Introduction
A Kruskal Wallis Test compares the difference between more than two independent groups, such as comparing the difference between groups A, B and C. If your data only has two groups such as Male/Female or Present/Absent you should consider the Independent-Samples t-Test
It is considered a non-parametric test and is suitable for non-parametric data. To check if your data is parametric, please check out the dedicated guide here: Parametric or Not Guide
If your data is parametric you should consider using a One-Way Between-Subjects ANOVA
Test Procedure
Step 1: Click Analyze > Nonparametric > Legagy Dialogs > K Indepenedent Samples
Step 2: Within the "Tests for Several Independent Samples" Window, select the test variable or dependent variable you are analysing and move it to the "Test Variables List" box. Then move your independent/grouping variable into the “Grouping Variable” box.
Step 3: Click "Define Range". Select the numbers corresponding to your maximum and minimums values, I have 3 groups labelled 1,2 and 3, so I will use 1 and 3.
Step 4: Click "Options". Within the "Several Independent Samples: Options" window select "Descriptives"
Step 5: Select Continue à OK
Results
SPSS will generate three tables, to correctly report this test we need three, the Descriptives, the ANOVA, and the ANOVA Effect Sizes.
Descriptives
This table shows a selection of descriptive statistics: the sample size of each group (N), the mean of each group (Mean), and the standard deviation of each group (Std. Deviation), best practice is to report them all.
Test Statistics
This table shows the specific test results including the H-statistic (H), the degrees of freedom (df) the two-tailed significance/p-value (Asymp. Sig), we need to report all three.
Reporting the Results in APA Formatting
Test scores of three groups (A, B, and C) were compared. On average, Group A (Mean Rank = 34.91) scored higher than Group B (Mean Rank = 30.71), but lower than Group C (Mean Rank = 46.43). A Kruskal-Wallis Test indicated there was a significant difference between the three groups, H(2) = 6.75, p = .034.
Post hoc comparisons were conducted using Mann-Whitney Tests and a Bonferroni-adjusted alpha level of .016 (0.05 ÷ 3)
The difference between Group B and Group C was statistically significant ( U(NGroup B=24, NGroup C=22)=145, Z=2.62,p=.009), and all other combinations were non-significant.
If your ANOVA is significant you must report your Post-Hoc results, these are indicated in green.
