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

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  • Bosch Jover, Oriol
  • Sturgis, Patrick
  • Kuha, Jouni
  • Revilla, Melanie

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.

Suggested Citation

  • Bosch Jover, Oriol & Sturgis, Patrick & Kuha, Jouni & Revilla, Melanie, 2024. "Uncovering digital trace data biases: tracking undercoverage in web tracking data," LSE Research Online Documents on Economics 124537, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:124537
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    File URL: http://eprints.lse.ac.uk/124537/
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    References listed on IDEAS

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    1. Ian R. White, 2010. "simsum: Analyses of simulation studies including Monte Carlo error," Stata Journal, StataCorp LLC, vol. 10(3), pages 369-385, September.
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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