Loading…
Psychometric assessment of dynamic risk factors for child molesters
To explore the relationship between dynamic risk factors and recidivism in child molesters, we studied a sample of men (N=495) who completed an intensive, prison-based treatment program in New Zealand. During the follow-up period (M=5.8 years), 9.9% were reconvicted for a sexual offense. A self-repo...
Saved in:
Published in: | Sexual abuse 2007-12, Vol.19 (4), p.347-367 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | To explore the relationship between dynamic risk factors and recidivism in child molesters, we studied a sample of men (N=495) who completed an intensive, prison-based treatment program in New Zealand. During the follow-up period (M=5.8 years), 9.9% were reconvicted for a sexual offense. A self-report psychometric battery was administered at pre-treatment that assessed a range of variables related to sexual attitudes and beliefs, emotional functioning, and interpersonal competency. Factor analysis showed that individual differences in the battery could be described by four dimensions-Social Inadequacy, Sexual Interests, Anger/Hostility, and Pro-Offending Attitudes. Factor scores for each dimension were significantly correlated with sexual recidivism. Logistic regression analyses confirmed that the Sexual Interests and Pro-Offending Attitudes factor scores, as well as an Overall Deviance score which combined the dimensions, provided significant additional validity for predicting recidivism beyond the Static-99 (Hanson and Thornton Law and Human Behavior 24:119-136, 2000). When added to the Static-99, the Overall Deviance score increased the area under the Receiver-Operating Characteristic curve (AUC) from 0.72 to 0.81. These results show that psychometric self-reports can provide valid measures of dynamic risk factors, and that inclusion of such measures can improve risk prediction beyond that achievable by static factors alone. |
---|---|
ISSN: | 1079-0632 1573-286X |
DOI: | 10.1007/s11194-007-9052-5 |