It is important to realize that the Kruskal-Wallis H test is an omnibus test statistic and cannot tell you which specific groups of your independent variable are statistically significantly different from each other it only tells you that at least two groups were different. Note: If you wish to take into account the ordinal nature of an independent variable and have an ordered alternative hypothesis, you could run a Jonckheere-Terpstra test instead of the Kruskal-Wallis H test.

Alternately, you could use the Kruskal-Wallis H test to understand whether attitudes towards pay discrimination, where attitudes are measured on an ordinal scale, differed based on job position (i.e., your dependent variable would be 'attitudes towards pay discrimination', measured on a 5-point scale from 'strongly agree' to 'strongly disagree', and your independent variable would be 'job description', which has three independent groups: 'shop floor', 'middle management' and 'boardroom'). It is considered the nonparametric alternative to the one-way ANOVA, and an extension of the Mann-Whitney U test to allow the comparison of more than two independent groups.įor example, you could use a Kruskal-Wallis H test to understand whether exam performance, measured on a continuous scale from 0-100, differed based on test anxiety levels (i.e., your dependent variable would be 'exam performance' and your independent variable would be 'test anxiety level', which has three independent groups: students with 'low', 'medium' and 'high' test anxiety levels). The Kruskal-Wallis H test (sometimes also called the 'one-way ANOVA on ranks') is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable.

Kruskal-Wallis H Test using SPSS Statistics Introduction

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