Chapter 6: Conclusion and Recommendations
3.3 Sampling Design
Cooper and Schindler (2003) explain that the fundamental idea of sampling is by selecting some of the elements in a population, conclusions may be drawn about the entire population.
3.3.1 Sample and population
A population element is the unit of study on which the measurement is being taken. A population is defined as being the total collection of all the elements about which one is required to make an inference (Cooper and Schindler, 2003).
The population of 1,100 in this study comprises of employees of SAPPI Saiccor, a division of SAPPI Limited, an international pulp and paper manufacturing company whom the researcher has regarded as being capable of providing the information required.
According to the judgement of the researcher, a sample of fifty individuals was adequate in providing the information required for the study. The richness of knowledge in the field of pulp and paper that these individuals boast made them exceptional participants for the study. Of the fifty individuals in the sample, thirty responded to the questionnaire.
3.3.2 Reasons for sampling
The reasons for sampling include lower cost, greater accuracy of results, greater speed of data collection and the availability of population elements (Cooper and Schindler, 2003). Sekaran (1992) states that in investigations concerning several hundreds or
examine every element. Even if this was possible, it would be prohibitive in terms of time, costs and other resources.
3.3.3 Sampling techniques
The two types of sampling techniques are probability and non-probability sampling.
In probability sampling, the elements within the population have a known chance or probability of being selected as sample subjects. In non-probability sampling, the elements do not have a known or predetermined chance of being selected as sample subjects.
Probability sampling designs are used when the representativeness of the sample is important for the purpose of greater generalizability. When time or other aspects rather than generalizability are of crucial importance, non-probability sampling is usually used (Sekaran, 1992).
Greenfield (2002) states that probability sampling refers to sample designs wherein units are chosen by a certain probability mechanism, thereby allowing no possibility for subjectivity.
3.3.3.1 Non-probability sample techniques
Of the fifty people who were identified that could provide the required information to the study, thirty individuals responded to the questionnaire.
3.3.3.1.1 Purposive sampling
Purposive or judgemental sampling enables the researcher to use judgement in selecting cases that will provide the best approach in meeting the objectives. This type of sampling is usually used when the sample is small (Saunders and Lewis, et al, 2003).
3.3.3.1.2 Quota sampling
Quota sampling is non-random and is usually utilised for interview surveys. This type of sampling is based on the assertion that the sample will be representative of the population as the variability in the sample for various quota variables is the same as that of the population.
The advantages of quota sampling relative to probabilistic techniques are reduced costs and the time taken to set up is generally quick (Saunders and Lewis, et al, 2003).
Sekaran (1992) states a disadvantage of quota sampling as not being easily generalizable.
3.3.3.1.3 Snowball sampling
Snowball sampling is often used when it is difficult to identify subjects of the desired population. The process of identifying subjects is to make the initial contact with one or two members and ask them to identify further cases. The new cases are also asked to identify new cases. The process ends when no new cases are given or until the sample size is as large as manageable (Saunders and Lewis, et al, 2003).
3.3.3.1.4 Convenience sampling
Convenience sample is chosen on the basis that it is simple to obtain and performs the task. This sampling type provides a swift and low-cost solution. It is however, prone to bias (Curwin and Slater, 2002).
3.3.3.2 Probability sampling
Table 3.1 compares the different types of probability sampling designs within the framework of description, advantages and disadvantages.
Table 3.1 Probability sampling designs Type
Simple random
Systematic
Stratified
Description
Every element within the population has an equal chance of being selected into the sample.
Chooses an element of the population at a beginning with a random start and following the sampling fraction selects every kth element.
Divides the population into subpopulations or strata and uses simple random on each strata. The results may be weighted and combined.
Advantages
Simple to implement with automatic dialling and computerised voice response systems.
Easy to design.
Simple to use in comparison to the simple random.
Easy to determine sampling distribution of the mean or proportion.
Cheaper than simple random.
The researcher controls the sample size in strata.
Increased statistical efficiency.
Generates data to represent and analyze subgroups.
Allows the use of
Disadvantages A listing of
population elements is required.
Requires more time for implementation Larger sample sizes are used.
Produces large errors.
Expensive method.
Periodicity that exists within the population may skew the sample and results.
Should the
population list have a monotonic trend, a biased estimate will result based on the start point.
Increased error will result if subgroups are selected at different rates.
Expensive.
Especially costly if strata on the
population is to be created.
Cluster
Double (sequential or
multiphase)
The population is divided into internally
heterogeneous subgroups.
Some of them are randomly selected for further study.
The process includes collecting data from a sample by utilising a previously defined method.
Based on the findings, a sub sample is selected for further study.
Provides an unbiased estimate of the
population parameters if done properly.
Economically more efficient than simple random.
Cost is lower per sample, especially in geographic clusters.
Easy to perform without a population list.
Costs may be reduced if the first stage results in adequate data to stratify or cluster the
population.
Often lower
statistical efficiency because of
subgroups being homogenous rather than heterogeneous.
Increased costs if indiscriminately utilised.
Source: Cooper and Schindler (2003), page 199 (Adapted)
A purposive sample of fifty individuals was utilised for the study. The richness of knowledge in the field of pulp and paper that these individuals boast made them exceptional participants for the study. Purposive sampling was utilised as the participants were chosen on the basis of their expert knowledge in the field of the paper industry.