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Prevention Science

Erschienen in:

01.10.2015

Methods for Multilevel Ordinal Data in Prevention Research

verfasst von: Donald Hedeker

Erschienen in: Prevention Science | Ausgabe 7/2015

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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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Titel: Methods for Multilevel Ordinal Data in Prevention Research
verfasst von: Donald Hedeker
Publikationsdatum: 01.10.2015
Verlag: Springer US
Erschienen in: Prevention Science / Ausgabe 7/2015
Print ISSN: 1389-4986
Elektronische ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-014-0495-x

Klimawandel und Gesundheit: Was Sie in der Hausarztpraxis wissen müssen und tun können

Springer Medizin

Methods for Multilevel Ordinal Data in Prevention Research

Abstract

Klimawandel und Gesundheit: Was Sie in der Hausarztpraxis wissen müssen und tun können

Springer Medizin

Abstract

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