New day, new question. We're running an event related novelty oddball
study. (Give people a task, surprise them every now and then with a
"novelty" event (Nov1) that is always something new, after a while, in
another run, repeat some of these novelty events (Nov2) to see what
happens). We scan the subjects over multiple runs (12). All of these
contain Nov1 events, all but the first contain Nov2 events. (None of
the novelty events are repeated in the same run, only in later ones).
So here's the question. It seems to me there are a number of ways to
analyze this data to see if there is a difference between first time
and repeated novel events (Nov1 vs Nov2).
1. In all but the first run (where there are no Nov2), model an EV that
gives some relationship between the Nov1 and Nov2 events. But I don't
want to a priori do this.
2. Have one EV for Nov1 and one EV for Nov2, do a contrast within each
run (all but run 1) of Nov1 vs Nov2. Then do a second level within
subject of this cope, then a third level across subjects.
3. Have one EV for Nov1 and one EV for Nov2. Do a contrast for each EV.
Do a second level analysis within subject of the Nov1 contrast and also
the Nov2 contrast (so, 12 copes for Nov1 and 11 for Nov2). Do a third
level within subject of the Nov1 cope vs the Nov2 cope. Then a fourth
across subjects. (As I'm writing this, I'm sure this is not the way to
So perhaps I answered my own question... thoughts?
Robin Goldman, Ph.D.
Hatch Center for MR Research
710 W. 168th Street, NIB-1
New York, NY 10032