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Cluster sampling is a likelihood sampling method that divides the target population into several groups known as clusters. The analysts using this technique then uses a simple or systematic random method to choose random groups to be used for data gathering or analysis. The population sampled using this technique is often made up of many sample parameters including habits, demographics, and any other attribute. These parameters are usually the main focus of the study. Also, cluster sampling is usually performed when the statistical population is made up of subgroups that are almost the same but diverse internally.
Simple Random Sampling
This is a method in sampling that gives every object or item in the target population an equal probability or chance of making up the selected sample. The likelihood of an item being chosen in simple random sampling is based on luck. It is for this reason that this technique is sometimes referred to as the method of chances. Simple random sampling is important and is usually employed in more complex sampling techniques.
Like cluster sampling, multistage sampling also divides the population of interest into clusters. It can be seen as a more intricate form of cluster sampling and sometimes referred to as multistage cluster sampling. To make the primary process of data collection easy, the clusters are further divided into subgroups at various sampling stages. The four main stages of multistage sampling are:
- Select your sampling frame while taking into account the target population
- Choose the sampling frame for the various sub-groups
- You can repeat step 2 if you deem it necessary
- Choose the items that should make up the sample from the sub-groups. A variation of probability sampling can help you with this step.