What I did is: I demeaned the values in EV1 and also used D option fir
randomise (somehow I did it correct:).
Yes, that's correct.
Then, I am not sure if I should model my mean or not? I did it both with and
without the EV with ³1² only, results look roughly similar, what does it
change and what does it mean?
If you use the -D option, there is no mean in the data to model, hence including it doesn't hurt anything, but it is fitting nothing.
In group comparison, I dont assume the same variances.
Randomise, as it is just using a vanilla GLM, assumes the variance is the same for all scans.
In correlation within one group I may. Is it connected to this mean modeling?
No, totally separate issues.
So is this single EV all I need (yes, my values are
demeaned and I used D option) or do I need to model my mean ? And then I have 2 EVs, does it change anything with demeaning?
As above, "-D" ==> No mean to model
> Also, the third contrast "x(y)" is probably not meaningful... it's testing if
> the average of the two regression coefficients are zero.
>
Hmm, I in fact thought this is the main contrast to look at:(. So is it then
the first contrast that gives me info I am looking for (how TBSS values
correlate with x, controlling for y)?