School of Mathematics and Statistics, Xi'an Jiaotong University, Shanxi Province, China.
Phytopathology. 2012 Nov;102(11):1064-70. doi: 10.1094/PHYTO-05-11-0128.
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.
摘要 在植物病理学和其他生物学中，通常对表型测量收集有序定性数据。当比较两组或多组时，通常使用 t 检验或方差分析等统计方法来分析有序数据。然而，对于定性数据，通常违反了正态性和同方差等基本假设。为此，我们研究了一种替代方法，即秩回归，用于分析有序数据。基于秩的方法本质上基于两两比较，因此可以自然地处理定性数据。它们既不需要正态性假设，也不需要数据转换。除了对离群值具有稳健性和高效率外，秩回归还可以像普通回归一样纳入协变量效应。通过重新分析来自小麦镰刀菌冠腐病研究的数据集，我们说明了秩回归方法的使用，并表明秩回归模型似乎更适合和合理地分析非正态数据和存在离群值的数据。
