Understanding and interpreting generalized ordered logit models

2016/01/02 by Richard Williams · 13 citations
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference

paper · doi:10.1080/0022250x.2015.1112384

crossref issued 2016/01/02 · crossref published 2016/01/02 · crossref published-print 2016/01/02 · openalex publication_date 2016/01/02 · crossref published-online 2016/01/29 · crossref created 2016/01/29 · crossref deposited 2019/09/04 · openalex created_date 2025/10/10 · crossref indexed 2026/07/27 · openalex updated_date 2026/07/28

Abstract

When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative. Gologit/ppo models can be less restrictive than proportional odds models and more parsimonious than methods that ignore the ordering of categories altogether. However, the use of gologit/ppo models has itself been problematic or at least sub-optimal. Researchers typically note that such models fit better but fail to explain why the ordered logit model was inadequate or the substantive insights gained by using the gologit alternative. This paper uses both hypothetical examples and data from the 2012 European Social Survey to address these shortcomings.

Citations

• Assessing Proportionality in the Proportional Odds Model for Ordinal Logistic Regression

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