Wilcoxon Signed rank test, Students's T-test, or a mixture of both?

[pepperpockets]

Hello!
For my dissertation I am looking at the temporal order of physiological and behavioural events before a voluntary action. I have 12 different repeated measures variables including: Time of intention (W), Readiness potential 1 (as measured by task A) Readiness potential 2 (task A), RPmax (task A), ERDalpha(task A), ERDbeta (task A), Time of probed intention (T), Readiness potential 1 (as measured by task B) Readiness potential 2 (task B), RPmax (task B), ERDalpha(task B), ERDbeta (task B).
And with these variables in order to establish the order of events I need to make many many comparisons (and use the appropriate bonferroni correction).
My issue is that some of the variables are normally distributed and some aren't - for example, time of intention (W) is normally distributed, but Readiness potential is not.
I know that if one of your varibles is not normallly distributed, you should use a non-parametric test ie Wilcoxons. But should I be using Wilcoxons for some of theses comparisons and then for the odd comparison jump back to a t-test? Or should I just use Wilcoxons for all my comparisons?
Thanks

 

[Karabiner]

My issue is that some of the variables are normally distributed and some aren't - for example, time of intention (W) is normally distributed, but Readiness potential is not.

Do you want to perform t-tests for repeated measures? Then its the
difference which is to be checked, not the original variables. And how large
is your sample size? With n > 30, normality is no longer an issue for t-tests.

With kind regards

K.

 

[pepperpockets]

My sample size is only 13 i'm afraid!
And yes, t-tests for repeated measures. Thank you, I was checking the normality of the original variables, but I will go and check the normality of the difference.

 

[Karabiner]

With n = 13 you better use Wilcoxon throughout. With such a small sample
one cannot reliably assess whether the normality assumption is met.

With kind regards

K.

 

[pepperpockets]

Thank you very much!