b'Corruption in Africa: What Factors Predict the Levels of Corruption across Countries?Jayla Elizabeth JacksonSponsor: Dr. James T. LaPlantThis quantitative study examines the research question what factors predict the levels of corruption across countries in Africa?. The independent variables in this study are urban population, high school graduation rate, political regime, net flows as a share of GDP, GDP based on natural resource rents, and media freedom score. The dependent variable is the Corruption Perception Index (CPI). Correlation and regression analysis as well as ANOVA (analysis of variance) examine the relationship between the dependent and the six independent variables. The multivariate regression analysis found that three out of the six independent variables were statistically significant: media freedom, high school graduation rate, and GDP based on natural resource rents. The correlation and regression analyses revealed that the high school graduation rate is a statistically significant positive predictor of transparency (less corruption) in African nations, while media freedom and GDP based on natural resource rents are significant negative predictors of transparency (greater corruption).2020 Presidential Elections: What Factors Predict the Vote by County in Florida for Donald J. Trump in the 2020 Presidential ElectionsDestiny LewisSponsor: Dr. James T. LaPlantThis study examines the research question of what factors predict the vote by county in Florida for Donald Trump in the 2020 presidential election? The dependent variable is the percentage of the vote by county for Trump. This study will look at nine independent variables: the 2016 Trump vote in Florida, percentage of a county that is African American, percentage of the county that is Latinx, the countys unemployment rate, percent of the population with a bachelors degree, percentage of the population above 65, median income, COVID-19 infection rate, and population density. The relationship between the independent variables and the dependent variable will be analyzed through correlation and regression analysis. The bivariate regression analysis reveals a powerful relationship between the Trump vote in 2016 and the Trump vote in 2020 across the sixty-seven counties of Florida. Five of the independent variables were statistically significant in the multivariate regression analysis: percentage of the county that is African American, the percentage of the county that is Latinx, per capita COVID-19 infection rate, median household income, and the percentage of a county that has a bachelors degree. The 2016 vote for President Trump, per capita COIVD-19 infection rate, and median household income have a positive relationship with the 2020 Trump vote, while percentage of the county that is African American, percentage of the county that is Latinx, and percentage of a county with a college degree have a negative relationship with the dependent variable. The results of this study highlight key indicators of which counties supported President Trump in his ultimately failed attempt to get re-elected.59'