Re: st: Sampling weights (pweights) and regression analysis

[Fatih Yilmaz <fyilmaz@ucalgary.ca>]

Thanks Steve, 
This is very helpful. 
fatih

On 2012-07-12, at 6:16 PM, Steve Samuels wrote:

> On Jul 11, 2012, at 4:15 PM, Fatih Yilmaz wrote:
> 
> 
>> I am having trouble with using sampling weights in my simple regression
>> analysis.
>> 
>> Here is the story:
>> 
>> The survey data I have is not representative, where some groups were
>> deliberately over or under-sampled.
>>    The weights I was provided ara computed as follows:
>> 
>> For group one (strata), population weight is 60%
>>         				  sample weight is 40%
>> 					Final Pweight = 60%/40%=1.5
>> 
>> My questions:
>> 
>> 1- I needed to drop some of the observations from the survey data: outliers,
>> missings obs and also unrelated data.
>>    so, can I still use the old (initial) weights or do I have to re-weight the
>> data with respect to the dropped observations?
>>    Or how problematic could it be to use old weights?
>> 
> 
> 
> You should reweight for non-response.. Not doing so could be quite problematic. 
> How you do thisdepends on what you know about the population. See the sections
> on nonresponse weighting in the books by Lohr or Groves et al. and in the PEAS page
> referenced below. If you are dropping observations because of missing data for
> some variables, you have a couple of choices. Probably best is to treat these as
> "nonrespondents". Better would be to  impute missing variables  with Stata's
> multiple imputation commands (see the help for -mi svyset-), but this would take
> your analysis out of the realm of the "simple".
> 
> Note that if you want to analyze a subgroup, it is an error to discard
> members of the sample who are not in the subgroup. Doing so risks standard
> errors that are too small. See the section on "subpopulations" in
> Stata's survey manual and in Lohr's book (reference)
> 
> 
>> 2- Since, my weights were computed as w=(pop%)/(sample%) (in general, some other
>> researchers may compute them as w=(sample%)/(pop%) ),
>> when I estimate weighted OLS should I use "reg y x [pw=1/w]" or  ""reg y x
>> [pw=w]".
>> 
> Other researchers may, but they would be wrong. From your description, I think that
> you have the right weights.  You can check by seeing if the stratum weight totals
> add up to the known stratum population sizes.  ("total w, over(stratum)"
> 
> To do survey regression in Stata, you -svyset- the data and identify weights,
> sampling strata, and clusters, if any. The regression estimation command is 
> s -svy, subpop(): regress-
> 
> 
>> Could you pls also recommend some resources on sampling weights and regression
>> analysis (preferably practical sources ),
>> 
> Resources:
> 
> Lohr, S. L. (1999 1st Ed & 2009 2nd Ed). Sampling: Design and Analysis (2nd
> ed.). Boston, MA: Cengage Brooks/Cole.
> 
> Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., &
> Tourangeau, R. (2004 1st Ed, 2009, 2nd). Survey methodology. Hoboken, N.J.: Wiley.
> 
> http://www.restore.ac.uk/PEAS/about.php
> especially http://www.restore.ac.uk/PEAS/theory.php
> with sections on weighting and non-response
> and the exemplars page
> http://www.restore.ac.uk/PEAS/aboutex2.php
> 
> 
> http://www.statcan.gc.ca/edu/power-pouvoir/ch13/5214895-eng.htm. See especially:
> http://www.statcan.gc.ca/edu/power-pouvoir/ch13/estimation/5214893-eng.htm.
> 
> help.pop.psu.edu/help-by-statistical-method/weighting
> 
> http://www.ats.ucla.edu/stat/stata/seminars/applied_svy_stata11/default.htm
> 
> 
> Steve
> sjsamuels@gmail.com
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> 


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