How to measure lineup fairness: Concurrent and predictive validity of lineup-fairness measures

Abstract

The current study examined the concurrent and predictive validity of four families of lineupfairness 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.