In general, sampling is conducted in two types of probability sampling and non-probability sampling. In probability sampling there is an equal chance for all members of a population to be selected for the study. Therefore, each member has a known chance of being randomly chosen in the sample. The types of probability sampling are presented below.
Simple random sampling
This type of sampling is suitable when the members of a population are highly homogenous. In this regard the researchers assign a number to each individual and randomly select them from a draw without specifying any criteria. As a result, members of the population have an equal chance of being selected to be a part of a sample.
Systematic random sampling
In this method, members of a population are chosen at regular intervals to be considered as members of a sample. It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. The main advantage of this method is that it is easy and time-saving.
Stratified random sampling
In stratified random sampling, the population is first divided into smaller sub-groups called strata. In the next step, members from each stratum are selected randomly. This method aims to enhance the homogeneity of sample members when the members of a population are not highly homogenous.
Cluster random sampling
This type of sampling is proper when a population is made up of various naturally occurring segments or clusters. Therefore, the researchers first divide the population into separate groups called clusters based on some shared characteristics, such as, location, gender, and age. Afterward, clusters are randomly selected to be included in the study. Finally, the members of samples are all the individuals taken from randomly selected clusters.
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