The effectiveness of cognitive behavioral therapy on criminal attitudes among juvenile- and adult offenders : a meta-analytic review

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FACULTEIT DER MAATSCHAPPIJ- EN GEDRAGSWETENSCHAPPEN Graduate School of Child Development and Education

The effectiveness of cognitive behavioral therapy on criminal attitudes among juvenile- and adult offenders: A meta-analytic review

Masterscriptie Forensische Orthopedagogiek Universiteit van Amsterdam Auteur: R.R. (Rebecca) Knooihuizen Studentnummer: 10813977

Begeleider: A. (Anner) Bindels Tweede beoordelaar: E. (Els) Kornelis

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COGNITIVE BEHAVIORAL THERAPY AND CRIMINAL ATTITUDES 2

Abstract

Several well-conducted meta-analyses provide strong indications for the effectiveness of cognitive behavioral treatment (CBT) in (re-)offending behavior among juvenile- and adult offenders. Although criminal attitudes are considered as the main hypothesized mediator of change in cognitive behavioral programs, no systematic review has statistically summarized the strength and direction of the effect of CBT on criminal attitudes. The present meta-analytic review consisted of 11 independent studies (s), reporting on 48 effect sizes (k) from 2002 to 2015, aimed to generate more specific knowledge on the effect of CBT on criminal attitudes among the offender population. The total sample of N = 795 consisted of: n = 422 subjects who received cognitive behavioral therapy, and n = 373 subjects constituted the comparison group. A significant small-to-medium overall effect on criminal attitudes was found (d = - 0.312). Trim-an-fill procedures suggested the presence of publication bias. After imputing missing effect sizes to the data, the overall effect of CBT on criminal attitudes remained a significant small-to-medium effect (d = -0.402). The effect of CBT on criminal attitudes was moderated by the proportion ethnic minority: studies with a larger proportion non-Caucasian in their sample (i.e., 70%) were found to yield larger effect sizes compared to studies with lower proportion non-Caucasian (i.e., 20%). As a consequence of low statistical power, moderators with small to moderate effects were likely not to be identified. Future research should focus on high quality intervention evaluations to gain more insight in the influence of individual-and contextual factors. We argue that results of our meta-analytic review should be used as a baseline for future studies examining the effectiveness of cognitive behavioral therapy on criminal attitudes.

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The effectiveness of cognitive behavioral therapy on criminal attitudes among juvenile- and adult offenders: A meta-analytic review

Despite the decline in crime in the Netherlands during the past decade (Centraal Bureau voor Statistiek [CBS], 2016), criminal behavior continues to be a serious societal problem (Cohen, Figuero, & Jennings, 2010). Multiple criminological theories posit that criminals ‘think differently’ compared to non-criminals in ways they justify or exhibit pride for their involvement in criminal activity, their attitudes toward other criminal associates, their impression of the legal system, and often have a belief in an external locus of control (Andrews & Bonta, 2007; Holsinger, 1999; Walters, 1990; Yochelson & Samenow, 1976). This has been referred to as criminal attitudes. Criminological research on attitudes consistently indicates a significant association with criminal (re-)offending, both in psychological and criminological theories (e.g., Ajzen & Fishbein, 1977; Hirschi, 1969; Sutherland, Cressey, & Luckenbill, 1992), and in empirical research (e.g., Andrews & Kandel, 1979; Bagozzi & Burnkant, 1979; Mills, Kroner, & Forth, 2002; Nesdale, Maass, Kiesner, Durkin, Griffiths, & James, 2009; Simourd, 1999; Stevenson, Hall, & Innes, 2003; Walters, 2005). In addition, Lipsey and colleague (2007) consider criminal attitudes as the main mediator of change in cognitive behavioral treatment. More specifically, two meta-analytical reviews showed that criminal attitudes are among the strongest predictors of criminal (re-)offending (Gendreau, Little, & Goggin, 1996; Simourd, Hoge, Andrews, & Leschied, 1994). As such, criminal attitudes have been labelled as one of the ‘big four’ risk factors of criminal (re-)offending (Andrews & Bonta, 2010). Therefore, changes in attitudes seem to be of most relevance in criminological theories, empirical studies, and development of rehabilitative interventions to predict, understand, and influence the probability of (re-)offending. Western societies are focused on incorporating evidence based rehabilitative treatment programs aimed at reducing criminal attitudes, and subsequently reduce criminal

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(re-)offending (Illescas, Sánchez-Meca, & Genovés, 2001). Synthesis of rehabilitation literature among offenders consistently indicates that cognitive behavioral treatment programs are more effective than traditional behavioral management and other correctional interventions in reducing criminal behavior (Koehler, Lösel, Akoensi, & Humphreys, 2013; McQuire & Hatcher, 2001; Pearson, Lipton, Cleland, & Yee, 2002). One of the main aspects of cognitive behavioral treatment programs is to alter dysfunctional and criminogenic thinking patterns (Lipsey, Chapman, & Landenberger, 2001; Turner, 2007). This might explain why cognitive behavioral treatments are generally found to have the strongest effect in reducing reoffending in both juvenile- and adult offenders (Lipsey, 2009). To date, however, no systematic meta-analytic review on the effect of cognitive behavioral therapy on criminal attitudes is available. It is important to understand whether criminal attitudes can be modified, and if so, which intervention program may best achieve change among offenders. Therefore, the aim of the current meta-analysis is to examine the effect of cognitive behavioral therapy on criminal attitudes among juvenile- and adult offenders.

Theoretical framework

Much controversy exist on the conceptualizing of the attitude construct: the conceptualization of the construct attitude is unequivocal and considered multidimensional. From a social psychological perspective, attitudes have been operationalized as “evaluative cognitions and feeling that organize the actor’s decision to act and behavior toward a person, thing or action” (Andrews & Bonta, 2010, p. 234). Subsequent, criminal attitudes can be described as a set of cognitions and feelings, which reflect criminal values, and rationalizations or neutralizations with regard to individuals own criminal behavior (Simourd & Olver, 2002; Stevenson, Hall, & Innes, 2004). Andrews and Bonta (2007) operationalized criminal attitudes into roughly four components: the way offenders justify or exhibit pride for their involvement in criminal activity, their attitude toward other criminal associates, their

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impression of the legal system, and a belief in an external locus of control. The attribution by which offenders attempt to construct causal explanations for their criminal behavior consists of two types of attribution: internal- and external attribution (Snyder, 1979). Internal attribution exists when the cause for criminal behavior is perceived as being ‘located’ within the individual: the offender is in control and responsible of one’s own criminal behavior. However, external attribution is the tendency to excuse their criminal behavior by blaming the crime on social circumstances, victim, or society (Gudjonsson, 1984).

Throughout the years, empirical research repeatedly indicated that criminal attitudes are related to criminal (re-)offending. Multiple studies have shown that criminal attitudes have been linked to gang involvement (Chu, Daffern, Thomas, Ang, & Long, 2013; Ireland & Power, 2012), misconduct in prison (Gendreau, Goggin, & Law, 1997), and recidivism (Gendreau et al., 1996). In addition, two meta-analytical reviews have shown that criminal attitudes are among the strongest predictors of criminal (re-)offending (Gendreau et al., 1996; Simourd et al., 1994). In line with prior research, Lipsey and colleague (2007) consider criminal attitudes as the main hypothesized mediator of change in judicial interventions.

Research into criminal attitudes among offenders can be viewed from a variety of theoretical perspectives. Over the years, several criminological and psychological theories described the attitude-behavior relationship. Some theories assume that criminal attitudes precede criminal behavior: these theories try to explain why individuals engage in criminal behavior. The theory of reasoned action as developed by Ajzen and Fishbein (1977) focuses on the links between attitude, intention, and behavior. This theory argues that the best predictor of behavior is intention, which is determined by attitudes toward and social normative perceptions regarding the behavior, therefore suggesting a clear link between attitudes and behavior. Similar ideas can be found in Sutherlands’ differential association theory (Sutherland, Cressey, & Luckenbill, 1992), which assumes that criminal behavior is

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acquired in interaction with criminal others by the adaption of deviant moral norms and values of others, as well as their thinking patterns. However, some theories posit that criminal behavior precedes and thus affects criminal attitudes. In this regard, the cognitive dissonance theory as developed by Festinger (1962) is an important and relevant theory. This theory argues that when an individual acts against one’s moral values and norms this will cause cognitive dissonance. In an attempt to overcome cognitive dissonance the individual will subsequently adjust his or her norms and attitudes in accordance with his or her behavior by means of post-hoc explanations of criminal behavior, referred to as neutralization. These techniques are operationalized as “justification for deviance that are seen as valid by the delinquent, but not by the legal system or society at large” (Sykes & Matza, 1957). As a consequence, neutralization techniques increase the presence of pro-criminal attitudes, and by doing so increase the probability of (re-)offending. Summarizing the abovementioned theories, it can be concluded that criminal attitudes, values and norms, and criminal behavior might affect each other reciprocally, indicating that criminal attitudes may both have a causal as well as a maintenance role in explaining persistent criminal behavior (e.g., Maruna & Copes, 2005; Maruna & Mann, 2006). Consequently, it is not only relevant to understand the theoretical mechanisms by which criminal behavior occurs, but it is equally important to understand whether criminal attitudes can be modified and, if so, which judicial intervention program may best achieve change among offenders.

Cognitive behavioral therapy

Cognitive behavioral therapy is originally developed in the 1960s for the treatment of depression and anxiety (Beck, 1963, 1967; Ellis, 1957, 1962). Cognitive behavioral therapy is based on the premises that cognitions affect behavior, that we can monitor and alter our cognitive activity, and that changes in cognitions lead to changes in behavior (Dobson & Block, 1988). The common element of these approaches is “an emphasis on broad human

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change, but with a clear emphasis on demonstrable, behavioral outcomes achieved primarily through changes in the way an individual perceives, reflects upon, and, in general, thinks about their life circumstances” (Dobson & Khatri, 2000, p. 908). To date, cognitive behavioral therapy has evolved to address a diverse array of problems (Beck, Freeman, Davis, & Associates, 2004) including: substance abuse (Miller, 1980), personality disorder (Beck, Freeman, & Associates, 1990), and criminal behavior (Wilson, Bouffard, & MacKenzie, 2005). In line with the cognitive behavioral approach, several well-established intervention programs have been developed, such as Reasoning and Rehabilitation (Ross & Fabiano, 1985), Moral Reconation Therapy (Little & Robinson, 1986), and Aggression Replacement Training (Goldstein & Glick, 1987). All cognitive behavioral intervention programs employ a set of structured techniques aimed at cognitive restructuring, anger management, problem solving and various supplementary components related to moral development, relapse prevention, and life skills training.

The importance of establishing the effectiveness of rehabilitative intervention programs for offenders was acknowledged and addressed in the late 1990s (HMIP, 1998; Pearson et al., 2002). Several well-conducted meta-analyses have identified cognitive behavioral therapy as a particularly effective intervention for reducing recidivism and criminal behavior among juvenile- and adult offenders. A meta-analytic review by Gendreau and Andrews (1990) found that the most effective rehabilitation programs are those that incorporate cognitive behavioral techniques to improve criminal attitudes. In line with Gendreau and Andrews (1990), Pearson et al. (2002) conducted a meta-analysis of 69 primary studies on the effectiveness of both behavioral (e.g., token economy) -and cognitive behavioral programs on recidivism. Results indicated that cognitive behavioral programs were more effective in reducing recidivism than traditional behavioral management programs. Similarly, Landenberger and Lipsey (2005) conducted a meta-analysis of 58 studies

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demonstrating the effectiveness of cognitive behavioral programs based on a range of rehabilitation programs, offender types, with both juvenile- and adult offenders in the field of correctional- and mental health facilities. This meta-analysis confirmed the effectiveness of cognitive behavioral intervention programs on the recidivism of offenders that have been reported in previous meta-analyses (Lipsey et al., 2001; Pearson et al., 2002, Wilson et al., 2005).

In 1990, Andrews and colleague (1990) published an article outlining three general principles for effective offender rehabilitation: The Risk-Need-Responsivity (RNR) model. The RNR-model has been widely regarded as one of the leading theoretical frameworks that outline both the central causes of persistent criminal behavior, and principles for reoffending. The model states that rehabilitation effects can be optimized when (a) the intensity of rehabilitation programs is proportional to the risk of reoffending – implying that high risk offenders require more intensive treatment, and (b) rehabilitation programs should address the criminogenic needs of offenders associated with reoffending (i.e., dynamic risk factors). The responsivity principle is disaggregated in: general and specific responsivity. The generic responsivity states that rehabilitation programs should employ behavioral, social-learning, and cognitive behavioral strategies. The specific responsivity principle is focused on the ‘fine-tuning’ of rehabilitation programs: interventions should be tailored to match to offender characteristics such as learning style, level of motivation, and the individual’s personal and interpersonal circumstances (Andrews & Bonta, 2010b). In line with the general responsivity principle, rehabilitative intervention programs should employ cognitive behavioral strategies in order to be effective. Additionally, cognitive behavioral programs focus on changing dynamic risk factors (e.g., criminal attitudes) in line with the needs principle.

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In sum, several well conducted meta-analyses based on a range of rehabilitation programs, offender types, outcome variables, and quality of study design provide strong indications for the effectiveness of cognitive behavioral treatment for reducing recidivism and criminal behavior among juvenile- and adult offenders. Although criminal attitudes are considered as the main hypothesized mediator of change in cognitive behavioral programs, no systematic review has statistically summarized the strength and direction of the effect of cognitive behavioral therapy (CBT) on criminal attitudes. In order to generate more specific knowledge on the effectiveness of CBT on criminal attitudes, possible moderators related to sample-, study-, and treatment characteristics were included (for a detailed description of used moderators: see Method). A meta-analytical review provides a summary of previously conducted studies more adequately and precisely than a narrative review (Lipsey & Wilson, 2001). Therefore, we chose to conduct a meta-analysis to assess the strength and direction of the effectiveness of CBT on criminal attitudes, and to examine factors that might influence this association. The current meta-analytic review addressed the following research questions: (1) What is the strength and direction of the relationship between cognitive behavioral therapy and criminal attitudes? (2) Which factors moderate the relationship between cognitive behavioral therapy and criminal attitudes?

Method Inclusion criteria

For the selection of studies, multiple inclusion criteria were formulated: (1) primary- or secondary outcome is criminal attitudes. In concomitance with Andrew and Bonta’s (2007) operationalization, criminal attitudes has been operationalized as [1] the way offenders justify or exhibit pride for their involvement in criminal activity, [2] offenders attitude toward other criminal associates, [3] offenders impression of the legal system, or [4] offenders belief in an external locus of control. (2) The treatment under investigation was a variant of cognitive

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behavioral therapy representing or substantially similar to such brand names as Reasoning and Rehabilitation (Ross & Fabiano, 1985) or Moral Reconation Therapy (Little & Robinson, 1986). In particular, the treatment was directed toward changing distorted or dysfunctional cognitions (cognitive restructuring) or teaching new cognitive skills and involved therapeutic techniques typically associated with CBT (i.e., interpersonal problem solving, social skills, and emotional control), and subsequently lead to changes in behavior. (3) Recipients of the intervention were general offenders, either juvenile or adult, treated while on probation, incarcerated/- institutionalized, or during parole. Studies that were solely focused on one type of specific offending behavior (e.g., sexual offenses, drug offenses, DUI) were excluded. As described by Moffitt (1993), developmental trajectories toward specific offending behavior are different from general offending behavior. Specific offending behavior can be explained by specific risk factors that are different from general types of offending behavior (e.g., Becker, 1990; Worling & Längstrom, 2006), and thus may not be comparable. Therefore, the focus of the current study is on general offending. (4) Studies used a randomized or quasi-experimental design that compared CBT treatment condition with a relevant control condition, receiving treatment as usual, waiting list or no treatment. Finally, (5) Studies had to provide sufficient statistical information to calculate an effect size.

Selection of studies

The search strategy to identify qualified studies involved the inspection of computerized databases for studies published before December 2015. Seven electronic databases were searched: ScienceDirect, Web of Knowledge, Google Scholar, Wiley, Proquest (including Dissertations and Theses and Sociological Abstracts), EBSCOhost, and PsycInfo. The keywords for searching were concatenations of describing the population (e.g., offend*, delinquen*), cognitive behavioral intervention (e.g., cognit* behavior*, CBT*), and criminal attitudes (e.g., crimin* think*, hostile attrib*). Computerized searching was restricted

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to keywords, title, and/- or abstract. For a specific search query, see Appendix A. Second, reference lists from several relevant review articles (e.g., Lipsey et al., 2007; Wilson et al., 2005) were screened for citations of potentially eligible studies. In total, our literature search strategy yielded 2511 studies (including review- and qualitative studies), of which abstracts and method section were read, and excluded in case the study did not fit one of the inclusion criteria. This resulted in 152 studies. Further examination of the full texts led to the final sample of 11 studies that met inclusion criteria, with 11 independent samples (s), and 48 effect sizes (k). For a flow chart of the search procedure, see Appendix B. Table 1 presents the characteristics of included studies

Coding the studies and potential moderators

Each study was coded using a detailed coding sheet (see Appendix C) for recording outcome and potential moderators following the guidelines of Lipsey and Wilson (2001). The primary outcome was a measure of criminal attitudes. In concomitance with Andrew and Bonta’s (2007) operationalization, criminal attitudes has been operationalized as [1] the way offenders justify or exhibit pride for their involvement in criminal activity, [2] offenders attitude toward other criminal associates, [3] offenders impression of the legal system, or [4] offenders belief in an external locus of control. The attribution by which offenders attempt to construct causal explanations for their criminal behavior consists of two types of attribution: internal- and external attribution (Snyder, 1979). A meta-analytic review by Hanson and Bourgoum (2005) found that the association between type of attribution and treatment outcome is unclear. Therefore, as for outcome characteristic, we coded criminal attitudes into a dichotomous variable (i.e., internal- vs. external attribution). In addition, potential moderators of treatment effects were grouped into study-, sample-, and treatment characteristics. As for study characteristics, we coded year of publication (continuous variable) and impact factor (0-2 = low to moderate; >2 = high, Stams et al., 2006) of the journal. As an indicator of increased prevalence of mental health problems, we coded the setting of the study (correctional- vs. mental health facility). Both male- and

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female mental disordered offenders have been reported to have increased levels of criminal attitudes, values and beliefs related to criminal behavior (Morgan, Fisher, Duan, Mandracchia, & Murray, 2010). In addition, the design of the study (randomized controlled trial vs. quasi-experimental) was coded, since it has been argued that quasi-experimental studies are more

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Table 1

Study, sample and treatment characteristics of the studies included in the meta-analysis.

Study Study characteristics Sample characteristics Treatment characteristics

Year N # r (M) Country Impact factor

Design Age % male % minority Mental disorder Internal attr. External attr.

Duration Contact Setting

Bosold 2010 96 4 (-0.793) GER 0.880 QE 20.10 100.00 23.96 No  20 60 COR

Brazão 2015 48 4 (-0.666) POR 1.587 RCT 28.42 100.00 - No   52 60 COR

Brugman 2010 77 4 (-0.375) NED 1.063 QE 16.44 100.00 68.25 No   13 49 COR

Cullen 2011 72 12 (-0.119) UK 5.938 RCT 35.40 100.00 67.90 Yes   29 72 MEN

Doyle 2013 126 1 (-0.594) UK 0.953 QE 27.50 100.00 15.87 Yes  5 45 COR

Golden 2002 90 3 (0.066) USA - QE 27.11 70.40 66.90 No  11 44 COR

Jotangia 2015 30 5 (0.183) UK 1.014 QE 38.39 0.00 - Yes  16 24 MEN

Nas 2005 56 4 (-0.340) NED 1.063 QE 16.83 100.00 - No   10 30 COR

Rees-Jones 2012 121 5 (-0.328) UK 2.210 QE 34.77 100.00 - Yes  16 24 MEN

Yip 2013 48 3 (-0.358) UK 2.210 QE 38.32 100.00 - Yes  16 24 MEN

Young 2012 31 3 (-0.238) UK 3.779 QE 40.25 100.00 12.90 Yes  15 22.5 MEN

Note. N = number of participants; # r (M) = number of effect sizes (mean d); country = country where the study was conducted; GER = Germany; POR = Portugal; NED = The Netherlands; UK = United Kingdom; USA = United States of America; impact factor = impact factor of journal; design = quasi-experimental (QE) or randomized controlled trial (RCT); age = mean age at start intervention; % male = percentage male in sample; % minority = percentage non-Caucasian; mental disorder = presence of a DSM-IV axis I diagnosis (yes or no); internal attr. = internal attribution for criminal behavior; external attr. = external attribution of criminal behavior; duration = duration of CBT in weeks; contact = total hours of intervention; setting = correctional facility (COR) or mental health facility (MEN).

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susceptible to bias, and may show larger effects than RCT-studies (MacLehose, Reeves, Harvey, Sheldon, Russell, & Black, 2000). With regard to sample characteristics, we coded age (continuous), proportion ethnic minority (i.e., non-Caucasian), and the presence of a mental disorder (i.e., DSM axis I-diagnosis: yes vs. no). Exploration of the data indicated a division of proportion ethnic minority in the study sample: studies with low proportion of ethnic minority in their sample (i.e., 20%), -and studies with high proportion of ethnic minority in their sample (i.e., 70%). Therefore, we chose to transform proportion ethnic minority into a dichotomous variable (i.e., 20% vs. 70%). Finally, we coded whether the comparison group received treatment as usual or waiting list, as it may be expected that this may influence the strength of the effect (Larun, Nordheim, Ekeland, Hagen, & Heian, 2006). In addition, the duration of incarceration (in weeks) was coded. Research on criminal attitudes showed that prisoners in interaction with criminal others adapt to each other’s values and norms, as well as their thinking patterns (Clemmer, 1940; Walters, 2003). Therefore, it may be expected that with the passage of incarceration duration individuals develop more criminal attitudes (Hyland & Boduszek, 2012). The last coded sample characteristics was type of offense (violent/ serious crimes [e.g., armed robbery, violent assault] vs. mixed [mix of minor offenses]). Valliant and Bergeron (1997) argue that criminal attitudes differ depending on the type of the offense, with more severe types of offending related to higher criminal attitudes. Since nine studies consisted of only male samples, we were not able to perform moderator analysis of gender. As for treatment characteristics, we coded duration of the intervention (in weeks), frequency of the intervention (in hours per week), and total hours of cognitive behavioral therapy. Finally, we coded the format in which the cognitive behavioral therapy was given (group vs. mixed).

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Effect sizes were transformed into Cohen’s d by using the calculator of Wilson (2013) and formulas of Lipsey and Wilson (2001). Cohen’s d were calculated based on reported means and standard deviations. A negative effect size indicates a larger decrease in criminal attitudes in the CBT intervention group than the comparison group. Effect sizes were calculated for both pre- and post-test. Pre-test effects were then subtracted from post-test effects to correct for pre-treatment differences. Continuous variables (i.e., proportion ethnic minority, frequency of intervention) were centered around the mean, while categorical variables were recoded into dummy variables. Outlying effect sizes were identified using a method based on standardized z-values. An outlier was defined as an effect size that is more than two standard deviations from the mean (-3.29 > z > 3.29; p <.001; Tabachnick & Fidell, 2013). Extreme outliers would be winsorized (i.e., replaced by the highest or lowest acceptable score falling in the normal range). However, no extreme outliers were identified.

By including multiple effect sizes per study the assumption of independent effect sizes, underlying traditional meta-analytic strategies, was violated (Hox, 2002; Lipsey & Wilson, 2001). To deal with the dependency of effect sizes within studies, we used a multilevel random effects model (Hox, 2002; Noortgate & Onghena, 2003). A multilevel approach allows for the hierarchical structure of the data, in which the effect sizes (the lowest level) are nested within studies (highest level). Therefore, maximum statistical power is generated (Wibbelink & Assink, 2015). In the current meta-analysis, we used a three-level random effects model to account for three levels of variance, including sampling variance for each effect size (level 1), the variance between effect sizes within a study (level 2), and the variance between studies (level 3) (Wibbelink & Assink, 2015). Analysis were conducted in the R environment (Version 3.3.0; R Core Team, 2015) with the metafor package, using a multilevel random effects model (Bussche, Noortgate, & Reynvoet, 2009; Houben, Noortgate, & Kuppens, 2015; Viechtbauer, 2010). Likelihood ratio tests were used to compare the

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deviance score of the full model to the deviance of the models excluding one of the variance parameters, making it possible to determine whether significant variance is present at level two or three (Wibbelink & Assink, 2015). Significant variance at level 2 or level 3 indicates a heterogeneous effect size distribution. In that case, moderator analyses were performed, as effect sizes could not be treated as estimates of a common effect size. Moderator analyses were performed in case each category of the potential moderator was filled with at least three studies.

Publication bias

In systematic reviews, the aim is to identify, retrieve, and include all previous conducted studies that meet inclusion criteria (Lipsey & Wilson, 2001). However, studies reporting non-significant results are less likely to be published than studies reporting significant results, referred to as publication bias or file drawer problem (Rosenthal, 1994). To examine the possibility of publication bias, we conducted the funnel-plot-based trim and fill method as described by Duval and Tweedie (2000a, 2000b) using the “trimfill” function of the metafor package (Viechtbauer, 2010) in the R environment (Version 3.3.0; R Core Team, 2015). In short, the trim-and fill method corrects the symmetry of an asymmetric funnel plot by imputing missing effect sizes, calculated based on existing effect sizes. In case the trim- and fill plot imputed effect sizes of missing studies, we included these estimations and reran the multilevel random effects model (Duval & Tweedie, 2000). Second, we calculated Rosenthal’s (1995) fail-safe number: the number of studies averaging null effects that would have to be added before the overall result increased to a non-significant result (p ≥ .05). In case the fail-safe number is larger than the critical value (5 * k + 10, in which k is the number of effect sizes in the current meta-analysis), it can be concluded that publication bias is unlikely (Rosenthal, 1991).

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Effect of cognitive behavioral therapy on criminal attitudes

The present study on the effect of cognitive behavioral interventions on criminal attitudes consisted of 11 independent studies (s), reporting on 48 effect sizes (k) from 2002 to 2015. The total sample of N = 795 consisted of n = 422 subjects who received cognitive behavioral therapy, and n = 373 subjects constituted the comparison group. In accordance to the criteria of Cohen (1988) for interpreting the magnitude of effect sizes, a significant small-to-medium effect (d = -0.314; p <.001) of cognitive behavioral interventions on criminal attitudes was found, indicating that cognitive behavioral interventions reduced criminal attitudes among juvenile- and adult offenders. Funnel plot examination suggested the presence of publication bias, as the trim and fill plot in Fig. 1. (Appendix D) shows the imputation of three estimated effect sizes with a negative effect size (represented by the white dots) on the left side of the funnel. This indicates the absence of studies reporting that juvenile- and adult offenders in the CBT intervention group showed a larger decrease in criminal attitudes than the comparison group. After imputing missing effect sizes to the data, the overall effect of cognitive behavioral interventions on criminal attitudes remained a significant small-to-medium effect (d = -0.402; p <.001). This indicates that, when controlled for publication bias, the true effect of cognitive behavioral therapy on criminal attitudes is somewhat stronger than before controlling for publication bias. Second, we computed a fail-safe N value, which showed that 973 studies with a mean effect size of 0 would need to be added to yield a non-significant summary effect. This result exceeded Rosenthal’s benchmark of 65 (5 * k + 10), suggesting that our findings are robust to the threat that excluded studies might have yielded a non-significant effect.

Moderator analysis

Concerning the heterogeneity of the effect sizes, the likelihood ratio test comparing models with and without between-study variance (level 3) indicated that significant variance

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was present at the between-studies level (σ2level3 = 14.65, χ2 (1) = -13.81, p <.001), indicating

a heterogeneous effect size distribution. The variance between the effect sizes within studies (level 2) was not significant (σ2

level2 = 0.00, χ2 (1) = -6.49, p = 1.000). Since the variance was

significant on the third level, moderator analyses of outcome-, study-, sample-, and treatment characteristics were conducted to assess the strength of potential moderators on the effect of cognitive behavioral therapy on criminal attitudes. Results of moderator analyses are presented in Table 2.

Outcome characteristics. The attribution by which offenders attempt to construct causal explanations for their criminal behavior (i.e., internal- versus external attribution) did not moderate the effect of cognitive behavioral therapy on criminal attitudes. Results indicate that the effect of cognitive behavioral therapy on criminal attitudes did not deviate for offenders who perceive the cause of criminal behavior as being ‘located’ within the individual compared to offenders who have the tendency to excuse their criminal behavior by blaming the crime on social circumstances, victim, or society.

Study characteristics. The year of publication, the impact factor of the journal in which the study was published, the design of the study (i.e., randomized controlled trial versus quasi-experimental), and setting of the study (i.e., correctional- versus mental health facility) did not moderate the effect of cognitive behavioral therapy on criminal attitudes. Even though differences in effect sizes of correctional- versus mental health facilities appear quite substantial (d = -0.179, p = 0.138 vs. d = -0.420, p < .001, respectively), treatment setting was not identified as a significant moderator.

Sample characteristics. The proportion ethnic minority (i.e., non-Caucasian) significantly moderated the effect of cognitive behavioral therapy on criminal attitudes (F(1, 25) = 5.044; p <.001). Studies with a larger proportion non-Caucasian in their sample (i.e., 70%) were found to yield larger effect sizes compared to studies with lower proportion

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non-Caucasian (i.e., 20%). Results indicate that studies with higher proportion ethnic minority in their sample yielded larger effects sizes of the effect of cognitive behavioral therapy on criminal attitudes compared with studies with lower proportion ethnic minority in their sample.

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COGNITIVE BEHAVIORAL THERAPY AND CRIMINAL ATTITUDES 20 Table 2

The overall results and moderator effects on the relationship cognitive behavioral interventions on criminal attitudes

# studies #

ES

β₀ (mean d) SE t0 P β₁ SE t1 p F(df1, df2)

Overall effect of CBT on criminal attitude 11 48 -0.312 0.084 -3.728 0.001

After trim and fill procedure 14 51 -0.402 0.092 -4.370 0.001

Moderator variables

Type of criminal attitude

Internal attribution (RC) 11 38 -0.300 0.118 -2.552 0.014

External attribution 4 10 -0.314 0.086 -3.652 0.001 -0.014 0.100 -0.145 0.886 F(1, 46) = 0.021

Study descriptors

Publication year (continuous) 11 48 -0.318 0.084 -3.784 0.001 -0.020 0.022 -0.923 0.361 F(1, 46) = 0.852 Impact factor (continuous) 10 45 -0.316 0.079 -3.999 0.001 0.061 0.045 1.376 0.176 F(1, 43) = 1.893

Design of the study

Quasi-experimental (RC) 9 32 -0.357 0.200 -1.787 0.081

Randomized controlled trial 2 16 -0.301 0.099 -3.037 0.004 0.056 0.223 0.250 0.804 F(1, 46) = 0.804

Setting of the study

Correctional facility 6 20 -0.179 0.118 -1.510 0.138

Mental health facility 5 28 -0.420 0.107 -3.921 0.001 -0.242 0.160 -1.514 0.137 F(1, 46) = 0.063

Sample descriptors Age Adult (RC) 8 36 -0.474 0.149 -3.187 0.003 Juvenile 3 12 -0.245 0.095 -2.571 0.013 0.230 0.177 1.300 0.200 F(1, 46) = 1.689 Percentage minority More than 50% (RC) 3 19 -0.551 0.142 -3.880 0.001 Less than 50% 3 8 -0.134 0.119 -1.127 0.271 0.417 0.185 2.246 0.034 F(1, 25) = 5.044

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Duration of incarceration (continuous) 7 34 -0.326 0.104 -3.125 0.004 -0.003 0.002 -1,266 0.214 F(1, 32) = 1.604

Mental disorder

Yes 6 29 -0.393 0.123 -3.205 0.002

No 5 19 -0.239 0.116 -2.055 0.046 0.155 0.169 0.915 0.365 F(1, 46) = 0.837

Type of comparison group

Waiting list 6 20 -0.424 0.116 -3.667 0.001

Treatment as usual 5 28 -0.203 0..114 -1.790 0.080 0.221 0.162 1.362 0.180 F(1, 46) = 1.855

Treatment descriptors

Duration of intervention (continuous) 11 48 -0.327 0.087 -3,772 0.001 -0.006 0.007 -0.866 0.391 F(1, 46) = 0.751 Frequency of intervention (continuous) 11 48 -0.312 0.088 -3.528 0.001 -0.005 0.085 -0.064 0.950 F(1, 46) = 0.004 Total contact hours (continuous) 10 45 -0.336 0.097 -3.482 0.001 -0.005 0.006 -0.828 0.412 F(1, 46) = 0.686

Treatment format

Group (RC) 7 32 -0.197 0.146 -1.345 0.185

Mixed (group and individual) 4 16 -0.371 0.105 -3.535 0.001 -0.174 0.180 -0.965 0.340 F(1, 46) = 0.931

Cognitive restructuring Yes (RC) 7 27 -0.336 0.144 -2.343 0.024 No 4 21 -0.297 0.113 -2.639 0.011 0.039 0.182 0.215 0.831 F(1, 46) = 0.046 Anger management No (RC) 8 37 -0.208 0.159 -1.308 0.197 Yes 3 11 -0.354 0.102 -3.485 0.001 -0.146 0.189 -0.772 0.444 F(1, 46) = 0.595 Emotional control Yes (RC) 6 32 -0.378 0.127 -2.979 0.005 No 5 16 -0.255 0.118 -2.170 0.035 0.123 0.173 0.711 0.481 F(1, 46) = 0.505 Critical reasoning No (RC) 6 20 -0.179 0.118 -1.510 0.138 Yes 5 28 -0.420 0.107 -3.921 0.001 -0.242 0.160 -1.514 0.137 F(1, 46) = 2.291 Note. # studies = number of independent studies; # ES = number of effect sizes; β₀ = intercept/ mean effect size (d); SE = standard error; t0 = difference in

mean d with zero; β₁ = estimated regression coefficient; t1 = difference in mean d with reference category; F(df1, df2) = omnibus test; RC = reference category +_{p < .10; * p < .05; ** p < .01; *** p < .001}

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In addition, no moderating effects were found for age (juvenile- versus adult offenders), duration of incarceration (in weeks), presence of DMS axis-I diagnosis, and type of comparison group (waiting list- versus TAU). Although, it should be noted that the differences in effect size of juvenile versus adult offenders (d = 0.245, p = .013 vs. d = -0.474, p = .003, respectively), and waiting list- versus TAU comparison group (d = -0.424, p <.001 vs. d = -0.203, p = .080, respectively) seemed quite substantial, these were not identified as significant moderators, possibly as a consequence of low statistical power.

Intervention characteristics. No moderating effects were found for intervention characteristics. More precisely, the duration and frequency of the intervention, the total hours of contact, treatment format (group vs. mixed), whether or not the intervention consisted of cognitive restructuring, anger management, emotional control, and critical reasoning did not moderate the effect of cognitive behavioral interventions on criminal attitudes. Finally, the difference in effect size of whether or not the intervention aimed to teach participants critical reasoning seemed quite substantial (d = -0.420, p <.001 vs. d = -0.179, p = 0.138, respectively), but was not identified as a significant moderator.

Discussion

Several well-conducted meta-analyses based on a range of rehabilitation programs, offender types, outcome variables, and quality of study design provide strong indications for the effectiveness of cognitive behavioral therapy for reducing the probability of (re-)offending among juvenile- and adult offenders. Cognitive behavioral treatment programs are designed to alter dysfunctional and criminogenic thinking patterns (i.e., criminal attitudes), and as a consequence reduce the risk of (re-)offending. Although, criminal attitudes are considered the main hypothesized mediator of change in cognitive behavioral therapy (Lipsey et al., 2007), no systematic review has statistically summarized the strength and direction of the effect of cognitive behavioral therapy on criminal attitudes. Therefore, the present multilevel

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meta-analytic review aimed to generate more specific knowledge on the effect of cognitive behavioral therapy (CBT) on criminal attitudes among juvenile-and adult offenders.

Overall, we found a significant small-to-moderate effect size (d = -0.314), supporting the hypothesis that juvenile- and adult offenders in a cognitive behavioral intervention group showed a larger decrease in criminal attitudes compared to comparison groups (Gendreau & Andrews, 1990). Trim-an-fill procedures suggested the presence of publication bias. After imputing missing effect sizes to the data, the overall effect of CBT on criminal attitudes remained a significant small-to-medium effect (d = -0.402). Therefore, we conclude that cognitive behavioral interventions are effective in reducing criminal attitudes among juvenile- and adult offenders. However, the distribution of effect sizes was heterogeneous, indicating variation between effect sizes across studies. Therefore, moderator analyses were conducted to examine the influence of outcome, -study, -sample, -and treatment characteristics.

Moderator analyses showed only the proportion ethnic minority (i.e., non-Caucasian) moderated the effect of cognitive behavioral intervention on criminal attitudes. Studies with a larger proportion non-Caucasian in their sample (i.e., 70%) were found to yield larger effect sizes. This is especially interesting given that previous studies did not report differences in treatment effects between Caucasian and ethnic minority offenders (Wilson, Lipsey, & Soydan, 2003). Lipsey (2009) argues that intervention effectiveness is associated with type of treatment, the quantity and quality of treatment implementation, and characteristics of the offender. The duration and frequency of the intervention did not deviate in ethnic minority- and Caucasian offenders, and thus could not explain current research findings. Therefore, differences in effectiveness of cognitive behavioral therapy between ethnic minority- and Caucasian offenders could possibly be attributed to individual- and contextual factors, which were not included in the current meta-analysis.

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In addition, no moderating effects were found for outcome (internal versus external attribution), -study (i.e., publication year, impact factor, design of the study, and setting of the study), -sample (i.e., age, duration of incarceration, presence of Axis I mental disorder, and type of comparison group), and treatment characteristics (i.e., duration of the intervention, frequency of intervention, total contact hours, and treatment format), suggesting that the studied CBT interventions were equally effective and equally suitable for various recipients and settings in decreasing criminal attitudes among delinquent- and criminal offenders. The relative robustness of cognitive intervention across juvenile- and adult offenders in both correctional- and mental health facility gives support to previous studies indicating the effectiveness of cognitive behavioral therapy based on a range of offender types, outcome variables, and setting (Landenberger & Lipsey, 2005).

Limitations

Several limitation of the present meta-analytic study should be taken into account. First, only eleven studies were found that met inclusion criteria of this meta-analysis. Currently, there appears to be a limited amount of research on the effectiveness of CBT on criminal attitudes which is surprising given (1) the extensive implementation of CBT as judicial intervention, and (2) criminal attitudes is considered as the main hypothesized mediator of change in cognitive behavioral therapy. One reason for this may be the use of different terminology and conceptual perspectives (i.e., social cognition [Blackburn, 1993], criminal attitudes [Simourd, 1997], criminal thinking [Walters, 2014], cognitive distortion [Barriga, Gibbs, Potter, & Liau, 2001, p. 1], and antisocial cognition [Andrews et al., 1990]), which have been used interchangeably. As a result, studies on the effectiveness of cognitive behavioral therapy on criminal attitudes could be harder to find in online databases. Second, the quality of some of the included studies was low. Various studies had small samples, and did not always provide enough information to code all the moderators. As a consequence of

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low statistical power, moderators with small to moderate effects are likely not to be identified. Differences in effect sizes of correctional- versus mental health facility seemed quite substantial (d = -.179 vs. d = -.420, respectively), but was not identified as a significant moderator. The same was observed for juvenile- versus adult study samples (d = -.245 vs. d = -.474, respectively), waiting list- versus TAU comparison group (d = -.424 vs. d = -.203, respectively), and whether or not the intervention aimed to teach participants critical reasoning (d = -.420 vs. d = -.179, respectively). Therefore, results of moderator analyses should be interpreted with caution. Finally, the studies included in the current meta-analysis consisted of general juvenile- and adult offenders. Studies that were solely focused on one type of specific offending behavior (e.g., sexual offenses, drug offenses, DUI) were excluded. As described by Moffitt (1993), developmental trajectories toward specific offending behavior are different from general offending behavior. Specific offending behavior can be explained by specific risk factors that are different from general types of offending behavior (e.g., Becker, 1990; Worling & Längstrom, 2006), and thus may not be comparable. As a result, the current meta-analytic review offers limited opportunity to determine the effectiveness of CBT for specific types of offending behavior, which negatively influence generalizability.

Despite these limitations, the current meta-analytic review is the first to examine the effectiveness of cognitive behavioral therapy on criminal attitudes. In addition, a three-multilevel random effects model was used, which increased statistical power and facilitate a more extensive moderator analysis. Furthermore, the findings of the current meta-analysis yield some important implications for future research and policy. Findings of the current meta-analysis provide support for the continuation of the implementation of cognitive behavioral therapy for decreasing criminal attitudes among juvenile- and adult offenders in both correctional- and mental health facilities. This finding is in line with previous conducted

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meta-analyses into the effectiveness of cognitive behavioral therapy based on a range of rehabilitation programs, offender types, and setting (e.g., Gendreau & Andrews, 1990; Lipsey et al., 2007; Landenberger & Lipsey, 2005). In addition, moderator analyses indicated that differences in effect sizes from several individual- and contextual factors seemed quite substantial, but were not identified as significant moderators. Possibly as a consequence of low statistical power. Therefore, future research should focus on high quality intervention evaluations to gain more insight in the influence of individual- and contextual factors on the effect of CBT on criminal attitudes (juvenile- vs. adult, correctional facility vs. mental health, especially). However, future research should not solely focus on criminal attitudes, although criminal attitudes is considered as the main hypothesized mediator of change. Criminal attitudes are the result of individual experience, environmental circumstances, and reinforcement histories. The development and persistence of criminal behavior is complex and multi determined. Therefore, we argue that criminal attitudes should be one of a series of variables to consider, along with substance abuse, criminal history, family dynamic, interaction with criminal others, and employment (Andrews & Bonta, 2010).

Conclusion

In sum, the present multilevel meta-analytic study showed that cognitive behavioral therapy appears to be effective in altering dysfunctional and criminogenic thinking patterns among juvenile- and adult offenders. As a consequence of low statistical power, moderator analyses from several individual- and contextual factors seemed quite substantial, but were not identified as significant moderators. Therefore, future research should focus on high quality intervention evaluations to identify individual- and contextual factors influencing the effectiveness of cognitive behavioral therapy on criminal attitudes. It is expected that increasing knowledge on individual- and contextual factors improves the effectiveness of cognitive behavioral therapy. All in all, we argue that results of our meta-analytic review

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should be used as a baseline for future studies examining the effectiveness of cognitive behavioral therapy on criminal attitudes.

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*Young, S., Hopkin, G., Perkins, D., Farr, C., Dodge, A., & Gudjonsson, G. (2012). A controlled trial of a cognitive skills program for personality-disordered offenders. Journal of Attention Disorders, 17, 598 – 607. Doi: 10.1177/1087054711430333

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1. Population

delinq* or crimin* or offen* or adjud* or probat* or incarcerat* or detent* or court* or justice*

2. Intervention program

treatment* or intervent* or program* or training* or therap* or rehabilitat*

3. Cognitive behavioral therapy

cognitive-behavioral or CBT or (cognit* and behavior*) or reconat* or reasoning* or restrict* or problem solving* or change or replacem* or coping skill* or (cognit* and restruc*) or behavior modific*

4. Criminal attitude

(crimin* and think*) or (crimin* and 39ttitude*) or (hostil* and 39ttitude39*) or (cognit* and distort*) or (antisoc* and 39ttitude*) or (antisoc* and think*) or (violen* and cognit*) or (think* and styl*) or (attit* and law* and violat*) or (attit* and court*) or (attit* and police*) or (attit* and law*) or (attit* and crimin* and other*)

(40)

2511 titles screened, abstracts read and methods briefly screened Web of Knowlegde : 728 ScienceDirect : 418 Wiley : 94 Google Scholar : 499 Proquest : 422 EBSCOhost : 46 PsychInfo : 304

152 full texts examined Reasons for exclusion:

Review article : 6

Independent variable no CBT : 22 No suitable dependent variable : 20 No control group available : 21

No full text available : 29 No criminal offender : 13 Qualitative study : 3 Total excluded: 114

11 studies (with 48 effect sizes) included in present meta-analysis Appendix B

Flow chart of search strategy

38 full texts thoroughly examined Reasons for exclusion:

Unable to calculate effect size : 13 Independent variable no CBT : 6 No suitable dependent variable : 5 No control group available : 3 Total excluded: 27