A Gibbs sampler for the multidimensional four-parameter logistic item response model via a data augmentation scheme.
Bayes estimation
Gibbs sampling
data augmentation
deviance information criterion
multidimensional four-parameter logistic item response theory model
Journal
The British journal of mathematical and statistical psychology
ISSN: 2044-8317
Titre abrégé: Br J Math Stat Psychol
Pays: England
ID NLM: 0004047
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
revised:
30
12
2020
received:
23
06
2019
pubmed:
19
5
2021
medline:
5
11
2021
entrez:
18
5
2021
Statut:
ppublish
Résumé
The four-parameter logistic (4PL) item response model, which includes an upper asymptote for the correct response probability, has drawn increasing interest due to its suitability for many practical scenarios. This paper proposes a new Gibbs sampling algorithm for estimation of the multidimensional 4PL model based on an efficient data augmentation scheme (DAGS). With the introduction of three continuous latent variables, the full conditional distributions are tractable, allowing easy implementation of a Gibbs sampler. Simulation studies are conducted to evaluate the proposed method and several popular alternatives. An empirical data set was analysed using the 4PL model to show its improved performance over the three-parameter and two-parameter logistic models. The proposed estimation scheme is easily accessible to practitioners through the open-source IRTlogit package.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
427-464Informations de copyright
© 2021 The British Psychological Society.
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