Stephanie M. Coffey, Jaya Damineni, John Eltinge, Anup Mathur, Kayla Varela, Allison Zotti
{"title":"Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods","authors":"Stephanie M. Coffey, Jaya Damineni, John Eltinge, Anup Mathur, Kayla Varela, Allison Zotti","doi":"10.1177/0282423x241235270","DOIUrl":null,"url":null,"abstract":"Adaptive survey design is a framework for making data-driven decisions about survey data collection operations. This article 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: (1) the goals for improvement through adaptation; (2) the design features that are available for adaptation; (3) the auxiliary data that may be available for informing adaptation; (4) the decision rules that could guide adaptation; (5) the necessary systems to operationalize adaptation; and (6) 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.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Official Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1177/0282423x241235270","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Adaptive survey design is a framework for making data-driven decisions about survey data collection operations. This article 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: (1) the goals for improvement through adaptation; (2) the design features that are available for adaptation; (3) the auxiliary data that may be available for informing adaptation; (4) the decision rules that could guide adaptation; (5) the necessary systems to operationalize adaptation; and (6) 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.
期刊介绍:
JOS is an international quarterly published by Statistics Sweden. We publish research articles in the area of survey and statistical methodology and policy matters facing national statistical offices and other producers of statistics. The intended readers are researchers or practicians at statistical agencies or in universities and private organizations dealing with problems which concern aspects of production of official statistics.