On Sun, 26 Dec 1999, Greg Hooper wrote:
> I want to use a one-way random effects anova intraclass correlation on
> the following data. 70 subjects, each with 30 measurements of the one
> property - their EEG alpha frequency - taken across a 5 minute interval.
Every ten seconds, then? How are you proposing to model the time
dependency(ies) across the 5 minutes? Is it reasonable to model EEG
alpha as constant during that time? One would suppose not, since you
write of "a one-way random effects anova" and the random effects in
question are, presumably, time effects? But then it would appear that
you would be considering the 70 first measurements (one for each subject)
as somehow equivalent, and possibly systematically (if randomly)
different from the 70 second measurements, and the 70 third measurements,
and so on. That doesn't sound to me any more reasonable than a constant
over the 5 minutes. But perhaps I misapprehend your purpose...
Presumably the 70 subjects are homogeneous with respect to possible
between-subject variables of interest (sex or age, e.g.), else you'd have
mentioned the design factors...
> I'm looking at both single and average measures for reliability.
> When assessing normality of the distribution ...
You must mean "assessing non-normality"?
> do I look at the entire data set, i.e. 70*30 measures, or do I look at
> the single columns of alpha, i.e. 30 distributions of 70 measures each.
By "alpha" I take it you mean the EEG alpha frequency measures, not the
reliability coefficient alpha. In general, the assumption associated
with anova is that the residuals -- i.e., the departures from the model
one is trying to fit -- be normally distributed. In practice, it usually
suffices if they're unimodal and not too asymmetric. It follows that you
cannot assess possible non-normality of residuals until after you have
attempted to fit a model.
> Is the within subject distribution important, i.e. 70 distributions of
> 30 measures each?
It certainly would have some bearing, one would expect, on how you chose
to model the time series. If your model were too simple for the universe
of discourse, the distributions of residuals -- and perhaps particularly
the time-dependent distribution of residuals -- would provide some
evidence that the model needed revision.
> Thank you for your time, I understand this is trivial but I find the
> statistic texts i have consulted quite opaque on this point.
Not all that trivial (in the corrupt modern sense; might well be trivial
as a metaphor for the classical sense of "belonging to the trivium", i.e.
the first three of the seven liberal arts). And textbooks do tend to be
opaque, I'm afraid.
-- DFB.
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Donald F. Burrill [EMAIL PROTECTED]
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