b'COVID-19 Response in the United States: What Factors Predict the Level of Restriction and Level of Openness across the 50 States?Kiya EzellSponsor: Dr. James T. LaPlantThis study examines the demographic, economic, and political predictors of how states have responded to the COVID-19 pandemic across the 50 states. The dependent variable, how states have responded to the pandemic, is operationalized as openness of the economy and scope of restrictions. This study analyzes ten independent variables to predict state policy responses: region, percentage tourism GDP, population density, total number of COVID cases per state, per capita healthcare spending, per capita income, percent of 65+ population, percent of African American population, percent of Hispanic population, and percent of the 2016 Trump vote. The relationship between these independent variables and the dependent variables of state restriction and openness scores is analyzed through correlation and regression analysis. The role of region is analyzed through an ANOVA (analysis of variance). Out of the ten independent variables, three proved to be statistically significant in the multivariate model: percentage Trump vote, per capita health expenditure, and percent 65 and older. Percentage Trump vote maintained a positive relationship with lack of COVID-19 response, while per capita health expenditure and percentage 65+ showed a negative relationship.Region revealed that the South and the North Central have lower levels of COVID-19 response, than their North East and West counterparts. Lastly, percentage tourism of GDP, population density, percentage African American/Hispanic, and per capita income did not present statistically significant results in the multivariate model. The results of this study highlight the key predictors of a states COVID-19 pandemic response which can better inform federal policy debates for how to create and apply a nationwide response plan in the United States.Populism in EuropeErik KacprzykSponsor: Dr. Mandi Bates-BaileyScholars have offered two explanations for the recent success of populism in Europe: economic grievances and socio-cultural threat. In this study, I aim to disentangle these two explanations and discern which one is the stronger predictor for support for populism by examining the most recent elections in the 27 European Union member plus the United Kingdom, Norway, and Switzerland. Using the total vote share of populist parties in each countrys most recent election as the dependent variable and economic conditions, immigration, anti-religious minority sentiment, post-communism, and feelings of socio-cultural superiority as dependent variables, I perform a multivariate regression analysis which yields no significant relationships save for the dichotomous post-communism variable. Further studies with more observations and better data are needed to more clearly understand the phenomenon of populism in Europe.58'