Abstract:
The COVID-19 pandemic has wreaked havoc around the globe and caused significant disruptions across multiple domains. Moreover, different countries have been differentially impacted by COVID-19 — a phenomenon that is due to a multitude of complex and often interacting determinants. Understanding such complexity and interacting factors requires both compelling theory and appropriate data analytic techniques. Regarding data analysis, one question that arises is how to analyze extremely non-normal data, such as those variables evidencing L-shaped distributions. A second question concerns the appropriate selection of a predictive modelling technique when the predictors derive from multiple domains (e.g., testing-related variables, population density), and both main effects and interactions are examined.
