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Although many studies supported the use of actuarial risk assessment instruments (ARAIs) because they outperformed unstructured judgments, it remains an ongoing challenge to seek potentials for improvement of their predictive performance. Machine learning (ML) algorithms, like random forests, are able to detect patterns in data useful for prediction purposes without explicitly programming them (e.g., by considering nonlinear effects between risk factors and the criterion). Therefore, the current study aims to compare conventional logistic regression analyses with the random forest algorithm on a sample of N = 511 adult male individuals convicted of sexual offenses. Data were collected at the Federal Evaluation Center for Violent and Sexual Offenders in Austria within a prospective-longitudinal research design and participants were followed-up for an average of M = 8.2 years. The Static-99, containing static risk factors, and the Stable-2007, containing stable dynamic risk factors, were included as predictors. The results demonstrated no superior predictive performance of the random forest compared with logistic regression; furthermore, methods of interpretable ML did not point to any robust nonlinear effects. Altogether, results supported the statistical use of logistic regression for the development and clinical application of ARAIs. [ABSTRACT FROM AUTHOR]

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To examine the predictive accuracy of four well established risk assessment instruments (PCL-R, HCR-20, SVR-20, and Static-99) in an important subgroup of sexual offenders, these instruments were assessed retrospectively based on information from forensic psychiatric court reports in a sample of 90 released male sexual homicide offenders (out of an original sample of 166) in Germany. Follow-up information about criminal reconvictions after release were obtained from the federal criminal records. Total scores as well as subscales and single items of these risk assessment instruments did not predict sexual recidivism, and only some of them had moderate predictive power regarding nonsexual violent recidivism. Possible explanations for these unexpected results are the retrospective study design with missing information about influences during the long duration of detention and time after release, the small sample size as well as the possibility that the risk assessment instruments investigated were valid for general sex offender samples, but not for the particular subgroup of offenders with sexually motivated homicides. (Contains 2 tables.)

Based on the theory of cognitive dissonance and the understanding of cognitive distortions as defense mechanisms, we argued that individuals convicted of sexual offenses diagnosed with exclusive pedophilia (epICSOC) report more child offense related cognitive distortions compared to individuals convicted of sexual offenses without such diagnosis (oICSOC). In the present study, we aimed to empirically validate these clinical observations and previous results by relating cognitive distortions to four measures of pedophilic preference, namely, a clinical diagnosis of pedophilia, viewing time captured and self-reported pedophilic preference (EISIP; as well as the SSPI-2). Results revealed that cognitive distortions in general and minimizing sexually offending behavior toward children or attributing sexual interest to children in particular, significantly correlated with all four measures of pedosexual preference. Regression analyses, furthermore, indicated that attributing sexual interest to children was the best predictor for a pedosexual preference. Results thus show that cognitive distortions typically associated with child molesting in general are, in fact, more common in individuals convicted of sexual offenses diagnosed with exclusive pedophilia than in those without such diagnosis. [ABSTRACT FROM AUTHOR]

The Severe Sexual Sadism Scale (SSSS) is a screening device for the file-based assessment of forensically relevant sexual sadism. The SSSS consists of 11 dichotomous (yes/no) items that code behavioral indicators of severe sexual sadism within sexual offenses. Based on an Austrian sample of 105 sexual offenders, the present study replicated the 1-dimensional scale structure of the SSSS, as evidenced by confirmatory factor analysis. More specifically, the scale was commensurate with the 1-parameter logistic test model (Rasch model). Reliability was estimated to be good. Criterion validity for the clinical diagnosis of sexual sadism was good. With a cutoff value of 7 points, sensitivity and specificity were estimated at 56% and 90%, respectively. (Contains 2 tables and 2 figures.)

Men convicted of rape are a heterogenous group among individuals with sexual offending history. This may contribute to the difficulty of establishing adequate treatment during institutionalization and preventing recidivism after release. Therefore, we aimed to identify an empirical typology of individuals convicted of rape based on (a) common criminological variables (age at first offense, number of prior convictions, relation to the victim), (b) offense behavior (alcoholization during the offense) and (c) clinical diagnoses (substance abuse and dependence, psychopathy, sexual sadism, Cluster B personality disorders, paraphilias, paraphilia-related disorders) associated with the risk of sexual offending. Data of N = 575 adult males with raping history were analyzed. We found four types with different profiles in the described variables: an antisocial impulsive, a sexualized, a highly violent and a non-criminal situational type. The types significantly varied in prognostic validity for violent, but not sexual, recidivism. Also, the typology exhibited a slightly higher predictive validity for violent recidivism than Static-99 scores. In contrast to Static-99, the typology failed to significantly predict sexual recidivism. Although the types explained more variance of violent recidivism than Static-99 scores, the value of applying a broad set of risk factors in contrast to combining only few are discussed. [ABSTRACT FROM AUTHOR]