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st: From: Marc Peters <[email protected]>
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st: From: Marc Peters <[email protected]>
Date
Fri, 13 Dec 2013 18:01:35 +0000
Dear Statalist users,
I am new to multilevel modeling and I am currently trying to estimate
a model using xtmixed.
Most of my models look reasonable, but when I add a specific (highly
significant) level-2 variable (var2) the level-2 variance component
becomes strangely small.
I use the following command where var1 is a level-1 variable and var2
and var3 level-2 variables.
xtmixed depvar var1 var2 var3||id:, var ml cov(un)
I receive the following output
Computing standard errors:
Mixed-effects ML regression Number of obs = 78
Group variable: id Number of groups = 28
Obs per group: min = 1
avg = 2.8
max = 5
Wald chi2(3) = 89.55
Log likelihood = 35.123461 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
depvar | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
var1 | .2780454 .0370738 7.50 0.000 .205382 .3507087
var2 | .0038315 .0009236 4.15 0.000 .0020213 .0056416
var3 | -.3500793 .0929506 -3.77 0.000 -.5322591 -.1678995
_cons | .6950143 .0682743 10.18 0.000 .5611993 .8288294
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Identity |
var(_cons) | 6.95e-21 6.63e-20 5.19e-29 9.31e-13
-----------------------------+------------------------------------------------
var(Residual) | .0237903 .0038095 .0173819 .0325612
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000
If I have understood it correctly, the variance component for id
should not become smaller when adding a significant level-2 variable
to the equation. Or am I completely mistaken?
Thank you,
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