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Stratified Sampling vs. Cluster Sampling

Cluster sampling is similar to stratified sampling since the clusters are treated as groups within the study. However, cluster sampling is less expensive than simple random sampling or stratified sampling and it does not provide the degree of accuracy found using the stratified method. It is often used when a study involves populations spread over a large geographic area. Following is a basic comparison of stratified sampling vs. cluster sampling.

Geographical Cluster Sampling

In geographical cluster sampling a group of residents of a particular area are selected as a cluster. The sampling should be homogeneous and have more participants in each cluster than would be included in strata groups. Each cluster is treated in the same way as a stratum and results are obtained by comparing the samples from various geographic areas within the study parameters, for example as in a state to find commonalities or differences.

Probability Proportionate to Size Sampling

It is usual to include clusters of the same size in studies, but when this is not possible, the probability proportionate to size method is employed. In this selection process, the probability of larger clusters being selected is greater than the probability of smaller clusters being selected. When clusters are selected using this method, the same number of interviews should be conducted in each cluster selected so the probability of each sample being selected is the same.

Applications for Cluster Sampling

Cluster sampling is often used to determine mortality rates from famines, wars and natural disasters. It may also be used to determine rates of certain diseases like cancers within a specific geographical area. Some cluster studies may be repeated over time intervals to obtain statistics on the specific rate of certain diseases in a geographic area over a period of several years. Cluster sampling is most often used when dealing with relatively large populations.

Disadvantages of Stratified Sampling vs. Cluster Sampling

Cluster sampling is considerably less expensive than stratified sampling especially when the study includes a large geographic area which would require significant traveling for interviews. Stratified sampling has a lower sampling error rate and provides a more complete picture of the general population. Multistage sampling may use both cluster and stratified sampling in a single study, breaking down clusters into strata in the later stages of selection and having the advantages of both methods.

Advantages of Stratified Sampling vs. Cluster Sampling

The primary disadvantage of cluster sampling is the margin of error, which while smaller than the percentage of error in random sampling, is much greater than the percentage in stratified sampling. It is used to greatest advantage when variation is between members of groups rather than between groups. The selection methods must include homogeneous representation of each sample cluster for the study to be effective and reasonably accurate.

There are a number of different methods of sampling, but the most commonly employed are simple random sampling, cluster sampling, stratified sampling and multistage sampling. Cluster sampling provides the least expensive method for sampling large groups of people, particularly those who live in distant geographical areas from one another. The margin of error in these studies is greater than that of some other methods, but can be calculated and analyzed at the conclusion of the study.
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