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/
Chapter 1 Applied Survey Data Analysis: Overview
Chapter 2 Getting to Know the Complex Sample Design
Chapter 3 Foundations and Techniques for Design-Based Estimation and Inference
Chapter 4 Preparation for Complex Sample Survey Data Analysis
Chapter 5 Descriptive Analysis for Continuous Variables
Chapter 6 Categorical Data Analysis
Chapter 7 Linear Regression Models
Chapter 8 Logistic Regression and Generalized LinearModels for Binary Survey Variables
Chapter 9 Generalized Linear Models for Multinomial,Ordinal, and Count Variables
Chapter 10 Survival Analysis of Event History Survey Data
Chapter 11 Multiple Imputation: Methods and Applications for Survey Analysts
Chapter 12 Advanced Topics in the Analysis of Survey Data
Appendix A: Software Overview
