An NCME Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

Autor/inn/en	Kim, Jee-Seon; Bolt, Daniel M.
Titel	An NCME Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods
Quelle	In: Educational Measurement: Issues and Practice, 26 (2007) 4, S.38-51 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0731-1745
DOI	10.1111/j.1745-3992.2007.00107.x
Schlagwörter	Placement; Monte Carlo Methods; Markov Processes; Measurement; Sampling; Item Response Theory; Computation; Inferences; Mathematics; Mathematics Tests; College Mathematics; Computer Software; Evaluation Methods + Suchen Sie Ihr Suchwort? Betriebspraktikum; Praktikum; Monte-Carlo-Methode; Markowscher Prozess; Messverfahren; Item-Response-Theorie; Inference; Inferenz; Mathematik
Abstract	The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain convergence. Model comparison and fit issues in the context of MCMC are also considered. Finally, an illustration is provided in which a two-parameter logistic (2PL) model is fit to item response data from a university mathematics placement test through MCMC using the WINBUGS 1.4 software. While MCMC procedures are often complex and can be easily misused, it is suggested that they offer an attractive methodology for experimentation with new and potentially complex IRT models, as are frequently needed in real-world applications in educational measurement. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10