If the rounds are independent, then
t=(avergage[round t]-average[round s])/sqrt(variance_t [average in
round t]+variance_s [average in round s])
is normal / t with sum of design degrees of freedom / t with
Satterthwaite corrected degrees of freedom, depending on how you want
to think about them. The quick and dirty solution is to save the four
above quantities as locals or scalars and form this t-statistic. If
you had strata and cluster IDs with some sort of insider access, you
could put those together with
use dataset1
append dataset2
*********
* make sure PSU labels are different in two years
*********
svy: sum whatever , over[year]
lincom [whatever]year2 - [whatever]year1
With separate bootstrap weights, that would not necessarily work, I am
afraid. If you have the same number of replicate weights in both
periods, it might.
On Fri, Jul 25, 2008 at 9:41 AM, Mark Latendresse
<[email protected]> wrote:
> Hello,
> I have data on 7 independent cross-sectional surveys 1999-2007 (not panels)
> for which the target populations were identical. We would like to test for
> trends among several sub-groups on average number of cigarettes smoked per
> day (continuous variable). How can I do a trend analysis in Stata that will
> take into account sampling weights and stratification? We have bootstrap
> weights for each survey, however, we can also obtain the design effects for
> each survey if necessary.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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