Type of Document Dissertation Author Burton, Clayton Clendenin Author's Email Address email@example.com URN etd-04092011-214746 Title The Influence of Budget Allocation by Function on Student Retention of Public Community Colleges in the Sixteen States in the Southern Region Education Board. Degree Doctor of Philosophy (Ph.D.) Department Human Resource Education Workforce Development Advisory Committee
Advisor Name Title Kotrilk, Joe Committee Chair Burnett, Michael Committee Member DeLaune, Laura Committee Member Machtmes, Krisanna Committee Member Stephen, Clifford Committee Member Keywords
- higher education
- community college
- Southern Region Education Board (SREB)
- Integrated Postsecondary Education Data System (IP
Date of Defense 2011-01-19 Availability unrestricted AbstractStakeholders of higher education expect a positive return for their investment. One of the measures of success for higher education is persistence; one of the measures of persistence is retention rate. One segment of higher education, community colleges, receives a significant outlay of public resources in terms of government appropriations, student financial aid, and individual investment. This study follows the framework of Ryan (2004) and Gansemer-Topf and Schuh (2006) who successfully created models using the allocation of financial resources to predict graduation and retention rates for four-year colleges. Using data from the U. S. Department of Educationís Integrated Postsecondary Education Data System (IPEDS), this study examined the relationship between the allocation of financial resources and selected institutional demographics with freshman retention for community colleges. This study found no relationship between financial allocation and selected institutional demographics with freshman retention. In addition, this study found no predictive model using financial allocation to determine freshman retention, but found a minimally effective model by adding selected institutional characteristics as predictors. Further research adding additional predictors may discover an effective predictive model for freshman retention.
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