Ordinal regression

In statistics, ordinal regression is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. The two most common types of ordinal regression models are ordered logit, which applies to data that meet the proportional odds assumption, and ordered probit.

Further reading

• Hardin, James; Hilbe, Joseph (2007). Generalized Linear Models and Extensions (2nd edition ed.). College Station: Stata Press. ISBN 978-1-59718-014-6. 

• McCullagh, Peter (1980). "Regression models for ordinal data". Journal of the Royal Statistical Society. Series B (Methodological) 42 (2). pp. 109–142.