Introduction to Structural Equation Modeling

Structural Equation Modeling (SEM) is a general statistical modeling technique to establish relationships among variables. A key feature of SEM is that observed variables are understood to represent a small number of "latent constructs" that cannot be directly measured, only inferred from the observed measured variables. This course covers the theory of SEM, and includes practical work with computer software and real data. It covers the key concepts in SEM - at the conclusion of the course students will be able to specify different forms of models, using observed, latent, dependent and independent variables. Student will be able to conduct confirmatory factor analysis, and diagram SEM models.

: Preliminaries

• LISREL software installation
• PRELIS
• Data entry and Data Edit issues
• Correlation and Covariance Data Files

: Modeling

• SEM Basics
• Regression models
• Diagramming Models
• Path Analysis Models

: Measurement Models

• Exploratory vs. Confirmatory factor analysis
• Latent Variables
• CFA models

: Developing Structural Equation Models

• Combining Path and Factor Models
• 5 Basic SEM steps
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Model Modification