Topic:  Latent Variability Models

This class deals with models with latent variables. It begins by
examining the structural equation model (SEM). 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.
Then, we merge the two parts to create the full SEM. The course will
also deal with other models that include latent variables, such as
latent class analysis.