--
Also, 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), the second citation that
Stas listed, compute the "Method 1" weights by hand and illustrate an
analysis in GLLAMM.
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 Thu, Jul 16, 2009 at 12:28 PM, Stas Kolenikov<[email protected]> wrote:
> Oh, I see. With 64 second level units, you are in a much better shape.
> I would probably have an urban/rural dummy as an explanatory variables
> for those second levels with -feq- option.
>
> If you sum up the weights, you are using the weights twice. And that's
> hardly a great idea: you are overcompensating for unequal
> probabilities of selection, if there were any. Were these
> states/ruran/urban areas selected via a sampling procedure? Or what
> you have is a complete list? In the latter case, you surely would need
> to specify unit weights at the second level.
>
> On the issue of weights in multilevel models, see:
> http://www.citeulike.org/user/ctacmo/article/711637,
> http://www.citeulike.org/user/ctacmo/article/850244,
> http://www.citeulike.org/user/ctacmo/article/3158754. There's probably
> more by now, but I am not tracking this literature very closely.
>
> On Thu, Jul 16, 2009 at 11:18 AM, Kanter, Rebecca<[email protected]> wrote:
>> Hi Stan and statalist,
>>
>> Regarding my second level it is more than 2 values...as there are 32 states in the country...that makes 64 values (or areas/clusters that i illustrate via one variable called urstate...e.g. if urstate=1 it is the urban area of the 1st state and if urstate=33 it is the rural area of the 1st state and so on) if one divides each state into its urban and rural areas, respectively. Each one I want to take its own intercept and slopes etc to better account and visualize the urban and rural differences in the country....
>>
>> Thus, is it better to sum the individual weights per urstate (1-64) or let all weights for this second level equal one and keep my individual pweights as is for the individual level (level 1)?
>>
________________
>> From: [email protected] [[email protected]]
>> On Wed, Jul 15, 2009 at 5:37 PM, Kanter, Rebecca<[email protected]> wrote:
>>> Hi,
>>>
>>> I am running 2 level multi-level models using gllamm. Level one is individuals and Level two is either the urban or rural part of the country's state (i.e. urstate).
>>>
>>> I would like to use the survey pweights I have...I only have pweights for the individual level (adul_sr), but it seems that you need pweights for all levels specified in gllamm (?) so this is what I did to create pweights for urstate based on these weights:
>>>
>>> collapse (sum) sadul_sr=adul_sr , by(urstate)
>>>
>>> then I merged them to the rest of my dataset
>>>
>>> and made this weight for the gllamm:
>>>
>>> *MLM-level pweights
>>> generate pwadulsr1=adul_sr
>>> *urstate summed adul_sr
>>> generate pwadulsr2=sadul_sr
>>>
>>> Then ran the most basic random-intercept only (around urstate) in gllamm and get the follow error below and am assuming it is a pweight problem but I do not know where the problem is coming from so if anyone has insight that would be much appreciated. Thanks so much!
>>>
>>> (note: diettag==1 & exwt==1 is the subpopulation i want to look at for this series of models)
>>>
>>> gllamm bmi2 if diettag==1 & exwt==1, i(urstate) pweight(pwadulsr) adapt nip(15)
>>>
>>> Running adaptive quadrature
>>>
>>> Convergence not achieved: try with more quadrature points
>>>
>>>
>>
Steven Samuels
[email protected]
18 Cantine's Island
Saugerties NY 12477
USA
845-246-0774
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