Simple ways to interpret effects in modeling ordinal categorical data

We survey effect measures for models for ordinal categorical data that can be simpler to interpret than the model parameters. For describing the effect of an explanatory variable while adjusting for other explanatory variables, we present probability-based measures, including a measure of relative size and partial effect measures based on instantaneous rates of change. We also discuss summary measures of predictive power that are analogs of $R$-squared and multiple correlation for quantitative response variables. We illustrate the measures for an example and provide { tfamily R} code for implementing them.