|dc.description.abstract||This dissertation investigated the variables associated with high-school dropout rates in a large metropolitan area in the southeastern United States. Data from 143 high schools with a combined population of 269,290 students was included. Variables included ethnicity, gender, school size, school location, special education status, limited English Proficiency status, and socioeconomic status. Multiple regression analysis was used to answer the following research questions.
1. Which variables—ethnicity, gender, school size, school location, special education status, socioeconomic status, and limited English proficiency status—are more likely to predict higher dropout rates among students who attend school in the large metropolitan area studied?
2. Which variables — ethnicity, gender, school size, school location, special education status, socioeconomic status, and limited English proficiency status —have the greatest impact on dropout rates?
Results from the multiple regression analysis revealed the variables gender, ethnicity, and school size are more likely to predict dropout rates. Furthermore, the variables having the greatest impact on dropout rates in the school districts included in this study were, in rank order, gender (male), ethnicity (Black and Hispanic), and school size (medium).
Since the results of this quantitative research study provide a means to predict dropout rates, legislators and school system personnel can use the regression formula to predict school dropout rates in order to prioritize the allocation of resources and focus on intervention efforts. Additionally, education specialists, practitioners, and school system personnel will have a better understanding of which student groups have the greatest impact on dropout rates and can tailor intervention strategies designed to help reduce the dropout rate.||