There are a plethora of sampling approaches in research. Below is a partial list of the more common approaches. Please remember that the population is the group you are studying. The sample is a smaller portion of the population. Often it is not practical to collect data from an entire population. Therefore, researchers collect data from a sample and make inferences about the population based on the sample.
The sampling approaches below are all forms of random sampling, which is a process in which any member of the population has an equal likelihood of being selected as part of the sample. Non-random sampling will be dealt with in a future post.
- Simple random sampling. The sample is derived via random numbers or lottery from the population
- Systematic sampling-The selection of every kth element in the population. For example, selecting every fifth student at a school
- Stratified sampling. Subdividing the population into subgroups and taking member at random from each subgroup. This helps to replicate the proportions of the population in the sample. For example, if a school is 75% men and 25% women these same proportions need to exist in the sample population
- Cluster sampling. Random selecting clusters from a population that is spread over a large geographical area. For example, subdividing a country into provinces and then randomly selecting some of the provinces to participate in the study
The sampling approach you used is determined by the purpose of the research, finances, and practicality.