Definition
The target population is divided into mutually exclusive and collectively exhaustive subpopulation called clusters. Then a random sample of clusters is selected based on probability sampling techniques such as simple random sampling.
For each selected clusters, either all the elements are included in the sample or a sample of elements is drawn probabilistically.
Explanation
• If all the elements in each selected cluster are included in the
sample, the procedure is called one stage cluster sampling.
• If a sample of elements is drawn probabilistically from each selected cluster, the procedure is called two-stage cluster sampling.
• The key distinction between cluster sampling and stratified
sampling is that in cluster sampling only a sample of subpopulations
(clusters) is chosen, whereas in stratified sampling all the subpopulations
are selected.
• The objective of the cluster sampling is to increase the sampling efficiency by decreasing costs.
Example
If the study requires studying the households in the city then in cluster sampling the whole city is divided into Blocks and to take each household on each block selected. Thus to get a representative whole of the universe.
Advantages
• Low population heterogeneity / high population homogeneity
• Low expected cost of errors.
• The main advantage of cluster sampling is the low cost per sampling unit as compared to other sampling methods.
Disadvantage
• High potential of sampling error as compared to other methods.
• For eg: The lower cost per unit and higher sampling error potential of a cluster sample is illustrated by considering a sample of 100 households to be selected for personal interviews from a particular city. In this method the city would be divided in blocks and 10 households from 10 selected blocks would be selected and interviewed.
Thus the cost of personal interview per unit will be low because of the close proximity of the units in the cluster. This sample may not be the exact representation of the entire city. Thus there is a possibility of sampling error.
The target population is divided into mutually exclusive and collectively exhaustive subpopulation called clusters. Then a random sample of clusters is selected based on probability sampling techniques such as simple random sampling.
For each selected clusters, either all the elements are included in the sample or a sample of elements is drawn probabilistically.
Explanation
• If all the elements in each selected cluster are included in the
sample, the procedure is called one stage cluster sampling.
• If a sample of elements is drawn probabilistically from each selected cluster, the procedure is called two-stage cluster sampling.
• The key distinction between cluster sampling and stratified
sampling is that in cluster sampling only a sample of subpopulations
(clusters) is chosen, whereas in stratified sampling all the subpopulations
are selected.
• The objective of the cluster sampling is to increase the sampling efficiency by decreasing costs.
Example
If the study requires studying the households in the city then in cluster sampling the whole city is divided into Blocks and to take each household on each block selected. Thus to get a representative whole of the universe.
Advantages
• Low population heterogeneity / high population homogeneity
• Low expected cost of errors.
• The main advantage of cluster sampling is the low cost per sampling unit as compared to other sampling methods.
Disadvantage
• High potential of sampling error as compared to other methods.
• For eg: The lower cost per unit and higher sampling error potential of a cluster sample is illustrated by considering a sample of 100 households to be selected for personal interviews from a particular city. In this method the city would be divided in blocks and 10 households from 10 selected blocks would be selected and interviewed.
Thus the cost of personal interview per unit will be low because of the close proximity of the units in the cluster. This sample may not be the exact representation of the entire city. Thus there is a possibility of sampling error.