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| From | jl591164@albany.edu |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: bivariate correlation analsis for longitudinal data |
| Date | Mon, 24 May 2010 13:18:43 -0400 (EDT) |
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 >>>>>> >>>>>> >>>>>> * >>>>>> * For searches and help try: >>>>>> * http://www.stata.com/help.cgi?search >>>>>> * http://www.stata.com/support/statalist/faq >>>>>> * http://www.ats.ucla.edu/stat/stata/ >>>>> * >>>>> * For searches and help try: >>>>> * http://www.stata.com/help.cgi?search >>>>> * http://www.stata.com/support/statalist/faq >>>>> * http://www.ats.ucla.edu/stat/stata/ >>>>> >>>> >>>> * >>>> * For searches and help try: >>>> * http://www.stata.com/help.cgi?search >>>> * http://www.stata.com/support/statalist/faq >>>> * http://www.ats.ucla.edu/stat/stata/ >>>> >>> >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >>> >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/