Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Morality, self-control, age, type of offence and sentence length as predictors of psychopathy amongst female incarcerated offenders in South Africa

Abstract

There has been an increase in female incarcerated offenders nationally and internationally. Despite this trend, literature and research on female offenders remain limited compared to their male counterparts. Evidence of the relationship between certain personality disorders and offending behaviour has led numerous countries to prioritise identifying and assessing personality disorders among the offender population. Psychopathic personality traits may contribute to women’s risk factors for expressing antisocial behaviours, resulting in their potential future incarceration. Thus, a need exists to understand possible factors that may predict the expression of psychopathic traits in females, which may have notable utility among female offenders. This study aimed to investigate possible predictor variables of psychopathy amongst incarcerated female offenders in South Africa. A quantitative research approach, non-experimental research type, and correlational research design were employed. A convenience sampling technique was used. The sample consisted of 139 (N = 139) female offenders housed in two correctional centres in South Africa who voluntarily participated in this study. Correlation analyses and hierarchical regression analysis procedures were conducted to analyse the results. Results indicated (i) a certain combination of predictor variables that statistically and practically significantly explained both primary and secondary psychopathy and (ii) individual predictor variables (e.g., Impulsivity, Simple Tasks, Risk-Seeking, and Self-Centredness) that explained both primary and secondary psychopathy statistically and practically significantly. This study provides valuable information about the possible predictor variables of psychopathy amongst female offenders within the context of South Africa. However, further research must be conducted to validate these findings and advance our knowledge on this topic.

Introduction

Recent years have shown an increase in the number of female incarcerated offenders both nationally and internationally [14]. In South Africa, the Department of Correctional Services (DCS) noted an increase from 3 380 in 2012/13 to 4 316 in 2018/19 [5], with the latest count at 3 724 as of March 2022 [6]. In contrast, the population of male offenders decreased exponentially from 150 467 in March 2020 to 139 499 467 by March 2022 [6], possibly due to DCS interventions, including the 2019 presidential remission, the March 2020 National State of Disaster, and the 2020/21 advancement of parole dates [7].

Although focused predominantly on male offenders, research underscores the relationship between personality disorders, particularly psychopathy, and offending behaviour [811]. Worldwide, psychopathy rates among incarcerated female offenders range from 9 to 15.5%, contrasting with approximately 31% in males [9, 1214]. Notably, psychopathic personality traits in women are identified as potential risk factors for antisocial behaviours and future incarceration [9, 15]. These findings purport the need to understand factors influencing psychopathic personality traits in adult females, holding significant clinical relevance for female offenders [16].

Levenson [17] proposes that psychopathy is a pattern of intrinsically antisocial behaviour, driven by judgements regarding one’s wishes versus the rights and well-being of others. Levenson et al. [18] distinguish between primary and secondary dimensions of psychopathy, with primary focusing on selfish, uncaring, and manipulative attitudes and secondary emphasizing impulsivity and a self-defeating lifestyle. Previous research links psychopathy to antisocial and offending behaviours [1921], particularly violent, serious and chronic offences [8, 11, 21]. Female offenders in South Africa commonly engage in economic, aggressive, and violent offences [2, 4, 8, 22] with environmental variables (i.e., poverty) [8, 23], social factors (i.e., social exclusion and lack of policing) [11, 24] and the normalisation of violence [11, 25] often cited as contributing factors. Despite the increasing number of female offenders, research on the connection between psychopathy and offending behaviour, especially recidivism, remains crucial [8, 21, 26]. However, prevailing beliefs about female criminality and lower offending rates compared to males have marginalized the study of female offenders in mainstream psychopathy research [8, 12, 2729]. Additionally, Douglas et al. [30] and Walters [31] argued that psychopathy may not possess inherent validity in relation to descriptive variables such as age and offender history. DeLisi [19] suggests using psychopathic theories to explore correlates of emerging psychopathic behaviours, allowing researchers to distinguish theories that share similar predictor variables [32] and identify mechanisms connecting psychopathy to offending behaviour. Various variables, including a history of victimisation, aggressive behaviours, personality disorders, substance use, and criminal thinking styles are proposed as potential mediators in explaining the relationship between psychopathy and offending, ultimately aiding in the prediction of psychopathy [8, 10, 11, 3336]. Moreover, variables such as morality, self-control, age, type of offence and sentence length can serve as possible predictors of psychopathy [8, 12, 21, 37, 38].

Morality, specifically moral metacognition, involves an individual’s awareness, monitoring, reflection, and regulation of their thinking within a moral reasoning framework [39, 40]. Moral metacognition is crucial for effective ethical decision-making, contributing to appropriate ethical behavioural outcomes [3942]. Malatesti et al. [43] argue that psychopaths, characterized by a lack of empathy and resistance to other’s emotional influence, tend to reject societal norms [43, 44]. Research further associates psychopathic behaviour with diminished neural reactivity when judging moral transgressions [4548], resulting in persistent aggressive behaviours [21]. The observed lack of aversive response to moral transgressions reinforces the link between psychopathy and morality [21].

Self-control, a key construct in the general theory of offending behaviour, is defined as the susceptibility to momentary temptations [49, 50]. Gottfredson and Hirschi [51] posit that an individual’s self-control influences offending behaviour when presented with opportunities shaped by structural or situational circumstances [49]. Van Gelder and De Vries [52] suggested that individuals are more likely to offend when perceived benefits outweigh perceived losses, particularly if low self-control leads to prioritizing immediate gratification over long-term consequences [51, 53]. Gottfredson and Hirschi [51] distinguish self-control from a lasting propensity to offend, emphasizing a persistent inclination to ignore long-term consequences, resulting in impulsivity, feebleness, recklessness, and indifference to others’ needs [49]. Armstrong et al. [53] note that psychopathy often manifests as callous egocentricity, stemming from a lack of affect rooted in physiological and neuropsychological emotional under-arousal [54, 55]. The connection between psychopathy, self-control, and offending behaviour suggests that exploring this relationship may establish the analytical validity of self-control and psychopathy as distinct aspects of offender propensity [37, 53, 56, 57]. DeLisi and Vaughn [58] argue that the inclination for offending behaviour is predominantly shaped by a psychopathic personality, low self-control, and poor morality. According to Van Gelder and De Vries [59], individuals with a proactive willingness to exploit others for personal gain, displaying self-enhancing and amoral behaviours, are prone to rule violations due to lower moral standards. These behaviours predict psychopathy, self-centeredness [60, 61], and engagement in offending behaviour [52]. Thus, psychopathy, poor morality and low self-control may collectively contribute toward the predisposition to offending behaviours.

Various demographic, genetic and environmental risk factors may contribute to psychopathy [62, 63]. Studies exploring age, type of offense, and sentence length in association with psychopathy reveal limited research on age differences among individuals with aversive personality traits, often focusing on limited age ranges [6467]. The age of female offenders typically ranges between 19 to 55 years, with an average of 33.5 years [12]. Severity and frequency of psychopathic offending decrease with age among females [8, 21], aligning with the maturity principle proposed by Roberts et al. [68]. Improvements in age-associated adjustment and emotional regulation lead to increased agreeableness, conscientiousness, and emotional stability [66, 69]. Psychopathic offenders habitually commit more non-violent and violent offences from late adolescence to their late forties [70], with a considerably higher number of sentences before the age of 17 [71, 72]. Psychopathic individuals are also more likely to take on a wider range and higher rate of violent behaviours compared to non-psychopathic offenders [8, 26, 73]. Hicks et al. [71] noted differences between primary and secondary psychopathy, with affective and interpersonal psychopathic traits associated with more non-violent offences and behavioural traits linked to more violent offences [71]. Given that sentence length is typically an indication of the seriousness of an offence(s), longer sentences tend to be imposed on individuals with multiple convictions and those who have committed more serious/violent crimes [74, 75].

With the high rate of females currently incarcerated over and above the predicted proliferation of offenders in the coming years [5, 6], it is vital to investigate factors that may predict offending behaviour amongst females in order to support these individuals, regardless of the contraventions that placed them there [76]. Therefore, this research aimed to investigate the possible individual predictor (independent) variables (i.e., morality, self-control, age, type of offence, sentence length) or combination(s) of predictor variables that explain a significant percentage of the variance in psychopathy amongst female incarcerated offenders.

Methodology

Research design and approach

A quantitative research approach and a non-experimental type of research were used. The main goal was to describe and measure the direction, strength, and practical significance of correlations between two or more quantitative variables [77, 78], and therefore, a correlational research design [79] was used.

Research sample

A non-probability sampling technique known as convenience sampling [79, 80] was utilised in this study as this sampling method was determined to pose the least amount of risk to participants and correctional officials. This research was conducted in two South African correctional centres, namely the Bizzah Makhate Correctional Centre (BMCC) in Kroonstad, Free State, and the Johannesburg Correctional Centre (JCC) in Johannesburg, Gauteng. In total, 139 incarcerated female offenders (N = 139) between the ages of 20 and 68 voluntarily participated in the research. Participants of all ages, ethnicities, linguistic groups, religious or spiritual orientations, types of offences, and sentence lengths were included to form part of the sample. Offenders 18 years of age or younger were excluded from the sample.

Participants. The frequencies for the research sample, as illustrated in Table 1, were calculated for the participants’ type of offence, sentence length, offender type, gang involvement, age, ethnicity, previous psychiatric disorder, and substance abuse.

thumbnail
Table 1. Frequency distribution of participants according to demographic variable.

https://doi.org/10.1371/journal.pone.0299847.t001

Based on the demographic information, most participants (n = 105; 75.5%) were sentenced for violent offences such as murder and sexual offences. In comparison, 24.5% (n = 34) were incarcerated for non-violent offences such as fraud, forgery, and theft. Furthermore, the majority of the participants were first-time offenders (n = 127; 91.4%). Almost all participants (n = 130; 93.5%) indicated that they do not belong to a gang in the correctional environment. Moreover, most participants were between 30 and 39 years old (n = 58; 41.73%). More than three quarters of the participants were Black (African) offenders (n = 108; 77.7%), while the remaining offenders were White (Caucasian) (n = 21; 15.1%), Coloured (individuals of a mixed race) (n = 6; 4.3%), and Indian (n = 3; 2.2%). Regarding previous psychiatric disorders and substance abuse, the majority of the participants were not previously diagnosed with psychiatric disorders (n = 116; 83.5%) and did not abuse any substances (n = 101; 72.7%). The average age of the participants was 35.22 years (SD = 9.602), and their average sentence length was 14.84 years (SD = 33.205).

Data collection procedures

Approval to conduct this research was first obtained from the General and Human Research Ethics Committee at the University of the Free State with ethical clearance number: UFS-HSD2019/0369/1308. Further approval to conduct this study was obtained from the Department of Correctional Services, South Africa. The research was explained in its entirety to several groups of incarcerated offenders who had been collected and assembled in predetermined venues. These offenders were invited to participate in the study. A total of 89 (n = 89) offenders from BMCC and fifty (n = 50) offenders from JCC chose to participate in the study. All participants were well-versed on their rights as research participants, both verbally and in writing, and that their participation in the research would not influence their sentence and parole outcome in any way.

Participants were also informed that they would not receive any benefits or privileges for their participation in the study. In summary, data collection occurred following the researchers’ request that participants sign the informed consent sheet if they wanted to participate in the study. Data collection commenced during 19 November 2021 and was completed during 17 March 2022. The researchers visited the correctional centres on different occasions to recruit participants.

Measurement scales

Each participant received a single booklet comprised of four distinct questionnaires. The questionnaires took one to one and a half hours to complete depending on the literacy level of the offenders. The researchers were always present to assist the participants in the completion of the questionnaires and created an environment conducive to the offenders feeling free to ask questions and request assistance. The researchers also ensured that the participants completed the questionnaires in a quiet and private space to reduce distractions and improve comprehension. The questionnaires were generated on EvaSys, an automated survey software programme that enabled the researchers to craft the questionnaire through a simple and efficient design to increase participation responses [81]. The instruments used to gather the necessary data included: (i) a self-compiled demographic questionnaire, (ii) the Levenson Self-Report Psychopathy Scale (LSRP), (iii) the Moral Metacognition Scale (MMS), and (iv) the Self-Control Scale-Modified (SCSM).

Each participant completed a self-compiled demographic questionnaire. The questionnaire included items relating to offender demographics such as age, type of offence, sentence length, prior incarceration experience, gang affiliation, diagnosis of a psychiatric disorder, and substance abuse.

The Levenson Self-Report Psychopathy Scale (LSRP) [18] was utilised to measure the psychopathy of incarcerated female offenders. The LSRP consists of 26 items, each endorsed according to a four-point Likert-type scale with response options ranging from 1 (“strongly disagree”) to 4 (“strongly agree”) [18]. Some items are reverse-scored to control for rater response sets [82, 83]. The LSRP focuses on two distinct dimensions of psychopathy, namely primary psychopathy and secondary psychopathy [18]. According to offender-based studies, adequate to good internal consistencies for the LSRP primary dimension and secondary dimension, ranging from .83-.84 and .69-.77, respectively, have been reported [16, 8489]. Acceptable internal consistencies were obtained in this study, namely .73 for primary psychopathy and .69 for secondary psychopathy. This is in line with previous studies. Using the two-dimensional model, higher scale scores suggest greater self-reported propensities for primary and/or secondary traits related to the construct of psychopathy [90]. Therefore, lower scale scores suggest diminished self-reported predispositions for primary and/or secondary psychopathic traits [90].

The Moral Metacognition Scale (MMS) [39] was utilised to measure the extent to which offenders monitor and regulate their thinking concerning ethical decision-making. The MMS has 20 items and four subscales, namely (i) Regulation of Cognition, (ii) Knowledge of Cognition (Declarative), (iii) Knowledge of Cognition (Procedural), and (iv) Knowledge of Cognition (Conditional). The items of the MMS are scaled on a six-point Likert-type scale, with response options ranging from 1 (“very strongly disagree”) to 6 (“very strongly agree”) [39]. The MMS produced adequate to exceptional internal consistencies for all subscales in previous studies, ranging from .78-.83, .82-.89, .79-.84, and .83-.90 for the Regulation of Cognition, Knowledge of Cognition (Declarative), Knowledge of Cognition (Procedural) and Knowledge of Cognition (Conditional) subscales, respectively [39, 91]. Internal consistencies ranging from .69 for Regulation of Cognition, .75 for Declarative Knowledge of Cognition, .54 for Procedural Knowledge of Cognition, and .60 for Conditional Knowledge of Cognition were obtained in this study. These are lower compared to the findings of previous studies. Higher scores on the MMS suggest that offenders self-report that they employ high levels of cognitive expenditure as they monitor, reflect on, and regulate their thinking during the ethical decision-making process [39]. Therefore, lower scores on the MMS suggest less overall self-reported cognitive expenditure of the offenders in their thought processes when making ethical decisions [39].

The Self-Control Scale-Modified (SCSM) [50] was utilised to measure several aspects of offenders’ self-control. The SCSM measures 24 items of self-control, divided into six subscales assessing four items each, namely (i) Impulsivity, (ii) Simple Tasks, (iii) Risk-Seeking, (iv) Physical Activities, (v) Self-centredness, and (vi) Temper [51]. Each item on the SCSM is rated on a seven-point Likert-type scale with response options ranging from 0 (“strongly disagree”) to 6 (“strongly agree”) [50]. For the purpose of this study, the element of physical activity was excluded from the questionnaire since this factor was not particularly relevant to the overall aim of the study. According to a study using a male-specific offender and undergraduate sample, the internal consistencies of the six subscales equalled .67 (Impulsivity), .73 (Simple Tasks), .74 (Risk-Seeking), .76 (Physical Activity), .73 (Self-centredness) and .79 (Temper), respectively [92]. The internal reliability of each factor of the SCSM in this study was calculated as .59 for Impulsivity, .74 for Simple Tasks, .61 for Risk Seeking, .77 for Self-Centredness, and .76 for Temper, which compares with the findings from previous studies. Using the six-factor model, higher subscale scores on the SCSM indicate low self-control among incarcerated offenders [50]. Low self-control entails certain characteristics, including risk-taking, impulsivity, lacking empathy, preferring simple and easy tasks, and preferring physical tasks [50]. According to Hirschi and Gottfredson [93], these are the same characteristics of criminality. Therefore, low self-control represents the propensity to engage in offending behaviour.

Data analyses

All data collected from the participants were analysed using the Statistical Package for the Social Sciences (SPSS, Version 28) [94]. Descriptive statistics for the LSRP, MMS and SCSM scales were calculated, including the biographical characteristics of the sample of incarcerated female offenders. Cronbach alpha coefficients were calculated in order to establish the internal consistencies of the various scales [95, 96] followed by Pearson product-moment correlation coefficients that were calculated to determine the strength, direction and significance of the correlations between variables. In order to determine which individual predictor variable(s) (e.g., morality, self-control, age, type of offence and sentence length) or combination(s) of predictor variables explain the highest percentage of variance in psychopathy amongst female incarcerated offenders, hierarchical multiple regression analyses were conducted. Furthermore, the variance in the criterion variable was examined by evaluating the effect size (f2) of the contribution made by an individual predictor or combination of predictors.

Ethical considerations

As this study endeavoured to obtain sensitive information from a population of female incarcerated offenders, who are considered to be a vulnerable population [2, 97], ethical considerations were paramount throughout the research process. Written informed consent [78] was obtained from participants at the beginning of the study to ensure voluntary participation. Additionally, the researchers maintained voluntary participation by consistently communicating to participants that they could decline participation or withdraw from the study at any point, with no adverse consequences throughout the entire research process. As there were also no direct benefits offered to participants due to their participation in the study, individuals who opted not to participate were not disadvantaged in any way. The study had the potential to cause emotional and/or psychological distress to participants. Consequently, the researchers took measures to ensure the availability of a psychologist and/or a social worker who could provide debriefing or counselling services to participants experiencing emotional distress as a result of the study. Importantly, participants’ identifying information was not required to complete the questionnaires. Moreover, a coding system was utilised to ensure the anonymity of participants. All data obtained for the study were stored securely, which further helped protect participants’ anonymity and/or confidentiality.

Results

Means, standard deviations, skewness, kurtosis and internal consistencies of the various measurement scales

The means, standard deviations, skewness, kurtosis as well as internal consistencies of the various subscales of the measuring instruments are illustrated in Table 2 for the total group of participants. Cronbach’s alpha coefficient (α) was calculated as an indication of the internal consistencies of the subscales.

thumbnail
Table 2. Descriptive statistics and reliability coefficients for the LSRP subscales, MMS subscales, and SCSM subscales.

https://doi.org/10.1371/journal.pone.0299847.t002

Table 2 illustrates that the internal consistencies for the LSRP, MMS and SCSM subscales range from .54 to .77. The majority of these scales, therefore, displayed acceptable levels of internal consistency [98] and were included in the subsequent analyses. However, the Knowledge of Cognition (Procedural) subscale was excluded from further statistical analysis in this study, as it had an unacceptable level of internal consistency (.54). As part of the descriptive statistics in this table, it was investigated whether the data were normally distributed by calculating the skewness and kurtosis values of the different subscales. According to Kahane [99], the cut-off point for skewness is > |2| and kurtosis > |4|. Table 2 shows that the scores on all the subscales are within these cut-off points and do not deviate substantially from normal.

Correlations between variables

The Pearson product-moment correlation coefficients were calculated for the independent (predictor) variables, namely Morality, Self-Control, Age, Type of Offence (non-violent versus violent) and Sentence Length, as well as the dependent (outcome) variable, namely Psychopathy, prior to conducting the regression analyses. All the assumptions of correlational analyses were met. The focus of the discussion will be conserved to correlations of statistical and practical significance. Both the 1% and 5% levels of significance were considered. According to Steyn [100], for correlations, an effect size of .10 is considered small, .30 is medium, and .50 is large. However, only findings with effect sizes of .30 or greater will be discussed. The correlation coefficients are illustrated in Table 3.

thumbnail
Table 3. Correlations between the LRSP subscales and age, type of offence, sentence length, MMS subscales, and SCSM subscales (N = 139).

https://doi.org/10.1371/journal.pone.0299847.t003

Table 3 demonstrates significant positive correlations between primary psychopathy, secondary psychopathy, and various factors. Both primary and secondary psychopathy exhibit statistically significant positive correlations with impulsivity, simple tasks preference, risk-seeking behaviour, self-centeredness, and temper. Additionally, both primary and secondary psychopathy show statistically significant negative correlations with age.

For instance, higher scores on primary psychopathy and secondary psychopathy are associated with elevated impulsivity, that might suggest that incarcerated female offenders with these psychopathy traits tend to display higher impulsivity. Similarly, a preference for simple tasks over complicated activities is linked to higher scores on both primary and secondary psychopathy. Moreover, risk-seeking behaviour seems to be more prevalent among incarcerated female offenders scoring higher on primary and secondary psychopathy, indicating a possible tendency for engagement in risky activities. Elevated self-centeredness is observed in individuals with higher scores on both primary and secondary psychopathy, implying a lack of sympathy among female incarcerated offenders with these traits. Additionally, individuals scoring high on primary and secondary psychopathy might be more likely to have short tempers. Finally, both primary and secondary psychopathy exhibit negative correlations with age, suggesting that younger incarcerated female offenders tend to score higher on these psychopathy dimensions.

Hierarchical regression analyses

Psychopathy was measured using two dimensions (subscales): Primary Psychopathy and Secondary Psychopathy. Two hierarchical regression analyses were conducted with one of the psychopathy dimensions as the criterion variable. All assumptions of regression analyses (i.e., sample size, normality, outliers, multi-collinearity, and normality, linearity, and homoscedasticity of residuals) were investigated. None of the assumptions was violated. Once again, the focus of the discussion will be on the contributions that were of statistical and practical significance. Both the 1% and 5% levels of significance were considered. When performing hierarchical regression analyses, an effect size of .02 is considered to be small, an effect size of .15 is considered to be medium, and an effect size of .35 is considered to be large [100]. Only findings with medium effect sizes will be discussed.

Hierarchical regression analyses with primary psychopathy as criterion variable

Table 4 depicts the results of the hierarchical regression analysis with Primary Psychopathy as the criterion variable.

thumbnail
Table 4. Contributions of age, type of offence, sentence length, MMS subscales, and SCSM subscales to R2 with primary psychopathy as criterion variable.

https://doi.org/10.1371/journal.pone.0299847.t004

It is evident from Table 4 that the combination of the independent (predictor) variables contributed to 55.9% (F11;127 = 14.626; p≤,001) of the variance in the Primary Psychopathy scores of the sample. This finding was statistically significant at the 1% level, and the large corresponding effect size (f2 = 1.070) suggested that it is of considerable practical significance. As a set of predictor variables, the SCSM subscales (Impulsivity, Simple Tasks, Risk-Seeking, Self-Centredness and Temper) were responsible for 31.3% of the variance in the Primary Psychopathy scores of the incarcerated female offenders. This finding was statistically significant at the 1% level, and the corresponding large effect size (f2 = .710) suggested that it is of considerable practical significance. Table 4 further indicates that Impulsivity, Simple Tasks, Risk-Seeking, and Self-Centredness, respectively, explained 15.8% (F7;131 = 34.728; p≤.01; f2 = .265), 17.0% (F7;131 = 38.134; p≤.01; f2 = .291), 12.3% (F7;131 = 25.536; p≤.01; f2 = .195), and 25.2% (F7;131 = 65.761; p≤.01; f2 = .502) of the variance in the participants’ Primary Psychopathy scores. The relevant effect sizes indicated that these findings were of practical significance.

Hierarchical regression analyses with secondary psychopathy as criterion variable

The results of the hierarchical regression analysis with Secondary Psychopathy as the criterion variable are reported in Table 5.

thumbnail
Table 5. Contributions of age, type of offence, sentence length, MMS subscales, and SCSM subscales to R2 with secondary psychopathy as criterion variable.

https://doi.org/10.1371/journal.pone.0299847.t005

It is evident from Table 5 that the combination of the independent (predictor) variables contributed to 60.9% (F11;127 = 18.014; p≤,001) of the variance in the Secondary Psychopathy scores of the sample. This finding was statistically significant at the 1% level, and the large corresponding effect size (f2 = .720) suggested that it is of considerable practical significance. As a set of predictor variables, the SCSM subscales (Impulsivity, Simple Tasks, Risk-Seeking, Self-Centredness and Temper) were responsible for 41.7% of the variance in the Secondary Psychopathy scores of the incarcerated female offenders. This finding was statistically significant at the 1% level, and the corresponding effect size (f2 = 1.066) suggested that it is of considerable practical significance. Table 5 further indicates that Impulsivity, Simple Tasks, Risk-Seeking, Self-Centredness, and Temper, respectively, explained 27.1% (F7;131 = 66.110; p≤.01; f2 = .505), 23.6% (F7;131 = 54.049; p≤.01; f2 = .413), 19.5% (F7;131 = 41.672; p≤.01; f2 = .318), 18.6% (F7;131 = 39.174; p≤.01; f2 = .299), and 30.9% (F7;131 = 81.120; p≤.01; f2 = .619) of the variance in the participants’ Secondary Psychopathy scores. The relevant effect sizes suggested that these findings were of practical significance.

Discussion

Statistically and practically significant positive correlations were found between the LSRP dimensions (Primary Psychopathy and Secondary Psychopathy) and SCSM subscales (Impulsivity, Simple Tasks, Risk-Seeking, Self-Centredness and Temper). These findings suggest that as the incarcerated female offenders’ levels of impulsivity, preference for simple tasks, risk-seeking, self-centredness and temper increase, their primary psychopathy increases. Similarly, these findings indicate that as the incarcerated female offenders’ levels of impulsivity, preference for simple tasks, risk-seeking, self-centredness and temper increase, their secondary psychopathy increases. These findings are congruous with previous studies that have found statistically significant correlations between self-control and psychopathy [53, 101, 102], with notable correlations identified between self-control and secondary psychopathy [9]. This is comparable with Prado et al.’s [103] result that those presenting with lower self-control (i.e., behaviour culminating in increased impulsivity, preference for simple tasks, risk-seeking, self-centredness and temper) are more likely to present with secondary psychopathic traits. This seems to suggest that female incarcerated offenders’ lower degree of self-control may be more likely explained by the behaviours associated with their secondary psychopathic traits (such as sensation-seeking, disinhibition, impulsivity, lack of responsibility and antisocial lifestyle) rather than their affective and interpersonal characteristics. Furthermore, the present study found three particularly significant correlations between Secondary Psychopathy and Impulsivity, Primary Psychopathy and Self-Centredness, as well as Secondary Psychopathy and Temper.

A study by Fekih-Romdhane et al. [9] supports the finding of a significant positive relationship between Impulsivity and Secondary Psychopathy. This relationship can be explained by the finding that impulsivity is often considered a cardinal feature, particularly associated with the behavioural facets of psychopathy [56]. Prado et al.’s [103] result further confirmed Snowden and Gray’s [104] observation that adult offenders with higher scores on the secondary dimension of psychopathy demonstrate elevated impulsivity, while those with higher primary psychopathy scores exhibit reduced impulsivity, suggesting notable differences between the impulsive nature of the two variants of psychopathy. In practical terms, this may indicate that female incarcerated offenders presenting with secondary psychopathy may engage more frequently in behaviours considered to be spur-of-the-moment or last-minute and may, therefore, not take the time to consider the consequences of their behaviours but instead take immediate action resulting in immediate gratification.

Furthermore, as with the results obtained by Armstrong et al. [53] (i.e., significant correlations between Self-Centredness and Primary Psychopathy and Temper and Secondary Psychopathy), these findings can be attributed to literature whereby primary psychopaths are theorised to be emotionally detached, which is supposed to result from an intrinsic deficit in one’s emotional processing and are thus more inclined to be callous, self-centred, and lacking empathy [105, 106]. In practical terms, this may indicate that female incarcerated offenders presenting with primary psychopathy may have poorer interpersonal relationships with others as they may not consider others’ perspectives, feelings or intentions and may prioritise their own well-being over others. Moreover, secondary psychopaths are conjectured to develop a proneness to poorly regulated negative affect often characterised by high levels of hostility and aggression and are thus more inclined to quick temper [103, 107]. In practical terms, this may indicate that female incarcerated offenders presenting with secondary psychopathy may behave with increased aggressive behaviours toward others without necessary provocation or may respond in an increasingly aggravated manner to a degree unnecessitated to the situation at hand.

Contrary to previous research, the present study found significant correlations between the Simple Tasks and Risk-Seeking dimensions of the SCSM with secondary psychopathy. These findings may not be wholly unjustifiable as secondary psychopaths are often characterised by impulsive, irresponsible, and antisocial behaviours [106]. They may, therefore, be more inclined to dislike challenging projects and take pleasure in risk-seeking activities. It could be suggested that the correlations between Simple Tasks and Risk-Seeking in relation to psychopathy may be specific associations unique to the South African female offender population.

Statistically and practically significant negative correlations were also identified between the LSRP dimensions (Primary Psychopathy and Secondary Psychopathy) and Age. These findings suggest that as the incarcerated female offenders’ age increases, psychopathy seems to decrease. These findings are congruous with previous studies that have found negative correlations between age and psychopathy [64, 65, 67, 108, 109]. According to Hartung et al. [66], socially adverse personality characteristics, for example, psychopathy, uniformly decrease with increasing age. Notably, the present study determined Age significantly correlated with Primary Psychopathy; the same was found to be true in a recent study among female offenders [109]. In practical terms, this may indicate that if female incarcerated offenders present with increased behaviours associated with primary psychopathic traits (such as callousness, grandiosity, fearlessness, lack of remorse and guilt), the younger they may be. Thus, female offenders presenting with primary psychopathy may be less likely to exhibit callous, grandiose, and remorseless behaviours as they grow older.

The hierarchical regression analyses revealed that the combination of the predictor variables (Age, Type of Offence, Sentence Length, MMS subscales, and SCSM subscales) statistically and practically significantly predicted both dimensions of the LSRP (i.e., Primary Psychopathy and Secondary Psychopathy). These findings are congruous with the literature. In a study conducted by Jonason and Tost [102], self-control was reported to be a robust regression coefficient of psychopathy, with a partial variance associated with the participant’s age [110]. Several studies have also found low self-control to consistently be a significant predictor of psychopathy among diverse populations [53, 101, 111115].

Significant predictors of primary psychopathy were self-centredness (25.2%), simple tasks (17%), impulsivity (15.8%), and risk-seeking (12.3%). In practical terms, these findings suggest that as female incarcerated offenders’ egocentricity, preference for simple tasks, impulsivity and recklessness increase, their affective and interpersonal psychopathic traits increase. For secondary psychopathy, temper (30.9%), impulsivity (27.1%), simple tasks (23.6%), risk-seeking (19.5%), and self-centredness (18.6%) were significant predictors. In practical terms, these findings suggest that as female incarcerated offenders’ temper, impulsivity, preference for simple tasks, recklessness and egocentricity increase, their affective and interpersonal psychopathic traits increase. These findings are congruous with researchers’ postulations that impulsivity is a significant contributor to individuals’ psychopathic traits and antisocial behaviours [9, 109, 116, 117]. These findings are further supported by literature, where impulsivity has been found to be associated with the socially deviant features of psychopathy in offenders [118], more specifically with poor impulse control of one’s executive function measures [119, 120], thrill and experience seeking traits, and disinhibition [121], characteristics typically ascribed to the secondary dimension of psychopathy. Furthermore, researchers have utilised impulsivity (i.e., a lack of self-control and cognitive complexity) to explain violence in female offenders [109]. This supports prior research that has shown violent offenders display deficits within their executive functioning skills, such as inhibitory control and cognitive flexibility [122], characteristics typically attributed to the primary dimension of psychopathy.

The findings of several significant individual predictors of psychopathy (e.g., simple tasks, risk-seeking, self-centredness and temper) appear to be a novel result of this study, as there is currently no literature available to support the significant variance between these individual predictor variables and psychopathy (EBSCOHost, 2022 August). This seems to suggest that previous research has likely not explored individual factors of self-control as possible predictors of psychopathy. Therefore, additional research is needed to duplicate the findings of simple tasks, risk-seeking, and self-centredness as significant predictor variables amongst female incarcerated offenders.

Limitations of the study

Several studies have detailed the psychopathy of incarcerated offenders [16]. However, a search on EBSCOHost (2022, August) did not deliver results on any previous or similar studies that investigated the predictors of psychopathy amongst female incarcerated offenders, specifically within the South African context. This was a pertinent limitation of the study, considering that there was no previous literature regarding this population group to draw upon or compare the results.

The generalisability of the results to the broader female offender population in South Africa was another significant limitation of this study. The paradigm of quantitative research places emphasis on the generalisation of results. With the use of convenience sampling, the sample was not representative of the population, and therefore, these results are not generalisable [80] to other populations of offenders.

Recommendations for future research

There are several important recommendations regarding potential future research, particularly regarding incarcerated female offenders. Firstly, South African researchers must undertake to conduct sustained research not only amongst incarcerated offender populations but, more specifically, female incarcerated offenders across correctional centres in South Africa. The Department of Correctional Services Research Agenda (2019–2023) [123, p. 3] unequivocally states that:

Research in corrections has a high value to society. It has provided important information about incarceration trends for planning and identifying risk factors to improve security in corrections. Research has led to significant discoveries, the development of new ways of rehabilitating offenders and improvements in correctional care.

Without continued research, it would be difficult to support offenders during their rehabilitation within the correctional environment over and above the broader community when released [76]. Although international and national interest in correctional-based research is rapidly expanding [124], research on incarcerated female offenders continues to lag. Thus, for this study, a paucity of research continues to exist amongst South African female incarcerated offenders regarding psychopathy and cannot be drawn upon.

This study investigated the predictors of female offenders’ psychopathy within the BMCC in Kroonstad, Free State, and the JCC in Johannesburg, Gauteng alone. This resulted in a limited sample size which was also not representative of the general South African female offender population. To adequately gauge how these predictors of psychopathy (impulsivity, simple tasks, risk-seeking, self-centredness and temper) drawn from international studies apply to female offenders in South Africa, a broader investigation of inquiry, including a larger sample obtained from several correctional centres, could yield more findings regarding this topic.

Also, the use of probability sampling is recommended for future research endeavours, as it would yield a representative sample of the larger population of South African female offenders that would be beneficial in generalising future results to the broader correctional context [80]. This would result in a better understanding of how the predictors of psychopathy interact with this concept in the broader spectrum of the multicultural South African landscape.

Furthermore, future research may investigate other possible predictors of psychopathy among female offenders that were not necessarily analysed as they were beyond the scope of the current study. This includes family history, aggressive behaviour towards others, previous convictions (i.e., first-time or repeat offenders), gang involvement, personality disorders and substance abuse [8, 10, 35, 62, 125128]. This may advance our understanding of the best predictors of psychopathy amongst incarcerated female offenders in the South African context.

Additionally, researchers have found support for different factor solutions of the LSRP across samples [16]. Therefore, future researchers can conduct similar research with a larger and more representative sample to validate the applicability of a different factor solution measure of female offenders’ psychopathy within the South African context.

Conclusion

The present study has identified correlations between several predictor and criterion variables. Statistically and practically significant correlations were identified between Primary Psychopathy and Impulsivity, Simple Tasks, Risk-Seeking and Self-Centredness, and between Secondary Psychopathy and Impulsivity, Simple Tasks, Risk-Seeking, Self-Centredness and Temper. The correlations between the other predictor variables, for example, Age, Sentence Length, and the MMS subscales with any of the criterion variables did not reach statistical and/or practical significance. Regarding the regression analyses, the percentage of the variance these predictor variables explained was statistically significant but of limited practical significance. Therefore, this study found that the individual predictor variables or the combination(s) of predictor variables, specifically the dimensions of self-control, statistically and practically significantly contributed to the variance of psychopathy amongst incarcerated female offenders in the South African context. This was the goal of this research study. Limitations and recommendations for future research have been noted. Regardless of the limitations, the study provided information contributing to the existing literature on incarcerated female offenders.

Supporting information

Acknowledgments

We would like to thank all volunteers for their participation in this study. We further express our gratitude to the Department of Correctional Services for assistance with the recruitment and assessment of study participants.

References

  1. 1. Agboola C, Rabe M. Intersectionality and crime: reflections from female ex-inmates in South Africa. Acta Crim. 2018; 31(1):1–18.
  2. 2. Duba S. Predictors of correctional adjustment amongst incarcerated female offenders in correctional centres. [Master’s thesis]. University of the Free State. 2022.
  3. 3. Krabbe M, Van Kempen PH, editors. Women in prison: the Bangkok rules and beyond. Cambridge (England): Intersentia; 2017.
  4. 4. Steyn F, Booyens K. A profile of incarcerated female offenders: implications for rehabilitation policy and practice. Acta Crim. 2018; 30(4):33–54.
  5. 5. South Africa. Department of Correctional Services. Annual report for the 2018/2019 financial year. 2019a. Available from: http://www.dcs.gov.za/?page_id=663
  6. 6. South Africa. Department of Correctional Services. Annual report for the 2021/2022 financial year. 2022. Available from: http://www.dcs.gov.za/?page_id=663
  7. 7. South Africa. Judicial Inspectorate for Correctional Services. Annual report for the 2020/2021 financial year. 2021. Available from: http://jics.dcs.gov.za/jics/?page_id=142
  8. 8. Botha R. Psychopathy and comorbid mental disorders among South African female offenders. [Doctoral dissertation]. University of the Free State. 2014.
  9. 9. Fekih-Romdhane F, Hsini A, Aouina AA, Ridha R, Cheour M. Correlates of psychopathy in a Tunisian sample of incarcerated women. J Forensic Leg Med. 2021; 82(102232):1–10. https://doi.org/10.1016/j.jflm.2021.102232
  10. 10. Flórez G, Ferrer V, García LS, Crespo MR, Pérez M, Saiz PA. Personality disorders, addictions and psychopathy as predictors of criminal behaviour in a prison sample. Rev Esp Sanid Penit. 2019; 21(2):62–79. https://doi.org/10.4321/S1575-06202019000200002 pmid:31642857
  11. 11. Loots S. Antisocial personalities among maximum security prisoners. [Doctoral dissertation]. University of the Free State. 2010.
  12. 12. https://doi.org/10.1192/pb.bp.106.009472https://doi.org/10.1002/bsl.663Basson S. Psychopathic traits and offender characteristics amongst female offenders in a South African correctional centre. [Master’s thesis]. University of the Free State. 2014.
  13. 13. https://doi.org/10.1016/j.ijlp.2008.11.002https://doi.org/10.1027/1015-5759.16.3.147Pinheiro M, Gonçalves RA, Cunha O. Criminal lifestyle, psychopathy, and prison adjustment among female inmates. J Crim Justice. 2021; 76(101849):1–10. https://doi.org/10.1016/j.jcrimjus.2021.101849
  14. 14. Vaurio O, Lähteenvuo M, Kautiainen H, Repo-Tiihonen E, Tiihonen J. Female psychopathy and mortality. Front Psychiatry. 2022; 13(831410):1–6. pmid:35360121
  15. 15. https://doi.org/10.1037/0021-843X.110.4.644Beaver KM, Boutwell BB, Barnes JC, Vaughn MG, DeLisi M. The association between psychopathic personality traits and criminal justice outcomes: results from a nationally representative sample of males and females. Crime Delinq. 2017; 63(6):708–730. https://doi.org/10.1177/0011128715573617
  16. 16. Brinkley CA, Diamond PM, Magaletta PR, Heigel CP. Cross-validation of Levenson’s Psychopathy Scale in a sample of federal female inmates. Assessment. 2008; 15(4):464–482. pmid:18567698
  17. 17. Levenson MR. Rethinking psychopathy. Theory Psychol. 1992; 2(1):51–71. https://doi.org/10.1177/0959354392021003
  18. 18. Levenson MR, Kiehl KA, Fitzpatrick CM. Assessing psychopathic attributes in a noninstitutionalized population. J Pers Soc Psychol. 1995; 68(1):151–158. pmid:7861311
  19. 19. DeLisi M. Psychopathy is the unified theory of crime. Youth Violence Juv Justice. 2009; 7(3):256–273. https://doi.org/10.1177/1541204009333834
  20. 20. DeLisi M. Psychopathy as unified theory of crime. London (England): Palgrave Macmillan; 2016.
  21. 21. DeLisi M, editor. Routledge international handbook of psychopathy and crime. Oxfordshire (England): Routledge; 2019.
  22. 22. Kruger W. Female economic offenders in South Africa. Servamus Community-Based Safety and Security Magazine. 2016; 109(12):52–53.
  23. 23. South Africa. South African Police Service. Annual report for the 2012/2013 financial year. 2013. Available from: https://www.gov.za/documents/annual-report
  24. 24. South Africa. South African Police Service. Annual report for the 2009/2010 financial year. 2010. Available from: https://www.gov.za/documents/annual-report
  25. 25. http://www.dcs.gov.za/?page_id=663Louw A. Crime and perceptions after a decade of democracy. Soc Indic Res. 2007; 81(2):235–255. https://doi.org/10.1007/s11205-006-9009-y
  26. 26. Glannon W. Intervening in the psychopath’s brain. Theor Med Bioeth. 2014; 35(1):43–57. pmid:24381085
  27. 27. https://doi.org/10.1521/pedi.2007.21.2.102Perri FS, Lichtenwald TG. The last frontier: myths and the female psychopathic killer. Forensic Exam. 2010; 19(2):50–67.
  28. 28. Babiak P, Neumann CS, Hare RD. Corporate psychopathy: talking the walk. Behav Sci Law. 2010; 28(2):174–193. pmid:20422644
  29. 29. Häkkänen-Nyholm H, Nyholm JO. Psychopathy and law: a practitioner’s guide. West Sussex (England): John Wiley & Sons; 2012.
  30. 30. Douglas KS, Lilienfeld SO, Skeem JL, Poythress NG, Edens JF, Patrick CJ. Relation of antisocial and psychopathic traits to suicide-related behavior among offenders. Law Hum Behav. 2008; 32(6):511–525. pmid:18080733
  31. 31. Walters GD. Psychopathy and crime: testing the incremental validity of PCL-R measured psychopathy as a predictor of general and violent recidivism. Law Hum Behav. 2012; 36(5):404–412. pmid:23030821
  32. 32. Agnew R. An alternative strategy focusing on motivational processes. J Res Crime Delinq. 1995; 32(4):363–398. https://doi.org/10.1177/0022427895032004001
  33. 33. https://doi.org/10.1111/j.1745-9125.1994.tb01151.xCulhane SE, Walker S, Hildebrand MM. Serial homicide perpetrators’ self-reported psychopathy and criminal thinking. J Police Crim Psychol. 2019; 34(1):1–13. https://doi.org/10.1007/s11896-017-9245-x
  34. 34. Edwards BG, Mills JL, Reynolds BL, Verona E, Kiehl KA. Psychopathy and substance use in relation to prostitution and pimping among women offenders. Pers Disord: Theory Res Treat. 2021; 12(5):411–420. pmid:32897095
  35. 35. Gray NS, Blumenthal S, Shuker R, Wood H, Fonagy P, Snowden RJ. The triarchic model of psychopathy and antisocial behavior: results from an offender population with personality disorder. J Interpers Violence. 2021; 36(17–18):1–30. pmid:31189393
  36. 36. Psederska E, Yankov GP, Bozgunov K, Popov V, Vasilev G, Vassileva J. Validation of the Levenson Self-Report Psychopathy Scale in Bulgarian substance-dependent individuals. Front Psychol. 2020; 11(1110):1–19. pmid:32581949
  37. 37. Lynam DR, Miller DJ, Vachon D, Loeber R, Stouthamer-Loeber M. Psychopathy in adolescence predicts official reports of offending in adulthood. Youth Violence Juv Justice. 2009; 7(3):189–207. pmid:22661910
  38. 38. Verschuere B, Ben-Shakhar G, Meijer E. Memory detection: theory and application of the concealed information test. Cambridge (England): Cambridge University Press; 2011.
  39. 39. McMahon JM, Good DJ. The Moral Metacognition Scale: development and validation. Ethics Behav. 2016; 26(5):357–394. https://doi.org/10.1080/10508422.2015.1028548
  40. 40. Narvaez D. Moral complexity: the fatal attraction of truthiness and the importance of mature moral functioning. Perspect Psychol Sci. 2010; 5(2):163–181. pmid:26162122
  41. 41. Hannah ST, Avolio BF, May DR. Moral maturation and moral conation: a capacity approach to explaining moral thought and action. Acad Manage Rev. 2011; 36(4):663–685. https://doi.org/10.5465/amr.2010.0128
  42. 42. Sadler-Smith E. Before virtue: biology, brain, behavior, and the “moral sense.” Bus Ethics Q. 2012; 22(2):351–376. https://doi.org/10.5840/beq201222223
  43. 43. Malatesti L, McMillan J, Šustar P, editors. History, philosophy and theory of the life sciences: psychopathy its uses, validity and status. Cham (Switzerland): Springer; 2022.
  44. 44. Maibom HL. Moral unreason: the case of psychopathy. Mind Lang. 2005; 20(2):237–257. https://doi.org/10.1111/j.0268-1064.2005.00284.x
  45. 45. Harenski CL, Harenski KA, Shane MS, Kiehl KA. Aberrant neural processing of moral violations in criminal psychopaths. J Abnorm Psychol. 2010; 119(4):863–874. pmid:21090881
  46. 46. Marshall J, Watts AL, Lilienfeld SO. Do psychopathic individuals possess a misaligned moral compass? A meta-analytic examination of psychopathy’s relations with moral judgment. Pers Disord: Theory Res Treat. 2016; 9(1):1–11. pmid:27797544
  47. 47. Brook M, Brieman CL, Kosson DS. Emotion processing in psychopathy checklist-assessed psychopathy: a review of the literature. Clin Psychol Rev. 2013; 33(8):979–995. pmid:24013478
  48. 48. Harenski CL, Edwards BG, Harenski KA, Kiehl KA. Neural correlates of moral and non-moral emotion in female psychopathy. Front Hum Neurosci. 2014; 8(741):1–10. pmid:25309400
  49. 49. Longshore D. Self-control and criminal opportunity: a prospective test of the general theory of crime. Soc Probl. 1998; 45(1):102–113. https://about.jstor.org/terms
  50. 50. Yu S. Does low self-control explain voluntary disclosure of personal information on the internet? Comput Human Behav. 2014; 37:210–215. https://doi.org/10.1016/j.chb.2014.04.055
  51. 51. Gottfredson MR, Hirschi T. A general theory of crime. California (United States): Stanford University Press; 1990.
  52. 52. Van Gelder JL, De Vries RE. Rational misbehavior? Evaluating an integrated dual-process model of criminal decision making. J Quant Criminol. 2014; 30(1):1–27. https://doi.org/10.1007/s10940-012-9192-8
  53. 53. Armstrong TA, Boisvert D, Wells J, Lewis RH, Cooke E, Woeckner M. Assessing potential overlap between self-control and psychopathy: a consideration of the Grasmick Self-Control Scale and the Levenson Self-Report Psychopathy Scale. J Crim Justice. 2020; 70(101725):1–9. https://doi.org/10.1016/j.jcrimjus.2020.101725
  54. 54. Glenn AL, Raine A. The neurobiology of psychopathy. Psychiatr Clin North Am. 2008; 31(3):463–475. pmid:18638646
  55. 55. Hawes DJ, Brennan J, Dadds MR. Cortisol, callous-unemotional traits, and pathways to antisocial behavior. Curr Opin Psychiatry. 2009; 22(4):357–362. pmid:19455037
  56. 56. Hart SD, Dempster RJ. Impulsivity and psychopathy. In Webster CD, Jackson MA, editors. Impulsivity: theory, assessment and treatment. New York (United States): Guilford Press; 1997. p.212–32.
  57. 57. Robinson MD, Bresin K. Higher levels of psychopathy predict poorer motor control: implications for understanding the psychopathy construct. J Psychopathol Behav Assess. 2014; 36(2):201–210. pmid:25419045
  58. 58. DeLisi M, Vaughn MG. Foundation for a temperament-based theory of antisocial behavior and criminal justice system involvement. J Crim Justice. 2014; 42(1):10–25. https://doi.org/10.1016/j.jcrimjus.2013.11.001
  59. 59. Van Gelder JL, De Vries RE. Traits and states: integrating personality and affect into a model of criminal decision making. Criminol. 2012; 50(3):637–671. https://doi.org/10.1111/j.1745-9125.2012.00276.x
  60. 60. De Vries RE, Van Kampen D. The HEXACO and 5DPT models of personality: a comparison and their relationships with psychopathy, egoism, pretentiousness, immorality, and Machiavellianism. J Pers Disord. 2010; 24(2):244–257. pmid:20420478
  61. 61. De Vries RE, De Vries A, De Hoogh A, Feij J. More than the big five: egoism and the HEXACO model of personality. Eur J Pers. 2009; 23(8):635–654. https://doi.org/10.1002/per.733
  62. 62. https://doi.org/10.1207/s15327906mbr3902_8García CH, Moral J, Frías M, Valdivia JA, Díaz HL. Family and socio-demographic risk factors for psychopathy among prison inmates. The European Journal of Psychology Applied to Legal Context. 2012; 4(2):119–134.
  63. 63. Gunter TD, Vaughn MG, Philibert RA. Behavioral genetics in antisocial spectrum disorders and psychopathy: a review of the recent literature. Behav Sci Law. 2010; 28(2):148–173. pmid:20422643
  64. 64. Ali F, Chamorro-Premuzic T. The dark side of love and life satisfaction: associations with intimate relationships, psychopathy and Machiavellianism. Pers Individ Differ. 2010; 48(2):228–233. https://doi.org/10.1016/j.paid.2009.10.016
  65. 65. Götz FM, Bleidorn W, Rentfrow PJ. Age differences in Machiavellianism across the life span: evidence from a large‐scale cross‐sectional study. J Pers. 2020; 88(5):978–992. pmid:32145085
  66. 66. Hartung J, Bader M, Moshagen M, Wilhelm O. Age and gender differences in socially aversive (“dark”) personality traits. Eur J Pers. 2022; 36(1):3–23. https://doi.org/10.1177/0890207020988435
  67. 67. Craker N, March E. The dark side of Facebook: the dark tetrad, negative social potency, and trolling behaviours. Pers Individ Differ. 2016; 102:79–84. https://doi.org/10.1016/j.paid.2016.06.043
  68. 68. Roberts BW, Walton KE, Viechtbauer W. Patterns of mean-level change in personality traits across the life course: a meta-analysis of longitudinal studies. Psychol Bull. 2006; 132(1):1–25. pmid:16435954
  69. 69. Staudinger UM, Kessler EM. Adjustment and growth: two trajectories of positive personality development across adulthood. In Smith MC, DeFrates-Densch N, editors. Handbook of research on adult learning and development. Oxfordshire (England): Routledge; 2009. p.242–68.
  70. 70. Porter S, Birt AR, Boer DP. Investigation of the criminal and conditional release profiles of Canadian federal offenders as a function of psychopathy and age. Law Hum Behav. 2001; 25(6):647–661. pmid:11771639
  71. 71. Hicks BM, Vaidyanathan U, Patrick CJ. Validating female psychopathy subtypes: differences in personality, antisocial and violent behavior, substance abuse, trauma, and mental health. Pers Disord: Theory Res Treat. 2010; 1(1):781–802. pmid:20582155
  72. 72. https://doi.org/10.1002/bsl.572Graeve CM. An exploratory look at career criminality, psychopathy, and offending persistence: convergence of criminological and psychological constructs? [Master’s thesis]. Iowa State University. 2007.
  73. 73. Piquero AR, Farrington DP, Fontaine NM, Vincent G, Coid J, Ullrich S. Childhood risk, offending trajectories and psychopathy at age 48 in the Cambridge study in delinquent development. Psychol Public Policy Law. 2012; 18(4):577–585. https://doi.org/10.1037/a0027061
  74. 74. Di Placido C, Simon TL, Witte TD, Gu D, Wong SCP. Treatment of gang members can reduce recidivism and institutional misconduct. Law Hum Behav. 2006; 30(1):93–114. pmid:16729210
  75. 75. Mallillin AZC. The criminal career profile: A measure of criminal careers. [Doctoral dissertation]. University of Saskatchewan. 2006.
  76. 76. Rogers C. Predictors of prison adjustment amongst male incarcerated offenders in a private maximum-security correctional centre. [Master’s thesis]. University of the Free State. 2019.
  77. 77. Abbott ML, McKinney J. Understanding and applying research design. New Jersey (United States): Wiley; 2013.
  78. 78. Creswell JW, Creswell JD. Research design: qualitative, quantitative, and mixed methods approaches. 5th ed. California (United States): Sage; 2018.
  79. 79. Stangor C. Research methods for the behavioural sciences. 5th ed. Massachusetts (United States): Cengage; 2015.
  80. 80. Maree K, editor. First steps in research. 3rd ed. Pretoria (South Africa): Van Schaik; 2019.
  81. 81. EvaSys. The software for paper-based and online surveys. 2021. Available from: https://evasys.de/en/evasys/
  82. 82. Barbosa F, Gonçalves S, Almeida PR, Ferreira-Santos F, Marques-Teixeira J. The Levenson Self-Report Psychopathy Scale (LSRPS): translation and adaptation to European Portuguese. 2014. Available from: http://www.fpce.up.pt/labpsi/data_files/09labreports/LabReport_7.pdf
  83. 83. Garofalo C, Noteborn MGC, Sellbom M, Bogaerts S. Factor structure and construct validity of the Levenson Self-Report Psychopathy Scale (LSRP): a replication and extension in Dutch nonclinical participants. J Pers Assess. 2019; 101(5):481–492. pmid:30362829
  84. 84. Brinkley CA, Schmitt WA, Smith SS, Newman JP. Construct validation of a self-report psychopathy scale: does Levenson’s Self-Report Psychopathy Scale measure the same constructs as Hare’s Psychopathy Checklist-Revised? Pers Individ Dif. 2001; 31(7):1021–1038. https://doi.org/10.1016/S0191-8869(00)00178-1
  85. 85. Epstein MK, Poythress NG, Brandon KO. The self-report psychopathy scale and passive avoidance learning: a validation study of race and gender effects. Assessment. 2006; 13(2):197–207. pmid:16672734
  86. 86. Salekin RT, Chen DR, Sellbom M, Lester WS, MacDougall E. Examining the factor structure and convergent and discriminant validity of the Levenson Self-Report Psychopathy Scale: is the two-factor model the best fitting model? Pers Disord: Theory Res Treat. 2014; 5(3):289–304. pmid:24773039
  87. 87. Sellbom M. Elaborating on the construct validity of the Levenson Self-Report Psychopathy Scale in incarcerated and non-incarcerated samples. Law Hum Behav. 2011; 35(6):440–451. pmid:20953972
  88. 88. Shou Y, Sellbom M, Han J. Evaluating the construct validity of the Levenson Self-Report Psychopathy Scale in China. Assessment. 2017; 24(8):1008–1023. pmid:26969688
  89. 89. Wang MC, Shou Y, Deng Q, Sellbom M, Salekin RT, Gao Y. Factor structure and construct validity of the Levenson Self-Report Psychopathy Scale (LSRP) in a sample of Chinese male inmates. Psychol Assess. 2018; 30(7):882–892. pmid:29565613
  90. 90. Borchert K. User manual: inquisit Levenson’s Self-Report Psychopathy Scale (LSRP). 2022. Available from: https://www.millisecond.com/download/library/v6/lsrp/lsrp/lsrp.manual
  91. 91. Duruk U, Acikgul Firat E, Akgun A. Turkish Moral Metacognition Scale (TMMS): the study of adaptation, validation and reliability. Int Online J Educ Sci. 2020; 12(3):153–163. https://doi.org/10.15345/iojes.2020.03.012
  92. 92. Williams MWM, Fletcher RB, Ronan KR. Investigating the theoretical construct and invariance of the self-control scale using confirmatory factor analysis. J Crim Justice. 2007; 35(2):205–218. https://doi.org/10.1016/j.jcrimjus.2007.01.007
  93. 93. Hirschi T, Gottfredson MR, editors. The generality of deviance. New York (United States): Routledge; 1994.
  94. 94. IBM Corporation. IBM SPSS statistics for Windows (28.0). 2022. Available from: https://www.ibm.com/products/spss-statistics
  95. 95. Aron A, Aron EN, Coups EJ. Statistics for psychology. 6th ed. New Jersey (United States): Prentice Hall; 2014.
  96. 96. Bonett DG, Wright TA. Cronbach’s alpha reliability: interval estimation, hypothesis testing, and sample size planning. J Organ Behav. 2015; 36(1):3–15. https://doi.org/10.1002/job.1960
  97. 97. Ayamba BN, Arhin AK, Dankwa JA. Counselling needs of Ghanaian prisoners: the case of Ankaful and Kumasi central prisons. Ife Psychol. 2017; 25(2):195–205.
  98. 98. Vogt WP, editor. Dictionary of statistics and methodology. 3rd ed. California (United States): Sage; 2005.
  99. 99. Kahane LH. Regression basics. 2nd ed. California (United States): Sage; 2008.
  100. 100. Steyn HS. Manual for the determination of effect size indices and practical significance. 2012. Available from: https://natural-sciences.nwu.ac.za/scs/manuals/determination
  101. 101. DeLisi M, Pechorro P, Maroco J, Simões M. Overlapping measures or constructs? An empirical study of the overlap between self-control, psychopathy, Machiavellianism and narcissism. Forensic Sci Int Synerg. 2021; 3(100141):1–6. pmid:33665594
  102. 102. Jonason PK, Tost J. I just cannot control myself: the dark triad and self-control. Pers Individ Differ. 2010; 49(6):611–615. https://doi.org/10.1016/j.paid.2010.05.031
  103. 103. Prado CE, Treeby MS, Crowe SF. Examining relationships between facial emotion recognition, self-control, and psychopathic traits in a non-clinical sample. Pers Individ Differ. 2015; 80:22–27. https://doi.org/10.1016/j.paid.2015.02.013
  104. 104. Snowden RJ, Gray NS. Impulsivity and psychopathy: associations between the Barrett Impulsivity Scale and the Psychopathy Checklist-Revised. Psychiatry Res. 2011; 187(3):414–417. pmid:21377739
  105. 105. Koenigs M, Kruepke M, Newman JP. Economic decision-making in psychopathy: a comparison with ventromedial prefrontal lesion patients. Neuropsychologia. 2010; 48(7):2198–2204. pmid:20403367
  106. 106. Pletti C, Lotto L, Buodo G, Sarlo M. It’s immoral, but I’d do it! Psychopathy traits affect decision-making in sacrificial dilemmas and in everyday moral situations. Br J Psychol. 2017; 108(2):351–368. pmid:27370950
  107. 107. Dean AC, Altstein LL, Berman ME, Constans JI, Sugar CA, McCloskey MS. Secondary psychopathy, but not primary psychopathy, is associated with risky decision-making in noninstitutionalized young adults. Pers Individ Differ. 2013; 54(2):272–277. https://doi.org/10.1016/j.paid.2012.09.009
  108. 108. Kowalski CM, Vernon PA, Schermer JA. Vocational interests and dark personality: are there dark career choices? Pers Individ Differ. 2017; 104:43–47. https://doi.org/10.1016/j.paid.2016.07.029
  109. 109. Thomson ND, Vassileva J, Kiehl KA, Reidy D, Aboutanos M, McDougle R, et al. Which features of psychopathy and impulsivity matter most for prison violence? New evidence among female prisoners. Int J Law Psychiatry. 2019; 64:26–33. https://doi.org/10.1016/j.ijlp.2019.01.001
  110. 110. Figueredo A, Vásquez G, Brumbach B, Schneider S, Sefcek J, Tal I, et al. Consilience and life history theory: from genes to brain to reproductive strategy. Dev Rev. 2006; 26(2):243–275. https://doi.org/10.1016/j.dr.2006.02.002
  111. 111. Jackson DB, Testa A, Vaughn MG. Low self-control and the adolescent police stop: intrusiveness, emotional response, and psychological well-being. J Crim Justice. 2020; 66(101635). https://doi.org/10.1016/j.jcrimjus.2019.101635
  112. 112. Mathesius JR, Lussier P, Corrado RR. The early temperamental correlates of antisocial propensity. J Crim Justice. 2020; 66(101630). https://doi.org/10.1016/j.jcrimjus.2019.101630
  113. 113. Petrich DM, Sullivan CJ. Does future orientation moderate the relationship between impulse control and offending? Insights from a sample of serious young offenders. Youth Violence Juv Justice. 2020; 18(2):156–178. https://doi.org/10.1177/1541204019876976
  114. 114. Tehrani HD, Yamini S. Parenting practices, self-control and anti-social behaviors: meta-analytic structural equation modeling. J Crim Justice. 2020; 68(101687). https://doi.org/10.1016/j.jcrimjus.2020.101687
  115. 115. Wojciechowski T. The relevance of the dual systems model of self-control for age-related deceleration in offending variety among juvenile offenders. J Crim Justice. 2020; 70(101716). https://doi.org/10.1016/j.jcrimjus.2020.101716
  116. 116. Ray JV, Thornton LC, Frick PJ, Steinberg L, Cauffman E. Impulse control and callous-unemotional traits distinguish patterns of delinquency and substance use in justice involved adolescents: examining the moderating role of neighborhood context. J Abnorm Child Psychol. 2016; 44(3):599–611. https://doi.org/10.1007/s10802-015-0057-0
  117. 117. Waller R, Hicks BM. Trajectories of alcohol and marijuana use among primary versus secondary psychopathy variants within an adjudicated adolescent male sample. Pers Disord: Theory Res Treat. 2019; 10(1):87–96. pmid:30080061
  118. 118. Ruiz MA, Skeem JL, Poythress NG, Douglas KS, Lilienfeld SO. Structure and correlates of the Barratt impulsiveness scale (BIS-11) in offenders: implications for psychopathy and externalizing pathology. Int J Forensic Ment Health. 2010; 9(3):237–244. https://doi.org/10.1080/14999013.2010.517258
  119. 119. Cheung AM, Mitsis EM, Halperin JM. The relationship of behavioral inhibition to executive functions in young adults. J Clin Exp Neuropsychol. 2004; 26(3):393–404. pmid:15512928
  120. 120. Spinella M. Self-rated executive function: development of the executive function index. Int J Neurosci. 2005; 115(5):649–667. pmid:15823930
  121. 121. Stanford MS, Mathias CW, Dougherty DM, Lake SL, Anderson NE, Patton JH. Fifty years of the Barratt impulsiveness scale: an update and review. Pers Individ Differ. 2009; 47(5):385–395. https://doi.org/10.1016/j.paid.2009.04.008
  122. 122. Baker SF, Ireland JL. The link between dyslexic traits, executive functioning, impulsivity and social self-esteem among an offender and non-offender sample. Int J Law Psychiatry. 2007; 30(6):492–503. pmid:17919728
  123. 123. Africa South. Department of Correctional Services. Department of Correctional Services research agenda (2019–2023). 2019b. Available from: http://www.dcs.gov.za/wp-content/uploads/2017/12/DCS-Research-Agenda-11-March-2019-.pdf
  124. 124. Tyson S. Philosophies of incarceration and the incarceration of philosophy. Paper presented at the 22nd Annual Philosophy Conference. 2017 March 17–18; Pennsylvania, United States. Available from: https://listserv.jmu.edu/cgi-bin/wa?A2=FEAST-L;2d0c467f.1612&S=
  125. 125. Banasik M, Gierowski JK. Predictors of aggressive criminality among sentenced women and men with psychopathic characteristics. Problems of Forensic Sciences. 2015; 102:96–114.
  126. 126. Botha R, Louw D, Loots S. Psychopathy and its relation to personality psychopathology in a South African female forensic context. S Afr J Psychol. 2018; 48(2):230–242. https://doi.org/10.1177/0081246317715351
  127. 127. Carabellese F, Felthous AR, La Tegola D, Rossetto I, Franconi F, Lucchini G, et al. Female psychopathy: a descriptive national study of socially dangerous female NGRI offenders. Int J Law Psychiatry. 2020; 68(101455):1–9. pmid:32033688
  128. 128. Sherretts N, Boduszek D, Debowska A, Willmott D. Comparison of murderers with recidivists and first time incarcerated offenders from US prisons on psychopathy and identity as a criminal: an exploratory analysis. J Crim Justice. 2017; 51:89–92. https://doi.org/10.1016/j.jcrimjus.2017.03.002