Structural Equation Modeling: Part 1- Remote
A 4-day Remote Seminar Taught by Paul D. Allison, Ph.D.
To see a sample of the course materials, click here.
This seminar is currently sold out. Email firstname.lastname@example.org to be added to the waitlist.
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 the basics of SEM from a master teacher, Professor Paul D. Allison, in just four days. This is an introductory course, and no previous knowledge of SEM is presumed.
Starting August 11, we are offering this seminar as a 4-day synchronous*, remote workshop. 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 your 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.
This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Participants will receive an email with information about joining the Zoom sessions prior to the course.
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 bring your own laptop 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.
- Introduction to SEM
- Linear regression with missing data
- Path analysis of observed variables
- Direct and indirect effects
- Bootstrapping in SEM
- Reliability: parallel and tau-equivalent measures
- Multiple indicators of latent variables
- Heywood problems
- Exploratory factor analysis
- Confirmatory factor analysis
- Goodness of fit measures
- Structural relations among latent variables
“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, ICF
“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
“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, with excellent time control. The training venue is facilitated to our learning.”
Jessica Li, The Polytechnic University of Hong Kong
“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
“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, Westat
“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
“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 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 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