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...
| Autores principales: | ; ; |
|---|---|
| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2025
|
| En: |
Psychology, crime & law
Año: 2025, Volumen: 31, Número: 6, Páginas: 666-690 |
| Acceso en línea: |
Volltext (lizenzpflichtig) |
| Journals Online & Print: | Gargar...
|
| Verificar disponibilidad: | HBZ Gateway |
| Palabras clave: |
| Sumario: | 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. |
|---|---|
| ISSN: | 1477-2744 |
| DOI: | 10.1080/1068316X.2024.2307358 |
