Adaptive Survey Design (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Science & Math

Wagner, James

Schouten, Barry

Peytchev, Andy

Mathematics

Chapman and Hall/CRC

A comprehensive, one-stop guide to identifying, reducing, and managing nonresponse in household surveys  Nonresponse and its impact on the sample selection mechanism of a survey is a common problem that often arises while collecting survey data. Handbook of Nonresponse in Household Surveys is a complete guide to handling the nonresponse problem, outlining statistical methods and techniques for improving response rates and correcting response data. The authors begin with an introduction to the nonresponse problem along with basic concepts and definitions. Subsequent chapters present current theories and methods that enable survey researchers to skillfully account for nonresponse in their research. Exploring the latest developments in the field, the book also features:   An introduction to the R-indicator as an indicator of survey quality   Discussion of the different causes of nonresponse   Extensive treatment of the selection and use of auxiliary information   Best practices for re-approaching nonrespondents   An overview of advanced nonresponse correction techniques   Coverage of adaptive survey design   Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. Discussion concludes with a summary of the main concepts as well as a glossary of key terms and a set of exercises that allows readers to test their comprehension of the presented material. Examples using real survey data are provided, and a related website features additional data sets, which can be easily analyzed using Stata® or SPSS® software. Handbook of Nonresponse in Household Surveys is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels.

Handbook of Nonresponse in Household Surveys

Bethlehem, Jelke

Cobben, Fannie

wiley

Mixed-Mode Official Surveys (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Mixed-mode surveys have become a standard at many statistical institutes. However, the introduction of multiple modes in one design goes with challenges to both methodology and logistics. Mode-specific representation and measurement differences become explicit and demand for solutions in data collection design, questionnaire design, and estimation. This is especially true when surveys are repeated and are input to long time series of official statistics. So how can statistical institutes deal with such changes? What are the origins of mode-specific error? And how can they be dealt with? In this book, the authors provide answers to these questions, and much more. 
Features 
Concise introduction to all the key elements of mixed-mode survey design and analysis 
Realistic official statistics examples from three general population surveys 
Suitable for survey managers and survey statisticians alike 
An overview of mode-specific representation and measurement errors and how to avoid, reduce and adjust them.

Van Den Brakel, Jan

Buelens, Bart

Giesen, Deirdre

Luiten, Annemieke

Meertens, Vivian

Adaptive survey designs (ASDs) provide a framework for data-driven tailoring of data collection procedures to different sample members, often for cost and bias reduction. People vary in how likely they are to respond and in how they respond. This variation leads to opportunities to selectively deploy design features in order to control both nonresponse and measurement errors. ASD aims at the optimal matching of design features and the characteristics of respondents given the survey budget. Such a goal is sensible, but ASD requires investment in more advanced technical systems and management infrastructure and asks for the collection of relevant auxiliary data. So what are current best practices in ASD? And is ASD worthwhile when the same auxiliary data are employed in the estimation afterwards? In this book, the authors provide answers to these questions, and much more.

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