Partisanship, Messaging, and the COVID-19 Vaccine: Evidence From Survey Experiments.

PURPOSE: To investigate partisanship in COVID-19 attitudes, and assess partisan or scientific messaging effects on COVID-19 vaccination intentions. DESIGN: Two-wave survey with two-arm randomized experiment. SETTING: Recruited Pennsylvania residents online. SAMPLE: 2037 (May 2020) and 1577 (October 2020) Pennsylvania residents, aged 18-94 years. INTERVENTION: Respondents saw messaging that presented either President Trump or scientists endorsing the vaccine, then reported their vaccination intentions. MEASURES: Likert scale items measuring COVID-19 attitudes (May), including mask wearing and vaccination intentions (May and October). ANALYSIS: Partisan differences in attitudes were analyzed by chi-square; differences in support for mask wearing and vaccination intentions were also analyzed by Mann-Whitney U. The messaging experiment was analyzed by chi-square, Mann-Whitney U, and survey-weighted multivariate logistic regression. RESULTS: Significant partisan differences were found in all attitudes. The partisan split in support for mask wearing increased from May to October, whereas the split in vaccination intentions decreased. Compared to partisan messaging, scientific messaging increased overall odds of intending to vaccinate by 32% in May (adjusted odds ratio [AOR]=1.32, 95% confidence interval [CI] = 1.06-1.65), and increased odds among Democrats by 142% in October (AOR = 2.42, CI = 1.29-4.55). Scientific messaging had no significant effect on independents or Republicans. CONCLUSION: Partisan COVID-19 attitudes were widespread and persistent. Partisan endorsement of the vaccine positively influenced those with congruent beliefs, while scientific messaging produced consistent effects across political affiliation.

Emotions and decisions in the real world: What can we learn from quasi-field experiments?

Researchers in the social sciences have increasingly studied how emotions influence decision-making. We argue that research on emotions arising naturally in real-world environments is critical for the generalizability of insights in this domain, and therefore to the development of this field. Given this, we argue for the increased use of the "quasi-field experiment" methodology, in which participants make decisions or complete tasks after as-if-random real-world events determine their emotional state. We begin by providing the first critical review of this emerging literature, which shows that real-world events provide emotional shocks that are at least as strong as what can ethically be induced under laboratory conditions. However, we also find that most previous quasi-field experiment studies use statistical techniques that may result in biased estimates. We propose a more statistically-robust approach, and illustrate it using an experiment on negative emotion and risk-taking, in which sports fans completed risk-elicitation tasks immediately after watching a series of NFL games. Overall, we argue that when appropriate statistical methods are used, the quasi-field experiment methodology represents a powerful approach for studying the impact of emotion on decision-making.