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Re: st: GLLAMM and weights


From   Steven Samuels <sjhsamuels@earthlink.net>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: GLLAMM and weights
Date   Sat, 13 Dec 2008 22:12:10 -0500

Second thoughts: Rescaled weights are not what your original article called for. They are designed to reduce bias in the estimated coefficients and variance components under a sampling model, not the observation model that you are studying. Therefore, -gllamm- with rescaled weights may be the wrong approach. You might write to the authors and ask what software they used.

-Steve
On Dec 13, 2008, at 9:52 PM, Steven Samuels wrote:

I haven't tried this in GLLAMM, so I'm not sure what goes wrong when you add the weights. Check that your version of -gllamm- is up- to-date. As requested in the Statalist FAQ, show exactly what you typed and what Stata produced. It is good practice, as others have pointed out, to start with the simplest model possible (e.g. no covariates) and try progressively more complex models (e.g. add treatment) to see where the problem starts. You probably have not rescaled the weights as the GLLAMM manual suggests. See: http:// www.stata.com/meeting/4nasug/Chantala.ppt and http:// www.cpc.unc.edu/restools/data_analysis/ml_sampling_weights. These contain links to the Stata program -pwigls- which will scale the weights. Rabe-Hesketh and Skrondal (2006) compute the "Method 1" weights by hand and illustrate an analysis in GLLAMM. Try to replicate their results.

-Steve

Rabe-Hesketh, S. & Skrondal, A. (2006). Multilevel modelling of complex survey data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(4), 805-827.

On Dec 13, 2008, at 2:06 PM, Ariel Linden wrote:

I posted a question a couple weeks ago about using multiple weights per person/period data (that is, using a different weight per person for each period). The problem was that xtgee will not accept different weights per person.One suggestion that was provided was to use GLLAMM. I tried that and the multiple weights were indeed accepted by the model. However, another issue presented itself. I am using 3 variables for the fixed effects; wave, treatment (dummy 1/0), and wave * treatment (for growth). Unfortunately, the model drops both the wave and wave * tx variables. This does not happen when I run an unweighted model, nor when I run this using regress or glm. Of course, I need both those variables in the model.




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