Uncovering digital trace data biases: tracking undercoverage in web tracking data
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- dc.contributor.author Bosch, Oriol J.
- dc.contributor.author Sturgisc, Patrick
- dc.contributor.author Kuha, Jouni
- dc.contributor.author Revilla, Melanie
- dc.date.accessioned 2025-09-30T06:27:03Z
- dc.date.available 2025-09-30T06:27:03Z
- dc.date.issued 2024
- dc.description.abstract Digital trace data is an increasingly popular alternative to surveys, often considered as the gold standard. This study critically assesses the use of web tracking data to study online media exposure. Specifically, we focus on a critical error source of this type of data, tracking undercoverage: researchers’ failure to capture data from all the devices and browsers that individuals utilize to go online. Using data from Spain, Portugal, and Italy, we explore undercoverage in online panels and simulate biases in online media exposure estimates. We show that undercoverage is highly prevalent when using commercial panels, with more than 70% of participants affected. Additionally, the primary determinant of undercoverage is the type and number of devices used, rather than individual’s characteristics. Moreover, through a simulation study, we demonstrate that web tracking estimates are often substantially biased. Methodologically, the paper showcases how auxiliary survey data can help study web tracking errors.en
- dc.description.sponsorship The work was supported by the H2020 European Research Council [849165]; H2020 European Research Council Ministerio de Ciencia e Innovación Leverhulme Trust Large Centre Grant LCDS Fundacion BBVA. Oriol J. Bosch is supported by an ERC Advanced Grant (835079, PI M.C Mills), Leverhulme Trust Large Centre Grant LCDS (RC-2018-003, PI M.C.Mills).en
- dc.format.mimetype application/pdf
- dc.identifier.citation Bosch OJ, Sturgis P, Kuha J, Revilla M. Uncovering digital trace data biases: tracking undercoverage in web tracking data. Commun Methods Meas. 2025 Apr 3;19(2):157-77. DOI: 10.1080/19312458.2024.2393165
- dc.identifier.doi http://dx.doi.org/10.1080/19312458.2024.2393165
- dc.identifier.issn 1931-2458
- dc.identifier.uri http://hdl.handle.net/10230/71306
- dc.language.iso eng
- dc.publisher Taylor & Francis
- dc.relation.ispartof Communication Methods and Measures. 2025 Apr 3;19(2):157-77
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/849165
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/835079
- dc.rights © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
- dc.subject.other Internetca
- dc.subject.other Interacció persona-ordinadorca
- dc.subject.other Webca
- dc.title Uncovering digital trace data biases: tracking undercoverage in web tracking dataen
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion