Missing data as data

Basiri, A. and Brunsdon, C. (2022) Missing data as data. Patterns, 3(9), 100587. (doi: 10.1016/j.patter.2022.100587) (PMID:36124308) (PMCID:PMC9481944)

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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.

Item Type:Articles
Additional Information:The authors acknowledge the support from the following projects and funding: The UK Research and Innovation (UKRI) Future Leaders Fellowship ”Indicative Data” MR/S01795X/2, SFI Investigator Programme ”Building City Dasboards: Addressing Fundamental and Applied Problems” Code 15/IA/3090, and the Turing-Roche strategic partnership project on ”Developing a coherent Bayesian modelling and imputation framework that accounts for, and utilises, Structured Missingness”.
Glasgow Author(s) Enlighten ID:Basiri, Professor Ana
Authors: Basiri, A., and Brunsdon, C.
College/School:College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:Patterns
Publisher:Elsevier (Cell Press)
ISSN (Online):2666-3899
Published Online:09 September 2022
Copyright Holders:Copyright © 2022 The Author(s)
First Published:First published in Patterns 3(9): 100587
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
312992Indicative Data: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)Ana BasiriUK Research and Innovation ( UKRI) (UKRI)MR/S01795X/2GES - Geography