RESEARCH DESIGN AND METHODOLOGY
4.6 Analysis of data
4.6.2 Reliability statistics
The two most important aspects of precision are reliability and validity (Diedenhofen and Musch, 2016; Silverman, 2016). Additionally reliability is computed by taking several
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measurements on the same subjects. Cho (2016) is of the view that a reliability coefficient, namely, the Cronbach’s Alpha Score of 0.60 or higher is considered as acceptable for a newly developed construct. The various reliability statistics for this research were computed using SPSS software. Table 5.1 reflects the Cronbach’s alpha score for all the key items that constituted the questionnaire. The reliability scores for all sections exceed the recommended Cronbach’s alpha value of 0.600 for a newly developed construct. This indicates a degree of acceptable consistent scoring for these sections of the research.
Table 5.1: Cronbach’s Alpha score for key items of the questionnaire
Section Name Number
of Items
Cronbach's Alpha
B1
(a) Which are the most important issues (top 5) for society at present?
(b) Which do you think are the most important issues (top 5) for industry or business in general at the moment?
26 0.936
B2
Which are the most important environmental issues that you
experience or impacts on your life at present? 17 0.925
B3
How important do you think the following environmental issues are
for the well-being of global society in general? 17 0.913 B8
Which of the following do you believe are the main causes of
climate change? 11 0.742
B12
Which, if any, of the following have you noticed (relating to where
you have lived) which may suggest that the climate is changing? 9 0.821 C1
Would you be prepared to change your behaviour to reduce your
contribution to climate change in any of the following ways? 11 0.846 D1
Whom do you think should be responsible for making any changes
to lessen the impacts of climate change? 12 0.947
D2
Please indicate how much you agree or disagree with the following
statements about climate change by ticking one box on each row. 11 0.599 D3
Whom would you trust in making any changes needed to lessen the
impacts of climate change? 12 0.960
D6
Whom would you trust most to give you reliable information on
climate change? 10 0.908
E1
Please rate your level of agreement with the following statements with regard to climate change climate change programme and environmental strategies in the Distribution Division
8 0.763
179 4.6.3 Factor analysis
Ott and Longnecker (2015) contend that factor analysis is a statistical technique whose main goal is data reduction and a typical use of factor analysis is in survey research, where a researcher wishes to represent a number of questions with a small number of hypothetical factors, for example, as part of a national survey on political opinions, participants may answer three separate questions regarding environmental policy, reflecting issues at the local, state and national level. Additionally, each question, by itself, is an inadequate measure of attitude towards environmental policy but together they may provide a better measure of the attitude. According to Maydeu-Olivares et al. (2017), factor analysis can be used to establish whether the three measures do, in fact, measure the same thing and if so, they can then be combined to create a new variable, a factor score variable that contains a score for each respondent on the factor. Additionally, factor techniques are applicable to a variety of situations and a researcher may want to know if the skills required to be a decathlete are as varied as the ten events, or if a small number of core skills are needed to be successful in a decathlon.
The matrix tables are preceded by a summarised table that reflects the results of Kaiser- Meyer-Olkin and Bartlett's Test. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in variables that might be caused by underlying factors. High values (close to 1.0) generally indicate that a factor analysis may be useful with the data. If the value is less than 0.50, the results of the factor analysis would not very useful. Bartlett's test of sphericity tests the hypothesis that the correlation matrix is an identity matrix, which would indicate that the variables are unrelated and therefore unsuitable for structure detection. Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with the data. In all instances for this research, the conditions are satisfied which allows for the factor analysis procedure.
Factor analysis is done only for the Likert scale items and certain components divided into finer components. Since the Kaiser-Meyer-Olkin Measure of Sampling Adequacy values are greater than 0.500 and the Bartlett's Test of Sphericity significant values are less than 0.05, all of the conditions are satisfied for factor analysis for all the questions (Table 5.2).
180 Table 5.2: Kaiser-Meyer-Olkin and Bartlett's Test
Kaiser-Meyer-Olkin
Measure of
Sampling Adequacy
Bartlett's Test of Sphericity
Section Name Approx. Chi-
Square Df Sig.
B1a
Which are the most important issues (top 5) for society at present?
0.787 510.203 78 0.000
B1b
Which do you think are the most important issues (top 5) for industry/business in general at the moment?
0.810 379.099 78 0.000
B2
Which are the most important environmental issues that you experience or impacts on your life at present?
0.861 732.806 136 0.000
B3
How important do you think the following environmental issues are for the well-being of global society in general?
0.841 927.161 136 0.000
B8
Which of the following do you believe are the main causes of climate change?
0.805 824.897 55 0.000
B12
Which, if any, of the following have you noticed (relating to where you have lived) which may suggest that the climate is changing?
0.771 924.713 36 0.000
C1
Would you be prepared to change your behaviour to reduce your contribution to climate change in any of the following ways?
0.871 649.470 55 0.000
D1
Whom do you think should be responsible for making any changes to lessen the impacts of climate change?
0.895 1647.221 66 0.000
D2
Please indicate how much you agree or disagree with the following statements about climate change by ticking one box on each row:
0.731 1295.304 55 0.000
D3
Whom would you trust in making any changes needed to lessen the impacts of climate change?
0.883 1161.977 66 0.000
D6
Whom would you trust most to give you reliable information on climate change?
0.900 810.965 45 0.000
E1
Please rate your level of agreement with the following statements with regard to climate change in the Distribution Division
0.798 1684.619 28 0.000
181 4.7 Conclusion
This chapter provided a detailed background of the methodological approaches used in this study. The Distribution Division of Eskom, a SOE, was chosen as a case study for this research for a number of reasons including the need to understand climate change learning in a national electricity utility which is a large employer in South Africa. The quantitative method made use of an online survey questionnaire which was hosted on the Distribution Division’s Group Executive’s Homepage. The case study, the Distribution Division, the key informant interviews and the focus group discussions represented the qualitative research approach in this study. The sampling processes used for the case study, the online survey, the key informant interviews and the focus group discussions were discussed briefly. The need to ensure validity and reliability was also emphasised in this chapter to ensure the credibility of this study. The techniques in which the quantitative and qualitative data would be interpreted and analysed was stated which included pairwise ranking, the chi-square technique and the use of SPSS for the statistical analysis of the data. The techniques implemented in this study to overcome some of the potential flaws and to ensure a robust analysis of the focus group discussions were also discussed.
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