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Offender Subtypes Based on Psychopathic Traits: Results from Factor-Mixture Modeling


Mokros, Andreas; Hollerbach, Pia Sofie; Eher, Reinhard (2020). Offender Subtypes Based on Psychopathic Traits: Results from Factor-Mixture Modeling. European Journal of Psychological Assessment:online.

Abstract

Psychopathy is a primary risk factor of re-offending in sexual offenders. Conceptually, both variable-centered (e.g., factor analysis) and clustering methods (e.g., latent profile analysis) have been used in previous research. Variable-centered and clustering methods were merged in a simultaneous modeling strategy for two purposes: First, to test assumptions on the emergence of psychopathic versus sociopathic (antisocial) sub-groups. And second to compare the predictive validity of clusters with that afforded by a dimensional cut-score. Using mixture modeling, two types of models were estimated: Latent class factor-analytic (LCFA) and factor-mixture models (FMM). The four-factor model of psychopathy as assessed with the Psychopathy Checklist-Revised (PCL-R) was estimated for up to 12 latent classes in a sample of adult male sexual offenders from Austria ( N = 1,266). Solutions with five (LCFA) and two latent classes (FMM) provided a good and parsimonious fit for the data. The two-latent-class FMM solution yielded higher predictive validity than a cut-score but only for general offense recidivism. Theoretically, this solution goes against etiological models that distinguish psychopathic from sociopathic (antisocial) individuals. Official data on offense recidivism (at a fixed 7-year-interval post-release) corroborate the importance of psychopathic offender subtypes. The rates of recidivism varied considerably between the subgroups.

Abstract

Psychopathy is a primary risk factor of re-offending in sexual offenders. Conceptually, both variable-centered (e.g., factor analysis) and clustering methods (e.g., latent profile analysis) have been used in previous research. Variable-centered and clustering methods were merged in a simultaneous modeling strategy for two purposes: First, to test assumptions on the emergence of psychopathic versus sociopathic (antisocial) sub-groups. And second to compare the predictive validity of clusters with that afforded by a dimensional cut-score. Using mixture modeling, two types of models were estimated: Latent class factor-analytic (LCFA) and factor-mixture models (FMM). The four-factor model of psychopathy as assessed with the Psychopathy Checklist-Revised (PCL-R) was estimated for up to 12 latent classes in a sample of adult male sexual offenders from Austria ( N = 1,266). Solutions with five (LCFA) and two latent classes (FMM) provided a good and parsimonious fit for the data. The two-latent-class FMM solution yielded higher predictive validity than a cut-score but only for general offense recidivism. Theoretically, this solution goes against etiological models that distinguish psychopathic from sociopathic (antisocial) individuals. Official data on offense recidivism (at a fixed 7-year-interval post-release) corroborate the importance of psychopathic offender subtypes. The rates of recidivism varied considerably between the subgroups.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Social Sciences & Humanities > Applied Psychology
Uncontrolled Keywords:Applied Psychology, psychopathy, PCL-R, factor mixture model, latent class, offense recidivism
Language:English
Date:29 April 2020
Deposited On:30 Dec 2020 13:11
Last Modified:31 Dec 2020 21:01
Publisher:Hogrefe & Huber
ISSN:1015-5759
OA Status:Closed
Publisher DOI:https://doi.org/10.1027/1015-5759/a000582

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