Structural Equation Modeling
A 5-Day Seminar Taught by Paul Allison, Ph.D.
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
- 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 10 through Friday, July 14 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 12.
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 before June 9 to reserve a room for a rate of $209 per night when checking in on Sunday. If you are arriving Monday, you will need to call the Reservations Manager directly at (312) 836-5970 since the rate is different. Approximately 0.2 miles from Gleacher.
Club Quarters, 75 East Wacker Drive, Chicago, IL 60601. Email memberservices@clubquarters.
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
“I took this course as part of my graduate training program in neuroscience. The 5-day course on structural equation modeling allowed for an in-depth explanation of Structural Equation Modeling with hands-on exercise and daily quizzes. I appreciated that I was able to learn more about Structural Equation Modeling from the very beginning to the nuances within one intensive week. It was worth the time away from lab to gain all of the knowledge in one week.”
Christine Paula Lewis-de-los-Angeles, Northwestern University
“Dr. Allison clearly has depth and breadth of knowledge of Structural Equation Modeling. I have a better understanding of Structural Equation Modeling, the use of MPlus and STATA, and the statistical theory behind Structural Equation Modeling as a result of the course. It was especially helpful in learning code or STATA to run Structural Equation Modeling analyses. Dr. Allison is a rock star statistician!”
Julia Phillips, Cleveland State University
“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
“The biggest worry for me about teaching myself structural equation modeling is that I would spend so much time understanding the key concepts of it all. This class provides the students a very strong foundation in Structural Equation Modeling, and it goes over many key concepts, and model nuisances.”
Ezkekiel Kaufman, Pennsylvania State University
“”The 5 day courses are an ideal balance of content depth and efficiency. I had no previous experience with structural equation modeling and after taking this course, I feel prepared to knowledgeably run these models for my own research. The quality of instruction and materials were excellent. I would enthusiastically recommend this course and it is superior to shorter 2-3 day courses I took through other groups.”
Christine Mair, University of Maryland
“Professor Allison’s 5-day Structural Equation Modeling workshop provides detailed examples throughout using four different software packages (SAS, Stata, MPlus, R). The provision of all this code for a wide range of applications is worth the price alone. The five day format allows ample time for questions and direct feedback on exercises in addition to broad coverage of classic factor models, path modeling, latent variables, and longitudinal applications, to name a few. I highly recommend this course to anyone interested in learning SEM for the first time or as a refresher course.”
Jonathan Brauer, Indiana 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 Structural Equation Modeling to answer a large variety of questions. I came to the course with little to no previous knowledge of Structural Equation Modeling and am leaving feeling that I am capable of running Structural Equation Modeling 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 Structural Equation Modeling.”
Moriah Sokolowski, University of Western Ontario
“It is an excellent course. The course provided good information for the model, particularly the analysis of psychometric concept and econometrics. I recommend this course or all people or statisticians in the statistics school.”
Hilaire Hounkpodote, CONFEMEN
“This is amazing workshop I attended. And this is second course with Statistical Horizons. Through this SEM, I feel confident to start SEM with MPlus. The 5-day is great! Thanks a lot Dr. Allison.”
Lixin Qu, University of Alabama
“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. I felt like I got an entire semester’s worth of SEM training in one week and at the fraction of the cost, making this a great value for the price. It was clear this course equipped me with new skills because I went home every night and used my new skills on my own data sets – pretty much immediately.”
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! It helped me to develop a systematic and deep knowledge about SEM, which allowed me to use SEM and learn more about SEM in the future.”
Yue Yin, UIC College of Education