Dear Richard,
thanks for the update. given this, i continue to wonder
if it is necessarily a priori biased to somehow weight each
subject by the quality of their fit when performing the second
stage t-test during an rfx analysis. on the one hand its seems
legitimate since a subject can be poorly fit regardless of the size
of the height estimate (or difference estimate). in practice however,
i would expect that subjects with small estimates would in
fact have larger residuals.
i would guess that the answer to this rests on exactly what one wants to
say about the "population". in the extreme such a weighting approach
would face the same problem as a fixed effects approach,
in that a minority of the sample could determine the conclusions
(i.e. really poorly fit subjects would be weighted negligibly).
nevertheless,
i can't help thinking that taking 15 or 20 gigabytes of subject data,
reducing it to 12 or so synthetic height estimates that are all considered
equally
valid, and then asking whether the 95 percent CI of these
encompasses zero is a bit extreme.
one alternative that we have tried is to start with the fixed effects
analysis,
and then followup by asking if the regions identified in the fixed
effects are reliable across the subjects in the sample using roi extraction
and random effects anova on the extracted timecourse data for each subject.
if the condition effects and condition by time interactions are significant,
one can assume the activation is reliable in the sample. the presumption
then is that given another sample of 7 or 8 right-handed, undergraduate,
native-
english speakers, i should expect a similar outcome. of course this
is an reasoned versus statistical "population" inference. i'd be
interested if you or anyone has any comments regarding the
veracity of this method.
cheers.
ian.
Richard Perry wrote:
> Dear Ian,
>
> Apologies: Rik Henson has pointed out an error in what I wrote yesterday.
>
> >Dear Ian...
> >...It's not so much that you are assuming that the observations from
> >all subjects are equally reliable (this is clearly not the case!)...
>
> Apparently you DO in fact assume equal 'efficiency' of estimates of
> the betas in each individual in a RFX analysis. I'm not quite clear
> why (and it worries me a bit, because I know that the variance often
> varies considerably from one individual to another!). I thought I
> should at least warn you of this immediately; perhaps someone will be
> able to write an e mail explaining why this is so later!
>
> Best wishes,
>
> Richard.
> --
> from: Dr Richard Perry,
> Clinical Lecturer, Wellcome Department of Cognitive Neurology,
> Institute of Neurology, Darwin Building, University College London,
> Gower Street, London WC1E 6BT.
> Tel: 0207 679 2187; e mail: [log in to unmask]
--
Ian G. Dobbins
MGH-NMR Center
Bldg. 149, 13th Street
Charlestown, MA 02129
(ph) 617-724-9989 (fax) 617-726-7422
[log in to unmask]
www.nmr.mgh.harvard.edu/people_final.htm
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