COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak
COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak
This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
Yamada, Yuki
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Ćepulić, Dominik Borna
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Coll-Martín, Tao
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Debove, Stéphane
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Gautreau, Guillaume
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Han, Hyemin
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Rasmussen, Jesper
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Tran, Thao P.
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Travaglino, Giovanni A.
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Liebroth, Andreas
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Capelos, Tereza
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COVIDiSTRESS Global Survey Consortium
Yamada, Yuki
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Ćepulić, Dominik Borna
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Coll-Martín, Tao
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Debove, Stéphane
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Gautreau, Guillaume
350115cd-fe7d-435b-bf0b-ca1c81c8285a
Han, Hyemin
adcc09d5-2e43-4091-a721-7675ae7cdf45
Rasmussen, Jesper
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Tran, Thao P.
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Travaglino, Giovanni A.
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Liebroth, Andreas
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Capelos, Tereza
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Yamada, Yuki, Ćepulić, Dominik Borna, Coll-Martín, Tao, Debove, Stéphane, Gautreau, Guillaume, Han, Hyemin, Rasmussen, Jesper, Tran, Thao P., Travaglino, Giovanni A. and Liebroth, Andreas
,
COVIDiSTRESS Global Survey Consortium
(2021)
COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak.
Scientific Data, 8, [3].
(doi:10.1038/s41597-020-00784-9).
Abstract
This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
Text
s41597-020-00784-9
- Version of Record
More information
Accepted/In Press date: 2 October 2020
e-pub ahead of print date: 4 January 2021
Additional Information:
Funding Information:
The COVIDiSTRESS consortium would like to acknowledge the additional contributions of numerous friends and collaborators in translating and sharing the COVIDiSTRESS survey, even if contributions were small or the person did not wish their name included as a member of the consortium. All funding information is listed in the supplementary material (Figure S1). We also want to address thanks to the IFB (Institut Français de Bioinformatique, https://www.france-bioinformatique.fr/) for hosting the server Shiny illustrating our results. This research was supported by JSPS KAKENHI Grants JP17H00875, JP18K12015, JP20H04581, JP20K14222, Czech Science Foundation GC19-09265J, Consejo Nacional de Ciencia y Tecnologia (Conacyt), Full National Scholarship - MSc degree (CVU: 613905), Research Foundation Flanders (FWO) postdoctoral fellowship, and The HSE University Basic Research Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Identifiers
Local EPrints ID: 484367
URI: http://eprints.soton.ac.uk/id/eprint/484367
ISSN: 2052-4463
PURE UUID: 79499e54-534a-483c-8768-f466f7210739
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Date deposited: 15 Nov 2023 18:32
Last modified: 18 Mar 2024 04:15
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Contributors
Author:
Yuki Yamada
Author:
Dominik Borna Ćepulić
Author:
Tao Coll-Martín
Author:
Stéphane Debove
Author:
Guillaume Gautreau
Author:
Hyemin Han
Author:
Jesper Rasmussen
Author:
Thao P. Tran
Author:
Giovanni A. Travaglino
Author:
Andreas Liebroth
Author:
Tereza Capelos
Corporate Author: COVIDiSTRESS Global Survey Consortium
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