Using Mixture Models to Examine Group Differences: Studying Juror Perceptions of the Strength of Forensic Science Evidence

• Activity Number: 395 - Addressing Individual Variation to Improve the Analysis of Forensic Evidence
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
• Sponsor: Advisory Committee on Forensic Science
• Author(s): Naomi Kaplan Damary* and Willian Thompson and Rebecca Hofstein Grady and Hal Stern
• Companies: Postdoctoral scholar, University of California, Irvine and University of California, Irvine and University of California, Irvine and University of California Irvine
• Keywords: Paired comparison models; Mixture Models; The EM algorithm; Forensic Science; Law and Statistics

Abstract

Forensic examiners compare items to assess whether they originate from a common source. In reaching conclusions, they consider the probability of the observed similarities and differences under alternative assumptions regarding the source(s) of the items (i.e., same or different source). These conclusions can be reported in various ways including likelihood ratios or random match probabilities. Thompson et. al., 2018 examined how laypeople perceive the strength of these reports through the use of paired comparison models, obtaining rank-ordered lists of the various statements and an indication of the perceived differences among them. The current study expands this research by examining whether the population is comprised of sub-populations that interpret these statements differently and whether their differences can be characterized. A mixture model that allows for multiple sub-populations with possibly different rankings of the statements is fit to the data and the possibility that covariates explain sub-population membership is considered. A deeper understanding of the way potential jurors perceive various forms of forensic reporting could improve communication in the courtroom.