Identifying the sampling frame

After identifying the desired population and selecting a sampling unit, it is then necessary to obtain a sampling frame from which to draw the sample. The sampling frame is also known as the

‘working population’ (Zikmund & Babin, 2010). The sampling frame can be defined as that subset of the population, which provides a broad and detailed framework for selection of sampling units (Singh, 2007). Babbie (2010) defines sampling frame as the list constituting a population from which a sample is drawn. Depending on the population and the sampling units being used, some examples of sampling frame would be a list of all the members of a church, factory workers, high school students or members of a club (Zikmund & Babin, 2010; Babbie, 2010). It is important for a sampling frame to be accurate, complete, up-to-date and relevant to the use of the study (ibid).

In the current study, a sampling frame of the residents of the City of Tshwane was not available

72 for selecting the sample elements, therefore non-probability quota sampling was used to determine the sample size for this study.

3.4.4 Select a sampling method

The purpose of sampling is to establish a small sample from an entire population, such that the sample is representative of the entire population. As such, it is essential to ensure that a sample is certainly representative (Richardson et al., n.d). Sampling is a process of selecting a portion of the population to represent the entire population. Singh (2007) describes sampling as ‘the process of selection of sampling units from the population to estimate population parameters in such a way that the sample truly represents the population. It would be impossible to attempt to include all the members of the population in a survey; but neither is it necessary to include every member of the population (Zikmund & Babin, 2010; Richardson et al., n.d).

The two most common ways of sampling a population are probability and non-probability sampling (Walliman, 2011). Table 3.3 presents the types of probability and non-probability sampling.

Table 3.3: Types of probability and non-probability sampling

Probability sampling Non-probability sampling Simple random sampling Convenience sampling

Systematic sampling Quota sampling

Stratified sampling Snowballing

Cluster sampling Purposive sampling

Multi-stage sampling Deviant sampling

Source: Adapted from Walliman (2011); Babbie (2010); Singh (2007)

Probability sampling: Probability sampling determines the probability that any element or member of the population will be included in the sample; while non-probability sampling cannot specify this probability (Mamun et al., 2014; Welman et al., 2013). Every unit of the study has the potential to be equally selected (van Thiel, 2014). Probability sampling is established on random methods of selecting the sample (van Thiel, 2014; Walliman, 2011). Probability sampling provides

73 the researcher with the confidence that the sample is not a biased one; and it enables him to assess how accurate the data are likely to be (Fowler, 2012).

Examples of probability sampling techniques are simple random sampling, cluster and stratified sampling, as shown in Table 3.3. Some of the advantages of probability sampling are that it saves time and cost (Oum, 2010). The main reason for using probability sampling is to generalise the findings from a large population (Steinberg, 2015). Probability sampling is more applicable to descriptive, experimental and correlational designs of research (ibid).

Non-probability sampling: Non-probability sampling is the method whereby a sample is selected deliberately and purposively to produce a representative across the population (van Thiel, 2014).

Non-probability sample does not use a random process; instead population elements are selected by assumptions, judgement, quotas or convenience (Blair & Blair, 2015;Walliman, 2011). While the ultimate goal of probability is to generalise; non-probability sampling aims to have access to information (Steinberg, 2015). Since non-probability is associated with exploratory studies, it is appropriate to use non-random methods to select a sample for the study. Some of the advantages of using non-probability sampling are that: it is convenient; it eliminates acomplex random process; it becomes possible to access population that would be otherwise impossible to reach; and it is impossible to develop a representative sample, for example, of homeless people in a large city (ibid). Non-probability sampling is useful to other studies, in which surveys are needed quickly;

and it is difficult to get access to the whole population. Decisions on inclusion/exclusion are possible with non-probability sampling; and in a way, this makes the study feasible. However, the findings from non-probability sampling cannot be generalised (Walliman, 2011).

Examples of non-probability sampling are: quota, snowball and purposive sampling, as shown in Table 3.3. The study on which the research is based is non-probability sampling, specifically quota sampling; as it improves the representation of particular stratum within the population, as well as ensuring that there is no over-representation of the strata (Ornstein, 2013). Quota sampling should not confused with stratified sampling (Steinberg, 2015).

Quota sampling can be used if probability sampling techniques are not possible (Welman et al., 2013). This research study could not use probability sampling because there was no sample list of the individual members of the population. The results of quota sample can be comparable to

74 stratified sampling data, if applied effectively (Steinberg, 2014; Fowler, 2012; Oum, 2010).

Various subgroups in a population are well represented (Oum, 2010). However, one of the disadvantages of quota sampling is that the selection process is not random.

The interviewers are most likely to pick participants from whom they feel they may get a good response; hence, such a selection is highly biased (Richardson et al, n.d). It is therefore important to consider reporting on aspects of context, such as gender, geographical location, racial composition (Steinberg, 2015). For the purpose of this study, geographical location (regions of the City of Tshwane) was used for quota sampling; so that all the regions in the City of Tshwane could be fully represented.

The population will be divided, according to the seven regions of the City of Tshwane and the samples will be taken from each region to meet the required quota. Table 3.3 shows regional population share percentages in the City of Tshwane. (See map of the City of Tshwane, Appendix A).

75 Table 3.4: Population according to the City of Tshwane’s regions

Region Regional Population (% share)

Region 1: Winterveld area 28

Region 2: Hammanskraal area 12

Region 3: Atteridgeville to Central Business

District to N1 Eastern border by-pass 20

Region 4: Centurion to R21 area 13

Region 5: Roodeplaat dam Cullinan area 3

Region 6: Mamelodi to South-East border 20

Region 7: Bronkhorstspruit to Eastern

border 4

Source: CoT (2016)

The next section to be discussed is research instrument.