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Re: st: Correlation between 2 variables overtime- accounting for repeated measures


From   "JVerkuilen (Gmail)" <jvverkuilen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Correlation between 2 variables overtime- accounting for repeated measures
Date   Sat, 16 Mar 2013 09:44:23 -0400

On Sat, Mar 16, 2013 at 8:32 AM, megan rossi <megan_rossi@msn.com> wrote:
> Hi All
> Can you please recommend what syntax would be most appropriate for my below senario
>
> 40 participants with three repeated measures (baseline, year 1, year 2)- observational study ie. no intervention
> At each of these three time points two continous variables (a) and (b) were measured. I want to know whether (a) and (b) are correlated. If I do a correlation at one time point ie. year 1 the correlation is not significant which I believe is due to the small numbers ie. 40. If I can find a method of accounting for the lack of dependence among these three time points ie. repeated measures I will effectively have 120 pieces of data, which should be sufficient to see a correlation if one really exists.
> Cheers,

You could approach this a few different ways. One might be to set up
the appropriate SEM with correlated residuals to take the longitudinal
dependence into account. The other would be to set the data up as a
linear mixed model and use say, AR(1) residuals. I think the SEM
approach is probably the most straightforward. That said, before you
do either, be sure to scatterplot both variables broken out over time.
SEM might wring some extra statistical efficiency, but don't get too
optimistic.

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