Covid 19: An Educational Intervention to Combat Whole Number Bias in Risk Perceptions in Ambiguous Health Context: COVID-19

Grants and Contracts Details

Description

Title: An Educational Intervention to Combat Whole Number Bias in Risk Perceptions in an Ambiguous Health Context: COVID-19; Project Type: Unsolicited Topic: Postsecondary and Adult Education; STEM Education; Cognition and Student Learning Purpose: COVID-19 is a worldwide pandemic. Prevention measures are key to “flattening the curve.” Beliefs about the likelihood of getting a disease, or one’s risk perceptions, are a key predictor of engagement in prevention behaviors. Information about one’s risk is often presented as ratios, like fractions, and people who can understand this health information may develop more accurate personal risk perceptions. Unfortunately, fractions are difficult because people commit whole number bias errors by focusing on numerators and denominators in isolation instead of the size of the fraction. Fractions are also disliked by many and math anxiety, a feeling of apprehension about math, makes people avoid numbers. In our research, people with higher math anxiety were worse at estimating the size of fractions. The public receives daily updates about the number of people locally, nationally, and globally who are infected with, and die from, COVID-19. If individuals commit whole number biases, they may disregard COVID-19 as a small threat and fail to heed preventative measures (i.e., social distancing). Our interdisciplinary team will employ a brief educational intervention shown to diminish whole number bias when reasoning about COVID-19 statistics. We will track impacts of the intervention on adults’ risk perceptions and preventative behaviors across 10 days. Setting: Study 1: Qualtrics panel; Study 2: local elementary and intermediate schools. Population/Sample: An online sample stratified by gender and educational attainment of over 2,000 adults will participate in Study 1 and 100 5th and 6th graders will participate in Study 2. Intervention/Assessment: Study 1 will test an intervention involving analogies, worked examples, and visual models to help adults learn how to combat whole number bias. Study 2 will assess improvements in children’s health decision-making accuracy after a similar intervention. Control Condition: Participants in the control condition will receive the same health statistics as those in the intervention condition, but will not receive tools to help them calculate rate. Research Design and Methods: Study 1: An online RCT (2,000 people in U.S.) recruited through Qualtrics’ panel. Wave 1 will assess predictors of whole number bias. As indicated above, participants will be randomly assigned to a business-as-usual or educational intervention condition. For the next 10 days, participants will complete evening daily diaries (Bolger et al., 2003) to track the impact of the intervention on preventative behaviors. In Wave 2, participants will be recontacted in Fall 2020 at the start of flu season to assess long-term benefits of the educational intervention. Study 2: RCT of 100 students will be randomly assigned to either the educational intervention to combat whole number bias or not. This approach is aligned with our lifespan development framework in which we can intervene on adults’ or children’s rational number understanding to improve health decision-making outcomes. Key Measures: Selected key measures include: (1) Rational number estimation and comparison, (2) Objective/subjective numeracy, (3), Math anxiety/attitudes, (4) Need for cognition, (5) Strategy choice, (6) Risk perceptions/preventative behaviors about COVID-19, and (7) trait anxiety. Data Analytic Strategy: We will assess whether posttest and transfer problem accuracy and strategy reports differed across experimental conditions with a logistic regression. We will ensure that our groups do not differ in baseline math performance, math attitudes and anxiety, age, gender, and trait anxiety. These variables will be included as covariates. Finally, we will use linear mixed effects models to assess whether the educational intervention had an impact on preventative behaviors across the 10-day period.
StatusFinished
Effective start/end date9/1/208/31/22

Funding

  • Kent State University: $12,350.00

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