Structural Equation Modeling

A 5-Day Seminar Taught by Paul Allison, Ph.D.

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

This course is currently full. If you would like to be added to the waitlist, please send us an email at

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.

This 5-day seminar assumes no previous knowledge of SEM, and covers almost a full semester’s worth of material. It is designed to make you a knowledgeable, effective and confident user of methods for structural equation modeling. Each day will include 1 to 2 hours of supervised, practical exercises that will help you achieve mastery of these methods.

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.

How This Seminar Differs From Paul Allison’s 2-Day Seminar “Introduction To Structural Equation Modeling”

This course includes all the material in the 2-day seminar, but in more detail, especially regarding models for categorical data. It also covers many other topics such as missing data, bootstrapping, formative indicators, interactions, and longitudinal data analysis. Lastly, much more time is devoted to exercises.


This is a hands-on course with lots of opportunities to practice SEM. The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will also be presented for SAS, Stata and lavaan (a new package for R). Mplus is our preferred SEM package 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 attend? 

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 you 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. 

For the later parts of the course, it is also desirable to be familiar with logistic regression (binary, ordinal, or nominal). To do the hands-on exercises, it is essential that you already be comfortable working with one of the four packages that will be covered in the seminar.

Location and Materials 

The course meets 9 a.m. to 5 p.m. on Monday, July 16 through Friday, July 20 at The Gleacher Center, The University of Chicago Booth School of Business, 450 North Cityfront Plaza Drive, Chicago, IL 60611.  

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 $1795.00 includes all seminar materials. The early registration fee of $1595.00 is available until June 18. 

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). 

Lodging Reservation Instructions

Room blocks have been reserved at the following nearby hotels. Due to several city-wide conferences taking place during this seminar, hotel availability is extremely limited. We recommend making travel arrangements as early as possible. Once the room block deadlines pass, we will be unable to assist with securing lodging. 

Embassy Suites, 511 N Columbus Drive, Chicago, IL 60611. Make a reservation online by clicking here or call 800-525-2509 before Friday, June 15 to reserve a room for a rate of $309 per night. Approximately 0.2 miles from the Gleacher Center.

Club Quarters, 75 E Upper Wacker Dr, Chicago, IL 60601. Make a reservation online by clicking here or call 203-905-2100 and reference group code STA715 before Monday, June 18 to reserve a room for a rate of $207 per night. Approximately 0.4 miles from the Gleacher Center.

Fairfield Inn & Suites, 216 E Ontario Street, Chicago, IL 60611. Make a reservation online by clicking here or call 855-476-6661 and reference Statistical Horizons before Monday, April 16 to reserve a room for a rate of $249 per night. Approximately 0.4 miles from the Gleacher Center.

Courtyard, 165 E Ontario, Chicago IL 60611. Make a reservation online by clicking here before Monday, June 25 to reserve a room for a rate of $299 per night. Approximately 0.4 miles from the Gleacher Center.

Westin, 320 N Dearborn St, Chicago, IL 60654. Make a reservation online by clicking here or call 888-627-8359 and reference Statistical Horizons before Friday, June 15 at 5 p.m. CST to reserve a room for a rate of $269 per night. Approximately 0.6 miles from the Gleacher Center.

Acme Hotel, 15 E Ohio Street, Chicago, IL 60611. Make a reservation by calling 312-894-0800 and reference Statistical Horizons before Friday, June 15 at 4 p.m. CST to reserve a room for a rate of $199 per night. Approximately 0.5 miles from the Gleacher Center.

Other nearby hotel options include:

Sheraton Grand Chicago, 301 E North Water St, Chicago, IL 60611
DoubleTree, 300 E Ohio St, Chicago, IL 60611
Inn of Chicago, 162 E Ohio St, Chicago, IL 60611
Comfort Suites, 320 N Michigan Ave, Chicago, IL 60601
Cambria Hotel & Suites, 32 W Randolph St., Chicago, IL, 60601
Hotel EMC2, 228 E Ontario St, Chicago, IL 60611

Room blocks are not available at these hotels, so reservations should be made directly or via a travel website.

Preliminary Seminar outline

1.Introduction to SEM
2. Path analysis
3. Direct and indirect effects
4. Bootstrapping in SEM
5. Nonrecursive models
6. Instrumental variables
7. Reliability and validity
8. Multiple indicators of latent variables
9. Exploratory factor analysis
10. Confirmatory factor analysis
11. Goodness of fit measures
12. Structural relations among latent variables
13. Formative indicators
14. Alternative estimation methods.
15. Missing data
16. Multiple group analysis
17. Interactions with latent variables
18. Models for ordinal and nominal data
19. Models for censored and event-time data
20. Models for longitudinal data

Comments From Recent Participants

“Paul Allison’s seminar on structural equation modeling was a great course to attend for those who want to really learn the fundamentals of SEM. In addition, the exercises enhanced the understanding of the different models and practical use. Thank you.”
  Bernice Gulek, Washington State University

“Dr. Allison is very knowledgeable and explains stuff very clearly. This course is also helpful since it presents codes for different software.”
  Yudong Zhang, University of Chicago

“This course helped me learn the fundamentals of structural equation modeling and it gave me confidence in my ability to fit and evaluate a range of different models appropriate to a variety of circumstances.”
  Christina Hardway, Merrimack College

“This course covers a wide range of SEM concepts including path analysis, logistic regression, longitudinal modeling, fixed, and random effects.”
  Sandra Donnay, Fordham University

“I came into this course with a master’s level knowledge of statistics but little research experience. I was surprised that I could grasp most of the material without the research background. I attribute this result to Dr. Allison’s easy going but comprehensive teaching style. He allowed time for questions and the ability to apply newly-learned material during each day of the course. I have a better grasp of SEM at the end of the course than at its beginning. Also, I learned the beginning steps of using Mplus even though I had no knowledge of it coming in.”
  Mark Barrish

“The pace and quality of the teaching was outstanding. Also, you are relatively free to delve as deep as you want into the concepts, meaning you can learn the math behind the concepts or you can he happy to be an “end user” who knows which input leads to which output.”
  Michael Puntiroli, Université de Neuchâtel

“SEM has for some time been my Achilles heel. However, by taking this class I have a better understanding of what to do and I have acquired the practical skill of estimating structural equation models. I feel I can now use SEM in estimating most of the complex models.”

“This course was a good introduction using software packages for SEM. It was also a great review and extension of what I had learned about SEM in the past. The printed material was very helpful and I am certain I’ll refer back to it while doing my dissertation analysis. For the most part, even complicated models and concepts were explained in a way that made them accessible. Thank you!”
  Nora Medina, University of Chicago

“The detailed notes from the course will be extremely beneficial after the course. I have learned so much in such a short time and it’s great to know that I can come back to specific code commands, or model specifications later from the notes provided. This course provides a great overview to get you started in SEM analysis!”
  Jennifer Hayward, Emory University