Local Data and Upstream Reporting as Sources of Error in the Administrative Data Undercount of COVID 19

Autor/in	Dubrow, Joshua K.
Titel	Local Data and Upstream Reporting as Sources of Error in the Administrative Data Undercount of COVID 19
Quelle	In: International Journal of Social Research Methodology, 25 (2022) 4, S.471-476 (6 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1364-5579
DOI	10.1080/13645579.2021.1909337
Schlagwörter	Data Use; COVID-19; Pandemics; Data Collection; Error Patterns; Data Processing; Mortality Rate + Suchen Sie Ihr Suchwort? Data capture; Datensammlung; Fehlertyp; Datenverarbeitung; Mortalitätsrate
Abstract	The COVID 19 pandemic illuminates the role data has in public policy-making, i.e. datafication of society, and the importance of exploring the local sources of data to reveal errors in what has assuredly been from the beginning an undercount of cases and deaths. I note four interrelated error sources. The first two are common to any quantitative data collection project: (1) representation, measurement, and data processing; and (2) problems of data standardization from unequally resourced local and national data providers. COVID 19 casts a special light on (3) the possibility of government intervention in at least the public presentation of these data; and (4) human errors in the data chain caused by a stressful data collection environment. To identify errors, we should look to national pressures and the local contexts from which these data are collected and the upstream reporting process. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01