The Kruskal-Wallis test (H-test) is an extension of the Wilcoxon test and can be used to analyse the effect of a classification factor on ordinal data. It tests the hypothesis that a number of unpaired samples originate from the same population.
- Variable: select a continuous variable.
- Factor codes: select a categorical or qualitative variable. This variable may either contain character or numeric codes. These codes are used to break-up the data into several subgroups.
- Optionally select a filter to include a subset of cases.
- Post-hoc test significance level: the desired significance level for the post-hoc test. If the Kruskal-Wallis test results in a P-value less than this significance level, SciStat.com performs a post-hoc test.
- Jonckheere-Terpstra trend test: when the qualitative factor is ordered the Jonckheere-Terpstra trend test can be used to test the hypothesis that the medians are ordered (increase or decrease) according to the order of the qualitative factor (Bewick et al., 2004; Sheskin 2011).
If the null-hypothesis, being the hypothesis that the samples originate from the same population, is rejected (P<0.05), then the conclusion is that there is a statistically significant difference between at least two of the subgroups.
If the Kruskal-Wallis test is positive (P less than the selected significance level) then SciStat.com performs a test for pairwise comparison of subgroups.
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- Conover WJ (1999) Practical nonparametric statistics, 3rd edition. New York: John Wiley & Sons.
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- Rosner B (2006) Fundamentals of Biostatistics. 6th ed. Pacific Grove: Duxbury.
- Sheskin DJ (2011) Handbook of parametric and nonparametric statistical procedures. 5th ed. Boca Raton: Chapman & Hall /CRC.
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