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Multiple Regression with Discrete Dependent Variables$
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John G. Orme and Terri Combs-Orme

Print publication date: 2009

Print ISBN-13: 9780195329452

Published to Oxford Scholarship Online: May 2009

DOI: 10.1093/acprof:oso/9780195329452.001.0001

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Regression with an Ordinal Dependent Variable Regression with an Ordinal Dependent Variable

Regression with an Ordinal Dependent Variable Regression with an Ordinal Dependent Variable

Chapter:
(p.123) 4 Regression with an Ordinal Dependent Variable
Source:
Multiple Regression with Discrete Dependent Variables
Author(s):

John G. Orme

Terri Combs-Orme

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195329452.003.0004

This chapter discusses ordinal logistic regression (also known as the ordinal logit, ordered polytomous logit, constrained cumulative logit, proportional odds, parallel regression, or grouped continuous model), for modeling relationships between an ordinal dependent variable and multiple independent variables. Ordinal variables have three or more ordered categories, and ordinal logistic regression focuses on cumulative probabilities of the dependent variable and odds and odds ratios based on those cumulative probabilities, estimating a single common odds ratio. The chapter discusses the proportional odds or parallel regression assumption; this is the assumption that the odds ratios for each cumulative level are equal in the population (although they might be different in a sample due to sampling error). The concepts of threshold, sometimes called a cut-point, proportional odds or parallel regression assumption, are also discussed.

Keywords:   ordinal variables, threshold, cut-point, proportional odds assumption, parallel regression assumption, ordinal logit model, ordered polytomous logit model, constrained cumulative logit model, proportional odds model, parallel regression model, grouped continuous model

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