Repeated Measures ANOVA in SPSS - checking normality assumption

The main requirement of the repeated measures ANOVA, next to the interval or ratio level for the test variable, is normal distribution.
For the repeated measures ANOVA the dependent variable for each point in time needs to be approximately normally distributed.
Despite there being three ways to test for normal distribution, the Shapiro-Wilk-test, a histogram or a Q-Q-plot, I will only show the latter for the following reason: As with all analytical tests, large samples have more power and will “find” significant deviations from normal distribution, even if those deviations are negligible. Therefore, caution is advised when blindly trusting a p-value.
Please refer, among many other publications, to Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492.
Eventually, put emphasis on the plots, mainly a histogram or a q-q-plot. I prefer the latter since one can manipulate the histogram with a proper "bin width".
Final note: a z-standardization before plotting a histogram or q-q-plot is optional. You will only see a slightly different histogram (reminder of the bin width) with less cliffs on the inside. The q-q-plot is not affected, hence my advise to use this as a test for normal distribution.
⏰ Timestamps:
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0:00 Introduction and overview
0:16 Shapiro-Wilk-Test - why not to use
0:35 Q-Q-Plot creation and interpretation
If you have any questions or suggestions regarding checking the normal distribution for the repeated measures ANOVA in SPSS, please use the comment function. Thumbs up or down to decide if you found the video helpful.
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