Cookies?
Library Header Image
LSE Research Online LSE Library Services

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

Bosch Jover, Oriol and Revilla, Melanie (2022) When survey science met web tracking: presenting an error framework for metered data. Journal of the Royal Statistical Society. Series A: Statistics in Society. ISSN 0964-1998

[img] Text (Bosch-Jover_when-survey-science-met-web-tracking--published) - Published Version
Available under License Creative Commons Attribution.

Download (866kB)

Identification Number: 10.1111/rssa.12956

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.

Item Type: Article
Official URL: https://rss.onlinelibrary.wiley.com/journal/146798...
Additional Information: © 2022 The Authors
Divisions: Methodology
Subjects: H Social Sciences > HM Sociology
H Social Sciences > HE Transportation and Communications
H Social Sciences > HA Statistics
Date Deposited: 02 Sep 2022 13:39
Last Modified: 24 Nov 2022 09:42
URI: http://eprints.lse.ac.uk/id/eprint/116431

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics