b'Exploring Predictors of College GraduationRates at Colleges and Universities across GeorgiaTia M. BrownSponsor: Dr. LaPlantThe purpose of this quantitative study is to examine the factors affecting graduation rates at colleges and universities across Georgia. The study will look at six factors believed to affect graduation rates at 50 institutions across the state. The six independent variables examined are in-state tuition cost, percentage of female students, percentage of white students, percentage of student body on Pell grants, percentage of application admitted, and school type. The impact of these independent variables on the dependent variable, graduation rate, are examined through a correlation analysis, box plot, scatterplot, and multivariate analysis. The study finds percentage of student body on Pell grants, percentage of applications admitted, and in-state tuition are highly significant predicators of graduation rates. The remaining variables, type of school, percentage of white students, and percentage of female students, were not statistically significant. Private institutions had slightly higher graduation rates than public institutions, but the difference was not statistically significant.2021 California Recall Election:What are the Key Predictors of the Recall Vote of Governor Gavin Newsom across the Counties of CaliforniaJacob C. RyanSponsor: Dr. LaPlantThe purpose of this quantitative study is to examine the key predictors of the yes vote to recall California governor Gavin Newsom. This study evaluates the factors that led to the unusual event of a recall election in the middle of the Governors elected term by reviewing data across the 58 counties of California. The study analyzed eight independent variables: percentage African American population, percentage Latino population, county unemployment rates, percent of the population who have a college degree, percent of the vote for Trump in the 2020 presidential election, population density per capita, the number of COVID-19 cases per 100,000 people, and a nominal level region variable. The impact of these variables on the dependent variable, the percentage of the vote of yes to recall Governor Newsom, is determined through a correlation analysis, scatterplots, boxplot, and multivariate regression analysis. Three of the eight variables proved to be highly significant. The percent of the vote for Trump in 2020 and the percent of those with a college degree per county were found to be highly significant at p.01. The region variable was found significant at p.05 when an independent t-test was conducted and found the North region to have higher values for the dependent variable. Interestingly, population density and number of COVID-19 cases were found significant at the bivariate level but washed out in multivariate analysis. The African American population and unemployment rate variables had minimal influence on the recall election. While not significant at the bivariate level, the Latino population variable was found significant in the multivariate model. The variable was found to have a negative relationship with the dependent variable. Newsoms recall election was a significant and unusual political event which he will carry with him into his 2022 reelection campaign. 53'