Nonparametric analysis of ordinal data in designed factorial experiments

被引:403
作者
Shah, DA
Madden, LV [1 ]
机构
[1] Cornell Univ, New York State Agr Expt Stn, Dept Plant Pathol, Geneva, NY 14456 USA
[2] Ohio State Univ, Dept Plant Pathol, Wooster, OH 44691 USA
关键词
distribution-free methods; normalized distribution; rank-based methods;
D O I
10.1094/PHYTO.2004.94.1.33
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Plant disease severity often is assessed using an ordinal rating, scale rather than a continuous scale of measurement. Although Such data Usually should be analyzed with nonparametric methods, and not with the typical parametric techniques (such as analysis of variance), limitations in the statistical methodology available had meant that experimental designs generally could not be more complicated than a one-way layout. Very recent advancements in the theoretical formulation of hypotheses and associated test statistics within a nonparametric Framework, together with development of software for implementing the methods, have made it possible for plant pathologists to analyze properly ordinal data from more complicated designs using nonparametric techniques. In this paper, We illustrate the nonparametric analysis of ordinal data obtained from two-way factorial designs, including a repeated measures design, and show how to quantify the effects of experimental factors on ratings through estimated relative marginal effects.
引用
收藏
页码:33 / 43
页数:11
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