This content requires Adobe Flash Player version
or later.
Either you do not have Adobe Flash Player installed,
or your version is too old,
or there is a problem with your Flash installation and we were unable to detect it.
As the factor analysis proved the scales reliable, the researcher could continue with the data analysis. To measure whether significant differences among means existed, the researcher made use of independent sample t-tests. Zikmund and Babin (2007: 351) state that an independent samples t-test is used to compare means for a single variable grouped into two categories. In measuring whether significant differences exist, the researcher measured statistical as well as practical significance. Results will have a statistical significance when the p value is ≤ 0.05. For practical significance Cohen (1988: 20-27) suggests the following effect sizes:
Ÿ d = 0.2 small effect with no or real significance; Ÿ d = 0.5 medium effect with significance; Ÿ d = 0.8 large effect indicating practical significance.
The following sections will include the results pertaining to the study. The section will include the demographic profile as well as the statistical analysis in testing the hypotheses.
Demographics
The demographic profile of respondents serves as the basis that can provide some insights into the type of respondent participating in the study. Table 4 indicates the demographic profile as captured in the data editing phase.
TABLE 4: DEMOGRAPHIC PROFILE OF RESPONDENTS Demographic variables
Gender Highest level of education Male Female Primary school completed