I would interpret significant regions from this test as those in which there was a "main effect of hair color correcting for eye color". The test would be directional (ie: the two contrasts would not show the same regions).
If I wanted to test the hypothesis that there is a main effect of eye color on FA "correcting for hair color" then I could set up the following design:
1 0 0.5
1 0 0.5
1 0 -0.5
1 0 -0.5
0 1 0.5
0 1 0.5
0 1 -0.5
0 1 -0.5
Contrasts:
1 -1 0
-1 1 0
If I wanted to test an interaction between hair color and eye color I could setup the following design file:
1 0 0.5 0
1 0 0.5 0
0 1 0.5 0
0 1 0.5 0
1 0 0 0.5
1 0 0 0.5
0 1 0 0.5
0 1 0 0.5
An example for that design file for my test would be:
1 -1 1 1
1 -1 1 1
-1 1 1 1
-1 1 1 1
1 -1 -1 1
1 -1 -1 1
-1 1 -1 1
-1 1 -1 1
Contrasts:
1 0 0 0
0 1 0 0
0 0 1 0
Ftests:
1 0 0
0 1 0
0 0 1
I would interpret the Ftests as "a main effect of hair color", "a main effect of eye color" and an "interaction between hair color and eye color" respectively.
So, here's my question again: how are t-tests with a dichotomous covariate, or t-tests with dichotomous covariate interaction different to a 2x2 anova? Are the t-tests that I outlined valid? Do they converge with the anova? I do appreciate that the anova is conducting an F test while the others are pulling from a t-distribution. But is that the only difference?
Any thoughts would be greatly appreciated.
Thank you very much, and apologies again for the long email.
Kx
--
Kirstie Whitaker, PhD
Research Associate
Department of Psychiatry
University of Cambridge
Mailing Address
Douglas House
18b Trumpington Road
Cambridge, CB2 8AH
Phone: +44 7583 535 307
Website: www.kirstiewhitaker.com