Department of
presents
Melanie Wall
Structural equation modeling of latent
classes
ABSTRACT
Latent class analysis typically involves
modeling a set of several observed measurements via a single underlying
(latent) categorical (class) variable which is meant to capture the
associations found amongst the observed variables. This latent class
variable can then be modeled as either an outcome or predictor variable to
address some research question of interest. Latent class models can be seen
applied within the health sciences to multiple diagnostic tests without a gold
standard, multiple source or informant data, and
multiple symptom assessments. As applications of this type of modeling of
a single latent class variable are becoming more common, it is natural to
consider models involving multiple latent class variables. In particular,
structural equation models (SEM) of latent class variables will be considered,
differing from traditional SEM in that all the latent variables are categorical
rather than continuous. In addition to basic main effects type models, models
involving interactions effects between different latent class variables on
outcomes will be demonstrated as well as structural model relationships between
multiple latent class processes (over time). Examples of traditional
applications of the single latent class variable models will be given and an
application relating social, familial, environmental and personal factors
associated with adolescent obesity will be used to demonstrate the new SEM of
latent classes.
Friday, November 9, 2007
3:35 - 4:35 pm
301 Riddick Hall
Refreshments will be served in the common area of 301 Riddick at 3:00 pm