Adaptive and Responsive Survey Designs: A Review and Assessment

Abstract

Summary The paper reviews the growing literature on responsive and adaptive designs for surveys. These designs encompass various methods for managing data collection, including front loading potentially difficult cases, tailoring data collection strategies to different subgroups, prioritizing effort according to estimated response propensities, imposing stop rules for ending data collection, monitoring key survey estimates throughout the field period, using two-phase or multiphase sampling for following up non-respondents and calculating indicators of non-response bias (such as the R-indicator) other than response rates to monitor and guide fieldwork. We give particular attention to efforts to evaluate these strategies experimentally or via simulations. Although the field seems to have embraced these new tools, most of the evaluation studies suggest they produce marginal reductions in cost and non-response bias. It is clearly difficult to lower survey costs without reducing some aspect of survey quality. Other issues limiting the effectiveness of these designs include weakly predictive auxiliary variables, ineffective interventions and slippage in the implementation of interventions in the field. These problems are not, however, unique to responsive or adaptive design. We give recommendations for improving such designs and for improving the management of data collection efforts in the current difficult environment for surveys.