Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model

Autor/inn/en	de la Torre, Jimmy; Stark, Stephen; Chernyshenko, Oleksandr S.
Titel	Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model
Quelle	In: Applied Psychological Measurement, 30 (2006) 3, S.216-232 (17 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621605282772
Schlagwörter	Computation; Monte Carlo Methods; Markov Processes; Item Response Theory; Models; Test Length; Sample Size; Error of Measurement; Test Items + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Markowscher Prozess; Item-Response-Theorie; Analogiemodell; Messfehler; Test content; Testaufgabe
Abstract	The 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. (Contains 1 figure and 9 tables.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10