Message-ID: <21876376.post@talk.nabble.com>
Date: 2009-02-06T17:07:31Z
From: Ian Fiske
Subject: one-sample t-test with correlated (clustered) observations
In-Reply-To: <21875193.post@talk.nabble.com>
To handle the correlations, you can treat individuals as random blocks. So
you have a mixed model with measurement technique crossed with measured
attribute and random intercepts for each individual. You can fit this with
lmer() in the lme4 package. Keep in mind there are a number of variations
on this... like whether or not to include a measurement*attribute
interaction, etc.
good luck,
ian
Paul Artes wrote:
>
> I would like to estimate the difference between two measurement
> techniques. With both techniques, 4 measurements were obtained in each of
> 15 individuals. (These are not *repeated* measurements though - each of
> the 4 is of a different attribute). The naive approach would be a paired
> t-test, but of course this assumes that the 4 measures contributed by each
> individual are not dependent (which they are), and would inflate the CI of
> the differences.
>
> I found t.test.cluster {Hmisc}, but this works for the 2-sample problem
> only as far as I understand...
>
> Could someone please point me in the right direction?
>
> Many thanks!
>
> Paul
>
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