A Bayesian algorithm based on auxiliary variables for estimating GRM with non-ignorable missing data

Jiwei Zhang (), Zhaoyuan Zhang () and Jian Tao ()
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Jiwei Zhang: Yunnan University
Zhaoyuan Zhang: Yili Normal University
Jian Tao: Northeast Normal University

Computational Statistics, 2021, vol. 36, issue 4, No 13, 2643-2669

Abstract: Abstract In this paper, a highly effective Bayesian sampling algorithm based on auxiliary variables is used to estimate the graded response model with non-ignorable missing response data. Compared with the traditional marginal likelihood method and other Bayesian algorithms, the advantages of the new algorithm are discussed in detail. Based on the Markov Chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarignal likelihood are employed to compare the different missing mechanism models. Two simulation studies are conducted and a detailed analysis of the sexual compulsivity scale data is carried out to further illustrate the proposed methodology.

Keywords: Bayesian inference; Graded response model; Item response theory (IRT); Missing mechanism models; Model assessments (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-021-01100-8

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