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RE: st: Interpretation of Variace Estimates (xtmixed)


From   "Cohen, Elan" <cohened@upmc.edu>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Interpretation of Variace Estimates (xtmixed)
Date   Wed, 2 Sep 2009 11:37:43 -0400

I'm not sure that it makes sense to talk about R-squared in a multilevel model (though I'd love to hear opinions on this).  You may want to think about reliability or intra-class correlation, both of which are talked about in Rabe-Hesketh & Skrondal.

- Elan


> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
> statalist@hsphsun2.harvard.edu] On Behalf Of Christian Weiß
> Sent: Wednesday, September 02, 2009 11:21 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Interpretation of Variace Estimates (xtmixed)
> 
> Dear John,
> 
> browsing to my edition of this book I could not find the respective
> topic (R-square in multilevel models). Could you provide any more
> detailed reference to the book?
> 
> Viele Grüße
> Christian
> 
> 
> 
> 
> On Tue, Sep 1, 2009 at 6:13 AM, John Antonakis<john.antonakis@unil.ch>
> wrote:
> > Hi:
> >
> > There should also be formulas for r-square, at level 1 and 2 are in:
> >
> > Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and Longitudinal
> > Modeling Using Stata. College Station, TX: Stata Press.
> >
> > Best,
> > J.
> >
> > ____________________________________________________
> >
> > Prof. John Antonakis
> > Associate Dean Faculty of Business and Economics
> > University of Lausanne
> > Internef #618
> > CH-1015 Lausanne-Dorigny
> > Switzerland
> >
> > Tel ++41 (0)21 692-3438
> > Fax ++41 (0)21 692-3305
> >
> > Faculty page:
> > http://www.hec.unil.ch/people/jantonakis&cl=en
> >
> > Personal page:
> > http://www.hec.unil.ch/jantonakis
> > ____________________________________________________
> >
> >
> >
> > On 01.09.2009 11:11, Maarten buis wrote:
> >> --- On Mon, 31/8/09, Christian Weiß wrote:
> >>> Please assume xtmixed ,variance yields the following
> >>> result.
> >>>
> >>>
> >>> ----------------------------------------------------------------------
> -----
> >>> Random-effects Parameters | Estimate Std. Err. [95% Conf.
> >>> Interval]
> >>>
> >>> -----------------------------+----------------------------------------
> -----
> >>> Variable1: Identity |
> >>> var(_cons) | 100.000 70.000 54.22297 415.9787
> >>>
> >>> -----------------------------+----------------------------------------
> -----
> >>> Variable2: Identity |
> >>> var(_cons) | 10.0000 6.000 4.254477 36.79754
> >>>
> >>> -----------------------------+----------------------------------------
> -----
> >>> var(Residual) | 400.000 40.000 397.8473 465.2598
> >>>
> >>> ----------------------------------------------------------------------
> -----
> >>>
> >>> Is it 'correct' to conclude that this estimated model
> >>> yields a total variance of 510 (100+10+400) and
> >>> accordingly variable1 explains about 20% and variable
> >>> 2% of the total model or is that a total
> >>> misinterpretation?
> >>
> >> I once knew the answer, now I only know the reference:
> >> chapter 7 of Tom Snijders and Roel Bosker (1999)
> >> "Multilevel Analysis, An introduction to basic and
> >> advanced multilevel modeling". Thousand Oaks: Sage.
> >>
> >> Hope this is still of some help,
> >> Maarten
> >>
> >> -----------------------------------------
> >> Maarten L. Buis
> >> Institut fuer Soziologie
> >> Universitaet Tuebingen
> >> Wilhelmstrasse 36
> >> 72074 Tuebingen
> >> Germany
> >>
> >> http://home.fsw.vu.nl/m.buis/
> >> -----------------------------------------
> >>
> >>
> >>
> >>
> >>
> >>      >
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