A Meta-Analytical Review of the Ordinal Logistic Regression Model: Applicability, Assumptions and Practical Constraints

Abstract

This meta-analytical review critically examines the Ordinal Logistic Regression (OLR) model, focusing on its applicability, underlying assumptions and practical constraints in empirical research. Drawing on Forty-five (45) peer-reviewed studies published between 2015 and 2025, the analysis synthesizes methodological insights from applications across diverse fields, including social sciences, health research, education and engineering. The review outlines the theoretical foundation of OLR, highlighting its suitability for modeling ordinal response variables where the proportional odds assumption holds. Key assumptions such as proportionality of odds, absence of multicollinearity, and correct model specification are systematically discussed, alongside diagnostic procedures for verification. Practical constraints identified include small sample bias, violation of proportional odds, challenges in interpreting interaction effects and the complexity of handling missing or imbalanced data. The findings underscore that while OLR offers robust analytical power for ordinal outcomes, its validity is contingent upon careful assumption testing, adequate sample size, and context-appropriate interpretation. This synthesis contributes to improved methodological rigor by offering a consolidated reference for researchers, emphasizing both the strengths and limitations of the OLR model in applied statistical modeling.