Get Analyzing Complex Survey Data (Quantitative Applications in PDF

By Dr. Eun Sul Lee, Dr. Ronald N. Forthofer

This e-book examines how one can research complicated surveys, and specializes in the issues of weights and layout results. This re-creation accommodates contemporary perform of studying advanced survey facts, introduces the recent analytic strategy for specific information research (logistic regression), experiences new software program and offers an advent to the model-based research that may be worthwhile studying well-designed, really small-scale social surveys.

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Extra info for Analyzing Complex Survey Data (Quantitative Applications in the Social Sciences)

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It is now commercially available, and a student version is also available. Although data can be imported from other systems, it is designed to be a stand-alone package. In addition to the sample weights for the full sample, it requires that each record in the input data file contain the replicate weight. For simple designs, the program can create the replicate weights before running any procedure. The program documentation is adequate, but new users may find instructions for preparing the replicate weights somewhat difficult to understand without help from experienced users.

If stratification is not used or the stratum identification is not available from the data, one can perform the analysis assuming an unrestricted cluster sampling. If there is no information on the stratum and PSU, it is important to investigate whether treating the data as SRS is reasonable for the given sample design. When stratification is used and the stratum identification is available, we need to make sure that at least two PSUs are available in each stratum. Otherwise, it is not possible to estimate the variance.

It uses the Taylor series method for variance estimation using PSUs (ultimate cluster approximation). Although it does not support complicated designs such as multilayered nesting designs, it can be used for analyzing most of the survey designs used in practice. Most of its survey analysis procedures are parallel to its general (nonsurvey) statistical procedures, which means that many general features in its statistical analysis can be integrated easily with the survey analysis components. The output is relatively easy to understand, and new users may find Stata easier to learn than SUDAAN.

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