Average Reviews:
(More customer reviews)Applied Survey Data Analysis (ASDA) is a crystal-clear survey of modern techniques for analyzing complex survey data. Note the word "analyzing". This is not a text on sampling methods per se. Rather, it is a guide to using existing data sets that result from a complex survey design that employs weighting, clustering, and stratification. The authors demonstrate how a correct analysis should be undertaken. In doing so, they review descriptive statistics, categorical methods, regression analysis (linear and logistic), survival analysis, and multiple imputation. Most examples use Stata, but some are in SAS.
The level of mathematical sophistication is not high, although "theory boxes" are interspersed to add additional detail. Anyone who is challenged by the mathematical level of this book probably should not be working with survey data in the first place.
In sum, this is an important -- and very well written -- contribution to the literature on survey data analysis.
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Taking a practical approach that draws on the authors' extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods.After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches.Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book's website: http://www.isr.umich.edu/src/smp/asda/
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