Why is cluster sampling used

The second step is to interview a random sample of the customers of those stores. There are two types of cluster sampling: one-stage cluster sampling and two-stage cluster sampling. One-stage cluster sampling involves choosing a random sample of clusters and gathering data from every single subject within that cluster.

Two-stage cluster sampling involves randomly selecting multiple clusters and choosing certain subjects randomly within each cluster to form the final sample. Two-stage sampling can be seen as a subset of one-stage sampling: sampling certain elements from the created clusters. This sampling method may be used when completing a list of the entire population is difficult as demonstrated in the example above. This is a simple, manual process that can save time and money.

In fact, using cluster sampling can be fairly cheap when compared to other methods. That's because there are generally fewer associated costs and expenses because cluster sampling requires choosing selected clusters at random rather than evaluating entire populations. This same process also allows for increasing the sample size. As a statistician is only choosing from a select group of clusters, they can increase the number of subjects to sample from within that cluster. The primary disadvantage of cluster sampling is that there is a larger sampling error associated with it, making it less precise than other methods of sampling.

This is because subjects within a cluster tend to have similar characteristics, meaning that cluster sampling does not include varied demographics of the population. This often results in an overrepresentation or underrepresentation within a cluster, and, therefore, can be a biased sample. For example, say an academic study is being conducted to determine how many employees at investment banks hold MBAs , and of those MBAs, how many are from Ivy League schools. It would be difficult for the statistician to go to every investment bank and ask every single employee their educational background.

To achieve the goal, a statistician can employ cluster sampling. The first step would be to form a cluster of investment banks. Rather than study every investment bank, the statistician can choose to study the top three largest investment banks based on revenue, forming the first cluster. From there, rather than interviewing every employee in all three investment banks, a statistician could form another cluster, which would include employees from only certain departments, for example, sales and trading or mergers and acquisitions.

This method allows the statistician to narrow down the sampling size, making it more efficient and cost-effective, yet still having a varied enough sample to gauge the information being sought. Though both systematic sampling and cluster sampling are forms of random sampling, they arrive at their sample size in completely different ways.

Systematic sampling chooses a sample based on fixed intervals in a population whereas cluster sampling creates a cluster from a population. Cluster sampling is better suited for when there are different subsets within a specific population, whereas systematic sampling is better used when the entire list or number of a population is known. Both, however, are splitting the population into smaller units to sample.

For systematic sampling it is important to ensure there are no patterns in the group, otherwise, you risk choosing similar subjects without representing the overall population. For cluster sampling, it is important to ensure that each cluster has similar traits to the whole sample. Cluster sampling is a form of random sampling that separates a population into clusters to create a sample.

Further clusters can be created from the initial clusters as well to narrow down a sample. Cluster sampling is best used to study large, spread out populations, where aiming to interview each subject would be costly, time-consuming, and perhaps impossible. Cluster sampling allows for creating clusters that are a smaller representation of the population being assessed, with similar characteristics.

Cluster sampling simply involves dividing the population being studied into smaller groups. These subgroups can be studied or further randomly divided into other subgroups. The primary difference between cluster sampling and stratified sampling is that the clusters created in cluster sampling are heterogeneous whereas the groups for stratified sampling are homogeneous.

There are a variety of sampling methods available to statisticians who seek to study information within groups. Because groups or populations tend to be large, it is very difficult to obtain data from every single subject. To overcome this problem, statisticians use sampling, creating smaller groups that are meant to be representative of the larger population. An important aspect of creating these smaller samples is to ensure they are selected at random and are a true representation of the larger population.

Systematic sampling and cluster sampling are two methods that statisticians can use to study populations. Both are forms of random sampling that can be time- and cost-efficient, separating populations into smaller groups for easier analysis. Select clusters using methods of random selection while keeping in mind the desired sample size. Collect data from the final sample group.

Get Expert's Sampling Guide. What are advantages of Cluster Sampling? Easy to implement: Cluster sampling is relatively easy to implement. Very efficient : This method of sampling is more cost-effective and time-efficient in contrast to some other forms of probability sampling, such as simple random sampling.

Improve Survey Response Rates. What are disadvantages of cluster sampling? Difficult to analyse: The results are usually also difficult to compute and interpret. Difficult to implement : This method of sampling also tends to be difficult to plan and execute, in comparison to some other forms of sampling. High Sampling Error: Cluster sampling is also relatively more prone to high sampling error. Book a Free Demo.

Cluster Sampling versus Stratified Random Sampling. The main objective in cluster sampling is to reduce costs, while in stratified sampling the objective is to accurately represent the population and obtain results that aptly represent the population. Voxco can help you in conducting cost effective survey research.

The subgroups in cluster sampling are called clusters, not all of these clusters are included in the sample group, some are eliminated. In stratified random sampling, on the other hand, elements are picked from each subgroup also known as strata so that each strata is equally represented in the sample group.

Elements from every strata are chosen in stratified random sampling, whereas in cluster sampling, whole clusters are chosen to be a part of the sample group. Within each stratum in stratified random sampling, the sub-population is homogeneous.

In contrast, each cluster has a sub-population that is heterogeneous. Members from randomly selected clusters are a part of this sample. Researchers consider individual components of the strata randomly to be a part of sampling units.

Researchers maintain homogeneity between clusters. Researchers maintain homogeneity within the strata. Researchers divide the clusters naturally. The researchers or statisticians primarily decide the strata division. The key objective is to minimize the cost involved and enhance competence. The key objective is to conduct accurate sampling, along with a properly represented population. Related Posts. Sampling bias in research, types, examples, and how to avoid it. A guide to choosing the right sample partner for research.

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Back to QuestionPro. On the other hand, the sub-groups occur naturally in cluster sampling. Stratified sampling achieves homogeneity within the strata, while cluster sampling achieves uniformity between the clusters.

So, why is cluster sampling a big deal in data collection? Frankly, there are several reasons. When dealing with a large target population and a strict time frame, it's impossible to gather all the data you need from every target audience member.

By adopting cluster sampling, researchers can gather quality responses from their target audience while saving time and resources. Common advantages of cluster sampling include:. Although cluster sampling isn't always the answer to data collection in a systematic investigation despite its many advantages, specifically, it has the following disadvantages:.

The hack to cluster sampling is identifying the fine lines between subgroups in your research population. This means that the parameters used must create research groups that are similar yet internally diverse. You can break your target audience into naturally-occurring clusters when you get this right and collect the information you need. Create Online Surveys for Free.

Create powerful online surveys in 90 seconds with Formplus. Different socio-demographic factors affect how and why a customer chooses one product over the other. Maybe they do not have enough money To build a successful business, it is important to gather useful insights through market research.

More than anything else, carrying out In product development, ideas are unpredictable. Everyone is thinking of the best ways to improve the new or existing product. While this What is a Guttman Scale? The Guttman scale is one of the three unidimensional measurement scales. It tests how a person responds to a Pricing Templates Features Login Sign up. What Is Cluster Sampling? Types of Cluster Sampling Cluster sampling is commonly classified by stages, although some researchers prefer a classification method based on group representation in each subset.

Against this background, we can identify three distinct types of cluster sampling: One-stage Sampling Two-stage Sampling Multi-stage Sampling One-Stage Cluster Sampling For one-stage cluster sampling, the researcher allows every member of the selected clusters to participate in the systematic investigation. Examples of One-Stage Cluster Sampling An organization is researching to discover how many people use its product in a community.

Using single-stage sampling, the researcher splits the community into districts and randomly selects clusters to form a sample.

Every member of the chosen clusters participates in the systematic investigation. To know what students think about the school's administration, the researcher chooses specific classes to provide feedback.