Rank regression for analyzing ordinal qualitative data for treatment comparison

Fu, L. Y., Wang, Y. -G. and Liu, C. J. (2012) Rank regression for analyzing ordinal qualitative data for treatment comparison. Phytopathology, 102 11: 1064-1070. doi:10.1094/PHYTO-05-11-0128


Author Fu, L. Y.
Wang, Y. -G.
Liu, C. J.
Title Rank regression for analyzing ordinal qualitative data for treatment comparison
Journal name Phytopathology   Check publisher's open access policy
ISSN 0031-949X
1943-7684
Publication date 2012-11-01
Sub-type Article (original research)
DOI 10.1094/PHYTO-05-11-0128
Open Access Status DOI
Volume 102
Issue 11
Start page 1064
End page 1070
Total pages 7
Place of publication St. Paul, MN, United States
Publisher American Phytopathological Society
Language eng
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.
Keyword Linear rank regression model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2013 Collection
 
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