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RE: st: Sample Wegihts

From   "Jason Dean, Mr" <>
To   "" <>
Subject   RE: st: Sample Wegihts
Date   Tue, 9 Mar 2010 16:33:41 -0500

Hi Michael and Guang, thank you very much for providing help.

Here is some more information about my samples:

- 5% random sample of the entire country (census data 1901 Canada).
- 8% sample of the urban areas of Montreal
- 6% sample of the urban areas of Toronto
- 24% sample of the urban areas of Vancouver
- 17% sample of the urban areas of Winnipeg

Now these last 4 city samples are clustered by census subdistrict (sorry I forgot to mention this in my post) - essentially they were sampled by selecting every 5th sub district and every household in those subdistricts where sampled. Many of the socio-economic characteristics of these samples match the census population figures.

- As well I have an additional 10% random sample of Vancouver.

I have taken out duplicated observations by using the page and line number of the census and left them in the 5% random sample (originally for what I was doing that was fine.) Sorry I did not mean between cities in my last post. I meant I dropped duplicates between the Toronto 5% sample and the Toronto extra sample (and the other three cities)

So if I ignore the clustering and duplicates issue, I would have a weight of 20 for the 5 percent random samples and then a weight of 12.5, 16.7, 4.1 and 5.8 respectively for the city over samples. Plus the extra Vancouver sample would have a weight of 10. I would apply these weights using svyset and have a strata id as per your previous response. Is this correct?

Then to deal with cluster could I use the ,cluster(id) command after regress where id is a variable that identifies the subdistirct in all samples?

For the duplicates could I adjust the weight for the city in which I remove the duplicated observations?

Please let me know if you need any more info. Again I really appreciate the help.



From: [] On Behalf Of Michael I. Lichter []
Sent: Tuesday, March 09, 2010 3:07 PM
Subject: Re: st: Sample Wegihts


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)

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.


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. <>
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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