## 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 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

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 Friday, September 27 and Saturday, September 28 at the the Courtyard by Marriott – Magnificent Mile, 165 East Ontario Street, Chicago IL.

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 seminar location, the Courtyard by Marriott – Magnificent Mile, 165 East Ontario Street, Chicago IL at a rate of \$229 per night. To make a reservation, please call 1-800-321-2211 or 312-573-0800 and identify yourself with Statistical Horizons. In order to guarantee rate and availability, reservations must be made by September 5, 2013.

A block of rooms has also been reserved at the Red Roof Inn, 162 E. Ontario Street, Chicago, IL at a rate of \$118.99 per night. This hotel is directly across from the Courtyard hotel. To make reservations, please call 1-800-RED-ROOF or 312-787-3580 and identify yourself with Statistical Horizons and refer to group block code “ST2013”. In order to guarantee rate and availability, reservations must be made by August 26, 2013.

### 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

“Excellent course. Very good concept of statistical concepts.”
Dave Barrett, Clemson University

“”This course provided a good introduction to many psychometric concepts in addition to SEM, and it was immensely helpful to be discussing both conceptual ideas and implementation (in SAS and MPlus) at each stage of the course.  The best part of the course was the ease of it – lecture notes were carefully and thoroughly created, making it easier to process /digest the information.”
Jen Faerber, Children’s Hospital of Philadelphia

“Excellent course. Well taught by an obviously very knowledgeable and experienced instructor. I learned a lot.”
Grace Mhango, Mount Sinai Medical Center

“I thought the course was an excellent resource for the mid-career profession who wants to update his or her skills to take advantage of advances in statistical modeling programs. The program assumed conceptual familiarity but no analytic experience with SEM. The pacing and presentation of the material was excellent and very helpful with regard to the application of computational programs to specific problem examples. The material was presented in a very logical, sequential manner, for the adult learner.”
Jonathan G. Tubman, American University

“Having read a variety of books on structural equation models, I still found this introduction course very useful. There are several concepts in SEM that I have struggled with (e.g. scale), but Dr. Allison’s description and personal interactions helped to clarify many of these concepts.”
Justin Fear, University of Florida/MGM