Email discussion lists for the UK Education and Research communities

## SPM@JISCMAIL.AC.UK

#### View:

 Message: [ First | Previous | Next | Last ] By Topic: [ First | Previous | Next | Last ] By Author: [ First | Previous | Next | Last ] Font: Proportional Font
 LISTSERV Archives SPM Home SPM July 2011

#### Options

Subject:

Re: SPM: MEG within-subject covariates

From:

Date:

Fri, 22 Jul 2011 17:35:26 +0100

Content-Type:

text/plain

Parts/Attachments:

 text/plain (40 lines)
 ```Dear Tal, The summary statistics are indeed con images and not t-images. t-images conflate effect size and error and it is therefore not meaningful to do further statistics on them (for instance compute their mean across subjects). The only thing that you can do with them is use them for computing p-values. With the summary statistic approach within-subject variance is not taken into account at the second level. The inference is made in relation to between-subject variance. This makes it possible to draw conclusions with respect to the population from which the subjects were taken. A fully hierarchical approach makes it possible to take into account both within and between subject variance but it is computationally expensive (or at least was when people worked on those things) and it has been shown that the summary statistic approach is a good approximation to it. See http://www.fil.ion.ucl.ac.uk/spm/course/slides11/05_Group_Analysis_FIL2011May.ppt for more details and references. Best, Vladimir On Fri, Jul 22, 2011 at 5:16 PM, Tal Linzen <[log in to unmask]> wrote: > Hi Vladimir, > > One point of confusion -- contrary to what I had thought, the contrast > images (con_0001.img) are not t-statistics, but the regression coefficients > themselves (in simple contrasts such as [0 1]). Does that make sense? > Shouldn't I take the t statistic maps instead, to account for the > within-subject variance? I thought that the mixed-effects model should take > into account both the within-subject variance and the between-subject > variance, and in the summary statistic approach this would amount to running > a t test on the t statistics rather than the raw beta values. > > Thanks, > Tal > ```