Re: st: Sample Wegihts

["Michael I. Lichter" <mlichter@buffalo.edu>]

Jason,

In general, probability weights are equal to 1/(probability of inclusion 
in the sample), so your 5% sample gets a weight 20 and if you sampled 
the 4 urban areas at a 10% rate, the weight for those cases should be 
10. This is a stratified design and should ideally be analyzed as such 
using -svyset [pw=your-pweight], strata(your-stratum-id)- where 
your-pweight is the weight you construct and your-stratum-id is a 
variable with a category for each stratum. If the sampling rate differs 
between the cities; e.g., if you sampled 1000 people regardless of the 
city size, you would need a different weight for each city and a 
different stratum ID.

Now, I wonder what you mean about having dropped "duplicate 
observations". Do you mean that you dropped the observations of Toronto 
from your first sample and are substituting those from the second, or do 
you mean that you combined the two samples and literally dropped only 
those observations that appeared in both? (And I wonder what kind of 
data you have that you would know they were duplicates.) If the former, 
what I said above applies; if the latter ... you probably shouldn't.

The other alternative is simply to combine the samples without dropping 
observations. In that case, you would need to decide how much relative 
weight to give to the "regular" sample vs. the "oversample"; if you want 
each to be weighed equally, you just divide their "natural "weights by 
two; that is, your-pweight = 10 instead of 20 for the 5% sample, and 
your-pweight = 5 instead of 10 for the oversample. Somebody who knows 
more than me can comment on the advisability of this course; it means 
that a sampling without replacement design (which is what I assume you 
have in each of the two datasets) becomes sampling with (limited) 
replacement.

I agree with Guang Dai (I saw his message after writing this) that how 
your samples are designed is important; you haven't given us a lot of 
information to go on.

Michael

Jason Dean, Mr wrote:
> I have a quick question. I currently have a 5% random sample of Canada. I also have 4 extra random samples of only the four largest urban cities (I have dropped duplicate observations between samples).
>
> What is the best strategy to include these extra samples and keep the sample representative of the country. I intend to conditon on these cities with dummy variable in my regression.  However, I would prefer to use sample weights but I am not sure the best way to go about creating them. Any suggestions would be greatly appreciated.
>
> Jason
>
>
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--
Michael I. Lichter, Ph.D. <mlichter@buffalo.edu>
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 126 / Phone: 716-898-4751 / FAX: 716-898-3536

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