Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm

Autor/inn/en	Monroe, Scott; Cai, Li
Titel	Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm
Quelle	In: Educational and Psychological Measurement, 74 (2014) 2, S.343-369 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164413499344
Schlagwörter	Item Response Theory; Models; Computation; Mathematics; Matrices; Comparative Analysis; Accuracy; Maximum Likelihood Statistics; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Mathematik; Matrizenrechnung; Monte-Carlo-Methode
Abstract	In Ramsay curve item response theory (RC-IRT) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the parameter covariance matrix as an automatic byproduct on convergence. In turn, researchers are limited in when they can employ RC-IRT, as the covariance matrix is needed for many statistical inference procedures. The present research remedies this problem by estimating the RC-IRT model parameters by the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm. An attractive feature of MH-RM is that the structure of the algorithm makes estimation of the covariance matrix convenient. Additionally, MH-RM is ideally suited for multidimensional IRT, whereas EM is limited by the "curse of dimensionality." Based on the current research, when RC-IRT or similar IRT models are eventually generalized to include multiple latent dimensions, MH-RM would appear to be the logical choice for estimation. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10