Introduction to Structural Equation Modeling

A 2-day seminar taught by Paul D. Allison, Ph.D.

Read reviews of this seminar

To see a sample of the course materials, 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 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 class will meet from 9 am to 4 pm each day with a 1-hour lunch break at Sheraton Boston Hotel, 39 Dalton Street, Boston, MA, 02199. 

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 $995.00 includes all seminar materials. 

Refund Policy

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

A block of rooms has been reserved at the Sheraton Boston Hotel, 39 Dalton Street, Boston, MA, 02199 at a special rate of $279 per night. In order to guarantee rate and availability, make your reservations by clicking here or by calling Sheraton Reservations 1-(888) 627-7054, giving the hotel name (Sheraton Boston Hotel), and the group code ‘SJ12AB” no later than 5 pm on Friday, September 15, 2017.


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

“Coming from a position with very little background in SEM, I found this course insightful and practical. I enjoyed the real-world advice that Dr. Allison offered.” 
  Casey Tak, University of Utah

“This course was very helpful to me. I have a better understanding of SEM. I especially can appreciate the different programs that are available. Thanks for providing great slides and allowing for hands-on activity.”
  Marcia Lowe, University of Alabama School of Nursing

“As usual, Paul Allison doesn’t disappoint. He is extremely didactic and offers great in-depth explanations to the subject matter deeply rooted in statistical concepts. His courses are a must for researchers across many fields who value statistical methods.”
  Grettel Castro, Florida International University

“As a beginning in SEM, I found this course to be well paced. Dr. Allison’s teaching style is well suited for beginners. Strengths of the course include discussion of software input and output, step-by-step interpretation of results, and the comprehensive nature of the workshop. The notes are also fantastic! I highly recommend.”
  Amoha Bajaj, University of Pittsburgh

“Excellent course, excellent professor. SEM is explained very clearly with examples and the commands needed combined with the ability to summarize a lot of information in a very didactic way. I highly recommend this course.” 
  Pura Rodriguez de la Vega, Florida International University