Types of Data Sampling

If a business organization has some raw data concerning a population that it intends to use for a particular analysis, this data can either be from primary or secondary sources. In order to make use of this data, it is important to sample it so as to easily and quickly obtain the required information (RDI Online materials 2012). Sampling involves choosing some subjects to represent the whole population. The sampling method used should be employed accurately so as obtained desired results (Scheaffer, 1996). There are probabilistic and non-probabilistic sampling methods that can be used to accomplish this purpose and they include;

1) Random Sampling

This is a probabilistic method where every member of the population has an equal chance of being included in the sample (RDI Online materials 2012). The choosing is purely made on random basis.

2) Stratified Sampling

In this method, the population is first divided into strata with each strata being representative of the population attributes. These strata form the sample to be used for analysis.

3) Non-Random Sampling

This involves dividing the entire population into clusters and from each of these clusters; a sample is obtained (RDI Online materials 2012). Random sampling may later be used to obtain the subjects from these clusters.

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