Complex Surveys

As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys for traditional analyses. Complex Surveys is a practical guide to analysing this kind of data using R, the freely available and downloadable statistical programming language. As the creator of the specific survey package for R, the author provides the ultimate presentation on how to successfully use the software for analysing data from complex surveys, utilising the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields.

The book begins with coverage of basic tools and topics within survey analysis, such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, emphasis is placed on graphics, regression modelling, and two-phase designs. Additionally, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All examples and figures in the book are generated using R, and a related website provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in complexity, and detailed appendices outline additional mathematical and computational descriptions that assist readers in comparing results across various software systems.

Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It also serves as a practical reference guide for applied statisticians and practitioners in the social and health sciences who regularly use statistics in their work.