Offers a coherent view on all methodological options to reduce mode effects Reviews inference procedures designed to minimize the effect of mode effects Discusses several different sources of mode-specific error Provides a roadmap for practitioners who design and analyse mixed-mode surveys Summarises the literature on mixed-mode surveys from questionnaire design to analysis

Offers a coherent view on all methodological options to reduce mode effects Reviews inference procedures designed to minimize the effect of mode effects Discusses several different sources of mode-specific error Provides a roadmap for practitioners who design and analyse mixed-mode surveys Summarises the literature on mixed-mode surveys from questionnaire design to analysis

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.