This website contains materials for the research master psychology courses Structural Equation Modeling 1: Confirmatory Factor Analysis and Structural Equation Modeling 2: Structural Equation Modeling, which I teach at the University of Amsterdam.
SEM 1: Confirmatory Factor Analysis
Week 0 – Statistics recap
Week 1 – Common Cause modeling
- Exercises
- Lecture 1-1: Course introduction
- Lecture 1-2: Measurement and CFA
- Lecture 1-3: Maximum likelihood fit function
- 2019 summary video
Week 2 – Fitting CFA models
Special thanks to Mijke Rhemtulla for some of the slides used this week!
- Exercises
- Lecture 2-1: Testing for exact fit
- Lecture 2-2: Fit indices
- Lecture 2-3: Sample size
- Lecture 2-4: Model Comparison
- Lecture 2-5: Software overview
- 2019 CFA software video series
- 2019 code examples
- 2019 summary video
Week 3 – Latent growth models and measurement invariance
- Exercises
- Lecture 3-1: Introduction
- Lecture 3-2: Mean structure
- Lecture 3-3: Latent growth
- Lecture 3-4: Multi-group CFA
- Lecture 3-5: Measurement invariance
- Lecture 3-6: Homogeneity
- Software videos and code
- 2019 video summary
Week 4 – Advanced CFA topics
- Lecture 4-1: Ordered and categorical data
- Lecture 4-2: Missing data
- Lecture 4-3: Assumptions
- Video
- Slides available in missing data lecture
- Lecture 4-4: Higher-order & bifactor models
- Lecture 4-5: Exploratory factor/graph analysis
- 2019 video Summary
- 2019 exercises
SEM 2: Structural Equation Modeling
Week 1 – Expectation and covariance algebra
- Introduction to SEM 2 video
- Video lecture on expectation and covariance algebra
- Practical exercises
- Practical solutions
- Blog on casino games
- Exercises
Week 2 – Structural Equation Modeling
- Exercises
- Lecture 2-1: Causal modeling
- Lecture 2-2: Path analysis
- Lecture 2-2: Path analysis
Week 3 – Causality & Equivalent models
- Exercises
- Lecture 3-1: Causality & DAGs
- Lecture 3-2: Partial Covariance
- Lecture 3-3: Conditional Expectation
- Lecture 3-4: Equivalent Models