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Analyzing survey data using Stata 10


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  • Roberto G. Gutierrez


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    Stata’s approach to the analysis of data from complex surveys is unique in that it clearly separates the declaration of the design aspects of the survey (accomplished by svyset) from the actual analysis. Such an arrangement is ideal because the design characteristics of the data do not change according to the analysis being performed. Whether you are constructing contingency tables or performing Cox regression, the sampling weights and primary sampling units (not to mention the other design specifications) remain constant. Stata’s treatment of survey data makes it easy to maintain that consistency. Most of Stata’s model fitting and other analysis commands can be applied easily to survey data, including (with the release of Stata 10) commands for Cox regression and parametric models for survival data in a survey setting. This talk is a tutorial on how to make full use of Stata’s capabilities for survey data. Alternative variance estimation is a key component of performing valid inference in light of complex-survey designs, and I will discuss several variance-estimation options. That discussion will include modern computationally intensive methods such as balanced and repeated replication, the jackknife, and the bootstrap, which are made feasible with the advent of better computer technology. For these three methods, variance estimation can be done directly or by using a series of replication weights.

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    Bibliographic Info

    Paper provided by Stata Users Group in its series Summer North American Stata Users' Group Meetings 2008 with number 18.

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    Date of creation: 29 Jul 2008
    Date of revision: 28 Aug 2008
    Handle: RePEc:boc:nsug08:18

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    Cited by:
    1. Michal Brzezinski, 2010. "Income Affluence in Poland," Social Indicators Research, Springer, vol. 99(2), pages 285-299, November.
    2. Lamichhane, Dirga Kumar & Mangyo, Eiji, 2011. "Water accessibility and child health: Use of the leave-out strategy of instruments," Journal of Health Economics, Elsevier, vol. 30(5), pages 1000-1010.


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