Introduction to Structural Equation Modeling

A 2-day seminar taught by Paul D. Allison, Ph.D.

Read reviews of this seminar

To see a sample of the course materials, click here.

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 two days.


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.


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 class will meet from 9 am to 5 pm each day with a 1-hour lunch break at the Hotel BLU Vancouver, 177 Robson St, Vancouver, BC V6B 2A8, Canada.

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 USD includes all course materials.

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50 USD). 

Lodging Reservation Instructions 

A block of guest rooms has been reserved at the Hotel BLU Vancouver, 177 Robson St, Vancouver, BC V6B 2A8, Canada, where the seminar takes place, at a special rate of $299 CAD per night. In order to make reservations, call 778-945-1933 or email and identify yourself as part of the Statistical Horizons LLC group. For guaranteed rate and availability, you must reserve your room no later than Monday, July 15, 2019.

We also recommend going directly to the hotel’s website or checking other online hotel sites. Pricing varies and you may be able to secure a better rate. 

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 is the perfect introductory level course for individuals who are interested in learning SEM. The course took you into just the right amount of depth without being too overwhelmingly complicated. I would recommend this to anyone interested in learning SEM without prior experience in it.”
  Danielle Sullivan, Boston University

“This course was very informative and allowed for practical applications to my data. I previously attended an LCA course and was pleased with what I learned, which is why I decided to return.”
  Ashley Hill, Texas A&M University

“Well developed and presented course. I have taken several courses from Paul and found them all to be excellent.”
  Barbara Lamberton, University of Hartford

“Coming from a position with very little background in SEM, I found this course insightful and practical. I enjoyed the real-world advice that Dr. Allison offered.” 
  Casey Tak, University of Utah

“This course was very helpful to me. I have a better understanding of SEM. I especially can appreciate the different programs that are available. Thanks for providing great slides and allowing for hands-on activity.”
  Marcia Lowe, University of Alabama School of Nursing

“As usual, Paul Allison doesn’t disappoint. He is extremely didactic and offers great in-depth explanations to the subject matter deeply rooted in statistical concepts. His courses are a must for researchers across many fields who value statistical methods.”
  Grettel Castro, Florida International University

“As a beginning in SEM, I found this course to be well paced. Dr. Allison’s teaching style is well suited for beginners. Strengths of the course include discussion of software input and output, step-by-step interpretation of results, and the comprehensive nature of the workshop. The notes are also fantastic! I highly recommend.”
  Amoha Bajaj, University of Pittsburgh

“Excellent course, excellent professor. SEM is explained very clearly with examples and the commands needed combined with the ability to summarize a lot of information in a very didactic way. I highly recommend this course.” 
  Pura Rodriguez de la Vega, Florida International University