From aggregations to multimethod case configurations. Case diversity in quantitative analysis when explaining COVID-19 fatalities

Philip Haynes, David Alemna

Research output: Contribution to journalArticlepeer-review

Abstract

Three quantitative methods are compared for their ability to understand different COVID-19 fatality ratios in 33 OECD countries. Linear regression provides a limited overview without sensitivity to the diversity of cases. Cluster Analysis and Dynamic Patterns Synthesis (DPS) gives scrutiny to the granularity of case similarities and differences, and reveals case exceptions. Qualitative Comparative Analysis (QCA) develops causal theory about what conditions are sufficient for explaining outcomes by using robust and transparent conventions. Configurational case-based methods offer important advantages over inferential statistics when there is a need to focus on diversity in small n. These techniques can be combined as multi-methods. DPS and QCA can be used concurrently to aid research insights. These methods are also strengthened by additional qualitative evidence about the cases.
Original languageEnglish
JournalInternational Journal of Social Research Methodology
Early online date13 Sep 2022
DOIs
Publication statusEarly online - 13 Sep 2022

Keywords

  • regression
  • cluster analysis
  • Dynamic Pattern Synthesis (DPS)
  • Qualitative comparative analysis (QCA)
  • causal complexity
  • covid-19

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