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Takeaways
- Bayesian methods align better with researchers' intuitive understanding of research questions and provide more tools to evaluate and understand models.
- Prior sensitivity analysis is crucial for understanding the robustness of findings to changes in priors and helps in contextualizing research findings.
- Bayesian methods offer an elegant and efficient way to handle missing data in longitudinal studies, providing more flexibility and information for researchers.
- Fit indices in Bayesian model selection are effective in detecting underfitting but may struggle to detect overfitting, highlighting the need for caution in model complexity.
- Bayesian methods have the potential to revolutionize educational research by addressing the challenges of small samples, complex nesting structures, and longitudinal data.
- Posterior predictive checks are valuable for model evaluation and selection.
Chapters
00:00 The Power and Importance of Priors
09:29 Updating Beliefs and Choosing Reasonable Priors
16:08 Assessing Robustness with Prior Sensitivity Analysis
34:53 Aligning Bayesian Methods with Researchers' Thinking
37:10 Detecting Overfitting in SEM
43:48 Evaluating Model Fit with Posterior Predictive Checks
47:44 Teaching Bayesian Methods
54:07 Future Developments in Bayesian Statistics
Thank you to my Patrons for making this episode possible!
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