Re: st: Number of Obs with svy , suppop()

[Phil Schumm <pschumm@uchicago.edu>]

On Mar 19, 2010, at 3:17 AM, Michael Norman Mitchell wrote:
> Thank you for your reply... I am still struggling to solidly  
> understand this. Perhaps I have a more fundamental question. What is  
> the formula for the "Number of obs" in the context of the -svy-  
> commands. It sounds like, in the absence of the -subpop()- option,  
> it is the number of observations with non-missing values on the  
> tabulated variable. And, in the presence of the -subpop()- option it  
> is the total number of observations minus the number of observations  
> that meet the -subpop()- option and are missing on the tabulated  
> variable. Am I on the right track here?


Yes, I believe this is correct (note however that I haven't looked  
into this carefully, so if you need confirmation of Stata's behavior  
WRT this issue, you'll need to get it from the manual or from someone  
like Jeff).  One more thing I should mention: How you proceed in cases  
like this may depend on the reason(s) that the data are missing.  For  
example, suppose the missing values for race are due to respondents  
refusing to answer the question or saying "I don't know."  In that  
case, Durbin argued that this should be taken into account when  
defining the subpopulation (also referred to in the survey literature  
as a domain).[1]  IOW, in your example, the subpopulation of interest  
would be "all males who, when asked, will provide an answer to this  
question."  In this case, you would augment your -subpop()-  
specification like this:

    svy, subpop(if sex==1 & !mi(race)):

in which case the "number of observations" reported by Stata should  
now correspond to the total number of observations in your dataset.   
More importantly, this would specify a slightly different variance  
calculation, though the actual result may only differ very slightly  
(if at all) depending on the circumstances.  Note that I almost never  
see anyone do this -- at least not in the applied social science  
literature.

Of course, what I just described does nothing to address the possible  
bias that might arise if those who don't respond differ (in terms of  
race) from those who do...


-- Phil

[1] J. Durbin. Sampling theory for estimates based on fewer  
individuals than the number selected. Bulletin of the International  
Statistical Institute, 36(3):113–119, 1958.


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