# Structural Equation Modeling (2020)

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 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 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

## Week 4 – Network models and temporal effects

• Exercises
• Lecture 4-1: The Network Perspective
• Lecture 4-2: Undirected Network Models
• Lecture 4-3: SEMs and GGMs
• Lecture 4-4: Panel data
• Lecture 4-5: Time-series data