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Over the past few decades, a number of typologies have been proposed for female sex offenders. However, these typologies have been products of small clinical samples or limited descriptive characteristics obtained from the public registry. For the current study, we created a unique dataset that consisted of face-to-face interviews, police reports, criminal histories, correctional reports, and offender assessment surveys. To explore the complex relationships between victims, offenders, and situational factors that impact offending, we used a hierarchical loglinear modeling (HLM) and cluster analysis on the population of female sex offenders in Arkansas from 1999-2014. Based on our findings, we provided policy implications in regard to crime prevention, risk assessment, and cognitive-based treatment programs.
Tusty ten Bensel, University of Arkansas at Little Rock
Benjamin GIbbs, University of Central Missouri
Linde Chris Burkey, University of Arkansas at Little Rock