Kruskal-Wallis test

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Description

The Kruskal-Wallis test (H-test) is an extension of the Wilcoxon test and can be used to test the hypothesis that a number of unpaired samples originate from the same population. In MedCalc, Factor codes are used to break-up the (ordinal) data in one variable into different sample subgroups. 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.

Required input

Dialog box for the Kruskal-Wallis test.

The following need to be entered in the dialog box: for Data select the variable containing the data, and for Factor codes the qualitative factor. The qualitative factor may either be character or numeric codes. These are the codes that will be used to break-up the data into several subgroups.

Options

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, MedCalc performs a test for pairwise comparison of subgroups according to Conover, 1999.

Results

Results for the Kruskal-Wallis test.

In this example, it is tested whether different treatment regimens coded A, B and C in the variable Treatment, have an influence on the data in the variable Pain_relief. Pain relief was recorded on an ordinal scale from 0 to 9. Since the null-hypothesis is not rejected (P=0.1995), the conclusion is that there is no statistical significant difference between the different treatments.

For a graphical representation of this test, refer to Multiple comparison graphs.

Post-hoc analysis

If the Kruskal-Wallis test is positive (P less than the selected significance level) then MedCalc performs a test for pairwise comparison of subgroups according to Conover, 1999.

Literature

  • Altman DG (1991) Practical statistics for medical research. London: Chapman and Hall.
  • Conover WJ (1999) Practical nonparametric statistics, 3rd edition. New York: John Wiley & Sons.
  • Sheskin DJ (2004) Handbook of parametric and nonparametric statistical procedures. 3rd ed. Boca Raton: Chapman & Hall /CRC.

See also

External links

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