Modeling and inference for an ordinal effect size measure.

An ordinal measure of effect size is a simple and useful way to describe the difference between two ordered categorical distributions. This measure summarizes the probability that an outcome from one distribution falls above an outcome from the other, adjusted for ties. We develop and compare confidence interval methods for the measure. Simulation studies show that with independent multinomial samples, confidence intervals based on inverting the score test and a pseudo-score-type test perform well. This score method also seems to work well with fully-ranked data, but for dependent samples a simple Wald interval on the logit scale can be better with small samples. We also explore how the ordinal effect size measure relates to an effect measure commonly used for normal distributions, and we consider a logit model for describing how it depends on explanatory variables. The methods are illustrated for a study comparing treatments for shoulder-tip pain.