SAMPLING DESIGN

RESEARCH METHODOLOGY

3.3 SAMPLING DESIGN

CHAPTER 3

Cooper and Schindler (2003:179) reinforces the preceding statement when they say, "the basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusion about the entire population.

Cooper and Schindler (2003:179) summaries the following as compelling reasons for sampling:

• lower cost

• greater accuracy of results

• greater speed of data collection

• availability of population elements 3.3.1 Population and Sample

Cooper and Schindler (2003:179) define population as the total collection of elements upon which inferences are wished to be made. They further define a sample as the elements selected from a population and from which conclusions about the entire population may be drawn. It should be noted that drawing conclusions about the population based on a sample depends on the representativeness of the sample. The population for the study is made up all petrol attendants working at the twenty five service stations of the oil company within the Durban Metro. The number of the population elements is estimated at 400 by the oil company. The sample for the study comprises 1 70 petrol attendants drawn from seventeen service stations. This sample size was seen to be acceptable based on Cooper and Schindler (2003)'s advocacy that in reality the size of the sample is a function of the variation in the population parameters under the study.

3.3.2 Sampling Techniques

There are a number of sampling techniques available and are classified as probability

and non-probability sampling. According to Cooper and Schindler (2003), probability

sampling is based on random selection - a controlled procedure that ensures each

population element to be given a known non-zero chance of selection. Non-probability

sampling is not random but the probability of selecting population elements is unknown.

They assert that the probability sampling has technical superiority over non-probability sampling. Cooper and Schindler (2003:199-203) list and define the different forms of probability and non-probability sampling as follows:

3.3.2.1 Probability Sampling:

• The simplest type of probability approach is simple random sampling. In this design, each member of the population has an equal chance of being included in the sample. In developing a probability sample, the researcher has to consider the relevant population, the parameters of interest, the sampling frame, the type of sample, the size of sample and the cost that will be incurred. The specification of the researcher and the nature of the population determine the size of a probability sample. Cost considerations are also often incorporated into the sample size decision.

• Complex sampling is used when conditions make simple random samples impractical or uneconomical. The four major types of complex random sampling are systematic, stratified, cluster and double sampling.

• Systematic sampling involves the selection of every 'k'th element in the population by beginning with a random start between elements from 1 to k. Its simplicity in certain cases is its greatest value.

• Stratified sampling is based on dividing a population into sub-populations and then randomly sampling from each of these strata. Stratified samples may be proportionate or disproportionate.

• In Cluster sampling, the population may be divided into convenient groups first and then randomly select the groups to study. It is typically less efficient from a statistical viewpoint than the simple random because of high degree of homogeneity within the clusters but has a great advantage of cost saving if the population is dispersed geographically or in time.

• At times it may be more convenient or economical to collect some information by sample and use it as a basis for selecting a sub-sample for further study. This procedure is called double sampling.

3.3.2.2 Non-Probability Sampling:

Non-probability sampling also has some compelling practical advantages that account for its widespread use. Probability sampling is often not feasible because the population is not always available. Furthermore, frequent breakdowns in the application of probability sampling discount its technical advantages. Finally, non-probability sampling is usually less expensive to conduct than is probability sampling. The following are examples of non-probability sampling:

• The simplest and least reliable forms of non-probability approach are convenience samples. In this type of sampling, the researchers have the freedom to choose any element of the population that they find. Their primary virtues are low cost and ease of conducting. This technique was adopted for the study, although efforts were made to use probability sampling by seeking consent from all gatekeepers (service station owners/managers) within the defined location to study the job satisfaction of petrol attendants employed by them. Consent was obtained from seventeen gatekeepers out of twenty five and as such a convenience sample had to be used based on approval obtained.

Some of the dealers who approved specified the time on which the researcher could conduct the interviews based on their quite times. This was acceptable to the researcher as the intention was not to disturb the flow of their business. The researcher therefore decided on the convenience sample based on the elements of the population available and willing to participate in the study.

• Purposive sampling is non-probability sample that conforms to certain criteria.

The two major types are judgemental sampling, which is used when the

researcher is interested in studying only selected types of subjects. It is

appropriate when used in the early stages of an exploratory study. The second type of purposive sampling is quota sampling from which subjects are selected to conform to certain pre-designated control measures that secure a representative cross section of the of the population .

• Snowball sampling uses a referral approach to reach particularly hard to find

respondents