Document Type

Article

Publication Version

Published Version

Publication Date

6-13-2020

Journal or Book Title

Behaviormetrika

Volume

47

First Page

355

Last Page

384

DOI

10.1007/s41237-020-00116-6

Abstract

Forensic science often involves the comparison of crime-scene evidence to a known-source sample to determine if the evidence and the reference sample came from the same source. Even as forensic analysis tools become increasingly objective and automated, final source identifications are often left to individual examiners’ interpretation of the evidence. Each source identification relies on judgements about the features and quality of the crime-scene evidence that may vary from one examiner to the next. The current approach to characterizing uncertainty in examiners’ decision-making has largely centered around the calculation of error rates aggregated across examiners and identification tasks, without taking into account these variations in behavior. We propose a new approach using IRT and IRT-like models to account for differences among examiners and additionally account for the varying difficulty among source identification tasks. In particular, we survey some recent advances (Luby 2019a) in the application of Bayesian psychometric models, including simple Rasch models as well as more elaborate decision tree models, to fingerprint examiner behavior.

Comments

This article is published as Luby, Amanda, Anjali Mazumder, and Brian Junker. "Psychometric analysis of forensic examiner behavior." Behaviormetrika (2020): 1-30. Posted with permission of CSAFE.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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