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Mertes, Scott J.; Hoover, Richard E. – Community College Journal of Research and Practice, 2014
Retention is a complex issue of great importance to community colleges. Several retention models have been developed to help explain this phenomenon. However, these models typically have used four-year college and university environments to build their foundations. Several researchers have attempted to identify predictor variables using…
Descriptors: Community Colleges, Predictor Variables, College Freshmen, Academic Persistence
Eickhoff, Mary Ann – ProQuest LLC, 2016
There is currently a nursing shortage in the United States. By 2022, the Bureau of Labor Statistics (BLS) expects, the number of job openings for Practical Nurses (PN) will be 168,500, an increase of 25% over 2012 (BLS, 2014). Nursing education does not currently meet present, much less future needs. Nursing programs have limited space; according…
Descriptors: Nursing Students, Predictor Variables, Success, Licensing Examinations (Professions)
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Walker, Eddie G., II – Journal of Higher Education Policy and Management, 2016
The accountability of colleges and universities is a high priority for those making policy decisions. The purpose of this study was to determine institutional characteristics predicting retention rates, graduation rates and transfer-out rates using publicly available data from the US Department of Education. Using regression analysis, it was…
Descriptors: Higher Education, Predictive Measurement, Predictive Validity, Prediction
Synco, Tracee M. – ProQuest LLC, 2012
Tinto, Astin and countless others have researched the retention and attrition of students from college for more than thirty years. However, the six year graduation rate for all first-time full-time freshmen for the 2002 cohort was 57%. This study sought to determine the retention variables that predicted continued enrollment of entering freshmen…
Descriptors: College Entrance Examinations, School Holding Power, Grade Point Average, Supplementary Education
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Cox, G. W.; Hughes, W. E., Jr.; Etzkorn, L. H.; Weisskopf, M. E. – IEEE Transactions on Education, 2009
This paper presents the results of an analysis of indicators that can be used to predict whether a student will succeed in a Computer Science Ph.D. program. The analysis was conducted by studying the records of 75 students who have been in the Computer Science Ph.D. program of the University of Alabama in Huntsville. Seventy-seven variables were…
Descriptors: Case Studies, Prediction, Computer Science Education, Doctoral Degrees