CHAPTER 4: EMPIRICAL RESULTS AND DISCUSSIONS OF QUANTITATIVE ANALYSIS
4.4. Discussion of empirical findings and hypotheses
135 For the 2008 academic year, holding all other variables at certain value, we will see 216 percent increase in the odds ratio of getting a final examination pass mark in ECON102 for a one-unit increase in ECON101 since exp(1.15) = 3.16. Holding all other variables at certain value, we will see 101 percent increase in the odds ratio of getting a final examination pass mark in ECON102 for a one-unit increase in the HG in Matric English since exp(0.70) = 2.01. Holding all other variables at certain value, we will see 90 percent increase in the odds ratio of getting a final examination pass mark in ECON102 for a one-unit increase in English as home first language since exp(0.64) = 1.90. Holding all other variables at certain value, we will see 189 percent increase in the odds ratio of getting a final examination pass mark in ECON102 for a one-unit increase in ACCT102 since exp(1.06) = 2.89.
136
137 In many instances the estimated coefficients that attained statistical significance were found to be of the expected signs conforming with the a priori expectations. Explaining why other coefficients were found to be of signs not conforming with the a priori expectations is purely speculative and the researcher felt that it is pointless to speculate on these signs. Some of the salient variables are found from the OLS regression analysis but not in the Logistic regression analysis and vice versa. Although some of the other variables had coefficients with the expected signs, none proved to be statistically significant at the 0.01, 0.05, or 0.10 levels to be considered as salient determinants of student success. Salient variables are discussed together16 since either one of the two models can be used for prediction because as has been discussed, the accuracy of prediction of both models is good and they are both fairly equally used in the existing literature. A brief discussion of salient predictors of students’ performance in the FMS follows.
TOTAL MATRIC POINTS AND MATRIC SUBJECT SCORES
As hypothesized, students are admitted with total matric points (or APS) calculated on their proficiency in matric subject scores at school leaving level. With certain minima, matric Maths score and English score are designated in the minimum requirements for admission at UKZN. The key finding is that total matric points (or APS) has positive causal effects (31 percent in relative frequency) on students’ performance for first-year accounting and ECON101 modules only. This trend is consistent over the years. Thus, total matric point (or APS) is, indeed, a predictor of university student success.
Evidence emanating from the empirical analysis indicates that matric Maths scores have positive causal effects (31 percent in relative frequency) on student performance for all the first-year accounting and economics modules. Matric Accounting scores have positive causal effects (13 percent in relative frequency) on student performance for all the first-year accounting modules only. This trend is consistent over the years.
In the Logistic regression analysis, total matric points equal or above 36 has causal effects (13 percent in relative frequency) on students’ performance for ACCT101 only. HG symbol D in matric Maths has causal effects (13 percent in relative frequency) on students’ performance for ACCT101 and ECON101 only. This trend is also consistent over the years.
16 OLS and logistic coefficients are not directly comparable as discussed in this study, since in the OLS model, the dependent variable is a continuous student success, while in the logit model it is a discrete student success.
138 This is interesting as evidence emanating from the empirical analysis indicates that, total matric points and matric Maths scores were confirmed as weak predictors of university student success at undergraduate level in correlations sweep and when data were fitted in the educational production functions. Of interest to this study is that, evidence emanating from the empirical analysis reveals that good total matric points and quantitative method skills help students do well in accountancy and economics modules in the FMS. Thus, premised upon this finding, the admission process is expected to play a vital role in highlighting the differences in the pool of student populations being educated in the FMS, and this must be borne in mind when student performance is interpreted and discussed in Chapter 6.
RACE OF THE STUDENT
As shown by descriptive statistics, for the entire sample of first-year students presented earlier, black Africans and Indians constitute the majority in the FMS. The race of the student has causal effects on students’ performance (25 percent in relative frequency). As hypothesized, the predicted students’ final examination marks for all the first-year accounting and economics modules would be higher for white students than for non-whites holding all other variables constant. This trend is also consistent over the years.
HOME FIRST LANGUAGE
There are some positive linear relationships between English as the home first language and students’
performance in first-year accounting and economics modules (38 percent in relative frequency). As hypothesized, English as the home first language has causal effects on first-year accounting and ECON102 modules but not on ECON101. This trend is consistent over the years. Of interest to this study is that, evidence emanating from the empirical analysis reveals that proficiency in English helps students do well in accountancy and economics modules in the FMS.
AGE OF THE STUDENT
As hypothesized, the age of student has negative causal effects (44 percent, the highest relative frequency in the Table 4-32) on students’ performance in first-year accounting and economics modules. This is consistent across the accountancy and economics modules and over the years.
139 Pair-wise correlations sweep between, on the one hand, the age of the student at the point of admission into the FMS, and on the other the hand, student performance in first-year accounting and economics modules across the same five academic years were tested. A perusal of results (See Appendix B) reveals that the student performance in first-year accounting and economics modules is strongly correlated with their age at the point of admission into the FMS across the five academic years. That is, the predicted final examination marks for younger students would be higher than for older students, holding all other variables constant.
GENDER OF THE STUDENT
As hypothesized, the gender of the student is intricately related to the student’s performance suggesting that a crude measure of gender of the student does not have a significant causal effect (zero percent in relative frequency) on students’ performance. Premised upon the empirical results, it is not, however, possible to draw any strong conclusion regarding the effectiveness of gender of the student. Male students seem to not perform better than female students and vice versa. Therefore, gender of the student in the FMS is to a large extent fixed. Even without any additional information, this finding provides strong evidence that important educational inputs (i.e.
characteristics of the student at the time of admission) can be influenced after the time of university entrance in the educational production function regardless of the gender of the students.
INTERACTION EFFECTS
As hypothesized, the interaction variables capturing students who coincidently have different attributes have causal effects on students’ performance. For example, the predicted final examination marks in ECON101 for students who declared having English as their home first language sorted by matric Maths at the school leaving level would be higher than others who do not have these attributes. In the Logistic regression analysis, positive causal effects in ACCT101 would stem from students having English as their home first language sorted by matric Accounting at the school leaving level. The predicted final examination marks in ECON102 for white students sorted by matric Maths would be higher than those who do not have these attributes. However, this trend is not consistent over the years (6 percent in relative frequency).
140 It is worth mentioning that there is a relationship between the students’ academic performance and other modules taught concurrently at university. In the OLS and Logistic regression analyses, the student success in the ECON101 module has been found to have causal effects (38 percent in relative frequency) on their success in the ACCT101 and ECON102 modules. The student success in the ACCT101 (25 percent in relative frequency), and MATHS134 and STAT181 modules (both have 13 percent in relative frequency) has also been found to have causal effects on the student success in the ACCT102 module. The students’
final examination marks in ACCT101, ISTN101 (13 percent in relative frequency), and the quantitative method MATHS134 modules have been found to have causal effects on the student success in the ECON101 module. This possibly indicates that first-year students who pass do better in both the accountancy and economics modules.
In the Logistic regression analysis the student success in the MATHS134 module has been found to have causal effects on the odds ratio of success in the ECON101 module. The students’ final examination marks in the ECON101 and ISTN101 modules have been found to have causal effects on the odds ratio of success in the ECON102 module. For the other first-year modules at the university level taught concurrently with economics, this possibly means that knowledge of Information Systems and Technology 101 helped students to understand Economics 101 and Economics 102 better. The results indicate that there is a fairly consistent relationship between students’ academic performance in ECON101 and ECON102 modules.
Of interest to this study is that, evidence emanating from the empirical analysis reveals that, although the ECON101 module is not a prerequisite for ECON102, students who do better in ECON101 are more likely to also do better in the ECON102 module. Knowledge of Information Systems and Technology for business and having well-rounded quantitative MATHS134 skills helped improve final examination marks in the ECON101 and ECON102 modules. These relationships appear to be fairly consistent across the semesters measured. These findings have a variety of education policy implications that are discussed in Chapter 6.