Structural Equation Models

About

This is an interactive course on data analysis focused on the application of multivariate analysis with latent variables using structural equation models (SEMs). These models can be used to explore research questions involving complex relationships among many variables (specifically multiple outcome variables) and these variables may be observed or latent.

Topics include:

- Introduction to and History of Structural Equation Models.
- Model Notation
- Path Tracing Rules and Covariance Algebra
- Total, Direct, and Indirect Effects
- Identification and Estimation with Observed Variables
- Understanding Measurement Error, Validity, and Reliability
- Confirmatory Factor Analysis
- Identification, Estimation, and Evaluation with Latent Variables
- Accounting for Missing Data
- Mean Structures and Latent Means Models
- Categorical Data and Multiple Group Models

This course will be helpful for researchers in any field—including psychology, sociology, education, business, human development, political science, public health, communication—and others who want to learn how to apply the latest methods in latent variable and structural equation models using readily-available software in R.

Course leader

Amanda Montoya

Target group

Master | PhD | Postdoc | Professional

Fee info