It would normally be impractical to study a whole population, for example when doing a questionnaire survey. Sampling is a method that allows researchers to infer information about a population, without having to investigate every individual. Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association.
If a sample is to be used, by whatever method it is chosen, it is important that the individuals chosen are representative of the whole population. This may involve specifically targeting hard to reach groups. For example, if the electoral roll for a town was used to identify participants, some people such as the homeless would not be registered and therefore excluded from the study by default.
There are several different sampling techniques available. The most common sampling methods:
1. Simple random sampling
2. Systematic sampling
3. Stratified sampling
4. Clustered sampling
5. Quota sampling
sample size
Once you have decided how determine sample size?!
The ever increasing need for a representative statistical sample in empirical research has created the demand for an effective method of determining sample size. Determination of sample size differs depending on the research design. For instance, survey research design requires huge sample size for the purpose of representation; in census, everyone in the target population is selected to participate in the study, hence the sample size is equal to the size of the target population; in experimental research design, with treatment and control groups, the sample size may differ in each group.
There are different ways of determining a sample size.
Below you find an indicative table on how to calculate your sample size.
