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Re: st: How to set calibrated weights


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: How to set calibrated weights
Date   Fri, 26 Oct 2012 13:41:39 -0400

You can't use  "//" , "/*",  "*/" or "///" on the command line.  
Put the code in a do file and log the results.  See the section
on "log your results" in http://folkesundhed.au.dk/fileadmin/www.folkesundhed.au.dk/uddannelse/stata/introduction/takecare.pdf


There won't be much harm in omitting the stratum variable, except slightly larger
standard errors, as you say. 

Steve


On Oct 26, 2012, at 12:27 PM, Veronica Galassi wrote:

I see...thank you for warning me!

I tried to run the code you gave me but I got back a couple of errors.
Below the output:

preserve

. bys cluster w2_gc_dc: keep if _n==1
(20701 observations deleted)

. bys cluster: gen nstrat= _n

. tab nstrat  //duplicates are nstrat >1
/ invalid name
r(198);

. sort cluster

. list cluster w2_gc_dc if nstrat>1 //show problem
1/ invalid name
r(198);

. save problem, replace
(note: file problem.dta not found)
file problem.dta saved

. restore


I told already the organisation about this funny thing when setting
the PSUs but they did not pay attention to it claiming that "cluster"
is the right PSU variable and end of the story.

I will try to raise the issue again. I know that not specifying the
strata gives me standard errors bigger than usual, but maybe this is a
good compromise.
What do you think?



2012/10/20 Steve Samuels <sjsamuels@gmail.com>:
> Veronica,
> 
> The PSU variable is not missing. It is the sampling unit at the first
> stage of sampling and it's one of your cluster variables, probably
> "cluster 1" (check). Your statement that one must know the PSU variable
> to use probability weights is also incorrect. One can get proper
> weighted estimates, though not standard errors, without knowing the PSU.
> 
> I'm not sure what wrong with your -concat- statement. I would have
> used "egen combination = group()". For it to have worked, the value of
> the "post-stratification weight" would have to be the population count
> for each combination of the three variables.
> 
> If the "post-stratification" weights are not integers, they are probably
> "calibration" weights that have already adjusted the probability
> weights. In that case, further post-stratification are likely to be
> superfluous. You would  then use the "post-stratification weight" in place of
> the probability weights. All weights should be
> described in the study documents (though usually not the"codebook"). If
> they are not, then contact the organization that did the study for
> details.
> 
> If sampling was without replacement at one or more stages,
> you could use the fpc() option for those stages. In practice,
> it makes a difference only for the first stage.
> 
> In any case, one guess at a -svyset- statement (assuming the
> "post-stratification weight" is a "calibration" weight) is:
> *************************************************************
> svyset w2_gc_prov [pw = w2_wgt], strata(w2_gc_dc) || w2_hhgeo
> **************************************************************
> 
> But I could be wrong, depending on how w2_wgt was calculated.
> 
> Before proceeding, I suggest that you learn more about sampling or take
> a survey course. I gave some references in:
> http://www.stata.com/statalist/archive/2012-09/msg01058.html.
> The Stata survey manual is also a very good resource, though the section on
> post-stratification is skimpy.
> 
> Steve
> 
> 
> On Oct 19, 2012, at 1:57 PM, Veronica Galassi wrote:
> 
> Dear Statalisters,
> 
> I am writing you concerning the application of calibrated weights to
> my dataset for the computation of descriptive statistics only.
> 
> The dataset I am working on collects information at household and
> individual level and comes from a stratified, two-stage clustered
> sample. The followings are the variables I have got:
> - probability weights: w2_dwgt
> - strata: w2_gc_dc
> - cluster 1: w2_gc_prov
> - cluster 2: w2_hhgeo
> - post-stratified weights: w2_wgt
> - age intervals:  w2_age_intervals
> - gender: w2_best_gen
> - population group: w2_best_race
> 
> In order to set the probability weights using the command svyset, I
> need the psu variable. As you may have noticed, this variable is
> missing and this makes me impossible to set pweights.
> In addition, from a couple of previous statalist conversations ( see
> in particular: http://www.ats.ucla.edu/stat/stata/faq/svy_stata_post.htm
> and http://www.stata.com/statalist/archive/2012-02/msg00584.html), I
> understood that:
> - when using calibrated weights I still have to set pweights and
> specify the original strata and clusters
> - In order to apply calibrated data I need to know the characteristics
> on the base of which the sample have been post-stratified ( in my case
> age intervals, gender and population groups).
> 
> Therefore, I tried to set my post-stratified weights using the
> following command:
> "svyset [pw=w2_dwgt], strata (w2_gc_dc) poststrata (w2_age_intervals
> w2_best_gen w2_best_race) postweight(w2_wgt)"
> which did not work because in Stata the poststrata must be mutually
> exclusive and thus only one variable can be specified.
> 
> In order to overcome this problem, I tried to generate a variable
> which is a combination of the three characteristics by using the
> command
> "egen combination=concat( w2_age_intervals w2_best_race w2_best_gen),
> format (float)".
> However, this command generated a variable containing only missing
> values and for this reason Stata gave me back the error:
> "option postweight() requires option poststrata()".
> The only way to make Stata set the post-calibrated weight was by using
> the command
> "svyset, poststrata (combination) postweight(w2_wgt)" with combination
> being a string variable. However I am scared that this command is not
> complete.
> 
> At this point, I would really appreciate any hint on what I am doing
> wrong and how to proceed to set my post-stratified weights.
> 
> Many thanks for your help!
> 
> Kind regards,
> 
> Veronica Galassi
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