Using mixture models to examine group difference among jurors: an illustration involving the perceived strength of forensic science evidence. (30th January 2021)

• Record Type: Journal Article
• Authors: Kaplan-Damary, Naomi; C. Thompson, William; Hofstein Grady, Rebecca; Stern, Hal S
• Is Part Of: Law, probability & risk. Volume 19:Number 3/4(2020)
• Page Start: 235
• Page End: 253
• Publication Date: 2021-01-30
• Subjects: forensic science -- testimony -- reporting -- identification -- probability -- evidence; Proximate cause (Law) -- Periodicals; Risk -- Periodicals; Law -- Mathematical models -- Periodicals; Law -- Methodology -- Periodicals; Probabilities -- Periodicals; Risk assessment -- Periodicals; Law -- Mathematical models; Law -- Methodology; Probabilities; Proximate cause (Law); Risk; Risk assessment; Periodicals; 340.1
• DOI: 10.1093/lpr/mgaa016
• Languages: English
• ISSNs: 1470-8396

Abstract

Abstract: The way in which jurors perceive reports of forensic evidence is of critical importance, especially in cases of forensic identification evidence that require examiners to compare items and assess whether they originate from a common source. The current study discusses methods for studying group differences among mock jurors and illustrates them using a reanalysis of data regarding lay perceptions of forensic science evidence. Conventional approaches that consider subpopulations defined a priori are compared with mixture models that infer group structure from the data, allowing detection of subgroups that cohere in unexpected ways. Mixture models allow researchers to determine whether a population comprises subpopulations that respond to evidence differently and then to consider how those subpopulations might be characterized. The reanalysis reported here shows that mixture models can enhance understanding of lay perceptions of an important type of forensic science evidence (DNA and fingerprint comparisons), providing insight into how the perceived strength of that evidence varies as a function of the language forensic experts use to describe their findings. This novel application of mixture models illustrates how such models can be used, more generally, to explore the importance of juror characteristics in jury decision making.