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From German Internet Panel to Mannheim Corona Study: Adaptable probability-based online panel infrastructures during the pandemic
[journal article]
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 inferen... view more
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.... view less
Keywords
ALLBUS; microcensus; epidemic; data quality; data acquisition; panel; probability; sample
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
COVID-19; Coronavirus; online panel; probability sample; social statistics; German General Social Survey - ALLBUS 2018 (ZA5272 v1.0.0)
Document language
English
Publication Year
2021
Page/Pages
p. 1411-1437
Journal
Journal of the Royal Statistical Society, Series A (Statistics in Society) (2021)
DOI
https://doi.org/10.1111/rssa.12749
ISSN
1467-985X
Status
Published Version; peer reviewed