The selection of a representative sample will be influenced by a number of factors such as: the type and size population, resources available, time constraints, etc. In general, two types of sampling are used: Probability sampling and Non-Probability Sampling.
In probability sampling, a random sample is selected that represents the whole population. Probability sampling can be classified into three types: simple random, stratified random, and cluster. Simple random sampling is a method where each person in the population has the same chance to be selected as a sample. In stratified sampling, the sample comes from a stratum or subgroup. Cluster sampling is a method where the entire population is split into small groups called clusters. A random sample is then selected from each cluster, including all the members of the group.
The non-probability method does not include random selection. It may also not be representative. The non-probability methods of sampling include convenience, purposeful, and snowball. Convenience sample involves choosing individuals that are the most accessible. The purpose of a deliberate sampling is to select individuals according to specific criteria that relate directly with the research question. Snowball sample involves obtaining participants via referrals by other participants.
In the end, the decision on which sampling strategy to use will be based upon the type of population under study, available resources, and specific research questions. It’s essential to carefully consider the pros and cons of each sampling method before selecting one for a study to ensure that the resulting sample is appropriate and can provide accurate and reliable results.