Thanks Stas & Steven
What I would like to do is to calibrate on some of the measures from the
first questionaire.
I have data on 3750 men from that first questionnaire and I would like to
transform my 600 man population into my 5000 man population so that the
distribution of chronic diseases and medication is the same as we would
expect it to be in the 5000 man population.
I know how the 5000 men differs from the 3750 men regarding age and
geaography. There was a slight effect of age, but geography was not
important for non-responders. So adjusting for age is really the only thing
needed at this step.
Then I know how the 600 differs from the 3750 men. The 600 are better
educated, smoke less and do more exercise and then they are slightly less
prone to have chronic diseases and then they are slightly younger.
So I'd like to weight each of the 600 men so that I can compensate for
education, smoking, physical activity, chronic diseases (and medication but
they are closely related so I think I'll just adjust for medication as it is
the most precise measure) and age.
So if I want to adjust for those, how do I go by that?
I can see that the code below will adjust on age and geography since those
data are present through the two steps, but the more detailed information on
smoing, health and lifestyle is only present in step two.
I don't know the tot_medgb (medicin) or tot_smokegp (smoking) amongst the
5000 but only amongst the 3750.
That is how do I incoorporate the two steps into the raking? Or should I use
the post stratification command instead since I know these data on the
individual level?
As I see it running two rakings after each other: one for step 1 and one for
step 2 would risk changing the what has been done in the first raking.
I might be stupid but I don't really see how I can do this using the code
below.
Also,how many variables is it adviseable to rake on?
Thank you for your help
Kristian
-----Oprindelig meddelelse-----
Fra: [email protected]
[mailto:[email protected]] På vegne af Steven Samuels
Sendt: Sunday, December 07, 2008 6:43 AM
Til: [email protected]
Emne: Re: SV: st: Survey - raking - calibration - post stratification -
calculating weights
--
Stas, I am envious of statisticians who draw samples from those
lists. This is a double sample and I agree with your advice: give
everyone the weight for their age stratum:
weight1 = N_i/n_i
where "N" denotes population and "n" denotes sample size. Kristian
apparently thinks of the 5,000 person sample as his "population"; the
figure that he linked to does not show the initial sampling step at
all. He may not have access to the one-year census counts. If he
does not, I suggest that he use the N's from the 5,000. I suggest
below that he also form geographic categories and rake those, with
population counts, if possible, otherwise with counts from the
5,000. I roughly calculate that with 5,000 in the first phase
sample, bias in estimates and in standard errors will be small.
Kristian, here is how to simultaneously match the age distribution
and the geographic distribution of the final sample to your
population. (This is called "sample balancing" or "raking".) Form
age groups (agegp) and geographical groupings (geogp) and get the
population counts(or percentages, see below) in each cell.
**************************CODE BEGINS**************************
* tot_agep = total for population in participant age group (agegp)
* tot_geogp = total for population in participant geographical group
(geogp)
**************************************************************
survwgt rake weight1 ///
by(agegp geogp) ///
totvars(tot_agegp tot_geogp ///
gen(weight2)
***************************CODE ENDS***************************
Raking can present problems, so so I suggest that you read http://
www.abtassociates.com/ presentations/raking_survey_data_2_JOS.pdf. If you
cannot get
population counts, perhaps you can get population percentages,
multiply by 10 or 100 and round to the nearest whole number (e.g.
5.12% = 51 or 512), so that the population "size" is 1,000 or 10,000.
For estimating means and proportions, these will yield nearly the
same results as actual population counts. The Denmark census counts
or percentages might be available only in larger age categories than
the ones you used to draw the sample: say (60-64, 65-70,70-74). If
so, use those for the raking calculations.
If you have, say, four geographical categories, you may be tempted to
use 4 x 15 =60 stratification combinations. However, with only 600
people in the final sample, the numbers in individual cells will be
too small for reliable estimation.
Theory for double sampling can be found in WG Cochran, 1973, Sampling
Techniques, pp 117-119, 327-334, or in most other texts.
Unfortunately, raking will not completely solve the problem of non-
response.
-Steven
On Dec 6, 2008, at 11:19 PM, Stas Kolenikov wrote:
> Steven,
>
> you might be shocked, but people in Nordic countries do have their
> population completely enumerated. Putting NJC's hat on :)), let me
> remind you that this is an international list, and different countries
> have different standards of how they collect and store their official
> data. Denmark has a register with an equivalent of SSN that makes it
> possible to combine the data three ways from economic, medical and
> social perspectives. That's a survey statistician and a
> microeconometrician dream... and they actually do have the capacity of
> drawing SRS. That is, the first 5000 were SRS of the population, and
> then Kristian continued a with stratified second phase sampling.
>
> I would probably just give everybody the weight = # in age group
> across Denmark (in some meaningfully defined period of the study) / #
> in age in group in the sample. If you treat sample groups as
> non-response adjustment cells, that's what this will probably boil
> down to after multiplication of three or so fractions. ches and help
> try:
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