[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Analyzing a subpopulation in Stata 10.1

From   "Michael I. Lichter" <>
Subject   Re: st: Analyzing a subpopulation in Stata 10.1
Date   Tue, 30 Jun 2009 15:48:22 -0400


It does make sense that the poststratification adjustment should be applied to the whole sample. The fact that the data is poststratified on nativity (which I purposely chose over sex, since there would have been no problem with female) means that the proportion female in the population is uncertain, which in turn means that the number of women who have ever born children should be that much more uncertain. I still have difficulty squaring the results from T3 from T4, since they are clearly contradictory and yet are *both* the result of "correct" adjustments of the survey weights.

Fortunately, I don't have any plans to report estimated counts in anything I'm working on right now; I'll just be sitting here worrying about whether my regressions, etc.., were performed with the correct weights.

In any event, I'll take a look at -svygen-.

Thanks again to you, and also to Figen.


Jeff Pitblado, StataCorp LP wrote:
The thing to keep in mind here is that the poststratification adjustment must
be applied to the entire estimation sample.

It is not possible to reweight at the subpopulation level unless there is
poststratification information at that level; i.e. if we had the postratum
population sizes for the four cells defined by sex and native status.

In table T3, -svy: tabulate- applies the weight adjustment to the 184
observations in the estimation sample.  The only way to prevent that is to fix
the adjusted weights ahead of time (see -help svygen-), but that isn't always
a good solution.  The poststratified sampling weights are designed to reduce
bias in the point estimates; however, with the postratum ID's -svy linearized-
can produce more efficient variance estimates than without.

Ultimately, it is the researcher/data-analyst that has the responsibility and
power to choose which analysis is most appropriate for themselves.

I can imagine real survey data where there are any number of different
poststratification adjustments one could apply for a given analysis.  Some
will make much more substantive sense than others.

Suppose we had a variable called -ns_postid- that simultaneously identified
the native status and sex of each individual in the dataset, and another
variable called -ns_postw- that contained the population size for the
corresponding group.  I think it is clear that this poststratification
information could be applied more broadly than the one in Michael's simulate


PS.  There is an undocumented -svygen- command that will generate
poststratification adjusted samling weights; see -help svygen-.

*   For searches and help try:

Michael I. Lichter, Ph.D. <>
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

*   For searches and help try:

© Copyright 1996–2022 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index