Steven,
1. Because only one person was interviewed in each dwelling, I don't see the
need to include a third stage in the design (there is no clustering of
individuals by dwelling, only by census tract).
2. I agree with dropping the age stratum.
3. I appreciate your advice on oversampling of the elderly. When listing and
selecting separately younger and elderly people in each dwelling, I see the
need to include the dwelling variable, because then you can have two
participants living in the same dwelling.
4. and 5. Census tracts were randomly selected with probabilities
proportional to the number of dwellings in them:
(#PSUs x #dwellings in PSUi)/ dwellings in all PSUs.
As probability of selection of each dwelling is:
12/#dwellings in PSUi,
#dwellings in PSUi cancels out and the result of these two components of the
weight is constant for all individuals in the stratum and can be dropped.
The only weights used were then: a) #people in the dwelling; b)
post-stratification weights to make age proportions match those of the
census.
Many thanks for your help.
�ngel Rodr�guez Laso
Institute of Public Health of the Region of Madrid
-----Mensaje original-----
De: [email protected]
[mailto:[email protected]] En nombre de Steven Samuels
Enviado el: lunes, 31 de marzo de 2008 19:30
Para: [email protected]
Asunto: Re: st: Definition of strata and PSUs when svysetting
Angel
"Gender" in point 2 should have been "age"-fixed below. I apologize
for the confusion.
-Steven
On Mar 31, 2008, at 9:32 AM, Steven Samuels wrote:
>
> --
>
> Angel, you had a three-stage, not a two stage design
>
> 1. The proper -svyset- should include the stage of selecting
> dwellings.
>
> -svyset censustract [pweight=???], strata(area) || dwelling || _n
>
> For the proper pweight, see point 4 below.
>
> 2. You did not really stratify on AGE, so drop all reference to an
> AGE stratum.
>
> 3. Your design, selecting one person at random, and hoping to get
> enough elderly people, is not one I recommend. There are standard
> approaches for oversampling sub-populations in household surveys.
> At the least, one can list older and younger people in each
> dwelling and select separately from each list.
>
> 4. The design makes it very difficult to calculate the sampling
> weights. You appear to be saying that you stopped interviewing
> when you had enough elderly and younger people ( or when you ran
> out of dwellings). This is a version of 'sequential
> sampling' (Sharon Lohr, Sampling: Design and Analysis, Duxbury, p.
> 403)
>
> Here are my best guesses at sample weights.
>
> 4a. person weight =
> 1/(prob sel tract) x (no. dwellings in tract)/(no. of dwellings
> where you obtained interviews) x (no. of people in the person's
> dwelling)
>
> 4b. If you listed the ages of all people in the 12 selected
> dwellings, not just those where you did interviewed, you can do more:
>
> weight for younger person =
> 1/(prob sel tract) x (no. dwellings in tract)/12 x (no. younger
> people in the 12 sampled dwellings)/(no. of younger people
> interviewed)
>
>
> weight for older person =
> 1/(prob sel tract) x (no. dwellings in tract)/12 x (no. older
> people in the 12 sampled dwellings)/(no. of older people interviewed)
>
> 4c. If you have ages of all people in the sampled dwellings,
> substitute 'no. of dwellings where you obtained interviews' for
> '12 sampled dwellings' in the formulas in 4b. These weights may
> slightly over-estimate the proportion of elderly people.
>
> 5. If there are census figures available for your target
> population, apply a post-stratification weighting to make the
> ratio of 'elderly' and 'younger' people match that in the census.
> See Lohr, Chapter 8.
>
> -Steven
>
>
> On Mar 31, 2008, at 6:27 AM, Angel Rodriguez Laso wrote:
>
>
>> Thank you, Steven, for your interest.
>>
>> Answering to your questions, I didn�t go into more details on the
>> sampling
>> procedure because I didn�t think they were needed for the
>> definition of
>> strata and PSUs. There was intermediate sampling of dwellings.
>> There was a
>> list of all dwellings in census tracts and from this list 12
>> dwellings in
>> each selected census tract were chosen at random. From each
>> dwelling one
>> person was taken at random (and his/her weight calculated from the
>> number of
>> people living in the dwelling). People were interviewed until a
>> sample of 7
>> bellow 65 and 3 over 65 was obtained in each census tract. The
>> reason why 12
>> dwellings were selected initially is that it was expected that
>> taking only
>> 10 would not yield the final 7/3 proportion desired. Nevertheless,
>> not in
>> all census tracts 7 and 3 individuals could be selected and that's
>> the
>> reason (more than the existence of missing items) why there are
>> census
>> tracts with only one individual over 65.
>>
>> I'm trying to check if following your advice (merging strata in
>> single PSU
>> per stratum census tracts) or just dropping the second stage
>> specification,
>> would give very different results, but when I run a svy: prop
>> under the
>> first specification:
>>
>> svyset censustract [pweight=pondef], strata(area) fpc
>> (#censustractsinarea)||
>> identificationvariable, strata(agegroupscorrected)
>>
>> I get the message: 'Missing standard error due to stratum with single
>> sampling unit; see help svydes.', but when I
>>
>> svydes variable, single stage(2)
>>
>> no single PSUs are displayed. Do you know why?
>>
>>
>> �ngel Rodr�guez Laso
>> Institute of Public Health of the Region of Madrid
>>
>> -----Mensaje original-----
>> De: [email protected]
>> [mailto:[email protected]] En nombre de Steven
>> Samuels
>> Enviado el: viernes, 28 de marzo de 2008 22:25
>> Para: [email protected]
>> Asunto: Re: st: Definition of strata and PSUs when svysetting
>>
>>
>> Angel-
>> I'm sorry that I missed your initial post; I was on vacation and
>> canceled my Statalist subscription. I agree with Stas's suggestion
>> for the first specification.
>>
>> I have some questions
>>
>> 1. Your description implies that you created a list of ALL people in
>> each selected tract, stratified by age. Then selected by simple
>> random sampling: 7 from the below 65 list; 3 from the over 65 list.
>> Is that a correct description? Or, was there intermediate sampling
>> of dwellings?
>>
>> 2. Your PSU's are census tracts, not people. ("Primary" refers only
>> to the first stage.) You are saying that in some of the census
>> tracts, you had only one person either under or 'over' 65. Is that
>> correct?
>>
>> For those tracts, I suggest that you go with option 1, but ignore
>> the stratification, but keep the sampling probabilities. That is,
>> create a single stratum for those tracts by recoding.
>>
>>
>> You may still analyze your outcomes by age. The analysis age groups
>> need not match the stratum age-groups.
>>
>> -Steven
>>
>>
>> On Mar 28, 2008, at 10:40 AM, Angel Rodriguez Laso wrote:
>>
>>
>>> Thank you for your answer, Stas.
>>>
>>> I�ve tried both specifications and the first surprise was that
>>> Stata 9
>>> ignores further stages when stage 1 is sampled with replacement. It
>>> was good
>>> to come across this warning because in our survey sampling was
>>> without
>>> replacement and the sampling fraction of the census tracts was
>>> quite high
>>> (more than one third in some strata) what precludes assuming that
>>> selection
>>> was with replacement.
>>>
>>> The problem with using age groups as second stage strata is that
>>> being 3 the
>>> number of people over 65 selected per census tract, whenever
>>> there are
>>> missing values in the variables some strata become single-PSU
>>> (person)
>>> strata, what prevents Stata from calculating standard errors. So,
>>> the two
>>> specifications I�ve tried are:
>>>
>>> svyset censustract [pweight=pondef], strata(area) fpc
>>> (#censustractsinarea)
>>> svyset censustract [pweight=pondef], strata(area-by-age) fpc
>>> (#censustractsin
>>> area)
>>>
>>> Not surprisingly standard errors with both specifications differ
>>> only in
>>> some hundreths. I believe this is mainly due to the fact that in
>>> both cases
>>> degrees of freedom are very large. This is something I want to
>>> check with
>>> you: From the reading of Korn and Graubard "Analysis of health
>>> surveys" I�ve
>>> understood that in complex surveys degrees of freedom are
>>> calculated as
>>> #PSUs - #strata (624 for the first specification and 1244 for the
>>> second,
>>> because Stata duplicates the number of census tracts because each
>>> of them
>>> belongs to two different strata). I do not follow you very well
>>> when you
>>> recommend doing a small simulation with census or simulated data to
>>> ascertain degrees of freedom or when you state that Taylor series
>>> expansion
>>> standard errors might be badly off with small samples. It�s usual
>>> practice
>>> to work with such low numbers of individuals per PSU (10 in my
>>> case) and
>>> I�ve never heard that there was a problem of a small sample size
>>> then.
>>>
>>> Unfortunately, I don�t have enough knowledge to go for option 3.
>>>
>>> To conclude, although both specifications yield similar results, I
>>> agree
>>> with you that the second one implies linked selection of PSUs while
>>> the
>>> first one is conceptually sounder.
>>>
>>>
>>>
>>> �ngel Rodr�guez Laso
>>> Institute of Public Health of the Region of Madrid
>>>
>>> -----Mensaje original-----
>>> De: [email protected]
>>> [mailto:[email protected]] En nombre de Stas
>>> Kolenikov
>>> Enviado el: jueves, 27 de marzo de 2008 20:06
>>> Para: [email protected]
>>> Asunto: Re: st: Definition of strata and PSUs when svysetting
>>>
>>>
>>> I would say your first specificaiton makes better sense, even though
>>> the design it produces is quite weird, and the degrees of freedom in
>>> that design are strange (and 7 initial strata won't get you very
>>> far,
>>> anyway). In Stata 10, that's doable with
>>>
>>> svyset tract, strata(area) || person, strata(age_group)
>>>
>>> if I am getting your design right.
>>>
>>> In the second specification with region by age strata, you have some
>>> sort of coupled sampling when selecting a PSU in one stratum implies
>>> selecting a certain PSU in the another stratum linked by geography.
>>> You could still analyze that, but you would need to get accurate
>>> pairwise probabilities of selection to compute Horwitz-Thompson
>>> estimator, and Grundy-Yates-Sen estimator of its variance (which I
>>> don't think is implemented anywhere commercially as those higher
>>> order
>>> probabilities of selection are rarely known; Jeff P, that might
>>> produce a cutting edge addition to Stata's set of -svy- tools,
>>> although I've no idea how to input and parse those :)). Any
>>> reasonably
>>> high level book would have it (Kish, Cochran, Mary Thompson's books
>>> spring to mind). For special cases, I think that can be
>>> programmed in
>>> Mata. Let's call that option 3. Note that the naive
>>> implementation as
>>>
>>> svyset tract, strata(area X age) || person
>>>
>>> produces wrong probabilities of selection, and the variances are
>>> likely to be understated, as there is more variability in this
>>> specification than in your actual design.
>>>
>>> If I were in your shoes, I would try both specifications you
>>> described
>>> and see whether they are producing comparable substantive results.
>>> Keep in mind that either way you are getting asymptotic Taylor
>>> series
>>> expansion standard errors, and they might be badly
>>> off with small samples like those you have. And I think you need to
>>> worry about your degrees of freedom, not your number of PSUs; I
>>> would
>>> do a small simulation to determine the approximate d.f.s for your
>>> main
>>> variables -- from census data if you have it, or from simulated data
>>> resembling the actual population. If I had infinite time to work on
>>> that project (meaning, a week or two of devoted programming), I
>>> would
>>> implement option 3 as the most proper.
>>>
>>> On 3/25/08, Angel Rodriguez Laso <[email protected]>
>>> wrote:
>>>
>>>> Greetings to all members of the list,
>>>>
>>>>
>>>>
>>>> I have the following questions on svysetting for an analysis of a
>>>> complex
>>>> survey:
>>>>
>>>>
>>>> We have carried out a regional health population survey. We
>>>> defined
>>>>
>>> strata
>>>
>>>> initially as geographic areas in the region (n=7) and allocated
>>>> to each
>>>>
>>> of
>>>
>>>> them a sample proportional to their population. But because we
>>>> wanted to
>>>> over-represent the elderly, we set that the number of people
>>>> over 65
>>>>
>>> years
>>>
>>>> sampled in all areas had to reach a minimum number. We didn't
>>>> change the
>>>> sample size of people bellow 65 obtained through the proportional
>>>> allocation. Therefore the sampling fractions (and consequently the
>>>>
>>> weights)
>>>
>>>> are different for each area by age group (bellow/over 65)
>>>> category.
>>>>
>>>> Then we selected census tracts in each geographic area with
>>>> probabilities
>>>> proportional to their total population, and randomly sampled 10
>>>>
>>> individuals
>>>
>>>> in those selected, always keeping the proportion 7 bellow 65
>>>> years/3 over
>>>>
>>> 65
>>>
>>>> years, which was the regional overall age distribution after the
>>>> oversampling explained above. My first question is if strata
>>>> should be
>>>> defined as geographic regions alone or as geographic area by age
>>>> groups
>>>> (bellow/ over 65 years) (n=14) when svysetting. The first
>>>> possibility
>>>>
>>> looks
>>>
>>>> more reasonable, because census tracts were selected within
>>>> geographic
>>>> areas, not within geographic-age groups areas. If this is
>>>> correct, then
>>>> probably the way to svyset would be declaring geographic areas as
>>>> first
>>>> stage strata, census tracts as first stage PSUs and age groups as
>>>> second
>>>> stage strata.
>>>>
>>>> Alternatively, if the answer is that strata should be defined as
>>>> region
>>>>
>>> by
>>>
>>>> two age-groups categories, then the same census tract can belong
>>>> to two
>>>> different strata (for example area A bellow 65/ area A over 65)
>>>> depending
>>>>
>>> on
>>>
>>>> the age of the individual considered. If I svyset: strata (region
>>>> by age
>>>> group categories) and PSU= census tracts, STATA interprets that
>>>> there are
>>>> twice the number of PSUs than real census tracts are. Is that
>>>> correct?
>>>>
>>>>
>>>>
>>>> Many thanks.
>>>>
>>>>
>>>> �ngel Rodr�guez Laso
>>>> Institute of Public Health of the Region of Madrid
>>>>
>>>>
>>>
>>>
>>> --
>>> Stas Kolenikov, also found at http://stas.kolenikov.name
>>>
>>> Small print: Please do not reply to my Gmail address as I don't
>>> check
>>> it regularly.
>>>
>>> *
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>>>
>>> ____________________________________________________________________
>>> _
>>> Mensaje analizado y protegido por Telefonica Empresas
>>>
>>>
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>>>
>>
>>
>> *
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>>
>> _____________________________________________________________________
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>>
>>
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>>
>
>
Steven Samuels
845-246-0774
18 Cantine's Island
Saugerties, NY 12477
EFax: 208-498-7441
*
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_____________________________________________________________________
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