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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright Owner
The Authors
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Luby, Amanda; Mazumder, Anjali; and Junker, Brian, "Psychometric analysis of forensic examiner behavior" (2020). CSAFE Publications. 52.
https://lib.dr.iastate.edu/csafe_pubs/52
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.