Linking self-reported social distancing to real-world behavior during the COVID-19 pandemic

In an effort to combat COVID-19 and future pandemics, researchers have attempted to identify the factors underlying social distancing. Yet, much of this research relies on self-report measures. In two studies, we examine whether self-reported social distancing predicts objective distancing behavior. In Study 1, individuals' self-reported social distancing predicted decreased mobility (assessed via smartphone step counts) during the COVID-19 pandemic. While participants high in self-reported distancing (+ 1 SD) exhibited a 33% reduction in daily step counts, those low in distancing (-1 SD) exhibited only a 3% reduction. Study 2 extended these findings to the group level. Self-reported social distancing at the U.S. state level accounted for 20% of the variance in states' objective reduction in overall movement and visiting nonessential services (calculated via the GPS coordinates of ~15 million people). Collectively, our results indicate that self-reported social distancing tracks actual social distancing behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Deviancy Aversion and Social Norms.

We propose that deviancy aversion-people's domain-general discomfort toward the distortion of patterns (repeated forms or models)-contributes to the strength and prevalence of social norms in society. Five studies (N = 2,390) supported this hypothesis. In Study 1, individuals' deviancy aversion, for instance, their aversion toward broken patterns of simple geometric shapes, predicted negative affect toward norm violations (affect), greater self-reported norm following (behavior), and judging norms as more valuable (belief). Supporting generalizability, deviancy aversion additionally predicted greater conformity on accuracy-orientated estimation tasks (Study 2), adherence to physical distancing norms during COVID-19 (Study 3), and increased following of fairness norms (Study 4). Finally, experimentally heightening deviancy aversion increased participants' negative affect toward norm violations and self-reported norm behavior, but did not convincingly heighten belief-based norm judgments (Study 5). We conclude that a human sensitivity to pattern distortion functions as a low-level affective process that promotes and maintains social norms in society.

Linking Self-Reported Social Distancing to Real-World Behavior During the COVID-19 Pandemic

In an effort to combat COVID-19 and future pandemics, researchers have attempted to identify the factors underlying social distancing. Yet, much of this research relies on self-report measures. In two studies, we examine whether self-reported social distancing predicts objective distancing behavior. In Study 1, individuals? self-reported social distancing predicted decreased mobility (assessed via smartphone step counts) during the COVID-19 pandemic. While participants high in self-reported distancing (+1 SD) exhibited a 33% reduction in daily step counts, those low in distancing (?1 SD) exhibited only a 3% reduction. Study 2 extended these findings to the group level. Self-reported social distancing at the U.S. state level accounted for 20% of the variance in states? objective reduction in overall movement and visiting nonessential services (calculated via the GPS coordinates of ?15 million people). Collectively, our results indicate that self-reported social distancing tracks actual social distancing behavior.

Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic.

Numerous polls suggest that COVID-19 is a profoundly partisan issue in the United States. Using the geotracking data of 15 million smartphones per day, we found that US counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous other factors, including counties' COVID-19 cases, population density, median income, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) was related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that US citizens' responses to COVID-19 are subject to a deep-and consequential-partisan divide.