Chapter 4: Research methodology
4.8 Sampling
In many cases, researchers are unable to conduct first-hand examinations of every unit within a population that they are investigating (a census), and they therefore gather data from a group of units (a sample) and use those findings to draw conclusions about the whole population (Nsiah Asare
& Prempeh, 2016:17). Population in this instance refers to the number of cases which are subject to
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the research. This enables the researcher to make quite accurate forecasts or observations on the conduct of the total population.
Sampling in qualitative research is described by Gentles et al. (2015:1775) as the “selection of specific data sources from which data are collected to address the research objectives”. Curtis et al.
(2000:1002) add that “sample selection is conceptually driven, either by the theoretical framework which underpins the research question from the outset, or by an evolving theory which is derived inductively from the data as the research proceeds”. This means that the sample selection is determined by the objectives of the study or by emerging theory as the researcher builds an understanding on a particular topic. It provides the opportunity for the selection and examination of observations. This is considered as an important component in the comprehension of new or current theories about a phenomenon under observation.
According to Miles and Huberman (1994:34) a sample needs to meet a number of conditions for it to be considered meaningful. In their view, a sample should possess the likelihood of generating a considerable amount of data on the phenomenon being researched. It should produce credible descriptions and explanations as well as make conclusions more generalisable. Furthermore, the researcher needs to examine whether or not the sample strategy is ethical and if at all the sampling plan is feasible in light of all considerations.
The most common reason or advantage of sampling is that it reduces costs. Because inferences may be drawn from selected subsets of the population rather than its entirety, the cost of data collection is greatly curtailed.
With sampling, the research is afforded greater flexibility with regards to the type of information that can be obtained. Furthermore, because the sample is only a portion of the whole, the researcher may collect data speedily and easily. It is however essential to mention that the chosen sample should be reflective of the broader population; as a result, the investigator must carefully examine how well the sample reflects that population.
The researcher may select from probability sampling or non-probability sampling techniques when conducting a research study. Probability sampling is a technique that involves randomly selecting individuals from a population (Etikan et al., 2016:1). Each individual has a fair and impartial probability of selection; in other words, the admission and exclusion of participants is solely based on chance. Systematic sampling, cluster sampling, stratified sampling, and simple random sampling are the four main types of probability sampling.
These sampling methods require the researcher to be in possession of an up-to-date database of the entire population. This on its own, makes the method relatively costly. In addition, the methods demand a considerable amount of time for the design and execution of the study. Probability
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sampling, however, reduces the possibility of systematic error and sampling bias which may occur if the researcher, because of convenience, collects data from readily available individuals.
Non-probability sampling, on the other hand, permits researchers to collect data from the participants non-randomly. Researchers are often confronted with time and financial constraints and, as such, it may be difficult to sample the entire population or even randomly select respondents. Non-probability sampling methods are thus employed to select samples based on their convenience, accessibility and cost effectiveness. With this in mind, non-probability sampling was used for this study, with more elaboration discussed below.
4.9 Non-probability sampling
Cargan (2007:242) highlights that non-probability methods “tend to be used for exploratory research where focus is on generating ideas and understanding behaviour”. For the purpose of this research and in line with set objectives, non-probability sampling was used. Its convenience and cost effectiveness allowed the researcher to select participants who were best placed to provide the most relevant information on the topic. Geographical access to these participants was also an added advantage and this insured that the cost of the research was manageable.
The most common methods of non-probability sampling include purposive (judgmental sampling), quota sampling, convenience sampling and snowball sampling. These sampling methods usually allow the researcher to decide which of the investigated population components will be selected.
Sampling is less stringent, easier to apply and therefore less costly.
Choosing a suitable technique is critical because it needs to be aligned with the research's objectives, the researcher's abilities, and the assessment of research participants (including assessment of accessing participants). An evaluation of the different sampling techniques was conducted, with specific consideration given to the study's objectives and the limitations of each technique. Critics of convenience sampling, for example, contend that the technique is prone to severe bias since the conveniently selected study participants may not actually reflect the whole population. Snowball sampling, on the other hand, is regarded time-consuming and costly to construct, whereas cluster sampling is difficult to evaluate and is prone to significant sampling error.
As such, purposive sampling was considered to be the most suitable technique and used for this research as it met all the criteria of this study.
Purposive sampling is a non-probability technique in which the researcher deliberately picks out a participant based on the qualities that he or she possesses. Participants are selected based on the purpose of the research. The researcher decides what requires investigation and proceeds to identify individuals who are in the best position to participate in providing information based on their expertise and experience (Bernard, 2002). Patton (2002:230) suggests that “it is typically used in qualitative research to identify and select the information rich cases from the most proper utilisation of available
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resources”. Based on Jenson's (1960) definition, a purposive selection involves selecting a group of participants in such a manner that its chosen subsets collectively represent, as close as possible, the entire population with respect to those attributes that are already known. Purposive sampling, according to Cohen (2005:138), enables for a complete range of topics to be examined. The purpose of this type of sampling is so that the research may draw the most information for a particular study.
Although purposive sampling is useful, Sharma (2017:751) notes that the method is prone to researcher bias as it is based on a judgment or selection process which the researcher is in charge of and will likely defend strongly. As a result, this level of subjectivity may be questioned by future researchers. In addition, the chosen participants may be deemed to be unreliable sources of information due to their organisational affiliation. Nevertheless, the researcher must justify the selection of participants by providing evidence that the selected participants are the most appropriate and that their contribution will be valuable for the study. The key objective here is to concentrate on the qualities which will allow the researcher to draw valuable responses to the study questions. The designations of the participants are therefore important as these provide concrete justification for their selection. The researcher is also able to corroborate information obtained through these interviews with secondary data thereby strengthening its credibility. Furthermore, since purposive sampling is ideal for the selection of samples from a small population, it makes sense to make use of this method with regards to MNCs in Zimbabwe.
As part of this study, the researcher used purposive sampling, which involved applying judgment on the most suitable interview participants to provide insight into the CSR strategies formulated by MNCs and how these may have added value to their investment propositions. A purposive sampling process thus provided the study selection with information-rich participants.