Introduction to Structural Equation Modeling

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

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 Friday, November 6 and Saturday, November 7 from 9 to 4 each day with a 1-hour lunch break at the Courtyard Washington Embassy Row,1600 Rhode Island Avenue, NW, Washington, DC  20036, 
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. 

Lodging Reservation Instructions
Blocks of rooms have been reserved at the hotels below.

Courtyard Washington Embassy Row,1600 Rhode Island Avenue, NW, Washington, DC  20036 with a special rate of $289 per night.  In order to make your reservations call 888-236-2427 or 202-448-8004 and identify yourself with Statistical Horizons. The room block will expire when it is full or on Monday, October 5.

Club Quarters, 839 17th Street NW, Washington, DC 20006 with a special rate of $224 for a Standard Room. In order to make your reservation, use this link or call 203-905-2100 during business hours and identify yourself using the group code STA115. The room block will expire when full or on Monday, October 5.

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 was a clear, comprehensive and well organized introduction to SEM. Unlike other courses that say no prior SEM knowledge needed, for this one it is actually true!! The instructor, Paul Allison, makes the material so accessible that you go from zero to hero quickly.”
  April Taylor, California State University

“Coverage of complex material was clear and well-paced. Dr. Allison’s expertise across a variety of SEM packages is impressive, making the course accessible to a wide range of researchers.”
  Paul Glavin, McMaster University

“The course materials are well organized. The lecture is clear and concise. I would highly recommend this course to some of the more advanced graduate students.”
  Philip Ender, UCLA

“University-offered SEM courses are usually much more theory-oriented with little practical advice about fitting these models in various software packages, which often have syntax different from standard statistics software.”
  Andy Lin, UCLA

“This course is comprehensive in that it covers a lot of the materials you are likely to encounter in working with SEM. Dr. Allison is a very good instructor – slow paced and receptive to questions throughout the course. Overall, though, it’s an excellent exposé on the topic. Thank you Dr. Allison!”
  Karabi Nandy, UCLA

“I was having a hard time in understanding SEM. After this course, I feel much more prepared and can start using this tool.”
  Richardo Alexandre de Souza, Federal University of São João del-Rei

“This course was an excellent introduction to SEM both from a theoretical and practical perspective. The instructor is very good and the course content covered many different aspects of SEM in a very efficient way.”
  Gabriel Gazzoli

“This course offers a very clear and step-by-step introduction to beginners of SEM. I strongly recommend researchers/graduate students, who are interested in SEM, to take this course to save a lot of time and energy from unnecessary mistakes and to gain a solid basic knowledge.”
  Matt Chang, South Dakota State University

“This course provides just the right level of detail and specific examples to get a new user of SEM started.”
  Ann Weber, Stanford University

“This course is extremely well-suited for graduate students who are interested in learning the basics of SEM with a focus on the application of this knowledge.”
  Patricia Moreno, UCLA

“This class offers a clear, concise & concrete introduction into SEM. It provides a treatment of the complexities without being overwhelming to those without much background in SEM. Additionally, Paul Allison goes into an excellent discussion of parallel, tau-equivalent & congeneric models, which for even more seasoned researchers, provides clarity to many of the defaults provided in the major statistical packages.”
  Joni L. Ricks, UCLA IDRE/OIT

“This is an excellent refresher course for those of us who have not done SEM for a while. Very good overview and nicely articulated and executed. Dr. Allison is a great lecturer who is able to teach the complex topics in a simple and understandable manner. Highly recommended.”
  Weiqiu Yu, University of New Brunswick 

“The workshop was very informative. The instructor was well versed in the theory and the application of SEM and explained key information in a clear and succinct manner.”
  Luci Martin, University of LaVerne