Benefits Of Cluster Sampling

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Benefits of Cluster Sampling

Cluster sampling refers to a sampling method that is used when natural groups are seen in a population. Here, the population is separated into smaller clusters and then a sample is taken from the groups. This sampling technique is mainly used when doing marketing research. A common incentive for doing cluster sampling lies in lowering the cost of the sampling project.

1. Financial benefits
The two main concerns of expenses with regards to sampling include listing and traveling. These two are reduced greatly when cluster sampling is used. For instance, compiling research data about each household in the city can be very hard. However, compiling data about several blocks in the city is going to be simpler. Here, listing and also travelling efforts are lessened significantly.

2. Practicability
Cluster sampling offers a practical manner of sampling large numbers of populations. In fact, without cluster sampling compiling certain research could be unachievable. With large populations, using other sampling methods would both be hard and expensive. Cluster sampling therefore provides practicability when research involves large numbers of people.

3. Lowers variability
Anytime you consider the estimates given by other probabilistic techniques of sampling, lessened variability in outcomes are observed. However, this might not always happen. Cluster sampling provides the benefit of increased variability with regards to results. Furthermore, cluster sampling assumes that a certain sample represents the whole cluster. As such, when a positive opinion is expressed in that cluster, the whole population is assumed to have a similar opinion. This makes sampling very easy and lowers variability.

Nevertheless, despite all these merits of cluster sampling, there are drawbacks to using this technique. For instance, beginners are discouraged to use cluster sampling because of the many errors linked to using it. Actually, other probabilistic sampling techniques provide lesser errors as compared to cluster sampling.

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