Predicting Serial Stranger Rapists: Developing a Statistical Model From Crime Scene Behaviors

Author:

Ramirez Meritxell Perez1ORCID,Framis Andrea Gimenez-Salinas1,Gonzalez-Alvarez Jose Luis2,Castro Juan Enrique Soto2

Affiliation:

1. Universidad Pontificia Comillas, Madrid, Spain

2. Ministry of the Interior of Spain, Madrid, Spain

Abstract

Stranger rapes are the most difficult cases to solve for the police, especially when a serial rapist is involved. Recent research in offender profiling has focused on generating inferences between crime scene variables and offender characteristics to aid the police investigation. This study aims to develop an empirical model to predict a new case of a serial stranger rapist by analyzing a Spanish sample of 231 one-off and 38 serial sexual offenders. A multivariate logistic regression model that included eight significant crime-related variables was able to predict whether an unknown offender is a one-off or serial rapist based only on the victim’s account. The predictive validity of the model was tested using receiver operating characteristic (ROC) analysis and the result of AUC value indicated a medium predictive capacity. The final model correctly classifies nearly 80% of serial stranger rapist cases. The implications of these findings for criminal investigation are discussed.

Publisher

SAGE Publications

Subject

Applied Psychology,Clinical Psychology

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