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Cluster sampling technique refers to a probability sampling method in which an overall population is split into clusters or groups of sampled data. Data collection and analysis is then proceeded by performing either simple or systematic random sampling on these groups. One good case of this method can be that of researchers endeavoring to find the average height of residents in the US. Considering that such a large-scale study is impossible to realistically conduct, researchers can divide this population into groups based on cities. These groups can then be selected via simple or systematic random sampling for data analysis.
A cluster by definition is any group or collection of things or individuals that represent a singular or common quality amongst its members. As such, cluster sampling basically boils down to dividing population into groups. This method is widely used when the population is geographically spread out.
Similar method is predominantly used in Telecom surveys nationwide almost in every country. It helps assess customer satisfaction for improved service delivery. Clusterization is also used when there are limitations for conducting on-field research.
The difference between cluster and stratified sampling stems from how populations are grouped together. In cluster method, populations are clustered and then individuals from it are randomly selected for your data set. On the other hand, in stratified method, selections are made from the entire population by randomly choosing a predefined criterion. Therefore, the probability of selecting an individual in cluster method is based on individual groups. Meanwhile, in stratified method, it's based on an entire population.
Now, let’s determine when is cluster sampling useful. This method is widely used when researchers can’t get universal information about the entire population. Instead, they can obtain necessary information from groups. This method is common in market research and sociology. For instance, you may study employment rate in Target groups cannot be misrepresented as clustering removes bias.
Cluster sampling formula delves into variables such as clusters in populations, clusters in sample, population observation, and mean score from a sample group. Use this formula to make your calculations:
In this formula, N is the total number of groups in a population while n represents a group in a sample. Capital M showcases total observations in a population. Mh specifies the number of observations in a group h. Finally, Xh provides the mean score from group h.
An example of cluster sampling can be taken if, for instance, a leading NGO wants to get a sample from different towns for underprivileged girls deprived of education. The NGO can group the towns and form a given sample using any available technique and consequently extend assistance. Also, if a manufacturer chooses to do quality control on their product, they can group product availability in different cities. After that, they can select random samples. Furthermore, any homogenous group selected through this method provides a reliable representation metric for different types of elements within a population.
Before answering exactly how to use cluster sampling, we need to understand the difference between single-stage and two-stage clustering. As its name suggests, in a single-stage method, researchers generate a sample only once using random selection of groups. Two-stage method is completed by determining the clusters and then performing a secondary random or systematic selection from the elements of groups.
The number of steps required to complete each of these 2 processes differs.
Here’s how to cluster sampling using a single-stage approach:
In a multistage process, there will be 1 small difference – instead of choosing the groups for data collection, you will need to select random individuals from these groups. Here’s how to do clusterization using a two-stage approach:
Now that you know how to complete each of these 2 processes, let’s discuss pros and cons.
There are various pros and cons for cluster sampling – from data collection to difference in clusters. Below you can find both advantages and disadvantages. Read them carefully to make sure that you choose the right method.
The advantages of cluster sampling, as was mentioned above, can range from the usage of less resources along with enhancing an overall feasibility of your study. A chosen topic might require information to be included from various sources. In this scenario, cluster sampling is preferable. Besides, it’s easy to add extra elements in your research since groups represent a whole population. Overall, the advantages of this method revolve around flexibility and a researcher’s ability to conduct less resource-intensive studies.
Disadvantages of cluster sampling mainly constitute the large error it generates with your final results. Since this method has human intervention, group selection can be rather biased. This may cause misrepresentation of your population. Critical studies such as medical surveys and constituency data should never be conducted using cluster sampling.
Cluster sampling is a unique process through which flexible sampling can be achieved. The technique used here is very simple and doesn’t require much effort. However, adhering to strict deadlines is important no matter your level of study.