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Learning Bayesian Statistics

#102 Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle

Mar 20, 2024
Explore Bayesian Structural Equation Modeling (SEM) in psychometrics with Ed Merkle. Learn about the significance of BSEM, challenges in model estimation, and the blavaan package in R. Discover the role of Bayesian methods in forecasting and wisdom crowdsourcing. Dive into the complexities of prior distributions, model convergence, and the development of blavaan. Gain insights on Bayesian workflow challenges, model placement in research papers, and future developments in patient psychometrics. Revolutionize math education and foster interest in STEM fields.
01:08:53

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Quick takeaways

  • Bayesian Structural Equation Modeling (SEM) aids in understanding latent variables and causal connections in psychometrics.
  • Bayesian models enhance model predictions and interpretations by sampling parameters and characterizing uncertainty.

Deep dives

Importance of Structural Equation Modeling in Psychometrics

Structural Equation Modeling (SEM) is vital in psychometrics, aiding in understanding latent variables and causal connections. Ed Merkel discusses his work on applying SEM to vision applications and model estimation, emphasizing its significance in psychometrics. Vision SEM enhances researchers' capabilities and addresses challenges in model estimation, contributing to method dissemination. Merkel's bliven package in R facilitates vision SEM implementation, expanding forecasting and crowdsourcing abilities.

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