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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