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<DIV><FONT face=Arial size=2>Hello!</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Thank you for your answers. Here are my replies to
them:</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>To avoid any misunderstandings here is again a
summary of my study design</FONT></DIV>
<DIV><FONT face=Arial size=2>- I have 11 subjects. </FONT></DIV>
<DIV><FONT face=Arial size=2>- Each of them performed 6 tasks related to working
memory.</FONT></DIV>
<DIV><FONT face=Arial size=2>- 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)</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Given my study design I thought something like a
one-way ANOVA for correlated samples would be in order.</FONT></DIV>
<DIV><FONT face=Arial size=2>(as I am comparing 6 conditions within-subjects as
every subject performed all sets)</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>-----------------</FONT></DIV>
<DIV><FONT face=Arial size=2><FONT color=#0000ff>> In your case you have
observed a "value" in 11 subjects, over 6 <BR>> conditions. That value
happens to be coherence, but could as well <BR>> have been something
else. Since you have manipulated the conditions <BR>> with each
subject, you can use a dependent-samples test: the observed <BR>>
values depend on the subject (an example of a dependent-samples test
<BR>> is a paired-t test).<BR></FONT>-------------------</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>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.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>I thought that I would first do an ANOVA and then
something like the Tukey HSD Test to cimpare individual conditions</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>--------------------<BR><FONT color=#0000ff>> If
the number of trials varies between subjects and/or sessions, it <BR>>
will be preferable to transform your coherence values to z-scores <BR>>
manually prior to submitting the values to
clusterrandanalysis.<BR></FONT>--------------------</FONT></DIV>
<DIV><FONT face=Arial size=2>Yes I agree that would need to be done
manually.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>-----------<BR><FONT color=#0000ff>> Now about
the 6 conditions, am I right that you have a 1x6 factorial <BR>>
design, or is there more strucure in the design? Do you have a <BR>>
specific hypothesis about the different conditions? If you only want
<BR>> to reject the null-hypothesis "the observed value (coherence) is
the <BR>> same (more accurately: stems from the same distribution) in
all my 6 <BR>> conditions", you would use an omnibus F-statistic,
i.e. <BR>> 'depsamplesF'. It might be that your hypothesis is more
specific, or <BR>> also in case you find an omnibus effect, then you
would probably want <BR>> to perform explicit tests between two subsets
of your 6 conditions <BR>> using a 'depsamplesT' test.<BR>>
</FONT></FONT></DIV>
<DIV><FONT face=Arial size=2>----------</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>I want to contrast all conditions at once (f -test
anova) AND also each combination of conditions (say a post- ANOVA
Q-test)</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>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</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>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.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Thank you for your help!</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Regards,</FONT></DIV>
<DIV><FONT face=Arial size=2>Jurij Dreo<BR></FONT></DIV></BODY></HTML>