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From |
"David Moore" <davem@hartman-group.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: RE: Missings in Survey Design (was: Bootstrap and percentages) |

Date |
Tue, 27 Aug 2002 09:52:45 -0700 |

Although it may not seem a general solution to the problem, since complex surveys can make it impractical, shouldn't a sample selection model handle this situation in theory? -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Joseph McCrary Sent: Tuesday, August 27, 2002 4:53 AM To: statalist@hsphsun2.harvard.edu Subject: st: Missings in Survey Design (was: Bootstrap and percentages) The answer pretty much depends on your research question. Are you interested in explaining the variance of the group of smokers (the question you posed, "Has a doctor ever told you to quit smoking" is a good example), or comparing the group of smokers to a larger population (e.g., "How effective do you think the latest Ad Council's commercials have been in preventing teen smoking?"). In the latter, you might be interested in comparing smokers to non-smokers. I'd love to see any references you collect as I will probably be undertaking quite a bit in the way of surveying shortly. Thank you. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Sayer, Bryan Sent: Monday, August 26, 2002 1:02 PM To: 'statalist@hsphsun2.harvard.edu' Subject: st: RE: Bootstrap and percentages We are in the middle of investigating this issue of "missings" (meaning Not In Universe) with regard to sample design, but my view right now is that Stata does not address this properly. Anyway, one fix is assign values to missing cases (use something that will cause problems if the cases get included like -999) and then use a subpop statement to restrict the analysis to the proper cases. For those of you interested in this issue, the philosphopical question is this. Consider a survey designed to sample from a specific target population, and assume that there are correct sample design variables for that population and that these design variables meet the criteria of at least two PSUs per stratum. Now assume that certain questions are asked only of a subgroup of the population. For example, we have a question "Has a doctor ever told you to quit smoking?" Clearly the question is asked only of smokers, and in our case, smokers who have seen a physician in the past 12 months. Now are the group of people who are asked this question a sub-population of the target population, or are they a population unto themselves? If they are a sub-population, then the sample design variables appropriate for the target population are sufficient to describe the sub-population, and Stata ought to estimate properly without consideration of missings for those not in the sub-group. But if this is a separate population, then yes, a new set of sample design variables is necessary. I'm collecting references. If enough people are interested I'll post them. Bryan Sayer Statistician, SSS Inc. bsayer@s-3.com -----Original Message----- From: Michael R. Smith [mailto:smith@leacock.lan.mcgill.ca] Sent: Monday, August 26, 2002 11:47 AM To: statalist@hsphsun2.harvard.edu Subject: st: Bootstrap and percentages I'm processing some data that was generated with a complex sample design but to which, for reasons of confidentiality, I don't have direct access. This means that the svy commands are not a practical option. Use of them requires correction for PSUs with only one case - but the PSUs with missing values that reduce them to one case will vary depending on variables in the analysis. Submitting my code to the person who runs it in order to find out when and where to merge PSUs would, then, become an extremely cumbersome process. So bootstrapping looks like the most practical method for inferential purposes. It's clear how to do that with regression and related procedures. But's it's not obvious to me how one should go about using the bs command to generate standard errors for a percentage table. The bs command requires specifying each coefficient to be bootstrapped. How does one specify cells in a percentage table? Part of the analysis requires generating percentage tables with quite large numbers of cells, so I need to generate a large number of standard errors. I've read what seem to be the relevant sections of the manual and rooted around in the FAQs and other documentation for an answer, so far with no success. Michael Smith * * 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/ * * 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/ * * 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/ * * 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/

**References**:**st: Missings in Survey Design (was: Bootstrap and percentages)***From:*"Joseph McCrary" <joemccrary@attbi.com>

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