Co-Principal Investigators: Jennifer Taber, Karin Coifman, Pooja Sidney (University of Kentucky), Percival Matthews (University of Wisconsin-Madison)

Beliefs about the likelihood of getting a disease, or one's risk perceptions, are a key predictor of engagement in prevention behaviors. Understanding the magnitude of one's risk may require making sense of numerical health information, often presented in the form of rational numbers, such as fractions, whole number frequencies, and percentages. Unfortunately, fractions are difficult because people are likely to commit whole number bias errors by focusing on numerators and denominators in isolation instead of the size of the fraction. During the COVID-19 pandemic, the public receives daily updates about the number of people locally, nationally, and globally who are infected with, and die from, COVID-19. Whole number bias may cause people to disregard COVID-19 as a small threat and fail to heed preventative measures (i.e., social distancing). The research team will test whether a brief education intervention diminishes whole number bias when reasoning about COVID-19 statistics.

The researchers will investigate whether an education intervention, aimed at decreasing whole number bias errors, can help adults 25 and older (Study 1) and emerging adults (ages 18-24) on a college campus (Study 2) to more accurately interpret health statistics about COVID-19. The education intervention involves analogies, worked examples, and visual models to help participants calculate the COVID-19 fatality rate to combat whole number bias. Study 1 includes an online randomized control trial (RCT) with adults recruited through a Qualtrics panel. Wave 1 of Study 1 will assess predictors of whole number bias. Participants will be randomly assigned to a business-as-usual or educational intervention condition. For 10 consecutive days, participants will complete evening daily diaries to track the impact of the intervention on risk perceptions and preventive health behaviors (e.g., social distancing, wearing a mask). In Wave 2 of Study 1, participants will be re-contacted during the 2020 flu season to assess long-term benefits of the education intervention. Study 2 involves an RCT of college students assigned to receive the education intervention or to a business-as-usual condition with no visualization of risk.

The researchers will assess whether the education intervention facilitates greater risk perceptions about COVID-19 and subsequently greater engagement in recommended health behaviors (e.g., social distancing, wearing a mask in public, etc.). In addition, the researchers will also assess whether unrealistic optimism is reduced following the intervention.

Key measures include rational number estimation/comparison, objective/subjective numeracy, math anxiety/attitudes, health decision-making accuracy and strategy reports, risk perceptions, preventive health behaviors, and trait anxiety. The researchers will assess whether health decision-making accuracy and strategy reports differed across experimental conditions with a logistic regression. Finally, linear mixed effects models will be used to assess whether the educational intervention had an impact on risk perceptions and preventive behaviors across the 10-day daily diary period.

The results from the studies will be disseminated through conferences, publications, social media, and presentations to researchers and the general public.