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Activity Number: 439
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320504 View Presentation
Title: A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer-Based Testing
Author(s): Zhuoran Shang*
Keywords: psychometrics ; item response theory ; mixture model

Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are modeling aberrant behavior via mixture modeling methods, and flagging aberrant behavior via residual based outlier detection methods. We propose a two-stage method that is conceived of as a combination of both approaches. In the first stage, a mixture hierarchical model is fitted to the response and response time data to distinguish normal and aberrant behaviors using Markov chain Monte Carlo (MCMC) algorithm. In the second stage, a further distinction between rapid guessing and cheating behavior is made at a person level using a Bayesian residual index. A simulation study shows that the two-stage method yields accurate item and person parameter estimates, as well as high power and low false detection rate, under different manipulated conditions. A real data example is given in the end to illustrate the potential application of the proposed method.

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

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