# Statistics - Cluster sampling

In cluster sampling, groups of elements that ideally speaking, are heterogeneous in nature within group, and are chosen randomly. Unlike stratified sampling where groups are homogeneous and few elements are randomly chosen from each group, in cluster sampling the group with intra group heterogeneity are developed and all the elements within the group become a pan of the sample. Whereas stratified sampling has intra group homogeneity and inter group heterogeneity, cluster sampling has intra group heterogeneity.

## Examples

### One stage cluster sampling

A committee comprising of number of members from different departments has a high degree of heterogeneity. When from number of such committees, few are chosen randomly, and then it is a case of one stage cluster sampling.

### Two stage cluster sampling

If from each cluster which has been randomly chosen, few elements are chosen randomly using simple random sampling or any other probability method then it is a two stage cluster sampling.

### Multi-stage cluster sampling

A cluster sample can be a multiple stage sampling, when the choice of element in a sample involves selection at multiple stages e.g. if in a national survey on insurance products a sample of insurance companies is to be drawn, then it requires developing clusters at multiple stages.

In the first stage the clusters are formed on the basis of public and private companies. At the next stage a group of companies is chosen randomly from each cluster developed earlier. In the third stage the office location of each chosen company from where data is to be collected is chosen randomly. Thus in multistage sampling, probability sampling of primary units is done, then from each primary unit a sample of secondary sampling units is drawn and then the third levels till we reach the final stage of breakdown for the sample units.