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Simple random sampling is a sampling method used in market research studies that falls under the category of probability sampling. This means that when employed, simple random sampling gives everyone in the target population an equal and known probability of being selected as a respondent in the sample group.
Simple random sampling assigns numbers to everyone within the population, so that a sample group may be selected using processes that pick random numbers from the list.
This method of sampling is most apt for when the main objective of a study is for its findings to be generalizable for a whole population. In other words, this method of sampling ensures that the data extracted from the chosen sample group is reflective of what it would be for the target population as a whole.
Simple random sampling is usually used for large populations, hence, it is important to ensure a sample size that is large enough to fittingly represent this population.
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In order to use simple random sampling in a market research study, a researcher must know the size of the population in order to ascertain the number of total units in the population. After finding the total number of units/people within the population, the researcher must then assign serial numbers to each one of them.
For example, if the study is on the employees of an organization that has 300 employees, each one of them must be assigned a number as they are the population from which the sample must be drawn.
After the numbers have been assigned, there are a few common ways in which the required sample size can be drawn from the population:
This method of sampling is also quite expensive, therefore in cases where cost is a primary consideration due to limited resources or funding, this isn’t a feasible sampling method.
Stratified sampling, also a form of probability sampling, is where populations are broken down into subgroups or subsets based on certain criteria and samples are picked from each one of subgroups.
This, in contrast to simple random sampling, ensures that all subgroups in a population are appropriately represented in the sample group. However, this does make it a more complicated and tedious sampling method in comparison and researchers must make sure there is no overlapping of different subgroups/stratas.
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