Thanks Matthew for your response.
I just want to confirm that you meant I need to run 2 F-tests for 2 contrasts as shown below, each of which will test both directions, right?
t1>t2 (F1) will also check (t2>t1)
t1>t2, G1>G2 (interaction effect) F2 that will also check (t2>t1, G2>G1)
Or do I need to make 4 contrasts and run F-tests?
If the former is correct, are we supposed to run separate t-test to get the direction?
Thanks,
Sara
________________________________________
From: FSL - FMRIB's Software Library [[log in to unmask]] on behalf of Matthew Webster [[log in to unmask]]
Sent: Wednesday, September 18, 2019 9:02 AM
To: [log in to unmask]
Subject: Re: [FSL] PALM design question
Hi Sara,
I believe Anderson recommends running F-tests on both contrasts as default, which will test both directions.
Permutation inference for the general linear model ( https://doi.org/10.1016/j.neuroimage.2014.01.060 ) explains F-tests and G-stats in the context of non-parametric statistics.
Hope this helps,
Kind Regards,
Matthew
--------------------------------
Dr Matthew Webster
FMRIB Centre
John Radcliffe Hospital
University of Oxford
> On 17 Sep 2019, at 23:08, [log in to unmask] <[log in to unmask]> wrote:
>
> Hi FSL team,
>
> I have used the design_ekaterina.ods example to create my design for a similar analysis. I have two groups (Treatment, waitlist) and two sessions (pre and post intervention) and I am mainly interested in the interaction effect. My question is should I only use the two contrasts defined in the example or should I also look at the t2>t1, and the interaction of t2>t1, G2>G1. Is this contrast somehow similar to the other one or not. I am a bit confused on that.
> I do not want run too many analysis that decreases my analysis power but I also do not want to miss potential significant results.
> Also, I was wondering if you could introduce a paper that explains the F-test and G-stats in more details.
>
> Thanks,
> Sara
>
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