Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial. (August 2019)

• Record Type: Journal Article
• Authors: Zhan, Peida; Jiao, Hong; Man, Kaiwen; Wang, Lijun
• Is Part Of: Journal of educational and behavioral statistics. Volume 44:Number 4(2019)
• Page Start: 473
• Page End: 503
• Publication Date: 2019-08
• Subjects: cognitive diagnosis modeling -- Bayesian estimation -- Markov chain Monte Carlo -- DINA model -- DINO model -- rRUM -- testlet -- longitudinal diagnosis -- polytomous attributes; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998619826040
• Languages: English
• ISSNs: 1076-9986

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.