# st: Svymean, svrmean and weighted binomial

 From "McKenna, Timothy" To 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.

_____________________________________________________________

*
*   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/
```