3.15 Data Analysis and Interpretation of Findings
3.15.4 The impact of the level of study and other socio-demographic variables on
142 FK1
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization. Source: SPSS
For the data used in this study, the value of the determinant of the correlation matrix is 0.001, which is higher than the necessary value of 0.00001. Therefore, multi-collinearity is not a problem for these data. This entails that all questions in the factors influencing
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A bivariate regression was used to establish the relationship between respondents’ socio- economic characteristics and financial capability. The primary purpose was to examine how well respondents’ socio-economic characteristics could predict the level of financial capability. A scatterplot of the analysis that demonstrates the relationship between the respondents’ socio-economic characteristics and financial capability suggested that it was negative and linear and did not reveal any bivariate outliers. The variable used in this model includes the respondents’ campus, level of study, year of study, current qualification, racial groups, parents’ education level, and pattern of savings. Herein, the correlation between the predictive variables (respondents’ socio-economic characteristics) and financial capability was statistically significant, with r(1578) = .197, p = .000.
Moreover, as determined by an ANOVA test in the regression analysis, the results suggested that the regression model works better with seven predictors (respondents’
socio-economic characteristics) than simply predicting using the mean, with F = 9.090; p
= .000. The p-value obtained is an indication that the regression model employed – using the seven predictors – was significantly more fitting than predictions without the seven predictors in the model. Hence, there is a statistically significant relationship between the predicting variables (respondents’ socio-economic characteristics) and the outcome variable (financial capability) – thus, respondents’ socio-economic characteristics were used to predict financial capability among accounting students.
The regression equation for predicting the financial capability of accounting students from the respondents’ socio-economic characteristics was ŷ = 4.348 – (0.016 + 0.161 + 0.200 + 0.028 + 0.057 + 0.010 + 0.034)x. The r2 for this equation was .039; that is, 3.9% of the variance in financial capability was predictable from the respondents’ socio-economic characteristics. This suggests that the coefficients for Level of study, Year of study, Current qualification, and Racial group were statistically significant. This is an indication that that respondents’ level of study, year of study, current qualification, and racial group influence their financial capability. These factors impact on the respondents’ financial capability, with a significant value of 0.000, 0.000, 0.006, and 0.011, respectively.
Table 3. 31 Regression model of financial capability
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Model Unstandardised Coefficients
Standardised Coefficient Beta
T Sig. 95,0%
Confidence interval for B B Std.
Error Lower
Bound Upper Bound
(Constant) 4.348 .096 45.336 .000 4.160 4.536
Campus .016 .030 .018 .018 .583 -.042 .075
Level of Study -.161 .034 -.209 -4.720 .000* -.229 -.094 Year of Study .200 .038 .226 5.231 .000* .125 .275 Current
Qualification .028 .010 .087 2.766 .006* .008 .048
Racial group -.057 .022 -.067 -2.543 .011* -.101 -.013 Parents’
Educational level
.010 .020 .012 .499 .618 -.029 0.50
Pattern of savings
.034 .019 .045 1.813 .070 -.003 .071
Dependent Variable: Financial Capability
*=p<0.05 Source: SPSS
Level of study vs financial capability among accounting students.
The analysis of the respondents’ financial capability according to their level of study suggested that most of the respondents from each class have high financial capability. It was found that the majority of the 180 respondents from a first-year non-accounting specialisation had high financial capability, with (n=151; 83.9%). Similarly, the analysis revealed that most of the 579 respondents from first-year mainstream had high financial capability, with (n=528; 91.2%). Finally, most of the 470 respondents in second year had high financial capability, with (n=428; 91.1%) as did the majority of the 353 third-year respondents with (n=309; 87.5%).
Table 3. 32 Financial capability versus level of study
145 Financial Capability (FC)
Level of Study High FC Moderate FC Low FC Total 1st Year non 151 (83.9%) 24 (13.3%) 5 (2.8%) 180 1st Year main 528 (91.2%) 41 (7.1%) 10 (1.7%) 579 2nd Year 428 (91.1%) 30 (6.4%) 12 (2.6%) 470 3rd Year 309 (87.5%) 41 (11.6%) 3 (0.8%) 353
Total 1416 (89.5%) 136 (4.8%) 30 (1.9%) 1582
Source: SPSS
A bivariate regression was used to ascertain the relationship between the level of study and financial capability. The primary purpose was to examine how well the respondents’
level of study could predict their level of financial capabilities. A scatterplot of the analysis that demonstrates the relationship between the respondents’ level of study and financial capability suggested that it was negative and linear and did not reveal any bivariate outliers. The variable used in this model includes respondents’ level of study, current qualification, and year of study. Herein, the correlation between the predictive variables (level of study, current qualification, and year of study) and financial capability was statistically significant, with r(1578) = .136, p = .000. Moreover, as determined by an ANOVA test in the regression analysis, the results suggested that the regression model works better with three predictors (level of study, current qualification, and year of study) than simply predicting using the mean, with F = 9.925; p = .000. The p-value here means that the regression model employed – using the three predictors – was more significantly fitting than predictions without the three predictors in the model. Hence, there is a statistically significant relationship between the predicting variables (level of study, current qualification, and year of study) and the outcome variable (financial capability) – these factors were used to predict financial capability among accounting students.
Therefore, the regression equation for predicting the financial capability of accounting students from the level of study, current qualification and year of study was ŷ = 1.034 – (0.69 + 0.013 + 0.083)x.
The r2 for this equation was .019; that is 1.9% of the variance in financial capability was predictable from the level of study, current qualification and year of study. The
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bootstrapped 95% confidence interval for the slope to predict financial capability from the level of study, current qualification, and year of study ranges from -0.103 to -0.035; 0.005 to 0.021; and 0.044 to 0.122. This suggests that for each unit increase in the level of study, financial capability decreases by about 0.04 to 0.1 points. It also suggests that for each unit increase in current qualification, financial capability increases by about 0.01 to 0.02;
and for each unit increase in year of study, financial capability increases by about 0.05 to 0.12.
Table 3. 33 Bivariate regression model of financial capability Coefficients
Model Unstandardised Coefficients Standardised
Coefficient Beta
T Sig. 95,0%
Confidence interval for B B Std.
Error
Lower Bound
Upper Bound
(Constant) 1.034 .027 38.922 .000 .982 1.086
Level of Study -.069 .017 -.172 -3.996 .000 -.103 -.035 Current
Qualification .013 .004 .079 3.150 .002 .005 .021
Year of Study .083 .020 .180 4.155 .000 .044 .122
Dependent Variable: Financial Capability
Source: SPSS