From German Internet Panel to Mannheim Corona Study: Adaptable probability-based online panel infrastructures during the pandemic. (24th September 2021)

• Record Type: Journal Article
• Authors: Cornesse, Carina; Krieger, Ulrich; Sohnius, Marie-Lou; Fikel, Marina; Friedel, Sabine; Rettig, Tobias; Wenz, Alexander; Juhl, Sebastian; Lehrer, Roni; Möhring, Katja; Naumann, Elias; Reifenscheid, Maximiliane; Blom, Annelies G.
• Is Part Of: Journal of the Royal Statistical Society. Volume 185:Number 3(2022)
• Page Start: 773
• Page End: 797
• Publication Date: 2021-09-24
• Subjects: COVID-19 -- data collection -- data quality -- online panel -- probability sample -- social statistics; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12749
• Languages: English
• ISSNs: 0964-1998

Abstract

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.