Predicting Serial Stranger Rapists: Developing a Statistical Model From Crime Scene Behaviors
Fecha
2022-10-01Autor
Estado
info:eu-repo/semantics/publishedVersionMetadatos
Mostrar el registro completo del ítemResumen
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.
Predicting Serial Stranger Rapists: Developing a Statistical Model From Crime Scene Behaviors
Tipo de Actividad
Artículos en revistasISSN
0886-2605Materias/ categorías / ODS
Derecho Penal y Criminología - Seguridad y Política CriminalPalabras Clave
offenders, sexual assault, cultural contexts, situational factors, preventionoffenders, sexual assault, cultural contexts, situational factors, prevention