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Prevention Science

Erschienen in:

10.03.2016

Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer

verfasst von: Kehui Chen, Yu Cheng, Olga Berkout, Oliver Lindhiem

Erschienen in: Prevention Science | Ausgabe 3/2017

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Abstract

In prevention trials, outcomes of interest frequently include data that are best quantified as proportion scores. In some cases, however, proportion scores may violate the statistical assumptions underlying common analytic methods. In this paper, we provide guidelines for analyzing frequency and proportion data as primary outcomes. We describe standard methods including generalized linear regression models to compare mean proportion scores and examine tools for testing normality and other assumptions for each model. Recommendations are made for instances when the assumptions are not met, including transformations for proportion scores that are non-normal. We also discuss more sophisticated analytical tools to model change in proportion scores over time. The guidelines provide ready-to-use analytical strategies for frequency and proportion data that are commonly encountered in prevention science.

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Titel: Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer
verfasst von: Kehui Chen
Yu Cheng
Olga Berkout
Oliver Lindhiem
Publikationsdatum: 10.03.2016
Verlag: Springer US
Erschienen in: Prevention Science / Ausgabe 3/2017
Print ISSN: 1389-4986
Elektronische ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-016-0643-6

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