#121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde
Learning Bayesian Statistics
Intro
This chapter provides an in-depth look at structural equation modeling and confirmatory factor analysis, highlighting the benefits of Bayesian approaches. It also discusses model validation, sensitivity analysis, and the relevance of these methodologies to both academia and industry, catering to a wide range of statistical practitioners.
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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, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan.
Links from the show:
- Modeling Webinar – Bayesian Causal Inference & Propensity Scores: https://www.youtube.com/watch?v=y9BeOr0AETw&list=PL7RjIaSLWh5lDvhGf6qs_im0fRzOeFN5_&index=9
- LBS #102, Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle: https://learnbayesstats.com/episode/102-bayesian-structural-equation-modeling-causal-inference-psychometrics-ed-merkle/
- Nate’s website: https://nathanielf.github.io/
- Nate on GitHub: https://github.com/NathanielF
- Nate on Linkedin: https://www.linkedin.com/in/nathaniel-forde-2477a265/
- Nate on Twitter: https://x.com/forde_nathaniel
- Confirmatory Factor Analysis and Structural Equation Models in Psychometrics: https://www.pymc.io/projects/examples/en/latest/case_studies/CFA_SEM.html
- Measurement, Latent Factors and the Garden of Forking Paths: https://nathanielf.github.io/posts/post-with-code/CFA_AND_SEM/CFA_AND_SEM.html
- Bayesian Non-parametric Causal Inference: https://www.pymc.io/projects/examples/en/latest/causal_inference/bayesian_nonparametric_causal.html
- Simpson’s paradox: https://www.pymc.io/projects/examples/en/latest/causal_inference/GLM-simpsons-paradox.html
Transcript:
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