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)

Abstract

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”.
• Status: Published
• Refereed: Yes
• 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: 2666-3899
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
				UK Research and Innovation ( UKRI) (UKRI)	MR/S01795X/2