Dear Statalist subscribers,
I have weighted survey (categorical)
data from which I wish to generate confidence intervals (CIs). I use:
. svyprop
. svymean
commands and I get confidence intervals no problem. Stata uses Wald binomial calculations. But when N is small (proportion < 0.01 (percentage < 1%)) Wald calculations are not appropriate (Stata happily still calculates them though). I could use exact calculations (cii command) but this is an appropriate solution only when survey data is approximately self-weighting. But my data is *not* approximately self-weighting, not by a galactic mile.
For *fun*, I tried using the cii command but sometimes (not surprisingly) I got weird results, such as 0.2% (0.5% - 1.1%) where 0.2% was the weighted point estimate obtained from svy commands and 0.5% - 1.1% was the CI from exact calculations (using cii command). Indeed, this was not a good look!
Does anyone know of any *analytic* methods to solve this problem? (as opposed to using bootstrap techniques)
I am interested in analytic techniques (assuming these are available) because I have other instances where my results are sometimes 100%, and sometimes 0%. Stata is not able to generate CIs for such results (remember, svy commands are being used because the data is weighted) and boostrap methods are useless here because all that one does when using bootstrap techniques is resample 100% (or 0%) over and over again without any uncertainty.
I am using Stata 10 (though mostly using Stata 9 commands within the Stata 10 environment).
cheers, peter
