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

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

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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 shows evidence that web tracking data might be, effectively, biased. Methodologically, the paper showcases how survey questions can be used as auxiliary 15 information to identify and simulate web tracking errors.

Item Type:	Article
Additional Information:	© 2024
Divisions:	Methodology Statistics
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 Aug 2024 23:16
URI:	http://eprints.lse.ac.uk/id/eprint/124537

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