Any research is based on data that helps comprehend the research framework; therefore, it is crucial to select the best approach for data collection to subsequently ensure proper analysis of the data. There are many different sampling methods in the literature and over 20 sampling types and subtypes are reviewed and discussed. For example Suri (2011) states that enlightened decisions when considering sampling are crucial for the credibility and quality of research.
Reviewing Patton’s work (1978-1990-1991-2002) and critically assessing the
proposals in these works, he lists a large number of sampling types, their definitions, uses, and results. Purposeful sampling is a recognised and commended type for qualitative research and it is based on finding key informants in the discipline who can point out rich data about the phenomena. Convenience sampling, a well-known and frequently used sampling type, targets easily accessible informants. Although it is of low cost, it is the least desirable as it is deemed neither purposeful nor important. Purposeful random sampling is considered by many researchers as a sample type that provide credible results, even though the sample size may not be large. Stratified purposeful sampling is when the researcher selects samples from within the samples, creating partial samples or strata. The purpose is to locate main differences within the samples, although eventually they data may converge to a common main idea. Snowball sampling asks key informants for advice about others who are sources of information, and this goes on in a chain-like process. In a maximum variation sample the researcher determines key elements of variations and looks for cases that greatly differ from one another. Other types of sampling include homogenous sampling, opportunistic, emergent sampling, critical case sampling, theory based‐ sampling, and many others (Suri, 2011)
Palinkas et al. (2015) state that qualitative research widely employs purposeful sampling in identifying and choosing respondents for information and that this technique is quite effective, especially when resources are limited. They
enumerate as many as 15 purposeful sampling strategies in implementation research.
In probability sampling, the population from which the sample is drawn is known to the researcher, and each element in the population (or sampling frame) has an equal chance of being included in the selection; the selected element cannot appear twice in the sample. Probability sampling is of different types:
random sampling (simple and stratified), cluster sampling, systematic sampling, and other types of sampling techniques. The probability sampling method includes some form of random choice of the elements (Marshall, 1996).
Other sampling techniques (subdivisions of probability and non- probability sampling) include convenience sampling, which comprises a number of subjects selected for their availability. However, there is no method of generalizing from it. It is largely used in qualitative research to identify subjects with rich information about the topic and requires that the available subjects be both willing and able to communicate their views and experiences about the issue being studied (Palinkas et al., 2015). It is the least rigorous and least costly technique (in terms of time and money) and is based on selecting the most accessible subjects. However, it may result in poor-quality data and may lack intellectual credibility (Marshall, 1996).
Another subdivision is purposeful sampling (also called selective or judgment sampling), where the researcher selects the sample that has the highest
probability of answering the research questions. It can involve using the researcher’s practical knowledge of the area of study, learnings from literature, and evidence from the study itself to develop a framework of the variables that may impact the contribution of the study subjects. Judgment sampling is when subjects are selected according to the discretion of the researcher due to familiarity with the pertinent characteristics of the population (Marshall, 1996). It is a practical necessity created by issues such as the time availability, the research model, the researcher’s interest, and other imposed restrictions on his observations (Coyne, 1997). Snowball sampling, also known as chain sampling, is a type of purposive sampling in which the researcher uses inputs from some of the study subjects to gain access to other prospective participants who can help provide more information regarding the objectives of the study (Marshall, 1996).
In qualitative study designs, samples are more or less driven by the theory that is gleaned from the emerging data and, subsequently, more samples are selected to test and further explore the theory (Marshall, 1996).
According to Marshall (1996), the size of the sample is dictated by the optimum number required for valid conclusions to be reached about the population. A larger sample size would minimize the chance of a random sampling error occurring. An appropriate sample size for a qualitative study is one that adequately answers the research question. Coyne (1997) argues that in qualitative research, when the randomly selected sample is small, the
representativeness of the sample is compromised, and the appropriateness of the informants is weak because the selected sample may not include good informants.
Concluding Remarks
The decision to include the total population in the UAE banking sector as the target population was taken during the conceptual phase of the project with the purpose of collecting data from as many respondents (bank employees) as possible. This is deemed a probability purposeful sampling, as the population is predetermined by both the location (UAE) and the type of industry (banking) and each employee in the UAE banking sector has an equal chance of being included in the sample. No fixed number of respondents was predetermined, as there is no control over the expected number of respondents or their demographics (geographical locations, gender, or country of origin, etc.). Such data would be obtained from the information provided by respondents and presented in the findings and discussion of the research. However, it was predicted that 200–300 bank employees would respond. This was considered a good representative sample, especially if the data was to reveal a reasonable representation of the demographics.
6.6 Surveys
According to Skinner and Wakefield (2017) sample surveys, in combination with experiments, have been important instruments for data collection. Survey design may seem interesting; however, it can prove to be complex especially when the issue of nonresponse arises, which is a common occurrence in social science human participants. Thompson, (2015) advises that when designing a survey, both the research questions and the scheme for data analyses should be determined at the beginning of the design process. If the survey of a longitudinal nature, a single sample will not provide reliable estimates of the different characteristics of the population. Analyzing the data from such studies is achieved by using either a design-based model or a model-based with substantive variables.
Fricker Jr. and Schonlau (2002) report that surveys conducted through E- mail and Internet have been both overstated and criticized. Admirers of these modes of delivery of surveys claim that they are not expensive, they are fast, and they produce better response rate than traditional paper and pencil modes.
Scrutinizing published literature about research that used a combination of both modes of delivery has shown without doubt that the above-stated claims are untrue. This conclusion is based on reviews that considered four factors: response rate, timeliness, data quality, and cost. These reviews indicate that Internet-based surveys involve the researcher technically deeper than mail or phone surveys; the idea that Internet-based surveys are less expensive does not have any supporting data; response rates for these surveys (if used alone) are found to get moderate or
poor responses; and while the argument regarding the time requirements is true when considering the time to dispatch the survey, there is not support for the claim when the time to receive responses is in question.
Surveys have several merits: They can be self-administered, they are cost- effective and cover wide geographical areas, they provide anonymity and allow respondents time to think about their responses, and the researcher cannot influence the responses. Their disadvantages are their limited length and complexity (brevity and clarity of questions), possible low response rates, and problems with identifying non-response bias (Bird, 2009).
6.7 The Instrument – “Questionnaire”
A quantitative, statistical survey instrument is used in this study by means of a self-administered questionnaire. The questions were designed to elicit responses regarding organizational factors contributing, or limiting, access to knowledge transfer practices. Organizational culture, organizational structure, leadership practices, and management support were deemed to influence knowledge transfer. It was anticipated that with an adequate number of responses, the study would glean information about factors of interest in talent-transfer practices in UAE banks. This quantitative survey was conducted among employees working in UAE banks to learn more about their attitudes toward KS, the perceptions about the same in banks, and how they impact their performance.
A self-administered questionnaire for the study was developed, which covers variables related to organizational factors (culture, structure, trust, leadership, and management support), personal factors (commitment, self-efficacy, and motivation), relationship factors (Communities of Practice), and technological factors.