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Ordinal Logistic Regression models and Statistical ...

Cornell Statistical Consulting UnitOrdinal Logistic Regression models and Statistical Software: What You Need to Know Statnews #91 Created June 2016. Last updated August 2020 Overview Ordinal Logistic Regression is a Statistical analysis method that can be used to model the relationship between an Ordinal response variable and one or more explanatory variables. An Ordinal variable is a categorical variable for which there is a clear ordering of the category levels. The explanatory variables may be either continuous or categorical. Estimating Ordinal Logistic Regression models with Statistical software is not difficult, but the interpretation of the model output can be cumbersome. Ordinal Logistic Regression is an extension of Logistic Regression (see StatNews #81) where the logit ( the log odds) of a binary response is linearly related to the independent variables. If instead the response variable has k levels, then there are k-1 logits.

applied after an ordinal logistic model provides one method for testing the assumption of proportional odds. In R, the nominal_test() function in the ordinal package can be used to test this assumption. SAS includes the test for the proportional odds assumption automatically in the output, as does SPSS’s ordinal regression menu.

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