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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- Partial requirement for: Ph.D., Arizona State University, 2015Note typethesis
- Includes bibliographical references (pages 142-148)Note typebibliography
- Field of study: Educational psychology