Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

Peida Zhan, Hong Jiao, Kaiwen Man and Lijun Wang
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Peida Zhan: Zhejiang Normal University
Kaiwen Man: University of Maryland
Lijun Wang: Zhejiang Normal University

Journal of Educational and Behavioral Statistics, 2019, vol. 44, issue 4, 473-503

Abstract: In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy “and†gate model; the deterministic inputs, noisy “or†gate model; the linear logistic model; the reduced reparameterized unified model; and the log-linear CDM (LCDM). Further, we introduce the unstructured latent structural model and the higher order latent structural model. We also show how to extend these models to consider polytomous attributes, the testlet effect, and longitudinal diagnosis. Finally, we present an empirical example as a tutorial to illustrate how to use JAGS codes in R.

Keywords: cognitive diagnosis modeling; Bayesian estimation; Markov chain Monte Carlo; DINA model; DINO model; rRUM; testlet; longitudinal diagnosis; polytomous attributes (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:44:y:2019:i:4:p:473-503

DOI: 10.3102/1076998619826040

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