[Eeglablist] 4 questions about Study statistics

[Andrew Hill]

I'm trying to understand the different statistical options in the study manger, when plotting channel measures.

I have data from 4 groups (3 different manipulations and one sham) that I gathered across sequential days of testing (e.g. Session 1,3,5), and my groups are unequal in size, e.g. 8,7,8,16 subjects (with 16 in sham group).   Each 32-channel data set has 600-800 ERP trials in it, per subject and session.

In my Study design I have a Group x Session design set up, using (unpaired data) Group as the first variable (with all 4 values included) and (paired data) Session as the second variable, with just sessions 1 and 5 in the design.

Then I run the ERSP/ITC/ERP/Spectral precomputation, which takes a good long while (24 hours).  

My questions:

1) Bootstrap and paired data: from what I gather I should not use bootstrap when looking at my Session data, so I've only looked at "Group" in this way.  If I look at paired data (Session) could I just used an alpha of .025 and assume equivalence to a .05 level if the bootstrapping was working properly with paired data?

2) Non-bootstrap options:  for permutation or parametric, and calculating stats on first and second variable, the matlab window output suggests that I should not be running this type of ANOVA on the parametric/permutation data, e.g.:

... (2 balanced 1-way ANOVAS and then 4 paired t-tests)
4 x 2, unpaired data, computing F values
Using balanced 2-way ANOVA (not suitable for parametric testing, only bootstrap)
...

So is the plot channel measures allowing options to be selected that are not legitimate?

3) Use single trials (when available):
I understand from reading old list posts that the null hypothesis when using trial based stats is restricted to consideration of a specific subject's trials against the possible population of trials for that subject.  What I don't understand is when I select "trials" with a multi-subject design, what is actually happening, e.g.:   Did I just violate the assumptions of the analysis, or is this analysis now running at the subject level for my paired data, and correcting somehow to calculate/plot the stats for a study with many subjects? 

For (3), when I use this in my study and calculate the Session variable differences in spectrum, many/most of the electrodes are significant when I check several 3-hz bins.  When I don't use trial-based stats, almost none are (unless I un-check FDR).  

4) FDR and Parametric:  Unchecking FDR for Parametric seems plausible, since wouldn't that mcorrect be some sort of Bonferroni, which would be overly conservative given the highly correlated local information in ERP waveforms?


So.. can anyone help me clarify what kind of stats (using the Study manger) I should be setting up for this kind of study design?  

Thanks,
Andrew

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