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A rapidly changing survey environment today requires a nimble, flexible design that leverages multiple data sources, frequently collects data in multiple modes, produces high quality data, and optimizes cost allocation around the survey life cycle. In this environment, surveys are ever more challenging and complex to implement. It was in response to these challenges that responsive and adaptive designs were born. This special issue includes twelve articles that give a glimpse of how these concepts are being used in diverse settings. Three phenomena are worth noting. First, evidence-driven policy makers as well as survey researchers have renewed their attention to administrative records, as a key additional data source to enhance survey data quality and minimize survey cost (Chun 2009; Nordbotten 2010; Chun and Scheuren 2011; Chun and Larsen forthcoming). Big data, sometimes called organic data, also create new opportunities when linked to survey data. Second, the computerization of survey data collection has made accessible real-time analysis of paradata, or process data. Paradata have proven useful for managing existing surveys and designing future ones (Couper 1998; Chun and Kwanisai 2010; Couper and Kreuter 2012). Third, methods from fields as diverse as machine learning, operations research, and Bayesian statistics have been found to be useful for the advancement of response propensity modeling and in the design of creative surveys (Early, Mankoff and Fienberg in this special issue; Calinescu et al. 2013; Little 2006). Responsive and adaptive survey designs are evolving in this dynamic environment. The JOS is publishing this special issue in memory of Professor Stephen Fienberg, University Professor of Statistics and Social Science at Carnegie Mellon University, who

JOS Special Issue on Responsive and Adaptive Survey Design: Looking Back to See Forward – Editorial