Academic journal article
By Gomez, Doris
Academy of Educational Leadership Journal , Vol. 17, No. 2
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 universities, and an intellectual loss for society. 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 students dropping out of school.
Research findings suggest 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). Institutions routinely collect a broad array of information on their students' backgrounds, socioeconomic status, past academic achievement, social involvement, and even personal characteristics. All factors that align well with the major theoretical models of student retention (Ash, 2004; Astin, 1984; Bean, 1985; Mansour, 1994; Tinto 1987, 1993). Several researchers therefore contend to make institution-specific predictions about retention and attrition based upon the increasing amount of student assessment data that are being collected by institutions of higher education (Seidman, 1995; Johnson, 1997; Murtaugh, Burns & Schuster, 1999). Their research indicates that analysis of readily available student data specific to a particular university and program can, indeed, be a valid predictor for student persistence and retention. Johnson (1997) suggested that each institution create its own predictor equations based on the characteristics of students who have succeeded in the past. Knowledge of students who are most likely to succeed and who are at risk to drop out may provide administrators and educators with the information necessary to develop strategies that encourage, guide, and motivate students through to degree completion. Once specific characteristics or tendencies are recognized, effort can be directed toward the development of programs and practices to help students overcome weaknesses and encourage a greater level of persistence.
Following these recommendations, this present study employed the analysis of secondary and program specific data to examine the predictive impact of student characteristics on persistence in an online doctoral leadership program. In the causal-comparative, ex-facto study, a logistic regression analysis was used to predict retention probability and identify student profiles with a higher likelihood of leaving the program prematurely. The sample for this study included doctoral students who enrolled in a multi-disciplinary online doctoral program in organizational and in strategic leadership at a private graduate university. Data for this study was collected from students who entered the program beginning in 1997 to 2006 and have since either dropped out or graduated. The subjects of this study are career professionals in various for-profit and non- profit organizations and range in ages from mid-twenties to late fifties. A total sample size of 303 students represented the full population of incoming students for the doctoral program out of whom 179 graduated and 124 attrited. In the graduated group, 113 were male and 66 were female. In the attrited group, 86 were male and 38 were female.
The literature review provided the justification for the selection of the independent variables used in the study. Each independent variable chosen has a theoretical relationship to retention. Graduation, an accepted standard of academic achievement, was used as the dependent variable in exploring the study questions. By utilizing the institution's database system, the following student demographic data was obtained and used as independent variables in this study:
Master's Level Grade Point Average (MGPA)
Application Summary Score (APSS)
Critical Thinking--measured by the Watson Glaser Critical Thinking Assessment (WGCTA)
Effective Leadership Behavior--measured through the Leadership Practices Inventory (LPI)
Challenging the Process (LPI-CHALL)
Inspiring a Shared Vision (LPI-INSP)
Enabling Others to Act (LPI-ENAB)
Modeling the Way (LPI-MODL)
Encouraging the Heart (LPI-ENC)
Psychological Type--based on the Myers-Briggs Type Indicator (MBTI)
Each of these variables can be associated with the concept of persistence and relate to characteristics that already existed at the time of matriculation of the student. …