Re: st: RE: gllamm for binary outcome - interpretation?

[Gitit Kadar Satat <G.Kadar-Satat@sms.ed.ac.uk>]

Many thanks!
(And sorry again I don't really know how to search for a specific  
post/thread in statalist)

Best wishes,

Gitit


Quoting Nick Cox <n.j.cox@durham.ac.uk> on Tue, 8 May 2012 19:24:03 +0100:

> There is never need for doubt over whether a posting was received,   
> meaning by the archives. Just look at the archives.
>
> What you want is at best a descriptive or heuristic measure as logit  
>  models, plain or fancy, are not estimated, even indirectly, by   
> maximising variance explained. But you may be able to do something   
> like
>
> FAQ     . . . . . . . . . . . . . . . . . . . . . . . Do-it-yourself  
>  R-squared
>         . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   
>  N. J. Cox
>         9/03    How can I get an R-squared value when a Stata command
>                 does not supply one?
>                 http://www.stata.com/support/faqs/stat/rsquared.html
>
> You are almost certainly going to need to take responsibility for   
> both calculation and interpretation.
>
> Nick
> n.j.cox@durham.ac.uk
>
> Gitit Kadar Satat
>
> (apologies for re-posting, not sure the first time has been received)
>
> I ran a gllamm multilevel model to explore a binary outcome. the data
> I'm using are individuals clustered in schools. The outcome of
> interest is whether or not they have passed an exam.
>
> I started by estimating an "unconditional model" with no explanatory
> variable and then ran additional model with the explanatory variables
> included.
>
> Based on the output (below), how can I estimate the percentage of
> variance explained at the "grouping" level (that is - schools)? Or is
> my output missing important information? In other words - I would like
> to be able to report the % variance in the outcome (whether or not
> individuals have passed the exam) that is explained by differences
> across schools. Is this possible?
>
> Here is the main output:
>
> *Model 1 (unconditional):
> . gllamm pass_exam, i(sptn00) pweight(pwt) link(logit)
> family(binomial) nip(30)  adapt
> number of level 1 units = 12552
> number of level 2 units = 335
>   Condition Number = 1.1434506
>   gllamm model
>   log likelihood = -8160.5935
>   Robust standard errors
> --------------------------------------------------------------------------------------
> pass_exam            |      Coef.   Std. Err.      z    P>|z|     [95%
> Conf. Interval]
> ---------------------+----------------------------------------------------------------
>                 _cons |   -1.31337   .0397836   -33.01   0.000
> -1.391345   -1.235396
> --------------------------------------------------------------------------------------
>   Variances and covariances of random effects
> ------------------------------------------------------------------------------
> ***level 2 (sptn00)
>      var(1): .2054349 (.03404487)
> ------------------------------------------------------------------------------
>
> *Model 2 (explanatories included)
> . xi: gllamm pass_exam i.S4_Parental_nssec0 i.S4_Parental_nvq
> i.S4_Income_quartiles ,
> i(sptn00) pweight(pwt) link(logit) family(binomial)  nip(30)  adapt
> number of level 1 units = 12379
> number of level 2 units = 335
>   Condition Number = 281.7895
>   gllamm model
>   log likelihood = -7870.4423
>   Robust standard errors
> --------------------------------------------------------------------------------------
> pass_exam             |      Coef.   Std. Err.      z    P>|z|
> [95% Conf. Interval]
> ---------------------+----------------------------------------------------------------
>         _IS4_Parent_2 |   .1579462   .1034318     1.53   0.127
> -.0447763    .3606688
>         _IS4_Parent_3 |   .2136482   .0943537     2.26   0.024
> .0287183    .3985781
>         _IS4_Parent_4 |    .205371   .1163226     1.77   0.077
> -.022617     .433359
>          _IS4_Parenta2 |   .2970159   .2201864     1.35   0.177
> -.1345416    .7285733
>         _IS4_Parenta3 |    .329872    .191429     1.72   0.085
> -.0453219    .7050659
>         _IS4_Parenta4 |   .3068724   .2005867     1.53   0.126
> -.0862702     .700015
>         _IS4_Income_2 |    .090987   .1359474     0.67   0.503
> -.1754649    .3574389
>         _IS4_Income_3 |    .127511   .1269022     1.00   0.315
> -.1212128    .3762348
>         _IS4_Income_4 |   .3252327   .1420127     2.29   0.022
> .046893    .6035724
>                 _cons |  -2.080896   .1809789   -11.50   0.000
> -2.435609   -1.726184
> --------------------------------------------------------------------------------------
>   Variances and covariances of random effects
> ------------------------------------------------------------------------------
> ***level 2 (sptn00)
>      var(1): .20224494 (.03582371)
> ------------------------------------------------------------------------------
>
>
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