Can you explain cluster sampling?
Cluster sampling is a probability sampling technique where the population is divided into groups or "clusters," typically based on geographic or natural groupings, and then a random sample of these clusters is selected. Instead of sampling individuals directly, entire clusters are chosen, and either all members of the selected clusters or a random sample of individuals within the clusters are included in the study.
How it works:
Divide the Population into Clusters: The population is split into distinct groups or clusters (e.g., schools, cities, neighborhoods).
Random Selection of Clusters: A random sample of clusters is selected.
Data Collection from Selected Clusters: Either all individuals in the chosen clusters are surveyed (one-stage cluster sampling) or a random sample of individuals within each selected cluster is taken (two-stage cluster sampling).
Where it is used:
Large and Geographically Spread-Out Populations: When the population is too large or spread over a wide area, such as in a national survey.
Cost and Time Efficiency: It reduces travel, time, and cost because only selected clusters need to be surveyed rather than the entire population.
Examples: National household surveys, education studies where schools are sampled instead of individual students.
Cluster sampling is practical when it's difficult or expensive to access the entire population, but it can increase sampling error compared to other probability sampling techniques due to potential homogeneity within clusters.
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