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st: gllamm for binary outcome - interpertation


From   Gitit Kadar Satat <G.Kadar-Satat@sms.ed.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   st: gllamm for binary outcome - interpertation
Date   Mon, 07 May 2012 16:45:18 +0100

Hello Statalist helpers!

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)
------------------------------------------------------------------------------

Thanks in advance,

Gitit



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