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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: generating confidence intervals from weighted (survey) data |

Date |
Mon, 17 Aug 2009 07:07:45 +0000 (GMT) |

--- On Mon, 17/8/09, Peter Ittak wrote: > I have weighted survey (categorical) data from which I wish to > generate confidence intervals (CIs). I use the -svyprop- and -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. Any method for calculating confidence intervals of a proportion, including the "exact" method, will get the coverage somewhat wrong, especially when you have such small proportions. Given this I would not waste too much effort on getting the estimated confidence interval exactly right, but instead pick a reasonable one and treat it as a roughly accurate indication. There is some evidence that the approximate methods actually do a better job than the exact methods: Agresti, Alan, and Coull, Brent A. Approximate is better than 'exact' for interval estimation of binomial proportions. The American Statistician 52: 119-126, 1998. Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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/

**References**:**st: generating confidence intervals from weighted (survey) data***From:*Peter Ittak <pittak@unimelb.edu.au>

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