Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265038 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of the Royal Statistical Society: Series A (Statistics in Society) [ISSN:] 1467-985X [Volume:] 185 [Issue:] 3 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2021 [Pages:] 773-797
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
The outbreak of COVID‐19 has sparked a sudden demand for fast, frequent and accurate data on the societal impact of the pandemic. This demand has highlighted a divide in survey data collection: Most probability‐based social surveys, which can deliver the necessary data quality to allow valid inference to the general population, are slow, infrequent and ill‐equipped to survey people during a lockdown. Most non‐probability online surveys, which can deliver large amounts of data fast, frequently and without interviewer contact, however, cannot provide the data quality needed for population inference. Well aware of this chasm in the data landscape, at the onset of the pandemic, we set up the Mannheim Corona Study (MCS), a rotating panel survey with daily data collection on the basis of the long‐standing probability‐based online panel infrastructure of the German Internet Panel (GIP). The MCS has provided academics and political decision makers with key information to understand the social and economic developments during the early phase of the pandemic. This paper describes the panel adaptation process, demonstrates the power of the MCS data on its own and when linked to other data sources, and evaluates the data quality achieved by the MCS fast‐response methodology.
Subjects: 
COVID‐19
data collection
data quality
online panel
probability sample
social statistics
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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