2017 Stata Winter School:

Introduction to Structural Equation
Modeling Using Stata

Taught by Paul Allison, Ph.D.
February 23-24, Hotel Birger Jarl Conference
Stockholm, Sweden 

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.


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. 


This seminar will use Stata for all the examples. Participants are encouraged to bring their own laptop computers with a recent version of Stata installed.  If you do not currently have Stata, we can provide a temporary license for Stata 13 which you can download and install before coming to the course. Power outlets will be available at each seat.


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. 


 Please go to the Metrika website for information on registration, and discounted hotel accommodations.


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


“This course provided a terrific overview of SEM, building from foundational concepts of path analysis and measurement to a wide variety of useful techniques. I found the course to be a highly efficient and effective way to refresh (and refine) my knowledge of SEM. I now feel much more confident applying SEM in my own research.”
  Michael Broda, Virginia Commonwealth University

“Dr. Allison clearly has depth and breadth of knowledge of SEM. I have a better understanding of SEM, the use of MPlus and STATA, and the statistical theory behind SEM as a result of the course. It was especially helpful in learning code or Stata to run SEM analyses. Dr. Allison is a rock star statistician!”
  Julia Phillips, Cleveland State University

“This course provided excellent guidance for me as I conceptualized my dissertation analyses. I feel prepared to answer new research questions using rigorous methodological approaches.
  Carolyn Bates, Loyola University Chicago

“This course provided an excellent way to learn how to use SEM to answer a large variety of questions. I came to the course with little to no previous knowledge of SEM and am leaving feeling that I am capable of running SEM analyses on my own data. I found the exercises particularly useful for solidifying the lesson material. As someone without much coding experience, I found that the slides with the codes for the different programs were well explained and therefore, easy to follow. I would definitely recommend this course to someone looking to learn SEM.”
  Moriah Sokolowski, University of Western Ontario

“This was a fantastic course – Professor Allison was knowledgeable and extremely clear when discussing the concepts underlying SEM. He patiently answered questions, which helped our understanding. The exercises allowed us to practice using the various software packages.”
  Cynthia Wang, Oklahoma State University

“Traveling from Senegal to Chicago is a long way. The contents of this training together with the way it is taught fully match my expectations when registering for this training. Professor Allison is a great and tireless teacher. I do feel privileged and lucky to benefit from this SEM course and wish success to Statistical Horizons in its efforts to skill researchers.”
  Oswald Koussihouede, CONFEMEN

“It’s rare to find a short course at the right level and of the right depth for those new to a concept and those more experienced alike. This course was just that – palatable for someone new to SEM methods (like me) and with enough depth offered for more experienced in Stata. Dr. Allison is a savvy and experienced teacher with a great wealth of knowledge. 
  Lorraine Dean, Johns Hopkins Bloomberg School of Public Health

“Beautifully organized. I have taken two courses taught by Dr. Allison and I am always impressed by how easily he explains a lot of complex stat concepts. That tells me he is an excellent instructor from experience. Highly recommend!”
  SeungYong Han, Arizona State University

“This is a very practical course. There are lots of detailed examples with ample time given to specifics of program code, interpreting output and testing assumptions. The course is ideal for professionals who are not professional statisticians, but who nevertheless need and would benefit from a practical and detailed introduction to SEM.”
  Ian Lyons, The University of Western Ontario

“I had a semester-long course in SEM eight years ago but have not used SEM a great deal since then. This was a great course that provided more up-to-date thinking on methods and analysis as well as an understanding of current software. I would highly recommend this course as an introduction to SEM or as a comprehensive refresher.”
  Cindy Guthrie, Bucknell University 

“I have been trying to teach myself SEM for years, but failed to find an effective and efficient way to do it. Finally your seminar helped me to connect all the SEM dots in my mind and even much more! 
  Yue Yin, UIC College of Education