Using structural equation modeling to model compliance with COVID-19 related non-pharmaceutical interventions amongst university students in the United States

Spencer Shumway, Jonas Hopper, Ethan Richard Tolman, Daniel Ferguson, Gabriella Hubble, David Patterson and Jamie Jensen

No 9nzr7, SocArXiv from Center for Open Science

Abstract: The world is currently dealing with a devastating pandemic. Although growing COVID-19 case numbers, deaths, and hospitalizations are concerning, this spread is particularly alarming in the United States where polarizing opinions, changing policies, and misinformation abound. In particular, American college campuses have been a venue of rampant transmission, with concerning spillover into surrounding, more vulnerable, communities. We surveyed over 600 college students from across the United States and modeled predictors of compliance with non-pharmaceutical interventions. We identified concern with severity (p < .001), constitutional originalist ideology (p < .001), news exposure (p < .001) and religiosity (p < .05) as significant positive correlates with compliance, and general trust in science (p < .05) as a significant negative correlate. To determine how applicable nationwide modeling might be to individual local campuses we also administered this same survey to nearly 600 students at two large universities in Utah County. In this population, concern with severity was the only significant positive correlate with compliance (p < .001); Additionally, feelings of inconvenience was negatively correlated (p < .001). The effects of feelings of inconvenience, and news exposure were significantly different between populations (p < .001, p < .001). These results suggest that we should focus our efforts on increasing knowledge about the pandemic’s effects on our society and informing about constitutionality amongst college students. However, we also show that nationwide surveys and modeling are informative, but if campuses are to efficiently curb the spread of COVID-19 this coming semester, they would be best served to utilize data collected from their student populations as these might significantly differ from general consensus data.

Date: 2020-12-10
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:9nzr7

DOI: 10.31219/osf.io/9nzr7

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