Advantages of Cluster Sampling. Cluster sampling involves identification of cluster of participants representing the population and their inclusion in the sample group. Cluster sampling (also known as one-stage cluster sampling) is a technique in which clusters of participants that represent the population are identified and included in the sample. Cluster sampling has been described in a previous question. Example: Cluster Sampling in R. Suppose a company that gives city tours wants to survey its customers. Two stage cluster sampling . When from number of such committees, few are chosen randomly, and then it is a case of one stage cluster sampling. Instead of sampling individuals from each subgroup, you randomly select entire subgroups. Cluster sampling is the sampling method where different groups within a population are used as a sample. The term cluster refers to a natural, but heterogeneous, … Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. Cluster sampling is a variation of sampling design. This makes it very simple for a survey creator to derive effective inference from the feedback. The cluster method comes with a number of advantages over simple random sampling and stratified sampling. 2 Clusters are natural groupings of people, and in the example above the cluster was the football club. A committee comprising of number of members from different departments has a high degree of heterogeneity. Examples One stage cluster sampling. For example, in stratified sampling, a researcher may divide the population into two groups: males vs. females. Cluster sampling is defined as a sampling technique in which the population is divided into already existing groupings (clusters), and then a sample of the cluster is selected randomly from the population. Cluster sampling. Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Conversely, in cluster sampling, the clusters are similar to each other but with different internal composition. 4. This is a popular method in conducting marketing researches. Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Out of ten tours they give one day, they randomly select four tours and ask every customer to rate their experience on a scale of 1 to 10. The fact that the precision of analyzing one sub-plot and analyzing four sub-plots is not very different is probably because of the relatively high intra-cluster correlation (see Spatial autocorrelation and precision).And this has likely to do with the geometric characteristics of forest fragmentation in the area of interest.