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.

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 15 through Friday, July 19 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 $1895.00 includes all seminar 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). 

Lodging Reservation Instructions

Embassy Suites, 511 N Columbus Drive, Chicago, IL 60611. Make a reservation online by clicking here and selecting ‘Attendee’ from the dropdown menu, or calling 800-525-2509 before Friday, June 14 to reserve a room for a rate of $259 per night. Approximately 0.2 miles from the Gleacher Center.

Courtyard, 165 E Ontario, Chicago IL 60611. Make a reservation online by clicking here before Friday, June 14 to reserve a room for a rate of $239 per night. Approximately 0.4 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 STAT71 before Friday, June 14 to reserve a room for a rate of $193 per night for a club room or $208 per night for a standard room. Approximately 0.4 miles from the Gleacher Center.

Acme Hotel, 15 E Ohio Street, Chicago, IL 60611. Make a reservation by calling 312-894-0800 and referencing the Statistical Horizons Seminar before Friday, June 14 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
Fairfield Inn & Suites, 216 E Ontario Street, 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
Westin, 320 N Dearborn St, Chicago, IL 60654
Cambria Hotel & Suites, 32 W Randolph St., Chicago, IL, 60601
Hotel EMC2, 228 E Ontario St, Chicago, IL 60611

Room blocks have not been reserved at these additional 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

“Dr. Allison’s SEM course was excellent. I had very limited experience/exposure to SEM previously and he did a great job of making it understandable and interesting. I also had limited experience with Mplus but examples made it easy to learn. This course provided an excellent foundation for me as an early scientist.”
  Amy Newman, University of Utah

“This was a fantastic introductory and higher-level course on SEM. I strengthened my fundamental knowledge while advancing my ability to plan, conduct, and interpret a large variety of SEM models. I will go on to not only use SEM better in ways I used it before but to apply it in new ways I hadn’t considered. I’ve taken a 2-day Statistical Horizons course on another topic and while it was very helpful, this 5-day format is really the way to go for anyone serious about using a statistical tool in their everyday work. Thank you, Paul!”
  Lindsay Nelson, Medical College of Wisconsin

“Professor Paul provided very clear instructions and a lot of valuable practical advice on SEM and how to best use it in my research. This is by far the best course in statistics and SEM particularly that I have taken, as the instructor knows how to explain complex concepts in an easy to understand manner and helps the students connect the concepts together.”
  Anh Luong, Baruch College

“This course is an intensive version of a whole SEM course which may last over a semester. That means you will be able to get familiar with both the fundamental concepts and up-to-date debates of SEM within five days. Learning SEM with most textbooks available on the market can be hard if you are baffled by some mathematical theorems. But I feel no difficulty in following each lecture and being able to learn and benefit from this course. The course also covers a good variety of statistical packages used in social sciences, which I found very helpful. I have been able to understand SEM better and feel more confident using SEM in my future research.”
  Yifei Hou, Purdue University

“I am a fan and user of Paul’s book on survival analysis because of its brief and concise presentation style. As such, I decided to attend this course. Hurray! My expectations are over met because of his in-depth knowledge and style of presentation. Paul is the epitome of a good teacher.”
  Francis Atsu, Ghana Institute of Management and Public Administration (GIMPA)

“As a novice in SEM, I learned both fundamental principles and practical aspects of setting up implementing models needed to apply this powerful approach to my research. I was impressed by how much useful information was clearly conveyed in a short time – this is a very high-yield course.”
  Natasha Marrus, Washington University

“The amount of information you learn in the time you enter and leave the seminar is exorbitant. Not only do you learn a great deal, but you get the opportunity to apply the material and receive immediate feedback and clarification. This is an invaluable course, taught by an effective educator and scholar.”
  Fabrice Stanley Julien, The University of Alabama at Birmingham

“Professor Allison’s course on SEM is very comprehensive yet easy to understand. The course packet comes with the data sets, syntax for different stat packages, and clear instructions to follow. I will use SEM models more in my own research work.”
  Lijun Chen, University of Chicago

“As a relatively new quantitative researcher and user of Stata, I was worried I would not be able to keep up with the course pace and content. However, thanks to the detail and thorough explanations given for each topic and step within SEM, I was pleasantly surprised and able to follow the course at each stage.”
  Laura Dunstan, University of Melbourne

“This course is excellent for people who know how to use SEM, but don’t really understand the mechanics of what they are doing or why they are doing it.”
  Rick Trinkner, Arizona State University

“This course is very practical with lots of useful examples and corresponding codes. It will be very helpful for me to apply SEM to my coming project analysis.”
  Qing Liao, Institute for Work and Health

“This course makes material available to people working within a wide range of systems – we have economists, doctors, social workers, and a wide range of others who gather and use social data working together. This emphasizes the way in which a common set of tools can be applied to data representing a wide range of social problems.”
  Christina Bruhn, Aurora University

“This course helped me in learning SEM models in detail and provided insights about the strengths and limitations of these models. I am now more aware of how these models can be applied in daily research practices.”
  Parul Agarwal, Icahn School of Medicine at Mount Sinai

“Coming into this class with a limited SEM background and little familiarity with Mplus and SAS, I was really nervous. I found Dr. Allison’s approach to teaching both the theory and mechanics of SEM incredibly enlightening, and I am leaving this seminar having run more complicated SEM models on my own data than I would have thought possible at the beginning of the class. What an incredible learning experience and one I would recommend wholeheartedly!”
  Meara Faw, Colorado State University

“This course is designed to have both theoretical depth and applicable tools for anyone who has an interest or would need to use SEM in his/her own research. Came in as a SEM newbie, got out feeling well-equipped for the battlefield!”
  Yunzhijun Yu, Simon Fraser University

“As always, Dr. Allison was great in teaching this course. He was patient to answer all the queries. This class is as suitable for those who have some background in SEM as to a totally novice person. Overall notes are excellent, covering many software packages.”
  Ashutosh Tamhane, University of Alabama

“This course provided excellent hands-on techniques for improving my understanding of SEM. Full information maximum likelihood was very insightful. Exploring interaction terms for latent variables was also very helpful.”
  Larry Weinzimmer, Bradley University

“This course helps me a lot for my dissertation work. The instructor is very kind, knowledgeable. I’d like to recommend it to anyone who wants to pursue knowledge of SEM. You can master the knowledge and skills in a 5-day workshop. This is amazing.”
  Lixia Zhang, University of Wisconsin, Milwaukee

“A very structured course that allowed me to collect the pieces of a puzzle about SEM in a consistent form.”
  Hager Khechine, Laval University

“During this course I enjoyed details of the different packages. I benefited from a better understanding of what these packages offer. Overall it brought many details of SEM which was great.”
  Robert Archer, Florida International University

“I have learned how to use Mplus during the course. My knowledge about SEM improved substantially after taking the course and I am more confident about doing SEM for my research after the course.”
  Elysa Widjaja, University of Toronto