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An Introduction to the DA-T Gibbs Sampler for the Two-Parameter Logistic (2PL) Model and beyond

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Title: An Introduction to the DA-T Gibbs Sampler for the Two-Parameter Logistic (2PL) Model and beyond
Language: English
Authors: Maris, Gunter, Bechger, Timo M.
Source: Psicologica: International Journal of Methodology and Experimental Psychology. 2005 26(2):327-352.
Availability: University of Valencia. Dept. Metodologia, Facultad de Psicologia, Avda. Blasco Ibanez 21, 46010 Valencia, Spain. Tel: +34-96-386-4100; Web site: http://www.uv.es/revispsi/
Peer Reviewed: Y
Page Count: 26
Publication Date: 2005
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Bayesian Statistics, Computation, Item Response Theory, Models, Sampling
ISSN: 0211-2159
Abstract: The DA-T Gibbs sampler is proposed by Maris and Maris (2002) as a Bayesian estimation method for a wide variety of "Item Response Theory (IRT) models". The present paper provides an expository account of the DA-T Gibbs sampler for the 2PL model. However, the scope is not limited to the 2PL model. It is demonstrated how the DA-T Gibbs sampler for the 2PL may be used to build, quite easily, Gibbs samplers for other IRT models. Furthermore, the paper contains a novel, intuitive derivation of the Gibbs sampler and could be read for a graduate course on sampling. (Contains 9 figures and 4 footnotes.)
Abstractor: As Provided
Number of References: 26
Entry Date: 2009
Accession Number: EJ844431
Database: ERIC
FullText Text:
  Availability: 0
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  Data: University of Valencia. Dept. Metodologia, Facultad de Psicologia, Avda. Blasco Ibanez 21, 46010 Valencia, Spain. Tel: +34-96-386-4100; Web site: http://www.uv.es/revispsi/
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  Data: The DA-T Gibbs sampler is proposed by Maris and Maris (2002) as a Bayesian estimation method for a wide variety of "Item Response Theory (IRT) models". The present paper provides an expository account of the DA-T Gibbs sampler for the 2PL model. However, the scope is not limited to the 2PL model. It is demonstrated how the DA-T Gibbs sampler for the 2PL may be used to build, quite easily, Gibbs samplers for other IRT models. Furthermore, the paper contains a novel, intuitive derivation of the Gibbs sampler and could be read for a graduate course on sampling. (Contains 9 figures and 4 footnotes.)
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