Friedman test
Compare three or more repeated measurements by their ranks.
When to use it
Use Friedman instead of a repeated-measures ANOVA when the same people are measured three or more times and the scores are ordinal or skewed.
Assumptions
Each row is one person. You need at least three measure columns. Kendall’s W is a separate analysis, not a field on this request.
Running it in Tensr
Options
Prop
Type
Reading the output
The three weekly scores. The later weeks tend to rank higher, without a person-by-person lockstep.
| Measure | Mean |
|---|---|
| week1 | 19.906 |
| week2 | 20.889 |
| week3 | 23.953 |
Significant differences across repeated measures (χ² = 9.556, p = .008, W = 0.133). Post-hoc pairwise comparisons recommended. Effect size (small; W < 0.1 negligible, 0.1 ≤ W < 0.3 small, 0.3 ≤ W < 0.5 moderate, W ≥ 0.5 large). Cutoffs: Tomczak & Tomczak, 2014. The primary result is significant (p = .008). This is large enough, in this sample, to treat the comparison this page is about as a real association rather than noise. Metrics: χ² = 9.556; df = 2; p-value = .008; Kendall's W = 0.133.
Reporting (APA 7)
Significant differences across repeated measures (χ² = 9.556, p = .008, W = 0.133). Post-hoc pairwise comparisons recommended. Effect size (small; W < 0.1 negligible, 0.1 ≤ W < 0.3 small, 0.3 ≤ W < 0.5 moderate, W ≥ 0.5 large). Cutoffs: Tomczak & Tomczak, 2014. This result is significant (p = .008). The effect is moderate, so report its size with the p-value.
Coming from SPSS
Analyze → Nonparametric Tests → K Related Samples.