Abstract

MYBURGH, Chris; POGGENPOEL, Marie  and  FOURIE, Cornelius M.. Predictors of aggression of university students. Health SA Gesondheid (Online) [online]. 2020, vol.25, pp.1-10. ISSN 2071-9736.  http://dx.doi.org/10.4102/hsag.v25i0.1096.

BACKGROUND: Post-secondary education forms the backbone of delivering high-powered persons in a country. Students are subjected to high levels of pressure to achieve success. This often promotes aggression towards self, others and even the environment. Predicting aggression is important as this can assist in managing aggression and the facilitation of the mental health of students. Little research has been published on the prediction of aggression in a university AIM: To describe the prediction of aggression of students in one faculty in a university SETTING: Students in one faculty in a university METHODS: A deductive quantitative methodology was applied. Multivariate statistical techniques were used to answer the research questions. An online survey was conducted by using a questionnaire that comprised items related to biographic, personality and aggression aspects. Data were analysed by calculating Cronbach's alpha values, various factor analyses (principal component analysis) and several multiple regression analyses to identify and describe the predictors of aggression RESULTS: Findings reflect that aggression can be predicted by intra- and interpersonal variables, such as 'positive inclination towards others', 'positive inclination towards self' and 'acting responsibly towards self.' Aggression is lower when a student's positive inclination towards others is higher and towards self is lower and when a student acts with greater responsibility towards self CONCLUSION: Students should understand and manage their own aggression. Overall, the findings showed that students are seemingly 'adjusted' conformists using an external locus of control. The facilitation of an internal locus of control and autonomous behaviour is imperative

Keywords : aggression; descriptive; exploration; factor analyses; multivariate regression; predictors; university students.

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