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Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit

Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive model checking (PPMC)—a popular Bayesian framework for model criticism—the performance of several discrepancy functions was investigated in a Monte Carlo simulation study.

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    Date Created
    • 2015
    Resource Type
  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2015
      Note type
      thesis
    • Includes bibliographical references (pages 142-148)
      Note type
      bibliography
    • Field of study: Educational psychology

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    by Derek M. Fay

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