Sampling and Rationale for Selecting Stratified Method

Although nowadays sampling is part and parcel of almost all research (Bryman and Bell, 2012), some experienced researchers who have not used this useful technique before seemingly feel uncomfortable and even puzzled by the reasons for the increased popularity and usage of this technique in both quantitative and qualitative research design. While valuable attempts have been made by some methodologists such as Yin (2009), Bryman and Bell (2007 & 2012) and Saunders and his colleagues (2009 & 2012) to reintroduce sampling and its functions in both quantitative and qualitative research, some very experienced but partly traditional academics are not as eager as younger researchers in accepting this technique and its wide functions that go much beyond its traditional boundaries (Lancaster, 2005).

One common misunderstanding is assuming that sampling is just for quantitative research (Patton, 1990). This problem arises from the historical fact that sampling techniques are initially developed by quantitative researchers to deal with difficulty of conducting research in a sizable research population (Krausz and Miller, 1974).

Gradually sampling has been used in qualitative studies too (Lancaster, 2005).

Sampling becomes much more popular in any forms of research due to adding new types (e.g. non probability) and techniques (e.g. quota sampling or snowball sampling) to it (Ragin, 1991).

Another common mistake among some researchers is this mentality that probability sampling is suitable for quantitative research and non-probability sampling is good for qualitative research design (Lancaster, 2005). Fortunately this assumption is seriously challenged by some pioneer methodologists such as Denzin (1978), Jick (1979) and Sieber (1973) with real life experience of conducting many small or large scale studies. These pioneers have shown that segregating research to just two or three domains (e.g. quantitative, qualitative) does not match to actual nature of practical research in real life that there is no a research that is 100%

quantitative or 100% qualitative (Denzin, 1978; Jick, 1979; Sieber, 1973). In any quantitative research there are some qualitative elements (e.g. networking to get access to participants, interpreting the statistical data) and in any qualitative research there are some quantitative aspects too (e.g. number of interview questions, number of participants, number of case studies) (Jick, 1979; Lancaster, 2005; Ragin, 1991).

Consequently, when in real life research, qualitative and quantitative phenomena are

inseparable (Patton, 1990; Tashakkori and Tedli, 2008); sharing some sampling, data collection and data analysis techniques between different types of research not only acceptable but also it is highly recommended (Jick, 1979; Van Maanen et al., 2007).

This research in contrast to unsubstantiated assumption of some other researchers that believe non-probability sampling methods are more appropriate for Case Studies, this research uses Stratified sampling, that is a probability method, intentionally in order to increase the 'Generalisability' of the findings of this study and promote the

‘Verification’ of the 11 propositions in this research. This research is a MAINLY qualitative research but with some quantitative elements especially in the data analysis stage based on Jick’s (1979) and Van Maanen’s (2007) recommendation, so a probability sampling method (stratified) with some basic statistical analysis have been used in this study to strengthen the findings and discussions.

Concept and Importance of Sampling

Sampling is a process or technique for selecting a suitable sample, representative of the population with the objective of collecting characteristics from a whole population. For the sake of a conclusion based on the population of the samples, inferential statistics should be used to help find out results or conclusions from the overall population. Sampling helps researchers to save time instead of going to the whole population, which is time-consuming and expensive (Webster, 1985).

There are advantages to sampling, but it also has disadvantages as the sampling is supposed to be considered representative but there is no assurance that the sample will exactly represent the population because everyone has different opinions. It is commonly observed that, during interviews, no two interviewees are alike and they give different answers to the same questions. It is also sometimes observed that respondents give incorrect answers to the interviewers merely to impress them and this type of error can seriously affect the quality and reliability of research (Webster, 1985).

Three types of sampling are most often used in research, i.e. convenience sampling, judgment sampling and random sampling. Convenience sampling from a population is, as implied, when the most convenient representatives are selected.

Judgment sampling is used when representatives are very familiar with the question concerned and the area of research. Random sampling is the most important type in

the process of sampling, which allows known probability from the representatives chosen and is referred to as a probability sample. There are a few other types of sampling: the simple random sample, systematic random sample, stratified sample and cluster sample.

A simple random sample is where, during the selection of the sample population, everyone has an equal chance of being selected. This type of sampling is not biased because it is based on randomisation.

A systematic random sample selects one unit as the random basis and additional units at intervals until the desired number of units is obtained.

A stratified random sample is where, after selecting a separate, simple random sample out of a population as a group, the population is then divided into different groups.

A cluster sample is where the researcher selects groups or clusters (e.g.

geographically), and then selects individual subjects either by simple random or systematic random sampling.

Rationale for Choosing Stratified Sampling Method

The method followed in order to choose the right academics and senior managers for this research was probability sampling or, to be more specific, a stratified sample.

Some of the reasons this method was chosen can be found in Cooper and Schindler (2008, p. 169), in order to gain efficiency and “to provide adequate data for analysing the various subpopulations or strata” (Cooper and Schindler, 2008, p.

169). There are many academics and authorities in different universities in Saudi Arabia and the United Kingdom, thus there are two main strata (Saudi and British) and within these there are two other strata (academics and authorities). Therefore, the only sampling method that can give a fair and reliable representation of these varied strata would be ‘Stratified Sampling’. Inside each stratum, a simple random sampling was conducted.

This research relies on a Case Study strategy in which one of its main difficulties is limitation of findings to only the relevant cases. The common concern about case studies is that “they provide little basis for scientific generalisation” (Yin, 2009).

Although, in case studies, it is more common to employ non-probability sampling methods such as convenient sampling or snowball sampling methods, there are no methodological limitations in the use of probability sampling methods such as simple

random sampling or stratified sampling methods (Bryman and Bell, 2007). Use of Stratified sampling, a probability method, is intentional to increase the 'Generalisability' of the findings of this study. Stratified sampling can be utilised in order to partly overcome the problem of lack of 'Generalisability' of findings that is one of the main weaknesses of the Case Study strategy (Cooper and Schindler, 2008).

Sampling Process and Sample Size

This is a mainly qualitative research. In qualitative research that generally relies on interviews for primary data collection, there is no specified and standard for sample size. Sample size can be as little as one or as big as 100 participants, depending on the nature of the research and access to participants. In this study, due to the exploratory nature of the research and reasonably good access to participants, the sample size consists of 63 participants (43 academics and 20 authorities).

The process of sampling was as follows: within the two main strata (Saudi and British) and for each stratum (each country) 5-9 universities were selected randomly by using simple random sampling. In other words, six universities were chosen from Saudi Arabia and nine universities were selected from Britain, which altogether totalled 15 universities from these two countries. Inside each university, either in Saudi or in the UK, there are two other strata (academics and authorities). So, randomly 2-3 academics and 1-2 authorities were selected for the interview from each university.

To put it simply, in the first step of sampling, 15 universities were chosen (six universities from Saudi Arabia and nine universities from Britain); and, in the second step of the sampling process, 3-6 participants (2-4 academics and 1-2 authorities) from each university were selected randomly. Thus, in general, 3-6 participants from 15 universities gives a total of 63 interviewees which shapes the sample size.