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How to survey citizens’ compliance with COVID-19 public health measures? Evidence from three survey experiments

Author

Listed:
  • Daoust, Jean-François
  • Nadeau, Richard
  • Dassonneville, Ruth
  • Lachapelle, Erick
  • Bélanger, Éric

    (McGill University)

  • Savoie, Justin

    (Impact Canada)

  • van der Linden, Clifton

    (McMaster University)

Abstract

The extent to which citizens comply with newly-enacted public health measures such as social distancing or lockdowns strongly affects the propagation of the virus and the number of deaths from COVID-19. It is however very difficult to identify non-compliance through survey research because claiming to follow the rules is socially desirable. Using three survey experiments, we examine the efficacy of different “face-saving” questions that aim to reduce social desirability in the measurement of compliance with public health measures. Our treatments soften the social norm of compliance by way of a short preamble in combination with a guilty-free answer choice making it easier for respondents to admit non-compliance. We find that self-reported non-compliance increases by up to 11 percentage points when making use of a face-saving question. Considering the current context and the importance of measuring non-compliance, we argue that researchers around the world should adopt our most efficient face-saving question.

Suggested Citation

  • Daoust, Jean-François & Nadeau, Richard & Dassonneville, Ruth & Lachapelle, Erick & Bélanger, Éric & Savoie, Justin & van der Linden, Clifton, 2020. "How to survey citizens’ compliance with COVID-19 public health measures? Evidence from three survey experiments," SocArXiv gursd_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:gursd_v1
    DOI: 10.31219/osf.io/gursd_v1
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    References listed on IDEAS

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    1. René Bekkers & Pamala Wiepking, 2011. "Accuracy of self-reports on donations to charitable organizations," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(6), pages 1369-1383, October.
    2. Diana C. Mutz & Robin Pemantle & Philip Pham, 2019. "The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean Data," The American Statistician, Taylor & Francis Journals, vol. 73(1), pages 32-42, January.
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    Cited by:

    1. Bogliacino, Francesco & Gómez, Camilo Ernesto & Montealegre, Felipe & Charris, Rafael Alberto & codagnone, cristiano, 2021. "Expert endorsement and the legitimacy of public policy. Evidence from Covid19 mitigation strategies," SocArXiv zbqjd_v1, Center for Open Science.

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