COVID-Dynamic: a large-scale longitudinal study of socioemotional and behavioral change across the pandemic

Creators: Rusch, Tessa; Han, Yanting; Liang, Dehua; Hopkins, Amber R.; Lawrence, Caroline V.; Maoz, Uri; Paul, Lynn K.; Stanley, Damian A.; Adolphs, Ralph; Alvarez, R. Michael; Camplisson, Isabella; Harrison, Laura; Hien, Denise; Lan, Tian; Lin, Chujusn; Lopez-Castro, Teresa; Nizzic, Marie-Christine; Rabkin Golden, Allison; Wahle, Iman; Yaffe, Gideon; COVID-Dynamic Team

Abstract

The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of >1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants' responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities.

Additional Information

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. We deeply appreciate all COVID-Dynamic participants, whose continued diligence and commitment during this difficult time made this data possible. We thank Jonathan Katz for his expert advice on panel data processing and analysis, and Ruby Basyouni for her help during data collection. Lastly, we are grateful to the various funding sources that supported this work: the National Institute of Mental Health (2P50MH094258), the Caltech Chen Neuroscience Institute, and the Caltech Merkin Institute (RA), by the Oscar M. Ruebhausen Fund at Yale Law School (GY), the Rutgers Center of Alcohol & Substance Use Studies (DH), the John Templeton Foundation and the Kay Family COVID-19 Rapid Response Research Awards at Chapman University (UM), and the Colin Powell School for Civic and Global Leadership, The City College of New York (TLC). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the funding agencies. These authors contributed equally: Tessa Rusch, Yanting Han, Dehua Liang. These authors jointly supervised this work: Lynn K. Paul, Damian A. Stanley. The authors declare no competing interests.

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