What you really need to do is create consistent *contrasts* not EVs.
It is contrasts that get fed up into the higher level analyses, not EVs.
If an EV is missing, then do not have it in the model. However, when
formulating your contrasts make sure that the numbering of the contrasts
is the same in each subject/session so that these feed up consistently.
You'll have to be careful to select the appropriate EVs each time, as
their numbering will change whenever some are missing.
For example, if you had a contrast like 1 0 -1 0 0 1 0 and in one case
EV2 was missing then the design matrix would only have 6 columns,
not 7, and the contrast would become 1 -1 0 0 1 0 in this
instance (assuming the other EVs were present).
Obviously these missing EVs cannot contribute to the contrast, but
as long as each contrast contains at least one EV then you are fine.
All the best,
Sam Harris wrote:
>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.