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

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Mon, 24 May 2010 09:38:04 -0700 |

Dear Junqing

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 JunqingIf 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. JunqingDear 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**:**Re: st: bivariate correlation analsis for longitudinal data***From:*jl591164@albany.edu

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