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From |
Rebecca Pope <rebecca.a.pope@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: How to interpret results from gllamm |

Date |
Tue, 6 Nov 2012 13:01:55 -0600 |

Hi Jurijs, To add to Stas's comments... If you haven't done so, I advise reading the GLLAMM Manual (Rabe-Hesketh, Sophia; Skrondal, Anders; and Pickles, Andrew, "GLLAMM Manual" (October 2004). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 160. http://biostats.bepress.com/ucbbiostat/paper160). The authors detail the interpretation of the output of -gllamm- for many different models. Even if you can't find your exact model there, you can usually find an example of something similar that will help. Note that -gllamm- is a user-written command. Depending on what version of Stata you have, much of what it accomplishes is now done within official Stata. Also, since you are new to multilevel models, I highly recommend Multilevel and Longitudinal Modeling Using Stata, 3rd Ed. by Sophia Rabe-Hesketh and Anders Skrondal. It is available from Stata Press at http://www.stata.com/bookstore/multilevel-longitudinal-modeling-stata. I used it in a course last semester & found the text quite approachable. It will also help you decide when to use the Stata command and when to use -gllamm- since sometimes one outperforms the other. Hope this helps, Rebecca On Tue, Nov 6, 2012 at 12:33 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > 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 >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: How to interpret results from gllamm***From:*Jurijs Ņikišins <jurijs.nikisins@lu.lv>

**Re: st: How to interpret results from gllamm***From:*Stas Kolenikov <skolenik@gmail.com>

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