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This session is devoted to discussing an evidence-based approach to guiding real-time design decisions during the course of survey data collection. We call it responsive and adaptive design (RAD), a scientific framework driven by cost-quality tradeoff analysis and optimization that enables the most efficient production of high-quality data. The notion of RAD is not new; nor is it a silver bullet to resolve all the difficulties of complex survey design and challenges. RAD embraces precedents and variants of responsive design or adaptive design that survey designers and researchers have practiced over decades (e.g., Groves and Heeringa 2006; Wagner 2008). In this session, we present the papers that discuss any of the four pillars of RAD: survey process data and auxiliary information, design features and interventions, explicit quality and cost metrics, and a quality-cost optimization tailored to survey strata. The papers will discuss how these building blocks of RAD are addressed and integrated like those papers published in the 2017 JOS special issue on RAD and the 2018 JOS special section on RAD (Edited by Chun, Schouten and Wagner). We are fond of RAD ideas implemented for survey-assisted population modeling, rigorous optimization strategies, and total survey cost-error modeling.

This session will present RAD papers, involving applied or theoretical contributions. For instance, 1) what approaches can be used to guide the development of cost and quality metrics in RAD and their use over the survey life cycle? 2) which methods of RAD are able to identify phase boundaries or stopping rules that optimize responsive designs? 3) what would be best practices for applying RAD to produce high quality data in a cost-effective manner? and 4) under what conditions can administrative records or big data be adaptively used to supplement survey data collection and improve data quality?