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

Oriol Bosch Jover and Melanie Revilla

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: digital trace data; error framework; metered data; passive data; total survey error; web-tracking (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2022-12-06
New Economics Papers: this item is included in nep-pay
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Citations: View citations in EconPapers (3)

Published in Journal of the Royal Statistical Society. Series A: Statistics in Society, 6, December, 2022, 185(Suppl 2), pp. S408 - S436. ISSN: 0964-1998

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