Target Population

Population can be defined as the complete universe of people or objects that are of interest to the study (Greener, 2008). According to Kumar (2011), the population of a study are all the targeted units of analysis for data collection, in order to address the identified research problem. It is, therefore, recommended that the researcher defines the population before proceeding with the study (Kumar, 2011). A population may be specific in size, or it might be impractical for the researcher to know the specific population size. However, where it is difficult to specify the size of the population, the characteristics of the population units of analysis should be clear (Howitt and Cramer, 2011). The population for this study included contractors based in the Gauteng Province and registered under the different grades with the CIDB. Table 3.1 presents the population size for the different classes of contractors registered with CIDB, as well as the aggregated population size.

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Table 3.1: Population for the Study

Population Group Size

Contractors (Grade 9) 22

Contractors (Grade 8) 41

Contractors (Grade 7) 101

Total 164

The above population size of 164 contractors was also augmented by clients and consultants who were linked to the current and completed construction projects in the Gauteng Province, those that were being executed (or already executed) by the contracting companies of the 164 contractors. The clients included those from both the private and public sectors, while the consultants included the quantity surveyors, health and safety practitioners, project managers and architects. The population size of 164 contractors, that was augmented by the clients and consultants for the projects being or already executed by the contracting companies for the 164 contractors, could not be used entirely in the study, due to the limited time and financial resources. This brings in another important element of research, that of sampling. The sampling procedure for the research participants is explained in the next section.

3.6.1 Sampling and Sample Size

Sampling is defined as a statistical process of selecting a subset of the population for the purpose of investigating the population to make inferences (Gill and Johnson, 2010; Bhattacherjee, 2012). Sampling is suitable when it is impractical for the researcher to work with the entire population due to the enormous size (Bhattacherjee, 2012). The researcher might have limited time and financial resources to collect data from the enormous population (Saunders, et al., 2009). Sampling was applied in this study, as a result of enormity of the population size.

There are two broad categories of sampling, namely probability and non-probability sampling. Probability sampling is concerned with selecting a representative sample for the purpose of generalising the research findings to the whole population (Gill and Johnson, 2010; Howit and Cramer, 2011). Population elements have therefore an equal chance of being selected (Saunders, et al., 2009). On the other hand, in non-probability sampling, the population

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elements have unequal chance of being selected (Bhattacherjee, 2012). In fact, the probability of selecting the population elements is unknown in non-probability sampling (Howitt and Cramer, 2011). Thus, non-probability sampling is not used in situations where researchers seek to generalise the research findings to the population. This study seeks to generalise the research findings to the whole population of contractors, clients and consultants in the Gauteng Province. The probability sampling method was therefore used in the study.

The probability sampling methods that can be employed in a study are simple random sampling, systematic sampling and stratified random sampling (Saunders, et al., 2009). In simple random sampling, all the population units have an equal probability of being selected and are selected randomly using random tables or computerised random numbers (Kumar, 2011). This method does not recognise the different groups in a population. Simple random sampling was ignored in this study due to its failure of recognising the different groups in the population.

In systematic sampling, the population is first arranged in order, followed by a random selection of the first sample element and then proceeding with selecting every nth _{element of} the population using a fixed interval (Gill and Johnson, 2010; Bhattacherjee, 2012). This method ensures there is no over-representation of either small or big size elements of the population (Bhattacherjee, 2012). This method is therefore only applicable if the population elements can be arranged orderly by size. The population for this study cannot reasonably be arranged by size and thus, the systematic sampling method was ignored.

In stratified sampling, the population is first divided into distinct homogeneous groups and then simple random sampling is applied to select members in each group (Howitt and Cramer, 2011). This method ensures that the different population groups are represented in the sample (Saunders, et al., 2009). The stratified random sampling was employed in the study, as the population for the study was made up of distinct homogeneous groups. First the population of contractors was made up of grade 9, grade 8 and grade 7 contractors. Stratified sampling was used to select 10 contractors from each group to reach a sample size of 30 contractors. Stratified sampling was further used to select 10 clients divided between the public and the private sector clients. The 50 consultants were also chosen by employing the stratified sampling to ensure that all the available professions of consultants were represented. The distribution of the selected consultants is shown in Table 3.2.

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Table 3.2: Distribution of Consultants

Profession Size

Project Managers 10

Architects 10

Quantity Surveyors 10

Engineers 10

Health & Safety Practitioners 10

Total 50

The use of stratified random sampling promoted the representation of all the groups in the population. However, it is important to note that the selection of clients and consultants was first linked to the 30 randomly selected contractors before applying the stratified sampling.

The size of a sample is determined by the size of the population, the researcher’s confidence

level and the level of sample error that is acceptable to the researcher (Saunders, et al., 2009). On the other hand, Neuman (2014) argues that if a researcher seeks

to generalise research findings, then a large sample should be selected at random. The selection of a large sample at random minimises sample error (Neuman, 2014). Greener (2008) is of the view that a large sample that gives a normal distribution is a minimum of 30 objects or people, that are selected at random. This study sought to generalise the research findings to all the contractors, consultants and clients in the Gauteng Province. Accordingly, a sample size of 30 contractors was considered large enough to generalise the research findings to all the contractors in the Gauteng Province. Sampling was further performed to select 50 consultants and 10 clients that are linked to the construction projects currently being executed, or those that have already been executed by the 30 selected contractors. Thus, the aggregated sample size used in the study is 90 contractors, consultants and clients.