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Personality and gang embeddedness
Personality and Individual Differences 51 (2011) 748–753
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Personality and gang embeddedness Vincent Egan ⇑, Matthew Beadman Department of Psychology – Forensic Section, University of Leicester, 106 New Walk, Leicester LE1 7EA, UK
a r t i c l e
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Article history: Received 3 February 2011 Received in revised form 17 June 2011 Accepted 20 June 2011 Available online 20 July 2011 Keywords: Gangs Personality Social learning models Individual differences Resilience Path analysis
a b s t r a c t Gang membership can be transient or persistent, and most persons who participate in such groups at one point in their lives grow out of the lifestyle. The personality dynamics driving gang membership are poorly understood, although the five-factor model of personality has proved an effective way to understanding many types of antisocial behaviour. Using dichotomous self-nomination 152 remand and sentenced participants incarcerated within a general prison in London, UK indicated their gang embeddedness as youths, prior to custody, within prison, and as an intent following release. This enabled behavioural intention to be incorporated into the model as well as historical factors. Constructs derived from a variety of personality measures and constructs were used to predict overall reported gang embeddedness. These predictors reduced to two underlying dimensions: personal resilience and antisocial personality. Path analysis showed the antisocial personality dimension predicted previous convictions and degree of gang embeddedness, whereas resilience did not. The direct and indirect effects of the composite antisocial personality dimension explained 50% of the overall observed variance in gang embeddedness. We suggest that gang membership may reflect normal assortative processes within the members of such groups. Ó 2011 Published by Elsevier Ltd.
1. Introduction Gang membership predicts prison misconduct (DeLisi, Berg, & Hochstetler, 2004), staff assault (Huebner, 2003), seditious and generic criminal activity (Krienert & Fleisher, 2001; Sheldon, 1991), and criminal recidivism (Huebner, 2003). Reducing gang activity reduces antisocial conduct and assists rehabilitation (Vittori, 2007). As elsewhere in the world, prison gangs operate within English prisons (Wood, 2006; Wood & Adler, 2001) and have similarly malignant influences on offender behaviour and possible rehabilitation. Risk factors for gang affiliation are similar to those facilitating delinquency, the same in and out of prison, and occur across individual, family, peer group, school, and community levels (Hill, Howell, Hawkins, & Battin-Pearson, 1999; Howell & Egley, 2005; Scott, 2001; Thornberry, Krohn, Lizotte, Smith, & Tobin, 2003). Though factors predicting gang membership also predict generic antisocial behaviour and criminal offending, research into gang membership tends to focus on social rather than individual factors (Warr, 1996). The current study examines individual differences influencing participation in criminal social groups, noting that many other offenders follow offending trajectories that eschew gangs. Costa and McCrae’s five-factor model of personality (FFM) is a compelling structure for explaining human variation, predicting a wide variety of behaviours (Ozer & Benet-Martínez, 2006). Along-
⇑ Corresponding author. Tel.: +44 116 252 3658. E-mail addresses: [email protected], [email protected] (V. Egan). 0191-8869/$ - see front matter Ó 2011 Published by Elsevier Ltd. doi:10.1016/j.paid.2011.06.021
side social learning, lower intelligence, higher neuroticism (N), lower agreeableness (A), and lower conscientiousness (C) routinely predict antisocial and criminal behaviour (Miller, Lynam, & Leukefeld, 2003; Samuels et al., 2004; Egan, 2011), Reciprocals of these dimensions reinforce individual resilience, moderating the effect of criminogenic environments. Persons lower in N, and higher in IQ, E, O, A, and C better resist antisocial influences, even in dysfunctional settings (Davey, Eaker, & Walters, 2003). There are other personality constructs associated with offending. Impulsivity predicts both overall criminal history and within-custody antisocial activity (Gordon & Egan, 2011; Lynam & Miller, 2004). van der Geest, Blokland, and Bijleveld (2009) found generic risk-taking (similar to low self-control (O’Gorman & Baxter, 2002)) measured using the Adolescent Temperament List related to irresponsible behaviour and drug use. More ambiguously, low (or excessive) self-esteem is correlated with offending (Oser, 2006; Rizzo, 2003). These overlapping antisocial and emotionally unstable constructs are separately associated with A, C, and N (Whiteside & Lynam, 2001). One way to avoid reifying ‘jangle’ constructs in psychology (which happen when ostensibly different measures measure the same underlying constructs; Block, 1995) is to use established measures to structure these other constructs. The FFM integrates a variety of conceptual models (McCrae & John, 1992), indicating where novel concepts are positioned empirically, relative to a familiar structural model. The current study uses the FFM to clarify such relationships in relation to persistent gang embeddedness. Social processes influence deviance through differential association for antisocial peers (Akers & Jensen, 2006). Social
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activity is personally rewarding and self-reinforcing (Winfree, Mays, & Vigil-Backstrom, 1994), so recidivism is higher in persons with negative peers (Gendreau, Goggin, & Little, 1996), and reducing antisocial peers reduces re-conviction (Warr, 1996). Gang membership intensifies delinquency beyond simple antisocial peer association (Esbensen & Huizinga, 1993), perhaps because negative peers disrupt the positive social relationships and prosocial activities that facilitate desistance. Assortative processes also influence criminality; excluded persons socialise with each other, mutually reinforcing their shared outlook, consolidating their interpersonal bonds and deviant attitudes (Granic & Dishion, 2003; Monahan, Steinberg, & Cauffman, 2009). Delinquent values decline with maturation (Anderson, 1999), and forming positive social bonds to legitimate institutions enables individuals to achieve previously unattainable objectives (Coleman, 1988), and something to lose if they remain antisocial (Maruna & Toch, 2005). Fearing such potential losses reduces the chance an individual will continue their criminal lifestyle, whereas those with looser ties to convention find it harder to re-integrate, making recidivism more likely. Decker and Van Winkle (1996) describe the process of gang membership in terms of ‘pushes’ (e.g., needing protection due to social marginalization) and ‘pulls’ (e.g., excitement and increased social status). Huebner, Varano, and Bynum (2007) found gang members excluded from prosocial activities and integration were ‘pushed’ toward offending. If prosocial integration wanes, individuals are ‘pushed’ back towards gang affiliation via marginalisation (Braithwaite, 1989). The power of this ‘pull’ to a deviant network may depend on a person’s degree of embeddedness to gangs and their values. For some, gang affiliation is transitory, whereas for others, gang affiliation affirms underlying values. Given prior attitudes predict future behaviour (Glasman & Albarracín, 2006), asking about potential future gang-related affiliation may strengthen the reliability of gangembeddedness assessment, relative to historical information alone. The current study explored personality constructs underlying gang embeddedness in British prisoners using the FFM to structure a variety of potentially relevant personality constructs into a simpler model. Given the literature on personality and crime, we expected negative feelings (including N, low self esteem, and greater social isolation) and antisocial personality (comprising criminality-related trait dimensions such as low A, low C, low self-control, and greater impulsivity) to form super-ordinate and separate constructs, and that deriving positive feelings from antisocial associates would be positively associated with antisocial rather than emotionally vulnerable personality traits. We expected the main determinant of criminal activity and gang embeddedness to be antisocial personality. 2. Method 2.1. Design The study followed a multivariate correlational design. The predictors comprised social variables (positive reinforcers, commitment to positive peers, commitment to negative peers, negative punishers, social isolation, previous criminal history), and personality (self-esteem, impulsivity, self-control, N, E, O, A and C). Our outcome variable was self-reported gang history. Ethical approval was provided by both the University and the penal establishment research committees involved in the study. 2.2. Participants Of 204 persons approached, 162 adult (over 21 years) male offenders (79.4%) were recruited from a general Category B prison population in London, UK. Category B prisons hold persons who do
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not require maximum security, but would still pose a risk of escape if security were lowered (Prisoner’s Advice Service, 2011). Of the participants, 33% described their ethnicity as White British, 13% White Other, 14% Black Caribbean, 6% Black African, 6% Black Other, 10% Other and 12% Asian, the remainder omitting this information. The mean age was 31.7 years (SD = 8.6 years, range = 21– 60). Wing Officers advised on persons thought unsuitable for participation due to vulnerability or heightened risk (e.g., those in segregation or healthcare wings). Given the frequency of adjustment disorder in new prisoners, recent admissions were also excluded. Non-participants typically expressed suspicion about the confidentiality of the study. 3. Materials 3.1. Gang membership Prior research has used officers or official records to operationalise gang membership (Fong & Buentello, 1991). These official methods underestimate actual rates of prison gang membership (Cooley, 1993). Classification reliability is also threatened by discrepancies between official sources (e.g., wing diaries, security information, staff reports). Esbensen and Weerman (2005) found self nomination able to define gang membership provided confidentiality was given and respected. While self-report data is also subject to difficulties (for example, impression management), optimising confidentiality reduces the personal benefit an individual may derive from such a strategy. We operationalised gang affiliation and membership using a four-question, self-report scale. Participants indicated whether they were members of a gang as youths, immediately before conviction, currently, and if they intended to join or rejoin a gang upon release. Data was coded in two ways; (1) summing the four dichotomous gang questions and (2) creating a weighted sum with more salience given to more recent gang membership. This hierarchy assumed recency in gang membership more salient, as would a declared intention to join a gang in the future. Thus, individuals disclosing gang membership as youths scored one point, those reporting gang membership immediately prior to current custody, two, those reporting current prison gang membership, three, and those reporting an intention to join a gang following release from prison, four. The maximum cumulative weighted score for an individual whose gang embeddedness spanned from youth to intent following release would be 10, producing a dimension reflecting the increasing importance of current and future gang membership. 3.2. Social variables Five social variables were measured: positive reinforcement, punishment, commitment to positive peers, commitment to negative peers, and social isolation (Akers, 1998). For positive reinforcement, the extent participants believed gang membership would lead to desirable outcomes was measured (e.g., ‘‘I would get respect’’). The more positive reinforcers associated with gang membership, the higher the score. This scale was taken from Winfree, Esbensen, and Osgood (1996), and had an internal alpha reliability of 0.84. Negative punishers were measured similarly, using items such as ‘‘I would get hurt’’; this scale having an internal reliability of 0.83 (Winfree et al., 1996). Items measuring commitment to positive and negative peers were taken from Esbensen and Osgood (1999), and had internal reliability coefficients of 0.77 and 0.84 respectively. The social isolation measure was taken from Esbensen, Deschenes, and Winfree (1999); no reliability was previously available for the latter, but our calculations proved it satisfactory (Table 1).
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Table 1 Mean, SD and reliability coefficients for all scales completed by prisoners (n = 152).
Impulsivity Self-control Social isolation Self esteem Commit to negative peers Commit to positive peers Positive reinforcement Negative reinforcement Neuroticism Extraversiona Openness to experienceb Agreeableness Conscientiousness
Mean
Standard deviation
Cronbach’s alpha
14.8 12.4 10.9 33.4 7.8 14.5 2.2 3.6 40.0 35.1 37.2 40.8 44.4
6.7 6.1 5.4 7.4 3.9 4.4 2.1 2.2 8.0 5.9 5.9 6.6 7.5
0.80 0.80 0.74 0.85 0.94 0.73 Single item Single item 0.75 0.61 0.62 0.67 0.82
a
Following exclusion of item E7 (‘‘I often feel as if I am bursting with energy’’). Following exclusion of item O1 (‘‘I enjoy concentrating on a fantasy or daydream and exploring all of it’s possibilities’’).
study used the comparative goodness of fit index as the criterion for judging the model’s effectiveness. 4.1. Descriptive statistics A third of participants reported gang membership as a youth or immediately prior to custody (n = 58), 13 reported being currently a member of a prison gang; and 15 intended to join a gang upon release. Table 1 presents mean, standard deviation and reliability statistics for all measures. All were reliable, though E and O were each improved by the deletion of an item. These items may have reflected comprehension difficulties in participants, given the poor literacy of some prisoners. 4.2. Correlations between measures
b
Four-item scales measuring self-control and impulsivity were adopted from Grasmick, Tittle, Bursik, and Arneklev (1993) (internal reliabilities of 0.82 and 0.74, respectively). Self-esteem items were taken from Esbensen et al. (1999). We measured personality using the NEO-FFI-R (McCrae & Costa, 2004), which has strong cross-situational and longitudinal consistency, and is reliable for the general British population (Egan, 2011). Prior conviction data (juvenile and adult) were obtained from the Prison Service database.
Pearson’s r was used to measure associations. To minimise spurious results, a significance value of .01 or less was adopted. Gang embeddedness was correlated positively with number of previous convictions, positive reinforcement associated with gang activity, commitment to negative peers, and negatively with commitment to positive peers. These associations show embeddedness in gangs is associated with measurable social and individual factors. Many correlations were found between the NEO-FFI-R and the other variables (Table 2). A correlated negatively with impulsivity, selfcontrol, positive reinforcement from antisocial associates, commitment to negative peers and previous convictions. A was positively correlated with commitment to positive peers and age. N and C were widely correlated with social variables, E and O having more sporadic associations.
3.4. Procedure
4.3. Data reduction
A forensic psychologist in-training approached prisoners individually when they were secured in their cells for the evening. The researcher explained the study, emphasising the confidential, optional nature of the research. The following day, the researcher asked the previously approached prisoners whether they would participate. Those who agreed completed a consent form and were given a questionnaire package, which was collected the following evening. This method reduced inhibition of participation due to apparent complicity with the prison regime. If a participant had literacy problems making it difficult for him to complete the questionnaire alone, the researcher read aloud, or explained the questions and entered their responses for them.
To reduce the data an exploratory principal components analysis was conducted, the factors being rotated using an oblique procedure. The 13 test variables had a Kaiser–Meyer–Olkin value of 0.78 (v2 = 519.1, p < .001), and formed a stable solution in six iterations. Parallel analysis indicated the minimum eigenvalue required for a non-chance factor was 1.46 or above. The initial factor-analysis found three factors, the third eigenvalue (1.31, explaining 10.6% of the variance) being below this criterion. ‘‘Negative punishers’’ was the highest loading on this third factor, which had very low communality relative to other measures. Given these observations, the ‘‘negative punishers’’ measure was dropped from the analysis (Table 3). Two oblique factors explained 44.6% of the observed variance. The first had high loadings for commitment to negative peers,
3.3. Personality
4. Results Analysis was conducted using SPSS version 16.0. Ten widely uncompleted questionnaires were excluded from analyses. Following data cleaning, normality testing, logarithmic transformations for skewed crime data, and inspection of collinearity diagnostics, data were subjected to a series of analyses. Initially, correlations were conducted between the gang scale and other variables. Factor analysis reduced these variables to their underlying dimensions, defining the number of dimensions using parallel analysis (O’Connor, 2000). These dimensions were fitted to age, the number of previous convictions and gang embeddedness using a path analysis calculated with AMOS 16.0 (Arbuckle & Wothke, 2003). Given the many measured variables and possible direct and indirect pathways and covariances between measures and outcomes possible, an a priori latent variable model generated by the data would be unidentified before it could be calculated. For this reason, factor scores for the personality dimensions were used. In smaller sample sizes, RMSEA indices of fit can be over-stringent, so the current
Table 2 Bivariate correlations for NEO-FFI-R subscales in relation to other predictor variables. N Age Previous convictions Impulsivity Self-control Social isolation Self Esteem Positive reinforcers Negative punishers Commitment to negative peers Commitment to positive peers * **
p < 0.05, two tailed. p < 0.01, two tailed.
E
O
A
C
0.05 0.15 0.37** 0.20* 0.42** 0.44** 0.20* 0.00 0.22**
0.25** 0.11 0.01 0.17* 0.15 0.29** 0.07 0.00 0.14
0.06 0.00 0.29** 0.04 0.05 0.30** 0.03 0.05 0.13
0.27** 0.31** 0.37** 0.36** 0.16 0.13 0.37** 0.18* 0.40**
0.23** 0.20* 0.43** 0.33** 0.25** 0.61** 0.12 0.13 0.27**
0.12
0.21**
0.18*
0.44**
0.36**
V. Egan, M. Beadman / Personality and Individual Differences 51 (2011) 748–753 Table 3 Structure matrix from principal components analysis (with oblique rotation) of psychometric measures collected from UK prisoners (loadings over 0.4 in bold, loadings reported in order of magnitude). Communality Commitment to negative peers Self control Agreeableness Impulsiveness Commitment to positive peers Positive reinforcers Self-esteem Conscientiousness Neuroticism Extraversion Openness Social isolation Eigenvalue % variance
Factor 1 antisocial personality
Factor 2 resilient personality
0.59
0.77
0.20
0.54 0.47 0.56 0.45
0.73 0.68 0.67 0.67
0.17 0.15 0.48 0.21
0.36 0.62 0.61 0.49 0.53 0.28 0.26
0.60 0.20 0.42 0.25 0.34 0.16 0.25 3.83 29.5
0.10 0.79 0.74 0.69 0.54 0.53 0.49 1.96 15.1
low self control, low A, impulsiveness, low commitment to negative peers, higher positive reinforcement gained from antisocial company, and low C. This was a clear antisocial personality factor, capturing many generic offender characteristics. The second factor had high loadings for low impulsivity, high self-esteem, high C, low N, E, and O, and low social isolation, and was deemed a resilience factor. Impulsivity and C had split loadings (the sign of the loading differentiating the direction of association for the two constructs). These factors correlated at 0.24, indicating a covariance for the subsequent path analysis.
4.4. Path analysis Fig. 1 presents an SEM between age, the two personality factors, the participant’s number of previous convictions, and their overall degree of unweighted gang embeddedness. Only significant paths (indicated by significant critical ratios in the output) are shown. The circles are error variances for the measured variables (represented by boxes). Covariance between the two personality dimensions at the factor level was represented by double-headed curved arrows between the variables. Predictive pathways are standardised regression coefficients, and all are significant at p < .001. The model itself was not significant (v2 = 3.81 with 3 d.f., p = 0.283) and fitness indices (Bentler–Bonnett fit index = 0.959, GFI = 0.976,
.24
e1
Age
e2
Resilence
-.35
e3
Previous .19 convictions
Gang adherence
-.22
e5
.44 .42
e4
Antisocial personality
Fig. 1. Path analysis predicting previous convictions and gang involvement from age and personality variables.
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RMSEA = 0.042, comparative fit index = 0.99) show the data fitted the model well compared to an independence model assuming all variables were unrelated (v2 = 116.96 with 15 d.f., p < 0.001)). The model shows age and resilient personality co-vary with antisocial personality; younger participants are higher in antisocial personality, and the older the participant, the greater their number of previous convictions. There are no significant pathways from resilient personality to either previous convictions or gang involvement. However antisocial personality significantly predicts previous convictions and gang involvement, showing both direct (0.42) and indirect (0.44 0.19 = 0.08) effects; antisocial personality has a total causal effect of 0.50 on gang embeddedness. Recalculating this model using weighted gang embeddedness generated a similar result (v2 = 4.05 with 3 d.f., p = 0.256; Bentler–Bonnett index = 0.960, GFI = 0.976, RMSEA = 0.048, comparative fit index = 0.988), showing weighting current and future gang embeddedness is unnecessary. While adding a path from resilience to the unweighted gang membership measure slightly increased model fit (v2 = 2.235 with 3 d.f., p = 0.156; Bentler–Bonnett fit index = 0.978), this pathway was not significant.
5. Discussion We examined whether personality helped structure a variety of personality measures used to assess offenders, and their relationship to criminal gang embeddedness. Personality reduced to two dimensions comprising resilience and antisocial personality. Path-analysis found antisocial personality the main determinant for prior convictions and gang embeddedness, with age and resilience operating indirectly through antisocial personality to influence general criminal convictions and gang involvement. Weighting gang embeddedness did not improve prediction. Our findings suggest individuals with low A seek out similar peers (in terms of disposition and attitudes) and this assortative process drives gang membership rather than socialisation alone. This is comparable with a recent meta-analysis of social learning theory that found differential association for antisocial peers a significantly stronger predictor for misconduct than differential reinforcement or modelling, both of which had only minor effects (Pratt et al., 2009). Seeking like-minded peers is normal and heritable; Kendler et al. (2007, 2008) found that as male twins mature, the importance of genetic factors on peer choice increases and the importance of previously shared environmental factors decreases. Close relationships with peers of compatible disposition provides a social group that positively reinforces behaviour. Anti-social group formation is strengthened if low-A individuals are rejected from prosocial peer groups, and peer group rejection predicts gang membership and deviance, even after controlling for anti-social behaviour and education (Dishion, Nelson, & Yasui, 2005). Agreeable individuals are motivated to maintain positive social relations and hold more positive views of others; individuals inclined to like others communicate warmth, so become popular themselves. Conventionality also protects individuals from violent victimisation; inner-city youth who eschew an identity derived from street culture are less likely to be victimised than ‘streetwise’ peers (Stewart, Schreck, & Simons, 2006), and victimisation is more common for those who are members of (or affect to be) in a gang (Taylor, Peterson, Esbensen, & Freng, 2007). There are limitations to this study. All participants were adult male prisoners on remand or serving a sentence at a local London prison, so findings may not apply to young or female offenders. We used self-reports, which can be compromised via subjective interpretation of questions, careless responding, acquiescence, or socially desirable responses (Paulhus, 1991). Another limitation is sample self-selection. Some refused to participate, either
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immediately or following inspection of the questionnaires, and others withdrew despite being suspected of affiliation to prison gangs. Our unfunded 6-month exploratory study found 8.6% persons reported active gang membership; Bennett and Holloway (2004) found 15% of arrestees had current or past membership of a gang, though they ran their study over 3 years, had substantial funding, and recruited over 2700 participants. Though Kline (2000) cautioned against the use of the NEO in clinical populations in the UK, 12 years of research using the NEO with clinical and forensic cohorts in the UK has not shown major problems with the scale’s reliability or validity (Egan, Austin, Elliot, Patel, & Charlesworth, 2003; Egan, Kavanagh, & Blair, 2005). Despite such concerns, our study recruited a substantial sample for research with a difficult population, reports a rich data set using reliable and validated measures, and uses rigorous analyses to test the results. The work contributes usefully to the literature both on gang membership, and on individual differences in offenders. Our findings suggest interventions seeking to reduce gang adherence focus on antisocial rather than emotional thoughts and behaviours, reiterating the importance of offence-focussed interventions (Hollin & Palmer, 2009).
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Measurement and control of response bias. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes (pp. 17–59). San Diego, CA: Academic Press. Pratt, T. C., Cullen, F. T., Sellars, C. S., Winfree, L. T., Jr., Madesen, T. D., Daigle, L. E., et al. (2009). The empirical status of social learning theory: A meta-analysis. Justice Quarterly, 9, 1–38. Prisoners Advice Service (2011). Categorisation – Male prisoners., Retrieved 27.05.11. Rizzo, M. (2003). Why do children join gangs? Journal of Gang Research, 11, 65–75. Samuels, J., Bienvenu, J., Cullen, B., Costa, P. T., Eaton, W. W., & Nestadt, G. (2004). Personality dimensions and criminal arrest. Comprehensive Psychiatry, 45, 275–280. Scott, G. (2001). Broken windows behind bars: Eradicating prison gangs through ecological hardening and symbolic cleansing. Corrections Management Quarterly, 5, 23–36. Sheldon, R. G. (1991). A comparison of gang members and non-gang members in a prison setting. The Prison Journal, 71, 50–60. Stewart, E. A., Schreck, C. J., & Simons, R. L. (2006). ‘‘I ain’t gonna let no one disrespect me’’: Does the code of the street reduce or increase violent victimization among African American adolescents? Journal of Research in Crime and Delinquency, 43, 427–458. Taylor, T. J., Peterson, D., Esbensen, F., & Freng, A. (2007). Gang membership as a risk factor for adolescent violent victimization. Journal of Research in Crime and Delinquency, 44, 351–380. Thornberry, T. P., Krohn, D. M., Lizotte, A. J., Smith, C. A., & Tobin, K. (2003). Gangs and delinquency in a developmental perspective. New York: Cambridge University Press.
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Contents lists available at ScienceDirect
Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Personality and gang embeddedness Vincent Egan ⇑, Matthew Beadman Department of Psychology – Forensic Section, University of Leicester, 106 New Walk, Leicester LE1 7EA, UK
a r t i c l e
i n f o
Article history: Received 3 February 2011 Received in revised form 17 June 2011 Accepted 20 June 2011 Available online 20 July 2011 Keywords: Gangs Personality Social learning models Individual differences Resilience Path analysis
a b s t r a c t Gang membership can be transient or persistent, and most persons who participate in such groups at one point in their lives grow out of the lifestyle. The personality dynamics driving gang membership are poorly understood, although the five-factor model of personality has proved an effective way to understanding many types of antisocial behaviour. Using dichotomous self-nomination 152 remand and sentenced participants incarcerated within a general prison in London, UK indicated their gang embeddedness as youths, prior to custody, within prison, and as an intent following release. This enabled behavioural intention to be incorporated into the model as well as historical factors. Constructs derived from a variety of personality measures and constructs were used to predict overall reported gang embeddedness. These predictors reduced to two underlying dimensions: personal resilience and antisocial personality. Path analysis showed the antisocial personality dimension predicted previous convictions and degree of gang embeddedness, whereas resilience did not. The direct and indirect effects of the composite antisocial personality dimension explained 50% of the overall observed variance in gang embeddedness. We suggest that gang membership may reflect normal assortative processes within the members of such groups. Ó 2011 Published by Elsevier Ltd.
1. Introduction Gang membership predicts prison misconduct (DeLisi, Berg, & Hochstetler, 2004), staff assault (Huebner, 2003), seditious and generic criminal activity (Krienert & Fleisher, 2001; Sheldon, 1991), and criminal recidivism (Huebner, 2003). Reducing gang activity reduces antisocial conduct and assists rehabilitation (Vittori, 2007). As elsewhere in the world, prison gangs operate within English prisons (Wood, 2006; Wood & Adler, 2001) and have similarly malignant influences on offender behaviour and possible rehabilitation. Risk factors for gang affiliation are similar to those facilitating delinquency, the same in and out of prison, and occur across individual, family, peer group, school, and community levels (Hill, Howell, Hawkins, & Battin-Pearson, 1999; Howell & Egley, 2005; Scott, 2001; Thornberry, Krohn, Lizotte, Smith, & Tobin, 2003). Though factors predicting gang membership also predict generic antisocial behaviour and criminal offending, research into gang membership tends to focus on social rather than individual factors (Warr, 1996). The current study examines individual differences influencing participation in criminal social groups, noting that many other offenders follow offending trajectories that eschew gangs. Costa and McCrae’s five-factor model of personality (FFM) is a compelling structure for explaining human variation, predicting a wide variety of behaviours (Ozer & Benet-Martínez, 2006). Along-
⇑ Corresponding author. Tel.: +44 116 252 3658. E-mail addresses: [email protected], [email protected] (V. Egan). 0191-8869/$ - see front matter Ó 2011 Published by Elsevier Ltd. doi:10.1016/j.paid.2011.06.021
side social learning, lower intelligence, higher neuroticism (N), lower agreeableness (A), and lower conscientiousness (C) routinely predict antisocial and criminal behaviour (Miller, Lynam, & Leukefeld, 2003; Samuels et al., 2004; Egan, 2011), Reciprocals of these dimensions reinforce individual resilience, moderating the effect of criminogenic environments. Persons lower in N, and higher in IQ, E, O, A, and C better resist antisocial influences, even in dysfunctional settings (Davey, Eaker, & Walters, 2003). There are other personality constructs associated with offending. Impulsivity predicts both overall criminal history and within-custody antisocial activity (Gordon & Egan, 2011; Lynam & Miller, 2004). van der Geest, Blokland, and Bijleveld (2009) found generic risk-taking (similar to low self-control (O’Gorman & Baxter, 2002)) measured using the Adolescent Temperament List related to irresponsible behaviour and drug use. More ambiguously, low (or excessive) self-esteem is correlated with offending (Oser, 2006; Rizzo, 2003). These overlapping antisocial and emotionally unstable constructs are separately associated with A, C, and N (Whiteside & Lynam, 2001). One way to avoid reifying ‘jangle’ constructs in psychology (which happen when ostensibly different measures measure the same underlying constructs; Block, 1995) is to use established measures to structure these other constructs. The FFM integrates a variety of conceptual models (McCrae & John, 1992), indicating where novel concepts are positioned empirically, relative to a familiar structural model. The current study uses the FFM to clarify such relationships in relation to persistent gang embeddedness. Social processes influence deviance through differential association for antisocial peers (Akers & Jensen, 2006). Social
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activity is personally rewarding and self-reinforcing (Winfree, Mays, & Vigil-Backstrom, 1994), so recidivism is higher in persons with negative peers (Gendreau, Goggin, & Little, 1996), and reducing antisocial peers reduces re-conviction (Warr, 1996). Gang membership intensifies delinquency beyond simple antisocial peer association (Esbensen & Huizinga, 1993), perhaps because negative peers disrupt the positive social relationships and prosocial activities that facilitate desistance. Assortative processes also influence criminality; excluded persons socialise with each other, mutually reinforcing their shared outlook, consolidating their interpersonal bonds and deviant attitudes (Granic & Dishion, 2003; Monahan, Steinberg, & Cauffman, 2009). Delinquent values decline with maturation (Anderson, 1999), and forming positive social bonds to legitimate institutions enables individuals to achieve previously unattainable objectives (Coleman, 1988), and something to lose if they remain antisocial (Maruna & Toch, 2005). Fearing such potential losses reduces the chance an individual will continue their criminal lifestyle, whereas those with looser ties to convention find it harder to re-integrate, making recidivism more likely. Decker and Van Winkle (1996) describe the process of gang membership in terms of ‘pushes’ (e.g., needing protection due to social marginalization) and ‘pulls’ (e.g., excitement and increased social status). Huebner, Varano, and Bynum (2007) found gang members excluded from prosocial activities and integration were ‘pushed’ toward offending. If prosocial integration wanes, individuals are ‘pushed’ back towards gang affiliation via marginalisation (Braithwaite, 1989). The power of this ‘pull’ to a deviant network may depend on a person’s degree of embeddedness to gangs and their values. For some, gang affiliation is transitory, whereas for others, gang affiliation affirms underlying values. Given prior attitudes predict future behaviour (Glasman & Albarracín, 2006), asking about potential future gang-related affiliation may strengthen the reliability of gangembeddedness assessment, relative to historical information alone. The current study explored personality constructs underlying gang embeddedness in British prisoners using the FFM to structure a variety of potentially relevant personality constructs into a simpler model. Given the literature on personality and crime, we expected negative feelings (including N, low self esteem, and greater social isolation) and antisocial personality (comprising criminality-related trait dimensions such as low A, low C, low self-control, and greater impulsivity) to form super-ordinate and separate constructs, and that deriving positive feelings from antisocial associates would be positively associated with antisocial rather than emotionally vulnerable personality traits. We expected the main determinant of criminal activity and gang embeddedness to be antisocial personality. 2. Method 2.1. Design The study followed a multivariate correlational design. The predictors comprised social variables (positive reinforcers, commitment to positive peers, commitment to negative peers, negative punishers, social isolation, previous criminal history), and personality (self-esteem, impulsivity, self-control, N, E, O, A and C). Our outcome variable was self-reported gang history. Ethical approval was provided by both the University and the penal establishment research committees involved in the study. 2.2. Participants Of 204 persons approached, 162 adult (over 21 years) male offenders (79.4%) were recruited from a general Category B prison population in London, UK. Category B prisons hold persons who do
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not require maximum security, but would still pose a risk of escape if security were lowered (Prisoner’s Advice Service, 2011). Of the participants, 33% described their ethnicity as White British, 13% White Other, 14% Black Caribbean, 6% Black African, 6% Black Other, 10% Other and 12% Asian, the remainder omitting this information. The mean age was 31.7 years (SD = 8.6 years, range = 21– 60). Wing Officers advised on persons thought unsuitable for participation due to vulnerability or heightened risk (e.g., those in segregation or healthcare wings). Given the frequency of adjustment disorder in new prisoners, recent admissions were also excluded. Non-participants typically expressed suspicion about the confidentiality of the study. 3. Materials 3.1. Gang membership Prior research has used officers or official records to operationalise gang membership (Fong & Buentello, 1991). These official methods underestimate actual rates of prison gang membership (Cooley, 1993). Classification reliability is also threatened by discrepancies between official sources (e.g., wing diaries, security information, staff reports). Esbensen and Weerman (2005) found self nomination able to define gang membership provided confidentiality was given and respected. While self-report data is also subject to difficulties (for example, impression management), optimising confidentiality reduces the personal benefit an individual may derive from such a strategy. We operationalised gang affiliation and membership using a four-question, self-report scale. Participants indicated whether they were members of a gang as youths, immediately before conviction, currently, and if they intended to join or rejoin a gang upon release. Data was coded in two ways; (1) summing the four dichotomous gang questions and (2) creating a weighted sum with more salience given to more recent gang membership. This hierarchy assumed recency in gang membership more salient, as would a declared intention to join a gang in the future. Thus, individuals disclosing gang membership as youths scored one point, those reporting gang membership immediately prior to current custody, two, those reporting current prison gang membership, three, and those reporting an intention to join a gang following release from prison, four. The maximum cumulative weighted score for an individual whose gang embeddedness spanned from youth to intent following release would be 10, producing a dimension reflecting the increasing importance of current and future gang membership. 3.2. Social variables Five social variables were measured: positive reinforcement, punishment, commitment to positive peers, commitment to negative peers, and social isolation (Akers, 1998). For positive reinforcement, the extent participants believed gang membership would lead to desirable outcomes was measured (e.g., ‘‘I would get respect’’). The more positive reinforcers associated with gang membership, the higher the score. This scale was taken from Winfree, Esbensen, and Osgood (1996), and had an internal alpha reliability of 0.84. Negative punishers were measured similarly, using items such as ‘‘I would get hurt’’; this scale having an internal reliability of 0.83 (Winfree et al., 1996). Items measuring commitment to positive and negative peers were taken from Esbensen and Osgood (1999), and had internal reliability coefficients of 0.77 and 0.84 respectively. The social isolation measure was taken from Esbensen, Deschenes, and Winfree (1999); no reliability was previously available for the latter, but our calculations proved it satisfactory (Table 1).
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Table 1 Mean, SD and reliability coefficients for all scales completed by prisoners (n = 152).
Impulsivity Self-control Social isolation Self esteem Commit to negative peers Commit to positive peers Positive reinforcement Negative reinforcement Neuroticism Extraversiona Openness to experienceb Agreeableness Conscientiousness
Mean
Standard deviation
Cronbach’s alpha
14.8 12.4 10.9 33.4 7.8 14.5 2.2 3.6 40.0 35.1 37.2 40.8 44.4
6.7 6.1 5.4 7.4 3.9 4.4 2.1 2.2 8.0 5.9 5.9 6.6 7.5
0.80 0.80 0.74 0.85 0.94 0.73 Single item Single item 0.75 0.61 0.62 0.67 0.82
a
Following exclusion of item E7 (‘‘I often feel as if I am bursting with energy’’). Following exclusion of item O1 (‘‘I enjoy concentrating on a fantasy or daydream and exploring all of it’s possibilities’’).
study used the comparative goodness of fit index as the criterion for judging the model’s effectiveness. 4.1. Descriptive statistics A third of participants reported gang membership as a youth or immediately prior to custody (n = 58), 13 reported being currently a member of a prison gang; and 15 intended to join a gang upon release. Table 1 presents mean, standard deviation and reliability statistics for all measures. All were reliable, though E and O were each improved by the deletion of an item. These items may have reflected comprehension difficulties in participants, given the poor literacy of some prisoners. 4.2. Correlations between measures
b
Four-item scales measuring self-control and impulsivity were adopted from Grasmick, Tittle, Bursik, and Arneklev (1993) (internal reliabilities of 0.82 and 0.74, respectively). Self-esteem items were taken from Esbensen et al. (1999). We measured personality using the NEO-FFI-R (McCrae & Costa, 2004), which has strong cross-situational and longitudinal consistency, and is reliable for the general British population (Egan, 2011). Prior conviction data (juvenile and adult) were obtained from the Prison Service database.
Pearson’s r was used to measure associations. To minimise spurious results, a significance value of .01 or less was adopted. Gang embeddedness was correlated positively with number of previous convictions, positive reinforcement associated with gang activity, commitment to negative peers, and negatively with commitment to positive peers. These associations show embeddedness in gangs is associated with measurable social and individual factors. Many correlations were found between the NEO-FFI-R and the other variables (Table 2). A correlated negatively with impulsivity, selfcontrol, positive reinforcement from antisocial associates, commitment to negative peers and previous convictions. A was positively correlated with commitment to positive peers and age. N and C were widely correlated with social variables, E and O having more sporadic associations.
3.4. Procedure
4.3. Data reduction
A forensic psychologist in-training approached prisoners individually when they were secured in their cells for the evening. The researcher explained the study, emphasising the confidential, optional nature of the research. The following day, the researcher asked the previously approached prisoners whether they would participate. Those who agreed completed a consent form and were given a questionnaire package, which was collected the following evening. This method reduced inhibition of participation due to apparent complicity with the prison regime. If a participant had literacy problems making it difficult for him to complete the questionnaire alone, the researcher read aloud, or explained the questions and entered their responses for them.
To reduce the data an exploratory principal components analysis was conducted, the factors being rotated using an oblique procedure. The 13 test variables had a Kaiser–Meyer–Olkin value of 0.78 (v2 = 519.1, p < .001), and formed a stable solution in six iterations. Parallel analysis indicated the minimum eigenvalue required for a non-chance factor was 1.46 or above. The initial factor-analysis found three factors, the third eigenvalue (1.31, explaining 10.6% of the variance) being below this criterion. ‘‘Negative punishers’’ was the highest loading on this third factor, which had very low communality relative to other measures. Given these observations, the ‘‘negative punishers’’ measure was dropped from the analysis (Table 3). Two oblique factors explained 44.6% of the observed variance. The first had high loadings for commitment to negative peers,
3.3. Personality
4. Results Analysis was conducted using SPSS version 16.0. Ten widely uncompleted questionnaires were excluded from analyses. Following data cleaning, normality testing, logarithmic transformations for skewed crime data, and inspection of collinearity diagnostics, data were subjected to a series of analyses. Initially, correlations were conducted between the gang scale and other variables. Factor analysis reduced these variables to their underlying dimensions, defining the number of dimensions using parallel analysis (O’Connor, 2000). These dimensions were fitted to age, the number of previous convictions and gang embeddedness using a path analysis calculated with AMOS 16.0 (Arbuckle & Wothke, 2003). Given the many measured variables and possible direct and indirect pathways and covariances between measures and outcomes possible, an a priori latent variable model generated by the data would be unidentified before it could be calculated. For this reason, factor scores for the personality dimensions were used. In smaller sample sizes, RMSEA indices of fit can be over-stringent, so the current
Table 2 Bivariate correlations for NEO-FFI-R subscales in relation to other predictor variables. N Age Previous convictions Impulsivity Self-control Social isolation Self Esteem Positive reinforcers Negative punishers Commitment to negative peers Commitment to positive peers * **
p < 0.05, two tailed. p < 0.01, two tailed.
E
O
A
C
0.05 0.15 0.37** 0.20* 0.42** 0.44** 0.20* 0.00 0.22**
0.25** 0.11 0.01 0.17* 0.15 0.29** 0.07 0.00 0.14
0.06 0.00 0.29** 0.04 0.05 0.30** 0.03 0.05 0.13
0.27** 0.31** 0.37** 0.36** 0.16 0.13 0.37** 0.18* 0.40**
0.23** 0.20* 0.43** 0.33** 0.25** 0.61** 0.12 0.13 0.27**
0.12
0.21**
0.18*
0.44**
0.36**
V. Egan, M. Beadman / Personality and Individual Differences 51 (2011) 748–753 Table 3 Structure matrix from principal components analysis (with oblique rotation) of psychometric measures collected from UK prisoners (loadings over 0.4 in bold, loadings reported in order of magnitude). Communality Commitment to negative peers Self control Agreeableness Impulsiveness Commitment to positive peers Positive reinforcers Self-esteem Conscientiousness Neuroticism Extraversion Openness Social isolation Eigenvalue % variance
Factor 1 antisocial personality
Factor 2 resilient personality
0.59
0.77
0.20
0.54 0.47 0.56 0.45
0.73 0.68 0.67 0.67
0.17 0.15 0.48 0.21
0.36 0.62 0.61 0.49 0.53 0.28 0.26
0.60 0.20 0.42 0.25 0.34 0.16 0.25 3.83 29.5
0.10 0.79 0.74 0.69 0.54 0.53 0.49 1.96 15.1
low self control, low A, impulsiveness, low commitment to negative peers, higher positive reinforcement gained from antisocial company, and low C. This was a clear antisocial personality factor, capturing many generic offender characteristics. The second factor had high loadings for low impulsivity, high self-esteem, high C, low N, E, and O, and low social isolation, and was deemed a resilience factor. Impulsivity and C had split loadings (the sign of the loading differentiating the direction of association for the two constructs). These factors correlated at 0.24, indicating a covariance for the subsequent path analysis.
4.4. Path analysis Fig. 1 presents an SEM between age, the two personality factors, the participant’s number of previous convictions, and their overall degree of unweighted gang embeddedness. Only significant paths (indicated by significant critical ratios in the output) are shown. The circles are error variances for the measured variables (represented by boxes). Covariance between the two personality dimensions at the factor level was represented by double-headed curved arrows between the variables. Predictive pathways are standardised regression coefficients, and all are significant at p < .001. The model itself was not significant (v2 = 3.81 with 3 d.f., p = 0.283) and fitness indices (Bentler–Bonnett fit index = 0.959, GFI = 0.976,
.24
e1
Age
e2
Resilence
-.35
e3
Previous .19 convictions
Gang adherence
-.22
e5
.44 .42
e4
Antisocial personality
Fig. 1. Path analysis predicting previous convictions and gang involvement from age and personality variables.
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RMSEA = 0.042, comparative fit index = 0.99) show the data fitted the model well compared to an independence model assuming all variables were unrelated (v2 = 116.96 with 15 d.f., p < 0.001)). The model shows age and resilient personality co-vary with antisocial personality; younger participants are higher in antisocial personality, and the older the participant, the greater their number of previous convictions. There are no significant pathways from resilient personality to either previous convictions or gang involvement. However antisocial personality significantly predicts previous convictions and gang involvement, showing both direct (0.42) and indirect (0.44 0.19 = 0.08) effects; antisocial personality has a total causal effect of 0.50 on gang embeddedness. Recalculating this model using weighted gang embeddedness generated a similar result (v2 = 4.05 with 3 d.f., p = 0.256; Bentler–Bonnett index = 0.960, GFI = 0.976, RMSEA = 0.048, comparative fit index = 0.988), showing weighting current and future gang embeddedness is unnecessary. While adding a path from resilience to the unweighted gang membership measure slightly increased model fit (v2 = 2.235 with 3 d.f., p = 0.156; Bentler–Bonnett fit index = 0.978), this pathway was not significant.
5. Discussion We examined whether personality helped structure a variety of personality measures used to assess offenders, and their relationship to criminal gang embeddedness. Personality reduced to two dimensions comprising resilience and antisocial personality. Path-analysis found antisocial personality the main determinant for prior convictions and gang embeddedness, with age and resilience operating indirectly through antisocial personality to influence general criminal convictions and gang involvement. Weighting gang embeddedness did not improve prediction. Our findings suggest individuals with low A seek out similar peers (in terms of disposition and attitudes) and this assortative process drives gang membership rather than socialisation alone. This is comparable with a recent meta-analysis of social learning theory that found differential association for antisocial peers a significantly stronger predictor for misconduct than differential reinforcement or modelling, both of which had only minor effects (Pratt et al., 2009). Seeking like-minded peers is normal and heritable; Kendler et al. (2007, 2008) found that as male twins mature, the importance of genetic factors on peer choice increases and the importance of previously shared environmental factors decreases. Close relationships with peers of compatible disposition provides a social group that positively reinforces behaviour. Anti-social group formation is strengthened if low-A individuals are rejected from prosocial peer groups, and peer group rejection predicts gang membership and deviance, even after controlling for anti-social behaviour and education (Dishion, Nelson, & Yasui, 2005). Agreeable individuals are motivated to maintain positive social relations and hold more positive views of others; individuals inclined to like others communicate warmth, so become popular themselves. Conventionality also protects individuals from violent victimisation; inner-city youth who eschew an identity derived from street culture are less likely to be victimised than ‘streetwise’ peers (Stewart, Schreck, & Simons, 2006), and victimisation is more common for those who are members of (or affect to be) in a gang (Taylor, Peterson, Esbensen, & Freng, 2007). There are limitations to this study. All participants were adult male prisoners on remand or serving a sentence at a local London prison, so findings may not apply to young or female offenders. We used self-reports, which can be compromised via subjective interpretation of questions, careless responding, acquiescence, or socially desirable responses (Paulhus, 1991). Another limitation is sample self-selection. Some refused to participate, either
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immediately or following inspection of the questionnaires, and others withdrew despite being suspected of affiliation to prison gangs. Our unfunded 6-month exploratory study found 8.6% persons reported active gang membership; Bennett and Holloway (2004) found 15% of arrestees had current or past membership of a gang, though they ran their study over 3 years, had substantial funding, and recruited over 2700 participants. Though Kline (2000) cautioned against the use of the NEO in clinical populations in the UK, 12 years of research using the NEO with clinical and forensic cohorts in the UK has not shown major problems with the scale’s reliability or validity (Egan, Austin, Elliot, Patel, & Charlesworth, 2003; Egan, Kavanagh, & Blair, 2005). Despite such concerns, our study recruited a substantial sample for research with a difficult population, reports a rich data set using reliable and validated measures, and uses rigorous analyses to test the results. The work contributes usefully to the literature both on gang membership, and on individual differences in offenders. Our findings suggest interventions seeking to reduce gang adherence focus on antisocial rather than emotional thoughts and behaviours, reiterating the importance of offence-focussed interventions (Hollin & Palmer, 2009).
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