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CHAPTER 4: RESEARCH DESIGN AND METHODOLOGY 105 

4.3  Research design 112 

4.3.4  Sampling technique 116 

Patton (1994:184) asserts that there are no rules in choosing the size in qualitative research. Sampling techniques provide a range of methods that enable a researcher to reduce the amount of data he or she needs to collect by considering only data from a subgroup rather than all the possible cases of elements. The choice of sampling techniques is dependent on the research questions and objectives. Therefore, the research questions and objectives that guide a researcher to estimate the characteristics of the population from a sample statically require probability sampling, whereas research questions and objectives that do not require such generalization can make use of non-probability sampling techniques (Saunders, et al., and 2003:178).

Cohen, et al. (2007:110) distinguish between the two main methods of sampling as probability sampling and non-probability sampling. They explain the difference between them as follows: in a probability sample, the chances of members of the wider population being selected for the sample are known and every member of the wider population has an equal chance of being included in the sample; inclusion or exclusion is a matter of chance and nothing else.

In a non-probability sample, chances of members of the wider population being selected for the sample are unknown while some members of the wider population being selected for the sample are known; some members of the wider population are excluded, and others are included. Not every member of the wider population had an equal chance of being included in the sample; the researcher deliberately, purposely selected a particular section of the wider population. The following paragraphs provide a brief explanation of how these methods of sampling work, since they influence the choice of sampling technique in this research.

4.3.4.1 Probability sampling

Probability sampling techniques are a kind of sampling in which subjects are drawn from a population in known probabilities. McMillan and Schumacher (2004:143) indicate that in probability sampling, the subject is drawn from a larger population in such a way that the probability of selecting each member of the population is known. He adds that this kind of sampling is conducted to provide the estimate of what is true for a population from that particular small group. Corbetta (2003:229) identifies different kinds of probability sampling designs and names them simple random sampling, stratified sampling or systematic sampling.

 Random sampling is where each member of the population or /group has an equal chance of being selected. A simple random sample is obtained when all units of the population have equal probability of being included in the sample.  Systematic sampling. In this sampling design, all sample units have equal

chances of being selected. The selection process is no longer random but systematically selected after a given interval.

 Stratified sampling. This is a sampling design whereby a researcher divides the population into sub-populations based on supplementary information. After dividing the sample into strata, a researcher randomly draws a sample from each sub-population.

4.3.4.2 Non-probability sampling

Non-probability sampling designs are used when the number of elements in a population is unknown or cannot be individually identified. As the sampling technique cannot be random, the selection of elements depends upon other considerations. Corbetta (2003:287) clarifies that a range of non-probability sampling techniques is available that should not be discounted, as they provide sensible alternatives to select cases to answer research questions and to address objectives. Kumar (2005:178) identifies four non-random sampling designs that are used in qualitative research and defines them as follows:

 Quota sampling. This is a sampling design where the researcher draws his sample from a location convenient to him or her. It is guided by some visible characteristics such as gender or race of the population under investigation. A researcher identifies people with visible, relevant characteristics and asks them to participate in the research until he reaches the number of suitable respondents.

 Accidental sampling/convenient sampling. This is a sampling design that is convenient and easy to access the sampling population by the researcher, but it is not guided by visible characteristics so some people consulted may not have the required information. It is used mostly in market research and newspaper reports. (Kumar, 2005:178)

 Snowball sampling. Kumar (2005:178) regards snowball sampling as a design whereby the sample is selected using networks. The researcher starts collecting information from individuals in a group or an organisation. After collecting the information, he asks them to identify other potential informants and the identified informants identify others until the information reaches its highest pick.

 Purposive sampling

Neuman (2006; 222) defines purposive sampling as a non-random sampling method in which the researcher uses a wide range of methods to locate all possible cases of a highly specific and difficult to reach population. Neuman (2000:519) maintains that purposive or judgmental sampling “enables you to use your judgment to select what will best enable you to answer your research questions and to meet your objectives”. This form of sample is often used to select cases particularly informative cases.

Neuman (1997: 206) highlights that purposive sampling is an acceptable kind of sampling for a special situation. It uses the judgment of an expert in selecting cases or it selects cases with a specific purpose in mind. Neuman (1997:206) mentions that purposive sampling is appropriate in the following three situations:

 The researcher uses it to select unique cases that are especially informative, e.g. a researcher who wants to use content analysis because it is trend setting;  A researcher may use purposive sampling to select members of a difficult to reach a specialized population. He or she uses subjective information and experts to identify a sample for inclusion in the research project;

 The third situation for purposive sampling occurs when a researcher wants to identify particular types of cases for depth investigation (Cohen, et al., 2007:115).

Cohen et al. (2007:115) also mentions that in many cases purposive sampling is used to access “knowledgeable people”, i.e. those who have in-depth knowledge about a particular issue maybe by virtue of their professional role, power, and access to networks, expertise or experience. In purposive sampling, often a feature of qualitative research, researchers handpick the cases to be included in the sample based on their judgement or the typicality or possession of the particular characteristics being sought i.e. they build a sample that is satisfactory to their specific needs (Cohen, et al., 2007:114).

The researcher has purposely selected purposive sampling. She used her judgment to select folktales that depict manipulative behaviour from the whole population of folktales in books. She believes that the selected folktales have the information needed to answer the research question and meet the research objectives.