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st: bivariate correlation analsis for longitudinal data


From   jl591164@albany.edu
To   statalist@hsphsun2.harvard.edu
Subject   st: bivariate correlation analsis for longitudinal data
Date   Sun, 23 May 2010 14:07:27 -0400 (EDT)

I try to do bivairate correlation analysis for longitudinal data. My
understanding is to use the data in wide format and then do regular
correlation of the x and y vairables, for example correlation of x at time
1 and y at time 2, x at time 2 and y at time 3, etc., or x and y both at
the same time point. By doing so, do i assume that time effect is the name
on the correlations at different time point? What is the appropriate way
of conducting bivairate correlation analysis for longitudinal data?

Eventually, i will fit randome intercept models for y on Xs. Thanks a lot.

Junqing


> Dear David
>
>    I think that it might help if we were able to see a picture of the
> graph that you have
> in mind. Of course, sharing such a picture is not easy via Statalist, but
> you could draw
> something and share it using Jing, see
>
> http://www.michaelnormanmitchell.com/stow/capturing-and-sharing-screen-images.html
>
>    I think that the growth model that you have in mind does not involve
> any of the
> interaction effects (note that age and age squared are not interacted with
> anything) thus
> when you look at the trajectory over time, the interactions you refer to
> just get
> "averaged out". I think it is possible that you might actually want more
> than one graph,
> for example you might like...
>
>    1) a graph with age on the X axis, the predicted outcome on the y axis,
> and separate
> lines for maternal IQ groups/treatment groups. This shows the growth curve
> over time for
> the six groups. Note that the growth curves will be parallel.
>
>    2) a graph with maternal IQ class on the X axis, separate lines for
> treatment groups.
> This will focus on the interaction.
>
>    There may be multiple ways to touch the elephant, but I don't think
> there is one way to
> touch the elephant to get the entire picture. It is also possible that you
> might be
> interested in interacting age (and perhaps age squared) with some of your
> other predictors
> if you think that the growth trajectory depends on these factors.
>
>    Finally, I would recommend "Applied Longitudinal Data Analysis:
> Modeling Change and
> Event Occurrence" by Judith D. Singer and John B. Willett to anyone who is
> interested in
> these kinds of models. It does not solve this exact problem, but is an
> outstanding
> reference, see
>
> http://www.ats.ucla.edu/stat/examples/alda.htm
>
> I hope this helps,
>
> Michael N. Mitchell
> Data Management Using Stata      -
> http://www.stata.com/bookstore/dmus.html
> A Visual Guide to Stata Graphics -
> http://www.stata.com/bookstore/vgsg.html
> Stata tidbit of the week         - http://www.MichaelNormanMitchell.com
>
>
>
> On 2010-05-22 5.52 PM, David Torres wrote:
>> Hello,
>>
>> I actually came upon the link Nick provided in a previous post
>> (http://www.ats.ucla.edu/stat/stata/faq/mar_graph/margins_graph.htm)
>> while I was working on this earlier today. The graph I was able to
>> produce does seem useful in that it shows the difference in slope
>> between child IQ scores of the different maternal IQ classes for both
>> the control and treatment groups. I wonder, though, if I'm missing
>> something by not showing growth. ???
>>
>> Since the data include assessments at several time points (8 to be
>> exact), should I not want a graph of the adjusted means at each time by
>> maternal IQ and treatment group assignment? And shouldn't it be
>> curvilinear given the quadratic? I guess I would like a growth curve
>> that takes into account significant interaction effects.
>>
>> --------------------------------------------
>>
>> David Diego Torres, MA(Sociology)
>> PhD Candidate in Sociology
>>
>>
>> *
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