Douglas Simpson, University of Illinois
Some Developments in Ordinal Regression Analysis with Applications
to Correlated Data
In social science research, questionnaires
often use Likert scale items. In medical research, a researcher may
grade tumors on an ordinal scale, or a patient may rate level of
discomfort on a 4 point scale. In toxicology, severity of
toxicological effect may be scored as 0="no effect", 1="mild effect",
2="severe effect". This talk will discuss ordinal regression analysis
of data from such studies. Ordinal regression analysis is used to
model the conditional probability distribution of the ordinal score as
a function of covariates such as exposure, social status, treatment,
and so on. A general class of models will be introduced along with
unified approach to model fitting. Then two approaches to the analysis
of correlated ordinal data will be discussed: mixed effect modeling
using latent variables, and marginal modeling, which is derived from
the theory of generalized estimating equations.