All Times ET
Keywords: COVID-19, Mixed Data, Partial Association
The outbreak of COVID-19 has lowered the well-being of college students across the world according to existing studies. In this paper, we study the association between well-being and common psychological factors. We analyze the data from two cohorts of first year undergraduates (in New Zealand) in April 2019 and 2020 (early pandemic), which enables a counterfactual to explore the impact of COVID-19. We found that by controlling for age and gender, the other covariates (students’ healthiness, loneliness and accommodation) account for more of the association between well-being and anxiety in 2020 than that in 2019, implying an increased moderating effect of these covariates on the association after the strike of COVID-19. Our empirical findings may deliver various insights to domain experts and lead to more specific studies to assist university policy makers and healthcare providers in decision making. The empirical analysis in this paper is based on our proposed framework of partial association analysis for mixed data. Specifically, we propose to assess partial association using the rank-based measure, Kendall’s tau, based on a unified residual that can be obtained from any general parametric model for continuous, binary and ordinal outcome. We show that the conditional independence between two outcome variables is equivalent to the independence between the corresponding pair of unified residuals. We also show several useful statistical properties of the proposed partial association measure. A practical guide that covers estimation and inference is provided.