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

Oriol J. Bosch, Patrick Sturgis, Jouni Kuha and Melanie Revilla
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Oriol J. Bosch: The London School of Economics and Political Science

No t2dbj, SocArXiv from Center for Open Science

Abstract: In the digital age, understanding people’s online behaviours is vital. Digital trace data has emerged as a popular alternative to surveys, many times hailed 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 commercial online panels and simulate biases in online media exposure estimates. The paper shows that tracking undercoverage is highly prevalent when using commercial panels, with more than 70% of participants affected. In addition, the primary determinant of undercoverage is the type and number of devices employed for internet access, rather than individual characteristics and attitudes. Additionally, through a simulation study, it demonstrates that web tracking estimates, both univariate and multivariate, are often substantially biased due to tracking undercoverage. This represent the first empirical evidence demonstrating that web tracking data is, effectively, biased. Methodologically, the paper showcases how survey questions can be used as auxiliary information to identify and simulate web tracking errors.

Date: 2023-10-08
New Economics Papers: this item is included in nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:t2dbj

DOI: 10.31219/osf.io/t2dbj

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