JSM 2004 - Toronto

Abstract #301337

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Activity Number: 299
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301337
Title: Nonparametric and Parametric Bayes Models for Item Response Data
Author(s): Kristin Duncan*+ and Steven MacEachern
Companies: Ohio State University and Ohio State University
Address: 2002 W. Schantz Ave., Kettering, OH, 45409,
Keywords: nonparametric Bayes ; item response ; cross-validation
Abstract:

Both classical nonparametric models and Bayesian parametric models are commonly used in psychometrics, but Bayesian nonparametric models have yet to receive much attention in this field and offer some improvements over current models. Ling Qin took a first look at the problem in her 1998 dissertation where she used the two-parameter logistic curve as the prior mean for the item response curve. We extend her work by using the three-parameter logistic curve as the prior mean. This allows us to study sensitivity to the prior distribution. Bayesian nonparametric models are computationally intensive, so we compare results with these models to those obtained with their faster classical nonparametric counterparts. Bayesian cross-validation is used to assess the fit of the models.

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