Introduction to Structural Equation ModelingA 2-Day Seminar Taught by Paul D. Allison, Ph.D.
To register for the Introduction to Structural Equation Modeling course on October 17-18, please 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 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
• 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.
The empirical examples and exercises in this course will emphasize Mplus, but equivalent code for SAS and Stata will also be demonstrated. 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, Stata, 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 Thursday, June 19 and Friday, June 20 at the the Boston Common Hotel and Conference Center, 40 Trinity Place, Boston, MA 02116.
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
Room blocks have been arranged at three locations.
The Courtyard Boston Downtown, 275 Tremont Street, Boston, MA 02116 is approximately a half mile from the seminar location. Call Marriott Reservations at (800) 321-2211 or (617) 426-1400 by Monday, May 26, 2014 for the special rate of $229 per night and mention that you are part of the Statistical Horizons Meeting group.
The Club Quarters, 161 Devonshire Street, Boston, MA 02110 is 1.2 miles from the seminar location. Public transportation is close by. Call (203) 905-2100 during business hours by Monday, May 19 for a rate of $239 per night and mention the code SHO618 to take advantage of this offer.
The Boston Common Hotel and Conference Center, 40 Trinity Place, Boston, MA 02116 is at the seminar location. Call (617) 933-770 by Monday, April 19 for a rate of $189 per night and mention that you are part of the Statistical Horizons Meeting group.
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
“Excellent teacher who really cares for the participants and a great teacher who helped me in thinking about the different application of the topic in my research area of interest.”
Pradeep Podila, University of Memphis
“Excellent course, easy to follow and leaves participants with applicable skills.”
Julia Felton, University of Maryland
“This course offers a wide body of knowledge on SEM that, in my opinion, has the right balance of review material and new material. More specifically, the demonstration of the concepts in multiple statistical package is a great plus, as it allows for both comparing similarities & differences as well as learning new features. I also liked Dr. Allison’s interaction with students & his subtle but much needed sense of humor, when dealing with a complex course such as this. ”
Monika Marko-Holguin, University of Illinois
“This course provided a great introduction to SEM. As a novice with the method, the lectures were informative in establishing fundamental knowledge. I would recommend this course to others who don’t know where to start with SEM.”
Andrea Cohee, Indiana University
“An entire day of statistics seems overwhelming but the way it was divided with breaks every hours really worked to maximize learning. Thank you!”
Maridekys Detres, Health Start Coalition of Pinellas
“This course provides a nice overview of SEM, and Dr. Allison makes everything easy to understand.”
Hyojung Park, Manship School of Mass Communication-LSU
“I had already taken a SEM course in my graduate program, but haven’t used it in several years. This course has a perfect refresher and added new insights as well. I am confident in my ability to begin using SEM in my every day work.”
Robert Lucio, Saint Leo University
Surprisingly, the course better helped me understand unobserved heterogeneity, which is a real help in economics.”
Diane Hite, Auburn Univeristy