CHAPTER 3: THEORETICAL FRAMEWORK, METHODOLOGY, AND DATA
3.4. Summary
100 or hiatus” when the physical articulation and re-organization of Faculties to a single UKZN campus occurred. These measurement academic years also have the merit of catching 2008 which is the most stable and complete year for which complete student trends data are available. This does not however suggest that major changes and policies at UKZN did not occur out of these indicator years. Though the study failed to undertake a longitudinal analysis, the results are gained for five indicator academic years in terms of cross sections suggesting that trends across time can duly be discerned as discussed in the following Chapter 4.
The regression results are similar and available on request (these results are to be published in another follow up study).
The primary data are computed from the focus group discussions with students, academic and non- academic (administrators and support) staff members at UKZN, and various education stakeholders in Durban. Focus group discussions are important as they emphasize the variables this study cannot grasp and the ones that are beyond measurement.
101 analyses. OLS regression model in which the students’ final examination marks are used as the dependent variable when they are continuous variables (ratio data) was differentiated from the Logistic regression model in which the students’ final examination marks are used as the dependent variable when they are treated as discrete variables (dichotomous/rank order). Therefore, regression analysis that incorporates the OLS regression model and the Logistic regression model are experimented. The difference being that, OLS method attempts to predict the average value of students’ final examination marks in first-year undergraduate accounting and economics modules in the FMS (continuous dependent variable) by knowing the fixed values of the educational inputs selected amongst the three broad categories of the student characteristics discussed earlier (independent variables). In Logistic method, the dependent variable is transformed to a qualitative, discrete, categorical, or specifically dichotomous variable in the case of this study and Logistic estimation technique is used. Therefore, student performance in this study is considered either discrete or continuous and the results of both the OLS and Logistic estimation techniques are reported in this study as discussed in the following Chapter 4.
In addition to the OLS and Logistic methods, this study is interested in testing the whole question of whether total matric points (or APS) or selected designated matric subject scores are good predictors of students’ academic performance in the FMS. The strength or degree of linear association between achievement on matric subject scores and final examination marks at university is one that can best be researched and tested by mining statistically significant pair-wise correlation coefficients between total matric points (or APS) or selected designated matric subject scores and final examination marks achieved by students in the undergraduate accounting and economics modules in the FMS. This study is interested more specifically, in finding to what extent a matric Maths score or a matric English I or II score is a good predictor of student performance in first-year accounting and economics modules in the FMS to test the hypothesis that a student with a high matric Maths score or English score is more likely to perform well in introductory accounting and economics modules.
The difference between correlation and regression analyses is that, in correlation analysis the final examination marks achieved by students and selected predictors variables are treated symmetrically and assumed to be random, while in the linear regression model the final examination marks achieved by students and selected predictors variables are treated asymmetrically. The dependent variable (the final examination marks achieved by students) is assumed to be statistical, random or stochastic having a probability distribution whereas the selected predictors (independent) variables are assumed to have fixed values in repeated sampling and estimates of parameters of the model are obtained at these level (Gujarati, 1995).
102 The chapter proceeded to discuss qualitative research. Focus group discussions allow this study to deal with measurement challenges that would typically render the grasp or measurement of qualitative and empirically intractable determinants of student success infeasible.
Finally in the data section, this thesis acknowledges a sizeable number of ghost students across the five indicators academic years measured. The amount of investment wasted, both of school achievement and tuition fees by this category of students before dropping out makes it imperative to try to find out more about this category of students who are increasing the incidence of student attrition in the FMS. The focus group discussions, dealt with in Chapter 5, specifically tackled the whole question of poor student success, student attrition, and poor graduation or throughput rates.
Four key empirical analyses are, therefore, the salient features of this chapter. First, total matric points hypothesized to be a key predictor of student success is tested using the pair-wise correlations sweep across the five indicator academic years: 2004, 2005, 2006, 2007, and 2008. Second, this analysis is then broken down for a number of designated matric subject scores and the pair-wise correlations sweep looked at whether designated matric subject scores are good predictors of student success. Third, an estimation of the parameters of the econometric model is undertaken using the OLS regression analysis. The model attempts to predict the attainment of students in first-year accounting and economics modules– as measured by student’s actual final examination marks (as dependant variable) – from selected independent predictors variables. Four, estimation of the parameters of the econometric model is undertaken using the Logistic regression analysis. The model attempts to predict the attainment of students in first-year accounting and economics modules– as measured by student’s actual final examination pass or fail marks (as dependant variable) – from selected independent predictors variables. It is worth mentioning that results and findings presented in the following Chapter 4 are from active students: they actually received final examination marks, as opposed to those who dropped out. Both SPSS and Stata software are used for econometric analyses.
Results are discussed in Chapters 4 and 5. Each of the four analyses listed above are expanded in the following Chapter 4 which is devoted to discuss all the quantitative analysis based results while Chapter 5 is devoted to discuss all the qualitative analysis based results.
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