Lecture 1: Overview & Path Analysis


Setting the stage: EFA, CFA, SEM, Path analysis

  • Goal: Understand relations among a large number of observed variables
  • Goal: Extend regression methods to (a) multiple outcomes, (b) latent variables, (c) accounting for measurement error or unreliability
  • Thinking: Equations -> Path diagram -> estimate, test, visualize

Lecture 2: Measurement models & CFA


  • Effects of measurement error
  • Testing equivalence of measures with CFA
  • Multi-factor, higher-order models
  • Multi-group models: Factorial invariance


  • CFA in lavaan. A nice tutorial on fitting CFA models using lavaan. It uses a larger version of the Holzinger-Swineford (1939) data used in the exercise and discusses goodness-of-fit measures, model comparison, and R tools to get nice output for write-ups.

Lecture 3: SEM with latent variables & other topics


  • The full SEM model
  • Longitudinal data
  • Power & sample size
  • SEM extensions

Copyright © 2019 Michael Friendly. All rights reserved.

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