CHAPTER SEVEN: RESEARCH METHODOLOGY
7.8 Target population
7.8.1 Sampling methods
The process of choosing a subset of the elements of a population (larger group) of people, events and/or events is referred to as sampling (Churchill, Brown & Suter, 2010). Sekaran and Bougie (2010) suggest two major categories of sampling design, i.e. probability and non- probability sampling. They clarify that probability sampling happens where the factors in the population have a recognised chance of being chosen, while non-probability sampling happens where the reverse of the known chance of being selected is the case. Wilson (2010) classifies probability sampling methods into systematic, simple random, stratified random, multi-stage sampling and cluster sampling. Below, probability and non-probability sampling are discussed in detail.
7.8.1.1 Probability sampling
In a probability sampling, all the elements in the population have a non-zero chance of being chosen as subjects in the sample (Leedy, 2014; Sekaran & Bougie, 2009; Struwig & Stead, 2013; Wiid & Diggines, 2010). Brown (1947) in Wilson (2010) suggests that probability sampling is beneficial because of its statistical characteristics and highly ranked as being free from biases. While Struwig and Stead (2013) report that it may be seen as the most correct method of sampling. It is flawed by the tediousness of its toll on researchers who may have to spend more time and money in conducting a study based on probability sampling. A few of the types of probability design are explained below.
7.8.1.1.a Simple random sampling
As the name implies, simple random sampling refers to that sampling technique that allows each element of the population of the study to have equal chance of being chosen (Sekaran &
Bougie, 2010). The approach according to Leedy and Ormrod (2014, p. 216) could be easily used where the population size is small and the whole members are known. In an illustration of simple random sampling, Wilson (2010) gave an example of a survey designed to sample
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125 employees among the total population of 500 employees. To ascertain the probability of inclusion into the study, he arrived at 0.25 chance of each one employee in every four being included from the following:
P (inclusion) = sample/total population I.e. 125/500 = 0.25 (1\4)
Implying that one out of every four employees stands the chance of being selected to be included in the survey. Likewise, if a sample of 200 is to be surveyed among a population of 800 people, the inclusion rate is 0.25 using Wilson’s (2010) formula. Therefore, bias levels are low in simple random sampling and in making generalisations about the study; it provides high reliability levels (Bryman & Bell, 2011). Bias as reported by Leedy and Ormrod (2014, p. 223) is any influence, condition or set of conditions that singly or in a combination distort the data. It is flawed when larger samples are used and has been described as impractical by Leedy and Ormrod (2014), and sometimes impossible. Sekaran and Bougie (2009) report that the weaknesses of the approach lie in its selection process which is complicated; a possible absence of current comprehensive list of events, people or cases being examined and most of all it is very time consuming.
The researcher adopted a simple random sampling approach in selecting the Heads of Departments and Heads of Units that were interviewed at the municipality. Pseudonyms were used in place of the names of the interviewees to protect their person in line with confidentiality and anonymity clauses of ethical clearance obtained for the study. Initially, sixteen Heads of Departments /Units were scheduled to be interviewed, but after interviewing eleven of the managers and with no new information being provided (information saturation), the researcher decided to stop conducting further interviews.
There are about 105 Heads of Departments and Units from 45 Departments/Units at the municipality. Therefore one person each was drawn from sixteen (16) major departments totalling sixteen (16) interviewees thereby giving every head of department/unit on the list equal opportunity of selection. However, only eleven (11) individuals in this category were available to be interviewed for this study. Following the ethical rule of voluntarism, this researcher interviewed 11 members of the top management that agreed to participate in the study. The same semi-structured questions were used in all the interviews in sync with the principles of convenience sampling technique (explained in section 5.8.1.2.4).
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Although, as the name implies, the simple random technique may be simple, it is often not appropriate, hence the use of other techniques like systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling explained below.
7.8.1.1.b Systematic sampling
This involves the selection of persons or sometimes clusters in accordance with a sequence that was prearranged (Leedy & Ormrod, 2014, p. 218). In the process, an initial point is selected and subsequently, every nth number listed is then chosen from there (Struwig &
Stead, 2013). According to Saunders et al. (2009), the elements could be drawn at constant intervals from the sample frame till the desired sample size is obtained. The sample fraction determines the regularity of the intervals at which the elements are drawn from the sample frame (Wilson, 2011; Sekaran & Bougie, 2009). Leedy and Ormrod (2014) suggest that the sampling order should originate by chance. For example, from a randomly jumbled list of individuals in a population under study, every fifth individual on the list could be selected.
If, say, a sample of 120 employees is needed for a study of 600 employees, systematically, we can select those to be sampled using the following procedure:
Sampling fraction = real sample size/total population
= 120/600
= 1/5
This implies that from the sampling frame, with every first out of five employees, selection of employees will be made and then at every fifth intermission (Wilson, 2011) another selection will be drawn until the desired sample size is attained. This is illustrated thus: 2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57, 62, and so on. This is done mainly through programmes used by computers (Bryman & Bell, 2011; Leedy & Ormrod, 2014). This method of sampling was not used in the present study.
7.8.1.1.c Stratified random sampling
The easiest way to understand stratified sampling is by looking at the grades used in the school system like grades 2, 3, 4 etc. (Leedy & Ormrod, 2014). It selects a prearranged number of elements from every stratum instead of making selection from the entire population (Struwig & Stead, 2013). Strata might consist of different groups of employees that work in an organisation, for example, directors, senior managers, managers, coordinators and supervisors. Upon categorising universe into many strata, use could be
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made of systematic random sampling or simple random sampling in drawing samples from each stratum until the size of the sample required is attained (Wilson, 2011; Bryman &
Bell, 2011). The key objective of this technique of sampling is to confirm that every stratum is sufficiently represented in the research. Struwig and Stead (2013) suggest three basic questions that a researcher must answer when using stratified random sampling. They are as follows:
a. On what basis are you grouping the sample?
b. How many strata do you wish to construct?
c. What sample size are you anticipating should make up each group?
7.8.1.1.d Proportional stratified sampling
When considering the simple stratified random sampling, it was observed that every strata of the universe is necessarily of equal size (Leedy & Ormrod, 2014). The case of the proportional stratified sampling is different. Here, sample is selected in accordance with the sizes of each group in the universe. For example, if an organisation employs 300 Blacks, 200 Whites and 100 Indians, imagine a survey circumstance where a researcher wants to find out the views of the employees with respect to serving a particular type of meal at organisational events. To be able to achieve the required sample, the researcher will draw his/her sample accordingly with respect to the proportions of each racial group in the organisation. It implies that for every one Indian, there will be two Whites and three Blacks in the sample; meaning that there can be no classification of the selected individuals into strata until the researcher identifies members of each sub-group using random sampling (Leedy & Ormrod, 20014).
7.8.1.1.e Cluster sampling
When the universe to be studied spreads across many locations, it often becomes challenging to compile the list of all the individuals in the area in order to choose a sample to be investigated. Cluster sampling technique assists researchers to split the whole universe under study into clusters (sets). Using random sampling, individuals are randomly drawn from each cluster to make up the sample for a study (Wilson, 2010). It is a cost- and time-effective technique (Sekaran & Bougie, 2009). Saunders et al. (2009) identified three major phases that should be followed in cluster sampling as stated below:
a) The sampling frame should be selected on the basis of a combination of clusters.
b) Every cluster should be given a distinctive number.
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c) In drawing the elements of the samples, simple random sampling is used.
In the present study, the researcher used cluster sampling in drawing the clusters to be studied by randomly selecting five out of the eight municipal clusters.
7.8.1.1.f Multi-stage sampling
This technique is designed to surmount the difficulties of sampling a population that is spread across a large geographical area; especially where person-to-person contact is essential (Saunders et al., 2009). It involves two or more stages of using a combination of some of the probability sampling methods (Struwig & Stead, 2013) discussed above. For instance, through the use of cluster sampling, a researcher can choose four categories of employees from a group of employees in the same organisational cadre. For instance, senior managers in an organisation can be selected from four different departments. Later, stratified sampling could be used to categorise them according to age and gender so as to draw the final sample (Struwig & Stead, 2013). Wilson (2010) reports that multi-stage sampling technique is time- and cost-effective.