Proficiency Testing of Forensic Fingerprint Examiners with Bayesian Item Response Theory

In recent years, the forensic community has pushed to increase the scientific basis of forensic evidence, which has included proficiency testing for fingerprint analysts. We used proficiency testing data collected by Collaborative Testing Services in which 431 fingerprint analysts were asked to identify the source of latent prints. The data were analysed using a Rasch model with a Bayesian estimation approach. Although these data provide valuable information about the relative proficiency of the examiners and the relative difficulty of the questions, it does not necessarily extrapolate onto general performance of examiners or difficulty in casework, which we show through sensitivity analysis and simulation. We show that a Bayesian Item Response Theory (IRT) analysis provides a deeper understanding of analysts’ proficiency and question difficulty than other forms of analysis. A large-scale adoption of IRT in this area would provide both more precise estimates of proficiency and quantitative evidence for the relative difficulty of different questions.