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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