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Re: st: Analyzing a subpopulation in Stata 10.1


From   "Michael I. Lichter" <MLichter@Buffalo.EDU>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Analyzing a subpopulation in Stata 10.1
Date   Sat, 27 Jun 2009 16:25:50 -0400

This is kind of long, but I hope that some folks, particularly those with expertise on poststratification and people from StataCorp will hear me out.

Fundamentally, Figen's question is about how Stata handles missing values under poststratification. It's one I don't know the answer to, but that perhaps somebody from StataCorp could help answer.

To illustrate the problem, I've included a program and edited output. For those not wanting to do too much scanning, I'll summarize what it does and shows.

I create a fictional dataset of men and women (femV1) who are randomly assigned to be either native or immigrant status (native; about 50% are native), and among the women I've created an ever-given-birth (everV1) variable (about 55% have). Although the sample is about 50% native/50% immigrant, the hypothetical population is 75% native and I've created a poststratification weight to deal with that. The everV1 variable is by definition missing for all males, but I also created a new variable everV2 that is missing at random for 15% of females.

The first table T1 below shows that there are, after weighting, 2374 females (110 obs) and 1626 males (90 obs) in the population of 4000 (200 obs). If I tabulate everV2 only (T2), without specifying a subpopulation, we learn that there are 2179 (50) people who have given birth and 1821 (44) who have not. Since there are only 2374 females and only 55% of them have given birth, 2179 is clearly too big a number. Of course, Stata doesn't know that some of the cases are missing by definition while others are missing at random; *it has simply reweighted the sample to the full population size.*

Now, if I tabulate everV2 for the subpopulation of females (T3), it shows that 1242 (50) have ever had a child and 1009 (44) have not, for a total of 2251 (94). Obviously, 2251 != 2374. *Why hasn't Stata adjusted the weights so that they add up to the full subpopulation size?* I don't know.

If I repeat T3 with the "missing" option (T4), I get different results. These are the same results that I would get if I were using a static poststratification weight: 1168 (50) yeses, 946 (44) nos, and 260 (16) missing, adding up to the subpopulation size of 2374 (110). (Note that this is the same as including a "if ! missing(everV2)" in the subpop() option.) This is probably better than the seemingly arbitrary result I get in T2, but I'd really like at least the option for my result to be adjusted up to the subpopulation size.

So, Stata does adjust the subpopulation weights, but it doesn't adjust them to the subpopulation size. What precisely is it doing? I wish I knew. It seems to me that adjusting to the full subpopulation size is the correct thing to do, but maybe I'm missing something.

Of course, Figen isn't calculating counts, he's calculating proportions. Nevertheless, the size of errors and proportions depends on how Stata is counting things internally.

Does this make sense? Is Stata doing the right thing? (And what *is* it doing in T3?)

Michael

-----------------------------------------------------------------------------------------
clear
set obs 200
set seed 06272009
gen byte femV1 = _n <= 110              // pop, 55% female
gen byte everV1 = (uniform() < .55) if (femV1==1)   // females only
clonevar everV2 = everV1
replace everV2 = . if (uniform() < .15)    // add missing values
gen byte native = (uniform() <= .5)    // about 50% native in sample
label define Lyes01 0 "0-No" 1 "1-Yes"
label val femV1 native everV1 everV2 Lyes01
gen postwt = cond(native, 3000, 1000)  // 75% native in population
svyset, poststrata(native) postweight(postwt)

svy: tab femV1, count format(%10.0f) obs   // [T1] femV1 only
svy: tab everV2, count format(%10.0f) obs   // [T2] ever2 only
svy, subpop(femV1): tab everV2, count format(%10.0f) obs // [T3] with subpop svy, subpop(femV1): tab everV2, count format(%10.0f) obs miss // [T4] with missing
-----------------------------------------------------------------------------------------

. svy: tab femV1, count format(%10.0f) obs   // [T1] femV1 only
(running tabulate on estimation sample)

Number of strata = 1 Number of obs = 200 Number of PSUs = 200 Population size = 4000 N. of poststrata = 2 Design df = 199

----------------------------------
   femV1 |      count         obs
----------+-----------------------
    0-No |       1626          90
   1-Yes |       2374         110
         |
   Total |       4000         200
----------------------------------
 Key:  count     =  counts
       obs       =  number of observations

. svy: tab everV2, count format(%10.0f) obs   // [T2] everV2 only
(running tabulate on estimation sample)

Number of strata = 1 Number of obs = 94 Number of PSUs = 94 Population size = 4000 N. of poststrata = 2 Design df = 93

----------------------------------
  everV2 |      count         obs
----------+-----------------------
    0-No |       1821          44
   1-Yes |       2179          50
         |
   Total |       4000          94
----------------------------------
 Key:  count     =  counts
       obs       =  number of observations

. svy, subpop(femV1): tab everV2, count format(%10.0f) obs // [T3] with subpop
(running tabulate on estimation sample)

Number of strata = 1 Number of obs = 184 Number of PSUs = 184 Population size = 4000 N. of poststrata = 2 Subpop. no. of obs = 94 Subpop. size = 2251.0638 Design df = 183

----------------------------------
  everV2 |      count         obs
----------+-----------------------
    0-No |       1009          44
   1-Yes |       1242          50
         |
   Total |       2251          94
----------------------------------
 Key:  count     =  counts
       obs       =  number of observations

. svy, subpop(femV1): tab everV2, count format(%10.0f) obs miss // [T4] with miss too
(running tabulate on estimation sample)

Number of strata = 1 Number of obs = 200 Number of PSUs = 200 Population size = 4000 N. of poststrata = 2 Subpop. no. of obs = 110 Subpop. size = 2374.4374 Design df = 199

----------------------------------
  everV2 |      count         obs
----------+-----------------------
    0-No |        946          44
   1-Yes |       1168          50
       . |        260          16
         |
   Total |       2374         110
----------------------------------
 Key:  count     =  counts
       obs       =  number of observations


-----------------------------------------------------------------------------------------

--
Michael I. Lichter, Ph.D. <mlichter@buffalo.edu>
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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