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From |
"McKenna, Timothy" <Timothy.McKenna@nera.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: Svymean, svrmean and weighted binomial |

Date |
Thu, 31 Mar 2005 12:57:56 -0500 |

I ran -svymean- on some data and got the following result: . svymean congo; Survey mean estimation pweight: wgt Number of obs = 937 Strata: dis Number of strata = 4 PSU: <observations> Number of PSUs = 937 Population size = 72787.999 ------------------------------------------------------------------------ ------ Mean | Estimate Std. Err. [95% Conf. Interval] Deff ---------+-------------------------------------------------------------- ------ congo | .0052457 .003009 -.0006595 .011151 1.624072 ------------------------------------------------------------------------ ------ I wanted to try the jackknife method of computing the variance, so I used -svrmean- by Nick Winter: . survwgt create jkn, strata(dis) psu(ssn) weight(wgt) stem(jkwgt_); Generating replicate weights........................... [snip a bunch of output about the replicate weights] . svrset set meth jkn; . svrset set pw wgt; . svrset set rw "jkwgt_1-jkwgt_937"; . svrmean congo; Survey mean estimation, replication (jkn) variance method Analysis weight: wgt Number of obs = 937 Replicate weights: jkwgt_1... Population size = 72787.999 Number of replicates: 937 Degrees of freedom = 933 ------------------------------------------------------------------------ ------ Mean | Estimate Std. Err. [95% Conf. Interval] Deff ---------+-------------------------------------------------------------- ------ congo | .0052457 .003009 -.0006595 .011151 1.624072 ------------------------------------------------------------------------ ------ It seems strange to me that this is the exact same result as -svymean-. Is this possible? My PSUs are the individual observations, would -svrmean- give the same result with such a survey design? On a related note, I have been looking for a way to do -ci congo, binomial wilson- using data with unequal sampling weights. I have not been able to find much of anything, even using software other than Stata. Using Gauss I run a bootstrap, assuming each stratum to be iid, but not iid across strata, and taking the empirical confidence interval from that (which is reassuringly close to the confidence interval from the unweighted -ci- results). From the software packages I have seen the full bootstrap is not used very often when it comes to computing the std errors of survey data. Why is that? Do more complicated survey designs make a full bootstrap intractable? For comparison below is the output -ci, binomial wilson- and my bootstrapped estimates. . ci congo, binomial wilson ------ Wilson ------ Variable | Obs Mean Std. Err. [95% Conf. Interval] -------------+---------------------------------------------------------- ----- congo | 937 .0032017 .0018455 .0010895 .0093708 My bootstrap results: Mean Std Error 95% confidence intervals 0.0052548 0.0030023 0.0000000 0.0122401 -Tim _____________________________________________________________ This e-mail and any attachments may be confidential or legally privileged. If you received this message in error or are not the intended recipient, you should destroy the e-mail message and any attachments or copies, and you are prohibited from retaining, distributing, disclosing or using any information contained herein. Please inform us of the erroneous delivery by return e-mail. Thank you for your cooperation. _____________________________________________________________ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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