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Article: Markov Chain Monte Carlo estimation of item parameters for the generalized graded unfolding model

TitleMarkov Chain Monte Carlo estimation of item parameters for the generalized graded unfolding model
Authors
KeywordsEstimation
Issue Date2006
Citation
Applied Psychological Measurement, 2006, v. 30, n. 3, p. 216-232 How to Cite?
AbstractThe authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response options, and sample size were manipulated. Results indicate that the two methods are comparable in terms of item parameter estimation accuracy. Although the MML estimates exhibit slightly smaller bias than MCMC estimates, they also show greater variability, which results in larger root mean squared errors. Of the two methods, only MCMC provides reasonable standard error estimates for all items. © 2006 Sage Publications.
Persistent Identifierhttp://hdl.handle.net/10722/228039
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 1.721

 

DC FieldValueLanguage
dc.contributor.authorDe La Torre, Jimmy-
dc.contributor.authorStark, Stephan-
dc.contributor.authorChernyshenko, Oleksandr S.-
dc.date.accessioned2016-08-01T06:45:02Z-
dc.date.available2016-08-01T06:45:02Z-
dc.date.issued2006-
dc.identifier.citationApplied Psychological Measurement, 2006, v. 30, n. 3, p. 216-232-
dc.identifier.issn0146-6216-
dc.identifier.urihttp://hdl.handle.net/10722/228039-
dc.description.abstractThe authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response options, and sample size were manipulated. Results indicate that the two methods are comparable in terms of item parameter estimation accuracy. Although the MML estimates exhibit slightly smaller bias than MCMC estimates, they also show greater variability, which results in larger root mean squared errors. Of the two methods, only MCMC provides reasonable standard error estimates for all items. © 2006 Sage Publications.-
dc.languageeng-
dc.relation.ispartofApplied Psychological Measurement-
dc.subjectEstimation-
dc.titleMarkov Chain Monte Carlo estimation of item parameters for the generalized graded unfolding model-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0146621605282772-
dc.identifier.scopuseid_2-s2.0-33646178269-
dc.identifier.volume30-
dc.identifier.issue3-
dc.identifier.spage216-
dc.identifier.epage232-
dc.identifier.eissn1552-3497-

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