Stata The Stata listserver
[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: using sample weights in the absence of stratification and clustering


From   frone@ria.buffalo.edu
To   statalist@hsphsun2.harvard.edu
Subject   st: using sample weights in the absence of stratification and clustering
Date   Wed, 13 Aug 2003 19:00:18 -0400

I would be grateful if someone could help me understand the basic issue 
involved in using sampling weights in the absence of stratification and 
clustering.

If I compute the weighted mean and standard error of a variable in 
SPSS(using the sample weight)  or in Stata using -ci- and aweights, I get 
the same estimates for the weighted mean and standard error.  However, if 
I use -svymean- with pweights, I get the same estimate for the weighted 
mean, but the standard error is different.  There is a note on page 350 of 
[U] stating the standard error provided by SPSS or -ci- with aweights is 
not correct.  I'm not sure why.

Also, if I compute a 2x2 table in SPSS using the weights, I get a Pearson 
chi-square value and p-value.  However, in Stata, using -tabluate- with 
aweights, the person chi-square cannot be  computed.  If I use -svytab- 
with pweights, I get an uncorrected chi-square (which is sometimes a bit 
different from that provided by SPSS) and a design based f-test.  It seems 
the recommendation is to use the design-based f-test.  If the sample 
weights are being used and there is no stratification or clustering, why 
use the design-based f tests as opposed to the uncorrected Pearson 
chi-square?

Thanks,
Mike Frone

*
*   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/



© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index