Applied Survey Data Analysis: (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Highly recommended by the Journal of Official Statistics, The American Statistician, and other top statistical journals, Applied Survey Data Analysis, Third Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first and second editions, this third edition further expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software procedures. New to the Third Edition: Applied Bayesian methods for the analysis of complex sample survey data using available software implementing these methods State-of-the-art methods and software for the analysis of survey data collected from non-probability samples Software for modern applications of machine learning techniques to complex sample survey data A completely revamped website providing code for replicating all the analyses illustrated in the book using Stata, SAS, SPSS, R, Mplus, SUDAAN, WesVar, and IVEware New end-of-chapter exercises, allowing for practice implementing the methods, including Bayesian analysis exercises Updated summaries of the newest literature on the analysis of survey data collected from complex samples An updated review of software packages currently available for the analysis of complex sample survey data Designed for readers working in a wide array of disciplines who conduct secondary analyses of survey data as part of their applied work, this book continues to provide a practical and accessible guide to the analysis of survey data. Continuing to use an example-driven approach to clearly illustrate analysis methods and software, the third edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also offer the newest code for replicating the examples in other popular software packages on the book’s revamped website.

Table of Contents:
0. Introduction. 1. Applied Survey Data Analysis: An Overview. 2. Getting to Know the Complex Sample Design. 3. Foundations and Techniques for Estimation and Inference. 4. Preparation for Complex Sample Survey Data Analysis. 5 Descriptive Analysis for Continuous Variables. 6. Categorical Data Analysis. 7. Linear Regression Models. 8. Logistic Regression and Generalized Linear Models for Binary Survey Variables. 9. Generalized Linear Models for Multinomial, Ordinal, and Count Variables. 10. Survival Analysis of Event History Survey Data. 11. Analysis of Longitudinal Complex Sample Survey Data. 12 Imputation of Missing Data: Practical Methods and Applications for Survey Analysts. 13. Advanced Topics in the Analysis of Survey Data.

About the Author :
Brady T. West is a Research Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He earned his PhD from the Michigan Program in Survey and Data Science in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, selection bias in surveys, responsive/adaptive survey design, interviewer effects, and multilevel regression models for clustered and longitudinal data. An author or co-author of more than 200 peer-reviewed publications in survey statistics, applied statistics, and public health, he is also the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Third Edition, Chapman Hall/CRC Press, 2022). He was elected as a Fellow of the American Statistical Association in 2022. Brady lives in Dexter, Michigan with his wife Laura, his son Carter, and his daughter Everleigh. Steven G. Heeringa is a Research Scientist Emeritus at the University of Michigan Institute for Social Research (ISR) and former Associate Director of the ISR Survey Research Center (SRC). He is a member of the faculty of the University of Michigan's Program in Survey and Data Science and the Joint Program in Survey Methodology. He is a Fellow of the American Statistical Association and elected member of the International Statistical Institute. He is the author of many publications on statistical design and sampling methods for research in the fields of public health and the social sciences. Steve has over 48 years of statistical sampling experience in the development of the SRC National Sample design, as well as research designs for ISR's major longitudinal and cross-sectional survey programs. Steve has collaborated extensively with scientific colleagues in the design and conduct of major studies in aging, psychiatric epidemiology and physical and mental health. He has been a teacher of survey sampling and statistical methods to U.S. and international students and has served as a sample design consultant to a wide variety of international research programs based in countries such as Russia, the Ukraine, Uzbekistan, Kazakhstan, India, Nepal, China, Egypt, Iran, the United Arab Emirates, Qatar, South Africa and Chile. Patricia A. Berglund is a semi-retired Senior Research Associate in the Survey Methodology Program at the Institute for Social Research. She has extensive experience in the use of computing systems for data management and analysis of complex sample survey data. She works on research projects focused on paediatric health, youth substance abuse, adult mental health, and survey methodology using data from Pediatrac, Monitoring the Future, the National Comorbidity Surveys, and various other national and international surveys. In addition, she has been involved in development and teaching of analysis courses and computer training programs at the UM Survey Research Center-Institute for Social Research and previously lectured in the SAS Institute-Business Knowledge Series.