Statistical Tests Used in the Data Analysis

Several statistical tests were carried out during the data analysis. The descriptive statistics recommended for this data analysis included mean for central tendency and standard deviation for variability. Additional data procedures included Pearson’s r, the t-test, ANOVA and regression analysis, as well as Likert data analysis specifically for questions 19, 21 and 23. Our method of data analysis was not based on individual question analysis but rather on composite score from the series of questions that represented the attitudinal scale.

In our questionnaire we had both Likert scale and Likert-type data. Variations of the Likert response alternatives are extremely common in research. However, it is essential that care be taken in the proper usage of Likert scale data (Boone, & Boone, 2011). One problem area is related to the difference between the Likert scale and Likert-type questions. In terms of Likert-type questions, multiple questions are used in a research instrument but the scores are not combined. Likert-type items such as question 24-29 are ordinal and the measuring scales that was used include a mode or median for central tendency and frequency for variability. Additional analysis were chi-square measure of association, Kendall Tau B, and Kendall Tau C.

Factor analysis was used to find the composite scores in Likert scale questions. The main

purpose of factor analysis was to reduce the number of variables, to find a relationship between variables and categorise them.

There are different methods for factor extraction and also different procedures for factor rotations and factor score calculations. The methods for factor extraction include principal components, generalised least squares, maximum likelihood, principal axis factoring, alpha factoring and image factoring, although the main two methods are principal component analysis and principal axis factoring. In factor analysis it is necessary to take into account the theoretical background as well as the empirical outcome. A summary of the tests carried out in this study are as follows:

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Kaiser-Meyer-Olkin Measure of Sampling Adequacy (MSA) Test and Bartlett’s Test of Sphericity were carried out on the Likert scale data. These are the standard test procedure

for a factor analysis. As a rule of thumb, KMO should be 0.60 or higher in order to proceed with a factor analysis of sample adequacy tests. The p-value of Bartlett’s test (represented by “Sig”) must be below 0.05 to indicate that the correlation structure is significantly strong enough for performing a factor analysis on the items.

Eigenvalue was used to decide on the number of factors. Methods such as eigenvalues and

scree plots were used for this purpose. An eigenvalue indicates how many factors should be extracted in the overall factor analysis. The Kaiser rule is to drop all components with an eigenvalue of under 1.0.

The scree test was used to plot the eigenvalue on the Y-axis and the components on the X- axis. As the x value increases, the eigenvalue decreases and, when the drop in eigenvalue was ceased, the curve made an elbow. None of the components after the drop (or elbow) were considered. The number of factors were selected from those components that were before the drop (elbow).

Graphical Interpretation were used after the extraction of the factors, each variable was

shown as a vector. The coordinates in this figure are factors. Factor loading, which is the cosine of the angle between the factor and one variable, was then calculated. Factors are rotated in order to make them more readable.

Communalities Tables were presented which indicate the extent to which the variables may

be explained by the factors. A value near 1 indicates an item that correlates highly with the rest of the items in a factor. Items with low communalities (near 0.2) were reconsidered.

Cronbach’s Alpha Coefficient was used to determine the internal consistency or average

correlation of items in a survey instrument. When the reliability was low, then the individual items within a scale was either changed completely or modified.

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Pearson Chi-square Test was used in this study. This is a statistical test that is used to

compare observed data with the expected data based on a hypothesis. The chi-square test is intended to test the likelihood that an observed distribution is due to chance. The Pearson chi-square value gives the p-value. If the p-value is smaller than 0.05, then a statistically significant association exists between different categories. This means that the results cannot be attributed to chance and that a real association exists between the variables.

Analysis of Variance (ANOVA) provides a statistical test of whether or not the means of

several groups are all equal. This is useful when comparing two, three, or more means. H0 = µ1= µ2= µ3= …..= µk

Where µ is the group population and k is the number of groups. The one-way ANOVA which was calculated in each case compares the means between the groups and checks whether any of the means is significantly different from others, testing the null hypothesis. If the one-way ANOVA produced a significant result, then the alternative hypothesis was accepted. This means that there are at least two group means that are significantly different from each other, without it being possible to identify them. In order to determine the groups, it is necessary to conduct a post-hoc test.

Statistical Significance (p-value) - It is the probability that the observed relationship

between items is as a result of either chance or luck and indicates that there is no

relationship between the components. It also provides the decreasing index of reliability. The results where p = 0.05 are considered to be borderline. The p-value still has a high probability of 5%. Results where p ≤ 0.05 are significant while results that are between 0.005 and 0.01 are highly significant. This number is an arbitrary selection based on experience and literature.