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Simple ways to interpret effects in modeling ordinal categorical data
Author(s) -
Agresti Alan,
Tarantola Claudia
Publication year - 2018
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12130
Subject(s) - categorical variable , ordinal data , ordinal regression , mathematics , measure (data warehouse) , statistics , explanatory power , ordinal scale , econometrics , simple (philosophy) , computer science , data mining , philosophy , epistemology
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 R code for implementing them.