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Model Criticism for Growth Curve Models via Posterior Predictive Model Checking

Abstract 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. The discrepancy functions of interest included two types of conditional concordance correlation (CCC) functions, two types of R2 functions, two types of standardized generalized dimensionality discrepancy (SGDDM) functions, the likelihood ratio (LR), and the likelihood... (more)
Created Date 2015
Contributor Fay, Derek M. (Author) / Levy, Roy (Advisor) / Thompson, Marilyn (Committee member) / Enders, Craig (Committee member) / Arizona State University (Publisher)
Subject Statistics / Quantitative psychology / Educational psychology / Bayesian hierarchical modeling / Growth curve modeling / Model criticism / Multilevel modeling / Posterior predictive model checking / Structural equation modeling
Type Doctoral Dissertation
Extent 211 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Educational Psychology 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis