Applied Survey Data Analysis

Steven G. Heeringa, Brady T. West, Patricia A. Berglund

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April 5, 2010 by Chapman and Hall/CRC
Professional - 487 Pages - 53 B/W Illustrations
ISBN 9781420080667 - CAT# C8066
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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Features

    • Demonstrates how design characteristics, such as stratification, clustering, and weighting, are easily incorporated into the statistical methods and software for survey estimation and inference
    • Presents many methods and models for survey data analysis, including the linear regression, generalized linear, Cox proportional hazards, and discrete time models
    • Explores developments in advanced statistical techniques, such as multilevel analysis of survey data
    • Supplies advice and recommendations based on the authors’ experiences as well as current thinking on best practices
    • Uses theory boxes to develop or explain a fundamental theoretical concept underlying statistical methods
    • Includes practical exercises that reinforce application of the methods
    • Offers software code, brief technical reports, links to example survey data sets, and more on the book’s website

    Summary

    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/