Assessing Proportionality in the Proportional Odds Model for Ordinal Logistic Regression
1990/12/01 by Rollin Brant · 1,821 citations
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Binary data #Binary number #Econometrics #Goodness of fit #Logistic distribution #Logistic regression #Logit #Mathematics #Odds #Optimal Experimental Design Methods #Ordered logit #Ordinal data #Ordinal regression #Statistical Methods and Bayesian Inference #Statistics
paper · doi:10.2307/2532457
published in Biometrics 46(4), 1171 (Oxford University Press)
openalex publication_date 1990/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Abstract
The proportional odds model for ordinal logistic regression provides a useful extension of the binary logistic model to situations where the response variable takes on values in a set of ordered categories. The model may be represented by a series of logistic regressions for dependent binary variables, with common regression parameters reflecting the proportional odds assumption. Key to the valid application of the model is the assessment of the proportionality assumption. An approach is described arising from comparisons of the separate (correlated) fits to the binary logistic models underlying the overall model. Based on asymptotic distributional results, formal goodness-of-fit measures are constructed to supplement informal comparisons of the different fits. A number of proposals, including application of bootstrap simulation, are discussed and illustrated with a data example.
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