The purpose of this study was to analyze the relationship between dropout variables and the race and gender of high school students in a mid-south suburban school district. Data was collected on 353 dropouts and 316 graduates that attended one of the district’s eight high schools between the years 2006 - 2011. Variables selected for study included ethnicity, gender, special education classification, socioeconomic status, retention occurrences, absentee rates, behavioral infractions, and grade point average. Descriptive statistics, Correlations, Tests for Differences, and Logistic regression analysis were run to determine both the predictability of these variables and their relationship among the two ethnic and gender groups. The analysis also provided the answers to thirteen research questions posed. Results from the various analyses revealed the variable grade point average was the best predictor for dropout occurrences. In each Hierarchical Logistic Regression model run, grade point average was highly significant. In the absence of grade point average, however, the other identified dropout variables became significant depending on which specific ethnic and/or gender group was being analyzed. Since the results of this quantitative research provide a method for predicting dropout occurrences, both school district administrators and legislators could use a similar data collection and regression testing to predict dropout rates across this nation. Having this accurate knowledge would prove beneficial in establishing intervention programs, allocating resources for prevention, and implementing appropriate graduation policies. Additionally, educators and other practitioners can better comprehend the impact that these variables have upon specific gender and ethnic groups. To this end, educators will be able to pinpoint the areas of need and develop effective intervention strategies that will aid in reducing the dropout rate
