The target population for the questionnaire distribution is all the professionals involved in Malaysian public sector projects. The probability sampling used in this study is stratified random sampling, whereby the target population is first separated into mutually exclusive, homogeneous groups, and then a simple random sample is selected from each stratum or group (Trochim, 2005). In order to reflect a balanced and unbiased point of view and ensure the validity of the research, two principal groups were targeted:
i) Public sector professionals working under the Malaysian public organisations engaged in delivering public projects.
ii) External private professionals from the private organisations engaged by the public organisations to undertake public projects: consultants (architects, quantity surveyors, engineers) and contractors.
Unlike simple random sampling, stratified random sampling is the best way to obtain the views of a representative sample from each stratum. It also allows the researcher to make inferences from within the strata or groups and comparisons across the groups (Trochim, 2005).
Malaysian Institute of Architects (PAM), the Malaysian Institute of Engineers, and the Malaysian Institution of Surveyors. For the contractors, the data was collected from the Construction Industry Development Board (CIDB). Letters of invitation to participate in the study were sent out along with the participant information sheet (see Appendix C) before the survey was conducted.
According to the PWD, the estimated number of construction professionals from the public organisations working in public projects in 2014 was 3,800. The total number of contractors in Malaysia registered with CIDB at the end of September 2014 was 66,953, (see the first row of the second column in Table 3.1). However, due to the large numbers, the researcher decided to reduce the sample group by targeting contractors registered under Class G7 (projects greater than RM10 million) from the state of Selangor and the Federal Territory of Kuala Lumpur (also known as Wilayah Persekutuan), (see Total G7 column of Table 3.1). These states were chosen as they are the centre areas of cultural, economic and administrative development in Malaysia and it is assumed that such characteristics qualify the sample to represent the Malaysian construction industry as a whole. The population size of these targeted contractors was thereby reduced to 2,754 (total of class G7 contractors in Selangor and Wilayah Persekutuan). However, 12% of these contractors are inactive, resulting in the final total population of 2,424 active contractors, as highlighted in the ‘active’ column in Table 3.1. The total number of registered consultants (architects, quantity surveyors, engineers) is listed in Table 3.2 below.
Table 3.1: Total Registered Class G7 Contractors by State (CIDB, 2014)
Table 3.2: Local Professional Consultants Registered by Type (CIDB, 2014)
Type of Professional Consultants 2011 2012 2013
Architect1 1,782 1,844 1,858
Quantity Surveyor2 888 930 975
Engineer3 6,841 N.A N.A
Source : 1 Board of Architects Malaysia
2 Board of Quantity Surveyors Malaysia 3 Board of Engineers Malaysia
Note : N.A – Not Available
Overall, a summary of the total estimated population size for this study is tabulated in Table 3.3 below. In order to determine the appropriate sample size (n) from the total population (N), the researcher adopted the formula given by Yamane (1967) whereby, for a 95% confidence level and the desired precision level of 10% (e), the optimum sample size for this study is 99 people. The calculation is shown below:
N 15,898 1 + N(e)2 1 + 15,898(.10)2
The total population (N) is based on Table 3.3. The level of precision (e), sometimes referred to as sampling error, is the range in which the true value of the population is estimated to be. This range is often expressed in percentage points (e.g., ±10%). This means that in this case, if 60% of professionals in the sample ranked strongly agree on the statement of supply chain flexibility with a precision rate of ±10%, then the researcher can conclude that 50% to 70% of the professionals in the population agree with the statement. The precision rate in previous studies normally ranges from 4% to 10% at the 95% confidence level, depending on the maximum sampling error that the researcher is willing to accept and the specific objectives or analysis used. Roscoe (1975) used 10% as a rule of thumb of acceptable precision level, whereas Israel (1992) suggested that for a population of 15,000, a sample of 99 should suffice for a 10% precision level, a confidence level of 95% and p=.5. Previous researchers in the Malaysian construction industry (Abdul-Karim, 2013; Al-Tmeemy et al., 2012) also used the same 10% precision rate, which they found acceptable in obtaining the required sample of construction professionals in Malaysia. Therefore, in line with the calculation above, it was established that an overall target of a minimum valid 99 respondents was acceptable for the analysis in this study.
Table 3.3: Summary of Total Estimated Population Size of Respondents
Targeted Respondents Number Source (Year)
Professionals in public
organisations 3,800
Public Works Department (2014)
Registered
Contractors (Class G7) 2,424 Construction Industry Development Board (2014)
Registered Architects 1,858 Board of Architects
Malaysia (2013)
Registered Quantity
Surveyors 975
Board of Quantity Surveyors Malaysia (2013) Registered Professional Engineers 6,841 Board of Engineers Malaysia (2011) Total 15,898 n = = = 99
response rate, the researcher distributed 220 questionnaires: 100 sets to the public organisations, 60 to contractors, and 60 to consultants. Overall, of the 220 questionnaires distributed covering the targeted population, 105 responses were received (response rate 48%), as listed in Table 3.4. The response rates from all respondents are within the acceptable range, as suggested by Fellows and Liu (2008). In line with the analysis of this study, research samples larger than 30 respondents can also ensure the benefits of central limit theorem (see for example, Roscoe, 1975, p.163; or Abranovic, 1997, p. 307-308). Fraenkel and Wallen (2007) also contended that for correlation studies, such as in the case of this study, a sample of at least 50 was deemed sufficient to establish the existence of a relationship.
Table 3.4: Questionnaire data collected
Respondents Targeted Sample Size Questionnaire sent Questionnaire received Response rate Public Organisations 50 100 54 54% Contractors 25 60 25 42% Consultants 25 60 26 43% Total : 100 220 105 48%
Overall, the sample size is acceptable within the context of the Malaysian construction industry, based on previous studies conducted by Sambasivan and Soon (2007), Alzan et al. (2011) and Abdul-Aziz and Ali (2004). Their sample sizes ranged between 50 and 150 respondents: Sambasivan and Soon (2007) studied the causes and effects of delays in the Malaysian construction industry with 150 respondents consisting of clients, consultants and contractors; Alzan et al. (2011) surveyed 100 contractors registered with CIDB Grade 7to gather their perceptions on factors contributing to project delay; and Abdul-Aziz and Ali (2004) questioned 47 quantity surveyors from the public organisations to assess the quality performance of outsourced quantity surveying services. Hence, it was established that an overall sample of 105 respondents is sufficient for the analysis of this study.
3.5.1.2 Questionnaire Design
The questionnaire design was developed based on existing literature of similar studies (Pettit et al., 2010; Stephenson, 2010; Zhao et al., 2001). An example of the questionnaire is presented in Appendix C. The questionnaire is divided into five sections: