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Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

Author

Listed:
  • Peida Zhan

    (Zhejiang Normal University)

  • Hong Jiao
  • Kaiwen Man

    (University of Maryland)

  • Lijun Wang

    (Zhejiang Normal University)

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.

Suggested Citation

  • Peida Zhan & Hong Jiao & Kaiwen Man & Lijun Wang, 2019. "Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 473-503, August.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:4:p:473-503
    DOI: 10.3102/1076998619826040
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Yaohui & Zhan, Peida & Fu, Yanbin & Chen, Qipeng & Man, Kaiwen & Luo, Yikun, 2023. "Using a multi-strategy eye-tracking psychometric model to measure intelligence and identify cognitive strategy in Raven's advanced progressive matrices," Intelligence, Elsevier, vol. 100(C).
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    4. Kazuhiro Yamaguchi & Jonathan Templin, 2022. "A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 24-54, March.

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