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http://hdl.handle.net/11531/60798
Título : | Predicting Serial Stranger Rapists: Developing a Statistical Model From Crime Scene Behaviors |
Autor : | Pérez Ramírez, Meritxell Gimenez-Salinas Framis, Andrea González Alvarez, Jose Luis Soto, Juan Enrique |
Fecha de publicación : | 1-oct-2022 |
Resumen : | 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 crimerelated variables was able to predict whether an unknown offender is a oneoff 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. 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 crimerelated variables was able to predict whether an unknown offender is a oneoff 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. |
Descripción : | Artículos en revistas |
URI : | DOI: 10.1177/08862605211044968 |
ISSN : | 0886-2605 |
Aparece en las colecciones: | Artículos |
Ficheros en este ítem:
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predicting.pdf | 303,35 kB | Adobe PDF | Visualizar/Abrir |
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