Journal of Probability and Statistics
Volume 2009 (2009), Article ID 537139, 18 pages
doi:10.1155/2009/537139
Review Article

The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling

Roy Levy

Division of Advanced Studies in Learning, Technology and Psychology in Education, Arizona State University, PO Box 870611, Tempe, AZ 85287-0611, USA

Received 7 August 2009; Accepted 26 November 2009

Academic Editor: Peter van der Heijden

Copyright © 2009 Roy Levy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the estimation of models that might otherwise be intractable, and frees the researcher from certain possible misconceptions about the models.