确保新数据生态系统的质量：注意数据和统计数据之间的差距！

Q3 Decision Sciences

Statistical Journal of the IAOS Pub Date : 2023-05-22 DOI:10.3233/sji-230008

Matthias Reister

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引用次数: 0

摘要

根据最近制定《联合国官方统计国家质量保证框架手册》以应对新的数据生态系统的工作，本文讨论了统计界目前面临的三个重要问题：（1）官方统计如何确保行政和其他来源的数据质量？（2）官方统计的质量保证框架能否适用于数据而不是统计？（3）数据和统计之间的差异对官方统计在新的数据生态系统中的作用还有什么其他影响？该文件认为，统计局应该大力支持建立国家数据管理员，但不应该自己承担这样的角色。将数据和官方统计的责任混为一谈，既有可能破坏官方统计，也有可能不公正地对待以负责任的方式将数据作为一种资产开发的必要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Assuring quality in the new data ecosystem: Mind the gap between data and statistics!

Drawing on recent work to develop the United Nations National Quality Assurance Frameworks Manual for Official Statistics to respond to the new data ecosystem, this paper addresses three important questions now facing the statistical community: (1) How can official statistics assure the quality of data from administrative and other sources? (2) Can the quality assurance framework for official statistics be applied to data as opposed to statistics? (3) What other implications does the difference between data and statistics have for the role of official statistics in the new data ecosystem? The paper argues that statistical offices should strongly support the establishment of national data stewards but should not take on such a role themselves. Mixing responsibilities for data and official statistics risks both undermining official statistics and not doing justice to the need to develop data as an asset in a responsible way.

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来源期刊

Statistical Journal of the IAOS Economics, Econometrics and Finance-Economics and Econometrics

CiteScore

1.30

自引率

0.00%

发文量

116

期刊介绍： This is the flagship journal of the International Association for Official Statistics and is expected to be widely circulated and subscribed to by individuals and institutions in all parts of the world. The main aim of the Journal is to support the IAOS mission by publishing articles to promote the understanding and advancement of official statistics and to foster the development of effective and efficient official statistical services on a global basis. Papers are expected to be of wide interest to readers. Such papers may or may not contain strictly original material. All papers are refereed.