Structural Equation Modeling: Part 1
An On-Demand Seminar Taught by
Paul D. Allison, Ph.D.
To see a sample of the course slides, click here.
This seminar is Part 1 of a two-part sequence on SEM. Part 2 covers more advanced SEM topics, like instrumental variables, alternative estimation methods, multiple group models, models for binary and ordinal data, and models for longitudinal data. You can learn more about Part 2 by viewing the page for our fall 2020 offering. Part 2 will be offered again in 2021, but has not yet been scheduled.
For the last several years, Dr. Paul Allison has been teaching his acclaimed two- and five-day seminars on Structural Equation Modeling to audiences around the world. This seminar covers structural equation modeling (SEM) basics. It is is an introductory course, and no previous knowledge of SEM is presumed.
The course takes place in a series of four weekly installments of videos, quizzes, readings, and assignments, and requires about 6-8 hours/week. You can participate at your own convenience; there are no set times when you are required to be online. The course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 12 modules:
- Introduction and linear regression
- FIML for missing data, path analysis
- Direct and indirect effects
- Indirect effects
- Bootstrapping and partial correlations
- Latent variable models and classical test theory
- Parallel, tau-equivalent and congeneric measures
- Confirmatory factor analysis
- Maximum likelihood estimation
- Goodness of fit
- Modification indices, correlated errors, the general structural equation model
- Concluding thoughts and advice
The modules contain videos of the 4-day remote version of the course in its entirety. Each module is followed by a short multiple-choice quiz to test your knowledge. There are also weekly exercises that ask you to apply what you’ve learned to a real data set.
Each week, there are 2-3 assigned articles to read. There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Allison.
Downloadable course materials include the following pdf files:
- All slides displayed in the videos.
- Exercises for each week.
- Readings for each week.
- Computer code for all exercises (in Mplus, SAS, Stata, and R formats).
- A certificate of completion.
MORE DETAILS ABOUT THE COURSE CONTENT
Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences. First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows for latent variables with multiple indicators. On the econometric side, SEM allows for multiple equations, possibly with feedback loops. In today’s SEM software, the models are so general that they encompass most of the statistical methods that are currently used in the social and behavioral sciences.
Here Are a Few Things You Can Do With Structural Equation Modeling
- Test the implications of causal theories.
- Estimate simultaneous equations with reciprocal effects.
- Incorporate latent variables with multiple indicators.
- Investigate mediation and moderation in a systematic way.
- Handle missing data by maximum likelihood (better than
- Adjust for measurement error in predictor variables.
- Estimate and compare models across multiple groups of individuals.
- Represent causal theories with rigorous diagrams.
- Investigate the properties of multiple-item scales.
Because SEM is such a complex and wide-ranging methodology, learning how to use it can take a substantial investment of time and effort. Now, you have the opportunity to learn SEM from a master teacher, Professor Paul D. Allison, in just four weeks.
The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be presented for SAS, Stata and lavaan (a new package for R). Mplus is one of the best SEM packages because of its superior capabilities for missing data, multi-level modeling, and ordinal and categorical data.
To fully benefit from the course, you should use your own computer loaded with a recent version of SAS, Stata, Mplus or R (with the lavaan package installed). Whichever package you choose, you should already be familiar with basic data management operations and the commands/procedures for doing linear regression, logistic regression, etc.
WHO SHOULD Register?
This course is designed for researchers with a moderate statistical background who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But participants should have a good 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.
REVIEWS OF STRUCTURAL EQUATION MODELING
“I left this course feeling like I had a good grasp on these methods for the first time, and feeling empowered to apply them in a practical setting with real-world data.”
Lauren Wilson, Duke University
“SEM has come up a lot in my current line of work at University of Michigan. Since I had never taken a formal training on this, I was very unclear on the concepts. This course emphasizes simple examples to explain new concepts. It develops concepts very systematically. At the end of this course, I have a very good idea about path analysis, latent factor models, and confirmatory factor models. I feel confident that I can draw a conceptual diagram of a theoretical model and implement it in a statistical program. Thank you so much!”
Bidisha Ghosh, University of Michigan
“This course provided a great conceptual and technical introduction to SEM. It packed a ton of great information into 2 days, even allowing time for application exercises. The instructor is an expert in SEM and it showed! Plus he provided clear explanations and showed great patience in answering all our questions. I feel I can now go back to my job and apply what I’ve learned and extend my SEM skills, now that I have a solid foundation.”
Elizabeth Tarlov, Edward Hines, Jr. VA Hospital
“This course is immediately applicable to my own work and work with students. Dr. Allison is available for detailed explanations. There was a clear integration of theoretical and statistical concepts.”
Paul Harrell, Eastern Virginia Medical School
“I learned a lot about SEM on principle and examples which will guide me to solve real problems in my work.”
You Li, University of Massachusetts
“This was a great course and covered a number of topics in depth. I signed up to learn more about how SEMs could be used to test mediation models but the section on FIML was interesting and I will try it out on my next missing data problem.”
Laura Pyle, University of Colorado