Higher Education Perceived Stress and Physical Stress: Big Data Analysis. (preprint)

Like other countries, China has suffered severe consequences as a result of the COVID-19 outbreak and pandemic. The lockdown, physical distancing, social isolation has disrupted the day-to-day activities of its citizens. The higher education sector has had to adopt state-of-the-art technologies and use online platforms to hold classes in order to engage students and ensure the continuity of education for an effective learning process. The second wave and Delta variant of the virus compelled numerous Higher Educational Institutes (HEIs) to start offline and remote work for both academic and non-academic staff; a stressful ordeal for them. Teaching has always been a stressful career path, and the stress faced by teachers has adverse consequences on the learning and performance of students. In this paper, we aim to identify and study the stress HEIs staff perceive and its impact on their physical stress and wellbeing. The resilient coping mechanism was evaluated as a moderator in the relationship between the staff and faculty’s perceived and physical stress. Using a standard survey questionnaire, 500 responses were collected. The findings revealed that the staff with the highest perceived stress also showed high symptoms of physical stress. It also indicated that resilience copers experienced reduced or very inconsequential symptoms of physical stress regardless of high perceived stress, thus validating the important moderating function of resilience in the relationship between the physical and perceived stress of the employees.

Tensor Completion for Causal Inference with Multivariate Longitudinal Data: A Reevaluation of COVID-19 Mandates (preprint)

We propose a new method that uses tensor completion to estimate causal effects with multivariate longitudinal data, data in which multiple outcomes are observed for each unit and time period. Our motivation is to estimate the number of COVID-19 fatalities prevented by government mandates such as travel restrictions, mask-wearing directives, and vaccination requirements. In addition to COVID-19 fatalities, we observe related outcomes such as the number of fatalities from other diseases and injuries. The proposed method arranges the data as a tensor with three dimensions (unit, time, and outcome) and uses tensor completion to impute the missing counterfactual outcomes. We first prove that under general conditions, combining multiple outcomes using the proposed method improves the accuracy of counterfactual imputations. We then compare the proposed method to other approaches commonly used to evaluate COVID-19 mandates. Our main finding is that other approaches overestimate the effect of masking-wearing directives and that mask-wearing directives were not an effective alternative to travel restrictions. We conclude that while the proposed method can be applied whenever multivariate longitudinal data are available, we believe it is particularly timely as governments increasingly rely on longitudinal data to choose among policies such as mandates during public health emergencies.