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Activity Number: 138
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #320827 View Presentation
Title: A Log-Linear Model Approach to Eyewitness Identification Data
Author(s): Amanda Luby*
Keywords: lineups ; eyewitness identification ; categorical data analysis

Although eyewitness identification is generally regarded as relatively inaccurate among cognitive psychologists and other experts, testimony from eyewitnesses continues to be prolific in the court system today. There is great interest among psychologists and the criminal justice system to reform eyewitness identification procedures to make the outcomes as accurate as possible. This involves both maximizing the true identification rate and minimizing the false identification rate. There has been a recent push to adopt Receiver Operating Characteristic (ROC) curve methodology to analyze lineup procedures, but has not been universally accepted in the field. This paper addresses some of the shortcomings of the ROC approach and proposes an analytical approach based on log-linear models as an alternative method to evaluate lineup procedures. We find that log-linear models can incorporate more information than previous approaches, and provide flexibility needed for data of this nature.

Authors who are presenting talks have a * after their name.

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