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Measuring polarization of risk perceptions

Olivia Fischer
University of Zurich ~ Cognitive and Behavioral Decision Research
Prof. Renato Frey
University of Zurich ~ Cognitive and Behavioral Decision Research

1. Background & Research question: Polarization is a complex and multifaceted issue that has gained increasing attention in recent years (e.g., Bail et al., 2018). But is society really as polarized as often assumed by popular media? Answering this question is no trivial matter given the lack of a clear and agreed-upon definition and measurement of the phenomenon. This heterogeneity can make it difficult to establish the true extent of polarization in society and to design effective interventions to reduce it. Our research contributes to the understanding of polarization by addressing the following two research questions: (1) To what degree does polarization manifest itself in the specific area of risk perceptions regarding Covid-19 measures? (2) How robust are our conclusions when we compare different operationalizations of polarization? 2. Methods & analytic pipeline: The debate around the appropriateness of Covid-19 measures often centers around the potential consequences on both public health and the economy. In order to investigate how individuals' risk perceptions differ depending on the perspective they take, we used a mixed-design study with two between-subjects conditions: One group answered questions from a health perspective and the other group from a finance perspective. Both groups were presented with the same three scenarios: (1) a lockdown scenario, (2) a mandatory Covid-19 certificates scenario, and (3) a vaccine mandate scenario. Importantly, we asked participants to report their risk perceptions regarding consequences both for themselves and society at large, as we believe that people differ in their perceptions regarding these consequences. This study design led to twelve unique combinations of between- and within-subjects conditions. In line with a pre-determined stopping rule, we collected data from a 768 participants in the United States using Amazon Mechanical Turk and followed best-practice recommendations for quality control of online samples (i.e., bot, VPN, and comprehension checks). As the main operationalization of polarization in participants' risk perceptions, we focused on the bimodality coefficient (BC; Lelkes, 2016). Using the runjags package in R, we conducted a preregistered Bayesian parameter estimation to estimate the BC's posterior distribution. In a novel approach, we defined the data-generating process of participants' risk perceptions as a beta distribution, which can assume a uniform, bimodal, or unimodal form. In addition to the BC, we computed seven further polarization indices to estimate the agreement between different measures. 3. Results: Regarding our first research question, we found that six out of the twelve unique combinations indicate credible polarization based on our pre-defined region of practical equivalence (ROPE) for the bimodality coefficient. Specifically, we found four cases of credible polarization in the finance condition and two in the health condition. In the finance condition, it was the mandatory certificate, lockdown, and vaccine mandate for one's financial situation and the vaccine mandate for others' financial situation that were polarized. In the health condition, it was the mandatory certificate and vaccine mandate for one's own health situation that were polarized. It is also notable that the other distributions are relatively uniformly distributed, indicating a high degree of variation and lack of agreement even in the non-polarized distributions of participants' risk perceptions. Regarding our second research question, we compared the posterior estimate of the bimodality coefficient to seven other operationalizations of polarization and found that there is relatively strong agreement between measures. The average absolute correlation (i.e., disregarding the sign) between measures is 0.58. 4. Conclusions & Significance of research: In conclusion, our results suggest that there is credible polarization in regards to certain Covid-19 measures, specifically those that have personal consequences. Moreover, we find that different measures of polarization tend to agree, at least regarding the relatively uniformly distributed data that we observed. Our research highlights the importance of considering the context in which polarization is measured and how it is conceptualized. Furthermore, these findings have important implications for public policy: They suggest that interventions aimed at reducing polarization should focus on addressing risks that individuals may perceive for themselves. In a second study, we use precisely these insights to implement an intervention based on one-on-one interactions between individuals with differing risk perceptions.



risk perception
Bayesian estimation

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Cite this as:

Fischer, O., & Frey, R. (2023, July). Measuring polarization of risk perceptions. Abstract published at MathPsych/ICCM/EMPG 2023. Via