Timely Doctoral Completion Rates in Five Fields: A Two-Part Study

• Main Author: Miller, Angela
• Format: Others
• Published: Scholar Commons 2013
• Subjects: Attrition; Completion; Doctoral; Faculty; Graduate Student; Program; Education; Educational Administration and Supervision; Higher Education and Teaching
• Online Access: http://scholarcommons.usf.edu/etd/4827; http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=6023&context=etd

Roughly half of all doctoral students who begin a program do not continue through graduation, and many of them face significant financial losses and emotional burdens as a result. Although this completion rate has stayed fairly constant for the past few decades, it has recently gained attention on a national level. In 2011, the National Research Council published the Assessment of Research Doctorate Programs in the United States, and provided a wealth of data on over 5,000 programs in 212 universities. This study used that dataset to examine the relationship between timely doctoral completion rates and 22 Program, Faculty and Student variables in the following five fields: Neuroscience, Chemical Engineering, Physics, Economics and English. The study also observed differences between programs with high completion rates and those with low completion rates in each field. The purpose of the study was to (1) determine which variables are significant in predicting doctoral completion rates, (2) discover if measurable differences exist between high and low completing programs, and (3) reveal the usefulness of collecting objective data in doctoral programs in order to assist doctoral programs as they create strategies to lower attrition rates. The sample in the study inculded over 10,000 students and over 12,000 faculty members from 365 programs in the five fields. The 22 variables in the study were: Availability of a Graduate Orientation, Existence of an Annual Student Review, Number of Academic Support Activities, Average First Year Enrollment Size, Total Number of Enrolled Students, Percentage of First Year Students with Full Financial Support, Percentage of Students that are Teaching Assistants, Percentage that are Research Assistants, Median Time to Degree, Average GRE Score, Percentage of Students that are Married, Percentage of Students with Dependents, Percentage of Students with Mentors, Average Satisfaction Rating, Average Sense of Belonging Rating, Percentage of Students that are Females, Percentage that are Minorities, Percentage of Faculty that are Females, Percentage that are Minorities, Percentage of Faculty with Grants, Total Number of Faculty, and Faculty to Student Ratio. All of the regression lines were significant at the p¡Ü.05 level. Furthermore, for Economics programs in the sample, 80%#37; of the variance in timely completion rates was explained by this specific set of variables, and the same set of variables explained between 40-66%#37; of the variance in timely completion rates for the other four fields in the study. When looking at all the programs in the dataset, the following variables were significantly related to timely completion rates: number of academic support activities, percentage of students with full financial support, 1st year size, annual student review, student satisfaction rating, number of faculty, percentage of students with teaching assistantships, percentage of faculty with grants, and time to degree. Between the high- and low-completion groups, the following variables were significantly different in the All Programs group: Student satisfaction rating, percentage of students with children, percentage of students with full financial support, number of academic support activities, time to degree, and percentage of students with teaching assistantships. Separate findings and implications are presented for each of the five fields (Neuroscience, Chemical Engineering, Physics, Economics and English). Program leaders and other interested parties can now use these results to focus their attention on significant variables as they create strategies for improving completion rates within their respective fields.