Markov Chain Monte Carlo for Item Response Models

Authored by: Brian W. Junker , Richard J. Patz , Nathan M. VanHoudnos

Handbook of Item Response Theory Volume Two Statistical Tools

Print publication date:  February  2016
Online publication date:  March  2017

Print ISBN: 9781466514324
eBook ISBN: 9781315373645
Adobe ISBN:

10.1201/b19166-19

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Abstract

Markov chain Monte Carlo (MCMC) has revolutionized modern statistical computing, especially for complex Bayesian and latent variable models. A recent Web of Knowledge search (Thompson ISI, 2012) for “MCMC” yielded 6015 articles, nearly half in statistics, and the rest spread across fields ranging from computational biology to transportation and thermodynamics. Of these, 72 articles appear in Psychometrika, Journal of Educational and Behavioral Statistics and Applied Psychological Measurement, and another seventeen in Psychological Methods, Journal of Educational Measurement, and Educational and Psychological Measurement. This is remarkable for an estimation method that first appeared in a psychometrics-related journal around 1990.

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