Fear of COVID-19: Data of a large longitudinal survey conducted between March 2020 and June 2021
Abstract
Research indicates that fear was an important factor in determining individual responses to COVID-19, predicting relevant behaviors such as compliance to preventive measures (e.g., hand washing) and stress reactions (e.g., poor sleep quality). Given this central role of fear, it is important to understand more about its temporal changes during the COVID-19 pandemic. This article describes a publicly available dataset that contains longitudinal assessment of fear of COVID-19 and other relevant constructs during the first 15 months of the pandemic. Particularly, the dataset contains data from two different samples. The first sample consists predominantly of Dutch respondents (N = 439) who completed a cross-sectional survey in March 2020. The second sample consists of a large-scale longitudinal survey (N = 2000 at T1), including respondents with a broad range of nationalities (though predominantly residing in Europe and North America; 95.6%). The respondents of the second sample completed the survey between April 2020 and August 2020 using the Prolific data collection platform. In addition, one follow-up assessment was completed in June 2021. The measures included in the survey were fear of COVID-19, demographic information (age, gender, country of residence, education level, and working in healthcare), anxious traits (i.e., intolerance of uncertainty, health anxiety, and worrying), media use, self-rated health, perceived ability to prevent infection, and perceived risk for loved ones. Additionally, at the follow-up assessment in June 2021, respondents were asked whether they were vaccinated against COVID-19 or were planning to get vaccinated. The datafiles of this study have been made available through the Open Science Framework and can be freely reused by psychologists, social scientists, and other researchers who wish to investigate the development, correlates, and consequences of fear of COVID-19.
Study specs
- Authors
- G Mertens,P Lodder,T Smeets,S Duijndam
- Institution
- Tilburg University
- Discipline
- Public Health,Psychology
- Year
- 2023
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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