## Introduction to Structural Equation Modeling

A two-day seminar taught by Paul D. 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.

Here Are a Few Things You Can Do With Structural Equation Modeling

• Test the implications of causal theories.
• 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).
• Adjust for measurement error in predictor variables.
• Estimate and compare models across multiple groups of individuals.
• Represent causal theories with rigorous diagrams.
• Investigate the properties of multiple-item scales.

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

### COMPUTING

The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be presented for SAS, Stata and lavaan (a new package for R). 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 lavaan) 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, October 13 and Friday, October 14 from 9 to 4 each day with a 1-hour lunch break at the Ontario Institute for Studies (OISE) in Education, 3rd Floor, Labs 1&2, 252 Bloor Street W, Toronto, ON M5S 1V4, Canada.

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 \$995 includes all course materials.

Lodging

There are several hotels within a short walk to campus. We suggest booking early as space fills up quickly. Here are a couple options:

Holiday Inn Toronto Bloor-Yorkville, 280 Bloor Street West, Toronto, ON M5S 1V8, Canada

• A rate of \$145 for one Queen bed is available until
Monday, September 12, 2016
• Follow this link to make a reservation and enter group code SHE

Intercontinental Toronto Yorkville, 220 Bloor Street West, Toronto, ON M5S 1T8, Canada

• Rates of \$225 for superior rooms and \$245 for deluxe rooms are available until Tuesday, September 13, 2016
• Proof of registration will need to be shown at check-in to receive the special rate. When booking online follow this link and use the University of Toronto code is 100217931.

### 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

“This course provided a richly detailed and in-depth introduction to SEM. The course worked through examples in very good detail, and Dr. Allison was very adept at answering questions and providing an exceptionally detailed overview of SEM procedures. Be prepared to be challenged and a bit exhausted, but also galvanized to use these procedures with your own data.”
Jonathan Mattanah, Towson University

“The course is well structured, it’s a good pace for someone with no SEM experience or background, and the information is presented in a very accessible way. I liked the multiple examples with specific data to illustrate more complex ideas. The course is very practical and I feel ready to apply SEM after only 2 days. The additional resources and references mentioned during the course will help me to dig into the SEM detail particularly relevant to my own work.”
Lilia Bliznashka, International Food Policy Research Institute

“This course was accessible for me with limited experience in SEM and no prior experience in MPlus. The pace was excellent with lots of breaks to digest the information. Also very helpful book and codes to make sure we can bring what we learned here in class back home and implement it.”
Carmen Logie, University of Toronto

“This course provided a good overview of SEM. I left it feeling more confident than when I arrived.”
Michael Miner, University of Minnesota

“This course is extremely useful for those who are beginners in the SEM field. The course is very well structured, allows “digestion” of the materials and has practical applications.”
Anonymous

“Dr. Allison is very knowledgeable and knows how to convey even very complicated concepts, such as SEM. He made it look very easy to me. I got many of my questions answered. Thank you.”