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RE: st: How to get the P values of the random effects after running an xtmixed command


From   SR Millis <srmillis@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: How to get the P values of the random effects after running an xtmixed command
Date   Thu, 6 Aug 2009 15:38:11 -0700 (PDT)

Martin,

I'd recommend that you get a copy of Multilevel and Longitudinal Modeling Using Stata by Rabe-Hesketh and Skrondal.  Chapter 5 provides an excellent demonstration of model building and evaluation.  Without knowing more details of your specific dataset and hypotheses, I find it difficult to give advice.  

Taking a simple example, let's say I have a growth-curve model.  I begin with a random intercept model.  In Stata's output, there's LR test vs linear regression.  If that's significant, there's support for that random effect. 

I then fit a random coefficient/slope model. Next, I conduct a likelihood-ratio test to compare this model with the random intercept model. If that's significant, there's support for including that additional random effect.

And it goes on from there.

Scott Millis



--- On Thu, 8/6/09, Martin kavao <mkavao@aphrc.org> wrote:

> From: Martin kavao <mkavao@aphrc.org>
> Subject: RE: st: How to get the P values of the random effects after running an xtmixed command
> To: statalist@hsphsun2.harvard.edu
> Date: Thursday, August 6, 2009, 1:37 AM
> Thanks Scott
> I thought about checking the confidence interval, but I
> thought they are
> always >0. 
> 
> About using the ICC to evaluate the model: does it mean if
> a model with an
> additional parameter (random effect) fits better, then the
> random components
> are all significant?
> 
> Subject: Re: st: How to get the P values of the random
> effects after running
> an xtmixed command
> 
> There's not a consensus regarding the nature, form, and
> effectiveness of
> single parameter tests for variance components.  With
> that caution in mind,
> you can examine the confidence interval to see if it
> contains 0--to get a
> rough idea regarding the rejection of the null.  A
> better method is to
> evaluate/compare models using the deviance statistic along
> with changes in
> ICC as you systematically build and evaluate your models.
> 
> Scott Millis 
> 
> 
> 
> --- On Wed, 8/5/09, Martin kavao <mkavao@aphrc.org>
> wrote:
> 
> > From: Martin kavao <mkavao@aphrc.org>
> > Subject: st: How to get the P values of the random
> effects after running
> an xtmixed command
> > To: statalist@hsphsun2.harvard.edu
> > Date: Wednesday, August 5, 2009, 11:14 AM
> > I am doing a multilevel analysis
> > using xtmixed command and I need to report
> > the significance of the random component in the final
> > models i.e. the p
> > values. Anyone has an idea how to compute it after
> running
> > the xtmixed
> > command
> > 
> > thanks 
> > 
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