Introduction In this chapter, the standard logistic model is extended to
handle outcome variables that have more than two cate-
gories. Polytomous logistic regression is used when the
categories of the outcome variable are nominal, that is,
they do not have any natural order. When the categories
of the outcome variable do have a natural order, ordinal
logistic regression may also be appropriate.
The focus of this chapter is on polytomous logistic regres-
sion. The mathematical form of the polytomous model and
its interpretation are developed. The formulas for the odds
ratio and confidence intervals are derived, and techniques
for testing hypotheses and assessing the statistical signifi-
cance of independent variables are shown.Abbreviated
Outline
The outline below gives the user a preview of the material
to be covered by the presentation. A detailed outline for
review purposes follows the presentation.I. Overview (pages 432–433)
II. Polytomous logistic regression: An example with
three categories (pages 434–437)
III. Odds ratio with three categories (pages
437–441)
IV. Statistical inference with three categories
(pages 441–444)
V. Extending the polytomous model toGoutcomes
andkpredictors (pages 444–449)
VI. Likelihood function for polytomous model
(pages 450–452)
VII. Polytomous vs. multiple standard logistic
regressions (page 453)
VIII. Summary (page 453)430 12. Polytomous Logistic Regression