How to test for goodness of fit in ordinal logistic regression models

Morten W. Fagerland (), David W. Hosmer () and Hajime Uno
Additional contact information
Morten W. Fagerland: Oslo University Hospital
David W. Hosmer: Stanford University
Hajime Uno: University of Vermont

Stata Journal, 2017, vol. 17, issue 3, 668-686

Abstract: Ordinal regression models are used to describe the relationship between an ordered categorical response variable and one or more explanatory variables. Several ordinal logistic models are available in Stata, such as the proportional odds, adjacent-category, and constrained continuation-ratio models. In this article, we present a command (ologitgof) that calculates four goodness-of-fit tests for assessing the overall adequacy of these models. These tests include an ordinal version of the Hosmer–Lemeshow test, the Pulkstenis–Robinson chi-squared and deviance tests, and the Lipsitz likelihood-ratio test. Together, these tests can detect several different types of lack of fit, including wrongly specified continuous terms, omission of different types of interaction terms, and an unordered response variable.

Keywords: ologitgof; Hosmer–Lemeshow test; Pulkstenis–Robinson chi-squared and deviance tests; Lipsitz likelihood-ratio test; ordinal models; propor- tional odds; adjacent category; continuation ratio (search for similar items in EconPapers)
Date: 2017
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