Upon collecting the necessary data for this study, appropriate research analytical methods were utilised for the analysis of the collated data using the Statistical Package for Social Science (SPSS) version 25 software. The collated data was firstly filtered and purified. Thereafter, a coding structure was devised to codify responses to each question. Then, each questionnaire was captured according to the coding structure, using the Microsoft Excel spreadsheet software and finally, the coded data was further analysed using the appropriate statistical tools. The analysis of the coded data using the necessary applicable statistical methods, is crucial to assessing the financial capability of the practising professionals under the broad themes of financial knowledge, financial behaviour, financial attitude, and financial self-efficacy.
Based on the inquiry of this study, research adopted appropriate analytical research methods for data description and inference. The descriptive research methods were utilised to describe the data characteristics, while inferential analytical methods were used to make inferences about the population based on the data collected from the sample.
Hence, to achieve the broad aim of the research study and the highlighted research objectives successfully, the appropriate statistical and analytical methods adopted as per the research objectives and questions are shown in the table below and subsequently discussed systematically:
Table 3. 3 Research Analytical Methods
Objective Question Methods
Objective 1 Question 1 Descriptive statistics using measures of central tendency such as mean averages, medians etc to compute the aggregate financial capability.
Objective 2 Question 2 Factor Analysis- Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA)
Objective 3 Question 3 ANOVA/ T-test will be used to determine significance and variations among the four different professional practises.
Objective 4 Question 4 Regression Analysis
Source: Researcher’s construction
3.8.1 Descriptive Analytical Method
3.8.1.1 Tables, Graphs and charts: Socio demographic information and Research Objective One
This research objective seeks to ascertain levels of financial knowledge, financial behaviour, financial attitude, as well as financial self-efficacy of the practising professionals in KwaZulu-Natal.
To achieve this, the researcher utilised descriptive analytical tools to compute the mean and median averages (in aggregate and comparatives) of the financial capability of the practising professionals. The determined results were presented clearly using tables, graphs and charts, to illustrate graphically results of analysis. Prior to this, the socio demographic information of the respondents was also visualised using tables, graphs, and charts to present clearly commonalities and disparities among the socio demographic metrics of the practising professionals in KwaZulu-Natal.
3.8.1.2 Factor Analysis: Research Objective Two
This research objective seeks to determine the factors that impact on the financial capability of practising professionals in KwaZulu-Natal.
To determine the factors that influence the financial capability of practising professionals in KwaZulu-Natal, the researcher employed both Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) methods to measure and present the relationships on the factors that influence the financial capability of KwaZulu-Natal practising professionals. In this study, EFA was used to measure the relationship among the variables by understanding the constructs of the underlying variables, while PCA was used to simply derive fewer variables that provide the same information that is found in the larger set of variables.
Therefore, to identify the factor structure of the determinants that influence the financial capability of practising professionals in KwaZulu-Natal province, a Principal Component Analysis (PCA) was conducted on all 45 items with an orthogonal rotation (Varimax). Using an Eigenvalue parallel analysis, four factors were extracted from this study, which were based on the Eigenvalue (Eigenvalue of >2). Here, the Eigenvalues related to each factor represents the variance explained by each linear component, which are also displayed in terms of the percentage of variance explained.
Hence, the factor analysis was appropriate in this study as a means of identifying the determinants of financial capability among practising professionals in KwaZulu-Natal province. In addition, the Kaiser-Meyer-Olkin (KMO) value and the Bartlett’s assessment of sphericity value in this study, further allowed the application of factor analysis which indicates that relationships between the items were sufficiently large for PCA.
As an analytical method, the core purpose of Principal Component Analysis (PCA) in this research study is:
➢ To visualise the correlations between the factors and the original variables. ➢ To reduce the original variables into a lower number of orthogonal (non-
correlated) synthesised factors (variables).
➢ To visualise proximities among mathematical units.
3.8.1.3 T-test and ANOVA: Research Objective Three
This research objective seeks to analyse comparatively the financial capability of finance related professionals vis-à-vis the financial capability of non-finance related professionals in KwaZulu-Natal province of South Africa.
To achieve this objective, the researcher adopted tests for variation and significance among means using T-test and ANOVA to understand the extent of variation in the financial capability levels of finance related professionals vis-à-vis the financial capability levels of non-finance related professionals. Hence, the practising professionals were categorised into subgroups via a median percentage of correct responses.
3.8.1.4 Regression Analysis: Research Objective Four
This research objective seeks to assess the influence of socio demographic variables (gender, age, profession, professional experience in practise, highest educational qualification, race, & monthly income) on the financial capability levels of practising professionals in KwaZulu-Natal.
To evaluate the impact of socio demographic variables (gender, age, profession, professional experience in practise, highest educational qualification, race, & monthly income) on the financial capability levels of practising professionals in KwaZulu-Natal, the researcher utilised a multiple linear regression analysis to estimate the relationship between the dependent variable and its gender, age, profession, professional experience in practise, highest educational qualification, race, & monthly income). Herein, the core purpose was to determine how well the respondents’ socio demographic profile could predict their financial capability. Furthermore, a scatterplot analysis will be performed to demonstrate the relationship between the predictors (gender, age, profession, professional experience in practise, highest educational qualification, race, & monthly income) and the dependent variable (Financial capability). Subsequently, both correlation and regression analysis will be done using a regression model to identify causal relationships between the predictors and the outcome variable:
ŷ = β0 + β1X1 + β2X2 +…...… βnXn + ei
Thus, in the context of this study, the regression model for this study can be expressed as:
ŷ = β0 + β1(GENDER) + β2(AGE) + β3(PROFESSION) + β4(PROFESSIONAL
EXPERIENCE) + β5(EDUCATIONAL QUALIFICATION) + β6(RACE) + β7(MONTHLY
INCOME) + ei Where:
Ŷ = dependent variable (Financial capability of the professionals).
β0 = y-intercept (constant term).
β1 = the slope of coefficients for each explanatory variable.
GENDER = the explanatory variable for X1
AGE = the explanatory variable for X2
PROFESSION = the explanatory variable for X3
PROFESSIONAL EXPERIENCE = the explanatory variable for X4
EDUCATIONAL QUALIFICATION = the explanatory variable for X5
RACE = the explanatory variable for X6
MONTHLY INCOME = the explanatory variable for X7 ei = the model’s error term.