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...
| Publicado en: | Psychology, Crime & Law Vol. 31; no. 6; pp. 666 - 691 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=186344609&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 186344609 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 1068316X 7WL jtl: Psychology, Crime & Law issn: 1068316X maglogo: N pubinfo: dt: Jul2025 vid: 31 iid: 6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 186344609 10.1080/1068316X.2024.2307358 ppf: 666 ppct: 25 formats: tig: atl: How to measure lineup fairness: concurrent and predictive validity of lineup-fairness measures. aug: au: Lee, Jungwon Mansour, Jamal K. Penrod, Steven D. affil: Department of Psychology, Hallym University, Chuncheon-si, Gangwon-do, South Korea Department of Psychology, University of Lethbridge, Lethbridge, Alberta, Canada Memory Research Group, Queen Margaret University, Edinburgh, UK Department of Psychology, John Jay College of Criminal Justice, New York, USA su: Witnesses Fairness Resemblance (Philosophy) Test validity Statistical correlation Predictive validity sug: subj: Witnesses Fairness Resemblance (Philosophy) Test validity Statistical correlation Predictive validity keyword: Filler similarity lineup bias lineup fairness lineup size mock witness Filler similarity lineup bias lineup fairness lineup size mock witness ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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