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I'm assuming you mean a two-sample t-test where you say Independence is NO
(e.g. measurements are dependent).

They are fundamentally different. The Paired T-test is equivalent to the
one-sample T-test of the difference images. This is the relationship defined
in all statistical texts for paired comparisons.

Setting Independence to NO tries to estimate the dependence, but has a
different error term and different degrees of freedom. As such, this
approach is not statistically valid.

Use the paired t--test. If you are looking for an easier way to input the
data, look at the flexible factorial designs.

I'll also point out the following issues that we are working on resolving:
Group effects (even if its one group*) in repeated measures designs and
N-way within-subject ANOVAs. If you want either of these cases, then SPM
won't produce the correct statistics in the flexible factorial model.

* This would be if you have pre and post scans and ask where the average is
different than 0. SPM will produce an inflated statistic from the flex.
factorial model. The only way to currently do this is to average the pre-
and post and then do a one-sample t-test.

Hope this helps.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and

Harvard Medical School
Office: (773) 406-2464
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On Tue, Aug 9, 2011 at 4:37 PM, Sweegers, Carly <[log in to unmask]>wrote:

>  Hello, I was wondering if there are any statistical differences between
> the 2nd level paired t-test and the dependent version of the two sample
> t-test? For my design everything is repeated measures so I wonder if a
> dependent two sample is acceptable instead of the paired t-test.
>
>
>
> thanks in advance,
>
>
>
> Carly
>