Dear statalists members
I have a dependent variable that is the logarithm of minutes of commuting time from home to school, and I want to do a multilevel regression with random intercept at municipal and neighborhood level. Reading the stata manual https://www.stata.com/manuals13/me.pdf, and previous questions on this forum, it seems as if I should get the same result from using mixed or meglm command, but I dont! I would really appreciate if someone could explain to me wich one I should shoose and why. Below I show an example using ethnicity as a categorical dependent variable, Kommun=municipality, desoomrade=neighborhood (wich are smaller areas within municipalities).
Best wishes
Susanne


1. Results from meglm:
meglm LogCommutingTimeMinutes i.ethnicity|| Kommun :|| desoomrade :

Integration method: mvaghermite Integration pts. = 7

Wald chi2(4) = 139.44
Log likelihood = -26444.087 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------------------
LogCommutingTimeMinutes | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
ethnicity |
2. gn1st | .1769682 .0270593 6.54 0.000 .123933 .2300034
3. gs1st | .1529214 .0180929 8.45 0.000 .1174601 .1883828
4. gn2nd | .060759 .0276835 2.19 0.028 .0065004 .1150175
5. gs2nd | .0978085 .013486 7.25 0.000 .0713763 .1242407
|
_cons | 3.416905 .0062768 544.37 0.000 3.404603 3.429207
-------------------------------+----------------------------------------------------------------
Kommun |
var(_cons)| .1288427 .0491893 .0609664 .2722887
-------------------------------+----------------------------------------------------------------
Kommun>desoomrade |
var(_cons)| .1641032 .0147931 .1375262 .1958163
-------------------------------+----------------------------------------------------------------
var(e.LogCommutingTimeMinutes)| .366027 .0038894 .3584827 .3737301
------------------------------------------------------------------------------------------------
LR test vs. linear model: chi2(2) = 0.00 Prob > chi2 = 1.0000



Results from mixed:
mixed LogCommutingTimeMinutes i.ethnicity|| Kommun :|| desoomrade :
Mixed-effects ML regression Number of obs = 18,068



Wald chi2(4) = 20.90
Log likelihood = -15305.266 Prob > chi2 = 0.0003

-----------------------------------------------------------------------------------------
LogCommutingTimeMinutes | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------+----------------------------------------------------------------
ethnicity |
2. gn1st | .1095635 .0248939 4.40 0.000 .0607723 .1583547
3. gs1st | .0309414 .0178491 1.73 0.083 -.0040422 .0659251
4. gn2nd | .0064226 .0248707 0.26 0.796 -.0423231 .0551684
5. gs2nd | .0066133 .0139866 0.47 0.636 -.0208 .0340266
|
_cons | 3.568386 .0569499 62.66 0.000 3.456766 3.680006
-----------------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Kommun: Identity |
var(_cons) | .0774919 .0238052 .0424395 .1414955
-----------------------------+------------------------------------------------
desoomrade: Identity |
var(_cons) | .1263648 .0060637 .1150219 .1388264
-----------------------------+------------------------------------------------
var(Residual) | .2774865 .0030279 .2716149 .283485
------------------------------------------------------------------------------
LR test vs. linear model: chi2(2) = 5912.76 Prob > chi2 = 0.0000