Predictive Analytics of Mental Health Problems Among College Students in the Covid-19 Pandemic

Abstract

Covid-19 pandemic has brought into focus the mental health of various segments of society. This paper presents an empirical study on mental health of college students due to the specific situations caused by the pandemic. A particular focus is on the effects of three stressors, namely, academic workload, separation from school, and fears of contagion among the college students. This initiative was a follow-up of the study by Yang et al. (2021) who proposed a research model that evaluates the impacts of these stressors on perceived stress which subsequently affects the mental health of the students. Using the data collected by Yang et al. (2021), an alternative predictive analytics approach, i.e., Partial Least Squares Structural Equation Modelling (PLS-SEM), was adopted to re-evaluate the research model. This has produced an improvement over the results of Confirmatory Factor Analysis (CFA) adopted by Yang et al. (2021), particularly in the test for the significance of correlation between academic workload and mental health. While the use of PLS-SEM that allowed for strategic refinements of the model produced a significant correlation between the two important constructs, the CFA failed to obtain a similar result. All the other significant correlations between the stressors and mental health and correlation between the mediating factor, the perceived stress, and mental health were also established using the PLS-SEM approach.