Rank Regression for Analyzing Ordinal Qualitative Data for Treatment Comparison

ABSTRACT Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the unde...

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Bibliographic Details
Published inPhytopathology Vol. 102; no. 11; pp. 1064 - 1070
Main Authors FU, L. Y, WANG, Y.-G, LIU, C. J
Format Journal Article
LanguageEnglish
Published St. Paul, MN American Phytopathological Society 01.11.2012
Subjects
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
Fusarium
Fusarium - drug effects
Fusarium - pathogenicity
Genotype
Gibberellins - pharmacology
Models, Statistical
Phytopathology. Animal pests. Plant and forest protection
Plant Diseases - microbiology
Plant Growth Regulators - pharmacology
Regression Analysis
Seedlings - drug effects
Seedlings - growth & development
Triticum - drug effects
Triticum - growth & development
Triticum - microbiology
Triticum aestivum
Plant pathogen
Plant
Treatment
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Summary:ABSTRACT Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers.
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ISSN:0031-949X
1943-7684
DOI:10.1094/PHYTO-05-11-0128