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# SV: SV: SV: st: Survey - raking - calibration - post stratification - calculating weights

 From "Kristian Wraae" To Subject SV: SV: SV: st: Survey - raking - calibration - post stratification - calculating weights Date Tue, 9 Dec 2008 17:25:07 +0100

```Thanks Steve

I was referring to the design.
What I meant by same probability was that if I include the distant islands
in the most distant non-island categories those categories would be weighted
too high due to the fact that too few of the 600 would be from that
category.

I think the only good solution is to drop the 164 men from the the 4975 and
rake on geography with fewer categories.

Thanks for all your help.

Kristian

There is no assumption "that all had the same probability of being
included in the final sample". People had different probabilities of
getting into that sample; that is why you are doing the response-
modeling.

-Steve

On Dec 9, 2008, at 10:23 AM, Kristian Wraae wrote:

> I think the reason why STATA complains about totals not being equal
> is that
> I have one geography category missing amingst the 600. We refrained
> from
> asking people who lived on distant islands, and thus had difficulty
> showing
> up, to participate in the final sample to avoid have too many
> dropouts.
>
> So I suppose we should drop all individuals living on islands
> amongst the
> 4975 (it is only 164) and later amongst the 3743 (120) in order to
> do the
> final raking with geography.

> Alternatively the final raking should be done
> without geography since there is really no reason to belive that
> geography
> should be a factor determining health.

>
> Another approach is to include the islands into the most distant
> zip-code
> category, but that will interfere with the assumption that all had
> the same
> probability of being included in the final sample.

You misunderstand the purpose of raking.  There is no such assumption
involved.
>
> My best suggesting will be not to rake on geography at in the last
> two steps
> (or maybe at all).
>
> Age is definately the most important variable to rake on.
>

>
>
> -----Oprindelig meddelelse-----
> Fra: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af Kristian
> Wraae
> Sendt: Tuesday, December 09, 2008 1:23 PM
> Til: statalist@hsphsun2.harvard.edu
> Emne: SV: SV: st: Survey - raking - calibration - post
> stratification -
> calculating weights
>
>
> Now I have continued to step 2 with this do file:
>
> *Step 2
>
> xi: logistic sample i.age_grp i.geo_grp  i.health_medication
> i.health_diseases
>
> predict p_r
>
> gen weight3x = weight2x * (1/p_r)
>
> keep if sample == 1
> 				*(reducing dataset to 600 men)
> survwgt rake  weight3x,   ///
>         by(age_grp  geo_grp) ///
>         totvars(tot_age_grp tot_geo_grp) ///
>         gen(weight4x)
>
>
>
> The problem now is that Stata says that "totals across dimensions 1
> and 2
> are not equal"
>
> Why is that? Should I generate new totals for tot_age_grp and
> tot_geo_grp?
> Should they be based on the 3743 Why?
>
> How do I deal with missing values in p_r (depending on which
> predictors I
> include in the logistisk regression I might get missing values for
> p_r).
>
>
>
> -----Oprindelig meddelelse-----
> Fra: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af Kristian
> Wraae
> Sendt: Tuesday, December 09, 2008 12:35 PM
> Til: statalist@hsphsun2.harvard.edu
> Emne: SV: SV: st: Survey - raking - calibration - post
> stratification -
> calculating weights
>
>
> I have now tried to do the first step of the raking.
>
> I have 15 age groups and 67 geographic groups (simply based on the zip
> codes).
>
> I tried to do the raking first with a smaller number of geographic
> groups
> (10) but the results were more accurate with all groups.
>
> The variable I have are:
> age = continuos variable containg the age of the subject at the
> time of
> sampling dist_study = continuous variable containing the distance
> from the
> individual to me. age_grp = categorial variable - 15 age strata.
> geo_grp =
> zip code quest = 1 if individual returned a filled out
> questionnaire pop = 1
> if individual was amongst the 4975 in the original sample (all had
> of course
> pop=1) sample = 1 for each finally included subject.
>
> The do file looks like this:
>
> *************
> *To get data from the orginal population
> tabstat age
> tabstat dist_study
>
> *Raking starts by generating totals in each age group and
> geographical group
> egen tot_age_grp =  count(pop),by(age_grp) egen tot_age_grp_q =
> count(pop)
> if quest==1, by(age_grp)
>
> egen tot_geo_grp =  count(pop),by(geo_grp)
> egen tot_geo_grp_q = count(pop) if quest==1, by(geo_grp) *Inital
> weight is
> generated gen weight1x = (tot_age_grp / tot_age_grp_q)
>
> keep if quest==1
> 			*(reducing the dataset to 3743 men)
> survwgt rake  weight1x,   ///
>         by(age_grp  geo_grp) ///
>         totvars(tot_age_grp tot_geo_grp) ///
>         gen(weight2x)
>
> svyset  [pweight=weight2x], strata(age_grp)
>
> *Description
> svydes
> *Now we estimate the average age in the 4975 men from the 3743 men
> svymean
> age *Now we estimate the average distance to travel to get to me
> for the
> 4975 men based on the 3743 men svymean  dist_study
>
> *These are the actual numbers for the 3743 men.
> tabstat age
> tabstat dist_study
> ******************
>
> The output from Stat8 is:
>
> . *************
> . tabstat age
>
>     variable |      mean
> -------------+----------
>          age |   66.6695
> ------------------------
>
> . tabstat dist_study
>
>     variable |      mean
> -------------+----------
>   dist_study |  25.90153
> ------------------------
>
> .
> .
> . egen tot_age_grp =  count(pop),by(age_grp)
>
> . egen tot_age_grp_q = count(pop) if quest==1, by(age_grp) (1232
> missing
> values generated)
>
> .
> . egen tot_geo_grp =  count(pop),by(geo_grp)
>
> . egen tot_geo_grp_q = count(pop) if quest==1, by(geo_grp) (1232
> missing
> values generated)
>
> .
> . gen weight1x = (tot_age_grp / tot_age_grp_q)
> (1232 missing values generated)
>
> .
> . keep if quest==1
> (1232 observations deleted)
>
> .                         *(reducing the dataset to 3743 men)
> . survwgt rake  weight1x,   ///
>>         by(age_grp  geo_grp) ///
>>         totvars(tot_age_grp tot_geo_grp) ///
>>         gen(weight2x)
>
> .
> . svyset  [pweight=weight2x], strata(age_grp)
> pweight is weight2x
> strata is age_grp
>
> .
> . svydes
>
> pweight:  weight2x
> Strata:   age_grp
> PSU:      <observations>
>                                       #Obs per PSU
>  Strata                       ----------------------------
>  age_grp    #PSUs     #Obs       min      mean       max
> --------  --------  --------  --------  --------  --------
>        1       346       346         1       1.0         1
>        2       333       333         1       1.0         1
>        3       304       304         1       1.0         1
>        4       297       297         1       1.0         1
>        5       284       284         1       1.0         1
>        6       275       275         1       1.0         1
>        7       249       249         1       1.0         1
>        8       246       246         1       1.0         1
>        9       231       231         1       1.0         1
>       10       209       209         1       1.0         1
>       11       212       212         1       1.0         1
>       12       210       210         1       1.0         1
>       13       184       184         1       1.0         1
>       14       174       174         1       1.0         1
>       15       189       189         1       1.0         1
> --------  --------  --------  --------  --------  --------
>       15      3743      3743         1       1.0         1
>
> .
> . svymean  age
>
> Survey mean estimation
>
> pweight:  weight2x                                Number of obs    =
> 3743
> Strata:   age_grp                                 Number of strata =
> 15
> PSU:      <observations>                          Number of PSUs   =
> 3743
>                                                   Population size
> = 4975
>
> ----------------------------------------------------------------------
> ------
> --
>     Mean |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
> ---------
> +--------------------------------------------------------------
> ---------+----
> --
>      age |   66.66605    .0067455    66.65283    66.67928    .0092211
> ----------------------------------------------------------------------
> ------
> --
>
> . svymean  dist_study
>
> Survey mean estimation
>
> pweight:  weight2x                                Number of obs    =
> 3742
> Strata:   age_grp                                 Number of strata =
> 15
> PSU:      <observations>                          Number of PSUs   =
> 3742
>                                                   Population size  =
> 4973.7235
>
> ----------------------------------------------------------------------
> ------
> --
>     Mean |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
> ---------
> +--------------------------------------------------------------
> ---------+----
> --
> dist_s~y |   25.90772    .3139459     25.2922    26.52325     1.01731
> ----------------------------------------------------------------------
> ------
> --
>
> .
> . tabstat age
>
>     variable |      mean
> -------------+----------
>          age |   66.5895
> ------------------------
>
> . tabstat dist_study
>
>     variable |      mean
> -------------+----------
>   dist_study |  25.93867
> ------------------------
>
> .
> end of do-file
>
> As one can see the average age amongst the 4975 men is: 66.6695
>
> Using raking and svymean Stata estimates the average age amongst
> the 4975
> men based on the information from the 3743 men to be: 66.66605
>
> As one can see those are quite similar.
>
> Now let us look at the distance to travel. We raked on zip codes
> which are
> not equivalent to distances but despite that the results are quite
> amazing:
>
> We know the average distance to travel is: 25.90153 km
>
> After raking and basing the results on the 3743 men Stata estimates
> the
> distance to be: 25.90772 km
>
> Strikingly similar. The true distributions amongst the 3743 are not as
> close: 66.5895 years and 25.93867 kms, but really not that far off.
>
> The differences will be far greater when raking the 600.
>
> I will now go on.
>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
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*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
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```

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