Replicating the Disease framing problem during the 2020 COVID-19 pandemic: A study of stress, worry, trust, and choice under risk

Abstract

In the risky-choice framing effect, different wording of the same options leads to predictably different choices. In a large-scale survey conducted from March to May 2020 and including 88,181 participants from 47 countries, we investigated how stress, concerns, and trust moderated the effect in the Disease problem, a prominent framing problem highly evocative of the COVID-19 pandemic. As predicted by the appraisal-tendency framework, risk aversion and the framing effect in our study were larger than under typical circumstances. Furthermore, perceived stress and concerns over coronavirus were positively associated with the framing effect. Contrary to predictions, however, they were not related to risk aversion. Trust in the government’s efforts to handle the coronavirus was associated with neither risk aversion nor the framing effect. The proportion of risky choices and the framing effect varied substantially across nations. Additional exploratory analyses showed that the framing effect was unrelated to reported compliance with safety measures, suggesting, along with similar findings during the pandemic and beyond, that the effectiveness of framing manipulations in public messages might be limited. Theoretical and practical implications of these findings are discussed, along with directions for further investigations.

Validity Testing of the Conspiratorial Thinking and Anti-Expert Sentiment Scales during the COVID-19 Pandemic Across 24 Languages from a Large-Scale Global Dataset

“…Measurement alignment was also performed to address the potential issue of measurement noninvariance reported by the measurement invariance test. Prior studies that analyzed international survey datasets addressing psychological aspects of COVID-19 also employed measurement alignment to address the issue (Han, 2021;Lieberoth et al, 2021;Rachev et al, 2021).…”

“…Because measurement invariance test and measurement alignment involve confirmatory factor analysis (CFA), following statistical guidelines, we analyzed responses in language groups where n ≥ 100 (Han, 2021;Rachev et al, 2021). After excluding language groups with n< 100, responses from 12,261 participants in 24 language groups were analyzed.…”

“…Measurement invariance was examined in terms of whether model fit indicators, i.e., RMSEA, SRMR, CFI, changed significantly when different levels of model constraints were applied (Rachev et al, 2021). We tested four different levels of measurement invariance, configural, metric, scalar, and residual invariance (Fischer & Karl, 2019).…”

Trust in the scientific research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset

“…Originally, the data was collected from 20,601 participants from 62 countries. However, as I employed mixed-effects model analysis to include the between-country effect in analysis, to prevent potential convergence issue [8,21], only data collected from countries where 100 or more participants completed the survey was used in the present study. As a result, I analyzed a subset of the data collected from 14,349 participants from 35 countries.…”

“…The same mixed-effects model analysed with lmerTest was tested with brms for each compliance variable. In this process, I employed the default Cauchy prior, Cauchy (0, 1), suggested by [30] following the previous studies [8,21]. After conducting Bayesian multilevel modelling for each dependent variable, the result was analysed with bayestestR package for Bayesian quantitative interpretation of effect sizes.…”