ArticlePrevention science : the official journal of the Society for Prevention Research2023
Ordered Regression Models: a Tutorial.
Article in Prevention science : the official journal of the Society for Prevention Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A beta way: A tutorial on Bayesian beta regression for psychological research.Behavior research methods · 2026Article
- Student Volunteer Retention and Operational Stability in U.S. Student-Run Free Clinics.Journal of community health · 2026Article
- Postpartum emotional fertility intentions in Ethiopia: an insight and correlates from a national women and newborns cohort study.BMC public health · 2025Article
- Racial/Ethnic Residential Clustering, Experiences with Discrimination, and Self-Rated Health across the Houston Area.Journal of racial and ethnic health disparities · 2025Article
- Trajectories of Health-Related Quality of Life 2 Years After Mild/Moderate Severe Acute Respiratory Syndrome Coronavirus 2 Infection in the Pre-Omicron Era.Open forum infectious diseases · 2025Article
- Patient's willingness to pay for improved community health insurance in Tanzania.Health policy OPEN · 2024Article
- Article
- Financial constraint and perceptions of COVID-19.Scientific reports · 2023Article
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2 authors.
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Abstract
Ordinal outcomes are common in the social, behavioral, and health sciences, but there is no commonly accepted approach to analyzing them. Researchers make a number of different seemingly arbitrary recoding decisions implying different levels of measurement and theoretical assumptions. As a result, a wide array of models are used to analyze ordinal outcomes, including the linear regression model, binary response model, ordered models, and count models. In this tutorial, we present a diverse set of ordered models (most of which are under-utilized in applied research) and argue that researchers should approach the analysis of ordinal outcomes in a more systematic fashion by taking into consideration both theoretical and empirical concerns, and prioritizing ordered models given the flexibility they provide. Additionally, we consider the challenges that ordinal independent variables pose for analysts that often go unnoticed in the literature and offer simple ways to decide how to include ordinal independent variables in ordered regression models in ways that are easier to justify on conceptual and empirical grounds. We illustrate several ordered regression models with an empirical example, general self-rated health, and conclude with recommendations for building a sounder approach to ordinal data analysis.
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