The Friedman ANOVA/Friedman-Test in SPSS usually consists of three parts:
- First, the Friedman Test/ANOVA itself, which tests the medians of the same test subjects across at least 3 point in time for differences. In addition it is common practice to report the effect size Kendall's w for the test itself.
- Second, in case of a significant Friedman Test/ANOVA, post hoc-tests to determine between which of the points in time differences exist, should be calculated.
- Third, the effect size r for time point differences that showed a low enough p-value.
Reporting
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The reporting is limited to the essentials: the test statistic (Chi-Square-value), the degrees of freedom, the p-value and results for post-hoc-tests, consisiting of p-value and effect sizes respectively.
The effect size can be classified with comparable studies or research field specific thresholds, optionally with Cohen (1992): A Power Primer.
It can be helpful to add the means and standard deviations for the time points as well as the Eta-squared effect size.
Please be aware that an effect size for the test itself is not really desirable since you are in almost all cases more interested in the specific pairwise differences. It is more frequently used to compare your study with others, for instance when it comes to a priori sample size calculations.
Sample formulation:
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"The Friedman test showed differences of the test score across the 3 points in time (Chi2 (2) = 28.8, p ◃ .001, W = 0.24.
Differences occured between t0 (MDN = 23) and t1 (MDN = 27.5) with p ◃ .001,r = 0.497 and between t0 and T2 (MDN = 28) with p ◃ .001, r = 0.646). Both effects tend to be large (Cohen (1992))."
⏰ Timestamps:
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0:00 Introduction and overview
0:09 Friedman Test/ANOVA results
0:22 Post-hoc-test results and respective effect sizes
0:31 Classifying results
If you have any questions or suggestions regarding the write-up for the Friedman Test/ANOVA in SPSS, please use the comment function. Thumbs up or down to decide if you found the video helpful.
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