Sampling design

According to Orodho (2002:65), sampling involves selecting a given number of subjects from a defined population so as to represent the entire population. For most research it is impossible and impractical to include in the sample every person in the population. Sampling is the part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for purposes of statistical inference (Palit, 2006: 3511).

Wegner (2001:172) advises that the size of sample should be determined by adequacy and resource considerations. According to Aosa (2006:125) adequacy means the sample should be big enough to enable reasonable estimates of variables to be obtained, capture variability of responses and facilitate comparative analysis.

Any statements made about the sample should also be true of the population. This study used probabilistic, also known as probability, sampling techniques.

7.4.3.1 Probability sampling techniques

According to Wegner (2001:171), probability sampling is based on the fact that any element or member of the population is chosen on a purely random basis. However, according to Saunders et al. (2007:171), in a probability sampling technique, the subjects of the sample are chosen on the basis of known probabilities. It uses methods such as simple random-, systematic- and stratified sampling.

a) Simple random sampling

168 A simple random sample is an unbiased surveying technique. In simple random sampling each possible sample combination within the population has an equal probability of being picked and included in the sample (Kothari, 2004:74). Amin (2005:64) argues that in small populations and often in large ones, Simple random sampling is typically done "without replacement", That is, one deliberately avoids selecting an element of the population more than once. Yates et al. (2008:56) support this view but argue that although simple random sampling can be conducted with replacement, it is less common. Denscombe (2008:39) adds that sampling without replacement is no longer independent, but still satisfies exchangeability.

Furthermore, for a small sample from a large population, sampling without replacement is more or less the same as sampling with replacement, since the odds of choosing the same individual twice is low.

b) Systematic sampling

Kothari (2004:74) describes systematic sampling as a technique where only the first unit of a sample is selected randomly and the remaining units of the sample are selected at fixed intervals. This method involves the selection of elements from an ordered sampling frame. According to Black (2004:41), the most common form of systematic sampling is the equal probability method. In this approach, the selection of elements is conducted in a cyclical fashion by again going back to the top of list once all elements had an opportunity of being chosen. Sekaran (2007:273) posits that the sampling should starts by selecting an element from the list at random and then every k^th element in the frame is selected thereafter. However, Black (2004:45) warns that systematic sampling should only be applied if the given population is logically homogeneous because systematic sample units are uniformly distributed over the population. Therefore the researcher must ensure that the chosen sampling interval does not have a pattern, as this will eliminate being chosen at random.

c) Stratified sampling

According to Sekaran (2007:272), stratified sampling involves a process of stratification or segregation, followed by random selection of subjects from each stratum. Särndal, Swensson and Wretman (2003:66) define stratification as the process of dividing members of the population into homogeneous subgroups known as strata which are mutually exclusive. Hunt and Tyrrell (2004:14) argue that when

169 subpopulations within the population vary, it is advantageous to sample each subpopulation (stratum) independently. The strata should be collectively exhaustive by not excluding any population element. Kothari (2004:77) adds that the sample drawn from each stratum can be either proportionate or disproportionate to the number of elements in the stratum. According to Sekaran (2007:273) once the population is stratified, simple random sampling or systematic sampling can be applied within each stratum. This often improves the representativeness of the sample by reducing the sampling error. However, Särndal et al. (2003:66) posit that stratified sampling produces a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population.

A summary of the probability sampling methods is provided in Table 7.3.

Table 7.3: Summary of probability sampling methods

Sampling method Description Authors

Simple random Each element within the population has an equal probability of being included in the sample

Amin (2005:64); Denscombe (2008:39); Kothari (2004:74); Yates et al. (2008:56)

Samples are drawn either proportionately or disproportionate to the number of valid statistical deduction since they avoid bias. The next section discusses non-probability sampling.

7.4.3.2 Non-probability sampling

A non-probability sampling technique is a technique in which the items or individuals included are chosen without regard to their probability of occurrence but based on convenience, judgement or quota (Sekaran, 2007:276). Non-probability sampling techniques cannot be used to infer from the sample to the general population since its disregards the probability of occurrence. These non-probability sampling methods are discussed below.

170 a) Convenience sampling

According to Leedy and Ormrod (2001:207), convenience sampling involves selecting cases or units of observation as they become available to the researcher. A convenience sample is a simple method. Wiederman (2009:59) asserts that it is a suiable method to use if data collection is expensive. According to Lucas (2013:54), since members of the population are chosen based on their relative ease of access, this method is biased because researchers may approach some respondents and on purpose avoid others. In addition, respondents who volunteer for the study may differ from non-participants considerably.

b) Purposive/judgmental sampling

Purposive or judgmental sampling allows a researcher to use cases that have the required information with regard to the objectives of the study (Mugenda & Mugenda, 2003:50). According to Marshall (2006:522), the researcher chooses the sample based on who they think would be appropriate for the study. However, Small (2009:12) is of the view that this method should mainly be used when there are a limited number of people that have expertise in the area being researched.

c) Quota sampling

Quota sampling includes various groups or quotas of the population in the study, based on some criteria (Mugenda & Mugenda, 2003:50). Berg (2006:76) concedes that the researcher selects a quota of the sample from specified sub-groups of the population. In supporting this view, Black (2004:43) argues that this method is similar to stratified sampling, but in quota sampling the selection of the sample is random. According to Small (2009:15), one of the disadvantages of the non-random sampling method, is that the sampling error cannot be assessed.

e) Snowball sampling

According to Kothari (2004:58), snowball sampling is applicable where the characteristics of the population are not fully known. Zikmund (2004:58) posits that a few identified subjects nominate others that they know have the required characteristics. The first respondent refers a friend which in turn refers it to another friend, and so on. This process is followed until the researcher has the required sample size. Berg (2006:43) asserts that such samples are biased because they

171 give people with more social connections an unknown but higher chance of selection.

A summary of the non-probability sampling methods is provided in Table 7.4.

Table 7.4: Summary of non-probability sampling methods

Sampling method Description Authors Purposive/judgmental Select cases that have the required

information needed by the researcher

Marshall (2006:522); Mugenda &

Mugenda (2003:50); Small (2009:12) Snowball The few identified subjects

nominate others know have the required characteristics until the required sample is achieved population, ultimately not making inferences or generalisations about the population (Mugenda & Mugenda, 2003:50).

In this study, the simple random probability sampling method was used. This was used to select the specific top and middle level managers in the state corporations in Kenya. In this method, any employee in top and middle level management had equal probability of being selected in the sample since samples was chosen on a purely random basis. However, the selection of top and middle categories of management was done using purposive or judgmental sampling. This allowed a researcher to use respondents that have the required information related to the objectives of the study (Mugenda & Mugenda, 2003:50). It is envisioned that top and middle level management would provide the information needed for the study (Marshall, 2006:522), especially with regard to strategy implementation since they are the strategy implementers in the organisation.

172 In the following sections the sampling frame of the study is outlined.