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
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
2121Informations de copyright
Copyright © 2020 Zhang, Lu, Du and Zhang.
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