What is Predictive Models of Retention

Student attrition, although some to be expected, comes at a high cost. Failure to complete studies is recognized as a personal loss for the individual, an economic loss for the universities, and an intellectual loss for society. As educational institutions increasingly develop and support online education programs to serve the instructional needs of adult population in a growing and ever changing global economy, student attrition becomes an even more significant issue. While national statistics for completion rates of distance education students are not easily available, dropout rates are believed to be 10-20% higher than for in-person learning (Carr 2000; Frankola 2001). Some scholars have indicated that, depending on the program, dropout rates for distance education are much higher, in the 30-50% range (Moore & Kearsley, 1996; Lorenzetti 2002). Whatever the attrition rate is, the reality is that too many students do not persist in their endeavor to achieve a degree in higher education although they made a conscious decision to enroll in higher education and took the steps needed to attend graduate school. While extensive research efforts have been used to develop and improve theoretical models of student retention or persistence, a concern of many administrators remains the ability to predict as early as possible the likelihood of a student dropping out of school. In light of research findings that the strongest predictor of graduation is a student’s conformity with the characteristics of those who have graduated from the same institution or program previously (Ash, 2004; Mansour, 1994), the purpose of this chapter is to determine the profile of students who are being retained and those who drop-out, by employing data obtained as early as possible in the application and matriculation process.