Antisocial cognition and crime continuity: Cognitive mediation of the past crime-future crime relationship

Journal of Criminal Justice 41 (2013) 135–140

Contents lists available at SciVerse ScienceDirect

Journal of Criminal Justice

Antisocial cognition and crime continuity: Cognitive mediation of the past crime-future crime relationship☆ Glenn D. Walters a,⁎, Matt DeLisi b a b

Kutztown University, United States Iowa State University, United States

a r t i c l e

i n f o

Available online 9 January 2013

a b s t r a c t Objectives: The purpose of this study was to assess whether antisocial cognition is capable of mediating the well-documented relationship between past and future criminality. Methods: Data for this study came from 812 members of the four-wave National Longitudinal Study of Adolescent Health (Add Health). Antisocial cognition was measured with nine self-report items reflecting a thrill-seeking, manipulative, callous, deceptive, and rule-breaking attitude. The predictor variable (delinquency), outcome variable (crime), and four observed confounding covariates (low self-control, delinquent peers, maternal attachment, and intelligence) were also measured via self-report. Results: Causal mediation analysis revealed that antisocial cognition, assessed during wave 3 of the Add Health study, partially mediated the relationship between delinquency at wave 2 and criminality at wave 4. This mediational effect was moderately robust to potential pre-treatment confounds from constructs central to four major criminological theories (low self-control, delinquent peers, maternal attachment, and intelligence) and to unobserved confounds from three demographic variables (age, gender, and race). Conclusions: These results suggest that antisocial cognition is both a cause and effect of antisocial behavior. Consequently, antisocial cognition is not only an important dynamic risk/needs factor, but should also be addressed in programs designed to ameliorate current criminality and prevent future antisocial behavior. © 2012 Elsevier Ltd. All rights reserved.

Introduction Researchers examining risk and needs factors in criminal justice samples have identified four factors that appear to do the best job of predicting future recidivism and criminal risk: antisocial history, antisocial personality processes, antisocial peer associations, and antisocial cognition. Andrews, Bonta, and Wormith (2006) refer to these four factors as the “big four,” and elaborate on how they figure prominently in criminal justice prediction, policy, and research. These are highly practical constructs but their theoretical underpinnings

☆ This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 ([email protected]). No direct support was received from grant P01-HD31921 for this analysis. The authors acknowledge that the original collector of the data, ICPSR, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses. ⁎ Corresponding author at: Department of Criminal Justice, Kutztown University, Kutztown, Pennsylvania 19530-0730, United States. E-mail address: [email protected] (G.D. Walters). 0047-2352/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jcrimjus.2012.12.004

are less well understood. Predicting future crime from past crime, also known as crime continuity is a well-established criminological fact (Gendreau, Little, & Goggin, 1996), yet this powerful relationship defies clear theoretical explanation (Barnes & Boutwell, 2012; Nagin & Paternoster, 2000). Antisocial cognition is another important predictor of future criminality but it too has yet to be satisfactorily placed within a coherent theoretical or nomological net (Andrews & Bonta, 2003). Perhaps by investigating the relationship between these two pragmatic constructs it may be possible to build the theoretical framework that both currently lack. Criminal history is a well-known and heavily studied construct in the criminology and criminal justice fields (DeLisi, 2005; Walters, 2012). Less is known, however, about antisocial cognition. In the risk literature antisocial cognition refers to attitudes, beliefs, and thoughts that support crime (Andrews et al., 2006). A callous, risk-taking, selfindulgent, and rule-violating attitude is part of what is commonly referred to as antisocial cognition. Meta-analyses indicate that cognitive variables predict future criminality as well as if not better than such traditional criminological variables as family structure and social class (Andrews & Bonta, 2003; Jones, Miller, & Lynam, 2011). Given the fact that cognitive factors often exert their effect by mediating relationships between other variables (Bandura, 1986), one possibility is that antisocial cognition mediates the well-documented relationship between past criminality and future criminality. By mediating

136

G.D. Walters, M. DeLisi / Journal of Criminal Justice 41 (2013) 135–140

the past crime-future crime relationship, antisocial cognition may not only help clarify crime continuity but may also provide information useful in criminal justice assessment and intervention. Recently, Walters (in press) examined two cognitive variables as possible mediators of the past crime-future crime relationship. In the first of two studies, Walters evaluated the role of general criminal thinking as a mediator of crime continuity in a large sample of male federal prison inmates. The results revealed that general criminal thinking partially mediated the relationship between past and future criminal involvement and was moderately robust in the face of potential covariate confounders like age, race, and education. In the second study, Walters tested the mediating effect of low self-efficacy for avoiding future police contact on crime continuity in a large sample of males and females from the 1997 National Longitudinal Survey of Youth. The results revealed the presence of a mediating effect that was robust to potential confounding from gender, social disadvantage (whether or not participant had been enrolled in Head Start as a youngster), and family structure (whether the father was present in the home at the time of initial interview). Although both studies showed evidence of cognitive mediation, important theoretical variables like low self-control and delinquent peers were not included in the analyses. The present study tested whether antisocial cognition mediated the relationship between prior delinquency and future criminality after controlling for important theoretical variables. Antisocial cognition was measured with nine self-report items from wave 3 of the Add Health study. These items assessed a thrill-seeking, manipulative, callous, deceptive, and rule-breaking attitude. The robustness of the mediating effect was tested against four pre-treatment confounding covariates derived from four major theories of criminology: low self-control from Gottfredson and Hirschi's (1990) general theory of crime, delinquent peers from Sutherland's differential association theory (Sutherland & Cressey, 1978), maternal attachment from Sampson and Laub's (1993) age-graded life-course theory, and intelligence from Wilson and Herrnstein's (1985) constitutional theory. In addition, all of the variables were measured with respondent selfreport, making it less likely that the results could be accounted for by differences in method variance. Method Participants Participants for this study were 812 members of the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative sample of youth interviewed in four waves (Udry, 2003). All members of the Add Health sample who had complete data on the variables of interest were included in the present study. Wave 1 data were collected in 1994–1995 when participants were in grades 7–12 and between the ages of 11 and 19. Wave 2 took place in 1996 when most participants were between the ages of 13 and 20. Data for Wave 3 were gathered between 2001 and 2002 when most participants were between the ages of 18 and 26. Wave 4 data were collected in 2007–2008 when most participants were between the ages of 24 and 32. The gender breakdown for the current sample was 506 males (62.3%) and 306 females (37.7%). Ethnically, 509 of the participants were white (62.7%), 163 were black (20.1%), 72 were Hispanic (8.9%), and 68 were Asian or Native American (8.4%). Measures Independent (predictor) variable The independent variable in this study was a self-report of criminal and delinquent involvement assessed at Wave 2, when participants were between the ages of 13 and 19. During the Wave 2 interview, participants were asked to recollect how often they engaged in the

following 14 criminal and delinquent acts within the past year: paint graffiti, damage property, lie to parents about whereabouts, shoplift, runaway from home, steal a car, steal something worth > $50, steal something worth b $50, burglarize a building, use or threaten to use a weapon, sell drugs, loud/rowdy in a public place, participate in a group fight, and initiated into a gang. Each criminal/delinquent act was rated on a four point scale: 0 = never, 1 = one or two times, 2 = three or four times, 3 = five or more times. The 14 individual scores were summed to create a total score (Crime-W2) with a potential range of 0 to 42. Dependent (outcome) variable The dependent variable in this study was a self-report of criminal involvement assessed at Wave 4, when participants were between the ages of 24 and 32. During the Wave 4 interview, participants were asked to recollect how often they engaged in the following 12 criminal acts within the past year: damage property, steal something worth > $50, steal something worth b $50, break into a building to steal something, use weapon to get something from someone, sell drugs, participate in a group fight, buy/sell stolen property, use credit card without owner's consent, deliberately write a bad check, involved in a serious physical fight, and hurt someone so bad they required care from a doctor or nurse. As with the dependent variable, participation in each crime was rated on a four-point scale: 0 = never, 1 = one or two times, 2 = three or four times, 3 = five or more times. The 12 individual scores were summed to yield a scale (Crime-W4) with a possible range of 0 to 36. Mediating variable An antisocial cognition scale was created by combining the following nine self-report items from Wave 3 of the Add Health sample: (1) I often try new things just for fun or thrills, even if most people think they are a waste of time; (2) When nothing new is happening, I usually start looking for something exciting; (3) I can usually get people to believe me, even when what I'm saying isn't quite true; (4) I often do things based on how I feel at the moment; (5) I sometimes get so excited that I lose control of myself; (6) I like it when people can do whatever they want, without strict rules and regulations; (7) I often follow my instincts, without thinking through all the details; (8) I can do a good job of stretching the truth when I'm talking to people; (9) I change my interests a lot, because my attention often shifts to something else. Each item was rated on a five-point scale—not true (1 point), a little true (2 points), somewhat true (3 points), pretty true (4 points), very true (5 points)—to produce a total score that ranged from 9 to 45. The internal consistency of the 9-item antisocial cognition scale was reasonably good (α = .88) and each of the items had a corrected item-total correlation of .60 or higher. Confounding covariates Four constructs from four different criminological theories served as pre-treatment (pre-independent variable) confounding covariates in the current study. Low self-control, a construct from Gottfredson and Hirschi's (1990) general theory of crime, was measured with five self-report items from Wave 1 of the Add Health study scored on either a four- or five-point scale: 0 = never, 1 = just a few times, 2 = about once a week, 3 = almost every day, 4 = every day. The five items that formed the low self-control scale (Perrone, Sullivan, Pratt, & Margaryan, 2004) included: trouble keeping my mind focused (0–3), trouble getting homework done (0–4), trouble paying attention in school (0–4), trouble getting along with teachers (0–4), and you feel like you are doing everything just about right (0–4: reverse scored). Scores on these five items were combined to create a low self-control scale with a range of 0 to 19. Delinquent peers, a construct central to Sutherland's differential association theory of crime (Sutherland & Cressey, 1978), was measured in the current study during Wave 1 of the Add Health study

G.D. Walters, M. DeLisi / Journal of Criminal Justice 41 (2013) 135–140

with the following three items: how many of your three closest friends smoke (cigarettes), how many of your three closest friends drink more than once a month, and how many of your three closest friends smoke pot more than once a month. Each item was scored on a four point scale (0 = no friends, 1 = one friend, 2 = two friends, 3 = three friends). Summing participant responses to the three items yields a ten-point scale (0–9) referred to as the Delinquent Peers scale (Beaver & Wright, 2005). Maternal attachment was a third pre-treatment confounding covariate included in the current study. This construct comes from Sampson and Laub's (1993) age-graded life-course theory. In the first wave of the Add Health study, maternal attachment was measured with two five-point items: how close are you to your mom and how much does your mom care about you? The five options (1 = not at all, 2 = very little, 3 = somewhat, 4 = quite a bit, 5 = very much) were reverse scored and summed to form a maternal attachment scale (Beaver, 2008) which ranged from 2 to 10. The reason why these items were reverse scored was to code all variables in the same direction so that high scores on a measure were associated with an increased level of criminality. The “not at all” option was selected for a small group of participants (n = 38, 4.7%) who indicated that they never knew their biological mothers. Intelligence, a construct central to Wilson and Herrnstein's (1985) constitutional theory, was assessed with a single self-report item: how would you rate your intelligence. The six points on the intelligence rating scale were: 1 = moderately below average, 2 = slightly below average, 3 = about average, 4 = slightly above average, 5 = moderately above average, 6 = extremely above average. This self-report measure of intelligence was used previously in a study by Beaver and Wright (2005) on biosocial development and delinquent involvement. The measure correlated .29 (p b .001) with a participant's raw score on the Add Health Picture Vocabulary Test, also administered during Wave 1. As was done with the maternal attachment scale, the self-report scale of intelligence was reverse scored so that higher scores on this measure would correlate positively with criminality. Unobserved covariates Variables classified as unobserved covariates by virtue of the fact that they were not included in this study as predictor, outcome, mediator, or observed confounding variables were used to test the robustness of the current results. These unobserved covariates were three demographic variables commonly used in criminological research: age, gender, and race. Participants included in the current study were between 11 and 19 years of age (M = 14.61, SD = 1.63) during the first wave of the Add Health study. Gender was coded as male and female and race was coded as white and nonwhite. Procedure The Add Health study was administered in four waves. All data came from an in-home interview where either the participant or interviewer entered participant responses on a laptop computer. The four pre-treatment covariate confounders (low self-control, delinquent peers, maternal attachment, and intelligence) were collected between 1994 and 1995 as part of Wave 1. A year or two later, in 1996, data for the independent variable (delinquency involvement, Wave 2; Crime-W2) were gathered. The mediator variable (antisocial cognition)

was constructed from nine self-report items measuring a thrill-seeking, manipulative, callous, deceptive, and rule-breaking attitude administered during Wave 3 of the Add Health study (2001–2002). The dependent or outcome measure (criminal involvement, Wave 4: Crime-W4) was collected between 2007 and 2008. MPlus 5.0 (Muthén & Muthén, 1998–2007) was used to perform a structural equation modeling (SEM) path analysis of the Crime-W2 → Antisocial Cognition → Crime-W4 relationship. In this analysis, Crime-W2 served as the independent (exogenous) variable, Antisocial Cognition served as the mediating (endogenous) variable, and Crime-W4 served as the dependent (endogenous) variable. Owing to the fact that there was no temporal overlap between the three variables (i.e., Crime-W2 preceded Antisocial Cognition at Wave 3, which preceded Crime-W4) the current study was clearly prospective in nature. Causal mediation analysis was conducted with algorithms derived and validated by Imai and colleagues (Imai, Keele, & Tingley, 2010). The (continuous) mediator and outcome models were both fit with linear least squares regression and a bootstrapped nonparametric mediational analysis was performed (b= 1000). Four confounding covariates (low self-control, peer delinquency, maternal attachment, and intelligence) were included in a sensitivity analysis of the cognitively mediated past crime-future crime relationship. Sensitivity analysis is designed to test the sequential ignorability assumption upon which causal mediation analysis rests. Sequential ignorability is based on two inter-related assumptions: (1) treatment assignment (independent variable) is ignorable or statistically independent of all potential values of the outcome and mediator variables given the presence of observed covariate confounders; and (2) the mediator is ignorable or statistically independent of all potential outcomes relative to the observed pretreatment and treatment covariates (Imai et al., 2010). Results Path analysis The standardized beta weights from the SEM path analysis conducted on the Crime-W2 → Antisocial Cognition → Crime-W4 relationship are listed in Fig. 1. These findings indicate that there was both a direct (Crime-W2 → Crime-W4) and indirect or mediated (Crime-W2 → Antisocial Cognition → Crime-W4) effect of past delinquent behavior on future criminal behavior. Despite the fact a prospective design was used, it was reasoned that a causal mediation analysis was required before any causal conclusions could be offered. Causal mediation analysis Using algorithms provided in an R language package created by Imai et al. (2010), a more stringent test of the mediation hypothesis was performed using Imai et al.'s bootstrapping procedure. In this analysis, Crime-W2 served as the independent (predictor) variable, antisocial cognition served as the mediating variable, Crime-W4 served as the outcome (dependent) variable, and low self-control, delinquent peers, maternal attachment, and intelligence served as observed covariate confounders. Descriptive statistics for and correlations between these eight variables are listed in Table 1. Causal mediation analysis disclosed a partial mediating effect for antisocial cognition that accounted for 18% of the total effect (see Table 2).1

.18*

Crime-W2

137

.20*

Crime-W4 .21*

AntiCog-W3

Fig. 1. Path analysis of the mediating effect of antisocial cognition on the past crime-future crime relationship. Note. Standardized beta coefficients are reported. *p b .001.

138

G.D. Walters, M. DeLisi / Journal of Criminal Justice 41 (2013) 135–140

Table 1 Descriptive Statistics for and Correlations between the Covariates, Independent Variable, Mediator Variable, and Outcome Variable Variable

M

SD

Range

Peers

Maternal

Intelligence

Crime-W2

AntiCog

Crime-W4

Low Self-Control Delinquent Peers Maternal Attachment Intelligence Crime-Wave 2 Antisocial Cognition Crime-Wave 4

6.82 2.48 3.00 3.09 3.06 22.13 0.37

2.68 2.60 1.96 1.07 4.05 8.46 1.10

1–19 0–9 2–10 1–6 0–33 9–45 0–9

.21⁎⁎⁎

.12⁎⁎⁎ .15⁎⁎⁎

.11⁎⁎ .17⁎⁎⁎ .09⁎⁎

.31⁎⁎⁎ .23⁎⁎⁎ .12⁎⁎⁎ .11⁎⁎

.21⁎⁎⁎ .05 −.01 .02 .20⁎⁎⁎

.17⁎⁎⁎ .12⁎⁎⁎ .07 −.01 .22⁎⁎⁎ .25⁎⁎⁎

Note. Maternal Attachment = self-reported attachment to biological mother (with higher scores indicating lower maternal attachment); Intelligence = self-reported intelligence (with higher scores indicating lower estimates of intelligence); Crime-Wave 2 = scope and frequency of self-reported antisocial behavior within one year of the Wave 2 interview; Antisocial Cognition = sum of 9 antisocial cognition items administered during Wave 3; Crime-Wave 4 = scope and frequency of self-reported antisocial behavior within one year of Wave 4 interview; N = 812. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

A sensitivity analysis of the mediation and outcome models, in which low self-control, delinquent peers, maternal attachment, and intelligence served as observed pretreatment covariate confounders, revealed a rho (ρ) at which mediation = 0 of .20 (see Fig. 2). Arranging the coefficients of determination (R 2) for the mediator and outcome models along the axes of a graph it was determined that an unobserved confounding covariate or a group of unobserved confounding covariates would need to account for approximately 18% of the variance in the mediator and 18% of the variance in the outcome to reduce the mediation effect to zero (see Fig. 3). To illustrate the robustness of the current mediation effect to unobserved covariate confounds, the mediator and outcome were regressed onto three unobserved covariate confounders (i.e., age, gender, and race). Regressing the mediator (Antisocial Cognition) onto the three unobserved covariates produced an R 2 of .092, whereas regressing the outcome (Crime-W4) onto the three unobserved covariate confounders produced an R 2 of .035. To reduce the mediating effect to zero in a situation where 9.2% of the variance in the mediator is explained by the unobserved covariate confounders would require that the confounders explain over 30% of the variance in outcome, when in actuality they only accounted for 3.5% of the variance in the outcome measure.

variables are controlled (low self-control, delinquent peers, maternal attachment, and intelligence) and above and beyond the contributions of age, gender, and race. It would appear that one of the ways antisocial cognition exerts its influence over crime-related constructs is by mediating important crime relationships. In the current study it helped shape future criminality by serving as a link between past and future criminality. There are three criteria of a causal relationship: (1) correlation, (2) direction, and (3) absence of viable alternative explanations. Correlation was clearly established to the extent that all three variables in the putative causal relationship (Crime-W2, Antisocial Cognition, and Crime-W4) correlated significantly with each other. The direction criterion was also satisfied in that delinquency at wave 2 preceded antisocial cognition at wave 3 and antisocial cognition at wave 3 preceded crime at wave 4. It is the third criterion that is the most difficult to establish. In the first study reported by Walters (in press), criminal thinking mediated the past crime-future crime relationship when age, race, and education were controlled and in the second Walters (in press) study weak efficacy expectancies for avoiding future police contact mediated the past crime-future crime relationship when

ACME(ρ)

Effect Type

Point Estimate

95% CI

Mediation Effect (Crime-W2 →AntiCog →Crime-W4) Direct Effect (Crime-W2 → Crime-W4) Total Effect Proportion of Total Effect via Mediation

0.0085 0.0397 0.0482 0.1755

0.0041–0.0138 0.0214–0.0576 0.0295–0.0666 0.1274–0.2875

Note. Crime-W2 = scope and frequency of self-reported antisocial behavior within one year of the Wave 2 interview; AntiCog = sum of 9 antisocial cognition items; Crime-Wave 4 = scope and frequency of self-reported antisocial behavior within one year of Wave 4 interview; Point Estimate = estimate of the size of the effect; 95% CI = 95% confidence interval of the point estimate; N = 812.

0.05 0.00 -0.05 -0.10

Table 2 Results of Causal Mediation Analysis of Antisocial Cognition as a Mediator of the Past Crime-Future Crime Relationship

Average Mediation Effect: δ(t)

The results of the current study, along with findings from the previously mentioned Walters (in press) investigation, support the conclusion that cognitive factors are partially responsible for crime continuity. In both studies, two of the “big four” predictors of criminal recidivism created a system of mediation that effectively predicted future official (Walters, in press: first study) and self-reported (Walters, in press: second study; current investigation) criminality. The current study adds to our knowledge on cognitive mediation of crime continuity by showing that antisocial cognition partially mediates the past crime-future crime relationship when important criminological

0.10

Discussion

-1.0

-0.5

0.0

0.5

1.0

Sensitivity Parameter: ρ Fig. 2. Sensitivity analysis of the continuous Crime-W4 outcome and continuous Antisocial Cognition mediator, with Crime-W2 as the independent variable. The dashed line represented the estimated mediation effect, the solid line represents the estimated average mediation effect at different levels of ρ, and the gray region represents the 95% confidence interval for estimated average mediation effect at different levels of ρ.

G.D. Walters, M. DeLisi / Journal of Criminal Justice 41 (2013) 135–140 ~2 ~2

0.5

-0.

0.4

-0.0

04

3

0.2 0.3

~2

RY

0.6

0.7

0.8

0.9

1.0

ACME(RM,RY), sgn(λ2λ3)=1

-0.02

0.1

-0.01

0.0

0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

~2 RM

Fig. 3. Sensitivity analysis of the continuous Crime-W4 outcome and continuous Antisocial Cognition mediator, with Crime-W2 as the independent variable. Contour lines represent the estimated average meditational effect at different levels of an unobserved confounder. The “0” line indicates how strong the unobserved confounder must be to completely eliminate the mediation effect.

gender, social disadvantage, and family structure were controlled. In the current study, antisocial cognition mediated the past crime-future crime relationship when low self-control, delinquent peers, maternal attachment, and intelligence were controlled and after age, gender, and race were correlated with the outcome and mediator variables. Hence, while all viable alternative hypotheses for cognitive mediation have not been ruled out, four core variables from criminological theory and three key demographic predictors of criminality were unable to eliminate the mediating effect identified in the current study. In comparing the effect sizes obtained by the four core variables from criminological theory with the effect size obtained by antisocial cognition it is evident that antisocial cognition correlated better with crime at wave 4 (r = .25) than any of the four variables from criminological theory (r = − .1–.17). In this comparison, however, antisocial cognition had a distinct time advantage in that while it preceded the outcome by six years, the four criminological variables, having been administered during the first wave of the Add Health study, preceded the outcome by 13 years. Antisocial cognition also performed well relative to the four criminological variables when comparisons were made with each variable's correlation with delinquency measured at wave 2. Here, the effect size for antisocial cognition (r= .20) fell short of two of the criminological effect sizes (r= .22–.31) and above the other two (r= .11–.12), despite the disadvantage of a three-fold longer span of time between it and the delinquency variable in wave 2. What this seems to indicate is that not only is antisocial cognition capable of mediating the past crime-future crime relationship, but that it predicts and postdicts crime on par with self-report measures of popular criminological constructs. The primary theoretical implication of the current results is that it is now possible to better understand crime continuity by examining potential mediating variables and their effects on the past crimefuture crime relationship. In the current study, antisocial cognition partially mediated the relationship between past criminality and future criminality. Given that antisocial cognition is subject to change and antisocial history is not, it makes sense that early criminality (Crime-W2) helped shape antisocial cognition at wave 3 which then

139

increased the individual's chances of criminal involvement at wave 4. With respect to statistical mediation, just as the outcome is affected by the mediator, so too can be mediator be affected by the predictor (Baron & Kenny, 1986). This could be what is occurring with cognitive mediation of crime continuity; the more malleable antisocial cognition being shaped by juvenile antisocial behavior and then partially contributing to continued criminal involvement during adulthood. Because the mediating effect is only partial, there may be other variables that play a role in mediating crime continuity. Two likely candidates are the remaining members of the “big four” risk factors: antisocial peer associations and antisocial personality processes. There are practical implications to these results as well, both in terms of assessment and intervention. Given the significant role cognitive factors appear to play in mediating the past crime-future crime relationship it would seem important that such factors be assessed in both juvenile and adult offenders. There are currently several measures of criminal thinking available, to include the Criminal Sentiments Scale-Modified (CSS-M: Simourd, 1997), the Measures of Criminal Attitudes and Associates (MCAA; Mills, Kroner, & Forth, 2002), the Texas Christian University Criminal Thinking Scales (CTS: Knight et al., 2006), and the Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 1995). Routinely evaluating adult and juvenile offenders as they enter probation or a confinement facility could go a long way toward improving the supervision and management of offenders. Intervention protocols could likewise be improved with the addition of programs designed to address and alter antisocial cognition. Meta-analyses clearly demonstrate that the most effective interventions are cognitive-behavioral in nature to where antisocial attitudes, beliefs, and thoughts are specifically targeted for change (Lipsey, 2009). The items used to measure antisocial cognition could be considered a serious limitation of this study that may have biased the results in favor of the mediation hypothesis. As the reader may recall, the antisocial cognition measure was composed of nine self-report items that seemed, from their content, to be measuring an attitude of callous disregard for the rights of others and one's own safety. In addition, the items displayed a reasonable degree of internal consistency and all nine items achieved corrected item-total correlations of .60 or higher. It could nonetheless be argued that these nine items did a less than satisfactory job of assessing antisocial cognition and that they achieved their effect primarily by way of shared method variance with the predictor and outcomes, both of which were also measured with self-report data. If this is true, however, then the effect would also extend to the measured covariate confounders because they were also based exclusively on respondent self-report. This, in turn, would tend to erase any advantage the antisocial cognition measure enjoyed with respect to shared method variance. It could also be argued that the four confounding covariates were less than adequately measured in this study. Though these scales were included in previous research (Beaver, 2008; Beaver & Wright, 2005; Perrone et al., 2004), they were measures of convenience and may not have mapped very well onto the constructs they were designed to represent. Low self-control, for instance, which Gottfredson and Hirschi (1990) state is best measured with behavioral indicators, was defined by self-reported problems with attention, concentration, homework, and getting along with teachers. The delinquent peers measure, on the other hand, was defined by the number of close friends who smoked, drank, or used marijuana. More serious forms of peer delinquency were missing from this definition. Maternal attachment was operationally defined in terms of how close respondents felt to their mothers and how much they thought their mothers cared about them. Self-reported intelligence provided what may have been the weakest operationalization of a construct in this study in that that it consisted of a self-appraisal of one's intellectual ability. It should be noted, however, that the self-report measure of intelligence correlated moderately well with performance on a picture vocabulary test and

140

G.D. Walters, M. DeLisi / Journal of Criminal Justice 41 (2013) 135–140

that causal mediation analysis results did not change when the picture vocabulary score replaced the self-report intelligence measure. Moreover, all four covariate confounders correlated significantly with at least one of the two crime measures (Crime-W2, Crime-W4) and two of the covariates (low self-control, peer delinquency) correlated significantly with both crime measures. Despite this, their combined effect could not eliminate the mediation effect of antisocial cognition on crime continuity. According to the results of this study, a cognitive variable labeled antisocial cognition partially mediated a moderately strong correlation between past delinquency and future criminality. This mediating effect surfaced even after variables from four popular criminological theories were controlled and was moderately robust in the face of unobserved pre-treatment covariate confounders. This study adds to the embryonic literature on cognitive mediation of crime continuity (Walters, 2012, in press) and suggests that mediation may be helpful in integrating the many diverse single-variable theories found in the criminology and criminal justice fields. What separates the current model from previous attempts to integrate criminological theory (Messner, Krohn, & Liska, 1989; Thornberry, 1987; Tittle, 1995) is the emphasis the current model places on mediation as a method by which integration can be achieved. Integrating variables from different theories of crime via mediation could help resolve the theoretical fragmentation that seems to have enveloped the criminological and criminal justice fields (see Ericson & Carriere, 1994) and lead to a more powerful and comprehensive theory of crime. Before this can occur, however, putative mediating variables must be identified and their effects thoroughly studied. Note 1. Because the Picture Vocabulary score correlated better with antisocial cognition (.08 vs. .01) and crime at Wave 4 (.04 vs. − .01) and worse with crime at Wave 2 (.02 vs. .11) than the self-report intelligence measure, and maternal support correlated better with crime at Wave 2 (.18 vs. .12) and antisocial cognition (.18 vs. .16) and worse with crime at Wave 4 (.04 vs. .07) when the 38 participants who did not know their mothers were removed from the sample, a second causal mediation analysis was conducted with the Picture Vocabulary score replacing self-reported intelligence and the sample restricted to the 774 participants who indicated that they knew their mothers. The results revealed a slight increase in the mediating effect of antisocial cognition, .0094 (.0049–.0145), on the past crime-future crime relationship that accounted for 19.95% of the total effect.

References Andrews, D. A., & Bonta, J. (2003). The psychology of criminal conduct. Cincinnati, OH: Anderson. Andrews, D. A., Bonta, J., & Wormith, J. S. (2006). The recent past and near future of risk and/or need assessment. Crime & Delinquency, 52, 7–27. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.

Barnes, J. C., & Boutwell, B. B. (2012). On the relationship of past to future involvement in crime and delinquency: A behavior genetic analysis. Journal of Criminal Justice, 40, 94–102. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Beaver, K. M. (2008). Nonshared environmental influences on adolescent delinquent involvement and adult criminal behavior. Criminology, 46, 341–369. Beaver, K. M., & Wright, J. P. (2005). Biosocial development and delinquent involvement. Youth Violence and Juvenile Justice, 3, 168–192. DeLisi, M. (2005). Career criminals in society. Thousand Oaks, CA: Sage. Ericson, R., & Carriere, K. (1994). The fragmentation of criminology. Pp. 89–109 in The Futures of Criminology, edited by D. Nelken (Ed). London: Sage. Gendreau, P., Little, T., & Goggin, C. (1996). A meta-analysis of the predictors of adult offender recidivism: What works! Criminology, 34, 575–607. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford, CA: Stanford University Press. Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334. Jones, S. E., Miller, J. D., & Lynam, D. R. (2011). Personality, antisocial behavior, and aggression: A meta-analytic review. Journal of Criminal Justice, 39, 329–337. Knight, K., Garner, B. R., Simpson, D. D., Morey, J. T., & Flynn, P. M. (2006). An assessment for criminal thinking. Crime & Delinquency, 52, 159–177. Lipsey, M. W. (2009). The primary factors that characterize effective interventions with juvenile offenders: A meta-analytic overview. Victims and Offenders, 4, 124–147. Messner, S. F., Krohn, M. D., & Liska, A. E. (Eds.). (1989). Theoretical Integration in the Study of Deviance and Crime: Problems and Prospects. Albany, NY: State University of New York Press. Mills, J. F., Kroner, D. G., & Forth, A. E. (2002). Measures of criminal attitudes and associates (MCAA): Development, factor structure, reliability, and validity. Assessment, 9, 240–253. Muthén, B., & Muthén, L. (1998–2007). Mplus User's Guide (5th ed.). Los Angeles, CA: Muthén and Muthén. Nagin, D. S., & Paternoster, R. (2000). Population heterogeneity and state dependence: State of the evidence and directions for future research. Journal of Quantitative Criminology, 16, 117–144. Perrone, D., Sullivan, C. J., Pratt, T. C., & Margaryan, S. (2004). Parental efficacy, self-control, and delinquency: A test of a general theory of crime on a nationally representative sample of youth. International Journal of Offender Therapy and Comparative Criminology, 48, 298–312. Sampson, R. J., & Laub, J. H. (1993). Crime in the making. Cambridge, MA: Harvard University Press. Simourd, D. J. (1997). The Criminal Sentiments Scale 2Modified and Pride in Delinquency Scale: Psychometric properties and construct validity of two measures of criminal attitudes. Criminal Justice and Behavior, 24, 52–70. Sutherland, E. H., & Cressey, D. (1978). Criminology (10 ed.). Philadelphia: Lippincott. Thornberry, T. P. (1987). Toward an interactional theory of delinquency. Criminology, 36, 863–891. Tittle, C. R. (1995). Control balance: Toward a general theory of deviance. Boulder, CO: Westview. Udry, J. R. (2003). The National Longitudinal Study of Adolescent Health (Add Health). Chapel Hill, NC: Carolina Population Center, University of North Carolina. Walters, G. D. (1995). The Psychological Inventory of Criminal Thinking Styles: Part I. Reliability and preliminary validity. Criminal Justice and Behavior, 22, 307–325. Walters, G. D. (2012). Crime in a psychological context: From career criminals to criminal careers. Thousand Oaks, CA: Sage. Walters, G. D. (in press). Cognitive mediation of crime continuity: A causal mediation analysis of the past crime-future crime relationship. Crime & Delinquency. Wilson, J. Q., & Herrnstein, R. J. (1985). Crime and human nature. New York: Simon and Schuster.