Chapter 6 Phase 2 (Qualitative data collection and analysis)
6.2 Sampling
An important design decision is appropriate data sources and their selection. Data may be collected from several sources, e.g., people, documents, IS artefact, etc. Sampling involves the selection of a subset of members (i.e., data sources) from a population for investigation. The design considerations are an appropriate population, sampling technique (i.e., how the sample may be selected), and adequate sample size (Saunders et al., 2015).
The main criterion for selecting a population and sample is the potential to provide evidence to solve a research problem. Thus, the research purpose defines the characteristics relevant to the investigation and, thereby, the population and the members to select. A well-delineated population is, therefore, important to ensure the selection of a relevant sample.
The factors which determine how a sample may be selected include the type of population, research approach, ethics, and practical constraints, e.g., time, money, and access (Saunders et al., 2015). For example, in the case of a rare-occurring phenomenon, the population may be small, which influences the sample size and selection techniques. There are two main sampling techniques: probability and non-probability sampling (Walliman, 2018). The primary distinction is the extent to which samples are representative and allow the generalization of research outcomes to the entire population.
Probability assumes that each member of the population has an equal chance of being selected.
Thus, the population needs to be well-defined, i.e., explicit knowledge about the population is necessary. This requires a sample frame, i.e., an accurate, complete, and up-to-date list of cases in the population. Statistical methods are applied to select a random, representative sample, which allows generalization from the sample to the population. Thus, this technique well-suited to research which seeks to extend knowledge about a population (Uprichard, 2013). In general, the larger the sample, the more representative it may be of the population and, therefore, the more accurate the generalization. Common examples include simple random, systematic, stratified, and cluster sampling.
By contrast, non-probability sampling is suitable for research that explores cases to understand the sample, not the population (Uprichard, 2013). Thus, the findings can provide useful information about the population but cannot be generalized (Sekaran & Bougie, 2016).
Examples include convenience, purposive, and snowball.
In convenience sampling, cases are selected based on easy access; purposive sampling allows the selection of members which bear characteristics suitable to the research purpose (Etikan et al., 2016). Uprichard (2013) argued that, in general, sampling is linked to research purpose and data analysis and, therefore, is conducted with a purpose in mind; i.e., purposive is central to all sampling techniques. In snowball, members of a population subsequently introduce others with the requisite characteristics; this is especially useful in cases where it is difficult to identify members of a population (Saunders et al., 2015).
In general, probability sampling is structured and, therefore, linked to the deductive approach, positivism and post-positivism, and survey strategies. By contrast, non-probability sampling is less-structured and associated with induction, constructivism, and case study. However, as shown above, the selection depends on, inter alia, suitability to research purpose and practical (e.g., feasibility) and ethical considerations (Saunders et al., 2015). Notwithstanding the distinction, probability and non-probability techniques may be combined in the same research, depending on the purpose.
Non-probability sampling is predominant in social research— sample frames and probability of elements in the population cannot be established, non-response bias, etc. (Rowley, 2014), and is widely employed in management research (Saunders et al., 2015). Data sources and selection have implications for this research design.
4.6.2.1 Implications for design
As stated above, data source, sampling techniques, and adequate sample size depend on the research purpose and characteristics of the population. People (rather than documents, artefacts, etc.) are well-placed to give an account of their practice (i.e., how e-government outcomes evaluation is done— decisions, actions, motivation, attitude, etc.). Thus, consistent with the descriptive research purpose, people (i.e., evaluation stakeholders) were considered an appropriate source of data.
Probability sampling was impractical (e.g., an up-to-date sample frame may not be available);
a purposive, snowball sampling technique was adopted. The goal was to enable the selection of people with the requisite e-government outcomes evaluation experience and to assure a larger sample size (see section 5.1).
Although the perspectives of all public sector stakeholder groups are necessary to enrich findings, only public employees were considered. The reason is twofold: public employees are central to service delivery, evaluation, and improvement as enjoined by public value and the Batho Pele (see section 2.7.1); and is it easier (e.g., time and budget) to access members of the public with requisite e-government outcomes evaluation experience. Pietersen (2014) selected public employees for similar reasons. Furthermore, Simonofski (et al., 2017) observed evaluation from the perspective of public organizations is uncommon; thus, this research, contributes to the literature.
The sampling strategy above means the findings cannot be representative of all stakeholders and, therefore, not generalizable beyond the sample. Although triangulation of data sources
(e.g., people, documents, artefacts, etc.) may improve the credibility of findings, this was considered impractical due to factors such as time and budget. Sampling decisions are considered in detail in section 4.8 and the respective phases (Chapters 5 and 6). The next section provides an overview of data generation methods and their implications for this research.