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#109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter

Learning Bayesian Statistics

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Navigating Model Selection and Overfitting in SEM

This chapter explores the complexities of model selection and overfitting in structural equation modeling (SEM), emphasizing the inadequacies of common fit indices. The discussion highlights the advantages of Bayesian methods over traditional approaches and the need for better detection methods for overfitting. Additionally, it touches on the importance of effective teaching strategies for Bayesian statistics and the challenges educators face in adapting to new statistical frameworks.

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