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This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN)

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes.

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    Date Created
    • 2014
    Resource Type
  • Text
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    Note
    • Partial requirement for: Ph. D., Arizona State University, 2014
      Note type
      thesis
    • Includes bibliographical references (p. 169-177)
      Note type
      bibliography
    • Field of study: Educational psychology

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    by AAron Crawford

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