ETD Collection
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Item Modelling learner discipline in a public school(2016) Ramokgadi, Mabala WilliamThe study explores the application of statistical methods to determine factors influencing learners to commit offences at school. Focus is on the time taken to commit a second offence and the number of repeat offences committed by learners after the first offence. The survival time from the first offence to a second offence is analysed by using Kaplan-Meier estimate of the survival function, the tests for equality of survivor functions, the Cox proportional hazards model, and the parametric hazards models. The number of repeat offences is analysed by fitting the Poisson regression model and Negative Binomial regression model. As many learners are expected not to offend again, the Zero Inflated Poisson (ZIP) model is also fitted to determine which factors influence learners not to commit a repeat offence. Factors that are related to the school environment were used in the study of learner offence. The categorical variables are learner grade (class), gender, home location, parental involvement, repeating a grade, suspected substance abuse, and hostel residence. The learner performance in the three selected subjects were used as continuous variables. The results of the analysis should also assist the schools in assessing the effectiveness of other forms of punishment since corporal punishment was abolished.Item An empirical evaluation of the Altman (1968) failure prediction model on South African JSE listed companies(2013-03-18) Rama, Kavir D.Credit has become very important in the global economy (Cynamon and Fazzari, 2008). The Altman (1968) failure prediction model, or derivatives thereof, are often used in the identification and selection of financially distressed companies as it is recognized as one of the most reliable in predicting company failure (Eidleman, 1995). Failure of a firm can cause substantial losses to creditors and shareholders, therefore it is important, to detect company failure as early as possible. This research report empirically tests the Altman (1968) failure prediction model on 227 South African JSE listed companies using data from the 2008 financial year to calculate the Z-score within the model, and measuring success or failure of firms in the 2009 and 2010 years. The results indicate that the Altman (1968) model is a viable tool in predicting company failure for firms with positive Z-scores, and where Z-scores do not fall into the range of uncertainty as specified. The results also suggest that the model is not reliable when the Z–scores are negative or when they are in the range of uncertainty (between 2.99 and 1.81). If one is able to predict firm failure in advance, it should be possible for management to take steps to avert such an occurrence (Deakin, 1972; Keasey and Watson, 1991; Platt and Platt, 2002).Item Simultaneous normalisation as an approach to establish equivalence in cross-cultural marketing research(2008-09-03T12:35:29Z) Strasheim, CatharinaSince bias threatens the validity of a study, it should be avoided where possible. Across all phases of a research project, bias could be introduced, and in most situations the researcher has reasonable control over processes that may be the source of bias. However, within a quantitative research context in social sciences, where the opinions, attitudes and intentions of people are often sought, response styles patterns due to cultural background, for example, are not within the control of the researcher. Typical response style patterns include acquiescence bias, a tendency to be agreeable to statements, which could be more prevalent in certain cultural groups than other. Another response style pattern is extremity ratings, where respondents tend to avoid the middle categories and mark the scale extremes. When practitioners sample respondents from different cultural groups, it is difficult, and depending on the research design, sometimes impossible to know whether significant differences are an artefact of substantive differences, or of differences in response styles. Adjusting scores for bias has a significant effect on the interpretation of research findings. To correct for bias, the method most commonly used to adjust scores within each cultural group is standardisation. In this research, SIMNORM, a target distribution estimation approach was used for the simultaneous estimation of a class of non-linear transformation functions that transform the composite scores within each cultural group to a standard normal distribution. SIMNORM was found to perform better than standardisation to obtain equivalence across cultural groups when composite scores are used. In addition, SIMITNORM, an item normalisation approach was developed, which is a simultaneous non-linear transformation of item scores to a standard normal target distribution. The results of seven nested SIMITNORM models were compared to raw item scores and standardised scores, using a multi-group confirmatory factor analysis approach, a method that is suitable to test for construct equivalence, metric equivalence and scalar equivalence. SIMITNORM had significant advantages over standardisation as an approach to obtain equivalence over items in a set of data where bias is present.