Free Introduction to Structural Equation Modeling

Our motivating goal is to provide an interactive and engaging instructional experience. We strive to strike an equal balance between core concepts of the underlying statistical model along with the practical application and interpretation of structural equation models fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of structural equation models and to be able to thoughtfully apply a variety these models to their own data.

This workshop is broadly targeted towards research applications in behavioral, health, educational and psychological sciences, although the methods apply to many other disciplines as well.

We recommend that participants have a working knowledge of the multiple regression model. Those who need a refresher may wish to check out episodes on our linear regression playlist on YouTube.

Additionally, like most multivariate statistical models, structural equation models are expressed using vectors and matrices, so some familiarity with linear algebra can be helpful (see our free Matrix Algebra review). Knowledge of linear algebra is not, however, required to fully benefit from the class.

Chapter 1. Introduction, Background, & Multiple Regression
1.1 Introduction
1.2 Linear Regression as a Structural Equation Model
1.3 Limitations of the Multiple Regression Model

Chapter 2. Path Analysis: Introduction
2.1 The Path Analysis Model
2.2 Model Identification
2.3 Model Estimation

Chapter 3. Path Analysis: Advanced Topics
3.1 Assessing Model Fit
3.2 Model Comparisons
3.3 Model Respecification and Modification Indices
3.4 Testing Direct and Indirect Effects
3.5 Assumptions (Self-Study)

Chapter 4. Confirmatory Factor Analysis
4.1 Confirmatory Factor Analysis
4.2 Issues and Extensions (Self-Study)

Chapter 5. Structural Equation Models with Latent Variables
5.1 Introduction to Structural Equation Models
5.2 Fitting and Evaluating Structural Equation Models
5.3 Additional Considerations: Estimation with Non-normal Distributions, Computing Power, and Equivalent Models (Self-Study)

Notes and video recordings of software demonstrations are provided for self-paced learning using R (focused on the lavaan package), Mplus and Stata. Note that R can be downloaded for free.

Lectures are software independent and focus on the conceptualization, specification, and interpretation of the models. Thus, while it is helpful to have some familiarity with one or more of these software programs, this is not necessary to benefit from the course.

Participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates. Pretty awesome, huh?

Content for this class is evergreen, meaning participants will always have access to the most current version of the course materials. Thus, if we replace a lecture recording or software demonstration, or update a syntax file, you will have immediate access to the updated content without needing to re-enroll in the class. Even more awesome.