Skip to content

Structural Equation Modeling: Part 2 - Online Course

A 4-day Livestream Seminar Taught by

Paul Allison
Course Dates: Ask about upcoming dates
Schedule: All sessions are held live via Zoom. All times are ET (New York time).

11:00am-2:00pm EDT: Live lecture via Zoom
After 2:00pm EDT: Exercise assignment to be completed on one’s own
7:00pm-8:00pm EDT: Live “office hour” via Zoom to review exercises and ask questions

Since 2015, hundreds of researchers have taken Paul Allison’s annual 5-day summer course on Structural Equation Modeling. This summer we are doing things a little differently. The course has been divided into two parts, and each part will be taught remotely (via Zoom) over a four-day period. Part 1 (July 7-10) covers the basics and is designed to get you up and running with SEM. This is an introductory course, and no previous knowledge of SEM is presumed.

Part 2 (July 14-17) covers more advanced topics, like instrumental variables, alternative estimation methods, multiple group models, models for binary and ordinal data, models for longitudinal data, and much more. To take Part 2, you should already have some knowledge of SEM, ideally by taking Part 1.

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
    multiple imputation).
  • 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 the basics of SEM from a master teacher, Professor Paul D. Allison, in just four days.

Starting July 14, we are offering this seminar as a 4-day synchronous*, remote workshop for the first time. Each day will consist of a 3-hour, live morning lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time. Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on one’s own that afternoon. A final session will be held each evening as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously.


Who should register?

Seminar outline

Payment information

“I highly recommend this course..."

“I highly recommend this course to everyone who has a basic statistical background and would like to learn a new technique of data analysis. The course is well structured and well-paced. It is also quite comprehensive, as a lot of material (different kinds of models, etc.) is covered in a rather short amount of time. It’s very hands-on, as one can apply newly learned concepts in exercises and is able to ask questions as well as receive feedback. Also, course reviews how to perform all analyses in different software and which ones are better for certain analyses. Overall, this is a great introductory course on SEM, very comprehensive. I highly recommend.”

Anna Sheremenko


"After this course, I feel more confident in handling SEM using different statistical softwares.”

“Professor Paul Allison is a wonderful teacher of SEM. It is my first time learning SEM and this course provides a full coverage of SEM topics. After this course, I feel more confident in handling SEM using different statistical softwares.”

Nicole W.T. Cheung

The Chinese University of Hong Kong

"I would highly recommend this course for beginners and advanced researchers..."

“The course, Structural Equation Modeling, offers good insight into the topic by displaying examples in statistical programs such as Mplus, Lavaan, Stata, and SAS. Before the start of the course the participants were questioned about which program they use so that the professor can adapt the use of the program to the individual class needs. Furthermore, all participants were free to ask questions during the class and breaks. Additional practice exercises for the specific programs with results were given. I would highly recommend this course for beginners and advanced researchers to increase their knowledge on SEM and related statistical methods.”

Franziska Safar

University of Trier

“I’m deeply thankful to Dr. Allison for his precise and concise presentation..."

“I’m deeply thankful to Dr. Allison for his precise and concise presentation, with excellent time control. The training venue is facilitated to our learning.”

Jessica Li

The Polytechnic University of Hong Kong

"SEM is now a very clear method that I am going to use in my future research.”

“I came here with zero knowledge about SEM. Throughout the course, Dr. Allison not only gave me a clear picture of how SEM functions but also improved my knowledge on other conventional methods and statistics. SEM is now a very clear method that I am going to use in my future research.”

Nguyen Nguyen

Auburn University

"I certainly recommend this course.”

“The course content was very comprehensive, and Dr. Allison is an excellent instructor. I certainly recommend this course.”

Mariana Toniolo Barrios

Simon Fraser University

“The strength of this course is its focus on the practical implementation of structural equation modeling, particularly how to use it in statistical software packages. Dr. Allison is extremely knowledgeable in each and every program and provides excellent teaching and exercises to help you improve your own skills and understanding. Knowing how to use these techniques in software packages reinforces and strengthens your total understanding of the material.”

Kevin Baier


"... a highly effective instructor."

“Professor Allison is not only an extremely knowledgeable scholar on the subject matter, but also a highly effective instructor. He makes advanced data analysis and statistics easier for students of all levels to learn and master. I benefitted from his course on SEM tremendously and am actually looking forward to using SEM to answer some research questions that I previously couldn’t due to lack of confidence (even after taking 1 grad course on SEM and spending many hours self-learning). Thank you, Professor Allison.”

Yuning Wu

Wayne State University

"... Dr. Allison welcomed all questions."

“During this course was my first time using Mplus. I got a lot of practice and feel somewhat comfortable using it. It was also great that Dr. Allison welcomed all questions. Finally, the detailed notes and slides are great.”



"... the exercises and activities allow opportunities to practice the techniques in class..."

“The course is taught so that people at various levels of understanding are able to understand the concepts. Furthermore, the exercises and activities allow opportunities to practice the techniques in class and after class. The course is also structured so that you can receive individual feedback if you desire.”

Juan Barthelemy

University of Houston

"I am confident to say I have learned a lot in a short space of time."

“I came into the program with bare minimum knowledge of structural equation modeling. At the end of the course, however, I am confident to say I have learned a lot in a short space of time. I am also excited now about using structural equation modeling and so it is definitely going to be an integral part of my research methods toolkit.”

Uchechi Anaduaka

Lingnan University