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.

陌生人强奸是警方最难解决的案件，尤其是当涉及连环强奸犯时。最近对犯罪者特征分析的研究集中在在犯罪现场变量和犯罪者特征之间产生推论，以帮助警方调查。本研究旨在通过分析 231 名一次性和 38 名连续性犯罪者的西班牙样本，开发一个经验模型来预测一个新的连环陌生人强奸犯案件。一个包含八个与犯罪相关的重要变量的多元逻辑回归模型能够仅根据受害者的描述来预测未知罪犯是一次性强奸犯还是连环强奸犯。使用受试者工作特征 (ROC) 分析测试了模型的预测有效性，AUC 值的结果表明预测能力中等。最终模型正确分类了近 80% 的连环陌生人强奸案。讨论了这些调查结果对刑事调查的影响。