I'm attempting to analyze event-related data acquired on 14 subjects, 3 runs each, in which
certain EVs, in any given run, were not represented. For example, subject #6, in run #2, may not
have provided a single example of EV 8 (while all other subjects did, as did subject #6 himself in
runs 1 and 3). I'm wondering if there is any way of creating a "place-holder" model (in three-
column format), so that all subjects and all runs can contain the same number of EVs, thereby
allowing analysis at the group level. Could I, in the above case, create a spurious model for EV 8
(in subject #6, run #2), with a fictional onset time and duration, and scale it at (or near) 0? Or
could I create an extra volume, tacked onto to the end of all scans, that represented the average
value of each functional run, and then reference this timepoint in my model as a dummy-EV?
Needless to say, I'm looking for a solution that will produce, in the worst case, a type 2 error.
Thanks for your help.