Uncovering digital trace data biases: tracking undercoverage in web tracking data

Bosch Jover, Oriol, Sturgis, Patrick ORCID: 0000-0003-1180-3493, Kuha, Jouni ORCID: 0000-0002-1156-8465 and Revilla, Melanie (2024) Uncovering digital trace data biases: tracking undercoverage in web tracking data. Communication Methods and Measures. ISSN 1931-2458

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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.

Item Type:	Article
Additional Information:	© 2024 The Author(s)
Divisions:	Methodology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science H Social Sciences > HA Statistics
Date Deposited:	13 Aug 2024 13:30
Last Modified:	13 Nov 2024 21:54
URI:	http://eprints.lse.ac.uk/id/eprint/124537

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