Introduction to Structural Equation Modeling - Online Course

This seminar provides an intensive introduction to the fundamentals of Structural Equation Modeling (SEM). The course will be roughly divided into four parts.

In Part 1, we will begin with a brief introduction to the SEM framework and what distinguishes SEM from other approaches to statistical analysis. We will then consider maximum likelihood estimation of the simple linear regression model within an SEM framework with both complete and missing data.

In Part 2, we will consider models with multiple, simultaneous equations. We will first explore simple path analysis, including the estimation of direct and indirect effects. We will then consider issues of identification that arise with more complex models, such as those with reciprocal effects.

In Part 3, we will turn to issues of measurement. We will begin by considering the concepts of reliability and validity and the advantages of multiple indicators when measuring a latent construct (e.g., capacity to specify and attenuate measurement error). We will then consider confirmatory factor analysis (CFA), including estimation, interpretation, and assessment.

In Part 4, we will explore a few advanced topics. First, we will integrate the measurement models of Part 3 with the structural models of Part 2 within a general SEM approach. We will then consider alternative methods of estimating these models beyond maximum likelihood. Finally, we will look at estimation with categorical observed variables.

Here are some of the things you will be able to do by the end of this course:

• • Handle missing data using full information maximum likelihood.
  • Investigate mediation in SEM models.
  • Investigate the properties of multi-item scales with confirmatory factor analyses (CFA).
  • Incorporate latent variables into your regression models.
  • Use different SEM estimation methods to address continuous and categorical observed variables.

There are several excellent software packages available for Structural Equation Modeling. This seminar will primarily use the freely-available R software and the R package ‘lavaan’ for in-class examples and exercises. No previous experience with R is needed for this course, as all code will be provided. While the focus will be on R, code will also be provided in Stata and Mplus.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.

This course is designed for researchers who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But participants should have a working knowledge of basic principles of statistical inference (e.g., standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the basic theory and practice of linear regression.

1. Introduction to SEM
2. Linear regression with complete and missing data
3. Path analysis
4. Direct and indirect effects
5. Issues of identification
6. Reliability and validity
7. Confirmatory factor analysis
8. Assessing goodness of fit
9. The general SEM model
10. Estimation beyond maximum likelihood
11. Models for categorical observed variables

The fee of $995 includes all course materials.

PayPal and all major credit cards are accepted.

Our Tax ID number is 26-4576270.