Understanding the Effects of Individual and State-Level Factors on American Public Response to COVID-19

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public response, COVID-19, multilevel predictors, structural equation modeling

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Purpose: To examine multilevel predictors on American public response to COVID-19.

Design: Multilevel study.

Setting: A national survey was conducted by Qualtrics from August 24 to September 11, 2020. The state-level variables were constructed on data from multiple sources.

Subjects: 2,440 respondents 18 years and older from all 50 states and D.C.

Measures: The outcome variable is the public response to COVID-19 measured by threat perception, behavioral adjustment, and policy support. The predictors include individual-level sociodemographic factors and state-level indicators about public health conditions, political context, and economic recovery.

Analysis: Multilevel structural equation modeling is used for statistical estimation.

Results: People from states with more COVID-19 cases (β = 0.020, p < 0.1), mandatory face mask policies (β = 0.069, p < 0.05), and liberal governments (β = 0.002, p < 0.05) are more likely to respond while people from states whose economies have recovered closer to the pre-pandemic level are less likely to do so (β = −0.005, p < 0.05). Regarding individual-level predictors, older people (β = 0.005, p < 0.001) and people with better education (β = 0.029, p < 0.01), leaning toward the Democrat Party (β = 0.066, p < 0.001) and liberal political ideology (β = 0.094, p < 0.001), and have stronger generalized trust (β = 0.033, p < 0.001) are more likely to respond than their counterparts.

Conclusion: Differences in the public response to the pandemic stem from variations in individual characteristics and contextual factors of states where people live. These findings contribute to the rapidly growing literature and have implications for public health policies.

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Citation / Publisher Attribution

American Journal of Health Promotion, v. 35, issue 8, p. 1078-1083