Re: st: How to interpret results from gllamm

[Stas Kolenikov <skolenik@gmail.com>]

The parameters var(1) and var(2) are your random intercepts and
slopes. The individual level coefficients have the same interpretation
as they would in a regular logistic regression: an increase of the
explanatory variable by 1 causes the linear prediction shift by {the
value of the regression coefficient}, and the change in probability
depends on the particular constellation of variables quantifiable via
marginal effects (and -gllamm- may not work very well with -margins-
that otherwise provides a great interface to describe and visualize
these marginal effects).

-- 
-- Stas Kolenikov, PhD, PStat (SSC)  ::  http://stas.kolenikov.name
-- Senior Survey Statistician, Abt SRBI  ::  work email kolenikovs at
srbi dot com
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer



On Tue, Nov 6, 2012 at 12:01 PM, Jurijs Ņikišins <jurijs.nikisins@lu.lv> wrote:
> Hello,
> I'm a newcomer to both Stata and multilevel analysis and I have some general understanding of theory but implementing it in practice is a real challenge for me so far, so I'd be really grateful for help on interpreting results I get from gllamm.
> Using the European Social Survey 25-country dataset, I'm studying the relationship between dichotomous outcome variable demonstration_rec (whether a person took part in a demonstration last year)
> and the following independent vars: gender, education, index of attitudes to gender, cultural and income equality (resp. geq_mean, ceq_mean, ieq_mean) and 4-rank democratic history variable new_demhist denoting period that a country has been a stable democracy.
> I treat new_demhist as a country-level variable, allowing it to vary at a country level (i.e. trying to build a random-coefficient model):
>
> gllamm demonstration_rec Gender_rec Education_rec geq_mean ceq_mean ieq_mean i.new_demhist, family(binomial) link(logit) i(country_rec) nrf(2) eqs(cntry_cons cntry_democr) nip(8)
> i.new_demhist     _Inew_demhi_1-4     (naturally coded; _Inew_demhi_1 omitted)
> -----------------------------------------------------------------------------------
> demonstration_rec |      Coef.          Std. Err.           z        P>|z|     [95% Conf. Interval]
> ------------------+----------------------------------------------------------------
>        Gender_rec |   .1956246          .0404499     4.84     0.000     .1163443    .2749049
>     Education_rec |   .2037315        .0162273    12.55    0.000     .1719265    .2355365
>          geq_mean |    .2762476         .0232011    11.91    0.000     .2307744    .3217209
>          ceq_mean |     .1077089         .0102165    10.54     0.000     .0876849    .1277329
>          ieq_mean |      .3145431          .0246306    12.77    0.000     .2662681    .3628181
>     _Inew_demhi_2 |  -.4142843    .1031448    -4.02    0.000    -.6164443   -.2121243
>     _Inew_demhi_3 |   .7189537    .0954098     7.54     0.000     .5319539    .9059535
>     _Inew_demhi_4 |  -.0171796    .0929596    -0.18    0.853     -.199377    .1650178
>             _cons |       -5.994235          .1476284   -40.60    0.000    -6.283581   -5.704889
> -----------------------------------------------------------------------------------
>  Variances and covariances of random effects
> ------------------------------------------------------------------------------
> ***level 2 (country_rec)
>      var(1): .20405822 (.10364398)
>     cov(2,1): .00757171 (.01157615) cor(2,1): .03609139
>      var(2): .21568821 (.02322925)
> ------------------------------------------------------------------------------
> My questions are:
>
> 1) How actually should I interpret var(1) and var(2)? Are they individual- and country-level variance, or variances of intercept and slope?
> 2) How do I interpret individual-level coefficients together with level 2 variances and covariances?
>
> Thanks a lot in advance,
>
> Jurijs Nikisins
> Sociology PhD student, University of Latvia
>
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