CHAPTER 5: RESEARCH METHODOLOGY
5.3 Research Design
5.3.4 Research Strategy (Survey)
A research strategy describes the overall plan on how the researcher will go about addressing the research questions of the study (Saunders et al., 2007). Peeling away the methodological choice of Saunders et al. (2015) onion, the research strategy is revealed (see Figure 5.1). This layer emphasised eight different research strategies that the researcher can select within the research design to answer the research questions of the study. In this study, the researcher used a survey strategy has to collect data from individuals who received training from the NDA on NPO governance. The pragmatic philosophy mainly informed the choice of the selection of the survey strategy of the researcher. According to Saunders and Tosey (2012:59), it is possible to select a research strategy that is informed or linked to a philosophical paradigm. As an example, ethnography is associated with both Interpretivism and realism, whereas both experiment and survey are conversely associated with positivism and used by realists and pragmatists researchers.
A survey research strategy is a descriptive research method, which examines the frequency and relationships between variables (Salkind, 2006), and it also describes the phenomenon that is not directly observed (Hakansson, 2013). In survey research, the selection in the sample of respondents from a population and questionnaire is administered and completed by the person being surveyed either through an online questionnaire, telephone interview or a standardised face-to-face approach (Kawulich, 2012). Survey research strategy has the advantage of having a great deal of information from a larger population, and it can be adapted to obtain personal and social facts, beliefs and
98 attitudes (Mathiyazhagan & Nandan, 2010). The study design for the qualitative was in-depth interviews.
5.3.4.1 Population and Sample
Population refers to the entire group of people, events or things that the researcher wishes to investigate (Sekaran & Bougie, 2013). In this study, the population is the total number of individuals that were trained on NPO governance by the NDA only in Gauteng province. The population size of the study is 589 individuals. Since the researcher is evaluating the effectiveness of governance training, the sample included elements that constitute the individuals who are members of the governing body/Board members of the NPOs. Therefore, the unit of analysis for this study were all trained board members of NPOs in Gauteng. The study participants included both male and female, young (youth) and the elderly. A sample, according to Sekaran and Bougie (Sekaran & Bougie, 2013, p. 212) comprises of a subset of the population, in other words, some but not all elements of a population. They further define an element as a single member of the population, and population as the entire group of people, events or things of interest that the researcher wishes to investigate. The researcher used sampling strategy due to the financial and time constraints associated with collecting data, testing or assessing every element of the population. Individuals that are employees or members of the NPOs that received governance training from the NDA are the unit of analysis of this study.
5.3.4.2 Sample Size
According to Wegner et al. (2015), a sample frame is crucial when applying a sampling strategy to select your sample from the population. The contact list of all the trained individuals in Gauteng was a sampling frame for this study. Some of the critical information on this list is the name, the municipality and the contact details of individuals. The determination of the sample size for the study was 95% level of confidence, with a margin of error of five; this then means that the sample size of the study is 253 respondents (survey system, n.d.). The researcher was able to maintain contact with the study participants because she is directly involved in the training program.
The sample size for the qualitative data was fifteen participants. According to Shetty (2018), a sample size for a qualitative study should be large enough to sufficiently describe the phenomenon
99 of interest, as well as being able to address the research question. However, if the sample size is large, there are high risks of the sample size having repetitive data. A sample size of ten for a qualitative study to a considerable extent, can be extremely fruitful and still yield relevant results (Malterud, 2016). Furthermore, Creswell (1998) recommends a size of between 5 and 25; whereas Morse (1994) suggests at least six. Sandelowski (1995) recommends that qualitative sample sizes are large enough to allow the unfolding of a ‘new and richly textured understanding’ of the phenomenon under study, but small enough so that the ‘deep, case-oriented analysis’ (p. 183) of qualitative data is not precluded. Additionally, qualitative samples are, selected by their capacity to provide richly-textured information, relevant to the phenomenon under investigation, and this means that they are purposive (Vasileiou, et al., 2018).
5.3.4.3 Sampling Technique (Simple Random and Purposive Sampling)
Once a sampling frame is in place, the decision is assumed on the sampling size required for the study, then followed by the method of sampling. Sampling can either be probability or non- probability (Sekaran & Bougie, 2015:245). In probability sampling, all elements of the population have an equal and known (non-zero) probability of being included in the sample (Alvi, 2016;
Garner et al., 2015). The researcher has access to the whole population and then randomly select the number needed to make up the sample in no particular or intentional order. Table 5.2 illustrates these advantages and disadvantages.
For qualitative data, purposive sampling was used to select the participants for in-depth interviews.
According to Vasileiou et al. (2018), purposive sampling, as opposed to probability sampling that is used for quantitative research, selects ‘information-rich’ cases. Purposive sampling has demonstrated the greater efficiency as compared to random sampling in qualitative studies (Patton, 1990), and this supports the related assertions long put forward by qualitative methodologists on adopting purposive sampling for qualitative study (Vasileiou, et al., 2018). “Qualitative inquiry typically focuses in-depth on relatively small samples, even single cases (n = 1), selected purposefully, whereas quantitative methods typically depend on larger samples selected randomly” (Patton, 1990, p. 169). In this research, fifteen participants were purposively sampled from the population as the information-rich cases for an in-depth study. The population was precisely defined to cover Chief Executive Officer, Chairperson of Board and Board Secretary of
100 the NPOs that were trained on NPO Governance between April 2016 and March 2017. The researcher applied a subjective judgement to select the sample for qualitative data.
Table 5. 2 Advantages and Disadvantages of Probability and Non-Probability Sampling
Source: Researcher’s Illustration adapted from (Alvi, 2016, pp. 12-14)
On the other hand, non-probability, as explained by Garner et al., (2015:88) is a non-random sample which involves people because of their ability and willingness to participate in the study. The researcher is aware that both sampling techniques are vulnerable to non-response bias. The researcher used the randomiser approach to eliminate any possibility of sampling biases and to have a sample that is representative of the population. In a simple random sampling technique, every element of the population has an equal chance of being selected in the sample (Alvi, 2016). A sample frame was a training list of all individuals trained by the NDA. The list contains the name and contact information for every element of the population. Figure 5.3 illustrates the process followed in randomly selecting the sample from the entire population.
Sampling Techniques
Advantages Disadvantages
Probability Sampling
Reduces the chance of systematic errors.
The methods minimise the chance of sampling biases.
A better representative sample is produced using probability sampling techniques.
Inferences drawn from the sample are generalizable to the population.
Lots of efforts are needed
Time-consuming
Expensive
Non- Probability Sampling
The techniques need less effort.
Less time is needed to finish up.
Not costly.
Prone to encounter with
systematic errors and sampling biases.
The sample cannot be claimed to be a good representative of the population.
Inferences drawn from the sample are not generalizable to the population.
101 Figure 5. 3 Researcher’s Illustration on the process followed for random sampling