Journal article
Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm
Psychometrika, Vol.87(4), pp.1390-1421
12/01/2022
DOI: 10.1007/s11336-022-09857-7
PMID: 35426059
Abstract
This paper proposes a novel collapsed Gibbs sampling algorithm that marginalizes model parameters and directly samples latent attribute mastery patterns in diagnostic classification models. This estimation method makes it possible to avoid boundary problems in the estimation of model item parameters by eliminating the need to estimate such parameters. A simulation study showed the collapsed Gibbs sampling algorithm can accurately recover the true attribute mastery status in various conditions. A second simulation showed the collapsed Gibbs sampling algorithm was computationally more efficient than another MCMC sampling algorithm, implemented by JAGS. In an analysis of real data, the collapsed Gibbs sampling algorithm indicated good classification agreement with results from a previous study.
Details
- Title: Subtitle
- Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm
- Creators
- Kazuhiro Yamaguchi - University of TsukubaJonathan Templin - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Psychometrika, Vol.87(4), pp.1390-1421
- DOI
- 10.1007/s11336-022-09857-7
- PMID
- 35426059
- ISSN
- 0033-3123
- eISSN
- 1860-0980
- Grant note
- DOI: 10.13039/501100001691, name: Japan Society for the Promotion of Science, award: JSPS Grant-in-Aid for JSPS Research Fellow 18J01312, KAKANHI 19H00616; DOI: 10.13039/501100001691, name: Japan Society for the Promotion of Science, award: KAKANHI 20H01720, KAKANHI 21H00936
- Language
- English
- Date published
- 12/01/2022
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984371296902771
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