Re: st: bivariate correlation analsis for longitudinal data

[jl591164@albany.edu]

Thanks, Michael. I wonder there is a way to get the generalized Person
between-variable and cross-time correlation coefficients without fitting
regression models.

The regression will give the regression coficient. But I am not sure how
the regression will give the person bivaiare correltaions at each time
point.

Junqing

   > Greetings Junqing
>
>   If you have not done so already, you probably want to reshape your data
> into a "long" format (e.g., see
> http://www.ats.ucla.edu/stat/stata/modules/reshapel.htm).
>
>   Having the data in a long format, you can then look at bivariate
> relationships between a continous predictor and continuous outcome
> like this (using the nlswork dataset as an example).
>
> . webuse nlswork
> . xtmixed ln_wage tenure || idcode:
>
>   The above looks at "tenure" predicting "ln_wage", with no random
> effects at level 2. If you are interested in assessing the variability
> of "tenure" as a random slope, you can do something like this...
>
> . xtmixed ln_wage tenure || idcode: tenure, cov(un)
>
>   As I mentioned in a previous post, I cannot recommend the following
> book highly enough for learning and as a reference for longitudinal
> analysis...
>
>   Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
> by Judith D. Singer & John B. Willett, see
> http://gseacademic.harvard.edu/alda/
>
> 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 Sun, May 23, 2010 at 11:07 AM,  <jl591164@albany.edu> wrote:
>> 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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