ESRA 2027 sessions by theme

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Session Details

Adaptive survey designs (ASD) aim to improve survey quality (e.g., response rates, nonresponse error, measurement error) and operational performance (e.g., total costs, effort) by tailoring data collection protocols to subgroups of the sample based on their characteristics. The allocation of survey protocols can follow simple rules that use a few readily available characteristics or a data-driven prediction process that draws on auxiliary data, paradata, survey responses, and other data sources. Despite their potential, ASDs face several challenges, for example identifying data sources that can inform the allocation of treatments in cross-sectional surveys, optimizing prediction, selecting efficient treatments and data collection protocols, improving allocation decisions for sample members, and dealing with undesired side-effects of protocol variation across the sample. New developments such as artificial intelligence (AI) and the growing availability of digital behavioral data in the social sciences offer chances to address these challenges but require further research to support the broader adoption of ASDs in practice.

This session invites presentations advancing adaptive survey designs from concept to practice. We welcome submissions that explore theoretical, methodological, statistical, or practical advancements, including but not limited to:
• Integrating established and emerging data sources for prediction and treatment allocation
• Using AI to inform ASDs in prediction, treatment allocation, and operational tasks
• Developing optimization strategies that jointly account for measures of data quality (e.g., nonresponse and measurement error) and cost constraints
• Exploring the combination of ASDs and postsurvey adjustment methods
• Translating methodological advances into practical blueprints, guidelines, and software tools that facilitate practical implementation of ASDs