Stereotype Ordinal Regression

There are a number of reasonable approaches to analysing an ordinal outcome variable. One common approach, known as the Proportional Odds (PO) Model, is implemented in Stata as ologit. If the assumptions of the PO model are not satisfied, an alternative is to treat the outcome as categorical, rather than ordinal, and use multinomial logistic regression (mlogit) in Stata. This insert describes an alternative form of ordinal regression model, the Stereotype Ordinal Regression (SOR) Model, which can be thought of as imposing ordering constraints on a multinomial model. The multinomial model provides the best possible fit to the data, at the cost of a large number of parameters which can be difficult to interpret. Stereotype regression aims to reduce the number of parameters by imposing constraints, without reducing the adequacy of the fit.