Institutions of higher education invest a significant amount of resources in recruiting, processing, and advising new students. When students leave the institution prior to graduation, the university loses considerable revenues. Therefore, it is important for colleges and universities to refine their student recruitment and retention strategies to avoid forgone revenues by predicting which students are likely to need particular types of support services (DeBerard et al, 2004). Current models of prediction utilize extensive surveys that are impractical to administer each term, and they do not adequately identify the broad range of student persistence categories needed in order to gain a greater understanding of persistence behavior (Davidson, 2005; Porter, 2000; Tinto, 1975). This study created a linear discriminant function to predict a broad range of persistence levels of first-time freshmen students at California State University, Bakersfield (CSUB), by identifying pre-enrollment and early enrollment student variables that existed within the database of the University. This information may be used to develop support service strategies to better assist incoming students predicted to have a greater probability of not persisting.