MDM_samp_cluster

=__Cluster Sampling__= =By: Abigail & Chelsea=

=Cluster Sampling is a technique when a population of interest is divided into groups or clusters, and from that, a random sample of these clusters is selected.= Cluster sampling is sampling in which groups, not individuals, are randomly selected. All the members of selected groups have similar characteristics.

Steps of cluster sampling: 1. Identify the population that you are sampling. 2. Determine the sample size. 3. List all clusters that make up the population. 4. Estimate the average number of members in each cluster. 5. Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster. 6. Randomly select the needed number of clusters. 7. Include all population members in each selected cluster

A disadvantage of Cluster Sampling is the lack of precision or less statistical efficiency. This is because within the clusters, the individuals are more alike than individuals in the population as a whole. Sometimes the biggest problem with sampling is being able to reach your targets, and having them be spread out over a large geographic area is a common experience.

An advantage of this type of sampling is that it is inexpensive, meaning you can have a larger cluster which gives you less statistical bias. This is also more convenient in comparison to random sampling. It also reduces costs when the population is scattered over a large area.

An example: You want to survey children in a school of a certain age in a specific area. If you drew a simple random sampling of school children, you will have to visit all schools in that area. With cluster sampling, you can select certain schools that will included in your sample and the the children within each school. This will reduce the number of school you will visit; reducing the cost.

One version of cluster sampling is area sampling.

Area Sampling is a method where an area is sampled and than subdivided into smaller blocks. These blocks are selected at random and then subsampled or fully surveyed.



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