John,
What was wrong with the paired T-test model? In this case, that would be the appropriate model to use given your pre-existing contrasts, and it should tell you where A and B are different. The model itself will look odd if you haven’t viewed design matrices like that before, as you’ll have 2 columns for you contrasts and a bunch of columns for your subjects.
Generally, if you have access to the original data, it would probably make more sense to just directly contrast A and B at the first level and enter that contrast into a one-sample T-test. But ultimately it will be an equivalent model (assuming that baseline_condition in your contrasts is indeed an identical baseline for both the A and B contrasts) because a one-sample T-test on a difference of conditions is identical to a paired T-test on the individual conditions.
-Mike
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Mike Angstadt
Research Computer Specialist / PANLab Lab Manager
Department of Psychiatry / University of Michigan
(734) 936-8229
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of John Gelburg
Sent: Wednesday, August 17, 2016 11:47 AM
To: [log in to unmask]
Subject: [SPM] simple question on random-effects second-level analysis
Hi all,
I have two sets of con* images from two first level contrasts: condition_A > baseline_condition and condition_B > baseline_condition. At the second level I need a contrast condition_A > condition_B. Is it achievable given data that I have? If so, which model should I define? - I tried a paired t-test but it seems to make something different.
Many thanks,
John
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