Gibbs-Slice Sampling Algorithm for Estimating the Four-Parameter Logistic Model.

Bayesian inference four-parameter logistic model item response theory model assessment potential scale reduction factor slice sampling algorithm

Journal

Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902

Informations de publication

Date de publication:
2020
Historique:
received: 17 12 2019
accepted: 30 07 2020
entrez: 12 10 2020
pubmed: 13 10 2020
medline: 13 10 2020
Statut: epublish

Résumé

The four-parameter logistic (4PL) model has recently attracted much interest in educational testing and psychological measurement. This paper develops a new Gibbs-slice sampling algorithm for estimating the 4PL model parameters in a fully Bayesian framework. Here, the Gibbs algorithm is employed to improve the sampling efficiency by using the conjugate prior distributions in updating asymptote parameters. A slice sampling algorithm is used to update the 2PL model parameters, which overcomes the dependence of the Metropolis-Hastings algorithm on the proposal distribution (tuning parameters). In fact, the Gibbs-slice sampling algorithm not only improves the accuracy of parameter estimation, but also enhances sampling efficiency. Simulation studies are conducted to show the good performance of the proposed Gibbs-slice sampling algorithm and to investigate the impact of different choices of prior distribution on the accuracy of parameter estimation. Based on Markov chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarginal likelihood are considered to assess the model fittings. Moreover, a detailed analysis of PISA data is carried out to illustrate the proposed methodology.

Identifiants

pubmed: 33041882
doi: 10.3389/fpsyg.2020.02121
pmc: PMC7530206
doi:

Types de publication

Journal Article

Langues

eng

Pagination

2121

Informations de copyright

Copyright © 2020 Zhang, Lu, Du and Zhang.

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Auteurs

Jiwei Zhang (J)

Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University, Kunming, China.

Jing Lu (J)

Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.

Hang Du (H)

Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
School Affiliated to Longhua Institute of Educational Science, Shenzhen, China.

Zhaoyuan Zhang (Z)

Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
School of Mathematics and Statistics, Yili Normal University, Yili, China.

Classifications MeSH