Does Criminal Thinking Predict Prison Misconduct? An Evaluation of TCU’s Criminal Thinking Scales

• Published date: 01 June 2023
• DOI: http://doi.org/10.1177/00938548231163111
• Author: Grant Duwe,Valerie Clark,Susan McNeeley
• Date: 01 June 2023
• Subject Matter: Articles

CRIMINAL JUSTICE AND BEHAVIOR, 2023, Vol. 50, No. 6, June 2023, 830 –848.

DOI: https://doi.org/10.1177/00938548231163111

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© 2023 International Association for Correctional and Forensic Psychology

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DOES CRIMINAL THINKING PREDICT PRISON

MISCONDUCT?

An Evaluation of TCU’s Criminal Thinking Scales

GRANT DUWE

VALERIE CLARK

SUSAN MCNEELEY

Minnesota Department of Corrections

To date, only one published study has tested the predictive validity of the Texas Christian University–Criminal Thinking

Scales (TCU-CTS), and no studies have tested whether these scales are predictive of prison misconduct. Using a sample of

more than 2,000 people incarcerated in Minnesota’s prison system, this study examined the predictive validity of the TCU-

CTS with multiple measures of prison misconduct. The results showed the overall TCU-CTS score significantly predicted

misconduct, although the strength of this association was relatively modest (AUC = 0.62). Among the six scales on the

TCU-CTS, Cold-Heartedness significantly predicted both measures of misconduct, whereas Power Orientation and Criminal

Rationalization were each significantly associated with one misconduct measure.

Keywords: criminal thinking; prison misconduct; TCU criminal thinking scales

The “what works” literature demonstrating the effectiveness of correctional interven-

tions in reducing recidivism led to the development of the risk-needs-responsivity

(RNR) model (Andrews et al., 1990, 2006; James, 2018; Lowenkamp, Latessa, & Holsinger,

2006; Lowenkamp, Latessa, & Smith, 2006; Serin & Lowenkamp, 2015). The RNR model

suggests effective rehabilitation relies on three components: Programming should be tar-

geted toward those with the highest risk of recidivism; programs must address criminogenic

needs that are related to criminal behavior; and programs must account for factors that

might limit the effectiveness of programming (i.e., responsivity). This model relies on

proper assessment for success; not only must high-risk individuals be identified, but each

person’s major criminogenic needs must be ascertained to steer them toward the most

appropriate rehabilitative programs.

AUTHORS’ NOTE: Correspondence concerning this article should be addressed to Grant Duwe, Research

Director, Minnesota Department of Corrections, 1450 Energy Park Drive, Suite 200, St. Paul, MN 55108-5219;

e-mail: grant.duwe@state.mn.us.

1163 111CJBXXX10.1177/00938548231163111Criminal Justice and BehaviorDuwe et al. / XXXX

research-article2023

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Four major criminogenic needs that should be targeted through treatment have been

identified—criminal thinking, criminal history, peer relationships, and antisocial personal-

ity (Andrews & Bonta, 2010; Andrews et al., 2006; Bonta & Andrews, 2017; Bonta et al.,

1998; Gendreau et al., 1996; Lipsey & Derzon, 1998; Olver et al., 2014). Criminal thinking

promotes criminal behavior by rationalizing and justifying inappropriate behavior (Andrews

et al., 2006). Adequately assessing criminal thinking is important for identifying individuals

who would be amenable to cognitive interventions that promote prosocial thinking patterns.

Thus, several assessment measures have been developed to identify those with problematic

levels of criminal thinking, including the Texas Christian University–Criminal Thinking

Scales (TCU-CTS; Knight et al., 2002, 2006).

The TCU-CTS is a self-reported assessment measure that takes about 5 to 10 min to

complete, making it cost-effective and easy to administer. Multiple studies have tested its

construct validity (Dembo et al., 2007; Knight et al., 2006; Taxman et al., 2011), demon-

strating that it appropriately measures criminal thinking. However, only one study has tested

its predictive validity using criminal justice outcomes (Taxman et al., 2011). To address this

shortcoming in the literature, the current study examines the TCU-CTS’s effectiveness at

predicting institutional misconduct among people incarcerated in Minnesota state prisons.

By examining institutional misconduct, the results will demonstrate whether it is useful for

correctional facilities to use the TCU-CTS as a screening tool at intake, not only as a needs

assessment but also for the purposes of classification.

COMMON CORRELATES OF INSTITUTIONAL MISCONDUCT

One of the most important tasks of prison administrators is to maintain the safety and

security of correctional facilities, and classification is one of the primary methods used to

achieve that goal (Hardyman et al., 2004). Classification determines an incarcerated per-

son’s appropriate level of custody and is generally based on current and past behaviors,

characteristics of the current offense, risk of escape, physical and mental health needs, and

programming needs, among other factors. This process helps prison administrators deter-

mine which of their facilities would be most appropriate for the incarcerated person, con-

sidering both the level of security and the availability of programming and other services.

To predict misconduct, correctional systems often use actuarial assessments or algo-

rithms that make use of reliable static predictors of misconduct (Austin, 2003; Austin &

Hardyman, 2004). These static measures include gender and age, given that past research

has shown that men are more likely to engage in misconduct than women, and younger

individuals are more likely to incur disciplinary sanctions than those who are older (Bench

& Allen, 2003; Gendreau et al., 1997; Steiner et al., 2014; Steiner & Wooldredge, 2008).

Past research has also demonstrated that disciplinary infractions incurred during prior peri-

ods of incarceration significantly increase the likelihood of disciplinary infractions in sub-

sequent periods of incarceration (Drury & DeLisi, 2010; Gendreau et al., 1997).

Another consistent static predictor of disciplinary infractions is criminal history

(Campbell et al., 2009; Gendreau et al., 1997; Steiner et al., 2014). People with lengthier

criminal histories accrue more disciplinary infractions than those with more limited crimi-

nal histories. Other common predictors of misconduct include offense type (with individu-

als convicted of sexual offenses less likely to engage in misconduct), sentence length

(individuals with sentence lengths less than 5 years engage in more misconduct), and