I have estimated a logistic multilevel model via runmlwin and meqrlogit. The coefficients and variance partition differ considerably between the two commands. I know that a small difference is normal and due to the different estimation methods, however my coefficient has almost twice the size with meqrlogit and the variance on the second level is twice as bis as well. Moreover, if I estimate a model with clustered standard errors, the results are comparable to the runmlwin model, which is why I assume I made a mistake in that specification and it does not account for the second level in runmlwin. I have checked my model several times, but just cannot see where I went wrong. Can anybody help me with that, maybe? I´ll attatch the two outputs (and sytaxes) below.
Thank you all!
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***syntax runmlwin***
use "C:\UXX\data", clear
global MLwiN_path "C:\Program Files (x86)\MLwiN trial\i386\mlwin.exe"
gen level1indicator=_n
gen cons=1
sort level2indicator level1indicator
runmlwin depvar cons i.varl1, level2(level2indicator: cons) level1(level1indicator) discrete(distribution(binomial) link(logit)denom(cons)) nopause
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***output runmlwin***
Run time (seconds) = 5.07
Number of iterations = 5
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stellefrei_sci| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | 1.380566 .1180445 11.70 0.000 1.149203 1.611929
_1_varl1 | -.0186297 .1457571 -0.13 0.898 -.3043084 .2670489
_2_varl1 | -.3663916 .1389778 -2.64 0.008 -.638783 -.0940001
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Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: indicator |
var(cons) | 2.293732 .2425133 1.818414 2.769049
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***Syntax meqrlogit***
meqrlogit depvar i.varl1 ||level2indicator:
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***output meqrlogit***
Integration points = 7 Wald chi2(2) = 20.24
Log likelihood = -746.48846 Prob > chi2 = 0.0000
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depvar | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
|
_1_varl1 | -.025434 .2384883 -0.11 0.915 -.4928625 .4419946
_2_varl1 | -.9323545 .2362082 -3.95 0.000 -1.395314 -.4693948
|
_cons | 3.527563 .2945633 11.98 0.000 2.95023 4.104897
------------------------------------------------------------------------------
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Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
level2indicator: Identity |
var(_cons) | 14.11532 1.947718 10.77052 18.49885
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LR test vs. logistic regression: chibar2(01) = 456.45 Prob>=chibar2 = 0.0000