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1 results

  • Bayesian Estimation of the DINA Q matrix

  • Yinghan Chen, Steven Andrew Culpepper, Yuguo Chen, Jeffrey Douglas
  • Journal:
    Psychometrika / Volume 83 / Issue 1 / March 2018
    Published online by Cambridge University Press:
    01 January 2025, pp. 89-108

  • Cognitive diagnosis models are partially ordered latent class models and are used to classify students into skill mastery profiles. The deterministic inputs, noisy “and” gate model (DINA) is a popular psychometric model for cognitive diagnosis. Application of the DINA model requires content expert knowledge of a Q matrix, which maps the attributes or skills needed to master a collection of items. Misspecification of Q has been shown to yield biased diagnostic classifications. We propose a Bayesian framework for estimating the DINA Q matrix. The developed algorithm builds upon prior research (Chen, Liu, Xu, & Ying, in J Am Stat Assoc 110(510):850–866, 2015) and ensures the estimated Q matrix is identified. Monte Carlo evidence is presented to support the accuracy of parameter recovery. The developed methodology is applied to Tatsuoka’s fraction-subtraction dataset.