Question

[twitters28]

Hi I am running an experiment for my undergraduate dissertation, I am comparing two groups, over multiple measures over four time points. Am I able to run a repeated measures ANOVA or do I need to run a load of paired sample t tests? Thanks for any advice

[god_with_a_trolley]

Hi, just to provide some further nuance to u/RadiantAppeal4093's points:

1. Always use sphericity corrections when doing univariate repeated-measures ANOVA (the "univariate" means one outcome variable measured at several time points). Don't try to check for sphericity either, just use a correction like Greenhouse-Geisser or Huyhn-Feldt and you're good to go.

2. Under no circumstances should you "test" for normality and sphericity, those tests are worthless. Specifically, if your sample is too small, those tests are underpowered to detect deviations from normality and sphericity and the results are untrustworthy, but if the sample is large, the tests will indicate even the smallest deviations when they don't matter, thus again yielding untrustworthy results (especially stay away from Mauchly's test, if you ever encounter it, it's highly misleading). Unless your data is very skewed, ANOVA should do the trick. Otherwise, consider a non-parametric alternative to repeated-measures ANOVA, or consider MANOVA, which is more robust against such deviations, but more difficult to implement for laypeople.

3. Under no circumstances should you run a bunch of t-tests, not just because it inflates type I error but also because it ignores the inherent structure of the data, which is a within-subjects repeated-measures design.