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From |
Mabel Andalon <mabel.andalon@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: gllamm & stratified sampling design |

Date |
Mon, 21 Apr 2008 22:11:05 -0400 |

Many thanks to Jay and Sebastian for the references. I just finished reading the paper, but I´m not sure I have fully understood what is going on.

Steven,

I appreciate your help and interest. I only have one cross-section. The main features of the survey design are the following:

*

1.* The sample was drawn from a household database of approximately 11 million households in the United States that are identified as Latino or Hispanic. The universe of analysis contains approximately 87.5% of the US Hispanic population.

*2.* The survey covers 15 states and the District of Columbia metropolitan area (including counties and municipalities in Virginia and Maryland). States were selected based on the overall size of the Latino/Hispanic population.

*3. *The sample is stratified by geographic designation, meaning that each state sample is a valid, stand-alone representation of that state´s Latino population.

*4.* Respondents were selected randomly from the Latino households in the jurisdictions covered (states) without replacement.

*5. *State sample sizes vary as a result of specific funders´ requests. The smallest sample size for any unit was 400, yielding a margin of error of less than ± 5% for each state.

*6. *A number of states were stratified internally. In each case but California, internal strata were represented proportionately in the final sample. In California, additional strata were imposed in a non-proportional fashion, owing in part to the larger sample size, to allow greater between-region comparisons.

*7.* I don´t have the formula for how weights were computed. The survey´s documentation says that national weights were constructed so that the numbers are accurately representative of the universe covered by the study.

Please let me know if you think my svyset statement is inaccurate:

svyset [pweight=wt_natio], strata(usstate)

wt_natio is the national weight described in *#7* above. usstate is the var that identifies the within-state strata.

I think I should care about conducting a multilevel analysis because I have merged two types of state-level characteristics to each individual in my sample. One reflects state-level characteristics of the persons' country of origin before s(he) arrived to the US. The other reflects the characteristics of the state in which the person lives currently.

Thanks very much,

Mabel

Steven Samuels wrote:

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?

Regards,

Steven

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.

Best,

Mabel

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**Follow-Ups**:**Re: st: gllamm & stratified sampling design***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**Re: st: Re: Question Tobit model***From:*Johannes Geyer <JGeyer@diw.de>

**st: gllamm & stratified sampling design***From:*Mabel Andalon <mabel.andalon@gmail.com>

**Re: st: gllamm & stratified sampling design***From:*Steven Samuels <sjhsamuels@earthlink.net>

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