I have just conducted an experiment in which I have measured glucose concentration in patients (4 different treatment groups) at 3 different weeks ( pre, 6 weeks and 12 weeks). I want to use Mixed Anova to test if there is any significant difference between treatments in glucose concentration (also possible time and time*treatment interaction).
However, Mixed Anova requires the data to be normally distributed. I am using SPSS. Must I perform for example a Kolmogorov‐Smirnov/Shapiro-Wilks test (glucose data (group1, time1,…,etc.) of every group separately (at each time point)? Or is there another way to look at normality before running Mixed Anova?
Best Answer
Sphericity explained here is also assumed and, when violated, increases the Type I error rate substantially. Sphericity is almost always violated so the problem is not to test for it (although that can be done) but rather how to deal with it. Violations of normaility of residuals make less difference unless they are severe. A good argument against testing for normality is that the a priori probability that the distribution is exactly normal is essentially 0, so you know the null hypothesis is false before you do the test.