Admission Criteria for Educational Leadership Doctoral Students in One U.S. Doctoral Program
The purpose of this study was to explore relationships between preadmission criteria and doctoral student performance ratings and to develop a model to predict student persistence in one doctoral program of educational leadership.
Individuals responsible for program admission decisions have a responsibility to minimize bias in the candidate selection process. Despite an interest in doctoral degree completion, few researchers have examined preadmission criteria and the ability to predict doctoral student performance, particularly in education programs.
Preadmission variables and postacceptance performance ratings were used in this cross-sectional predictive study (Type 5; Johnson, 2001) of 102 doctoral students in one educational leadership program. Analyses included descriptive statistics, a Pearson r correlation matrix, and predictive discriminant analysis.
In addition to strengthening the extant literature base, we attempted to respond to the charge levied by other researchers for faculty members in educational preparation programs to reassess current practices used to recruit and retain students.
Using predictive discriminant analysis, we determined that separate models for students of color and White students most accurately predicted program performance, indicating that a one-size fits all approach was not optimal. The GRE-Q and undergraduate GPA were useful predictors of doctoral student persistence. Additionally, the GRE-V and graduate GPA were also useful predictors but differentially so for students of color and White students.
We found value in using the GPA and GRE in admission decisions with some modifications. Programs directors are advised to evaluate their own selection processes to understand the utility of their preadmission criteria.
Although the functions that worked best in predicting continuance were grouped by ethnicity in this study for our students, future researchers might consider disaggregation by gender or some other characteristic to optimally identify a model specific to the student groups represented in their sample.
Working from an activist stance, we use our awareness of the positive correlation between degree attainment and socio-economic mobility in the United States, coupled with the existing realities of students of color who seek access to a space within the dominant culture, to urge admission committees to evaluate closely the variables used in their admission selection and to understand to what extent the selection process results in a fair selection across student groups.
Future studies could be conducted to understand why these differences exist. Other variables for future researchers to consider include time since the candidates obtained their master’s and bachelor’s degrees, the length of time to obtain those degrees, and the type of degree obtained.