Re: st: Using survey weights in regression models

[Richard Williams <Richard.A.Williams.5@nd.edu>]

At 09:19 AM 11/24/2003 -0600, Dick Campbell wrote:
> Regarding the recent discussion of how to handle
> weights in regression, I have found the following
> paper to be very informative.
>
> TI:     Sampling Weights and Regression Analysis
> AU:     Winship, Christopher; Radbill, Larry
> SO:     Sociological Methods and Research, 1994, 23, 2, Nov, 230-257
I just read this and it is very good.  Thanks for recommending it.

A key question I still have: One of its key arguments is that most programs 
get the standard errors wrong when weighting is used.  The article was 
written in 1994; is it still true of Stata in 2003?  In particular, do the 
svy commands (which I have never used) take care of the concerns raised by 
the authors?

To paraphrase their key arguments: Where sampling weights are solely a 
function of IVs included in the model (e.g. minorities are oversampled and 
race is an IV), unweighted OLS estimates are preferred because they are 
unbiased, consistent, and have smaller standard errors than weighted OLS 
estimates (because weighting induces heteroskedasticity in the error terms).

Where sampling weights are a function of the DV (and thus of the error 
term; oversampling low income groups and using income as a DV would be an 
example) unweighted estimates will be biased and inconsistent.  The authors 
recommend trying a couple of things before you resort to weighting.  But in 
some cases, weighting will be appropriate, and you should use the White 
heteroskedastic consistent estimator for the standard errors.  In this 
case, weighting will produce consistent estimates of the true regression 
slopes; but it will also induce heteroskedasticity in the error terms, 
which is why you use White's estimator.

There is other good stuff in the article too, e.g. as was mentioned on the 
list before, they suggest comparing weighted and unweighted results as a 
check on model specification.  Unless I missed it, they don't mention that 
standardized coefficients and R-square will be wrong if you don't weight, 
so be aware of that if you like to use such things.


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