When survey science met web tracking: presenting an error framework for metered data

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• dc.contributor.author Bosch, Oriol J.
• dc.contributor.author Revilla, Melanie
• dc.date.accessioned 2023-05-12T06:24:13Z
• dc.date.available 2023-05-12T06:24:13Z
• dc.date.issued 2022
• dc.description.abstract Metered data, also called web-tracking data, are generally collected from a sample of participants who willingly install or configure, onto their devices, technologies that track digital traces left when people go online (e.g., URLs visited). Since metered data allow for the observation of online behaviours unobtrusively, it has been proposed as a useful tool to understand what people do online and what impacts this might have on online and offline phenomena. It is crucial, nevertheless, to understand its limitations. Although some research have explored the potential errors of metered data, a systematic categorisation and conceptualisation of these errors are missing. Inspired by the Total Survey Error, we present a Total Error framework for digital traces collected with Meters (TEM). The TEM framework (1) describes the data generation and the analysis process for metered data and (2) documents the sources of bias and variance that may arise in each step of this process. Using a case study we also show how the TEM can be applied in real life to identify, quantify and reduce metered data errors. Results suggest that metered data might indeed be affected by the error sources identified in our framework and, to some extent, biased. This framework can help improve the quality of both stand-alone metered data research projects, as well as foster the understanding of how and when survey and metered data can be combined.
• dc.description.sponsorship Fundación BBVA; H2020 European Research Council, Grant/Award Number: 849165; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-106867RB- I00 /AEI/10.13039/501100011033
• dc.format.mimetype application/pdf
• dc.identifier.citation Bosch OJ, Revilla M. When survey science met web tracking: presenting an error framework for metered data. J R Stat Soc Ser A Stat Soc. 2022;185(Suppl 2):S408-36. DOI: 10.1111/rssa.12956
• dc.identifier.doi http://dx.doi.org/10.1111/rssa.12956
• dc.identifier.issn 0964-1998
• dc.identifier.uri http://hdl.handle.net/10230/56799
• dc.language.iso eng
• dc.publisher Oxford University Press
• dc.relation.ispartof Journal of the Royal Statistical Society: Series A (Statistics in Society). 2022;185(Suppl 2):S408-36.
• dc.relation.isreferencedby https://osf.io/3t7jz/
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/849165
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-106867RB-I00
• dc.rights © 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword digital trace data
• dc.subject.keyword error framework
• dc.subject.keyword metered data
• dc.subject.keyword passive data
• dc.subject.keyword total survey error
• dc.subject.keyword web-tracking
• dc.title When survey science met web tracking: presenting an error framework for metered data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion