Sampling is that part of statistical practice concerned with the selection of an unbiased or random subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern, especially for making predictions based on the statistical inference (Ader, Mellenberg & Hand: 2008). There are quite a number of sampling methods that can be employed in research and these include simple random sampling, systematic sampling, stratified sampling, cluster sampling, matched random sampling, quota sampling, convenience sampling, line intercept sampling, to mention just a few.
Simple Random Sampling:
In a simple random sample of a given size (elements are randomly chosen until a desired sample size is obtained), all such subsets of the frame are given an equal chance or probability. Each element of the population thus has an equal probability of selection: the frame is not subdivided or partitioned. Furthermore, any given pair of elements has the same chance of selection as any other pair and similarly for triples, quads and so on. This minimizes the bias and simplifies analysis of the results. However, simple random sampling method can be vulnerable to sampling error because the randomness of the selection may result in a sample that does not reflect the makeup of the population, for instance, a simple random of ten people from a given town will on average produce five men and five women, but any give trial is likely to over-represent one sex and under-represent the other thus leading to bias misrepresentation of what is actually happening on the ground. Simple random sampling may also be cumbersome and tedious when sampling from an unusually large target population…………(add)
Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random... [continues]
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