Affective polarization, local contexts and public opinion in America

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Abstract

<p class="first" id="d747615e109">Affective polarization has become a defining feature of twenty-first-century US politics, but we do not know how it relates to citizens' policy opinions. Answering this question has fundamental implications not only for understanding the political consequences of polarization, but also for understanding how citizens form preferences. Under most political circumstances, this is a difficult question to answer, but the novel coronavirus pandemic allows us to understand how partisan animus contributes to opinion formation. Using a two-wave panel that spans the outbreak of COVID-19, we find a strong association between citizens' levels of partisan animosity and their attitudes about the pandemic, as well as the actions they take in response to it. This relationship, however, is more muted in areas with severe outbreaks of the disease. Our results make clear that narrowing of issue divides requires not only policy discourse but also addressing affective partisan hostility. </p>

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Sensitivity Analysis in Observational Research: Introducing the E-Value.

Peng Ding, Tyler J. VanderWeele (2017)

Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.