Multilevel models with survey data
Stata’s xtmixed command for fitting linear multilevel models
now supports survey data. Sampling weights and robust/cluster standard
errors are available.
Sampling weights are handled differently by xtmixed than by other
commands:
- Weights can (and should be) specified at every model level unless you
wish to assume equiprobability sampling at that level.
- Weights at lower model levels need to indicate selection conditional on
selection of the higher-level cluster and not merely indicate overall
selection.
- The scaling of weights at lower levels needs to be considered. Unlike
a standard analysis where the scale of the sampling weights is not an
issue (only their relative sizes matter), in multilevel models weight
scales need to be made "consistent" across lower-level clusters.
See [XT] xtmixed and, in particular, section Survey data
in that entry for all the technical details.
We demonstrate using xtmixed to fit a two-level model for data from a
two-stage sampling design with sampling weights at both stages. Schools
were sampled at the first stage, students at the second.
In the above, we specified the student-level weights using standard Stata
weight syntax [pw=w_fstuwt] and the school-level weights with the
pweight(wnrschbw) option as part of the school random-effects
equation. We also specified pwscale(size) to rescale the
student-level weights using one of three available methods.
As is the case with other commands, sampling weights imply robust standard
errors, and in the case of xtmixed, standard errors are clustered at
the highest level (schools in this example) unless you specify otherwise.
See [XT] xtmixed for more details.
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