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

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.

A Comprehensive Taxonomy of Research Designs, a Scaffolded Design Figure for Depicting Essential Dimensions, and Recommendations for Achieving Design Naming Conventions in the Field of Mixed Methods Research

“…This language can be critiqued for the problem of inviting confusion with well-established and growing use of complexity theory across the social and health sciences. This is particularly relevant given growing interest in using complexity theory with mixed methods research (Kallemeyn et al, 2020; Poth et al, 2021).…”

Special Issue on COVID-19 and Novel Mixed Methods Methodological Approaches During Catastrophic Social Changes

“…In the third article, Poth et al (2021) who have affiliations in education and evaluation, provided an example of how data mining within a mixed methods design informed by complexity theory can rapidly generate complex case study descriptions. Key concepts of complex adaptive systems (emergence, interdependence, and adaptation) informed the generation of the case description.…”

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“…This means that, with this machine learned output, researchers now have to read the content (quotes) and assign a meaningful label, descriptor, or code (Saldaña, 2013) to such latent codes (González Canché, 2022a, 2022b, 2023b, 2023c. A recent description of this labeling process can be found in Chang et al (2021), González Canché (2022a, 2022b, 2023b, 2023c, Ho et al (2021), andPoth et al (2021). 7 Finally, note that the ordering of these latent codes and texts followed the linear evolution based on each participant's speeches or discourses.…”