2.8 Data Analysis
2.8.1 Data Analysis using the study’s objectives
The data analysis methods were:
2.8.1.1 Objective One
To establish the levels of financial capability (financial knowledge, financial attitudes, financial behaviour and numeracy skills) among accounting students at universities in KwaZulu-Natal.
a) Descriptive analysis, to examine the statistics of the data and describe the data in a sample without making any inferences to the data.
i. Justification: the information received from descriptive statistics seeks to clarify the mean, median, mode and standard deviation of financial capability.Other studies have also considered the descriptive statistics as basic statistical information(Xiao and O'Neill, 2016).
b) Bivariate regression analysis, a type of statistical analysis that involves analysing two variables – in this case, socio-economic factors and financial capability.
i. Justification: Bivariate regression analysis was used to assess the connection of socio-demographic factors and financial capability with other research on financial literacy(Finke et al., 2016).
c) Cross-tabulations were used to analyse the relationship between financial capability and demographic variables, specifically the level of study. The cross-
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tabulations also enabled the results for one or more variables to be analysed and compared.
i. Justification: In order to answer the research objectives, it was necessary to examine the levels of study with demographical information. (Rasoaisi and Kalebe, 2015) in study on financial literacy used cross-tabulation to explain socio-demographic information with financial literacy.
d) One-way Analysis of Variance (ANOVA) was used to analyse group mean differences between socio-demographic variables and financial capability.
i. Justification: The analysis of variance (ANOVA) was needed to determine whether a statistical connection exists between socio-demographic data and scores of financial capability. (Zakaria et al., 2017) in their study on financial literacy examined the statistical relationship between savings and investment and socio-demographic variables
2.8.1.2 Objective Two
To determine the factors that influence financial capability among accounting students at universities in KwaZulu-Natal.
a) Exploratory factor analysis (EFA) from the multivariate statistics family to detect the underlying structure of a relatively large number of variables of financial capability.
i. Justification: It was used to identify the associate relationship between financial knowledge, financial behaviour, financial attitudes and numerical skills. None of the existing studies examined to this extent especially using EFA. This was essential as the definition used in this research(WorldBank, 2013) already meant that elements of financial capability were financial knowledge(literacy), financial behaviour, financial attitudes, and numerical skills. None of the financial capability research examined used EFA, but other non-related studies used EFA such as (Ahmad et al., 2017).
b) Confirmatory Factor Analysis (CFA) – to test whether measures of a construct are consistent with the researcher's understanding of the financial
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capability factors (financial knowledge, numeracy skills, financial attitudes and financial behaviour).
i. Justification: Although exploratory factor analysis was done, it was necessary to confirm whether the capability constructs are closely related to each other. Likewise, none of the financial capability research examined used CFA, but other non-related studies used CFA such as (Huang, 2017).
2.8.1.3 Objective Three
To establish the level of financial socialisation among accounting students at universities in KwaZulu-Natal.
a) Multinomial Logistic regression, a method that generalises logistic regression to multiclass problems.
i. Justification: Multinomial logistic regression was necessary to identify a set of categories which financial socialization falls under which could have been two categories such as financial capability and professional skills in relation to socio-demographic. (Agarwalla et al., 2015) in a study on financial literacy used multinomial logistic regression to explain socio- demographic information with financial literacy.
2.8.1.4 Objective Four
To determine the factors that influence financial socialisation among accounting students at universities in KwaZulu-Natal.
a) Exploratory factor analysis (EFA) from the multivariate statistics family to detect the underlying structure of a relatively large number of variables of financial socialisation.
i. Justification: It was used to identify the associate relationship between family influence, social media influence and peer influence. None of the existing studies examined to this extent especially using EFA. None of the financial socialisation research examined used EFA, but other non-related studies used EFA such as (Ahmad et al., 2017).
62 1.8.1.5 Objective Five
To establish the level of professional skills among accounting students at universities in KwaZulu-Natal.
a) Multinomial Logistic regression, a method that generalises logistic regression to multiclass problems.
i. Justification: Multinomial logistic regression was necessary to identify a set of categories which professional skills fall under which could have been two categories such as financial capability and financial socialisation in relation to socio-demographic. Agarwalla et al. (2015) in a study on financial literacy used multinomial logistic regression to explain socio-demographic information with financial literacy.
2.8.1.6 Objective Six
To determine the factors that influence professional skills among accounting students at universities in KwaZulu-Natal.
a) Exploratory factor analysis (EFA) from the multivariate statistics family to detect the underlying structure of a relatively large number of variables of professional skills.
i. Justification: It was used to identify the associate relationship between critical skills, problem-solving skills, ICT skills and communication skills.
None of the existing studies examined to this extent especially using EFA. None of the professional skills research examined used EFA, but other non-related studies used EFA such as (Ahmad et al., 2017).
63 2.8.1.7 Objective Seven
To evaluate the differences in professional skills between SAICA accredited institutions (UKZN) and non-SAICA accredited institutions (DUT and MUT).
a) Cross tabulations (often referred to as cross-tabs) to analyse the relationship between two or more variable – SAICA and non-SAICA institutions.
i. Justification: In order to answer the research objective, it was necessary to examine the SAICA accredited and non- SAICA accredited if there is any difference. (Rasoaisi and Kalebe, 2015) in study on financial literacy used cross-tabulation to explain socio-demographic information with financial literacy.
2.8.1.8 Objective Eight
To evaluate the impact of socio-economic factors on financial capability, financial socialisation and professional skills.
a) Chi-square Independent T-test analysis, to examine if the two categorical variables are related.
i. Justification: The chi-square test was applied with the objective of determining whether financial capability, financial socialization and Professional skills vary with socio-demographic variables. The chi-square test analysis is used to examine the relationship between two discrete variables(each)(Bahovec et al., 2015).
b) Bivariate regression analysis, a type of statistical analysis that involves analysing two variables – in this case, socio-economic factors and financial capability, financial socialisation and professional skills.
i. Justification: Only the unweighted sample was used for the statistical analysis of bivariates and multivariates. It is suitable to use an unweighted sample for sophisticated statistical analysis when the research question focuses on examining connections between interesting factors(Nielsen and Seay, 2014).
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c) One-way Analysis of Variance (ANOVA) was used to analyse the group mean differences between socio-demographic variables and financial capability, financial socialisation and professional skills.
i. Justification: The analysis of variance (ANOVA) was needed to determine whether a statistical connection exists between socio-demographic data and scores of financial capability, professional skills and financial socialisation. (Zakaria et al., 2017) in their study on financial literacy examined the statistical relationship between savings and investment and socio-demographic variables
2.8.1.9 Objective Nine
To examine the relationships among financial capability, financial socialisation and professional skills.
a) Correlation analysis was used to statistically evaluate the strength of the relationships among financial capability, financial socialisation and professional skills.
i. Justification: The correlation was used to examine the connection of ' net intensity ' between two continuous variables. The coefficient of correlation between product and moment Pearson is the correlation coefficient used to view the correlation between variables from 0 to 1(Albeerdy and Gharleghi, 2015a).
b) Pearson Chi-square was used to statistically test categorical data sets to assess how likely it was that any observed difference between the sets arose by chance.
Justification: The chi-square test was applied with the objective of determining whether financial capability, financial socialization and Professional skills vary with socio-demographic . The chi-square test analysis is used to examine the relationship between two discrete variables(each)(Bahovec et al., 2015).
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