Using Data Mining for Rapid Complex Case Study Descriptions: Example of Public Health Briefings During the Onset of the COVID-19 Pandemic. (July 2021)

• Record Type: Journal Article
• Authors: Poth, Cheryl N.; Bulut, Okan; Aquilina, Alexandra M.; Otto, Simon J. G.
• Is Part Of: Journal of mixed methods research. Volume 15:Number 3(2021)
• Page Start: 348
• Page End: 373
• Publication Date: 2021-07
• Subjects: COVID-19 -- case study–mixed methods research design -- mixed methods case study -- public health -- convergent sequential; Mixed methods research -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Research -- Evaluation -- Periodicals; 507.21
• DOI: 10.1177/15586898211013925
• Languages: English
• ISSNs: 1558-6898

Abstract

The methodological purpose of this article is to demonstrate how data mining contributes to rapid complex case study descriptions. Our complexity-informed design draws on freely accessible datasets reporting the public health response surrounding the onset of the COVID-19 pandemic in Alberta (Canada) and involves the cross analysis of integrated findings across six periods of fluctuation identified in the initial quantitative phase of a convergent sequential approach. We discuss how our case meta-inferences, informing how public health briefings can build credibility and trust, were derived by attending to three key concepts of complex adaptive systems: emergence, interdependence, and adaptation. This article serves as an essential reference for using data mining within a case study–mixed methods design for studying complex phenomena.