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Bayes modal estimation in item response models. (English) Zbl 0596.62114

Summary: This article describes a Bayesian framework for estimation in item response models, with two-stage prior distributions on both item and examinee populations. Strategies for point and interval estimation are discussed, and a general procedure based on the EM algorithm is presented. Details are given for implementation under one-, two-, and three-parameter binary logistic IRT models. Novel features include minimally restrictive assumptions about examinee distributions and the exploitation of dependence among item parameters in a population of interest. Improved estimation in a moderately small sample is demonstrated with simulated data.

MSC:

62P15 Applications of statistics to psychology
62F15 Bayesian inference

Software:

BILOG
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References:

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