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Practical tools for designing and weighting survey samples. (English) Zbl 1282.62027

Statistics for Social and Behavioral Sciences. New York, NY: Springer (ISBN 978-1-4614-6448-8/hbk; 978-1-4614-6449-5/ebook). xxi, 670 p. (2013).
The book is aimed at providing practical aspects of applying sampling and weighting approaches in surveys. It addresses to students as well as to survey and social scientists with basic knowledge of sampling methods. The eighteen chapters in the book are organized into four principal parts (besides the introduction in chapter 1) dealing with designing single-stage sample surveys (chapters 2 - 7), multistage designs (chapters 8 - 11), area sample designs (chapters 12 - 16) and other related topics (chapters 17 and 18). Each part starts with the description of a practical example.
Chapter 1 provides a very brief introduction into basic survey methodology and terminology. After the presentation of a project to design a personnel survey in chapter 2, the following two chapters focus on sample size determination based on targets for coefficients of variation (chapter 3) and on hypothesis testing (chapter 4). Four different approaches for multicriteria optimization in single-stage designs are illustrated in chapter 5 using the software packages Microsoft Excel Solver, SAS and R. The first part of the book closes with a discussion on outcome rates and study eligibility in chapter 6 and with a solution for the practical projects presented in chapter 2. The second part of the book on sample design decision in multiple stages starts with a practical project presentation in chapter 8 dealing with the allocation of units to geographic clusters. The formal contributions of chapters 9 and 10 on designing multistage samples and area sampling are illustrated in chapter 11 giving the solutions to the practical projects described in chapter 8. Chapter 12 introduces the reader to a practical project on calculating weights for a personnel study. Basic concepts of weighting in surveys including adjustments for ineligible units and nonresponse are presented in chapter 13 and calibration and the use of auxiliary data in weighting are introduced in chapter 14. Methods on variance estimation are summarized in chapter 15 and the third main part of the book closes in chapter 16 with the description of a solution to the practical project presented in chapter 12. The last two chapters are devoted to two specialized topics: multiphase designs (chapter 17) and process control and quality measures (chapter 18). The volume ends with an appendix on notation glossary, a description of the datasets and R functions used in the book.
In summary, the book under review is recommended to students and researchers with basic knowledge in sampling theory. All concepts are presented within a strong practical context. The format of each of the main parts is well laid out, starting with the description of a practical project and quickly bringing the reader to the questions of interest and ending with a solution to the project. The concepts and methods are illustrated throughout the book using various software packages (R, SAS and Microsoft Excel Solver). In addition, supportive exercises are provided in each chapter with solutions given in the appendix.

MSC:

62D05 Sampling theory, sample surveys
62-04 Software, source code, etc. for problems pertaining to statistics
62-02 Research exposition (monographs, survey articles) pertaining to statistics
62P25 Applications of statistics to social sciences
62P35 Applications of statistics to physics
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