Sociology | Topics in Quantitative Sociology (Structural Equation Modeling)
S651 | 3713 | Scott Long

Structural equation modeling (SEM) is a general method for analyzing
the relationship among variables. In its most complex form, SEM allows
you to have multiple equations with errors in equations and errors of
measurement for all the variables in the model. The course will
approach this very general model by progressing through a series of
special cases that are quite useful and powerful in and of themselves.
First, we will consider simultaneous equations systems for multiple
regression. In this form of the model, all of the variables are
measured without error. Next, we consider the case in which all
variables are measured with error, but that there is a structural
relationship among the variables. This form of SEM is referred to as
confirmatory factor analysis. The advantage of confirmatory factors
analysis compared to exploratory factor analysis is considered.
Finally, we will consider the full model in which simultaneous
equation systems and measurement error are combined. Note that the SEM
goes by various names, including covariance structure analysis and
LISREL modeling.

This is a graduate level class in applied statistics that assumes
familiarity with the linear regression model as taught in Sociology
554. Students will be required to write a substantive paper using
structural equation modeling, or a methodological paper dealing with a
problem in SEM.