Scholars and law enforcement agencies have been analyzing criminals’ behavioral and psychological characteristics for decades in order to create profiles, which can be used by authorities to guide serial murder investigations and help apprehend suspects. Within criminology, multiple subgroups of serial murderers have been created to try to understand their motives and to help identify future perpetrators. Although behavioral profiling has traditionally consisted of qualitative techniques, it is the goal of this study to demonstrate the value of quantitative analysis in the profiling process. The primary research question that this project aims to answer empirically is whether there are certain crime scene and personal background characteristics common to serial murders that can be used to uncover latent or underlying classes of serial murderers. To answer this question, this project will rely on serial murder data obtained through a database provided by Radford University/Florida Gulf Coast University, assembled through examination of public documents. The data set includes roughly 175 variables on serial murderers (n= 1,131) who have killed in the United States with three or more victims. To determine if there are underlying clusters of serial murderers, this project will conduct a latent class analysis. If latent classes exist, not only will behavioral profilers be able to use this information, but so too will police departments working on a serial murderer case. Identification of latent classes will allow for individuals without significant experience in profiling to look at the different classes and determine the likelihood that a serial murderer fits into that class, and further know what other variables are commonly observed within this cluster, thus helping them apprehend the perpetrator. This quantitative approach to profiling through data-driven predictive modeling will add to the larger understanding of criminal behavior patterns that can be expanded to other forms of criminal offending.
