ROC analysis of lineups obscures information that is critical for both theoretical understanding and applied purposes

Our previous article (Wells et al., 2015a. Journal of Applied Research in Memory and Cognition) showed how ROC analysis of lineups does not measure underlying discriminability or control for response bias. Wixted and Mickes (2015. Journal of Applied Research in Memory and Cognition) concede these points. Hence, in this article we focus more on how forcing the 3. ×. 2 lineup into the 2. ×. 2 structure required for ROC analysis obscures important underlying phenomena of theoretical value. Moreover, ROC analysis fails to account for the unique diagnostic properties of exonerating eyewitness behaviors (filler identifications and rejections). We describe how an examination of the full 3. ×. 2 structure helps reveal the critical underlying phenomena that ROC analysis hides. We also show how a Bayesian approach yields a family of diagnosticity functions that exposes the unique diagnosticity of all three eyewitness behaviors (suspect identifications, filler identifications, and rejections). Moreover, we show how Bayesian methods can examine diagnosticity as a function of witness confidence for all three eyewitness behaviors, which gives it a significant applied advantage over ROC analysis.