Bayesian Analysis of Retention and Graduation of Female Students of Higher Education Institution: The Case of Hawassa University (HU), Ethiopia
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 2, March 2019, Pages: 47-66
Received: Apr. 1, 2019;
Accepted: May 23, 2019;
Published: Jun. 10, 2019
Views 16 Downloads 8
Tsega Kahsay Gebretekle, Department of Mathematics, Kotebe Metropolitan University, Addis Ababa, Ethiopia
Ayele Taye Goshu, Department of Mathematics, Kotebe Metropolitan University, Addis Ababa, Ethiopia
The study was conducted on female students who were 2005, 2006, 2007, and 2008 entries in the fields of Natural Science, Agriculture, and Social Science. From 1931 female students a sample of 605 was taken using stratified random sampling, Primary and secondary data were collected using questionnaire and analyzed using the Bayesian logistic regression analysis. The results showed that the percentage of graduation among 362 females who were enrolled in 2005, 2006, and 2007 was 72.1%. Similarly the retention rate among 243 females of 2008 entry was 75.7%. From the Bayesian logistic regression analyses, significant predicators of both graduation and retention were choice of field, preparatory average result, entrance exam score and first year cumulative GPA. Moreover pregnancy, organizing studying and leisure time, habit of chewing Khat, satisfaction with instructors, parent income, habit of smoking cigarette and using drugs, and feel safe to study at night in classrooms appeared as significant predictors of retention. The graduation rate and retention rate for the students who assigned to the field they did not choose were lower than that for those assigned to the field they chose. Those with first year CGPA less than 2.0 were having lower rates of graduation and retention than those having greater than 2.0. The graduation and retention rates for the students having higher preparatory average result and higher entrance exam score were higher than that for those having lower. The students having parents’ income less than 500 were less likely to retain than those having parents’ income greater than 1500. The retention rate for the students who were not satisfied with their instructors was lower than those were satisfied. The students who cannot organize their study and leisure time easily were less likely to retain than those can organize. In conclusion, the factors those mainly affect female students’ graduation and retention were more of academic variables; hence we recommend that assigning to the field they choose by their interest may help female students’ graduation and retention. The teaching method at secondary and preparatory schools should be designed to challenge and motivate them to adequately prepare them for Higher Education Institutions. Moreover, campus and Department administrators in collaboration with the students themselves and academic staff need to work hard to bring change in behavior, academics, and social aspects of female students at the University.
Tsega Kahsay Gebretekle,
Ayele Taye Goshu,
Bayesian Analysis of Retention and Graduation of Female Students of Higher Education Institution: The Case of Hawassa University (HU), Ethiopia, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 2,
2019, pp. 47-66.
Rose, P. (2003). Can Gender Equality in Education Be Attained?: Evidence from Ethiopia Background Paper for 2003 UNESCO Global Monitoring Report. Centre for International Education, University of Sussex.
Asresash Demise, Ruth Shinebaum, KassahunMelesse. (2002). The Problems of Female Students at Jimma University, Ethiopia, with Some Suggested Solutions. Ethiopian Journal of Health, 16 (3): pp 257-266.
Carmel, A., Gold S. (2007). The effects of course delivery modality on student satisfaction and retention and GPA in on-site vs. Hybrid courses. Journal of Distance Education, 8 (2), 1302–6488.
Foster, K. (2003). Libraries and Student Retention: Some Thoughts about the Issues and an Approach to Evaluation. Journal of SCONUL Newsletter: 28, pp. 12-16.
Stephenson, C., Derbenwick Miller, A., Alvarado, C., Barker, L., Barr, V., Camp, T., Frieze, C., Lewis, C., Cannon Mindell, E., Limbird, L., Richardson, D., Sahami, M., Villa, E., Walker, H., and Zweben, S. (2018). Retention in Computer Science Undergraduate Programs in the U. S.: Data Challenges and Promising Interventions. New York, NY. ACM.
Ferreira, MM., (2009). “Trends in women’s representation in science and engineering”, Journal of Women and Minorities in Science and Engineering, vol/issue: 15 (3), pp. 191-203.
Mastekaasa, A., Smeby, J., (2008) “Educational choice and persistence in male and female dominated fields”, Higher Education, vol/issue: 55 (6), pp. 189-202.
Military Leadership Diversity Commission. (2010). Differences in Promotion and Retention Rates by Race/Ethnicity and Gender: Considerations When Interpreting Overall Continuation Rates [Issue Paper #30]. Arlington, VA: Military Leadership Diversity Commission.
Mississippi University for Women website. (2019). MUW Graduation Rate & Retention Rates. Accessed from https://www.collegefactual.com/colleges/mississippi-university-for-women/academic-life/graduation-and-retention/ on May 15, 2019.
Zhang, G., Anderson, T. J., Ohland, M. W., Carter R. and Thorndyke, B. (2004). Identifying Factors Influencing Engineering Student Graduation: A Longitudinal and Cross-Institutional Study. Journal of Engineering Education, 93 (4), pp. 313-320.
Toolkit for Women. (2005). United Nations 3rd World Conference Nairobi. Accessed from www. Earthsummit2002.org/toolkits/women/un-doku/un-conf/nairobi.htm.
KassaShurke. (2008). Factors Affecting Females’ Participation in Education: The Case of TochaWoreda in SNNPR. Addis Ababa University. Accessed from http://hdl.handle.net/123456789/876.
Greenberger, M. D., Samuels, J., Chaudhry, N. K., Graves, F. G., Kaufmann, L., Keeley, T., Null, T. W., Seemeyer, L., Gupta, K., Kuznick, A., Petronko, K., Robinson, K., White, E., and Wong, K., with LeMair, L. M. (2007). When Girls Don’t Graduate We All Fail. National Women’s Law Center. Washington, DC 20036, www.nwlc.org.
Seyoum, Tefera. (1991). The Participation of Girls in Higher Education in Ethiopia: In the Proceeedings of the First University Seminar on Gender Issues in Ethiopia held in December 24-26. pp. 99-108. Institute of Ethiopian Studies, Addis Ababa University.
DemewezAdmassu, MehadiAbdo and TesfayeSemela. (2005). Impact of Varying Entry Behaviour on Students‟ Academic and Psychological Outcomes in Higher Education: The Case of PPC and FPC Students at Debub University. The Ethiopian Journal of Higher Education: 2 (2), pp 49-86.
TesfayeSemela. (2005). Higher Education Expansion and the Gender Question in Ethiopia: A Case Study of Women in a Public University. Proceedings of the Conference on the Future Direction of Higher Education in Ethiopia.
Mezick, E. M. (2007). Return on Investment: Libraries and Student Retention. Journal of Academic Librarianship: 33 (5), pp. 561-566.
Bean, J. and Eaton, S. B. (2001). The Psychology Underlying Successful Retention Practices. Journal of College Student Retention: Research, Theory and Practice. 3 (1), pp. 73 – 89.
Felder, R. M., Felder, G. N., Mauney, M., Hamrin, C. E., Jr., & Dietz, E. J. (1995). A Longitudinal Study of Engineering Student Performance and Retention. III. Gender Differences in Student Performance and Attitudes. Journal of Engineering Education: 84 (2), pp. 151–174.
Gupta, K. (2008). When Girls Don’t Graduate, We all Fail: Helping Girls Stay in School: Spotlight on Pregnant and Parenting Teens: NAPE/Women Work! Conference, Taryn Wilgus Null, National Women’s Law Center, Abby Kahn, Healthy Teen Network.
Central Statistical Agency (CSA). (2007). Demographics of Ethiopia.
Cochran, W. G. (1997). Sampling Techniques. (3rd Ed.). John Wiley and Sons, Inc, New York.
Al-Subaihi, A. A. (2003). Sample Size Determination: Influencing Factors and Calculation Strategies for Survey Research. Journal of Saudi Med J: 24 (4), pp. 323 – 330. Accessed from www.smj.org.sa, on July 5, 2010.
Stamps, D. K. (2006). Markov Chain Monte Carlo Methods for Regression Splines with a Penalized Acceptance Ration. Dissertation. 600. University of Missouri, St. Louis.
Geyer, C. J. (1992). On the convergence of Monte Carlo maximum likelihood calculations. Technical report 571, School of Statistics, University of Minnesota.
Merkle, E., Sheu, C., and Trisha, V. Z. (2005). Simulation – Based Bayesian Inference Using WinBUGS.