#121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde

Learning Bayesian Statistics

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Takeaways:

• CFA is commonly used in psychometrics to validate theoretical constructs.
• Theoretical structure is crucial in confirmatory factor analysis.
• Bayesian approaches offer flexibility in modeling complex relationships.
• Model validation involves both global and local fit measures.
• Sensitivity analysis is vital in Bayesian modeling to avoid skewed results.
• Complex models should be justified by their ability to answer specific questions.
• The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity.
• Divergences in model fitting indicate potential issues with model specification.
• Factor analysis can help clarify causal relationships between variables.
• Survey data is a valuable resource for understanding complex phenomena.
• Philosophical training enhances logical reasoning in data science.
• Causal inference is increasingly recognized in industry applications.
• Effective communication is essential for data scientists.
• Understanding confounding is crucial for accurate modeling.

Chapters:

10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)

20:11 Application of SEM and CFA in HR Analytics

30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA

33:58 Evaluating Bayesian Models

39:50 Challenges in Model Building

44:15 Causal Relationships in SEM and CFA

49:01 Practical Applications of SEM and CFA

51:47 Influence of Philosophy on Data Science

54:51 Designing Models with Confounding in Mind

57:39 Future Trends in Causal Inference

01:00:03 Advice for Aspiring Data Scientists

01:02:48 Future Research Directions

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy,