Adaptive Survey Design

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Adaptive Survey Design by Barry Schouten, Andy Peytchev, James Wagner, CRC Press
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Author: Barry Schouten, Andy Peytchev, James Wagner ISBN: 9781351650014
Publisher: CRC Press Publication: July 28, 2017
Imprint: Chapman and Hall/CRC Language: English
Author: Barry Schouten, Andy Peytchev, James Wagner
ISBN: 9781351650014
Publisher: CRC Press
Publication: July 28, 2017
Imprint: Chapman and Hall/CRC
Language: English

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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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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