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Cluster Sampling: Definition, Types, Pros & Cons

  • Vrinda Mathur
  • May 09, 2022
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Cluster sampling is a sample approach in statistics in which the total population of the study is split into superficially homogenous but internally diverse groupings known as clusters. Each cluster is essentially a micro-representation of the total population.

 

Cluster sampling is a type of probability sampling. This means that when cluster sampling is utilized, each unit/person in the population has an equal and known probability of being chosen for the sample group.

 

 

What is Cluster Sampling?

 

Before beginning with cluster sampling lets understand what is sampling, Researchers conduct internet surveys to learn about the opinions of their target audience (the population that interacts with the product or offerings). 

 

However, not everyone is part of the target audience, thus it is critical for researchers to identify who to include in the survey study in order to increase the quality of the insights gained.

 

To achieve high survey response rates, researchers might employ market research solutions that include a survey panel manager before conducting surveys utilizing online survey software.

 

Cluster sampling is a sampling technique in which researchers split a population into smaller groups known as clusters. They next choose a sample at random from these clusters.

 

It is a probability sampling technique that is frequently used to examine large populations, particularly those that are geographically distributed. As clusters, researchers often employ pre-existing entities such as schools or cities.

 

Cluster Sampling is a way of getting a representative sample from a population that has been separated into clusters by researchers. Individual clusters are subgroups that reflect the variety of the entire population, whereas the collection of clusters is similar to one another. 

 

This method is commonly used by researchers when examining big, geographically scattered populations as a cost-cutting tool. Researchers do not need to collect samples from all clusters because each one represents the complete population, and their uniformity makes them interchangeable, making the sampling procedure easier. 

 

These organizations should be mutually exclusive—no one should be a member of more than one. The groupings should include all members of the population being studied as a whole. Typically, existing groups, such as cities, are used as clusters by academics.


 

Types of Cluster Sampling

 

Although some studies prefer a categorization technique based on group representation in each subset, cluster sampling is generally characterized by phases. Against this backdrop, three main categories of cluster sampling can be identified as :- 


Types of Cluster Sampling- 1. One- Stage Sampling 2. Two- Stage Sampling 3. Multistage Sampling

Types of Cluster Sampling


 

  1. One-Stage Sampling

 

One-stage sampling, also known as single-stage cluster sampling, is a method in which every element inside the chosen clusters becomes a member of the sample group. This is frequently not possible when the target population is huge and the clusters are too large to encompass completely.

 

For example, if you wanted to conduct a research on soda consumption in a specific city, you might use area sampling to split the city into distinct locations, known as clusters, and then pick certain clusters to be a part of the sample group.

 

Examples

 

  • An organization is doing research to determine how many individuals in a neighborhood utilize its product. 

 

The researcher divides the population into districts and picks clusters at random to produce a sample using single-stage sampling. The systematic investigation is carried out by every member of the selected clusters.

 

  • The researcher selects certain courses to offer feedback in order to learn what students think about the school administration. All pupils in the chosen courses are given the chance to express their thoughts on the school's administrative procedures.

 

Pros

 

  • It offers a big sample size for data collecting.

 

  • It is simpler to customize your inquiries to the particular needs and experiences of those in a single cluster.

 

Cons

 

  • If you have a high number of clusters, this approach of data collecting is not viable.

 

  • It has the potential to slow down the data collecting process.

 

Also Read | Sampling Distribution and its Types

 

 

  1. Two-Stage Sampling

 

In circumstances where the population is too huge or is dispersed across a broad geographical region, two-stage sampling is a more practicable and realistic form of sampling. 

 

Simple random sampling (along with other sampling methods such as systematic sampling) is used in this approach to choose items from the specified clusters, reducing down to the required sample size.

 

Continuing from the previous example, if your sample is still too large after deleting the clusters that were not chosen, you may use two-stage sampling to reduce it down even more. 

 

You may use simple random sampling to choose items from each of the specified clusters with two-stage sampling. The selected responders will be the units of the trimmed down sample group.

 

Examples

 

  • Following the selection of a certain class to engage in educational research, the instructor selects individual pupils from the class to reply to survey questions.

 

  • An organization conducts market research by randomly selecting volunteers from an age group within its target audience. These persons make up the survey sample and respond to it.

 

Pros

 

  • It reduces the size of your research sample.

 

  • It accelerates the data collecting procedure.

 

Cons

 

  • The validity of the data gathering process might be influenced by researcher bias.

 

  • There may be significant sampling errors.

 

 

  1. Multistage Sampling

 

Multistage sampling extends the process of acquiring the intended sample group by adding a step, or a few steps, to the process of obtaining the desired sample group. This means that the researchers go through several procedures to achieve the necessary sample, and at each level, they end up with a smaller and smaller sample group. 

 

This is the most complicated of the three, but it is also the most useful for very big populations and/or groups that are geographically distributed.

 

To expand on the soda consumption study, suppose the city you're examining is a densely populated one like New York. In such a circumstance, it's possible that even with two-stage sampling, you won't get the appropriate sample size. 

 

Examples

 

  • When doing research on multilingualism in a community, the investigator uses the single-stage approach to identify clusters. Then he used the two-stage procedure to choose a subset from among the selected groups. 

 

Finally, the researcher selects research participants from the subgroups using the basic random selection approach.

 

  • A researcher wants to discover how many people in South America make more than $5,000 per month. He begins by identifying certain countries for the investigation, then narrows it down to individual states within the country.

 

Pros

 

  • It allows the researcher more freedom. You have the option of taking additional time to select an appropriate sample for your data gathering operation.

 

  • It assists you in gathering primary data from a large, geographically scattered population.

 

  • The validity and quality of research data are improved by multi-stage cluster sampling.

 

Cons

 

  • It is very subjective and prone to researcher bias.

 

  • Findings from research can never be completely representative of the population.

 

Also Read | Market Research Analysis


 

Advantages and Disadvantages of Cluster Sampling 

 

There are many practical benefits of Cluster sampling, although it also has certain drawbacks in terms of statistical validity.

 

Advantages

 

  1. Cluster sampling saves time and money, especially for samples that are geographically dispersed and would be difficult to sample otherwise.

 

  1. Since cluster sampling employs randomness, your study will have high external validity if the population is appropriately clustered because your sample will mirror the features of the wider population. 

 

  1. In a systematic examination, cluster sampling minimizes data inaccuracy—large clusters cover up for one-time occurrences of erroneous data.

 

  1. Cluster sampling is quite easy to execute, especially if you use the one-stage sample method.

 

  1. Reduced variability is another advantage of cluster sampling. As every cluster represents the general population being researched, a lower variability in the results is offered by the the information gained via this approach as the reflection of the group in its entirety becomes more precise and accurate. 

 

Disadvantages 

 

  1. Internal validity is weaker than with simple random sampling, especially when more levels of clustering are used.

 

  1. If your clusters do not give a decent mini-representation of the population as a whole, it will be more difficult to rely on your sample to produce meaningful results.

 

  1. Cluster sampling is far more difficult to arrange than other types of sampling.

 

  1. Inequality in sample size might jeopardize the research process. For example, if one cluster is much bigger than the other, the findings might be skewed and data discrepancy can occur.

 

  1. When compared to other types of sample selection in research, the cluster technique is more prone to sampling error.

 

Also Read | Types of Research Methods


 

Cluster Sampling Vs Stratified Random Sampling

 

Both cluster and stratified sampling divide the population into subgroups. So here are some of the differences between Cluster sampling and Stratified Random Sampling :

 

  1. The primary goal of cluster sampling is to decrease expenses, whereas the primary goal of stratified sampling is to correctly represent the population and generate findings that accurately represent the population. Voxco can assist you in doing low-cost survey research.

 

  1. Clusters are the subgroups in cluster sampling; not all of these clusters are included in the sample group; some are eliminated. Stratified random sampling, on the other hand, selects elements from each subgroup (also known as stratum) to ensure that each strata is represented equally in the sample group.

 

  1. In stratified random sampling, elements from each stratum are picked, but in cluster sampling, entire clusters are chosen to be a member of the sample group.

 

  1. In stratified random sampling, the subpopulation is homogenous within each strata. Each cluster, on the other hand, has a diverse subpopulation.

 

  1. Stratified random sampling necessitates the use of the complete population for the sampling frame, whereas cluster sampling necessitates the use of only a subset of the clusters.

 

For market research, the cluster sampling approach is utilized in an area or geographical cluster sample. When compared to surveys delivered to clusters defined by region, surveys sent to a large geographic area might be more expensive. 

 

The sample size must be expanded to provide reliable findings, however the cost reductions make this process of increasing clusters feasible.

 

The approach is commonly employed in statistics when the researcher is unable to collect data from the full population. It is the most cost-effective and feasible option for statisticians doing research. 

 

Consider the case of a researcher attempting to comprehend smartphone usage in Germany. Germany's cities will form clusters in this situation. This sampling strategy is also employed in events such as wars and natural disasters to make population conclusions when gathering data from every individual dwelling in the population is impracticable.

 

Identifying the tiny distinctions between subgroups in your study population is the cluster sampling hack. This implies that the parameters utilized must result in study groups that are comparable yet internally heterogeneous. When you do this right and acquire the necessary information, you may automatically divide your target audience into clusters. 

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