Structural equation modeling of psychopathic traits in Chilean female offenders using the Self-Report Psychopathy-Short Form (SRP-SF) Scale: a comparison of gender-based item modifications versus standard items.

Publisher: Associação Brasileira de Psiquiatria Country of Publication: Brazil NLM ID: 100895975 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1809-452X (Electronic) Linking ISSN: 15164446 NLM ISO Abbreviation: Braz J Psychiatry Subsets: MEDLINE

Original Publication: São Paulo, SP, Brasil : Associação Brasileira de Psiquiatria, [1999]-

Self Report*; Antisocial Personality Disorder/*diagnosis ; Prisoners/*statistics & numerical data; Adult ; Aged ; Analysis of Variance ; Chile ; Female ; Humans ; Middle Aged ; Models, Theoretical

Objective: To examine the utility of the Self-Report Psychopathy-Short Form (SRP-SF) to assess psychopathic traits in female offenders and to test gender-based item modifications.
Method: A South American sample of female offenders (n=210) was assessed with the Psychopathy Checklist-Revised (PCL-R); 110 subjects also completed the standard SRP-SF, while 109 completed a version with items rewritten to be more relevant for females. The underlying latent structure of the PCL-R and both versions of the SRP-SF were examined.
Results: Most of the modified items showed higher average item responses. The PCL-R showed a stronger association with the modified SRP-SF than with the standard SRP-SF. The four-factor model showed very good fit in accounting for the PCL-R data, consistent with previous research. For both SRP-SF versions, the results indicated good model fit. Structural equation models were tested separately, in which a superordinate SRP-SF factor was set to predict a broad factor reflecting chronic misconduct. Both versions showed good model fit, and the SRP-SF superordinate factor significantly predicted a chronic misconduct factor.
Conclusions: Both versions of the SRP-SF adequately reflected psychopathic features in this female sample; the modified items added robustness to representation of these features.

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Date Created: 20181018 Date Completed: 20190222 Latest Revision: 20191027

20240829

PMC6781698

10.1590/1516-4446-2018-0106

30328969