Date of Award

Spring 5-5-2026

Document Type

Thesis

Degree Name

Master of Arts (MA)

Department/Program

Forensic Psychology

Language

English

First Advisor or Mentor

Steven D. Penrod

Second Reader

Kelly McWilliams

Third Advisor

Dilhan Toredi

Abstract

Eyewitness descriptions play a crucial role in investigations, as they have a significant influence on investigation leads and lineup construction. However, little attention has been given to how descriptions are compiled into a single descriptive profile (i.e., descriptor techniques) in research and practice when there are multiple eyewitnesses. We examined how different descriptor techniques—comprehensive (all non-contradictory descriptions provided by eyewitnesses), random (a random description subset provided by eyewitnesses), and consensus (descriptions commonly provided by eyewitnesses)—influence eyewitness performance and lineup fairness. We expected that lineups constructed with comprehensive descriptors would yield arrays of faces with the highest fairness and lowest discriminability, as they would have the highest suspect-filler similarity due to inclusion of more details. Participants (N = 363) watched two mock-crime videos in random order, completed a distractor task, gave their lineup decisions and confidence ratings for either a target-present or target-absent lineup, and completed attention-check and demographic questions. Lineups constructed with comprehensive descriptors had the highest discriminability but the lowest fairness, while lineups constructed with random and consensus descriptors were fairer but showed reduced discriminability. Discriminability only differed significantly between the lineups constructed with comprehensive and random descriptors. Therefore, the compilation of descriptions into descriptors may have an impact on lineup performance and fairness—with both methodological and practical implications.

Available for download on Thursday, August 26, 2027

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