Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods

PhD. Stephanie Coffey, Jaya Damineni, PhD. John Eltinge, PhD. Anup Mathur, Kayla Varela and Allison Zotti

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: Adaptive survey design is a framework for making data-driven decisions about survey data collection operations. This paper discusses open questions related to the extension of adaptive principles and capabilities when capturing data from multiple data sources. Here, the concept of “design” encompasses the focused allocation of resources required for the production of high-quality statistical information in a sustainable and cost-effective way. This conceptual framework leads to a discussion of six groups of issues including: (i) the goals for improvement through adaptation; (ii) the design features that are available for adaptation; (iii) the auxiliary data that may be available for informing adaptation; (iv) the decision rules that could guide adaptation; (v) the necessary systems to operationalize adaptation; and (vi) the quality, cost, and risk profiles of the proposed adaptations (and how to evaluate them). A multiple data source environment creates significant opportunities, but also introduces complexities that are a challenge in the production of high-quality statistical information.

Keywords: administrative-records-first and survey-first designs; auxiliary data; field experiments; dimensions of data quality, risk and cost; production systems (search for similar items in EconPapers)
Pages: 63 pages
Date: 2023-02
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https://www2.census.gov/library/working-papers/2023/adrm/ces/CES-WP-23-03.pdf First version, 2023 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:23-03

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