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Re: st: Testing equality of means with correlated samples?
Once I faced a similar problem in which I wanted to compare the mean
of a variable y for two (unpaired) groups, say A and B, without
imposing the iid assumption.
My solution was to run a regression of y on dummies for A and B
(noconstant) and allow the "robust cluster" options to take care of
the non-iid errors.
Since you don't exactly say what your variables A and B are, and
whether you have individual data (vs. only means and sd's), it is hard
to know if my suggestion would help...
On 4/27/05, Nick Cox <firstname.lastname@example.org> wrote:
> This problem is discussed, among other places,
> in Rupert Miller's book "Beyond ANOVA". As
> you say, the issue is getting a more accurate P-value.
> I think you can do that if you also have a serial
> correlation, i.e. the dependence arises because
> these values are from a time series. I am not aware
> that the procedure is canned as a Stata program, but
> it should yield to calculator-style manipulations.
> Berk Sensoy
> > Thanks for your answer, but I don't think it will work because ttest
> > assumes that variables in each sample are iid.
> > Essentially, because observations of each variable are correlated, I
> > have fewer effective observations than actual observations, so ttests
> > understate the standard error of the mean.
> > Anyone know how to test mean(A)=mean(B) when cov(ai,aj) != 0 and
> > cov(bi,bj) != 0?
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