In cluster sampling, the entire population is divided into distinct sub-divisions known as clusters based on the problem being studied, and a straightforward random sample of these clusters is taken. Then, we examine and quantify each unit in the chosen clusters.
For instance, if we are interested in learning about the income or opinion statistics in a city the entire city may be divided into distinct blocks or locations, and a simple random sample is used to choose the individuals in each block. The cluster sample is based on the people in the chosen blocks. We now list some of the pros and cons of the cluster sampling method.
Advantages of Cluster Sampling:
- The ease of cluster sampling is its biggest advantage. Sometimes it might be difficult to create a random sample of the entire population. On the other hand, it is considerably simpler to choose clusters and perform measurements on randomly chosen clusters.
- Another benefit of cluster sampling is that only the clusters chosen at the very last step need to have detailed frames developed. Since there is no need to prepare frames for the entire population, this results in significant time and resource savings.
- If all sampling methods were to employ the same total sample size, cluster sampling would typically not yield estimates that were as accurate as simple random sampling or stratified sampling. However, a bigger cluster sample may be chosen at the same cost as that which is possible using the other sampling schemes thanks to the significantly lower cost and administrative simplicity. The higher sample size will lead to a reasonably high level of precision.
- Given that lists of clusters may be the only readily accessible frames for the target population, cluster sampling may be the only practical approach. If that is the case, creating a list of people (or even homes) just to conduct a survey is almost never possible in terms of time and resources. The sampling frame for clusters can be created very quickly from lists of blocks or other geographic entities.
- Since listing and travel expenses are among the lowest of any feasible approach, cluster sampling is usually the most affordable form of sampling.
- Cluster sampling makes it considerably simpler to get data about consumer preferences in marketing research.
Disadvantages of Cluster Sampling:
- The main limitation of cluster sampling is how inaccurate it is. Although it is believed that the clusters’ distributions of preferences are similar, such an assumption may not be entirely correct, which would cause the results to be wrong. Therefore, it is recommended to only employ cluster sampling when it is economically viable.
- Each cluster should have roughly the same number of sample units. Thus, if we are sampling parts of the city where there are private residential homes, business and industrial complexes, apartment buildings, etc., with greatly different numbers of persons or households, cluster sampling is not to be recommended.
- A drawback is that determining how many clusters should be sampled versus how big each duster should be is hard.
- The need for more difficult calculations compared to simple random sampling is a further disadvantage.
