The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters

Autor/inn/en	Jang, Yoonsun; Cohen, Allan S.
Titel	The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters
Quelle	In: Educational and Psychological Measurement, 80 (2020) 5, S.975-994 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Jang, Yoonsun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164419898228
Schlagwörter	Markov Processes; Item Response Theory; Accuracy; Inferences; Monte Carlo Methods; Simulation; Models; Evaluation Methods + Suchen Sie Ihr Suchwort? Markowscher Prozess; Item-Response-Theorie; Inference; Inferenz; Monte-Carlo-Methode; Simulation program; Simulationsprogramm; Analogiemodell
Abstract	A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the accuracy of model parameters estimated with different degree of convergence. Results indicated the accuracy of the estimated model parameters for the mixture item response theory models decreased as the number of iterations of the Markov chain decreased. In particular, increasing the number of burn-in iterations resulted in more accurate estimation of mixture IRT model parameters. In addition, the different methods for monitoring convergence of a Markov chain resulted in different degrees of convergence despite almost identical accuracy of estimation. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01