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A Comparison of Two Models for Cognitive Diagnosis MCMC IRT

Yan, Duanli; Almond, Russell G.; Mislevy, Robert J.
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
Cognitive Diagnosis, Fusion Model, Binary Skills Model, Latent Class Models, Bayesian Network, Markov Chain Monte Carlo (MCMC), Item Response Theory (IRT)


The paper also attempts to characterize the kinds of problems for which each type of measurement model is well-suited. A general Bayesian psychometric framework provides common language, making it easier to appreciate the differences. In addition, this paper explores some of the strengths and weaknesses of each approach based on a comparative analysis of a cognitive assessment, the mixed number subtraction data set. In this case, both the fusion model and Bayesian network approaches yield similar performance characteristics and also seem to pick up on different characteristics.

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