How to measure lineup fairness: concurrent and predictive validity of lineup-fairness measures
The current study examined the concurrent and predictive validity of four families of lineup-fairness measures – mock-witness measures, perceptual ratings, face-similarity algorithms, and resultant assessments (assessments based on eyewitness participants’ responses) – with 40 mock crime/lineup sets...
| Authors: | ; ; |
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2025
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| In: |
Psychology, crime & law
Year: 2025, Volume: 31, Issue: 6, Pages: 666-690 |
| Online Access: |
Volltext (lizenzpflichtig) |
| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
| Keywords: |
| Summary: | The current study examined the concurrent and predictive validity of four families of lineup-fairness measures – mock-witness measures, perceptual ratings, face-similarity algorithms, and resultant assessments (assessments based on eyewitness participants’ responses) – with 40 mock crime/lineup sets. A correlation analysis demonstrated weak or non-significant correlations between the mock-witness measures and the algorithms, but the perceptual ratings correlated significantly with both the mock-witness measures and the algorithms. These findings may reflect different task characteristics – pairwise similarity ratings of two faces versus overall similarity ratings for multiple faces – and suggest how to use algorithms in future eyewitness research. The resultant assessments did not correlate with the other families, but a multilevel analysis showed that only the resultant assessments – which are based on actual eyewitness choices – predicted eyewitness performance reliably. Lineup fairness, as measured using actual eyewitnesses, differs from lineup fairness as measured using the three other approaches. |
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| ISSN: | 1477-2744 |
| DOI: | 10.1080/1068316X.2024.2307358 |
