Structural equation modeling (SEM) is a technique to test hypothesized models with observed and latent variables. It encompasses many techniques, such as linear regression, multivariate regression, and factor analysis as special cases. This course provides a practical introduction of structural equation modeling using the open source R statistical platform.
- Introduction to structural equation modeling with R
- Special cases of SEM: regression, path model, confirmatory factor analysis
- Model specification
- Model evaluation
- Using the lavaan package in R
- Testing measurement invariance with multiple group analysis
- Latent growth model
- Testing mediation effect with bootstrapping
- Testing moderation effect with latent variables
- Handling missing data
- Handling non-normal data robust statistics
- Handling binary and ordinal variables
Who Should Attend
This course is designed for individuals with knowledge in regression and would like to extend it to handle more complex research questions involving observed and latent variables.
Participants are expected to have basic knowledge in regression analysis. Knowledge in R is not required. We will introduce R in the workshop.