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Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed. Researchers usually use pre-existing units such as schools or cities as their clusters.
Cluster sampling is used when the target population is too large or spread out, and studying each subject would be costly, time-consuming, and improbable. Cluster sampling allows researchers to create smaller, more manageable subsections of the population with similar characteristics.
What is Cluster Sampling? Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research.
What is Cluster Sampling? Cluster sampling is a method of obtaining a representative sample from a population that researchers have divided into groups. An individual cluster is a subgroup that mirrors the diversity of the whole population while the set of clusters are similar to each other.
In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected.
At its core, cluster sampling is a method of collecting data from a large population by dividing it into smaller groups, or clusters. Each group or cluster makes up a subgroup that researchers can then study in detail. For example, let's say you want to collect information about athletes in a particular city.
Cluster sampling is a statistical method used when studying large populations, especially when individual elements are not easily accessible. Unlike simple random sampling, where each member of the population has an equal chance of being selected, cluster sampling divides the population into groups, or 'clusters', before making a random selection.
Cluster sampling is a probability sampling method used in research studies where the population is large and geographically dispersed. In cluster sampling, the population is divided into groups, or clusters, based on some criterion, such as geographic location, and a random sample of clusters is selected.
Cluster sampling is a type of probability sampling where the researcher randomly selects a sample from naturally occurring clusters. On the other hand, stratified sampling involves dividing the target population into homogeneous groups or strata and selecting a random sample from the segments.
Cluster sampling is a sampling technique where the population is divided into separate groups, known as clusters, and a random sample of these clusters is selected for study.