Research and Research Interests
My research is primarily at the nexus of American politics and social psychology, examining the role of affect and emotion and how they relate to political outcomes of interest to contemporary scholars of American politics. My work has looked at fear and American foreign policy, and anxiety and the 2016 election. The first substantive chapter of my dissertation considers the role of anxiety among members of marginalized and disadvantaged subgroups. Namely, how political anxiety among members of these groups has a unique influence on their levels of political trust and political efficacy, among other normative outcomes. My work utilizes a mixed methods approach, employing experiments, surveys, observational studies, and other unique approaches.
Other research interests of mine include network science and the collaboration networks formed between congressional campaigns. A working paper with Janet M. Box-Steffensmeier and Benjamin W. Campbell is below.
I Get By With a Little Help from My Friends: Electoral Collaboration Among Congressional Campaigns
Co-Authored with Janet M. Box-Steffensmeier, Benjamin W. Campbell, and Seth J. Walker.
Central to the study of Congress is the study of collaboration among members. Notwithstanding the breadth of scholarship on legislative collaboration, scholars have limited understanding of how members of Congress collaborate to achieve reelection. Electoral collaboration increases the influence of members who choose to collaborate, allows members to support other members they align with ideologically, aids in legislative collaboration, and allows members to find cooperative usage for their donor and sup- porter lists. Using a quasi-experiment of the 2016 election and nearly 3.2 million FEC records from 2010-2016, we explore the network and political dynamics that influence electoral collaboration. We find that strategic considerations and shared policy goals motivate members who are likely to collaborate electorally with co-partisans, across chambers, with those serving on the same committee(s), and those facing contentious reelection races. Furthermore, personal relationships, which are measured as network features of preferential attachment and triadic closure, are found to characterize collaboration. These findings build upon our understanding of congressional collaboration, the networks of members of Congress, and the congressional power structure.
*Presented at the 11th Annual Political Networks Conference, George Mason University, Arlington, Virginia, June 6-9, 2018
*Presented at the 114th Annual Meeting of the American Political Science Association, Boston, MA, August 2018.
Anxiety Among Marginalized Groups Most Prone to Anxiousness and What it Means for Politics
Previous study frames the effects of anxiety among the populace in mostly neutral or even positive normative language. Affective Intelligence Theory (AIT) scholars find that anxious individuals shift from autopilot-type thinking to conscious deliberation, relying less on heuristics likes party and more on substance like candidate positions and candidate personal qualities. Furthermore, those seeking out new information do so in an un- biased manner. Second-generation scholars (Albertson and Gadarian 2015) find that anxiety increases levels of trust in relevant actors and experts. Taken in full, many scholars argue that a little anxiety may bring political activation, thereby making it a normatively good thing. But anxiety is not always normatively warranted or personally beneficial. Consider veterans, African Americans living in urban areas, welfare recipients, and Medicaid recipients. Government policy (or politics more generally) can be at the root of or cause much of their anxiety. How persons in these disadvantaged subgroups fare in the political landscape, and what this anxiety then means for civic attitudes like trust and efficacy, is understudied in political science. Scholars know very little about how government policies towards groups affects their levels of anxiety for politics, and what that then means for political involvement and civic engagement by members of these groups who are now anxious.
Public Opinion, Societal Differences, and American Foreign Policy: The Moderating Role of Religion and Theology on War
China has manipulated it’s currency relative to the U.S. Dollar, stolen countless intellectual properties from private American companies, and hacked over 22 million confidential records from the federal government’s Office of Personnel Management. Yet, China and the United States are not at war. Yes, they are military and techno- logical rivals, battling for global hegemony, but much keeps them from engaging in a militarized interstate dispute (MID). Without much hesitation, much of the literature in international relations argues this is due to mutually assured destruction (MAD) and economic interdependence. I launch a survey experiment to test the moderating effect of religious theology on fear of a physical threat, which in turn affects the likelihood of a MID. China may be a strategic threat to the United States, but unlike many Muslim countries in the Middle East, Americans are not afraid of China because it will not declare jihad against us, or duplicate terrorist attacks like 9/11. In essence, it is not The West vs. radical Islam. No matter how strong economic interdependence might be, and no matter how pertinent MAD may be, I predict an additional causal factor preventing war between the United States and China is lack of a religious component to the animosity. MAD and economic interdependence do indeed account for reasons the United States and China do not engage in war, but scholars have yet to universally quantify to what extent this is the case. Perhaps religious theology should be added to the list alongside MAD and economic interdependence as causal mechanisms preventing war between the United States and China.
*Presented at the annual meeting of the Southern Political Science Association, New Orleans, Louisiana, January 12-14, 2017.
**Please contact me for replication data.