Abstract
Multilevel covariance structure models have become increasingly popular in the psychometric literature in the past few years to account for population heterogeneity and complex study designs. We develop practical simulation based procedures for Bayesian inference of multilevel binary factor analysis models. We illustrate how Markov Chain Monte Carlo procedures such as Gibbs sampling and Metropolis-Hastings methods can be used to perform Bayesian inference, model checking and model comparison without the need for multidimensional numerical integration. We illustrate the proposed estimation methods using three simulation studies and an application involving student's achievement results in different areas of mathematics.
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The authors thank Ian Westbury, University of Illinois at Urbana Champaign for kindly providing the SIMS data for the application.
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Ansari, A., Jedidi, K. Bayesian factor analysis for multilevel binary observations. Psychometrika 65, 475–496 (2000). https://doi.org/10.1007/BF02296339
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DOI: https://doi.org/10.1007/BF02296339