Introduction to Structural Equation Modeling

A two-day seminar taught by Paul D. Allison, Ph.D.

Registration ends Tuesday, August 2

Read reviews of this seminar

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 complex causal theories with multiple pathways.
  • 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).
  • Analyze longitudinal data.
  • Estimate fixed and random effects models in a comprehensive framework.
  • Adjust for measurement error in predictor variables.

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 two days.


The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be demonstrated 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. Although not required, you are encouraged to bring your own laptop (loaded with SAS, Stata, Mplus or the Mplus demo) and do the optional exercises.


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. 

Location, format, materials.

The seminar meets Monday, August 8 and Tuesday, August 9 from 9 to 4 each day with a 1-hour lunch break at the Ohio State University, School of Communication, 216 Journalism Building, 242 W 18th Avenue, Columbus, Ohio 43210.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 

Registration and Lodging

The fee of $995 includes all course materials. 


For a list of hotels that are close to campus, click here.

Course Outline

1. Introduction to SEM
2. Path analysis
3. Direct and indirect effects
4. Identification problem in nonrecursive models
5. Reliability and validity
6. Multiple indicators of latent variables
7. Exploratory factor analysis
8. Confirmatory factor analysis
9. Goodness of fit measures
10. Structural relations among latent variables
11. Alternative estimation methods.
12. Multiple group analysis
13. Models for ordinal and nominal data

Comments from Recent participants

“This course provided a richly detailed and in-depth introduction to SEM. The course worked through examples in very good detail, and Dr. Allison was very adept at answering questions and providing an exceptionally detailed overview of SEM procedures. Be prepared to be challenged and a bit exhausted but also galvanized to use these procedures with your own data.”
  Jonathan Mattanah, Towson University 

“The course is structured well, it’s a good pace for someone with no SEM experience or background, and the information is presented in a very accessible way. I liked the multiple examples with specific data to illustrate more complex ideas. The course is very practical and I feel ready to apply SEM after only 2 days. The additional resources and references mentioned during the course will help me to dig into the SEM detail particularly relevant to my work on my own.”
  Lilia Bliznashka, International Food Policy Research Institute 

“This course was accessible for me with limited experience in SEM and no prior experience in MPlus. The pace was excellent with lots of breaks to digest the information. Also very helpful book and codes to make sure we can bring what we learned here in class back home and implement it.”
  Carmen Logie, University of Toronto 

“This course provided a good overview of SEM. I left it feeling more confident than when I arrived.”
  Michael Miner, University of Minnesota 

“This course is extremely useful for those who are beginners in the SEM field. The course is very well structured, allows “digestion” of the materials and has practical applications.”  

“Dr. Allison is very knowledgeable and knows how to convey even very complicated concepts such as SEM. He made it look very easy to me. I got many of my questions answered. Thank you.”
  Maryam Ghobadzadeh, University of Minnesota 

“A very nice overview of SEM. But the course also gave me additional understandings of regression, measurement error and other important topics. I feel more comfortable with methods in general now than I felt before this course.”
  Johan Westerman, Stockholm University 

“This course was excellent. Very clear instruction, explanations, and materials. Definitely give one the background and tools needed to begin exploring SEM.”
  Sydney Martinez, University of Oklahoma Health Sciences Center