File Name: advantages and disadvantages of cluster sampling .zip
Keep up-to-date on postgraduate related issues with our quick reads written by students, postdocs, professors and industry leaders. Multistage sampling, also called multistage cluster sampling, is exactly what it sounds like — sampling in stages. It is a more complex form of cluster sampling, in which smaller groups are successively selected from large populations to form the sample population used in your study.
Home QuestionPro Products Audience. Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups clusters for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection and data analysis. Select your respondents. It is impossible to conduct a research study that involves a student in every university.
Image: Cluster Sampling. Definition: Cluster sampling studies a cluster of the relevant population. It is a design in which the unit of sampling consists of multiple cases e. Cluster sampling is also known as area sampling. Some authors consider it synonymous with multistage sampling. In the multistage sampling, the cases to be studied are picked up randomly at different stages. For example, in studying the problems of middle class working people in a state, the first stage will be to pick up a few districts in the state.
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Cluster sampling refers to a sampling method that has the following properties. Assuming the sample size is constant across sampling methods, cluster sampling generally provides less precision than either simple random sampling or stratified sampling. This is the main disadvantage of cluster sampling. Given this disadvantage, it is natural to ask: Why use cluster sampling?
When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study. Ensures a high degree of representativeness of all the strata or layers in the population.
Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.
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