Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
jl591164@albany.edu |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Mon, 24 May 2010 11:52:32 -0400 (EDT) |

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/

**Follow-Ups**:**Re: st: bivariate correlation analsis for longitudinal data***From:*"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>

- Prev by Date:
**Re: st: AW: RE: renaming variables** - Next by Date:
**st: Exporting summary tables, appending a column** - Previous by thread:
**Re: st: bivariate correlation analsis for longitudinal data** - Next by thread:
**Re: st: bivariate correlation analsis for longitudinal data** - Index(es):