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Re: st: gllamm & stratified sampling design

From   Steven Samuels <>
Subject   Re: st: gllamm & stratified sampling design
Date   Mon, 21 Apr 2008 21:21:41 -0400

I have not read the article Sebastian referred to so I will ask only about your design. This is a multistage design, so, for a start, your -svyset- statement is incomplete. Please give more details. Exactly what was the sampling protocol? What was frame? What were the target populations at each stage of ssampling. How did the surveysors get from states to communities to individuals? Was there intermediate sampling of households or areas smaller than communities, or both? Was sampling with or without replacement, and, at what stages? How were the weights computed? Were Was there post- stratification weighting? Have you multiple years of data?



On Apr 21, 2008, at 10:51 AM, Mabel Andalon wrote:

Dear All,

I am estimating a model of community participation (1-0) using individual-level data. These data are of immigrants in the US and comes from a stratified simple random sampling survey. The strata are US states (usstate). I've always used the svy option when analyzing these data setting:

svyset [pweight=wt_natio], strata(usstate)

I just merged these data with contextual data from people's state of origin in a foreign country based on year of arrival to the US. And I also merged US state-level data based on current state of residence. That is, any two people who arrived in the same year from the same state and country and who live in the same US state were merged the same state-level data.

My questions are two:
1. Is this considered multilevel data?
2. If so, how can I conduct a true multilevel analysis using glamm and still include the features of sampling design (i.e. stratification).

So far, I have estimated:

gllamm participation $xvars , i(individual fostate year usstate) pweight(wt) f(binom) l(logit) adapt

i = individuals/inmigrants
fostate = foreign state of residence
year= year of arrival to the US
usstate= current state of residence

I'm not even sure that I have correctly defined the hierarchical, nested clusters in the i() option. The weights are individual's sampling weights.

Any suggestions will be highly appreciated.



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