Statistical Package for the Social Sciences (SPSS) was adopted to analyse the data collected from returned questionnaires. SPSS is designed to carry out various statistical data, data management and analysis (Sekaran and Bougie, 2020). To initiate the analysis, pre-testing had been carried out in advance.
After this, the data extracted from the returned questionnaires were coded into IBM SPSS Statistics version 22 for analysis purpose.
3.8.1 Cronbach’s Alpha Reliability Test
Cronbach’s alpha was employed to test the consistency of responses to a set of questions. Prior to data analysis, it is crucial to conduct reliability test in order to determine the statistical reliability of the data (Shan, et al., 2017; Owusu, et al., 2020). With a scale from 0 (no reliability) to 1 (full reliability), the threshold for establishing a statistically reliable dataset is minimum 0.70 (Fellows and Liu, 2015; Saunders, Lewis and Thornhill, 2016). In this research, questionnaires were distributed to collect data regarding Objective 1, Objective 2 and Objective 3. Hence, a total of three Cronbach’s alpha reliability tests were conducted in regard to the three objectives. Each of the tests shall achieve minimum coefficient of 0.7, indicating that the data achieved adequate level of internal consistency and reliability.
3.8.2 Shapiro-Wilk Test
Normality test was conducted to demonstrate the nature of data distribution.
The type of data determines the tests required for further analysis, which normally distributed data requires parametric tests, whereas data that is not normally distributed requires non-parametric tests (Owusu and Chan, 2018).
There are various approaches of normality test. In this research, Shapiro-Wilk test was employed. Shapiro-Wilk test is a common test that determines the data distribution, and it has been widely adopted in previous studies of similar nature (Gel, Miao and Gastwirth, 2007; Owusu and Chan, 2018). The function of Shapiro-Wilk test is defined as testing the normality of distribution, in order to determine whether the distribution as a whole for a variable has significant difference from a comparable normal distribution (Saunders, Lewis and
Thornhill, 2016). A value of 0.05 from Shapiro-Wilk test indicates that the possibility of the actual data distribution differing from a comparable normal distribution is 5 %. Hence, when the probability is 0.05 or lower, the data are not normally distributed. In contrast, if the probability is greater than 0.05, it indicates that the data are normally distributed (Saunders, Lewis and Thornhill, 2016).
3.8.3 Mean Ranking
The general forms of unethical professional practices, the respective influential factors and preventative strategies were ranked via the mean score approach.
The mean score method has been widely adopted in previous construction management studies as instrument to rank relevant variables (Ameyaw, et al., 2017; Chan and Owusu, 2017; Yap, Lee and Skitmore, 2020). The mean values range from 1 to 5 in accordance with the five-point Likert scale applied in questionnaire. A higher mean score indicates that the respondents viewed the variable as important. Standard deviation was applied in the situation where various items have the same mean score, with the lower standard deviation indicates higher level of criticality (Wang and Yuan, 2011; Zhang, et al., 2017; Akinrata, Ogunsemi and Akinradewo, 2019).
Basing on the five-point Likert scale, this research embraced 3.00 as the cut-off point of mean score, indicating that any mean score more than or equal to 3.00 is considered noteworthy variable (Yap, Lee and Skitmore, 2020). Mean rankings were conducted on each sampling group, including developer, consultant, contractor, and government agency. The results of mean score were then structured to Chapter 4 iteratively on the three key sections of questionnaires, which are general forms, influential factors, and preventative strategies of unethical professional practices.
3.8.4 Kruskal-Wallis Test
Kruskal-Wallis test is a one-way analysis of variances by ranks (Cooper and Schindler, 2014). It was adopted to examine whether is there any difference in respondent groups’ perception on a continuous measure (Pallant, 2013). A value higher than 0.05 suggests that there is no significant difference in
perception across the respondent groups. On the other hand, a value equal to or less than 0.05 suggests that there is significant difference, which the alternative hypothesis (H1) is accepted, and the null hypotheses (H0) is rejected (Chua, 2013).
3.8.5 Factor Analysis
Factor analysis was applied in this research to discover the patterns and analyse the relationships among variables that are correlated and hard to interpret. It was employed to group the variables into manageable sets of data so that the underlying structure of the variables can be studied (Cooper and Schindler, 2014; Fellows and Liu, 2015). Factor analysis was applied to Objective 3, in order to simplify and summarise the 22 preventative strategies of unethical professional practices into a smaller set of variables by looking into the intercorrelations among the variables.
There are two major types of factor analysis, namely explanatory factor analysis and confirmatory factor analysis. Explanatory factor analysis (EFA) was employed in this research as it is a common approach in early stages of study to identify the intercorrelation among a set of variables (Pallant, 2013).
In fact, EFA has been widely adopted in many previous construction management studies (Zhang, et al., 2017; Deng, et al., 2018; Yap, Chow and Shavarebi, 2019; Yap, Lee and Skitmore, 2020). On top of that, the technique adopted in this research was principal component analysis (PCA), to transform the 22 variables into a smaller set of linear combinations, applying all of the variances in the variables (Pallant, 2013).
To check the appropriateness and adequacy of the data for exploratory factor analysis, Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett’s Test of Sphericity were assessed beforehand. The KMO measures the appropriateness of sampling by comparing whether the magnitudes of the observed correlation coefficients to the size of the partial correlation coefficients are small enough (Zhang, et al., 2017; Owusu and Chan, 2018). Meanwhile, Bartlett’s Test is used to test homogeneity of variance (Zhang, et al., 2017). Pallant (2013) argued that the data is considered
adequate for factor analysis, if the KMO value is 0.6 or above with a significant value of Bartlett’s Test of Sphericity value which at 0.05 or below.
3.8.6 Spearman’s Correlation Test
Spearman’s correlation test was conducted to assess the strength of relationship between two variables. The correlation coefficient value ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation).
Correlation coefficient between -1 and +1 represents weaker negative and positive correlation, whereas a value of 0 indicates that the variables are perfectly independent (Saunders, Lewis and Thornhill, 2016). The interpretation of values of the correlation coefficient are presented in Figure 3.2 (Hair, et al., 2013). In this research, Spearman’s correlation test was employed to examine the relationship between influential factors (objective 2) and preventative strategies (objective 3) of unethical professional practices in construction industry.
Very strong negative Strong negative Moderate negative Weak negative None None Weak positive Moderate positive Strong positive Very strong positive
Figure 3.2: Interpretation of Values of The Correlation Coefficient