After an appropriate research design and suitable ways of creating or measuring the relevant variables were decided on, an appropriate statistical procedure was chosen for analysing the
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data to be obtained eventually (Gilham, 2005:88). It cannot be emphasised strongly enough that statistical techniques merely serve as aides in assisting the researcher to come to a justifiable decision as to whether or not the data obtained support the hypothesis originally formulated (Babbie, 2007:37).
3.10.1 Result presentation
The research data needs to be presented in a meaningful way so that it provides necessary information (Dunne et al, 2005: 47). Once the stage in simplification of the research data has been reached, the results need to be summarised using any of the following to present the research data: tabulation, diagrams and charts; and frequency distributions (George, 2011:76).
3.10.2 Software to be used
Among the software used by social and behavioural scientist, the SPSS (Statistical Package for the Social Sciences), BMDP (Biomedical Data Processing System) and the SAS (Statistical Analysis System) are the best known (Huysamen, 2001:195). Not only are the above programmes versatile and less expensive to use than to develop programmes specifically for individual use, but they yield highly reliable results (Silverman, 2006:124).
The current research made use of the SPSS software programme. Data were descriptively and inferentially analysed.
3.10.3 Statistical techniques
Statistical techniques cannot select themselves, nor can they interpret the results that have been obtained by them or make conclusions on behalf of the person applying them (Treiman, 2008:87). The choice of the appropriate statistical techniques and the interpretation of the results obtained remain the exclusive responsibility of the researcher using them (Huysamen, 2001:194).
3.10.3.1 Descriptive Statistics
After the information has been collected and captured on computer as numbers, called data or raw data, the analysis process usually starts with descriptive statistics (Maree, 2007:183). The term descriptive statistics is a collective name for a number of statistical methods that are used to organise and summarise data in a meaningful way. This serves to enhance the properties in a meaningful way. Descriptive statistics can be divided into two ways of representing or describing data: graphical ways; and numerical ways.
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3.10.3.2 Inferential Statistics
More often than not, researchers want to go beyond just summarising and describing the data they have collected (Maree, 2007:183). The purpose of most research is to use the findings from the sample data to generalise or draw conclusions about the population. This is called statistical inference, a field of statistics that relies heavily on probability theory (Maree, 2007:183). It is a means of probability statements that inferences are made, for the simple reason that one can never report anything about a whole (population) with certainty if it is based only on a part sample.
Maree (2007:183) states that statistical tests of hypothesis may be classified as belonging to one of two groups:
1. Parametric methods
2. Non-parametric or distribution-free methods
The reason for this distinction lies in the fact that statistical tests rely on certain population characteristics for their outcome to be valid. In general, parametric methods are used when one has knowledge of the underlying distribution of the study variable. Non-parametric methods are used when very little is known about the variable’s distribution in the population.
3.10.3.2.1 The Mann–Whitney U test
In statistics, the Mann–Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test (WRS), or Wilcoxon–Mann–Whitney test) is a non-parametric test of the null hypothesis that two samples come from the same population against an alternative hypothesis, especially that a particular population tends to have larger values than the other. It can be applied on unknown distributions contrary to t-test which has to be applied only on normal distributions, and it is nearly as efficient as the t-test on normal distributions (Wikipedia:
online).
3.10.3.2.2 The t-test
Maree (2007:183) states that this technique is used under the following circumstances:
• When two independent groups need to be compared based on their average score on a quantitative variable.
• When the average scores on two quantitative variables need to be compared in a single sample.
• When the average of a quantitative variable needs to be compared with a specified constant value in a single sample.
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3.10.3.2.3 The Kruskal–Wallis test
The Kruskal–Wallis test by ranks (named after William Kruskal and W. Allen Wallis) is a non- parametric method for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. It extends the Mann–Whitney U test when there are more than two groups. The parametric equivalent of the Kruskal-Wallis test is the one-way analysis of variance (ANOVA) (Wikepedia: online).
3.10.3.2.4 Analysis of variance (ANOVA)
Maree (2007:183) states that this technique is used when there are more than two independent groups that need to be compared on a single quantitative measure or score. Specifically, it tests whether the groups have different average scores.
ANOVA is appropriate if:
• the quantitative variable is normally distributed in each population;
• the spread (variance) of the variable is the same in all populations.
The statistical techniques to be used in this study are the Kruskal Wallis test and the Mann Whitney U test.