Applied Ordinal Logistic Regression Using Stata Official

: For cases where the proportional odds assumption is violated, the seminal paper on the gologit2 command by Richard Williams (2006) is the industry standard for learning how to fit partial proportional odds models in Stata. Applied Research Examples

If you specifically need shorter, peer-reviewed papers that demonstrate the application or technical nuances in Stata, consider these options: Technical & Comparative Papers Applied Ordinal Logistic Regression Using Stata

While there are several excellent papers and guides, the definitive core resource on this topic is actually a textbook: by Xing Liu (2016) . : For cases where the proportional odds assumption

: This paper by Xing Liu (2009) is excellent for seeing how Stata’s ologit command compares to other software, illustrating how it fits proportional odds models using real educational data. : This article on PMC demonstrates using ordinal

: This article on PMC demonstrates using ordinal logistic regression to determine household socioeconomic factors, explicitly recommending the use of partial proportional odds models (PPOM) when covariates violate proportionality. Quick Reference Guides