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Assessing Recidivism Risk Across Female Pathways to Crime

NCJ Number
215819
Journal
Justice Quarterly Volume: 23 Issue: 3 Dated: September 2006 Pages: 384-405
Author(s)
Michael D. Reisig; Kristy Holtfreter; Merry Morash
Date Published
September 2006
Length
22 pages
Annotation
This article draws on Daly’s (1992, 1994) pathways to crime framework to evaluate the effectiveness of the Level of Supervision Inventory-Revised (LSI-R), which predicts recidivism risk, across subgroups of female offenders.
Abstract
Overall, the results indicate that the LSI-R is not a gender-neutral tool that is capable of accurately classifying the recidivism risk of both male and female offenders. More specifically, the LSI-R was found to correctly classify female offenders only when: (1) they did not follow gendered pathways into offending; (2) they offended in contexts similar to that of male offending; and (3) they occupied a relatively advantaged social position in society. In particular, the LSI-R was effective in predicting recidivism for economically motivated female offenders. The LSI-R misclassified a significant portion of female offenders who were socially and economically marginalized and who offended within typical gendered contexts, particularly female offenders who were drug-connected or who could be classified as “harmed and harming” women. The findings call into question theories of criminal behavior that claim to be gender-neutral and support feminist critiques that claim many women enter into criminality through pathways that are different from men. Participants were 402 female offenders who completed the LSI-R and who were interviewed prior to beginning community supervision in the Minneapolis/St. Paul area and in 2 counties in Oregon. During the course of the next 18 months, 235 of the participants were reinterviewed about their social situations, their program involvement, their quality of life, exposure to intimate partner violence, and their involvement in criminal activities. Participants’ self-reports were checked against official records, such as arrest records and officer logs. Data analysis involved the use of one-way analysis of variance, chi-square calculations, and multivariate logistic regression models. Future research is clearly needed to improve the understanding of recidivism risk among female offenders. Tables, footnotes, references