Hallo Amy,
I do not see why using an a priori predefined ROI is any different
from using a restriction to an a priori predefined contrast. It all
depends on whether the a priori criterion is really a priori, or is in
reality ex post masqueraded as a priori.
In some specific instance, using the mask approach follows a clear
substantive logic. For example, if you are investigating individual
differences in cognitive capacity, you may be justified in carrying
out a contrast first, and then look at how individual differences
modulate the activation say, in prefrontal and parietal areas.
You do have to pay for the increased power (if the procedure is really
a priori), the price being that you potentially miss an effect in the
voxels outside the mask.
I do not see any simple way in which the concept of bias relates to
this specific situation; I'd rather say that these tests are
conditional on the a priori criterion. If the criterion is not a
priori, they have wrong significance values (too small), with inflated
type I errors.
When you use a cluster approach, you also have to specify a priori a
cluster definition threshold. Your p values are conditional on this
threshold. If you try several thresholds, your test will have wrong p
values.
All the best,
Roberto Viviani
University of Ulm, Germany
Quoting Amy Clements <[log in to unmask]>:
> Dear Experts,
>
> I am pretty far away from having statistical expertise, which is why
> I am posing my question to the group. Recently, I have seen a
> multitude of papers that are using a multi-masking approach to deal
> with corrections for multiple comparisons (using main effect or
> other effects of interest contrasts masks). While on the surface
> this appears to seem like an optimal approach because you are
> restricting the number of voxels included in the multiple
> comparison, it seems like an opportunity for biasing the data and
> obtained results--especially if you are not masking the data based
> from a priori hypotheses (e.g., using a previously defined
> functional ROI mask because you're interested in face processing).
>
> I'm not sure that I've articulated this is the best way. It seems,
> like I mentioned previously, to have the potential to bias results,
> but would greatly appreciate feedback. The questions typically
> asked from the lab that I've worked in have been better suited to
> utilizing a cluster-based approach; however, could also be served by
> multi-masking.
>
> Thanks!
>
>
> Amy Stephens
>
>
>
>
>
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