mixed - Stata

mixed — Multilevel mixed-effects linear regression 43
. mixed isei female high_school college one_for both_for test_lang
> [pw=w_fstuwt/wnrschbw] || id_school:, pweight(wnrschbw) pwscale(gk)
(output omitted )
Mixed-effects regression Number of obs = 2069
Group variable: id_school Number of groups = 148
Obs per group: min = 1
avg = 14.0
max = 28
Wald chi2(6) = 291.37
Log pseudolikelihood = -7270505.6 Prob > chi2 = 0.0000
(Std. Err. adjusted for 148 clusters in id_school)
Robust
isei Coef. Std. Err. z P>|z| [95% Conf. Interval]
female -.3519458 .7436334 -0.47 0.636 -1.80944 1.105549
high_school 7.074911 1.139777 6.21 0.000 4.84099 9.308833
college 19.27285 1.286029 14.99 0.000 16.75228 21.79342
one_for -.9142879 1.783091 -0.51 0.608 -4.409082 2.580506
both_for 1.214151 1.611885 0.75 0.451 -1.945085 4.373388
test_lang 2.661866 1.556491 1.71 0.087 -.3887996 5.712532
_cons 31.20145 1.907413 16.36 0.000 27.46299 34.93991
Robust
Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]
id_school: Identity
var(_cons) 31.67522 6.792239 20.80622 48.22209
var(Residual) 226.2429 8.150714 210.8188 242.7955
The results are somewhat similar to before, which is good news from a sensitivity standpoint. Note
that we specified [pw=w fstwtw/wnrschbw] and thus did the conversion from w ij to w i|j within
our call to mixed.
We close this section with a bit of bad news. Although weight rescaling and the issues that arise
have been well studied for two-level models, as pointed out by Carle (2009), “. . . a best practice
for scaling weights across multiple levels has yet to be advanced.” As such, pwscale() is currently
supported only for two-level models. If you are fitting a higher-level model with survey data, you
need to make sure your sampling weights are conditional on selection at the previous stage and not
overall inclusion weights, because there is currently no rescaling option to fall back on if you do not.