The use of receiver operating characteristic curve analysis for academic progress and degree completion

College student retention and graduation are important to students, institutions, and the community. Institutions must commit to understanding why students persist and depart in order to address student success. As a result, institutions and governmental entities have increased the emphasis they place on using data to improve student success and degree completion. An abundance of research suggests that background factors (such as high school GPA and ACT score) combined with environmental factors (such as one's major and first semester GPA) are predictive of student success. However, the literature has yet to explore the value of ROC curve analysis as a statistical technique to improve decision making. The purpose of this study was to identify the variables that best predicted satisfactory academic progress and degree completion, and model the use of ROC curve analysis.;This study utilized a quantitative approach and secondary data from the institutional research office of a State University System institution in Florida. Logistic regression was successful in identifying factors that were predictive of each outcome. ROC curve analysis successfully discriminated the success/non-success groups based on predicted probability scores and a cumulative risk index. Students with three or more risk factors were less likely to make academic progress or graduate in four years. Students with two or more risk factors were less likely to graduate in six years.