Changing Criminal Attitudes Among Incarcerated Offenders

Abstract

The present study investigated the effect of a criminal attitude treatment program to changes on measured criminal attitudes and postprogram recidivism. The criminal attitude program (CAP) is a standardized therapeutic curriculum consisting of 15 modules offering 44 hr of therapeutic time. It was delivered by trained facilitators to a total of 113 male offenders incarcerated in one of five state correctional institutions. Pretreatment and posttreatment comparisons were made on standardized measures of criminal attitudes, response bias, and motivation for lifestyle changes. Results found statistically significant lower criminal attitudes at posttreatment that were unaffected by response bias. There were also increases in motivation for lifestyle changes, but these did not reach statistical significance. Fifty-seven participants were released into the community following the program and were eligible for recidivism analyses. Comparisons between participants who completed the CAP and those who did not complete the CAP revealed 7% lower rearrest among CAP completers. Although preliminary, these results indicate that the CAP had a positive effect on changes to criminal attitudes and recidivism. The findings are discussed in terms of conceptual and practical considerations in the assessment and treatment of criminal attitudes among offenders.

Keywords

criminal attitudes offender rehabilitation

There is a broad consensus in the social-psychological literature that attitudes play a significant role in behavior. It is also widely accepted that the relationship between how or what a person thinks and how they act is complex and subject to considerable influence. The theory of reasoned action (Ajzen & Fishbein, 1980) is one of the most well-known models of the attitude–behavior relationship and illustrates the complexity between cognition and action. Accordingly, there is a sequence of events that begins with certain attitudes about a behavior (“attitude toward the behavior”), which are then evaluated against perceived social reactions (“subjective norm”), which give rise to the formation of self-instructions whether or not to engage in a certain act (“intention”). Research spanning a wide spectrum of attitude–behavior relationships has found support for this model, with intention playing a particularly strong role (Webb & Sheeran, 2006).

Linkages Between Criminal Attitudes and Criminal Behavior

Attitudes, specifically criminal attitudes, also have a robust history in the criminological and forensic psychological literature. At least two popular theories of criminality (e.g., Differential Association, Sutherland, 1947; Techniques of Neutralization, Sykes & Matza, 1957) place significant weight on the issue of criminal attitudes. Sykes and Matza (1957) proposed that criminal thinking involved reasons or excuses to justify committing offenses; within their model, such thinking could just as well occur before or after a criminal act was committed. Sutherland’s (1947) model stressed the role of social learning influences in the acquisition of criminal behavior, through exposure to antisocial role models with whom the individual associates, and a resultant formation of antisocial attitudes. Although these theories were first introduced over 50 years ago, they continue to receive recognition in present day literature. D. A. Andrews and Bonta (1994, 2010) designated criminal attitudes to be one of the “big four” covariates of criminality and assign them a primary role in their general personality and cognitive social learning (GPCSL) theory of criminal conduct. More specifically, situated in conjunction with an antisocial peer network, antisocial personality pattern, and history of antisocial behavior, antisocial attitudes feature prominently in the origin and maintenance of criminal behavior (D. A. Andrews & Bonta, 1994, 2010). Among these approaches is a common theoretical thread woven throughout a tapestry of attitude–criminal behavior relationships; that is, attitudes matter. They are relatively stable (yet changeable) affect–cognition–behavior conglomerates with causative relevance in the origin and maintenance of criminal behavior, although the mechanisms by which attitudes form and are made manifest in behavior may vary with specific models of attitude change.

The relevance of criminal attitudes in antisocial conduct is not merely a theoretical proposition, it is also supported by empirical evidence. The advent of meta-analytic techniques, which allows for the quantitative aggregation of various phenomena under consideration, has been particularly illuminating in this regard. Gendreau, Little, and Goggin (1996) were among the first to investigate the relationship between various risk factors and recidivism among adult male offenders using meta-analytic procedures. They found that a composite measure of “criminal peers” and “criminal attitudes” was the strongest predictor of male adult offender recidivism. Subsequent meta-analyses examining adult prison misconducts (Gendreau, Goggin, & Law, 1997), juvenile offenders (Cottle, Lee, & Heilbrun, 2001; L. Simourd & Andrews, 1994), and sexual offenders (Helmus, Hanson, Babchishin, & Mann, 2013) have replicated the substantive findings that criminal attitudes are a robust risk factor for criminality among various offender types and circumstances. A specific meta-analysis dedicated to the examination of criminal attitudes in the intervention and treatment process (Banse, Koppehele-Gossel, Kistemaker, Werner, & Schmidt, 2013) extended and replicated the findings of previous meta-analyses, and assigned a causal role to criminal attitudes in criminal behavior.

Criminal Attitude Intervention Models and Programs

Understanding the theoretical mechanisms by which criminal conduct occurs and knowing the relative importance of certain risk factors for such behavior are important, but it is equally important to understand whether criminal behavior can be modified and, if so, what methods may best achieve change among correctional clients. Compelling research has accumulated over the years that indicate correctional rehabilitation programs produce positive outcomes, particularly in reducing recidivism. The seminal meta-analysis of the correctional treatment literature by D. A. Andrews, Zinger, et al. (1990) found that offenders who participated in therapeutic activities had an approximate 10% lower rate of recidivism compared with those offenders who did not participate in treatment. Reductions in recidivism were enhanced in treatment contexts in which the guiding model of Risk, Need, and Responsivity (RNR; D. A. Andrews, Bonta, & Hoge, 1990) was followed. That is, rehabilitation efforts can be optimized when (a) the intensity of services is titrated to the criminal potential of the client (Risk)—higher risk clients receive more intensive services; (b) rehabilitation services attend to the key criminogenic characteristics of the client (Need), with criminogenic characteristics being those related to crime; and (c) services are delivered that are attuned to clients’ learning styles/abilities (Responsivity)—such that the opportunities for learning and behavior change are greatest. Indeed, the D. A. Andrews, Zinger, et al. (1990) meta-analysis demonstrated that “appropriate” services (i.e., broadly adhering to RNR) were associated with a 30% reduction in future criminal behavior. Further research on this model (D. A. Andrews & Bonta, 2010; D. A. Andrews & Dowden, 2005) has found a strong proportional link between adherence to the RNR principles and reductions in recidivism—the greater the adherence to the principles, the greater reductions in recidivism.

Given that criminal attitudes are a robust factor linked to criminal conduct and that meta-analytic reviews document the positive effect offender treatment has on altering offender behavior, it seems logical that the treatment of criminal attitudes would be a popular topic in the broader correctional literature and applied work with offenders. Unfortunately, this does not appear to be the case. More than 10 years ago, D. J. Simourd and Olver (2002) noted that the treatment of criminal attitudes was virtually absent in corrections, and there has been only a modest change in the intervening period. One reason for this may be the use of different terminology and conceptual perspectives. For example, terms such as social cognition (Blackburn, 1993), criminal attitudes (D. J. Simourd, 1997), and criminal thinking (Mitchell & Tafrate, 2012; Walters, 2014) have all appeared in the literature and are often used interchangeably. From a conceptual perspective within the RNR model, criminal attitudes have traditionally been regarded as a criminogenic need factor within the RNR model, which is defined as a “subset of offender attributes that, when changed, are associated with changes in the probability of recidivism” (D. A. Andrews & Bonta, 1994, p. 176). Recently, however, there has been a suggestion that criminal attitudes also represent a responsivity issue within the RNR model (e.g., Mitchell, Tafrate, Hogan, & Olver, 2013; Taxman, Rhodes, & Dumenci, 2011). The focus of the present study was on the treatment of criminal attitudes rather than a discussion of definitional and conceptual issues. As such, the notion of criminal attitudes is considered to be those attitudes, values, beliefs, and rationalizations related to criminality, and these will be considered as criminogenic need factors in the tradition of D. A. Andrews and Bonta (1994, 2010).

Another reason for the dearth of attention to criminal attitudes may be the relative lack of treatment programs focusing on them. Several years ago, Walters (1999) described a treatment approach that was linked to a criminal thinking assessment instrument, which demonstrated positive outcomes. More recently, Kroner and Morgan (2014, see also Kroner & Yessine, 2013) and Walters (2014) have described approaches to criminal attitude treatment by way of cognitive-behavioral techniques. Although these approaches have merits, these are more conceptual strategies than detailed practical curricula for directly changing criminal attitudes. D. J. Simourd and Olver (2002) have distinguished between indirect and direct criminal attitude interventions. Indirect programs are those in which attention to criminal attitudes is a secondary treatment target to other issues, whereas in direct programs the restructuring of criminal attitudes is the primary treatment target. The Cognitive Living Skills (Ross & Fabiano, 1985) and Cognitive Self-Change (Bush & Bilodeau, 1993; Powell, Bush, & Bilodeau, 2001) programs are examples of indirect programs in which decision-making skills is the primary therapeutic target. Banse et al. (2013) provided a detailed review of programs to address criminal attitudes, which appear to be a mixture of broader indirect as well as direct approaches. Some of the most detailed descriptions of direct criminal attitude programs (CAPs; of which there appear to be comparatively few), however, are rather dated (e.g., D. A. Andrews, Wormith, Kennedy, & Daigle-Zinn, 1977; D. A. Andrews, Young, Wormith, Searle, & Kouri, 1973). Parenthetically, an excellent, albeit brief, mention of the intent of direct CAPs can be found in D. A. Andrews and Bonta (2010).

The CAP (D. J. Simourd, 2007) was developed in part to address some of the shortcomings of previous criminal attitude treatment initiatives. The CAP is a direct criminal attitudes treatment program that integrates contemporary correctional theory, research and practice into the curriculum. It is a cognitive-behavioral program that focuses specifically on the attitudes, values, beliefs, and rationalizations conducive to criminal conduct. The program consists of 15 modules and 22 two-hour sessions, which use a variety of instructional techniques such as lectures, discussions, movies, role-plays, and homework. Brief information on the CAP is presented in Table 1.

Table 1.

Criminal Attitude Program Modules, Content, and Number of Sessions.

Module	Content	No. of sessions
1. Preassessment	Client interview, preassessment testing	1
2. Introduction	Program outline, expectation, rules	1
3. Assessment review	Feedback of preassessment testing	1
4. Definitions (attitudes)	Attitudes, attitudes–behavior	1
5. Movie	Pretreatment attitude identification	1
6. Definitions (crime)	Crime, criminal attitudes–behavior	2
7. Moral reasoning	Morals, morals–behavior	1
8. Criminal rationalizations	Justifications	2
9. Cycle of offending	Motivation–situation–attitude model	2
10. Risk factors	Big 10 predispositions to crime	2
11. Companions	Criminal values, companions	2
12. Prosocial skills	Being a “straight-up”	2
13. Relapse prevention	Avoiding temptations and problems	2
14. Movie	Posttreatment attitude identification	1
15. Postassessment	Postassessment testing	1

One feature of the CAP is the inclusion of a comprehensive assessment and a program evaluation process integrated into the CAP curriculum. Pretreatment and posttreatment assessment measures, content tests, graded homework, and facilitator ratings of client performance are all part of the CAP curriculum, which allows for relatively straightforward evaluation of client progress and program evaluation. To date, the CAP has been implemented within a private community corrections agency (D. J. Simourd & Blette, 2008), but the program was not subject to systematic evaluation. More recently, the CAP was implemented in several correctional institutions of the Alaska Department of Corrections (ADOC), which created the opportunity to examine its effectiveness in that setting.

Present Study

In summary, there is a wide recognition that criminal attitudes are linked to criminal conduct but there has been limited attention to the issue of treatment-induced changes to criminal attitudes. The CAP is a structured treatment program that directly addresses criminal attitudes; however, there are no extant data as to its effectiveness. The present study sought to examine the effectiveness of the CAP among offenders incarcerated within a State correctional organization. Comparisons were made on pretreatment and posttreatment psychometric measures of criminal attitudes, response bias, and motivation for lifestyle changes. Comparisons were also made on the reoffending rates between those who completed the CAP and those who did not. It was expected that CAP participants would have reduced criminal attitudes as reflected in lower criminal attitude measure scores postprogram. It was also expected that those offenders participating in the CAP would have lower reoffending rates than those who did not participate in the CAP.

Method

Setting and Program

The ADOC is a government-run correctional agency that provides custody and community supervision to offenders. Offenders with custody dispositions are housed in 1 of 13 in-State and 1 out-of-State institutions that are of multilevel security (e.g., minimum, medium, and maximum). One institution houses only female offenders, whereas the others house both males and females in separate areas. Other than the female-only institutions, there are no significant differences in the constitution between institutions. There are approximately 5,100 offenders incarcerated within ADOC facilities. As is common among State-operated institutions, various rehabilitation programs are available among the ADOC institutions such as educational upgrading, employment readiness, substance abuse treatment, and domestic violence reduction. The ADOC sought to augment these rehabilitative resources by implementing the CAP.

The CAP curriculum is set out in the Facilitator’s Manual, which includes all material to deliver the program. As noted above, a considerable emphasis within the CAP is placed on the client performance measurement, which is integrated into the curriculum. A standardized facilitator training curriculum exists consisting of separate Basic Training and Booster Training sessions. The Basic Training is a 4-day training that involves background information and detailed discussion of the components of the CAP Manual. The Booster Training is a 1-day refresher training that involves review of progress to date and offers remedies for any difficulties. This training occurs after CAP trainees have had the opportunity to facilitate at least one CAP group. The ADOC selected “education coordinators,” who are registered teachers, as facilitators of the CAP in all institutions where the program was delivered. CAP Basic Training was conducted in a group format for all potential CAP facilitators, after which the CAP was available to offenders at various ADOC institutions. CAP Booster Training was conducted in a group format with all CAP facilitators approximately 6 months later. In addition, periodic contact occurred between the CAP developer and the ADOC administrative staff who had oversight of the CAP.

Participants

Participants were 113 male inmates with a mean age of 34.0 (SD = 9.4; range = 20-61). Ninety-five percent of participants had a least one prior arrest, which ranged between 1 and 15 (M = 5.6, SD = 3.5). Broad designation of participant’s current arrest revealed that the majority of participants (81.4%) had current arrests for felony offenses, followed by parole violations (16.8%) and misdemeanor offenses (1.8%). Participants were housed in one of five institutions, although there were disproportionate contributions of data from these institutions, with one institution providing 62.8% of the data and the others providing proportionally less (e.g., 11.5%, 10.6%, 9.7%, and 5.3%, respectively). To determine the comparability of participants, comparisons were made on two criteria (age and number of prior arrests) across both institutional setting and offense designation. For institutional setting, there were no statistically significant differences between participants on age, F(4, 112) = 0.5, n.s., or number of prior arrests, F(4, 109) = 0.8, n.s. For offense designation, there were no statistically significant differences on age, F(2, 112) = 1.5, n.s, or number of prior arrests, F(2, 109) = 1.1, n.s. These findings suggest that the participants were comparable across institutions for the purposes of the present study.

Measures

Psychometric tests were used to assess the domains of criminal attitudes, amenability for change, and response bias. Given the CAP is oriented toward criminal attitudes, it was logical and essential to include a measure of criminal attitudes. The domain of client amenability for change was examined primarily because discussion in the broad psychological literature (e.g., Miller & Rollnick, 2002) has suggested that motivation plays a role in therapeutic success. The domain of response bias, defined as “a systematic tendency to respond to a range of questionnaire items on some basis other than the specific item content” (Paulhus, 1991, p. 17), is widely recognized in applied psychology as having a potentially confounding influence on psychometric test results. Inclusion of an index of response bias can serve as a gauge to the validity of psychological test data.

Criminal Sentiments Scale–Modified (CSS-M)

The CSS-M (Shields & Simourd, 1991) is a 41-item self-report instrument measure that measures criminal attitudes, values, beliefs, and justifications directly related to criminal activity. There are five subscales: Attitudes Toward the Law, Attitudes Toward the Courts, Attitude Toward the Police, Tolerance for Law Violations, and Identification With Criminal Others. The first three subscales are typically combined to form the Law-Court-Police subscale that assesses respect for the law and criminal justice system. The Tolerance for Law Violations subscales assesses specific justifications for law violations and the Identification with Criminal Others measures personal evaluative judgments about law violators. The CSS-M is scored according to a 3-point Likert-type scale with each endorsement of an antisocial statement yielding 2 points, a rejection of an antisocial statement (or acceptance of a prosocial statement) yielding 0 points, and undecided responses yielding 1 point. Higher scores on the CSS-M reflect greater criminal attitudes. Research (D. J. Simourd & Olver, 2002) attests to the psychometric integrity and construct validity of the CSS-M.

Self-Improvement Orientation Scheme–Self-Report (SOS-SR)

The SOS-SR (D. J. Simourd & Olver, 2011) is a 72-item self-report instrument designed to measure the degree to which a person possesses skills, attributes, and circumstances related to behavior change. It consists of a Total score, which is the sum of all items, and 12 subscales that reflect different amenability to change domains. The SOS-SR is scored using a 5-point Likert-type scale, with higher scores reflecting greater motivation to change. Research (D. J. Simourd & Olver, 2011) shows that the SOS-SR has acceptable psychometric properties and construct validity among offenders.

Marlow–Crowne Social Desirability Scale (MCSDS)

The MCSDS (Crowne & Marlowe, 1960) is a 33-item self-report instrument designed to measure response bias, or faking. It consists of a Total score, which is the sum of all items. Higher scores on the MCSDS reflect a greater response bias. Research (P. Andrews & Meyer, 2003; Tatman & Schouten, 2008) has found the MCSDS to be valid in forensic contexts.

Procedure

Participants volunteered for the CAP. A standardized advertisement describing the program and contact information was posted in various locations within the institutions. Interested participants approached CAP facilitators, and an appointment was scheduled at which time detailed information related to the program was provided to the participant and the pretreatment battery of psychometric measures was administered. These occurred either individually or in small groups. No information is available regarding the number of participants who initially approached CAP facilitators and the number who eventually began the program. The CAP was delivered in a group format with a maximum of 10 participants per group, although this varied across groups and each site due to factors such as initial available pool of participants, dropouts, and so on. Groups were conducted either midmorning or midafternoon to accommodate the CAP facilitators and institutional schedules. Sessions were 1 to 1½ hr in length, and were conducted twice a week for approximately 8 to 10 weeks, until all components of the CAP were completed.

Data analyzed in the present study were collected from two main sources: CAP facilitators and archival data from the ADOC electronic database. CAP facilitators entered data into the database that included basic information such as institution, client date of birth, CAP start and end date, and total scores of the psychometric tests, both pretreatment and posttreatment. Data entry was conducted after their program cohorts were completed.

Recidivism information was collected from the ADOC electronic database. The criterion variable used in the present study was postrelease arrest, which was scored dichotomously (yes/no). Follow-up data were available for 57 participants (50.4% of the sample) who had been released at the time of follow-up and were included in the analyses.

Results

Pre–Post Treatment Comparisons

The first step in the examination of the effectiveness of the CAP was a comparison between the pretreatment and posttreatment psychometric measures. Of primary interest was whether there were changes in the measure of criminal attitudes. As can be seen in Table 2, there were lower mean scores on the CSS-M at posttreatment compared with pretreatment levels, which reached statistical significance (t = 7.1, p < .001), suggesting reductions in criminal attitudes. Although enhancing motivation for lifestyle changes is not a specific treatment target within the CAP, it can be an unintended benefit of therapeutic involvement and was a secondary analysis in the present study. As can be seen in Table 2, greater mean SOS-SR scores were found at posttreatment compared with pretreatment levels, suggesting enhancements to motivation for lifestyle changes, although these approached but did not reach statistical significance (t = 1.7, p = .10). Scores on the MCSDS were examined to determine whether the obtained results on the criminal attitude and amenability to change data were affected by participant response bias. As can be seen in Table 2, there was virtually no change from pretreatment to posttreatment on this measure (t = 0.8, p = .4). To further explore the relationship between response bias and change, the CSS-M and SOS-SR change scores were correlated with posttreatment MCSDS. Neither these correlations (CSS-M and MCSDS: r = .13, p = .17; SOS-SR and MCSDS: r = .03, p = .74) were statistically significant, which suggests that scores on the CSS-M and SOS-SR were not affected by response bias.

Table 2.

Mean Pretreatment and Posttreatment Assessment Scores (n = 113).

Assessment instrument	Time		t	d
Assessment instrument	Pretreatment M (SD)	Posttreatment M (SD)	t	d
CSS-M	33.9 (16.5)	25.3 (12.9)	7.1**	0.58
SOS-SR	41.5 (26.3)	44.5 (28.3)	1.7	0.11
Marlow–Crowne Social Desirability	15.2 (6.6)	15.5 (6.1)	0.8	0.05

Note. CSS-M = Criminal Sentiments Scale–Modified; SOS-SR = Self-Improvement Orientation Scale–Self-Report.

p < .001.

The interpretation of statistical comparisons can be enhanced in applied research by examining the effect size—which represents the size of the association between two phenomena. One of the most common effect size estimates is that of Cohen’s (1988) d , which is the standardized difference between two mean scores. The general guidelines indicate that a d = 0.20 is a small effect, d = 0.50 is a medium effect, and d = 0.80 is a large effect. In the present study, the Cohen’s d for the CSS-M change score was 0.58, which is considered a medium effect. The Cohen’s d for the SOS-SR was 0.11, which is small.

One element of the contemporary model of offender intervention (RNR; D. A. Andrews, Bonta, & Hoge, 1990) is that therapeutic outcomes can be enhanced when there is congruence between dosage of treatment and criminal risk level. To date, there have been no investigations as to which risk level clients may respond best to the CAP. This issue was explored in the present study by way of comparisons of change scores between participants of different criminal risk potential. Unfortunately, there were no data available on participants’ risk level from standardized risk assessment instruments. As an alternative, CSS-M change scores were correlated with three proxy variables of risk: age at program admission, number of prior arrests, and pretreatment CSS-M score. First, age at program admission was nonsignificantly inversely related to CSS-M change (r = −.13, p = .186), demonstrating that younger offenders had nonsignificantly greater attitude change. Second, prior arrest was significantly positively correlated with CSS-M change score (r = .23, p = .016), indicating that offenders with more serious offense histories had more substantive attitude change. A repeated-measures ANCOVA demonstrated that pre–post CSS-M changes did not attain significance after controlling for age, F(1, 108) = 2.41, p = .124, but did attain significance after controlling for prior arrests, F(1, 108) = 5.30, p = .023. Finally, CSS-M change was highly positively correlated with CSS-M pretreatment score (r = .64, p < .001), indicating that individuals with more pronounced criminal attitudes (i.e., higher scores) demonstrated more positive change in their criminal attitudes in the CAP. Taken together, these proxies provide indirect support that the CAP is servicing clientele matched to the dosage of treatment.

These findings underscore the importance of controlling for pretreatment group differences in the context of examining pre–post changes and their relationship to outcome; the lattermost finding has a particular salience in this regard. Individuals with higher CSS-M scores naturally have more room to change and generate larger change scores (hence the positive correlation); however, they are also more likely to reoffend as they have more problematic criminal attitudes than individuals with lower scores, even if they make little change. One way to address this is to compute residualized change scores as a means of controlling for pretreatment score through regressing the change score on the pretreatment score and obtaining the residual. The residual is the amount of change independent of pretreatment score. For all remaining analyses involving examination of CSS-M pre–post change, both residual and raw change scores were used.

Recidivism Analyses

Criminal recidivism is a core criterion for determining the success of correctional rehabilitation. One way of examining this is to compare the rearrest rates of participants who participate in a treatment initiative with those who do not participate in a treatment initiative. In the present study, 57 participants who had some exposure to the CAP were released into the community and were eligible for follow-up. Of these, 42 (73.7% of the CAP exposure sample) completed the CAP curriculum and were considered to be “CAP Completers,” whereas 15 (26.3% of the CAP exposure sample) did not complete the CAP curriculum and were considered “CAP Non-Completers.” The reasons for CAP noncompletion were predominately related to participants being transferred (45%), or voluntarily withdrawing from the CAP (36%), with a minority being placed in segregation (9%) or released prior to CAP completion (9%). The time at risk to recidivate following release ranged from 7 to 375 days (M = 140.1, SD = 83.4), which was the follow-up period. Sixteen participants (28.1% of the recidivism eligible sample) were subsequently rearrested, which represents the base rate of recidivism. Of these, 11 (26.2% of recidivism sample) were CAP completers, whereas 5 (33.3% of the recidivism sample) were CAP noncompleters, which was not statistically significant (χ² = .3, p = .60). However, the proportional difference between groups was 7% favoring CAP completers.

It is common in rehabilitation initiatives for participants to have differential performance on treatment targets—some improve, some deteriorate, and some are unchanged. The use of pretreatment and posttreatment test data, specifically change scores (difference between pretreatment and posttreatment test data), is an effective method of determining the degree to which participants benefit from intervention. Although the use of change scores is not without its limitations and criticisms (e.g., Serin, Lloyd, Helmus, Derkzen, & Luong, 2013), it is considered to be an effective and popular method within criminal justice treatment programming (e.g., Carlson & Schmidt, 1999; Gendreau & Smith, 2007). In the present study, there was an aggregate reduction in the CSS-M scores at posttreatment, with a mean difference of 8.3 (SD = 11.7, range = 40 to −14). To examine the change score relationship with recidivism, the CSS-M raw and residual change scores were each correlated with the dichotomous rearrest variable and yielded an inverse relationship (r = −.17 and −.22, respectively), with a greater change related to lower recidivism (see Table 3). It is noteworthy that the relationship improved after controlling for pretreatment score. Although these relationships with outcome fall below the threshold for significance, it can be argued that these effects are not trivial in magnitude. For instance, invoking the binomial effect size display (see Gendreau & Smith, 2007), there is a 22% difference in rates of rearrest among CAP completers who demonstrated higher versus lower amounts of attitude change after controlling for pretreatment score.

Table 3.

Relationship of Key Predictor Variables and Criminal Attitude Change to Recidivism and CAP Noncompletion.

Measure	n	Rearrest		Noncompletion
Measure	n	r _pb	95% CI	r _pb	95% CI
CSS-M pre	55	.18	[−.09, 0.43]	.14	[−.13, 0.39]
CSS-M post	41	.19	[−.13, 0.47]	_—	_—
CSS-M change	41	−.17	[−.45, 0.15]	_—	_—
CSS-M RCZ	41	−.22	[−.49, 0.09]	_—	_—
Age	57	−.10	[−.35, 0.16]	−.06	[−.32, 0.20]
Prior arrests	57	.13	[−.14, 0.38]	.04	[−.22, 0.29]
Noncompletion	57	.07	[−.19, 0.32]	_—	_—
CCC	55	−.19	[−.43, 0.08]	_—	_—
CCC RCZ	55	−.26*	[−.49, 0.00]	_—	_—

Note. CAP = criminal attitude program; r_pb = point biserial correlation; CI = confidence interval; CSS-M = Criminal Sentiments Scale–Modified; RCZ = residualized change score (controlling for CSS-M pretreatment score); CCC = change-completion composite.

p = .052.

Owing to restricted sample size and a lack of change data for CAP noncompleters, a composite variable was created combining CSS-M change and treatment completion information.¹ As the sample registered a mean of 8.3 points of change (SD = 11.7) on the CSS-M, change groups were created corresponding to high change (above the mean, n = 18) and low change (below the mean, n = 23). The low change and treatment noncompletion groups were combined to form a single low change-noncompletion group, given that there were no differences in the rates of recidivism, which would further justify combining the two subgroups. When treatment completion and change information were combined in this simple manner, high change completers demonstrated even lower rates of recidivism (16.7%) than the combined low change-noncompletion group (35.1%), χ² = 2.00, p = .157; an approximate 18 percentage point difference. After controlling for pretreatment CSS-M score, the relationship between group membership and subsequent recidivism was marginally significant, r = .26, p = .052.

Finally, Cox regression survival analyses were conducted to examine the relationship of attitude change and program noncompletion to outcome, controlling for important risk-relevant covariates and individual differences in the follow-up time. As CSS-M pretreatment score, age at program admission, and prior arrests were all found associated with recidivism in the expected direction (see Table 3), these are particularly salient covariates and controlling for these three risk-relevant variables would thus entail a comprehensive control of risk. Three Cox regressions are reported in Table 4. First, consistent with bivariate analyses, CSS-M measured change was associated with reductions in postrelease recidivism after controlling for pretreatment score, age, and prior arrests; the relationship of attitude change to outcome improved and obtained a marginal level of significance after imposing these controls and adjusting for individual differences in follow-up time (e^B = .92, p = .061). A hazard ratio of .92 would, thus, be interpreted to mean a predicted 8% decrease in the hazard of rearrest for every 1 point increase in attitude change, after controlling for these covariates. In the second Cox regression, CAP noncompletion was nonsignificantly associated with increases in postprogram recidivism, controlling for the three risk-relevant covariates and follow-up time, with the relationship decreasing somewhat in magnitude compared with bivariate analyses. The third and final Cox regression entailed entering the binary change-completion composite variable along with the three covariates. The binary composite variable trended toward significance in predicting decreased hazard of recidivism and performed considerably better than the binary completion–noncompletion variable.

Table 4.

Cox Regression Survival Analysis Examining the Relationship of Attitude Change and CAP Noncompletion to Recidivism Controlling for Risk-Relevant Covariates.

						95% CI
Regression model	B	SE	Wald	p	e^B	Lower	Upper
Model 1 (n = 39)
CSS-M pretreatment	.04	0.04	1.54	.214	1.04	0.98	1.12
Age	.02	0.03	0.30	.585	1.02	0.96	1.09
Prior arrests	.10	0.09	1.03	.310	1.10	0.96	1.32
CSS-M change	−.08	0.04	3.52	.061	0.92	0.85	1.00
Model 2 (n = 52)
CSS-M pretreatment	.01	0.02	0.69	.406	1.01	0.98	1.05
Age	−.01	0.03	0.20	.656	0.99	0.93	1.05
Prior arrests	.03	0.06	0.31	.578	1.03	0.92	1.16
CAP noncompletion	.23	0.59	0.16	.689	1.26	0.40	3.98
Model 3 (n = 52)
CSS-M pretreatment	.02	0.01	1.91	.168	1.02	0.99	1.05
Age	−.02	0.03	0.41	.521	0.98	0.93	1.04
Prior arrests	.03	0.06	0.20	.657	1.03	0.91	1.15
Change-completion composite	−1.16	0.65	3.16	.076	0.31	0.09	1.13

Note. CAP = criminal attitudes program; CI = confidence interval; CSS-M = Criminal Sentiments Scale–Modified.

Discussion

The present study sought to examine what effect a structured criminal attitude treatment program had on offenders’ existing criminal attitudes and any future criminal behavior. Importantly, the present study was a prospective examination of the CAP (D. J. Simourd, 2007), in which the participants were treated and followed up in real-time postprogram, which adds an important element of ecological validity to the study design. The results of the present study provide preliminary and tentative support for completion of the CAP by a sample of prison inmates to be associated with positive changes in criminal attitudes. Among the standardized questionnaires measuring criminal attitudes, amenability to change, and response bias, there were statistically lower criminal attitude scores at posttreatment and a slight increase in amenability to change. There were no changes in the response bias measure at pretreatment and posttreatment, which suggests that the scores on criminal attitudes and amenability to change were not affected by unintended respondent influence. In addition, the recidivism rate of the CAP completers was 7% lower than noncompleters during an approximate 4-month follow-up time; however, when high change participants were compared with group members who demonstrated low change or failed to complete CAP, the magnitude of this difference in rates of recidivism improved to 18%.

An illuminating finding was the examination of within-treatment criminal attitude change and its relationship to possible reductions in recidivism. Controlling for pretreatment score, a nontrivial effect size was observed corresponding to a 22% difference in rates of rearrest between individuals making higher versus lower amounts of attitude change through the CAP when invoking the binomial effect size display (BESD; see Gendreau & Smith, 2007).

In Cox regression survival analyses, which provide added rigor through controlling for important possible confounds as well as individual differences in follow-up time, the relationship between criminal attitude change and outcome improved, despite the limited sample size. The magnitudes of the observed change effects with reductions in recidivism are also quite comparable in many respects with other examinations of dynamic risk factors in offender populations (cf. Beggs & Grace, 2011; Olver, Nicholaichuk, & Wong, 2014). Taken together, the essence of these findings is that CAP participants were attitudinally different following participation in the program. The findings would also suggest that the amount of attitude change made appears to matter more in terms of relative reductions in recidivism. These results require replication and extension.

For many years, there has been an expanding knowledge within the offender rehabilitation literature showing positive effects of treatment, as exemplified by reductions of offender recidivism. The most common reduction is approximately 10% (D. A. Andrews, Zinger, et al., 1990) but this rate can be augmented considerably based on characteristics of the intervention and the manner in which programs are delivered (e.g., D. A. Andrews & Dowden, 2005). Research (e.g., Gendreau et al., 1996) also shows that there are multiple criminal risk factors that may be more or less active during a period of time leading to a new criminal act (Zamble & Quinsey, 1997). Thus, addressing one risk factor therapeutically can be expected to account for a fraction of an offender’s behavioral intention. Moreover, the integration of treatment services within correctional organizations is a comprehensive undertaking that requires expertise and commitment and several junctures of the organization (see Gendreau, Goggin, & Smith, 2001). Within these contexts, the results of the present study, although preliminary, are considered to be consistent with the base reductions in recidivism.

Implications for Clinical Practice

One clinical implication of the present study is the notion that client and program evaluations can be relatively uncomplicated, but yield meaningful results. Without doubt, sophisticated experimental designs such as randomized clinical trials provide greater depth and confidence of findings than those of basic statistical examination of pretreatment and posttreatment scores. However, the benefit of the latter as articulated by Gendreau and Smith (2007) is that they can be very informative to multiple stakeholders (i.e., correctional organizations, program developers/evaluators). In the present study, facilitators of the CAP were front-line workers who received enough training and supervision, such that they could competently collect the necessary client treatment performance and program evaluation data. While there can be a sense within correctional organizations that comprehensive statistical evaluations are the only method of feedback on the success of interventions, this clearly does not have to be the case.

An underlying notion of the need principle in rehabilitation is that recidivism is only altered when there are changes in the criminogenic needs of offenders in what D. A. Andrews and Bonta (1994) described as intermediate treatment goals. The use of standardized assessment instruments administered at the outset and conclusion of intervention with the difference between the scores reflective of changes in the client is an example of this concept. The key to success in this way, however, is that there must be a conceptual concordance between the measurement instruments and the treatment process. In the present study, the CSS-M (Shields & Simourd, 1991) was used to measure criminal attitudes. The selection of this instrument was based on the conceptual concordance with the underpinnings of the CAP, in that it addresses the criminal attitudes, values, beliefs, and justifications directly related to criminal activity.

The issue of treatment dosage (the number of therapeutic hours required to effect change) has received some attention in the offender treatment literature of late, with suggestions that between 100 and 300 hr of treatment over an approximate 4-month period are necessary to effect positive change (e.g., Bourgon & Armstrong, 2005; Sperber, Latessa, & Makarios, 2013). Self-improvement is a complex process that involves an interaction between broad treatment and client variables that go beyond a prescribed number of treatment hours. Arguably, one of the most persuasive indicators of treatment success is whether the client is measurably different following an intervention (e.g., has improved cognitive perspectives and behavior skills). The CAP is a structured treatment program focusing on criminal attitudes, which is a known criminogenic need area, and attends to the principles of effective treatment (see Gendreau et al., 2001). The program consists of 44 hr of treatment, which is far below the suggestion of 100 to 300 hr; yet, there was a drop in measured criminal attitudes and reductions in recidivism for participants who completed the CAP. Bivariate analyses demonstrated that higher risk offenders, broadly speaking, made greater gains in the current program; specifically, greater criminal attitude changes were found among participants who had higher pretreatment levels of criminal attitudes, more serious offense histories, and who were younger on intake (although the latter variable did not attain significance). Although it is possible that more treatment hours could produce more enhanced treatment effects, the present findings support the D. A. Andrews et al. (1990) Andrews, Zinger, et. al., (1990) notion of “appropriate treatment” rather than simply a prescribed number of treatment hours.

Limitations and Future Directions

The results of the present study are considered preliminary based on several methodological limitations. Perhaps most obvious is the sample size, which was approximately 100, although a sample size of this magnitude is not uncommon in the correctional literature (see D. A. Andrews, Zinger, et al., 1990). This was particularly obvious in the recidivism analyses where the sample size was reduced to approximately 50 participants with a base rate of approximately 27%. Related to the recidivism analyses was the relatively short follow-up period that ranged between 7 and 375 days, with a mean of 140 days. The net impact of the shrinking sample size and a shorter variable follow-up period is that some of the analyses were likely underpowered. This is offset, however, through the use of analytic techniques (e.g., Cox regression) that can control for variability in follow-up and risk-relevant covariates; that the relationship of criminal attitude change to reductions in recidivism improved after controlling for these covariates and trended toward significance would seem to be a testament to this.

A related shortcoming is that criminal attitude change data were generally not available for CAP noncompleters. Given that noncompleters registered somewhat higher rates of recidivism and likely registered fewer positive changes, on average, in criminal attitudes owing to their noncompletion, it is quite possible that having such data could have augmented the relationship between criminal attitude change and recidivism to strengthen the findings. With these considerations in mind, the results of Cox regression (Model 1, Table 4) are viewed to be a particularly conservative test of the risk relevance of measured criminal attitude changes, given that these could only be examined primarily for CAP completers. The creation of a simple change-completion composite variable afforded greater power through added sample size and inclusion of change information, although there was a resultant loss of power through the use of a simple binary variable in contrast to having complete change information (and hence greater variance) for all CAP participants. Future research would be well served to obtain pre- and postmeasures on noncompleters if possible.

It is also important to bear in mind that the present study is not a controlled treatment outcome study; there was no control group of offenders who were untreated strictly speaking, and this, in turn, limits the generalizability of findings. There was also a relative lack of control variables (e.g., a measure of risk and need containing static and dynamic variables) to account for risk-relevant differences between treatment and comparison groups that can affect the outcome variable. In the absence of an untreated control group and with the relative lack of formal controls, the present study endeavored to impose comprehensive and theoretically relevant controls of salient risk-relevant variables (i.e., age, criminal history, and pretreatment attitude score) to ascertain the relationship of criminal attitude change to reductions in recidivism; doing so served to improve, rather than diminish, the link between attitude change and reductions in recidivism. Consistent with the RNR framework, the men who needed to change the most seemed to evidence greater risk reduction. The results also provide important insights regarding the importance of relative changes among CAP completers. The present evaluation is consistent with the possibility that the amount of criminal attitude change made by participants had a greater bearing on postprogram outcome than whether individuals simply completed, or failed to complete, the program; this would be consistent with smaller differences found between completers and noncompleters in postprogram recidivism, and an increase in the magnitude of differences in recidivism rates after collapsing attitude change and program completion information.

Finally, the broad social-psychological literature shows that attitudes play a functional role in behavior, but according to the theory of reasoned action (e.g., Ajzen & Fishbein, 1980), the heartbeat of the link between attitudes and behavior is the desire to engage in the behavior. This suggests that attitudes toward a specific behavior and social perceptions of engaging such behavior are statistically subservient to intention. This likely occurs within correctional settings in which criminal attitude measures are found to be modestly related to future criminality (D. J. Simourd & Olver, 2002; D. J. Simourd & van de Ven, 1999). In correctional practice, intention to engage in criminal activity is rarely, if ever, measured. In the present study, it was anticipated that a possibility existed to examine the role of criminal attitudes in criminal behavior in line with the theory of reasoned action. The expectation was that subscales of the CSS-M would reflect the “attitude toward behavior” and “subjective norm” component of the model and subscales of the SOS-SR would reflect the “intention” component. Unfortunately, the absence of complete test data prevented this examination. It is anticipated that data will be available in the future to conduct these analyses, which may shed greater light on the link between criminal attitudes and criminal behavior.

In all, the aforementioned study limitations underscore the logistical challenges and complexities of applied offender research within criminal justice field settings, such as prisons. Such challenges range from obtaining access and clearance from the institution, to navigating institutional politics, rules, and procedures, managing (often unexpected) program attrition from different sources, and the institution’s overarching objective to maintain tight security and control for the safety of staff and inmates (Ferszt & Chambers, 2011). This clinical–environmental–methodological context provides an important backdrop for the efforts of trying to provide clinically appropriate services to support a rehabilitative mandate and, when possible, to evaluate the effectiveness of program initiatives to achieve this. Small sample sizes, missing data, uneven (or nonexistent) controls are par for the course, but they also speak to the necessity of creative and effective solutions to maintain program and method integrity.

In summary, modifying the criminal attitudes of offenders seems to be a logical and achievable therapeutic endeavor given the prominence of criminal attitudes in the theory, research, and practice among offenders. In spite of the fact that addressing this important criminal risk factor is not an arduous or difficult task, relatively scant attention has been devoted to it. Bonta, Bourgon, Rugge, Gress, and Gutierrez (2013) have provided a training initiative for front-line workers geared toward increasing attention to relevant criminogenic risk factors, including discussion of criminal attitudes, within normal interactions with offenders. Whether basic indirect attempts such as these or more comprehensive direct approaches such as the CAP are used is less important than the need for greater clinical attention to criminal attitudes. The CAP is a module-based curriculum with an integrated client and program evaluation protocol. Training of staff is relatively straightforward, and the program can be integrated into correctional agencies without undue difficulty. Based on the results of the present study, which obviously require replication, it also appears the CAP has a positive effect on both intermediate treatment targets (e.g., reduced criminal attitudes) and longer term outcomes (e.g., reduced reoffending), which is what offender treatment is all about.

Footnotes

Acknowledgements

The authors acknowledge the contributions of several individuals who influenced the success of the present project: Joseph Schmidt, Commissioner of Alaska Department of Corrections, who maintained an open mind about offender rehabilitation; and the criminal attitude program (CAP) facilitators at the various institutions who added to their normal work duties to deliver the curriculum and supply program data. Appreciation is extended to Dr. John Blette who had a strong influence on the development and maturation of the CAP, and to Dr. Thomas Powell, Dr. Linda Simourd, and Dr. Paul Gendreau who offered editorial suggestions.

Declaration of Conflicting Interests

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

Funding

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

Notes

References

Ajzen

Fishbein

(1980). Understanding attitude and predicting behavior. Englewood Cliffs, NJ: Prentice Hall.

Andrews

D. A.

Bonta

(1994). The psychology of criminal conduct. Cincinnati, OH: Anderson Publishing.

Andrews

D. A.

Bonta

(2010). The psychology of criminal conduct (5th ed.). New Providence, NJ: LexisNexis.

Andrews

D. A.

Bonta

Hoge

R. D.

(1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17, 19-52.

Andrews

D. A.

Dowden

(2005). Managing correctional treatment for reduced recidivism: A meta-analytic review of program integrity. Legal and Criminological Psychology, 10, 173-187.

Andrews

D. A.

Wormith

J. S.

Kennedy

D. J.

Daigle-Zinn

W. J.

(1977). The attitudinal effects of structured discussions and recreational association between young criminal offenders and undergraduate volunteers. Journal of Community Psychology, 5, 63-71.

Andrews

D. A.

Young

J. G.

Wormith

J. S.

Searle

C. A.

Kouri

(1973). The attitudinal effects of group discussions between young criminal offenders and community volunteers. Journal of Community Psychology, 1, 417-422.

Andrews

D. A.

Zinger

Hoge

R. D.

Bonta

Gendreau

Cullen

F. T.

(1990). Does correctional treatment work? A psychologically informed meta-analysis. Criminology, 28, 419-429.

Andrews

Meyer

R. G.

(2003). Marlowe-Crowne Social Desirability Scale and short form C: Forensic norms. Journal of Clinical Psychology, 59, 483-492.

10.

Banse

Koppehele-Gossel

Kistemaker

Werner

V. A.

Schmidt

A. F.

(2013). Pro-criminal attitudes, intervention, and recidivism. Aggression and Violent Behavior, 18, 673-685.

11.

Beggs

S. M.

Grace

R. C.

(2011). Treatment gains for sexual offenders against children predicts reduced recidivism: A comparative validity study. Journal of Consulting and Clinical Psychology, 79, 182-192.

12.

Blackburn

(1993). The psychology of criminal conduct: Theory research and practice. Chichester, UK: Wiley.

13.

Bonta

Bourgon

Rugge

Gress

Gutierrez

(2013). Taking the leap: From pilot project to wide-scale implementation of the Strategic Training Initiative in Community Supervision (STICS). Justice Research and Policy, 15, 17-35.

14.

Bourgon

Armstrong

(2005). Transferring the principles of effective treatment into “real world” prison setting. Criminal Justice and Behavior, 32, 3-25.

15.

Bush

Bilodeau

(1993). Options: A cognitive change program. Longmont, CO: National Institute of Corrections.

16.

Carlson

K. D.

Schmidt

F. L.

(1999). Impact of experimental design on effect size: Findings from the research literature on training. Journal of Applied Psychology, 84, 851-862.

17.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

18.

Cottle

C. C.

Lee

R. J.

Heilbrun

K. K.

(2001). The prediction of criminal recidivism in juveniles: A meta-analysis. Criminal Justice and Behavior, 28, 367-394.

19.

Crowne

D. P.

Marlowe

(1960). A new scale of social desirability independent of psychopathology. Journal of Consulting Psychology, 24, 349-354.

20.

Ferszt

G. G.

Chambers

(2011). Navigating the challenges of prison research with women. Nurse Educator, 36, 256-259.

21.

Gendreau

Goggin

Law

M. A.

(1997). Predicting prison misconducts. Criminal Justice and Behavior, 24, 414-434.

22.

Gendreau

Goggin

Smith

(2001). Implementation guidelines for correctional programs in the “real world.” In Bernfeld

G. A.

Farrington

D. P.

Leschied

A. W.

(Eds.), Offender rehabilitation in practice (pp. 248-268). Chichester, UK: John Wiley.

23.

Gendreau

Little

Goggin

(1996). A meta-analysis of predictors of adult recidivism: What works! Criminology, 34, 401-433.

24.

Gendreau

Smith

(2007). Influencing the “people who count”: Some perspectives on the reporting of meta-analytic results for prediction and treatment outcomes with offenders. Criminal Justice and Behavior, 34, 1536-1559.

25.

Helmus

Hanson

R. K.

Babchishin

K. M.

Mann

R. E.

(2013). Attitudes supportive of sexual offending predict recidivism: A meta-analysis. Trauma, Violence, & Abuse, 14, 34-53.

26.

Kroner

D. G.

Morgan

R. D.

(2014). An overview of strategies for the assessment and treatment of criminal thinking. In Tafrate

R. C.

Mitchell

(Eds.), Forensic CBT: A handbook for clinical practice (pp. 87-103). Chichester, UK: John Wiley.

27.

Kroner

D. G.

Yessine

A. K.

(2013). Changing risk factors that impact recidivism: In search of mechanisms of change. Law and Human Behavior, 37, 321-336.

28.

Miller

W. R.

Rollnick

(2002). Motivational interviewing: Preparing people for change (2nd ed.). New York, NY: Guilford Press.

29.

Mitchell

Tafrate

R. C.

(2012). Conceptualization and measurement of criminal thinking: Initial validation of the criminogenic thinking profile. International Journal of Offender Therapy and Comparative Criminology, 56, 1080-1102.

30.

Mitchell

Tafrate

R. C.

Hogan

Olver

(2013). An exploration of the association between criminal thinking and community program attrition. Journal of Criminal Justice, 41, 81-89.

31.

Olver

M. E.

Nicholaichuk

T. P.

Wong

S. C. P.

(2014). The predictive and concurrent validity of a psychometric battery used to assess sex offenders in a treatment program: An 18-year follow up. Journal of Sexual Aggression, 20, 216-239.

32.

Paulhus

D. L.

(1991). Measurement and control of response bias. In Robinson

J. P.

Shaver

P. R.

Wrightsman

L. S.

(Eds.), Measures of personality and social psychological attitudes (pp. 17-59). San Diego, CA: Academic Press.

33.

Powell

Bush

Bilodeau

(2001). Vermont’s cognitive self-change program: A 15 year review. Corrections Today, 63, 116-120.

34.

Ross

R. R.

Fabiano

E. A.

(1985). Time to think: A cognitive model of delinquency prevention and offender rehabilitation. Johnson City, TN: Institute of Social Science and Arts.

35.

Serin

R. C.

Lloyd

C. D.

Helmus

Derkzen

D. M.

Luong

(2013). Does intra-individual change predict recidivism? Searching for the Holy Grail in assessing offender change. Aggression and Violent Behavior, 18, 32-53.

36.

Shields

I. W.

Simourd

D. J.

(1991). Predicting predatory behavior in a population of young offenders. Criminal Justice and Behavior, 18, 180-194.

37.

Simourd

D. J.

(1997). The Criminal Sentiments Scale-Modified and Pride in Delinquency Scale: Psychometric properties and construct validity of two measures of criminal attitudes. Criminal Justice and Behavior, 24, 52-70.

38.

Simourd

D. J.

(2007). The Criminal Attitudes Program: User’s manual (CAP). Kingston, Ontario, Canada: Algonguin Correctional Evaluation Services (ACES).

39.

Simourd

D. J.

Blette

(2008, June). Program implementation and evaluation in a large community correctional center: Issues and challenges. Paper presented at the annual conference of the Canadian Psychological Association, Halifax, Nova Scotia, Canada.

40.

Simourd

D. J.

Olver

M. E.

(2002). The future of criminal attitudes research and practice. Criminal Justice and Behavior, 29, 427-446.

41.

Simourd

D. J.

Olver

M. E.

(2011). Use of the Self-Improvement Orientation Scheme-Self Report (SOS-SR) among incarcerated offenders. Psychological Services, 8, 200-211.

42.

Simourd

D. J.

van

Ven

(1999). Assessment of criminal attitudes: Criterion-related validity of the Criminal Sentiments Scale-Modified and Pride in Delinquency Scale. Criminal Justice and Behavior, 26, 90-106.

43.

Simourd

Andrews

D. A.

(1994). Correlates of delinquency: A look at gender differences. Forum on Correctional Research, 6, 26-31.

44.

Sperber

K. G.

Latessa

E. J.

Makarios

M. D.

(2013). Examining the interaction between level of risk and dosage of treatment. Criminal Justice and Behavior, 40, 338-348.

45.

Sutherland

E. H.

(1947). Principles of criminology (6th ed.). Philadelphia, PA: J. B. Lippincott.

46.

Sykes

G. M.

Matza

(1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22, 664-670.

47.

Tatman

A. W.

Schouten

(2008). Measuring social desirability in adult male probation and parole clients. Perspectives: The Journal of the American Probation and Parole Association, 32, 49-53.

48.

Taxman

F. S.

Rhodes

A. G.

Dumenci

(2011). Construct and predictive validity of criminal thinking scales. Criminal Justice and Behavior, 38, 174-187.

49.

Walters

G. D.

(1999). Short term outcome of inmates participating in the lifestyle change program. Criminal Justice and Behavior, 26, 322-337.

50.

Walters

G. D.

(2014). Applying CBT to the criminal thought process. In Tafrate

R. C.

Mitchell

(Eds.), Forensic CBT: A handbook for clinical practice (pp. 104-121). Chichester, UK: John Wiley.

51.

Webb

T. L.

Sheeran

(2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132, 249-268.

52.

Zamble

Quinsey

V. L.

(1997). The criminal recidivism process. Cambridge, UK: Cambridge University Press.