My research falls into three areas with the common focus on group-based animosities. My primary research interest is the underrepresentation of nonreligious Americans and the effects of this group’s exclusion on US politics. My second area of research investigates the effects that partisans’ affective evaluations have on their perceptions and decisions. My third area of research examines the interactions between partisanship, vaccination in core social networks, and Covid-19 vaccine attitudes. The thread linking my work is an interest in social identity and the effects that group identities have on individuals’ animosities.
I have been published in American Politics Research, have three papers under review, and eight ongoing projects utilizing state of the arts methods such as conjoint analysis, machine learning, and multidimensional scaling. As an early career scholar, I aim to publish further articles on the nonreligious as a political group and turn my dissertation into a book on the group’s exclusion from representation.
Below are summaries of my furthest along projects along with some interesting figures generated from original surveys I designed and fielded. If you'd like to learn more about my work please see my research statement linked below.
Non-Religious Identity Salience for Candidate Choice
Religion is on the decline in the United States. Americans increasingly report low religiosity, have less attachment to religion, and a rapidly growing number identify as nonreligious. In Congress the story is different. While a quarter of the public identifies as nonreligious, only one member of Congress does. Why are the nonreligious vastly underrepresented in government? I use a conjoint candidate choice experiment to causally link religious voters bias against nonreligious candidates to reduced support for them in electoral settings. I demonstrate that bias against the nonreligious affects electoral decisions and is causally linked to the exclusion of the nonreligious from government.
Conjoint Analysis of Religious Respondents in a Nationally Representative Sample
Affective Polarization in a Word:
Open-Ended and Self-Coded Evaluations of Partisan Affect
Word Cloud of The Most Common Words Out-Partisans Use
Partisanship, scholars suggest, drives negative emotional evaluations of out-partisans. Yet, scholars base these insights on measures – like thermometers, candidate evaluations, and social-distance measures – that discount the sentiment attached to individuals' negative attitudes. We introduce a new measure of affect capturing the motivation underpinning partisans' attitudes. Our measure asks respondents for one-word to describe voters in their party and the opposing party. Then respondents code the sentiment behind their word choice themselves. Together, our measure produces qualitative and quantitative measures of respondents' affect. We find that our self-coded open-ended measure has strong face validity, correlates strongly with existing affect measures, and reveals a theoretically relevant dimension of affective polarization. This measure advances our understating of partisan affect by allowing scholars a window into respondents' state of mind. Scholars can easily apply our measure’s procedure beyond partisanship to other groups of interest.
Distribution of The Most Common Words About Out-Partisans With Average Affect Scores Displayed on Each Bar
Polarization In COVID-19 Vaccine Discussion Networks
The ubiquitous issue of our time—COVID-19—presented an exceptional opportunity to apply my interest in novel experimental research to an important context. Like most office workers I spent the pandemic working from home. Unlike most office workers, I had the training and tools to design and field an experiment that could inform me about why—despite the existence of widely available, incredibly effective, and completely safe vaccines—it took an exceedingly long time for the US population to achieve high levels of vaccination. Work I conducted with graduate student co-authors, published in American Politics Research and as two Monkey Cage articles in the Washington Post, illuminates the role that social pressure from close associates played in both inhibiting and facilitating widespread vaccination.
During the lowest levels of vaccination, I designed and fielded a novel survey to examine the role that core social networks play in individuals’ likelihood of being vaccinated. This survey built egocentric social networks composed of respondents’ closest associates with whom they “discuss COVID-19, vaccines, and politics.” The vaccination rate of this network was highly predictive of a respondents’ vaccination status. Furthermore, there is polarization in vaccination networks such that the vaccinated and unvaccinated individuals are clustered and isolated from each other. Surprisingly, these network effects are unmoderated. This work had implications for the effectiveness of vaccination campaigns and is important for understanding the public’s response to future pandemics.
Distribution of Vaccination in Respondents’ Social Network Conditional on Respondents’ Vaccination Status