A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models

This paper demonstrates Markov chain Monte Carlo (MCMC) techniques that are particularly well-suited to complex models with item response theory (IRT) assumptions. MCMC may be thought of as a successor to the standard practice of first calibrating the items using E-M methods and then taking the item parameters to be known and fixed at their calibrated values when proceeding with inference regarding the latent trait, in contrast to this two-stage E-M approach, MCMC methods treat item and subject parameters at the same time; this allows us to incorporate standard errors of item estimates into trait inferences, and vice versa. We develop a MCMC methodology, based on Metropolis-Hastings sampling, that can be routinely implemented to fit novel IRT models, and we compare the algorithmic features of the Metropolis- Hastings approach to other approaches based on Gibbs sampling. For concreteness we illustrate the methodology using the familiar two-parameter logistic (2PL) IRT model; more complex models are treated in a subsequent paper (Patz & Junker, in press).

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Year of publication:	1999
Authors:	Patz, Richard J. ; Junker, Brian W.
Published in:	Journal of Educational and Behavioral Statistics. - Vol. 24.1999, 2, p. 146-178
Subject:	item response theory \| Markov chain Monte Carlo \| National Assessment of Educational Progress (NAEP)

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Extent:	text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010776002