The prediction of the academic performance of MBA students by means of specific aptitudes And competencies

dc.contributor.authorKotzè, M.
dc.contributor.authorGriessel, L.
dc.contributor.otherCentral University of Technology, Free State, Bloemfontein
dc.date.accessioned2015-09-22T09:48:06Z
dc.date.available2015-09-22T09:48:06Z
dc.date.issued2008
dc.date.issued2008
dc.descriptionPublished Articleen_US
dc.description.abstractThe Council on Higher Education (CHE) (2004) states that graduation rates across all provider types of MBA qualifications in South Africa are not very high. Various studies have reported that, in order to address poor throughput rates, one of the important aspects that needs to be addressed, is the criteria used to select students. The purpose of this study was to identify valid predictors and measures of the academic performance of MBA students. Multiple regression analysis was used to determine the significance of different competencies and aptitudes in predicting academic success. The sample consisted of 135 MBA students from a South African School of Management. The results show that certain aptitudes and competencies, namely numerical aptitude, personal motivation, verbal aptitude, and resilience, contributed statistically significant to academic success.en_US
dc.format.extent117 605 bytes, 1 file
dc.format.mimetypeApplication/PDF
dc.identifier.issn16844998
dc.identifier.urihttp://hdl.handle.net/11462/505
dc.language.isoen_USen_US
dc.publisherJournal for New Generation Sciences, Vol 6, Issue 2: Central University of Technology, Free State, Bloemfontein
dc.relation.ispartofseriesJournal for New Generation Sciences;Vol 6, Issue 2
dc.rights.holderCentral University of Technology, Free State, Bloemfontein
dc.subjectMBA graduation ratesen_US
dc.subjectMBA selection criteriaen_US
dc.subjectPredictors and measures of academic performanceen_US
dc.subjectAptitudes and competenciesen_US
dc.titleThe prediction of the academic performance of MBA students by means of specific aptitudes And competenciesen_US
dc.typeArticleen_US

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