Cluster Sampling

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

Cluster sampling is the method used by researchers for 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 is commonly used in statistics for different projects. Moreover, this method of sampling is reliable and affordable for the researchers.

Random cluster sampling is a statistical process, in which a data set or a population is divided into small clusters or groups based on different factors or criterias. After that some random samples are selected from the clusters to for statistical study.

It is a very cost saving and practical way in which study based on samples are done in situations where the population is huge or creating a sample study is challenging. However, there may be deviations or variability in the study which need some adjustments later. It is important to note that this method is widely used in case the groups or data sets are heterogeneous among themselves but as a whole they are homogeneous in nature. Market research projects often use this process. Without an change in the parameters, in case each cluster is approximately of the same size, then the sampling method will remain unbiased. However, if the cluster sizes are very small, then there may be the element of biasness. There are some solutions proposed to eliminate these biases.

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

There are three types of cluster sampling method which are as follows:

1. Single-Stage: In this sampling stage, one will do it only once. They selected random samples only once at a time. For instance, an NGO wants to sample girls across six neighboring cities to grant education. They chose 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 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.

There are many examples of cluster sampling method as if a researcher opts to conduct a study to review the presentation of the sophomore in business culture in the US, so it is impossible to involve sophomores to organize research in every university in the US. Using this sampling method, researchers can easily club all the universities in the US, with each city diversifying into one cluster. These clusters specify all the sophomore strengths of students in the country. The next step is picking up clusters for the study or research. However, by using systematic sampling or simple sampling, one can pick each selected cluster 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 population data, 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 clusters and then select the cities with the highest population. So that researchers filter the ones using mobile phones. This multiple sampling is called cluster sampling.

Let us look at some requirements of the concept of cluster sampling technique.

• These sampling elements should be heterogeneous. The population's research should enclose a distinct subpopulation of altered types.
• Every cluster should be created as a representation of the whole population of the sample.
• Every cluster in the cluster sampling technique should be arranged in a mutually exclusive nature so that it would not be possible for the cluster to occur simultaneously.

Cluster sampling is used by researchers in statistics when natural groups are there in the population. The entire population divides into clusters in such a way as to create random sampling. It is typically used in market research where the researcher cannot get information regarding the entire population. On the contrary, they can get information regarding clusters.

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

Some advantages of the concept of cluster sampling procedure are as follows

• Requires fewer resources: This method is the most effective as it requires fewer resources to research as there is a selection of certain clusters out of the entire population. Hence, it is cheaper than other sampling methods and is also considered cost-effective.
• More Feasible: This technique is more feasible in terms of complexity, as it is very helpful in geographical research.
• Cost saving – Due to less resources it becomes cost saving, especially I the population is spread widely.
• Representaion – It offers a great representation of the population if it is large in size.

Here are some disadvantages of the process of cluster sampling procedure.

• Biased Samples: This sampling is very biased as clusters are randomly selected from the entire population. It has also formed a biased opinion regarding research.
• High Sampling Error: The samples are generally error-based compared to another simple sampling method.
• Homogeneity – The clusters may differ significantly among themselves but ther may be internal homogeneity. This may lead to bias.
• Inefficiency – There is a chance of the process being inefficient than any other method in case the clusters are not defined elaborately.

Both the above are two different statistical method where data is sampled from a large population for study. But some differences between them are as follows:

• The former divides the data into clusters or groups but the latter divides the data into strata or layers.
• In the former, the entire cluster is selected and samples are taken from them, whereas in case of the latter some samples are selected form each strata or levels to form a sample.
• The former is more easy and cost effective but the latter is more detailed study or analysis, which may lead to higher cost.

However, it is to be noted that both the methods have their own pros and cons. Therefore they should be used as per the nature of the population and the type of study that is to be conducted.

This article has been a guide to what is Cluster Sampling. We explain it with examples, differences with stratified sampling, advantages, limitations & types. You may learn more about financing from the following articles: -

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