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When survey science met web tracking: Presenting an error framework for metered data

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  • Oriol J. Bosch
  • Melanie Revilla

Abstract

Metered data, also called web‐tracking data, are generally collected from a sample of participants who willingly install or configure, onto their devices, technologies that track digital traces left when people go online (e.g., URLs visited). Since metered data allow for the observation of online behaviours unobtrusively, it has been proposed as a useful tool to understand what people do online and what impacts this might have on online and offline phenomena. It is crucial, nevertheless, to understand its limitations. Although some research have explored the potential errors of metered data, a systematic categorisation and conceptualisation of these errors are missing. Inspired by the Total Survey Error, we present a Total Error framework for digital traces collected with Meters (TEM). The TEM framework (1) describes the data generation and the analysis process for metered data and (2) documents the sources of bias and variance that may arise in each step of this process. Using a case study we also show how the TEM can be applied in real life to identify, quantify and reduce metered data errors. Results suggest that metered data might indeed be affected by the error sources identified in our framework and, to some extent, biased. This framework can help improve the quality of both stand‐alone metered data research projects, as well as foster the understanding of how and when survey and metered data can be combined.

Suggested Citation

  • Oriol J. Bosch & Melanie Revilla, 2022. "When survey science met web tracking: Presenting an error framework for metered data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 408-436, December.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s2:p:s408-s436
    DOI: 10.1111/rssa.12956
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    1. Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021. "Integrating explanation and prediction in computational social science," Nature, Nature, vol. 595(7866), pages 181-188, July.
    2. D. L. Oberski & A. Kirchner & S. Eckman & F. Kreuter, 2017. "Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1477-1489, October.
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    1. Keusch, Florian & Pankowska, Paulina & Cernat, Alexandru & Bach, Ruben L., 2023. "Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation," SocArXiv n9rx3, Center for Open Science.
    2. Camilla Salvatore, 2023. "Inference with non-probability samples and survey data integration: a science mapping study," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 83-107, April.

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