Some Considerations before Beginning the Analysis of the Data

The next section discusses three critical aspects that need to be taken into consideration before analysing the data. The first relates to the level of reliability of the data, the second is the normality of the distribution of the data and the final aspect considers the missing values and outliers in the data set.

4.6.1 Internal Reliability of the Data

Reliability is concerned with the ability of a measure to generate consistent results. Zikmund (2003, p. 300) defines reliability as “a degree to which measures are free from error and therefore yield consistent results”. Cronbach‟s coefficient alpha is the most commonly used measure for examining the scale reliability (Pallant, 2007). A reliability test was conducted for this study. Cronbach‟s Alpha Coefficient was run to test the internal consistency in areas of the questionnaire using interval scales including the Likert scale. Specifically, it was used to measure how well a set of items (or variables) hung together as a set (Pallant, 2007;

100 Sekaran, 2003). Cronbach‟s Alpha Coefficient can be explained as a correlation coefficient and the alpha value is the range from value 0 to 1.0 (Coakes & Steed, 2003).

Table 4.4 presents the internal reliability of areas in the questionnaire using interval scales, for this research.

Table 4.4: Internal reliability of areas in the questionnaire using interval scales

Variable Number of Items (n) Cronbach’s Alpha Mean inter-item correlation Section B:

Waterfront re Regulation Terms 10 0.891 0.464

Section C:

Guidelines for the riverfront developments concept. Best practice for waterfront developments. 4 18 0.841 0.778 0.592 0.165

As illustrated in Table 4.4 above, all variables showed Alpha values close to 1, which indicates high internal consistency and the achieving of reliability values. As recommended by DeVellis (2003) and Pallant (2007), Alpha values greater than 0.7 were considered acceptable however, a value above 0.8 was preferable. Churchill (1979) however, suggested that an Alpha value of 0.60 or greater also should be considered adequate for developing a new questionnaire. Thus, based on the Alpha values as presented in Table 4.4 above, it can be concluded that the respective respondents were able to understand all questions in the questionnaire and that they agreed on the necessity of the researcher for asking the questions. 4.6.2 Normality of the Distribution in the Data

The most fundamental assumption underlying the statistical analysis was the normality of the data (Coakes & Steed, 2003). Normality refers to the “degree to which the distribution of the sample data corresponds to a normal distribution” (Hair, et al., 2006, p. 40). According to

Pallant (2007), Skewness and Kurtosis are two indications of normality,40 giving information

about the shape of the distribution of the data. Skewness refers to the symmetry of a distribution compared with a normal distribution and Kurtosis is used to describe whether the

40 There are several ways to explore the assumption of normality: graphically; Histogram, Stem-and-leaf plot, Boxplot, Normal probability plot, Detrended normal plot and, statistically; Kolmogorov-Smirnov statistic with a Lilliefors significance level and the Shapiro-Wilk statistic and Skeweness and Kurtosis (Coakes & Steed, 2003; Pallant, 2007)

101 peak of a distribution is taller or shorter than a normal distribution (Morgan & Griego, 1998; Pallant, 2007). In this research, Skewness and Kurtosis were used to assess the normality distribution of the score. The assumption of normality was tested using the Explore option of the Descriptive Statistics menu in SPSS for Windows (Pallant, 2007). Table 4.5 presents the Skewness and Kurtosis for the data set.

Table 4.5: Test of normality – Skewness and Kurtosis

Item Skewness Kurtosis

Environmental Impact Assessment (EIA) is compulsory.

Maintenance and rehabilitation costs are shared between stakeholders.

Use environmentally friendly materials in construction.

Provide flood mitigation (e.g. by planting more trees).

Protection of natural resources (water and environment).

Personal security is maintained by means of policing, surveillance cameras, etc.

Provision of sufficient public facilities and amenities (such as pedestrian, landscaping, access ways, recreation areas, etc.

Upgrading and maintaining established settlement along the waterfront area.

Upgrading and maintaining sewage system. Continuous river rehabilitation.

River reserve beautification. Restrict type of developments.

Integrate both modern and heritage aspects into developments.

Encourage economic activities.

Sharing waterfront benefits (such as views, financial rewards, etc.) among stakeholders (e.g. community, government and developer).

Stakeholders‟ participation.

Continuously educate public about environmental concerns.

Provide regulations and policies that mitigate market speculation for waterfront properties.

-0.448 0.249 -0.305 -0.263 -0.081 0.171 0.101 -0.283 -0.421 -0.494 0.447 0.096 0.121 -0.181 -0.512 -0.714 0.097 -0.471 -0.667 0.046 -1.106 -0.791 -0.771 -1.267 -1.064 -0.373 -0.888 -0.978 -1.862 -0.468 -0.987 -0.874 -0.806 -0.874 -0.784 -0.445

102 As determined by many scholars, a distribution was perfectly normal if it had Skewness and Kurtosis values of zero (0) (George & Mallery 2009, pp. 98-99; Morgan, Leech, Gloeckner, & Barrett, 2007, p. 50; Pallant, 2007, p. 56). However, for the purposes of statistical analysis, Skewness and kurtosis values of more than ±1.0 were excellent, and a value ± 2.0 in many cases was also acceptable (George & Mallery 2009, pp. 98-99). Even though most of the items indicated a negative kurtosis value, it did not seem to affect the results of most of the statistical analyses. Therefore, based on the results (as presented in table 4.5), the data was considered to be normally distributed and appropriate for statistical analysis. The actual shape of the distribution for each item can be seen in the Histogram as presented in Appendix H. 4.6.3 Missing Values and Outliers

The non-response rate for the items in this research was zero, which meant that all the items provided complete information in all cases. Moreover, the data set was also screened for outliers. An outlier may have a disproportionate impact on the statistical analysis. Therefore, it was necessary to identify data that may be unduly influential on the results of the analysis. However, there were no outliers detected in the data set, meaning that all scores were within the possible range for the items or there were no extreme points in the data set (Pallant, 2007, p. 63). Thus, the data set in this research was satisfactory for statistical analysis.

Chapter four has presented the sequence of steps involved in the developments of the methods used for this research; the design of the survey and questionnaire including its content, as well as the process of conducting interviews and distributing the questionnaires were outlined. A number of issues regarding the limited number of responses obtained in the initial phase of the data collection, as well as the representativeness of the sample were discussed. Finally, the data set was screened for the appropriateness of the analytical procedures.

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