Cluster Sampling

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Cluster Sampling Definition

Cluster sampling, a cost-effective method in comparison to other statistical methods, refers to a variant of sampling method in which the researchers rather than looking at the entire set of the available data, distribute the population into individual groups known as clusters and select random samples from the population to analyze and interpret results.

Explanation

This type of sampling is used in statistics by choosing random samples among the population. Under this method, instead of choosing all subjects from the population, the researchers focus only on a few samples. The researchers also opt for the entire cluster and not the subset from the cluster. The most famous cluster used in statistics is the geographical cluster.

Examples of Cluster Sampling

There are many examples as if a researcher opts to conduct a study to review the presentation of the sophomore’s in business culture in the US, so it is not possible to involve sophomore to organize research in every university of the US. By using this method of sampling, researchers can easily club all the universities of the US, with each city diversification into one cluster. These clusters specify all the sophomore strength of students in the country. The next step is picking up clusters for the study or research. However, by using systematic samplingSystematic SamplingSystematic sampling is a method of selecting various elements ordered from a sampling frame. This statistical procedure begins with a random selection of elements from a list, and then selects each sampling interval from the frame.read more or simple sampling, each selected cluster can be picked for sophomores of every University for successful research. This method is done on a sample that contains multiple parameters like background, habits, demographics, or other attributes, which are the core of research. This technique will justify that instead of selecting the whole data of the population, select only the bifurcated data for more effectiveness.

Another example is where an organization is surveying the performance of smartphones in Germany. They can diversify the whole population into different clusters and then select the cities that have the highest population. So that researchers filter the one using mobile phones. This multiple sampling is called Cluster sampling.

Types

There are three types which are as follows:

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1. Single-Stage: In this stage of sampling, it will be done only once. Random samples were selected only once at a time. For instance, An NGO wants to do a sampling of girls across six neighboring cities to grant education. They choose a random sample of selected towns of girls who were deprived of education.
2. Two-Stage: This stage of a cluster is better than a single-stage cluster as it shows more reliable results. Under this method, more filters are preferred, which gives improved results. Instead of choosing the entire cluster, it will work over the handful of clusters, which are necessary for the sampling through simple or systematic random sampling.
3. Multiple Stage: This method is a kind of complicated one as compared to other stages. For multiple geographies, research should be more complex, and it has been done through multiple stage clusters technique of sampling.

Requirements

• These sampling elements should be heterogeneous. The research of the population should enclose with a distinct subpopulation of altered types.
• Every cluster should be created as a representation of the whole population of the sample.
• Every cluster should be arranged in a mutually exclusiveMutually ExclusiveMutually exclusive refers to those statistical events which cannot take place at the same time. Thus, these events are entirely independent of one another, i.e., one event's outcome has no impact on the other event's result.read more nature so that it would not be possible for the cluster to occur at the same time.

When to use Cluster Sampling?

Cluster Sampling is used by researchers in statistics when natural groups are there in the population. The entire population is divided into clusters in such a way to create random sampling. It is used typically in the market research where the researcher is unable to get the information regarding the entire population. On the contrary, they can get information regarding clusters.

Applications

This sampling method is used in geographical as well as market research at large. Research on geographical clusters is expensive as compared to other areas of research. The number of samples has been increased in this case for more accuracy. This method is also cost-effective for researchers. This technique is used in scenarios like natural calamities and wars. The application of this method is on a large scale while implementing it by researchers.

Advantages

• Requires fewer resources: This method is the most effective one as it requires fewer resources to research as there is a selection of certain clusters out of the entire population. Hence it is a cheaper method as compared to other sampling methods and considered as cost-effective as well.
• More Feasible: This technique is more feasible in terms of complexity also, as it is very helpful in geographical research.

Disadvantages

• Biased Samples: This sampling is very biased as clusters are selected from the entire population randomly. It has also formed a biased opinion regarding research.
• High Sampling Error: The samples are generally error based as compared to another simple sampling method.

Conclusion

Cluster Sampling is the sampling method used by the researchers for researching geographical data and market research. The population is subdivided into different clusters to select the sample randomly. It is a very helpful technique for researchers. It has many advantages and disadvantages, but it is commonly used in statistics for different kinds of projects. This method of sampling is reliable and affordable by the researchers.

Recommended Articles

This has been a guide to cluster sampling and its definition. Here we discuss examples, types, requirements, applications, and when to use this sampling along with advantages and disadvantages. You may learn more about financing from the following articles –

• Scenario Planning
• Stratified Sampling
• Sampling Distribution Formula
• Type II Error
• Simple Random Sampling