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Methods for Multilevel Ordinal Data in Prevention Research

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Abstract

This paper discusses statistical models for multilevel ordinal data that may be more appropriate for prevention outcomes than models that assume continuous measurement and normality. Prevention outcomes often have distributions that make them inappropriate for many popular statistical models that assume normality and are more appropriately considered ordinal outcomes. Despite this, the modeling of ordinal outcomes is often not well understood. This article discusses ways to analyze multilevel ordinal outcomes that are clustered or longitudinal, including the proportional odds regression model for ordinal outcomes, which assumes that the covariate effects are the same across the levels of the ordinal outcome. The article will cover how to test this assumption and what to do if it is violated. It will also discuss application of these models using computer software programs.

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Acknowledgments

The project described was supported by Award Number P01CA098262 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

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Authors and Affiliations

  1. Division of Epidemiology and Biostatistics (M/C 923), School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Room 955, Chicago, IL, 60612-4336, USA

    Donald Hedeker

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  1. Donald Hedeker
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Correspondence to Donald Hedeker.

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Hedeker, D. Methods for Multilevel Ordinal Data in Prevention Research. Prev Sci 16, 997–1006 (2015). https://doi.org/10.1007/s11121-014-0495-x

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