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From |
Michael Mitchell <Michael.Norman.Mitchell@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: bivariate correlation analsis for longitudinal data |

Date |
Sun, 23 May 2010 12:32:13 -0700 |

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/

**References**:**st: bivariate correlation analsis for longitudinal data***From:*jl591164@albany.edu

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