摘要
As a nonparametric method,the Kruskal-Wallis test is widely used to compare three or more independent groups when an ordinal or interval level of data is available,especially when the assump-tions of analysis of variance (ANOVA) are not met.If the Kruskal-Wallis statistic is statistically signifi-cant,Nemenyi test is an alternative method for further pairwise multiple comparisons to locate the source of significance.Unfortunately,most popular statistical packages do not integrate the Nemenyi test,which is not easy to be calculated by hand.We described the theory and applications of the Kruskal-Wallis and Nemenyi tests,and presented a flexible SAS macro to implement the two tests.The SAS macro was demonstrated by two examples from our cohort study in occupational epidemiology.It provides a useful tool for SAS users to test the differences among three or more independent groups using a nonparametric method.
As a nonparametric method,the Kruskal-Wallis test is widely used to compare three or more independent groups when an ordinal or interval level of data is available,especially when the assump-tions of analysis of variance (ANOVA) are not met.If the Kruskal-Wallis statistic is statistically signifi-cant,Nemenyi test is an alternative method for further pairwise multiple comparisons to locate the source of significance.Unfortunately,most popular statistical packages do not integrate the Nemenyi test,which is not easy to be calculated by hand.We described the theory and applications of the Kruskal-Wallis and Nemenyi tests,and presented a flexible SAS macro to implement the two tests.The SAS macro was demonstrated by two examples from our cohort study in occupational epidemiology.It provides a useful tool for SAS users to test the differences among three or more independent groups using a nonparametric method.
基金
supported by a grant from the National Basic Research Program of China (No. 2011CB503804)
