5.3 Comparison of Sample Survey with the main Database
5.3.1 Livelihood Assets at the University Stage
The survey sample focused on disadvantaged students (low quintile students) and this has higher percentage of students from quintile 1 compared to other quintiles. There were more students from quintile 1 (41.5%) and quintiles 2 and 3 had 29.3% each. Table 35 below provides an overview of these results. From the main database we gather that quintile distribution increased as one goes up the quintile categories. In other words there are more students from quintiles 4 and 5 than in quintiles 1 and 2 (see table 6 in chapter four). Further, the quintile factor had a significant impact on the academic progress of students. The survey sample picked students from quintiles 1 – 3 only as in the table below.
Table 35 Quintile Frequenc
y Percent
Valid Percent
Cumulative Percent
Valid 1.00 17 41.5 41.5 41.5
2.00 12 29.3 29.3 70.7
3.00 12 29.3 29.3 100.0
Total 41 100.0 100.0
5.3.1.2 Gender and GPA
About two thirds (68.3%) of the students in the sample were males (see table 36 below).
Table 36 Gender
Gender Frequency Percent
Male 28 68.3
Female 13 31.7
Total 41 100.0
The reasons for this vary, with the most significant being that females were particularly reluctant to disclose sensitive information such as academic records which contain GPAs.
They felt that disclosing this information to a stranger could lead to it being used for dubious purposes other than research. Some could have been worried about their performance and therefore would not allow anyone access to their academic records. The other reason is that I, a male, administered the questionnaire myself and my research assistants were also male.
This could have resulted in some mistrust. Some students actually lied about their student numbers and their questionnaires had to be discarded. The difference in the ratio of females
to males could also be attributed to the low number of females who come from low quintile schools. This could be compounded by the fact that low quintile students tend to come from big families of five and more. Given the resource constraints in their households, their families may prefer to send males rather than females to university.
This said, however the trend in most South African universities is that there are more females enrolled than males, although male students have always performed better than their female compatriots (see Cosser and du Toit, 2002). In 2007 men continued to dominate in science, engineering and technology where they constituted 57% of enrolments in 2007 while in all other fields of study, more women were enrolled than men (see CHE, 2009).
Contrary to the gender distribution noted in survey sample, the analysis in chapter four shows that there were more females (56.1%) than males (43.9) in the university system (see table 4 in chapter four). However, the most important factor should not be registration figures, but the graduation rates of female students taking into account their field of study.
5.3.1.2.1 Gender and GPA
In tables 36 and 37, GPA mean scores of gender groups are provided. Males scored slightly lower (mean= 50.98) than females (mean= 52.55) for 2008.
Table 37 2008 GPA versus gender Gender N Mean Std. Deviation GPA2008 Male 19 50.98 13.290
Female 7 52.55 7.325 t = 0.293 with a p-value of 0.722.
For GPA 2009 males scored a mean of 52.45 while females achieved a mean of 50.19.
Table 38 2009 GPA versus gender Gender N Mean Std. Deviation GPA2009 Male 27 52.45 10.221
Female 13 50.19 12.677 t = 0.604 with a p-value of 0.549.
However, the results indicate that there was no significant difference between males’ and females’ academic performance in terms of GPA for 2008 with a p-value of 0.722, and 2009 with a p-value of 0.549. Thus, gender did not matter as far as academic progress was concerned. Based on the findings of this study, UKZN can be lauded for levelling the field in terms of gender disparity. This finding also resonates with the analysis in chapter four where gender was found to have no significant impact on academic progress in terms of the mean GPA (refer to chapter four). From an SLA approach adopted in this study, gender is viewed as a livelihood asset; however it did not have an impact on livelihood outcome (GPA).
Equally, compared with the analysis from the main database in chapter four (table 5b) it (gender) was not an influential variable on academic progress (outcome) of students at university.
5.3.1.3 Matric Scores and GPA
29.4% of low quintile students had matric scores of between 32 and 39 points; and 15% had matric scores of between 25 and 28 (see table 39 below). These matric scores are comparable with UKZN’s current admission criteria. The minimum admission requirements at UKZN for the different colleges (the new college system effective from 2012) are (using a Swedish formula as described in section 1.7.5 Academic Progress):
College of Health Sciences = 30/38
College of Law and Management Studies = 28/36 College of Humanities:
• Mainstream = 24/36
• Extended Programme = 20/24
College of Agriculture, Engineering and Science:
• Mainstream = 28/40
• Foundation Programme = 16/20
• Augmented Programme = 22/28 (adapted from the University of KwaZulu-Natal Undergraduate Prospectus, 2012).
The mean matric score of 31.88 (table 40) of the 41 low quintile students in this analysis is also comparable with the minimum admission requirements indicated above. Thus interestingly, low quintile students in the survey sample are not all underperformers, despite their socio-economic status.
Table 39 Matric scores
Valid Frequency Valid Percent
0 2 4.9
24 1 2.4
25 3 7.3
27 2 4.9
28 3 7.3
30 2 4.9
31 2 4.9
32 4 9.8
33 2 4.9
34 2 4.9
35 4 9.8
36 2 4.9
37 2 4.9
38 1 2.4
39 4 9.8
40 2 4.9
41 1 2.4
42 1 2.4
43 1 2.4
Total 41 100.0
Table 40 below shows that the mean matric score of low quintile students was 31.88 against the mean GPAs for 2008 of 51.41 and 51.71 for 2009. The GPA is in line with the finding that low quintile students were just performing at the ‘survival’ level of just below and above 50 in terms of their mean GPA (refer to chapter four).
Table 40 Mean Matric Score and GPA of surveyed students Mean Std. Deviation N
Matric score 31.88 8.883 41
gpa2k8 51.41 11.856 26
gpa2k9 51.71 10.965 40
5.3.1.4 The Relationship between GPA and Matric Score for 2008 and 2009
A scatter plot was used to investigate the relationship between mean GPA and the matric score of low quintile students in the survey sample in 2008 and 2009. One of the principles used in interpreting scatter plots (diagrams) is that if the points cluster in a band running from lower to upper right there is positive correlation. For 2008, the points on figure 6 below cluster in a band, showing no linear pattern which indicates a no correlation between mean GPA and matric score with a p-value=0.757.
Figure 6 GPA versus matric score for 2008
r = 0.064 with a p-value of 0.757.
For 2009 the principle applied above in figure 6 was almost fulfilled, signifying only a weak correlation between GPA and matric score. Thus, there is weak positive linear correlation between matric score and GPA 2009 at a p-value of 0.012 (see figure 7 below).
Figure 7 GPA versus matric score for 2009
r = 0.402 with a p-value of 0.012.
0 10 20 30 40 50 60 70 80
0 10 20 30 40 50
m at score
GPA2008
0 10 20 30 40 50 60 70 80
0 10 20 30 40 50
m at score
GPA2009
The findings based on figure 7 resonate somewhat with the results in chapter four which showed that matric score was a strong predictor of mean GPA at university. Thus, from an SLA perspective, the livelihood asset of Matric score significantly impacted on academic progress (livelihood outcome) of students based on the main database in chapter four while it did not have such a positive influence based on the survey sample in this chapter (see figure 6). Variation in the results of both analyses (main database in chapter four and survey analysis in chapter five) could be accounted for by sample sizes and/or that livelihood assets played a minimal role in the academic progress of students based on GPA in some contexts, and not in others.
5.3.1.6 Year of Study Level Distribution
More than three quarters of the students have been studying for three years or less (see table 41). This study focused on undergraduate students mainly in three- and four-year degree programmes. The purpose of including all the undergraduate levels or categories stipulated above was to capture the perceptions and experiences of students at different levels of their studies. Students in five-year degree programmes or more such as medicine were also included and they constituted 7% of the sample. The distribution of the year of study is shown in table 41below.
There were more ‘sophomores’ (2nd year students) at almost 42%, and first years (22%) followed by 3rd and 4th years at 15% of the sample, respectively. The other (specify) category refers to medical students. Students at different levels of their studies will provide different perspectives on the problems faced by students from low quintile schools, thus helping capture their livelihood in context. Students’ experiences and perceptions about their learning environment and academic progress are looked at in context, based on the fact that students go through a development trajectory as they pursue their studies (refer to chapter two).
The main database sample in chapter four included all undergraduate levels of study at university such as number of years taken to graduate or dropout. The survey sample, on the other hand, could only go as far as capturing and analysing experiences and perceptions during the period of their studies at university, and not dropout or years of registration. The latter begs for further or follow-up research on the sample.
Table 41 Number of years studying at tertiary institutions
5.3.1.7 Type of Degree
For frequencies in terms of degree programmes BSc, BEd, BCom and BSocSci dominate (see table 41 below). Table 42 shows that low quintile students pursue degree programmes almost across all Faculties of the university.
Table 42 Type of degree Degree Frequency
B Pharm 1
BA 1
BAdmin 2
BCom 6
BEd 8
BSc 12
BSocSci 5
ComDev 1
MbChB 4
Nursing 1
Total 41
Interestingly, most of the 41 low quintile students were enrolled in the sciences, which require mathematical and numeracy skills. The expectation would have been to find fewer of these students in the sciences because of their low school SES which has been associated
Number of years
Frequency Percent
Cumulative Percent
1 year 9 22.0 22.0
2 years 17 41.5 63.4
3 years 6 14.6 78.0
4 years 6 14.6 92.7
Other (specify)
3 7.3 100.0
Total 41 100.0
with low academic achievement, and more in the humanities. Although this researcher did his utmost to gather a sample that matches both quintile and faculty distributions, in the end it was possible only to work with the valid survey responses which show a faculty skew towards science students. As there are too few students in the sample it was not possible to do analysis comparing faculty averages. Thus rather than continuing to compare the survey sample with the analysis of the main database in chapter four this chapter now moves to analyse the results of the survey questionnaire to elucidate the relationship between assets, context and outcomes. Where GPA is used it will be the average across the survey sample and not faculty specific. Furthermore, a new variable was created based on the GPA:
strugglers are defined as those scoring less than 50% on their GPA.