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According to Saunders at al (2003 p 151) “Sampling techniques provide a range of methods that enable you to reduce the amount of data you need to collect by considering only data from a subgroup rather than all possible cases or elements”. Additionally there are some research questions that will require sample data to generalise about all the cases from which the sample has been selected others may not require such generalisations. However, in undertaking case study research within a large organisation it is also necessary to select the case study (sample) organisation and a group (sample) of employees to question and managers to interview. Therefore techniques for selecting samples are very important. The sample size will depend on time, access to potential participants, planned methods of analysis, and the degree of precision and accuracy required. In general, the larger the sample the better, but beyond a certain point increasing the sample size has smaller and more marginal benefits (De Vaus 2001).

According to, Sekaran (2003 p. 269) “probability sampling designs are used when the representativeness of the sample is of importance in the interests of wider generalisability”, however when other factors such as time become critical, non- probability sampling is generally used. In an unrestricted probability sampling design, more commonly known as simple random sampling, every element in the population has a known and equal chance of being selected as a subject. This sampling design has the least bias and offers the most generalisability. However, the process can become cumbersome and expensive. As an alternative to the simple random sampling design, several complex probability sampling designs can be used. These probability sampling procedures offer a viable, and sometimes more efficient alternative to the unrestricted design.

The five most common complex probability sampling designs include systematic sampling, where “the population is divided by the required sample size (n) and the sample chosen by taking every ‘nth’ subject”, Collis and Hussey (2003 p 155). In

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stratified random sampling however the main feature is proportional representation of different groups within the sample, (Bryman 2001), but if this process of stratification is continued too far the research can end up with tiny groups that may not be representative of anything, Easterby-Smith et al. (2002). There are two other forms of sampling which also produce less representative pictures than the above methods these are quota and cluster samples. Quota sampling is a form of proportionate stratified sampling, in which a predetermined proportion of people are sampled from different groups, but on a convenience basis, Sekaran (2003). Cluster sampling is, on the surface, similar to stratified sampling as the population needs to be divided into discrete groups prior to sampling. The groups are termed clusters in this form of sampling and can be based on any naturally occurring grouping, Saunders et al. (2003). Cluster sampling involves making a random selection from a sampling frame listing groups of units rather than individual units. Every individual belonging to the selected groups is then interviewed or examined, Collis & Hussey (2003). This selection process makes cluster sampling a probability sampling technique, however, the technique normally results in a sample that represents the total population less accurately than stratified random sampling. Quota sampling is also type of purposive sampling, which ensures that certain groups are adequately represented in the study through the assignment of a quota. Generally, the quota fixed for each subgroup is based on the total numbers of each group in the population. However, since this is a non-probability sampling plan, the results are not generalisable to the population.

Non-probability sampling provides a range of alternative techniques based on the research’s subjective judgement, this judgemental sampling technique; present the research with opportunity to select purposively and to reach difficult-to-identify members of the population. The term non-probability sampling, “ is essentially an umbrella term to capture all forms of sampling that are not conducted according to the canons of probability sampling”, Bryman (2001 p. 97) at least one of which—the quota sample—is claimed by some practitioners to be almost as good as a probability sample. Other forms of non-probability sampling include convenience sampling which involves choosing the nearest and most convenient persons to act as respondents. The process is continued until the required sample size has been reached. This type of sampling is sometimes used as a cheap and dirty way of doing a sample survey and there is no real way of knowing whether or not findings are representative, (Robson 2002). Alternatively snowball sampling can be used this is where the research identifies one or

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more individuals from the population of interest and after they have been interviewed, they are used as informants to identify other members of the population who are themselves used as informants, and so on. Snowball sampling is useful when there is difficulty in identifying members of the population and can be seen as a particular type of purposive sample, (Robson 2002). Sampling is about finding a group to survey Non- probability sampling provides a number of alternative techniques based on the research’s subjective judgement, this research judged that as the topic of management development was the main area of interest, all managers within both companies were asked to complete the questionnaire, the tools of analysis used will depend on whether quantitative or qualitative data has been collected.