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Attack the bot: Mode effects and the challenges of conducting a mixed-mode household survey during the Covid-19 pandemic

Yazici, Edanur; Wang, Ying; (2024) Attack the bot: Mode effects and the challenges of conducting a mixed-mode household survey during the Covid-19 pandemic. International Journal of Social Research Methodology , 27 (6) pp. 791-796. 10.1080/13645579.2023.2241797. Green open access

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Abstract

Constant changes to COVID-19 restrictions have required adaptability from social scientists including responding to new challenges such as infiltration by bots. This research note presents unexpected encounters of bot infiltration and recruitment during survey data collection under pandemic conditions. The note draws from a household survey on a social housing estate in London, UK conducted in 2021. The survey investigates residents’ lived experiences of the estate and housing turnover. The note discusses the limitations of online data collection, focusing on infiltration by bots and exclusion of marginalised groups. It adds to the emerging literature on bots in survey methods, making recommendations for an iterative verification and sequential multi-stage data cleaning process. It finds that online-only approaches can exclude marginalised groups. The note argues that even under pandemic conditions, face-to-face data collection can have greater reach than online only approaches. It concludes that mixed-mode household surveys can a) mitigate the challenges of a changing research environment; b) reach a broader sample; and c) provide qualitative insight for future research.

Type: Article
Title: Attack the bot: Mode effects and the challenges of conducting a mixed-mode household survey during the Covid-19 pandemic
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/13645579.2023.2241797
Publisher version: https://doi.org/10.1080/13645579.2023.2241797
Language: English
Additional information: Copyright © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Bot infiltration, mixed-mode survey, household survey, Covid-19, data validity, mode effects
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Planning
URI: https://discovery.ucl.ac.uk/id/eprint/10175496
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