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Activity Number: 525 - Applications of Statistical Measurement to a Range of Social Issues: From Cost of Living to Social Unrest
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #320928
Title: Partial Association Between Mixed Data: Assessing the Impact of COVID-19 on College Student Well-Being
Author(s): Shaobo Li and Zhaohu Fan* and Dungang Liu and Ivy Liu and Philip S. Morrison
Companies: University of Kansas and University of Cincinnati and University of Cincinnati and Victoria University of Wellington and Victoria University of Wellington
Keywords: COVID-19; ordinal data; well-being; correlation; adjacent category logit model
Abstract:

The COVID-19 outbreak negatively affected the well-being of college students worldwide, according to existing research. We examine the association between psychological factors and well-being in this paper. This paper examines data collected from two first-year undergraduate cohorts in April 2019 and 2020 (early pandemic) in order to examine the impact of COVID-19. Observed differences in well-being and anxiety following the strike of COVID-19 were found to be mostly influenced by student healthiness, loneliness, and housing (controlling for age and gender). This suggests that these covariates have an increased moderating effect after the strike of COVID-19. As a result of our empirical findings, domain experts may be able to gain more insight and conduct more specific studies to assist university policy makers and healthcare providers. This empirical analysis is based on our proposed framework of partial association analysis based on mixed data.The proposed method uses a rank-based measure of partial association, Kendall's tau, derived from a unified residual that can be obtained from any general parametric model for continuous, binary, and ordinal outcomes.


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