Criminal Thinking in a Sample of Men and Women Attending a Multi-Site Batterer Intervention Program

Abstract

The study investigated differences and similarities in criminal thinking between men and women attending a multi-site Batterer Intervention Program (BIP). Overall, rates of criminal thinking were low. However, the results of the multivariate analyses suggest that, after controlling for demographic characteristics (i.e., race, ethnicity, education, SES) and personal experiences (i.e., prior convictions, mental health problems, substance abuse, experience of abuse) female participants significantly differed from men in patterns of Power Orientation. If replicated by other studies, especially studies that use larger samples of women enrolled in BIPs, these findings may contribute to identifying rehabilitation programs that best address women’s needs.

Keywords

criminal thinking gender Batterer Intervention Programs power orientation power and control

Introduction

In the 1980s, a coordinated community response to the rising awareness about domestic violence led to the design and implementation of Batterer Intervention Programs (BIPs) throughout the United States (Buzawa & Buzawa, 2003; Chesney-Lind, 2006; Stuart et al., 2007). The Duluth Model for Domestic Abuse Intervention developed by Ellen Pence in the early 1990s is recognized as the first BIP model in North America (Pence et al., 1993). The Duluth model employs a feminist sociocultural approach to educate individuals under the supervision of the criminal justice system for domestic violence offenses on the influence of patriarchal societal values that prioritize men’s dominance and control over women in intimate relationships, in the family, and in society (Lawson et al., 2012; Miller, 2005; Pence et al., 1993). Another BIP model, known as the Cognitive Behavioral Treatment (CBT) model, was later introduced by Murphy and Eckhardt (2005). The CBT model focuses on retraining the attitudes, beliefs, and behaviors of justice-involved individuals by targeting learning patterns and thought processes (Lawson et al., 2012), or criminal thinking patterns (Walters, 2006). The CBT model is the most common approach used by BIPs across the United States today (A. H. Lee & DiGiuseppe, 2018).

One of the most debated issues over the use of CBT is its (in)ability to address gender differences in patterns of aggressive behavior and whether gender specific programs that take into consideration men’s and women’s needs should replace current BIP protocols (Burgess-Proctor, 2006; Kernsmith, 2005). While research shows that women’s aggressive behavior often occurs in response to abuse and victimization and is often associated with stressors, such as mental health problems, substance abuse, or economic marginalization (Link & Oser, 2018; Miller, 2005), gender-neutral BIP protocols continue to be the mainstream legal response to domestic violence (Link & Oser, 2018), with programs employing the same curricula for both justice-involved men and women. Because BIPs that employ CBT protocols aim to influence individuals’ criminal thinking patterns, one way to examine the use of gender-neutral programs is to measure differences in criminal thinking patterns by gender (Benson & Harbinson, 2020; Dembo et al., 2007; Folk et al., 2018; Hubbard & Matthews, 2008; Jones et al., 2021; Stevenson et al., 2004; Vaske et al., 2017; Walters, 2014; Walters et al., 1998). In the literature, criminal thinking is defined as “thought content and process conducive to the initiation and maintenance of habitual lawbreaking behavior” (Walters, 2006, p. 88). We employ theories of Intimate Partner Violence (IPV) (Brush et al., 2007; Chesney-Lind, 2006; R. E. Dobash & Dobash, 1979; Johnson, 2008, 2011, 2017; Miller, 2005) and theories of criminal thinking (R. E. Dobash & Dobash, 2011; Knight et al., 2006; Link & Oser, 2018; A. Mennicke, 2019; Vaske et al., 2017; Walters, 2006, 2020) to investigate criminal thinking patterns among male and female participants attending a multi-site BIP that utilizes CBT programming.

Gender and IPV

The argument over whether BIP curricula should address gender differences or should be implemented equally for all IPV perpetrators stems from the long-standing debate over “gender symmetry” versus “gendered problem” in patterns of IPV. Feminist theorists have defined IPV as an act of violence perpetrated by men against women—or as “gendered problem” (Brush et al., 2007; Burgess-Proctor, 2006; Chesney-Lind, 2006; R. E. Dobash & Dobash, 1979; Miller, 2005), a perspective that finds global consensus, with international research showing that violence against women affects the lives of millions of women and girls worldwide (Reed et al., 2010). Some research evidence exists that women and men engage in IPV behaviors at similar rates, a phenomenon labelled as “gender symmetry” (Robertson & Murachver, 2007; Ross & Babcock, 2009; Straus, 2008; Straus & Ramirez, 2007). However, experts argue that research that employs the gender symmetry perspective has largely ignored the context in which violence occurs, the motivation behind the violence, and its consequences (Archer, 2000; R. P. Dobash & Dobash, 2003; Johnson, 2017; Langhinrichsen-Rohling, 2010; A. Mennicke, 2019; Reed et al., 2010; Robertson & Murachver, 2007). As Weston et al. (2005) point out, violence perpetrated by women against men is not equal to violence perpetrated by men against women. Within intimate contexts, men’s violence tends to be more frequent and more severe, and it tends to result in more injuries than violence perpetrated by women against men (Catalano, 2013; Larson & Hamberger, 2015; Tjaden & Thoennes, 2000; Weston et al., 2005). Women’s aggression in intimate relationships often stems from their own experiences of victimization, and it is—in many cases—motivated by self-defense (Chesney-Lind, 2006; Miller, 2005; Stuart et al., 2006).

Johnson’s (2008, 2011, 2017) IPV typology is particularly relevant to the “gender symmetry” argument. Johnson’s IPV typology is organized around the idea of coercive controlling behavior and includes four major types: (1) intimate terrorism; (2) violent resistance; (3) situational couple violence; (4) mutual violent control.

Intimate terrorism involves physical and/or sexual violence along with non-violent control tactics like economic and emotional abuse, blaming the victim, and constant monitoring, among others. Violent resistance refers to situations in which victims of domestic violence respond to violence with violence. Situational couple violence refers to situations in which disagreements between partners turn into arguments that are likely to become violent. Mutual violent control refers to situations in which both partners use control and violence in the relationship (Johnson, 2008). According to Johnson, while situational couple violence is the most common form of IPV (Johnson, 2011), this should not indicate support for gender symmetry (Johnson, 2017). Conversely, intimate terrorism, which is most often perpetrated by men against women, reflects men’s coercive control over women within patriarchal familial systems (Johnson, 2017). Control, however, may affect intimate relationships even in the absence of violence (Frankland & Brown, 2014), an issue further explored by A. Mennicke (2019) in a study that highlights the importance of identifying victims of power/control abuse that might not experience situational violence (p. 395). The consequences of power/control dynamics can be deleterious in intimate relationships. For instance, financial control may contribute to a woman’s complete financial dependence on her abuser, a major risk factor of IPV (Hoge et al., 2019; Singh et al., 2022). While it is important to focus on all forms of abuse, including those that stem from other underlying issues (e.g., mental health problems, substance abuse problems, financial burden, etc.), a deeper understanding of the association between IPV and power/control dynamics is necessary (A. Mennicke, 2019; Walters, 2020).

Other relevant differences in gendered patterns of IPV must be mentioned. In a review of gender differences in IPV behaviors, Larson and Hamberger (2015) found that, while men and women involved in IPV incidents had the same likelihood of arrest, women were less likely than men to be prosecuted; moreover, men had lengthier criminal histories and higher rates of recidivism. Recent research shows that among individuals involved in IPV incidents, men are more likely than women to face multiple charges and to face charges for non-physical violence and serious assault, while women are more likely than men to face charges for minor assault (Bader et al., 2019). Furthermore, research also shows that among IPV perpetrators, women tend to exhibit higher levels of psychopathologies than men, although women are less likely than men to manifest antisocial personality disorders (Larson & Hamberger, 2015). While the body of literature that focuses on gender differences in IPV is growing, more studies are needed to examine the influence of cognitive patterns (such as criminal thinking patterns) on IPV behaviors. A deeper understanding of the association between criminal thinking and IPV can help researchers better assess whether motivations behind IPV behaviors vary by gender, which in turn would contribute to the design of effective prevention and rehabilitation programs.

Criminal Thinking

The theoretical framework for the study of criminal thinking as a predictor of criminal or delinquent behavior was formulated by criminologists in the 1950s. Drawing upon the theoretical premises of Sutherland’s theory of differential association (Sutherland, 1939), Sykes and Matza (1957) argued that while criminal and delinquent behaviors are learned behaviors, the emphasis is most often on the processes through which individuals learn rather than on what they learn and how they internalize it. Delinquency is often associated with one’s proclivity to justify or rationalize their behavior in ways that are considered unacceptable by those who hold mainstream social values (Sykes & Matza, 1957). More specifically, the element of rationalization allows individuals who break the rules/laws to protect themselves from self-blame; instead, the element of justification allows the individual engaging in criminal/delinquent behaviors to see themselves as “sinned against” rather than “sinning” (Sykes & Matza, 1957, p. 667).

The principles of rationalization and justification of delinquent behavior highlighted by Sykes and Matza (1957), which are referred to as neutralization techniques, provide the theoretical foundations for the development of criminal thinking instruments. Among the criminal thinking instruments commonly used in IPV research are the Criminal Sentiments Scale (CSS, Gendreau et al., 1979; Simourd, 1997), the Psychological Inventory of Criminal Thinking Styles (PICTS, Walters, 1995, 2005), and the Texas Christian University Criminal Thinking Scale (TCU-CTS, Knight et al., 2006). Walters (2020) grouped criminal thinking measures into proactive and reactive measures. Proactive measures focus on cognitive patterns that are planned, calculated, and amoral, and focus on the individual’s proclivity to exercise power/control over others for one’s personal advantage. Reactive measures identify cognitive patterns that are impulsive, irresponsible, and emotional. The present study employs measures of criminal thinking using the TCU-CTS (Knight et al., 2006) to explore criminal thinking patterns among individuals referred to a BIP. The TCU-CTS was purposefully selected among a range of criminal thinking instruments because it best matched the principles included in the design of the BIP the participants attended. According to Walters (2020), the TCU-CTS primarily assesses proactive criminal thinking (i.e., power/control motives).

Criminal thinking scales employed in IPV research treat IPV as any other form of delinquent/behavior. Recent IPV research found a strong correlation between non-physical controlling behaviors against intimate partners and criminal thinking (Solinas-Saunders, 2022), supporting theories of developmental criminology that link IPV to the broader constellation of antisocial behaviors (Capaldi et al., 2012; Ehrensaft et al., 2004; O’Leary et al., 2014; Smith et al., 2015).

Criminal Thinking and IPV

Studying criminal thinking among individuals convicted of IPV or domestic violence is important in that it can shed light on the types of cognitive mechanisms one might develop to justify and rationalize abuse perpetrated against intimate partners. If violence, like other criminal and delinquent behaviors, is learned behavior (Bandura, 1969, 1978; Sutherland & Cressey, 1966), understanding the complexity of the set of beliefs and attitudes that allow individuals to justify the harm inflicted on loved ones can better inform us about the patterns of IPV offending and recidivism (Besemer et al., 2017; Widom & Wilson, 2015).

A few recent studies have explored the association between criminal thinking and IPV. In a study that employed a sample of male and female BIP clients (Solinas-Saunders, 2022), found that criminal thinking was significantly correlated with both perpetration and victimization of emotional/psychological abuse in intimate relationships. Furthermore, Stacer and Solinas-Saunders (2018) found that among men attending a BIP, those with military background scored higher than civilians in Criminal Rationalization. In a study that evaluated the effectiveness of a cognitive behavioral domestic violence treatment program in correctional settings called STOP (Survey, Think, Options, and Prevention) and Change Direction, A. M. Mennicke et al. (2015) found that the 20-week program involving weekly group and individual sessions significantly reduced criminal thinking in a sample of 176 incarcerated men. In a mixed method study that employed 24 men in a substance abuse therapeutic community inside a California maximum security prison, Yorke et al. (2010) found that voluntary participation in a BIP significantly decreased Power Orientation levels among participants. In a study that assessed the influence of criminal thinking on IPV behaviors and recidivism using a sample of 1,238 men released from a federal correctional facility, Walters (2020) found that criminal thinking moderated the association between past IPV and IPV recidivism. As the author explained, “[b]y providing those who perpetrate IPV with an enhanced sense of power and control, IPV may be viewed as a way of compensating for feelings of powerlessness by expressing dominance over others” (Walters, 2020, p. 951).

Because women are underrepresented among justice-involved populations (Mahony et al., 2011), especially among serious offenders (van Wormer & Bartollas, 2014), research that examines the role of criminal thinking in the interpretation of criminal and delinquent behaviors has somewhat neglected the analysis of gender differences in patterns of criminal thinking (Hubbard & Matthews, 2008; Link & Oser, 2018). We contribute to the growing body of literature on criminal thinking and gender (Hubbard & Matthews, 2008; Link & Oser, 2018; Vaske et al., 2017; Walters, 2020) by including both men and women participants referred to a BIP in our study.

Criminal Thinking and Gender

Contemporary research indicates that the association between criminal thinking and gender is complex with mixed findings. Some studies found that men exhibit higher levels of criminal thinking than women (Folk et al., 2018; Walters & Lowenkamp, 2016). In contrast, some studies found no differences between men and women in patterns of criminal thinking (Dembo et al., 2007; Stevenson et al., 2004; Taxman et al., 2011; Walters et al., 1998), and others found gender differences in some measures of criminal thinking but not in others (Scott & Straus, 2007; Walters, 2001). In general, women are less likely to exhibit criminal cognitions that indicate denial of responsibility (Klenowski et al., 2011). Furthermore, even when women exhibit similar patterns of criminal thinking, they do so differently from men. In a review of gender theories of criminal behavior, Benson and Harbinson (2020) asserted that both men and women use neutralization techniques to justify and rationalize their illegal behaviors, but men tend to do so more frequently than women. In addition, women are more likely than men to explain their behaviors within the context of dysfunctional interpersonal relationships. A recent pilot study with 216 admissions to a criminal justice day reporting center in Connecticut (Jones et al., 2021) that used the Criminogenic Thinking Profile (CTP, Mitchell & Tafrate, 2012) found no gender differences in criminal thinking patterns. However, the analysis indicated that male participants exhibited higher tendencies toward justification of criminogenic thinking patterns, while women manifested a greater propensity toward manipulative thinking (Jones et al., 2021).

When studying gender differences in criminal thinking patterns, it is important to point out that scientists agree that gender might influence the way participants interpret criminal thinking concepts as framed in criminal thinking measures. For instance, in 1998, Walters and collaborators estimated that criminal thinking patterns look similar for men and women. But later, Walters (2014) found that, when the overall power of predictability of the criminal thinking scale was investigated, some evidence existed that men and women involved with the criminal justice system rated scale items differently, even when they exhibited similar latent levels of criminal thinking. This finding was later confirmed by Vaske et al. (2017) in a study that used two measures of criminal thinking, the Criminogenic Cognition Scales (CCS, Tangney et al., 2012) and the modified version of the Criminal Sentiments Scale (CSS-M, Gendreau et al., 1979). This might be explained by the fact that men and women tend to interpret scale items differently, an issue known in statistics as differential item functioning (DIF, Wang, 2008), which has been previously demonstrated in the assessment of gender differences in depressive symptoms (Gelin & Zumbo, 2003) and in IPV behaviors (Wareham et al., 2022). Because of such differences in interpretation, some scholars support a gender-informed approach in the development of criminal thinking measures (Jones at al., 2021). Understanding whether men and women interpret criminal thinking concepts differently is important in survey research that aims to assess the effectiveness of rehabilitation programs. Furthermore, scientists argue that, even when criminal thinking patterns among justice-involved men and women are similar, the origins of the thought processes that accompany criminogenic attitudes might be quite different (Hubbard & Matthews, 2008). This suggests that the presence of stressors might contribute to the development of criminal thinking patterns. Researchers have found that criminal thinking is often associated with past negative experiences (Dembo et al., 2007; Engels et al., 2004) or mental health problems (Hubbard & Matthews, 2008). Prior criminal justice involvement and history of substance abuse tend to be positively correlated with criminal thinking (Dembo et al., 2007). The influence of such stressors varies by gender. Among justice-involved individuals, women, especially minority women, are more likely than men to report past experiences of economic and social marginalization and sexual victimization (Link & Oser, 2018). These experiences tend to increase the likelihood of mental health problems, and it is possible that mental health problems contribute to criminal thinking (Hubbard & Matthews, 2008).

The Present Study

With the present study, we aim to investigate patterns of criminal thinking among BIP participants and whether these patterns vary by gender. Drawing from extant theories of IPV (Brush et al., 2007; Chesney-Lind, 2006; R. E. Dobash & Dobash, 1979; Johnson, 2008, 2011, 2017; Miller, 2005) and theories of criminal thinking (R. E. Dobash & Dobash, 2011; Knight et al., 2006; Link & Oser, 2018; A. Mennicke, 2019; Vaske et al., 2017; Walters, 2006, 2020), we address the following question: Does gender influence criminal thinking patterns among individuals mandated or referred to BIPs? In addition, we explore whether adding other demographic characteristics (race, ethnicity, age, education, and socio-economic status [SES]) and personal experiences (mental health problems, substance abuse, abuse in the home, court mandated BIP participation, and past incarcerations) control for the association between gender and criminal thinking. The focus on gender differences and similarities in patterns of criminal thinking is relevant to both theory and practice as it may influence the design and implementation of future BIPs for women and men.

Methodology

Participants

The study uses data collected from a multi-site BIP that offers classes to both men and women referred by criminal courts in five counties in a Midwestern state. The program is a state certified BIP that employs a cognitive behavioral protocol and aims to rehabilitate clients by targeting their ability to take personal responsibility, avoid blame shifting, and recognize the harm caused to others. Although men and women attend classes separately, the curriculum uses the same CBT protocol for both. The study was approved by the Institutional Review Board at the first author’s institution with a full review protocol that also included a member of the community. In addition, the study was authorized by the judges that referred participants to the BIP. In total, 584 clients were surveyed; 495 were men (84.8%) and 89 were women (15.2%). Participants were referred to the program between 2010 and 2012. Data come from self-reports collected through a survey administered at intake. The survey was completed by the participants in paper and pencil. None of the participants invited to the study refused to take the survey, resulting in a 100.0% response rate. A trained research assistant entered the data in a database created by the first author.

Measures and Description of the Sample

In the study, the dependent variable is Criminal Thinking. Gender is the main independent variable, while participant demographic characteristics (race, ethnicity, age, education, and SES) and personal experiences (mental health problems, substance abuse, abuse in the home, court mandated, and past incarcerations) are included as control variables. Because we describe all the measures included in the analysis by gender, we introduce the variable gender first.

Gender

Participants were asked the question “Are you. . .?” Participants could choose “Male,” “Female,” or “Other.” None of the participants selected the category “Other.” For this reason, the variable gender included in the study only refers to “Male” and “Female” participants. To investigate sexual orientation, we asked participants to respond to the question “Is your significant other. . .?” Participants could choose “Male,” “Female,” or “Other.” We then created a new variable that would combine the participant’s sex and significant other’s sex. However, due to a low count of same sex relationships (N = 13), we did not include the variable sexual orientation in the analysis. In the study, 84.8% of participants identified as male, while 15.2% identified as female.

Criminal thinking

The dependent variable for the study is Criminal Thinking, measured through a modified version of the Texas Christian University Criminal Thinking Scale (TCU-CTS, Knight et al., 2006). The TCU-CTS is widely used to assess criminal thinking among correctional populations, and a recent study reaffirmed its good internal reliability as well as its validity using item response theory (Sease et al., 2022). The scale is comprised of 36 items that measure six aspects of the individual’s attitudes, beliefs, and behaviors: Personal Irresponsibility, Criminal Rationalization, Cold Heartedness, Power Orientation, Justification, and Entitlement. According to Knight et al. (2006), Personal Irresponsibility refers to an individual’s willingness to accept ownership of their wrongdoing. Criminal Rationalization is a measure of an individual’s general attitude toward the law and authority figures.Cold Heartedness addresses an individual’s lack of emotional attachment in interpersonal relationships. Power Orientation measures an individual’s need for power and control. Justification measures cognitive/thinking patterns that lead to minimizing the harm inflicted and justifying one’s own actions based on some external circumstances. Entitlement measures an individual’s sense of ownership or privilege over others (Knight et al., 2006, pp. 163–164). In addition to the six TCU-CTS scales, a scale comprised of all TCU-CTS items is included in the analysis labeled All TCU-CTS. For each item in the TCU-CTS, participants responded on a 5-item Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. Six of the 36 items were reverse coded.

Table A1 in the Appendix provides the results of the scale and sub-scales’ reliability analysis with Cronbach’s alphas (Cronbach, 1951). The overall Cronbach’s alpha for the full TCU-CTS was .9, suggesting that all the items belong together in the measure of criminal thinking. In addition, the sub-scales’ Cronbach’s alphas ranged between .76 and .80, also suggesting that the sub-scales efficiently measured specific aspects of the participants’ criminal thinking. Table A1 in the Appendix also displays the results of the reliability analysis for all the TCU-CTS and sub-scales by gender using Cronbach’s alpha, and the effect sizes using Cohen’s (1992) coefficient. Cronbach’s alphas indicated that there were no significant differences between the two groups, suggesting that the TCU-CTS may be an effective way to assess gender differences in criminal thinking patterns. The results of the effect size analysis with Cohen’s d coefficient between the variable gender and each one of the TCU-CTS scales indicated an overall moderate to large effect size for the TCU-CTS sub-scales (with Cohen’s d ranging between 0.49 and .85), and a moderate effect size for the overall TCU-CTS (Cohen’s d = 0.45). The largest effect size was between the variable gender and the TCU-CTS subscale Power Orientation, suggesting that gender differences were especially pronounced in patterns of power orientation.

Table 1 provides details about the criminal thinking measures and other continuous variables/indices included in the analysis and specifies the range, mean (M), and standard deviation (SD).

Table 1.

Describing the Sample | Continuous Variables and Scales N = 584.

Variable	Range	All mean (SD)	Male mean (SD)	Female mean (SD)	T-test (p-value)	Missing (%)
Dependent variables
Entitlement	1–4	1.49 (0.49)	1.5 (0.49)	1.4 (0.44)	1.4 (.148)	0.0
Justification	1–4	1.49 (0.59)	1.5 (0.58)	1.4 (0.63)	1.4 (.176)	0.0
Power orientation	1–5	2.07 (0.85)	2.1 (0.82)	2.02 (1.0)	0.56 (.573)	0.0
Cold heartedness	1–5	2.4 (0.74)	2.4 (0.73)	2.01 (0.67)	5.6 (<.001)	0.2
Criminal rationalization	1–5	2.4 (0.75)	2.4 (0.75)	2.15 (0.7)	3.7 (<.001)	0.0
Personal irresponsibility	1–4	1.6 (0.6)	1.6 (0.59)	1.4 (0.53)	3.5 (<.001)	0.0
All criminal thinking	1–3	1.9 (0.4)	1.9 (0.4)	1.7 (0.46)	3.7 (<.001)	0.0
Independent variables
Age (years)	16–67	33.15 (9.9)	33.41 (10.06)	31.69 (9.3)	1.6 (.115)	0.03
Education (years)	10–14	12.01 (1.2)	11.99 (1.2)	12.12 (1.3)	−0.8 (.424)	11.1
SES	0–2	0.94 (0.38)	0.94 (0.38)	0.93 (0.39)	0.16 (.87)	0.3
Mental health problems	0–15	4.7 (3.6)	4.4 (3.5)	6.2 (3.6)	−4.3 (<.001)	3.9
Substance abuse	0–14	2.3 (2.9)	2.4 (2.9)	1.7 (2.7)	1.9 (.051)	3.4
Past incarcerations	0–33	2.06 (3.5)	2.29 (3.7)	0.83 (1.7)	5.8 (<.001)	1.5

Note: The Table provides a comparison between men (84.8%) and women (15.2%) in the sample. T-test values are included. Probability values are reported in parentheses. SD refers to standard deviation.

Participant responses to the All TCU-CTS ranged between 1 and 3 (M = 1.9, SD = .4). Participant responses ranged between 1 and 4 in Entitlement (M = 1.49, SD = .49), Justification (M = 1.49, SD = .59), and Personal Irresponsibility (M = 1.6, SD = .6), but ranged between 1 and 5 in Power Orientation (M = 2.07, SD=.85), Cold Heartedness (M = 2.4, SD = .74), and Criminal Rationalization (M = 2.4, SD = .75).

Table 1 also displays gender comparisons for criminal thinking using two-sample t-test. Considering that men and women might be significantly different from one another, we used the assumption that equal variance is not assumed and employed Welch’s test (Welch, 1951) to control for Type 1 error in group comparison measurements (Derrick et al., 2016). In the study, male participants exhibited slightly higher rates of Entitlement (M = 1.5, SD = .49) and higher rates of Justification (M = 1.5, SD = .58) than female participants (M = 1.4, SD = .44, M = 1.4, SD = .63, respectively), but these differences were not statistically significant. Similarly, men in the study exhibited slightly higher levels of Power Orientation (M = 2.1, SD = .82) than women (M = 2.02, SD = 1.0), and this difference was not statistically significant. On average, male participants exhibited higher levels of Cold Heartedness (M = 2.4, SD = .73) than female participants (M = 2.01, SD = .67), and the difference was statistically significant (p-value <. 001). Male participants also scored higher in Criminal Rationalization (M = 2.4, SD = .75) and Personal Irresponsibility (M = 1.6, SD = .59) than female participants (M = 2.15, SD = .7 and M = 1.4, SD = .53, respectively) and these differences were statistically significant (both were p < .001). Male participants scored higher in All Criminal Thinking (M = 1.9, SD = .4) than female participants (M = 1.7, SD = .46) and the difference was statistically significant (p < .001).

Control variables

In the study, 10 variables were included as control variables. Among them, five were continuous (age, years of education, socio-economic status, mental health problems, substance use, and past incarcerations) and four variables were coded as binary (race, ethnicity, abuse while growing up, and court mandated).

In the survey, participants were asked to specify their age in years. As displayed in Table 1, among participants, age ranged between 16 years and 67 years (M = 33.15; SD = 9.9). On average, men in the sample were older than women (33.15 years vs. 31.69 years), but the difference was not statistically significant.

Participants were also asked to provide information about their education as number of years of education completed. The number of years of education completed by participants ranged between 10 and 14. On average, participants completed 12.01 years of education (SD = 1.2). Women reported slightly higher educational attainment with 12.12 years of education, compared to 11.99 years for men, but the difference was not statistically significant.

To measure participants’ socio-economic status (SES), we created an instrument by combining three variables from the survey: combined household income, number of children under the age of 18 years living in the household, and number of adults (including participant) living in the household. Combined household income included nine income categories, from “no income to $30,000” to “$100,000 or more.” Both variables specifying the number of adults and the number of children in the household were continuous variables. The three variables were recoded as binary variables to create a socio-economic status indicator. The variable income was recoded as a binary variable with a cut-off set at “$70,000 or less;” (the cut-off was determined based on labor market estimates) (DeNavas-Walt, & Proctor, 2015). The cut-off for the variables number of adults and number of children in the household was set to four for each variable. The combined measure is an SES indictor that ranged between 0 “low SES” and 2 “high SES.” For this methodology see also (Solinas-Saunders, 2021). Participants’ SES ranged between 0 and 2 (M = .94, SD = .38). While women had lower SES than men (.93 vs. .94 respectively), the difference was not statistically significant.

Two clinical instruments for the assessment of mental health and substance abuse were included in the survey. Participants’ mental health problems were measured through a modified version of the Mental Health Screening Form III (Carroll & McGinley, 2001). The 15 questions focus on numerous mental health symptoms and experiences throughout the participant’s lifetime. Examples of these are: “Have you ever been advised to take medication for anxiety, depression, hearing voices, or for any other emotional problems?”; “Have you ever heard voices no one else could hear or seen objects or things which others could not see?”; “Was there ever a period in your life when you spent a lot of time thinking and worrying about gaining weight, becoming fat, or controlling eating? For example, by repeatedly dieting or fasting, engaging in much exercise to compensate for binge eating, taking enemas, or forcing yourself to throw up?” For each question, participants could choose either “Yes” or “No.” We created an additive index for mental health problems including all 15 items. The reliability analysis (Cronbach’s alpha = .85) shows the 15 items belong together in the index to measure the participants’ mental health problems. In the study, the measure for mental health problems ranged between 0 (if participant answered “No” to all the mental health questions) and 15 (if participant answered “Yes” to all the mental health questions) (M = 4.7, SD = 3.6), with men exhibiting lower levels of mental health problems than women (M = 4.4, SD = 3.5 for men and M = 6.2, SD = 3.6 for women), a difference that was statistically significant (p < .001).

Substance abuse was measured through a modified version of the “Drug Abuse Screening Test” (Skinner, 1982). Twenty items were selected to investigate participants’ substance abuse during the 6 months prior to the survey. Items included questions like “Has drinking or other drug use caused problems between you and your family or friends?”; “Have you lost your temper or gotten into arguments or fights while drinking or using drugs?”; “Are you needing to drink or use drugs more and more to get the effect you want?” We created an additive index to measure participants’ substance abuse problems including all 20 items. The reliability analysis (Cronbach’s alpha = .85) suggests that all items in the index belong together to measure substance abuse. Participants were directed to answer each question by choosing either “Yes” or “No.” Participants’ responses to the substance abuse problems index ranged between 0 (if participant answered “No” to all the substance abuse questions) and 14 (if participant answered “Yes” to 14 of the 20 substance abuse questions included in the index; no participants answered “Yes” to more than 14 of the 20 questions) (M = 2.3, SD = 2.9). In the study, male participants reported higher levels of substance abuse problems than female participants (M = 2.4, SD = 2.9 for men and M = 1.7, SD = 2.7 for women), and the difference between the two groups was statistically significant (p < .05).

Participants were also asked about past incarcerations. More specifically, participants were asked to provide the number of times they had been incarcerated prior to intake in the BIP. Overall, participants reported a range of past incarcerations between 0 and 33 (M = 2.06, SD = 3.5). As expected, male participants scored higher than their female counterpart in the number of past incarcerations. Furthermore, the difference in the average number of past incarcerations between male participants (M = 2.29, SD = 3.7) and female participants (M = .83, SD = 1.7) was statistically significant (p-value < .001).

The survey asked participants about their race. Participants could self-identify as White; Black or African American; American Indian/Native American or Alaskan Native; Asian American, Hawaiian or Pacific Islander; or Other. Due to low frequencies in the non-White categories, all racial minorities were grouped into one category labeled as “non-White.”

Table 2 provides the descriptive statistics for the variable race and the other binary variables included in the study. Table 2 also displays gender comparisons for all the binary variables and the results of the Chi-square tests.

Table 2.

Describing the Sample—Binary Variables and Chi-Square Test (χ²), N = 584.

Variable	Groups	All (%)	Male (%)	Female (%)	χ² (p-value)	Missing (%)
Race					2.1 (.147)	4.45
Race	White	85.5	84.6	90.6
Non-White		14.5	15.4	9.4
Ethnicity					0.28 (.593)	7.4
	Non-Hispanic	95.2	95.4	94.0
	Hispanic	4.8	4.6	6.0
Abuse in the home					9.5 (.002)	1.9
	No	59.2	61.9	44.3
	Yes	40.8	38.1	55.7
Court mandated					9.5 (.002)	7.2
	No	11.8	10.1	22.4
	Yes	88.2	89.9	77.6

Note. The Table provides a comparison between men (84.8%) and women (15.2%) in the sample measured with Chi-square test. Probability values are included in parentheses.

In the sample, 85.5% of participants identified as “White,” while 14.5% identified as “non-White.” In the study, men were more likely than women to identify as non-White (15.4% vs. 9.4%), but the difference between the two groups was not statistically significant.

Participants were also asked about their ethnicity. More specifically, participants were asked whether they identified as Hispanic/Latinx, and participants could choose either “Yes” or “No.” In total, 4.8% of participants identified as Hispanic/Latinx, representing 4.6% of the men and 6.0% of the women in the sample, but the difference between the two groups was not statistically significant.

One of the survey questions asked participants whether there was abuse in the home while growing up, and participants could choose either “Yes” or “No.” Over 40% of participants reported that there was abuse in the home while growing up (40.8%). Women reported higher rates of abuse than men (55.7% vs. 38.1%) and the difference between the two groups was statistically significant (p < .01).

Participants were also asked to specify the court that mandated their attendance from a drop-down menu that listed all the counties included in the study. But participants also had the option to mark “Not court mandated” if enrollment in the BIP was recommended by the judge or a case manager/counselor due to the individual’s involvement in a dysfunctional intimate relationship but not mandated as part of a criminal conviction. While it is often assumed that all those who attend BIPs are mandated to do so following a court decision, this is sometimes not the case (Miller, 2005; Saunders, 2008), with several studies showing that non-court-mandated participants might enroll in a BIP following referral from case managers/counselors, or simply decide to join a BIP due to their involvement in dysfunctional relationships (Hamilton et al., 2013; Tutty et al., 2020). Among the participants, 88.2% were mandated to attend the BIP as part of their sentence (court mandated). The remaining 11.8% were not court mandated. The proportion of non-court-mandated participants in previous studies conducted in North America ranged between 14% and 23% (see review by Tutty et al., 2020, p. 294). We do not have details about the specific reason(s) each individual among the non-court-mandated group decided to enroll in the BIP. However, in the study, women were more likely than men to attend the BIP without a court mandate (22.4% vs. 10.1%) and this difference was statistically significant (p < .01). Participants who were court mandated were those convicted by the criminal court in their respective county. None of the participants attended the BIP as part of a diversion program. Unfortunately, no information about the participants’ criminal conviction was made available to us. It is important to note that community-engaged research requires close collaboration with community partners, and such collaboration is based on relevant compromises in research design. While access to more detailed information about participant criminal justice involvement would have improved the design of the study, the need to protect participants’ anonymity led to restrictions in the inclusion of specific details about their offenses.

Missing Data

Across all the variables and instruments employed for the study, missing data were low, ranging between .0% and 11.1%, which suggests that no statistical technique was needed to compute the impact of missing data on the empirical models (Dong & Peng, 2013). Missing data for each variable included in the analysis are specified on Tables 1 and 2.

In the sample, missing data were due to skipped items. Among participants, skipped items ranged between 0 and 11. Furthermore, an analysis of missing data across the different variables showed that there was no specific pattern in the skipped items, suggesting that missing data occurred at random. Importantly, while women represent the smaller group in the sample, the missing data among women were lower than among men. This is important, as it ensured women were sufficiently represented in the analysis even though it was the smaller group (N = 89). All 584 surveys submitted by participants were included in the analysis.

Empirical Strategy

Generalized Linear Models (GLZMs) were employed for the multivariate analyses. The dependent variables that measure criminal thinking are all continuous within the parameters of the Likert Scale. This suggests that Ordinary Least Square (OLS) models might not be appropriate for the analysis (Park, 2005) because criminal thinking behaviors are not distributed according to a linear function. Moreover, OLS is not able to adjust for the increased risk of Type I error when repeated measurements are conducted. Conversely, GLZMs use the Wald test, which provides a more conservative estimate when compared to other tests, and it controls for Type I error in repeated measurements (Nugent & Kleinman, 2021). GLZMs also provide more robust findings when the data present a hierarchical structure (Y. Lee & Nelder, 1996), as it is the case of the present study, with participants nested within BIP sites and gender groups. Seven equations were used for the multivariate analysis, one for the full TCU-CTS and one for each of the six sub-scales.

Results of the Multivariate Analyses

Table 3 reports the findings of the GLZM analyses. The results of each model employed in the analyses are displayed by column.

Table 3.

Regression Analysis, N = 584.

Variable	Model 1: All TCU-CTS		Model 2: Personal irresponsibility		Model 3: Criminal rationalization		Model 4: Cold heartedness
Variable	B (SE)	Wald (p-value)	B (SE)	Wald (p-value)	B (SE)	Wald (p-value)	B (SE)	Wald (p-value)
Gender (female)	−0.25 (0.06)	17.6 (<.001)	−0.25 (0.08)	8.5 (.004)	−0.35 (0.11)	10.5 (.001)	−0.35 (0.1)	11.7 (<.001)
Race (non-White)	0.14 (0.06)	5.1 (.024)	0.26 (0.08)	9.2 (.002)	0.22 (0.11)	4.2 (.042)	0.11 (0.11)	1.2 (.279)
Ethnicity (Hispanic)	−0.03 (0.17)	0.03 (.866)	−0.13 (0.23)	0.29 (.587)	0.33 (0.3)	1.2 (.267)	−0.29 (0.29)	0.98 (.323)
Abuse in the home	0.02 (0.04)	0.28 (.595)	0.03 (0.06)	0.24 (.628)	0.06 (0.08)	0.58 (.445)	−0.12 (0.08)	2.03 (.154)
Court mandated	−0.014 (0.07)	0.11 (.738)	0.25 (0.1)	6.2 (.013)	−0.01 (0.13)	0.004 (.952)	−0.13 (0.12)	1.14 (.291)
Age (years)	−0.01 (0.002)	20.21 (<.001)	−0.01 (0.003)	4.3 (.037)	−0.01 (0.004)	10.4 (.001)	−0.004 (0.004)	1.3 (.248)
Education (years)	0.005 (0.02)	0.09 (.764)	−0.02 (0.02)	0.58 (.447)	0.06 (0.03)	2.9 (.088)	−0.03 (0.03)	0.97 (.325)
SES	−0.02 (0.06)	0.01 (.976)	−0.05 (0.09)	0.3 (.584)	0.07 (0.11)	0.35 (.552)	0.13 (0.11)	1.4 (.233)
Substance abuse	0.01 (0.01)	1.2 (.279)	−0.01 (0.01)	1.4 (.244)	0.01 (0.01)	0.5 (.479)	−0.02 (0.01)	2.9 (.084)
Mental health problems	0.02 (0.006)	8.9 (.003)	0.01 (0.01)	1.7 (.186)	0.02 (0.01)	3.8 (.05)	−0.02 (0.01)	4.7 (.031)
Past incarcerations	0.01 (0.01)	1.1 (.316)	0.002 (0.01)	0.09 (.765)	−0.005 (0.01)	0.21 (.651)	0.02 (0.01)	4.3 (.037)
Intercept	1.8 (0.3)	44.4 (<.001)	1.9 (0.4)	24.6 (<.001)	2.19 (0.5)	18.9 (<.001)	2.3 (.5)	22.2 (<.001)
Diagnostics
Likelihood ratio Chi-square		61.6 (<.001)	34.69 (<0.001)		36.78 (<.001)		42.97 (<.001)
Degrees of freedom		11	11		11		11
Model N		382	382		382		382

Note. The Table Displays GLZM results for Models 1–4. Probability values for Wald Test are included in parentheses.

Variable	Model 5: Power orientation		Model 6: Justification		Model 7: Entitlement
Variable	B (SE)	Wald (p-value)	B (SE)	Wald (p-value)	B (SE)	Wald (p-value)
Gender (female)	−0.31 (0.1)	9.4 (.002)	−0.15 (0.08)	3.7 (.056)	−0.12 (0.07)	3.2 (.075)
Race (non-White)	0.17 (0.1)	2.6 (.11)	0.04 (0.08)	0.29 (.586)	0.03 (0.07)	0.24 (.627)
Ethnicity (Hispanic)	0.03 (0.29)	0.01 (.916)	−0.17 (0.2)	0.62 (.432)	0.004 (0.19)	0.00 (.984)
Abuse in the home	0.14 (0.08)	2.9 (.086)	0.08 (0.06)	1.97 (.16)	−0.07 (0.05)	1.5 (.217)
Court mandated	−0.3 (0.12)	5.9 (.015)	0.08 (0.09)	0.8 (.383)	−0.04 (0.08)	0.31 (.58)
Age (years)	−0.02 (0.0)	36.4 (<.001)	−0.01 (0.0)	7.19 (.007)	−0.005 (0.0)	3.6 (.058)
Education (years)	0.04 (0.03)	1.8 (.183)	−0.01 (0.02)	0.36 (.548)	−0.005 (0.02)	0.05 (.82)
SES	−0.15 (0.11)	1.96 (.161)	−0.03 (0.08)	0.36 (.551)	0.05 (0.07)	0.53 (.468)
Substance abuse	0.04 (0.1)	9.4 (.002)	0.03 (0.01)	7.3 (.007)	0.003 (0.01)	0.13 (.714)
Mental health problems	0.06 (0.01)	27.7 (<.001)	0.02 (0.01)	4.4 (.037)	0.02 (0.01)	7.6 (.006)
Past incarcerations	0.01 (0.01)	0.9 (.341)	0.003 (0.01)	0.14 (.712)	0.01 (0.01)	0.69 (.407)
Intercept	1.96 (0.49)	16.3 (<.001)	1.6 (0.4)	17.6 (<.001)	1.4 (.3)	(<.001)
Diagnostics
Likelihood ratio (Chi-squared)	115.7 (0.000)		39.76 (0<.001)		19.03 (0.061)
Degrees of freedom	11		11		11
N	382		382		382

Note. The Table Displays GLZM results for Models 5 to 7. Probability values for Wald Test are included in parentheses. The main independent variable in the analysis is Gender.

Model 1: All TCU-CTS

In Table 3, Model 1 for All TCU-CTS illustrated that women scored significantly lower than men in levels of overall criminal thinking (p < .001), even after controlling for demographics (race, ethnicity, age, education, and SES) and personal experiences (abuse in the home, court mandated, mental health problems, substance abuse, and past incarcerations). As for the control variables, the analysis indicated that non-White participants reported higher levels of criminal thinking compared to White participants (p < .05). In addition, younger participants scored significantly higher than older participants in levels of criminal thinking (p < .001). Participants who reported higher levels of mental health problems also scored higher in criminal thinking than those with lower levels of mental health problems (p < .01).

Model 2: Personal Irresponsibility

Model 2 for Personal Irresponsibility indicated that women scored significantly lower than men in levels of Personal Irresponsibility (p < .01), after controlling for other variables. Three of the control variables were statistically significant in this model. Non-White participants scored higher than White participants in levels of Personal Irresponsibility (p < .01), and participants who were court mandated also scored higher than those who were not court mandated (p < .05). In addition, younger participants scored higher than older participants in levels of Personal Irresponsibility (p < .05).

Model 3: Criminal Rationalization

Model 3 for Criminal Rationalization confirmed the findings in Models 1 and 2 that gender is a relevant variable in the analysis, with women exhibiting significantly lower levels of criminal rationalization than men in the study (p < .01). Among the control variables, both race and age were statistically significant. More specifically, non-White participants scored higher in levels of Criminal Rationalization than White participants (p < .05). Furthermore, younger participants scored higher than older participants in levels of Criminal Rationalization (p < .01).

Model 4: Cold Heartedness

In Model 4 for Cold Heartedness, the results of the analysis indicated that women scored lower than men in levels of cold heartedness (p < .001). Participants who scored higher in mental health problems scored lower in levels of cold heartedness (p < .05). In addition, individuals who reported more past incarcerations scored higher in cold heartedness than participants who reported fewer past incarcerations (p < .05).

Model 5: Power Orientation

Model 5 for Power Orientation showed that women scored significantly lower than men in levels of power orientation (p < .01). In this model, several of the control variables were statistically significant. Participants who were court mandated scored lower than participants not court mandated in levels of power orientation (p < .05). Older participants scored lower than younger participants in levels of power orientation (p < .001). Participants who scored higher in levels of substance abuse and mental health problems also scored higher in power orientation (p < .01 and p < .001, respectively).

Model 6: Justification

In Model 6 for Justification, the variable gender was not statistically significant (p > .05). In this model, three of the control variables were statistically significant. Younger participants scored higher than older participants in levels of justification (p < .01). Participants who scored higher in substance abuse and mental health problems also scored higher in levels of justification (p < .01 and p < .05, respectively).

Model 7: Entitlement

In Model 7 for Entitlement, no gender differences were observed among participants (p > .05). Among the control variables, only the variable mental health problems was statistically significant, with participants who scored higher in mental health problems also exhibiting higher levels of entitlement (p < .01).

Model Diagnostics

The diagnostics for the GLZMs indicated that six out of the seven models were statistically significant, with the Omnibus test for the Entitlement model showing a Likelihood Ratio (Chi-square) equal to 19.03 (p-value > .05). Models 1 through 6 were statistically significant with p < .001. A test of multi-collinearity showed VIF levels between 1.031 and 1.376, and tolerance levels between .727 and .97, suggesting that no tolerance issues were present in the empirical models. To make sure the correlation levels among the independent variables would not affect the results of the multivariate analysis, we computed the bivariate correlations for all the independent variables included in the models using Pearson’s r test. Table A2 in the Appendix displays the bivariate correlations among the independent variables in the study. The strongest bivariate correlation in the analysis was observed between the variables “abuse in the home” and the index for “mental health problems” (Pearson’s r = .421, p-value < .001), indicating a moderate correlation between the two variables.

Further Examination of the Data: Factor Analysis

To add rigor to the analysis, we examined whether gender differences existed in participants’ representation of criminal thinking domains using Factor Analysis (Rummel, 1988; Yong & Pearce, 2013). Table A3 in the Appendix provides the summary of the Factor Analysis for all the TCU-CTS subscales. Using Chi-square tests, we found no statistically significant differences in patterns of interpretation of criminal thinking concepts by gender. Following Vaske et al. (2017), we created a composite measure for each sub-scale and employed two-sample t-test procedures to compare the mean score by gender (factor loading). This method allowed for a more rigorous interpretation of gender differences in the representation of criminal thinking domains. We found statistically significant differences for three out of the six TCU-CTS subscales: Personal Irresponsibility (mean difference = .383, p-value < .001); Cold Heartedness (mean difference = .592, p-value < .001); and Criminal Rationalization (mean difference = .427, p-value < .001), suggesting that men and women differ in the way they interpret the concepts represented by these sub-scales. No statistically significant differences were found for the sub-scales Entitlement, Justification, or Power Orientation, allowing us to confirm the findings from the GLZMs in reference to these three TCU-CTS sub-scales.

Discussion

The study examined differences in criminal cognitions between men and women attending a multi-site BIP in a Midwestern state (N = 584). Overall, rates of criminal thinking among participants were low, with most participants disagreeing with the statements included in the criminal thinking measures employed for the study (TCU-CTS, Knight et al., 2006). However, the multivariate analyses (GLZMs) provided evidence that women scored lower than men in all aspects of criminal thinking, except for Justification and Entitlement. Further examination of the data through Factor Analysis (Rummel, 1988; Yong & Pearce, 2013) revealed that gender differences in Personal Irresponsibility, Cold Heartedness, and Criminal Rationalization might be at least partly due to men and women interpreting certain criminal thinking concepts differently, an issue known as Differential Item Functioning (DIF, Wang, 2008) explored in prior criminal thinking research (see for instance Vaske et al., 2017). Conversely, the findings of the multivariate analysis in reference to the TCU-CTS sub-scales Entitlement, Justification, and Power Orientation were confirmed by Factor Analysis, thereby providing evidence that women and men in the study differed in patterns of Power Orientation, after controlling for demographics (race, ethnicity, age, education, and SES) and personal experiences (mental health problems, substance abuse, abuse in the home, court mandated, and past incarcerations). While we believe these findings should not be ignored, before drawing conclusions, this study’s findings need to be replicated by other studies, especially studies that use larger samples of female participants.

Theoretical and Practical Implications

This study contributes to the development of criminal thinking theories (Walters, 2006) and to an understanding of the gendered patterns of neutralization techniques some individuals use to feel better about their behaviors while rationalizing and justifying the harm they inflict upon others (Benson & Harbinson, 2020; Klenowski et al., 2011; Sykes & Matza, 1957). Overall, the study confirmed that women are less likely than men to exhibit power and control motives in intimate relationships, supporting the premises of the feminist perspective, which posits that women are less likely than men to use power and control tactics to assert themselves in intimate relationships (Brush et al., 2007; Chesney-Lind, 2006; Miller, 2005). However, more studies are needed to better understand the depth of such differences between men and women involved in dysfunctional intimate relationships.

If replicated by other studies that use larger samples, these findings may become relevant from a practical standpoint for several reasons: First, differences in patterns of Power Orientation may suggest that specific components of curricula included in criminal justice programs could be diversified to accommodate gender-specific needs of clients (Hubbard & Matthews, 2008; Link & Oser, 2018). As Sease et al. (2022) indicated, the criminal justice system needs to provide services that help reduce offenders’ risk of recidivism. As they stated, “[c]riminal thinking is a dynamic cognitive process malleable through an intervention that, if accurately measured, could serve as a crucial component of an individual’s treatment plan” (Sease et al., 2022, p. 145). Second, at the statistical level, studies that focus on gender differences in patterns of criminal cognitions must pay attention to gender differences in response patterns and interpretation of concepts represented in the instruments employed to provide accurate findings (Hubbard & Matthews, 2008; Jones et al., 2021; Vaske et al., 2016; Walters, 2014).

Several variables were included in the analysis as control variables and their influence on the statistical models are also relevant to discuss. The results illustrated that non-Whites scored higher on the overall criminal thinking scale (All TCU-CTS) and on the Personal Irresponsibility and Criminal Rationalization subscales, but race was not significant in any other model. The findings about race must be further explored before drawing conclusions. Racial minorities tend to experience stressors that stem from prejudice and social marginalization (Link & Oser, 2018), and such stressors might contribute to one’s propensity to exhibit criminal thinking patterns.

Participants who were court mandated to attend the BIP were more likely than participants who were not court mandated to score higher in patterns of Personal Irresponsibility, but less likely to exhibit patterns of Power Orientation. Participants who decided to attend a BIP without a mandatory referral from a judge might have been more likely to recognize their own shortcomings or more willing to work on their issues (Miller, 2005). It must be noted that women in the program were less likely than men to be court-mandated, which might contribute to explain differences in power and control motives between the two groups of participants.

In the study, younger participants exhibited higher levels of overall criminal thinking. This is not surprising, as a propensity toward delinquent attitudes and antisocial behavior tend to be inversely correlated with maturation, with most individuals exhibiting antisocial behavior desisting at some point during adulthood (Loeber & Le Blanc, 1990); although exceptions do exist among individuals with life-course persistent antisocial behavior (Moffitt, 1993). A younger age among the participants in the study was strongly associated with Power Orientation, Criminal Rationalization, Personal Irresponsibility, and Justification.

Participants’ substance abuse was not significantly associated with overall criminal thinking; however, the analysis showed that substance abuse was strongly associated with Power Orientation and Justification. Interestingly, in the study, men had significantly higher levels of substance abuse than women. Mental health problems experienced by participants throughout their life-course are also relevant. Overall, mental health problems were significantly associated with five of the seven measures of criminal thinking (All TCU-CTS, Cold Heartedness, Power Orientation, Justification, and Entitlement). Considering that women in the study were significantly more likely than men to report mental health problems, a further examination of the association between criminal thinking and mental health problems is relevant.

Limitations

The study’s findings must be interpreted considering several limitations. First, data for the study come from a cross-sectional survey that was administered at intake to the BIP and measures criminal thinking at one point in time. Second, some of the measures included as control variables are explored in retrospective (abuse in the home, substance abuse, and mental health problems) in that participants are asked to refer to incidents that occurred in the past, and participants might not remember all the details of their experiences (Henry et al., 1994); although studies show that the main issue with retrospective investigation is associated with participants not remembering exactly when it happened rather than whether it happened (Dupre & Meadows, 2007). Third, the study only included 89 women representing 15.2% of the total sample. Studies that include larger samples of women are needed in research on criminal thinking. Fourth, while the study included measures of substance abuse and mental health, it is important to further explore how substance abuse and mental health problems influence the association between gender and criminal thinking in more depth (Vaske et al., 2017). Another relevant limitation in the study is that the TCU-CTS is mostly concerned with proactive criminal thinking. As Walters (2020) points out, IPV studies that seek to analyze the association between behaviors and cognitive patterns must include both proactive and reactive measures of criminal thinking. Despite some limitations, we employed rigorous methodologies, providing a robust framework of research for further studies employing larger samples.

Conclusion

The association between criminal thinking and gender is complicated. Careful consideration of participant responses through Factor Analysis indicated that men and women might have interpreted differently some of the questions in the criminal thinking measures employed for the study. Furthermore, the results of the multivariate analysis illustrated that, after controlling for demographic characteristics and personal experiences, men and women differed in patterns of Power Orientation but not in other areas of criminal thinking examined through the TCU-CTS (Knight et al., 2006). Differences is patterns of Power Orientation between men and women BIP clients support evidence provided by previous feminist studies (Brush et al., 2007; R. E. Dobash & Dobash, 1979; Chesney-Lind, 2006; Miller, 2005) that women are less likely than men to use power and control tactics to dominate their intimate partners. If confirmed in future studies that employ larger samples, these findings could help better understand the cognitive risk factors in IPV perpetration and recidivism. However, future research must employ measures of criminal thinking that include both proactive (power/control) and reactive (impulsivity/low self-control) criminal thinking patterns when comparing BIP participants by gender (Walters, 2020). Furthermore, an examination of the moderating effect of mental health problems on the association between gender/sexual orientation and criminal thinking should be considered. As women’s rates of involvement in the criminal justice system for IPV continue to grow (Bader et al., 2019), it is important to identify IPV treatments that specifically target women’s unique risks and needs.

Footnotes

Appendix

Table A3.

Texas Christian University—Criminal Thinking Scale, Factor Analysis (N = 584).

Item	Male	Female
Entitlement
You deserve special consideration	0.802	0.740
You have paid your dues in life and are justified in taking what you want	0.642	0.729
You feel you are above the law	0.622	0.657
It is OK to commit crime to pay for the things you need	0.724	0.816
Society owes you a better life	0.577	0.778
Your good behavior should allow you to be irresponsible sometimes	0.650	0.254
It is OK to commit crime to live the life you deserve	0.793	0.849
Chi-square = 151.8; p-value > .05
Mean difference = −.189; p-value > .05
Justification
You rationalize your irresponsible actions with statements like “Everyone else is doing it. . .”	0.563	0.494
When questioned about the motives for engaging in crime, you justify your behavior because you had a hard life	0.601	0.562
You find yourself blaming the victim of some of your crimes	0.491	0.527
Breaking the law is not a big deal as long as you do not harm physically someone	0.520	0.413
You find yourself blaming society for your problems	0.599	0.748
You justify the crimes you have committed by telling yourself someone else might have one the same	0.629	0.742
Chi-square = 137.3; p-value > .05
Mean difference = .172; p-value > .05
Personal irresponsibility
You are under the supervision of the criminal justice system because of bad luck	0.337	0.586
Nothing you do here is going to make a difference	0.283	0.553
You are not to blame for everything you have done	0.468	0.649
You may be in the system, but the environment made you this way	0.596	0.525
Laws are just a way to keep poor people down	0.647	0.801
The real reason why you are under the supervision of the criminal justice system is because of your race	0.631	0.840
Chi-square = 152.6; p-value > .05
Mean difference = .383; p-value < .001
Power orientation
When people tell you what to do you become aggressive	0.553	0.737
When not in control you feel the need to exert power	0.591	0.696
You like to be in control	0.382	0.506
If someone disrespect you, you have to straighten them out	0.682	0.634
You think you have to pay back people who mess with you	0.708	0.768
The only way to protect yourself is to be ready to fight	0.628	0.638
Chi-square = 283.8; p-value > .05
Mean difference = .112; p-value > .05
Cold heartedness
You get upset when you hear someone lost everything ®	0.373	0.307
Seeing someone cry makes you sad ®	0.586	0.698
You are sometimes so moved by an experience that you cannot describe ®	0.473	0.540
You worry when a friend is having personal problems ®	0.654	0.712
You feel people are important to you ®	0.482	0.330
Chi-square = 228.5; p-value > .05
Mean difference = .592; p-value < .001
Criminal rationalization
This country’s justice system was designed to treat everyone equally ®	0.243	0.963
Everything can be fixed in court if you have the right connections	0.423	0.420
Bankers, lawyers, and politicians get away with breaking the law	0.577	0.632
Police do worse things than those they lock up	0.602	0.639
Prosecutors often tell witnesses to lie in court	0.537	0.594
It is unfair that you are under the supervision when lawyers and politicians get away	0.456	0.702
Chi square = 367.4; p value > .05
Mean difference = .427; p value < .001

Note: The Table displays the results of the factor analysis and compares scores for men (84.8%) and women (15.2%) in the sample. The ® symbol indicate “reverse coding.” The Initial value for each item is consistently 1.000 across the scales in the factor analysis. The extraction value is the proportion of variance that is explained by the retained factor. Values are high, indicating that the items are well-represented in the analysis.

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.

ORCID iD

Monica Solinas-Saunders

Author Biographies

Monica Solinas-Saunders is an Associate Professor in the School of Public and Environmental Affairs at Indiana University Northwest, Gary, Indiana, USA. She holds a PhD in Sociology from the University of Pittsburgh, Pittsburgh, PA. Her research focuses primarily on issues of interpersonal violence and corrections. Her most recent work appeared in Homicide Studies, Crime & Delinquency, Journal of Interpersonal Violence, and The Prison Journal.

Melissa J. Stacer is a Professor of Criminal Justice at the University of Southern Indiana in Evansville, Indiana, USA. Her research interests include institutional corrections, justice-involved veterans, and criminal justice education, as well as reentry initiatives including faith-based programs and ban the box policies. Some of her work has been published in Victims and Offenders, the Prison Journal, the Journal of Offender Rehabilitation, and the Journal of Criminal Justice Education.

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