Any form of research that depends on information-gathering techniques such as interviews, questionnaires or biographical research needs to involve the careful selection of a study sample. When studying an entire group is impossible, a researcher needs to choose a sample that represents the group as a whole. A number of different methods exist for selecting this sample.
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Many sampling techniques draw on random number generation to prevent the possibility of researcher bias. The researcher identifies the target population, then selects the percentage of the population needed to create a representative sample. Each individual within the population is then assigned a number. In some cases, this can be done using an existing unique identifier, such as a national insurance number or telephone number. A random number generator then generates a list of individuals to be studied.
Other probability sampling techniques
Random sampling by simply generating and matching numbers is known as simple random sampling. Other types of random sampling technique give the researcher a greater degree of control. Stratified random sampling breaks down the target population into smaller groups and then samples randomly among them. For instance, a researcher who wanted to be sure that the different traits of both men and women in a target population were studied could divide the population by gender and then sample randomly within those groups. Other techniques divide a target populations into groups, choose a group randomly and then study that group comprehensively. For instance, a researcher might randomly choose streets within a city and then interview every resident on chosen streets.
Non-probability sampling techniques
Not all sampling techniques use probabilistic methods. Some non-probability methods are based on the realities of research: for instance, convenience sampling uses a sample population because it is available. A researcher might choose to study a particular city because he or she was already located there, for instance. Purposive sampling relies on the researcher's judgment to choose appropriate subjects for study. This usually means that the sample set will be small compared to the larger groups associated with random sampling.
Choosing a sampling technique
The sampling technique used in any thesis depends on the type of research being done as well as the time and resources available to the researcher. Probability sampling techniques tend to work best for large-scale studies, particularly those dealing with quantitative data. For exploratory studies, purposive sampling can be a good choice -- if a case is going to be studied in depth, the researcher will want to choose it with care. Each method should be carefully explained in the thesis so that the reader can evaluate its appropriateness.
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