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

Author:

Rusch TessaORCID,Han YantingORCID,Liang DehuaORCID,Hopkins Amber R.,Lawrence Caroline V.,Maoz Uri,Paul Lynn K.ORCID,Stanley Damian A.,Adolphs Ralph,Alvarez R. Michael,Camplisson Isabella,Harrison Laura,Hien Denise,Lan Tian,Lin Chujusn,Lopez-Castro Teresa,Nizzic Marie-Christine,Golden Allison Rabkin,Wahle Iman,Yaffe Gideon,

Abstract

AbstractThe 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.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

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