Structural Equation Modeling

About

Structural equation modeling (SEM) is a very general statistical technique, as it has regression analysis, path analysis, and factor analysis as special cases. It is also possible to combine the advantages of these techniques, which makes SEM one of the most general and most flexible techniques available to researchers. As a result, SEM presently is also one the most widely used techniques in the social and behavioral sciences.

This course will introduce you to the fundamentals of SEM by first translating some familiar methods (t tests and ANOVA, regression and correlation) into mean and covariance structure (MACS) analyses. Then you will see how path analysis is more general than the general(ized) linear model and better able to facilitate testing hypotheses about mediation. The second day will introduce tactics for evaluating data–model correspondence, methods for modeling moderation, and measurement models for latent variables. Day 3 will cover path analysis and moderation involving latent variables—the latter of which requires evaluating measurement invariance—and end with how to handle common nonideal data.

Course leader

Terrence D. Jorgensen, PhD - University of Amsterdam

Target group

Professionals, Researchers, and Students, who wish to develop high-quality surveys and employ up-to-date statistical methods for survey data analysis. Potential attendees include survey practitioners, marketing professionals, social science students, and researchers.