Hi,
I have a repeated measure experiment (pre, post intervention), and I would like to test for changes induced by the intervention, and if those changes are associated with a between-person behavioral score.
I previously found two networks that have differential weight after intervention with the following example design matrix and contrast (ex. for 3 subjects) :
1 0 0 1
0 1 0 1
0 0 1 1
1 0 0 -1
0 1 0 -1
0 0 1 -1
Contrast = [ 0, 0, 0, 1]
1) To test if a between-person score predict these changes, is the interaction the only way of doing that ? That would mean that my main test would now be on the interaction column of the intervention * behavioral score. Then I would have no "main effect", rather only an interaction for my question?
2) The alternative to that would be to manually compute the connectivity matrix of the change between z_scored(post_intervention_adj) - z_scored(pre_intervention_adj), then to test the main effect of the behavioral variable on the connectivity.
Should those two option be equivalent? I am very greatful for any advice.
Thank you for this tool!
Best,
Dylan
Hi Dylan,
these two approaches won't necessarily give you the exact same result, but they are both reasonable approaches. I would probably start with the computing differences a priori - second approach.
Neither approach will tell you whether change in connectivity "predicts" the behavioural score. They will reveal whether there is a statistical association between change in connectivity and behavioural scores.
Andrew
Originally posted by dylan sutterlin:
Hi,
I have a repeated measure experiment (pre, post intervention), and I would like to test for changes induced by the intervention, and if those changes are associated with a between-person behavioral score.
I previously found two networks that have differential weight after intervention with the following example design matrix and contrast (ex. for 3 subjects) :
1 0 0 1
0 1 0 1
0 0 1 1
1 0 0 -1
0 1 0 -1
0 0 1 -1
Contrast = [ 0, 0, 0, 1]
1) To test if a between-person score predict these changes, is the interaction the only way of doing that ? That would mean that my main test would now be on the interaction column of the intervention * behavioral score. Then I would have no "main effect", rather only an interaction for my question?
2) The alternative to that would be to manually compute the connectivity matrix of the change between z_scored(post_intervention_adj) - z_scored(pre_intervention_adj), then to test the main effect of the behavioral variable on the connectivity.
Should those two option be equivalent? I am very greatful for any advice.
Thank you for this tool!
Best,
Dylan