Re: st: bivariate correlation analsis for longitudinal data

[jl591164@albany.edu]

Thanks, Michael for your kind responses.
Junqing

 > Dear Junqing
>
>    My apologies for misreading your first email... I only saw the term
> "bivariate" but
> overlooked that you used the term "correlation". My standard script kicked
> in about the
> steps for doing bivariate regressions as a first step for a random effects
> model. I have
> not given much thought to looking at correlations in the context that you
> describe beyond
> what you have already done. Perhaps someone else on the list has more
> experience with this
> idea than myself.
>
> Thanks for following up and gently noting my misunderstanding!
>
> 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 2010-05-24 8.52 AM, jl591164@albany.edu wrote:
>> 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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