将缺失数据视为数据。
Missing data as data.
作者信息
Basiri Anahid, Brunsdon Chris
机构信息
School of Geographical and Earth Sciences, The University of Glasgow, Glasgow, G12 8QQ Glasgow, UK.
National Centre for Geocomputation, National University of Ireland, Maynooth, Ireland.
出版信息
Patterns (N Y). 2022 Sep 9;3(9):100587. doi: 10.1016/j.patter.2022.100587.
Our "digified" lives have provided researchers with an unprecedented opportunity to study society at a much higher frequency and granularity. Such data can have a large sample size but can be sparse, biased, and exclusively contributed by the users of the technologies. We look at the increasing importance of missing data and under-representation and propose a new perspective that considers missing data as useful data to understand the underlying reasons for missingness and that provides a realistic view of the sample size of large but under-represented data.
我们的“数字化”生活为研究人员提供了前所未有的机会，能够以更高的频率和粒度来研究社会。这类数据可能具有很大的样本量，但可能是稀疏的、有偏差的，且完全由技术使用者提供。我们探讨了缺失数据和代表性不足问题日益增加的重要性，并提出了一种新观点，即将缺失数据视为有助于理解数据缺失潜在原因的有用数据，且能对规模大但代表性不足的数据样本量提供一个现实的看法。
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