Statistics for coherence in a within-subject experiment

[Jurij Dreo]

Hello!

Thank you for your answers. Here are my replies to them:

To avoid any misunderstandings here is again a summary of my study design
- I have 11 subjects. 
- Each of them performed 6 tasks related to working memory.
- Each subject performed anywehere from 25 to 60 repeats (trial) of each condition (the standard number of trials per task was 30 - but some subjects performed the entire experiment twice (ergo 60 potential trials) .... and in some some of their trials are unusable - artifacts ... ergo 25 min number of possible trials)

Given my study design I thought something like a one-way ANOVA for correlated samples would be in order.
(as I am comparing 6 conditions within-subjects as every subject performed all sets)


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> In your case you have observed a "value" in 11 subjects, over 6  
> conditions. That value happens to be coherence, but could as well  
> have been something else. Since you have manipulated the conditions  
> with each subject, you can use a dependent-samples test: the observed  
> values depend on the subject (an example of a dependent-samples test  
> is a paired-t test).
-------------------

I´m not sure what you are saying here.... I agree with the dependent samples idea... but I cannot preform a t-test ... ie. more of them as that would inflate my p-rate overall.

I thought that I would first do an ANOVA and then something like the Tukey HSD Test to cimpare individual conditions

--------------------
> If the number of trials varies between subjects and/or sessions, it  
> will be preferable to transform your coherence values to z-scores  
> manually prior to submitting the values to clusterrandanalysis.
--------------------
Yes I agree that would need to be done manually.

-----------
> Now about the 6 conditions, am I right that you have a 1x6 factorial  
> design, or is there more strucure in the design? Do you have a  
> specific hypothesis about the different conditions? If you only want  
> to reject the null-hypothesis "the observed value (coherence) is the  
> same (more accurately: stems from the same distribution) in all my 6  
> conditions", you would use an omnibus F-statistic, i.e.  
> 'depsamplesF'. It might be that your hypothesis is more specific, or  
> also in case you find an omnibus effect, then you would probably want  
> to perform explicit tests between two subsets of your 6 conditions  
> using a 'depsamplesT' test.
> 
----------

I want to contrast all conditions at once (f -test anova) AND also each combination of conditions (say a post- ANOVA Q-test)

But Im still not sure which statistic to use and even if the one I use will be able to deal with the fact that I have a different number of conditions per condition per subject

As far as I understand it... we must use some kind of ANOVA here... A simple "paired" t-test will not do... as we are comparing more than 2 conditions at once and I do not want to inflate my p-rate.

Thank you for your help!

Regards,
Jurij Dreo
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