Friedman Test Assignment Help — Non-Parametric Repeated Measures in SPSS

What Is the Friedman Test and When Do You Use It?

The Friedman test is the non-parametric alternative to repeated measures ANOVA. It compares three or more related measurements from the same subjects, using ranks within each subject rather than assuming normally distributed differences and sphericity. Use it when your repeated measurements are ordinal, or the data clearly violates repeated measures ANOVA’s normality or sphericity assumptions, often the case with small samples or Likert-scale outcomes.

If your data reasonably meets repeated measures ANOVA’s assumptions, use that test instead.

Assumptions You Must Check Before Running It in SPSS

• The measurements must come from the same subjects across three or more time points or conditions.
• The dependent variable should be at least ordinal.
• No normality or sphericity assumption: that’s the reason to use this test instead of repeated measures ANOVA.

How to Run It in SPSS (Step by Step)

1. Go to Analyze > Nonparametric Tests > Legacy Dialogs > K Related Samples.
2. Move your three or more related variables into Test Variables.
3. Ensure Friedman is checked under Test Type, then click OK.

How to Interpret the Output

1. In the Test Statistics table, read the Chi-Square value, df (number of conditions − 1), and Asymp. Sig., your p-value.
2. Use Kendall’s W as the effect size (available via the same dialog or computed from the chi-square value); it ranges from 0 (no agreement/effect) to 1 (perfect agreement).
3. If significant, run post-hoc pairwise comparisons: Wilcoxon signed-rank tests between each pair of conditions, with a Bonferroni-adjusted alpha to control for multiple comparisons.

How to Report the Results in APA Format

A Friedman test showed a significant difference in anxiety ratings across the three time points, χ²(2) = 11.27, p = .004, Kendall’s W = .28. Post-hoc Wilcoxon tests with Bonferroni correction showed anxiety at 3 months (Mdn = 3) was significantly lower than at baseline (Mdn = 6), p = .002.

Friedman Test vs Repeated Measures ANOVA: Understanding the Difference

Both compare three or more measurements from the same subjects. Repeated measures ANOVA compares means and requires normality and sphericity. The Friedman test compares within-subject ranks and requires neither, at some cost in statistical power. For small samples with ordinal or clearly non-normal repeated measures, a common situation with Likert-scale data collected at several time points, the Friedman test is the safer, more defensible choice.

Not sure which one your data needs? See the full SPSS statistical test guide, or get help with this specific assignment.