582
Statistical Models for the Analysis of Ordered Categorical
Data in Public Health and Medical Research
Ruth D. Etzioni,
Stephen E. Fienberg,
Zvi Gilula, and
Shelby J. Haberman
Abstract:
In the late 1970's statisticians extended the methods for analyzing
loglinear and logit models for cross-classified categorical data to
incorporate information about the ordinal structure of the categories
corresponding to some of the classification variables. In this paper
we review one class of such extensions known as association
models. We consider association models with and without order
restrictions on the parameters and we use these models to answer
research questions about several medical examples involving ordered
categorical data. We emphasize the interpretation of parameters in
the association models and how this relates to the research questions
of interest.
Key Words: Association models; correlation models; linear by
linear interactions; logit models; loglinear models; ordinal
variables.