Learning Bayesian Statistics

#121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde

Dec 11, 2024
Guest
Nathaniel Forde
Nathaniel Forde, a staff data scientist at Personio and a contributor to the PyMC ecosystem, shares insights on Bayesian structural equation modeling. He highlights the importance of confirmatory factor analysis in validating constructs and discusses the flexibility of Bayesian methods in analyzing complex relationships. Forde emphasizes the necessity of model validation and sensitivity analysis to ensure robust findings. He also reflects on his journey into data science, noting how early challenges shaped his approach to data quality and causal inference.
Ask episode
AI Snips
Chapters
Transcript
Episode notes
INSIGHT

Confirmatory Factor Analysis

  • Confirmatory Factor Analysis (CFA) is a dimensional reduction technique.
  • CFA differs from classic factor analysis by focusing on pre-defined theoretical constructs, like "mathematical aptitude".
INSIGHT

Structural Equation Modeling

  • Structural Equation Modeling (SEM) extends CFA by adding explicit structure, like regression relationships between latent constructs.
  • SEM allows for complex dependency chains, increasing the model's explanatory power.
ANECDOTE

HR Analytics and SEM/CFA

  • Nathaniel's work at Personio, an HR platform, involves analyzing employee engagement surveys.
  • These surveys, often complex and multi-themed, are well-suited for SEM and CFA.
Get the Snipd Podcast app to discover more snips from this episode
Get the app