The rationale for taking up the con images is that the test you will then be implementing at the second level will, on average, be equivalent to a full mixed effects analysis.
You can read more about this in Chapter 12 of the SPM book (co-authored by Andrew Holmes):
http://www.fil.ion.ucl.ac.uk/~wpenny/publications/spm-book/rfx.pdf
Best, Will.
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
> On Behalf Of Stephen J. Fromm
> Sent: 22 March 2012 14:36
> To: [log in to unmask]
> Subject: Re: [SPM] 2nd level analyses on t statistics vs. contrast
> images
>
> I've thought about this issue in the past, but not enough to have a
> definitive answer.
>
> Here's a post I made previously which cited former SPM stats guru
> Andrew Holmes:
> http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;fadc02b2.1109
>
> In particular, AH wrote, "The statistic (SPM) images (SPMt_????.img &
> SPMF_????.img) should *not* be entered into a second level analysis if
> you want to effect a random effects analysis. This would basically be
> assessing the significance (across subjects) of the individual subjects
> significance! (Rather than the significance (across subjects) of the
> response."
>
> I think the basic reasoning is that we're interested in drawing
> inferences on the effect size, not on the significance.
>
> As others have stated, I'm pretty sure that if you use Z scores rather
> than the contrast itself, you're not going to be doing random effects.
>
> Perhaps the way to settle the issue would be to write down a bunch of
> models and compare what this method estimates to what you _want_ to
> estimate. (That would show, for example, that it's not measuring the
> between-subject variation.)
>
> If you're really dying to know you could try asking on one of the
> sci.stat.* newsgroups. :-)
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