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Re: st: STATA multilevel modelling
From
Steve Samuels <[email protected]>
To
[email protected]
Subject
Re: st: STATA multilevel modelling
Date
Fri, 20 Jul 2012 11:57:04 -0400
On Jul 11, 2012, at 9:12 AM, Kate Xu wrote:
> I am new to STATA and I would like to check a few things about the
> possibility under STATA to model multilevel data with sampling
> weights. I am mainly interested inoutcomes that are continuous,
> categorical, and count. So basically multilevel models for linear,
> poisson, logistic, ordinal and multinomial regressions with random
> intercepts and random slopes.
Welcome to Statalist!
Small point: correct spelling is "Stata", whereas GLLAMM is also correct
spelling for that command. "GLLAMM" is an acronym; "Stata" is not. See the FAQ.
> 1. Sampling weight. It seems that the xtmixed can model continuous
> outcomes with possibility of controlling for sampling weight, but not
> for xtmelogit (logistic) and xtmepoisson (poisson)?
Correct, and you can tell this from each command's -help- file, since [weight] is not mentioned as an option. If it had been, you would need to check if "pweight" was a possibility.
> 2. Is it possible to use multilevel model for ordinal and multinomial
> data under STATA, with the weighting taken into account?
With GLLAMM. Note that sampling weights are not the only issue: you must
decide if you want to base standard errors on the model or on the survey design.
> 3. What is the difference of these STATA based commands and GLLAMM? I
> understand that GLLAMM is a user written package of STATA that can
> also model multilevel data. What is the advantage of it. And is GLLAMM
> able to model the ordinal and count data with random slopes and
> weighting?
>
Just answered.
Some people report that GLLAMM can be
slow. But if you run -xtmelogit- first, you can get good starting values, which
should speed things up. Note that GLLAMM requires a different sampling weight
for each level.
See: http://www.stata.com/statalist/archive/2008-08/msg01194.html for some references.
You can calculate the two weights from contributed commands -pwigls- or -mpml_wt
found at: http://www.cpc.unc.edu/research/tools/data_analysis/ml_sampling_weights
Steve
[email protected]
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