Disparities in Methodology, Assumptions and Applications between Ordinal and Multinomial Logistic Regression: A Meta-Analysis

This meta-analysis examines the methodological, assumption-based and application-related disparities between Ordinal Logistic Regression (OLR) and Multinomial Logistic Regression (MLR), drawing on 60 peer-reviewed empirical and methodological studies published between 2015 and 2025. While both models are widely employed for categorical outcome analysis, their theoretical underpinnings, statistical assumptions and practical suitability differ significantly. OLR is optimal for ordered categorical data, relying heavily on the proportional odds assumption, which, if violated, can compromise interpretability and model fit. Conversely, MLR accommodates unordered categorical outcomes without assuming proportionality, but at the cost of greater model complexity and reduced statistical power when categories have an inherent order. The review synthesises findings from diverse disciplines, including social sciences, health research, and education and engineering, highlighting that the choice between OLR and MLR is often driven more by researcher preference and software defaults than by rigorous diagnostic testing or data characteristics. Studies also reveal a persistent gap in assumption testing, with limited reporting on proportional odds verification and model fit comparison. The analysis underscores the need for clearer methodological guidance, improved reporting standards and context-driven model selection. The study lies in its cross-disciplinary meta-analysis that uniquely integrates empirical and methodological evidence on OLR and MLR, exposing overlooked assumption-testing gaps and offering a framework for context-driven model selection.