A survey sample is a smaller representation of a larger population targeted by the survey in question. The use of sampling allows for more manageable data to work with and save time and resources in getting closer to realistic results using extrapolation and other techniques. It also gives more flexibility in allowing using different type of analyses to the collected data because of the manageable size or returned results.
Sampling is a scientific method that needs knowledge and discipline to stick to the process.
While doing sampling one should be cognizant of these key items that might affect the following areas:
- the definition of the population
- the size of the survey sample
- the representative-ness of the sample
In regard to the definition of the population, It is important to choose a population that really represents your purpose of the study and will allow you to generalize your finding. In regard to size of sample, consideration must be given to the persistent disappearance of cases in a breakdown analysis. This disappearance should be foreseen as clearly as possible. The essential requirement of any sample is that it is as representative as possible of the population from which it is taken.
Three methods of sampling are commonly used: simple random sampling, stratified sampling, and judgmental or purposive sampling.
Random sampling.
A random sample is one that is drawn in such a way that every member of the population has an equal chance of being included in the sample.
Stratified sampling
The aforementioned method assumes that the composition of the total group is not known, and that a representative sample will be best approximated by a strictly random selection or a selection by regular intervals.
Judgmental or Purposive sampling
When practical consideration preclude the use of probability sampling, researchers may seek a representative sample by other means. They may look for a subgroup that is typical of the population as a whole.
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