0.00 −0.36† −0.15 −0.66

0.26* −0.01 −0.51** −0.01 −0.32

b

Model 4

17

Flexon et al.

existing research had yet to assess whether the ability of low self-control to account for the overlap between victimization and offending may differ for males and females. To evaluate these two issues, data were collected from adult subjects concerning their self-control and recent offending and victimization experiences. We used these data to estimate a series of bivariate probit regression models to determine whether low self-control accounted for the joint association between victimization and offending and whether it did so equally across sex. Our analysis yielded three main findings. First, as other studies have found, we observed a modest but statistically significant association between offending and victimization for both males and females, as indicated by the significant value for ρ in our constant-only models. Second, in the full sample, we found that low self-control was positively and significantly related to both offending and victimization in general and in more fully specified models that considered age, sex, ethnicity, and school attachment. Yet, consideration of self-control did not do much to eliminate the overlap observed in the full sample between offending and victimization. Thus, at least in the overall sample, a key prediction regarding Gottfredson and Hirschi’s theory was only partially supported. Third, when we disaggregated the analysis by sex, which prior research has not adequately concentrated upon, a very distinct set of results emerged. Among males, a small-to-modest and significant association between offending and victimization continued to be observed in both a naïve model and a partially specified model that did not include a measure of self-control. Yet, inclusion of low self-control in the model eliminated the previously significant parameter estimate for ρ, indicating support for the general theory of crime, at least among males in our sample of young adults, that self-control accounts for the overlap between victims and offenders. Among females, however, a slightly but importantly different pattern of results emerged. Specifically, although low self-control was positively related to both offending and victimization among females, consideration of low self-control did little to alter the parameter estimate for ρ. Thus, among females in our sample of young adults, low self-control does virtually nothing with respect to accounting for the association between offending and victimization. Before we discuss the implications of our findings, we must acknowledge some limitations. First, although self-reports of offending and victimization are frequently used to examine the victim–offender overlap (see Jennings et al., 2012), as was done in the current study, social desirability bias may limit the ability to reliably assess the overlap and the factors that account for it (see, for example, MacDonald, Morral, & Piquero, 2011). Second, although recent research suggests that differences between college students and the


18


general population with regard to antisocial behavior may not be as divergent as previously thought (Wiecko, 2010), our sample is not based on a random selection of college students, and this should be kept in mind when considering the generalizability of our findings. Third, due to constraints on time and resources, we could not consider additional covariates for the analysis, nor could we explore alternative self-control indicators that might perform better than items from the Grasmick et al. (1993) scale, such as those appearing in Tangney, Baumeister, and Boone (2004) or Wiebe (2006). Although we do not think such considerations would materially alter our substantive findings, especially given the manner in which different forms of low self-control relate to both offending and victimization regardless of the covariates included in analytic models, future work should consider variables from other criminological theories as well as alternative measures of selfcontrol. Fourth, we were only able to assess the victim–offender overlap when considering a limited set of offending and victimization indicators. As noted earlier, we were unable to consider sexual offending and victimization within our analyses. Given that past research indicates that sexual offending (e.g., Kahn & Chambers, 1991) and victimization patterns (e.g., Erickson & Rapkin, 1991; Shrier, Pierce, Emans, & DuRant, 1998) are gendered, it is possible that our results may be sensitive to the behaviors considered in the analysis. Thus, it will be important for subsequent studies to consider sexual offending and victimization experiences when assessing the extent to which self-control accounts for the overlap between offending and victimization. Fifth, while we used dichotomized measures of offending and victimization to accommodate a methodology that enabled us to directly assess the victim–offender overlap (i.e., bivariate probit regression), this naturally restricted the variability in the measures. Likewise, some of the items used to measure victimization and offending can be considered more serious than others; a value of 1 in the analysis for one person may reflect a more or less serious form of victimization or offending than another person with a value of 1. To consider whether the results were sensitive to our methodology of dichotomizing victimization and offending, we conducted additional analyses before any transformation of the indicators. Results using the untransformed items indicated the following: (a) a statistically significant association between victimization and offending for the whole sample and for males and females separately and (b) the association between victimization and offending was reduced to a far greater extent when accounting for low self-control among males.4 These alternative results, therefore, reinforce the results based on the dichotomized measures by showing that self-control is predictive of both victimization and offending and that it does a much better job of accounting for the overlap between the two outcomes among males.


19

Flexon et al.

With regard to theoretical implications, our findings can be seen as partially supportive and partially negative for the general theory of crime. We found, as Gottfredson and Hirschi would anticipate, a shared overlap between offending and victimization as well as a consistent and positive effect of low self-control on both outcomes. Yet, our gender-specific analysis provided only partial confirmation that self-control could account for the association between the two outcomes, as we observed this theoretically expected pattern for males but not for females. The general theory, then, must revisit this specific expectation. More generally, there is a larger issue with respect to the role of gender in the general theory and its postulates. Unfortunately, there has not been as much research on the extent to which the general theory and all of its postulates operate in the same manner for males and females, though there is enough in the literature to warrant some rethinking on gender/selfcontrol relationships (Greenberg, 2008). Given the above, future researchers examining the victim–offender overlap may be well served by tapping into factors including and beyond individual-level propensities, such as that found with the general theory of crime. While in the present study low self-control accounted for the overlap in males, it did much less so for females. Consistently, Stewart et al. (2004) examined other correlates beyond self-control, including risky lifestyles, yet the balance of the overlap was not fully explained among females. Clearly, low self-control contributes to the overlap for females to some minor extent (Stewart et al., 2004). However, relying on more socially based explanations, including the social learning perspective, may be particularly useful when it comes to further explaining the overlap between victimization and offending for females. For example, recent research finds that individuals entering into mixed-sex peer groups at younger ages are at a significantly greater risk for problem behaviors (Molloy, Gest, Feinberg, & Osgood, 2014), which may extend to later victimization. Other socially based explanations for the overlap between victimization and offending that could be gendered may include vulnerability to peer influence (Meldrum, Miller, & Flexon, 2013) or gang membership. These possibilities are somewhat speculative, and additional inquiry is clearly needed and would complement the research on the victim– offender overlap well. In addition to the potential factors noted above, the historical position of women in society in contrast to their male counterparts likely deserves attention for those seeking to understand victim–offender concordance across gender. Additional explanations for females, then, might also include examining whether females have had previous abusive relationships (e.g., for predicting cycles of violence for offending and victimization) or are currently in an abusive relationship (Brett, 1993). Work on assortative mating, crime, and


20


interpersonal violence should also be exploited on this point (Tillyer & Wright, 2014), as should research on sexual offending, victimization, and intimate partner violence (IPV; the most important cause of non-fatal, female injury in the United States; Kyriacou et al., 1999). Examining the role of selfesteem among females, which also compliments research on domestic violence, IPV, and assortative mating, may also elucidate this overlap, as it has been found to play a role in offending behavior among female populations with a higher than average rate of being victimized (Burrow, Bynum, Koons, & Morash, 1997). Finally, cultural, racial, and strain-related processes may also play a role for women, in particular (Richie, 1996, 2001). Undeniably, this area is ripe for further inquiry and theoretical refinement. In sum, we believe we have made a valuable contribution to the extant literature in this area by culling information from data on victimization and offending—data that are not often readily available (Jennings et al., 2012). Although the victim–offender overlap has been acknowledged by criminologists for some time, the explanations behind this phenomenon are lacking. The present research represents an effort toward filling some voids, both in understanding the phenomenon and in testing Gottfredson and Hirschi’s (1990) proposition that those with lower self-control are susceptible to both offending and victimization. In addition, the lack of attention to gender differences across research examining the victim–offender overlap makes our study novel. As such, the current study represents an important contribution to the literature for unpacking the phenomenon of victim–offenders, as well as guiding future research concerned with the generality of Gottfredson and Hirschi’s (1990) focus on self-control as the preeminent explanation for offending and victimization.

Appendix Correlation Matrix (N = 475). Variable 1 2 3 4 5 6 7

Age Male Hispanic School attachment Low self-control Victimization Offending

1 .12** −.13** .02 −.18*** −.08 −.04

2

−.01 .00 .03 .00 .12**

3

−.02 .06 −.11* −.08

4

−.19*** −.12* −.10*

*p < .05. **p < .01. ***p < .001 (two-tailed).


5

6

.20*** .23***

.22***

21

Flexon et al. Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes 1. The first week of the semester is an ideal time to recruit participants, given that relatively few individuals are absent the first day of class. 2. Given the strong likelihood that some participants would be present in multiple classes where recruitment was taking place, we asked individuals who had already been recruited in prior classes to simply hold on to the survey being passed out to them and then to turn it in with everyone else without filling it out again. In this manner, we were able to identify the number of individuals who had already participated. 3. We did not include all 24 items found in the original Grasmick, Tittle, Bursik, and Arneklev (1993) scale to keep the survey at a reasonable length to encourage participation and to minimize respondent fatigue. As the data demonstrate, the selected 8 items correlate with several other measured variables as expected, particularly victimization and offending. Indeed, its correlation with offending and victimization in the current study parallels the mean effect size of correlations between low self-control and victimization and offending revealed in metaanalytic work (see de Ridder, Lensvelt-Mulders, Finkenauer, Stok, & Baumeister, 2012; Pratt & Cullen, 2000; Pratt, Turanovic, Fox, & Wright, 2014). Furthermore, we found that subsets of the items produced results that were substantively identical to those that emerged using the 8-item measure, and preliminary factor analysis indicated a single factor emerged for the 8 items, with an eigenvalue of 2.32. The eigenvalue for the second factor that emerged was only 0.50. 4. Specifically, we treated the ordered response options for the victimization and offending items as counts. We then created a summed count of offenses and victimization experiences, which correlated at r = .32, and used negative binomial regression where offending was treated as the dependent variable and victimization treated as the independent variable. In the sample of males, the coefficient for the effect of victimization on offending was reduced by 28% when low selfcontrol was added to the model. However, in the sample of females, the coefficient for the effect of victimization on offending was only reduced by 1% when low self-control was added to the model. Similar differences emerged when we created variety indices for victimization and offending ranging from 0 to 6 (r = .35). Again in negative binomial models, among males, inserting low selfcontrol into the model reduced the coefficient for the effect of victimization on offending by 33%, but the coefficient was only reduced by 7% among females.


22

Journal of Interpersonal Violence Thus, regardless of the scale on which offending and victimization were measured, self-control consistently accounted for a substantively greater portion of the victim–offender overlap among males.

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Flexon et al. Author Biographies

Jamie L. Flexon is an associate professor in the Department of Criminal Justice at Florida International University in Miami, Florida. Her research interests involve juvenile violence and delinquency, minorities and crime, and evaluating criminal justice policy. Her recent work appears in journals such as Crime and Delinquency, Journal of Quantitative Criminology, and American Journal of Criminal Justice, among others. Ryan C. Meldrum is an assistant professor in the Department of Criminal Justice at Florida International University in Miami, Florida. His current research focuses on tests of criminological theory, with a particular emphasis on the interrelationships between self-control, peers, and involvement in delinquency. His recent publications have appeared in journals such as Criminology, Journal of Quantitative Criminology, Criminal Justice and Behavior, and Preventive Medicine, among others. Alex R. Piquero is the Ashbel Smith professor of criminology at the University of Texas at Dallas and adjunct professor at the Key Centre for Ethics, Law, Justice, and Governance, Griffith University Australia. His research interests include criminal careers, criminological theory, and quantitative research methods. He has received several awards for his research, teaching, and service and is a fellow of both the American Society of Criminology and the Academy of Criminal Justice Sciences.


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