U.S. flag

An official website of the United States government, Department of Justice.

Predicting Rapist Type From Crime-Scene Variables

NCJ Number
170755
Date Published
January 1998
Author(s)
R. A. Knight, J. I. Warren, R. Reboussin, B. J. Soley
Agencies
NIJ-Sponsored
Annotation
Secondary data analyses of two analogous data sets assessed the ability of crime scene variables observed in serial rapes to predict rapist classification in the Massachusetts Treatment Center (MTC) Rapist Typology Version 3.
Abstract
Data sets from the Federal Bureau of Investigation (FBI), which contained extensive coding of crime scene information but minimal offender data, were analyzed to select optimum predictors by examining frequencies and both within-crime and cross-crime consistencies of crime scene indicators. Analogous predictors were identified in the MTC database, which contained extensive offender data but minimal crime scene data, and these indicators were used to predict rapist type. The study focused on three subject samples, two rapist samples from the FBI's Behavioral Science Unit (BSU) and one rapist sample from the MTC. The BSU crime scene sample included 116 subjects who committed 565 offenses against adult women, while the BSU interview sample included a subset of 41 serial rapist cases. The MTC sample consisted of 254 repetitive and/or aggressive male rapists who had been civilly committed between 1959 and 1991 to the MTC as sexually dangerous. Promising predictive results emerged in the domains of adult antisocial and expressive aggression. In addition, domains of sadism, offense planning, and relation with victim showed high internal consistency and good to high cross- crime consistency, suggesting predictive scales are possible for these domains. Findings are detailed with respect to adult antisocial behavior, expressive aggression, pervasive anger, sadism, sexualization, offender relationship with victim during the crime, vindictiveness, and offense planning. 34 references, 8 tables, and 2 figures
Date Created: December 17, 2008