<abstract xmlns="http://www.w3.org/1999/xhtml"><p>We discuss 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 and adaptive design that survey designers and researchers have practiced over decades. In this paper, we present 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. We discuss how these building blocks of RAD are addressed by articles published in the 2017 JOS special issue and this special section. It is a tale of the three perspectives filling in each other. We carry over each of these three perspectives to articulate the remaining challenges and opportunities for the advancement of RAD. We recommend several RAD ideas for future research, including survey-assisted population modeling, rigorous optimization strategies, and total survey cost modeling.</p></abstract>

We discuss 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 and adaptive design that survey designers and researchers have practiced over decades. In this paper, we present 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. We discuss how these building blocks of RAD are addressed by articles published in the 2017 JOS special issue and this special section. It is a tale of the three perspectives filling in each other. We carry over each of these three perspectives to articulate the remaining challenges and opportunities for the advancement of RAD. We recommend several RAD ideas for future research, including survey-assisted population modeling, rigorous optimization strategies, and total survey cost modeling.

Responsive and Adaptive Design for Survey Optimization

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Responsive and Adaptive Design for Survey Optimization"}

We discuss an evidence-based approach to guiding real-time design decisions during the course of survey data collection. We call it responsive and adaptive...

Responsive and Adaptive Design for Survey Optimization

Publicado en línea: 01 sept 2018

Páginas: 581 - 597

DOI: https://doi.org/10.2478/jos-2018-0028

Palabras clave
Responsive design, adaptive design, survey errors, survey costs, optimization, paradata, auxiliary data, total survey error

© 2018 Asaph Young Chun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Responsive and Adaptive Design for Survey Optimization

Publicado en línea: 01 sept 2018

Páginas: 581 - 597

DOI: https://doi.org/10.2478/jos-2018-0028

Palabras claveResponsive design, adaptive design, survey errors, survey costs, optimization, paradata, auxiliary data, total survey error

© 2018 Asaph Young Chun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Responsive design, adaptive design, survey errors, survey costs, optimization, paradata, auxiliary data, total survey error