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

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

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.

Suggested Citation

  • Shumway, Spencer & Hopper, Jonas & Tolman, Ethan Richard & Ferguson, Daniel & Hubble, Gabriella & Patterson, David & Jensen, Jamie, 2020. "Using structural equation modeling to model compliance with COVID-19 related non-pharmaceutical interventions amongst university students in the United States," SocArXiv 9nzr7, Center for Open Science.
    • Handle: RePEc:osf:socarx:9nzr7
    DOI: 10.31219/osf.io/9nzr7
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    1. Smriti Mallapaty, 2020. "Why COVID outbreaks look set to worsen this winter," Nature, Nature, vol. 586(7831), pages 653-653, October.
    2. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    3. Giuliana Viglione, 2020. "Scientists strongly back Joe Biden for US president in Nature poll," Nature, Nature, vol. 586(7831), pages 654-654, October.
    4. Lynne Peeples, 2020. "Face masks: what the data say," Nature, Nature, vol. 586(7828), pages 186-189, October.
    5. Megan Scudellari, 2020. "How the pandemic might play out in 2021 and beyond," Nature, Nature, vol. 584(7819), pages 22-25, August.
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Distancing and Lockdown > Compliance

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