Introduction to Structural Equation Modeling

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

Read 9 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 was 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 a the opportunity to learn the basics of SEM from a master teacher, Professor Paul D. Allison, in just two days.

Computing

Both Mplus and SAS will be used for the empirical examples and exercises in this course. Mplus is one of the best SEM packages because of its superior capabilities for missing data, multi-level modeling, and ordinal and categorical data. Although not required, you are encouraged to bring your own laptop (loaded with SAS, Mplus or the Mplus demo) and do the optional exercises.


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 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 seminar meets Friday, April 12 and Saturday, April 13 at the Courtyard Washington Embassy Row,1600 Rhode Island Avenue, NW, Washington, DC  20036, 

The class will meet from 9 to 4 each day with a 1-hour lunch break.
 
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 $895 includes all course materials. 

Lodging Reservation Instructions
A block of rooms has been reserved at the Courtyard Washington Embassy Row,1600 Rhode Island Avenue, NW, Washington, DC  20036 at a special rate of $159 per night.  In order to guarantee rate and availability, make your reservations by clicking on the hotel link or  call 800-321-2211 or 800-503-1432 no later than Thursday, March 21 and identify yourself with Statistical Horizons with the group code of SHMSHMA. 

 


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

“I found this course very helpful for solidifying concepts that I had read about prior to the course. The concrete examples with MPlus were useful and it helped that he explained how to read the output because it is not always clear. This course is great for people who want a “big picture” introduction to SEM without being bogged down or overwhelmed by the specific details of matrix algebra.”
  Sarah Farstad, University of Calgary

“This course offered a very comprehensive introduction to SEM. I liked that it wasn’t formula-heavy and offered many examples with how-to instructions.”
   Leah Sheppard, Univeristy of British Columbia

“This is a very structured and comprehensive lecture on SEM. Dr. Paul Allison did a fantastic job explaining the complicated materials in a very clear way and easy to understand approach.”
   Haijun Tian, Novartis

“Learn to use MPLUS for all your SEM research!”
   Sylvia Hurtado, UCLA

“An excellent introduction on interpreting SEM models.”
   Janet Schneiderman, University of Southern California

“Great course in a great location! Learned a lot! Dr. Allison is a wonderful teacher and is just as clear of a lecturer as he is a writer. For anyone who reads his books and enjoys them, the course is also very clear, succinct and helpful. Great overview of SEM & M-PLUS!”
   Jessica Allison, Kaiser Permanente

“Excellent exposure to the logic and practice of SEM. You will leave with the tools for formulating latent variable models and the programming skills to execute the model in a statistical package.”
   Jacob Young, Arizona State University

“This course is good for those who have some basic knowledge of SEM. The pace of instruction is well maintained so that you would not feel overwhelmed or time wasted.”
   Fei Sun, Arizona State University

“This is a great introductory course on SEM. Paul is a wonderful teacher and keeps the class interesting. I really enjoyed the experience and learned a lot.”
   Jane OReilly, University of British Columbia