Cluster Sampling Vs. Stratified Sampling

In statistics and research, we’re interested in collecting the data so we can test a certain theory.

And to collect such data, we implement sampling.

Sampling is when you collect data from a selected population group instead of from the entire population. You do that primarily with the help of cluster and stratified sampling. 

Of course, these aren’t the only methods of data collection; they’re just the most prevalent and efficient ones.

This blog weighs in on the cluster sampling vs. stratified sampling comparison. Furthermore, it will also explain in brief each of the sampling techniques, their differences, and their similarities.

So, let’s jump into it!

What is Cluster Sampling?

Cluster sampling is a technique where the population is first divided into groups or clusters. Then you randomly pick a subset of these clusters to be your sample.

Instead of randomly selecting individuals or items from the population, you pick out clusters to analyze. This technique is extremely cost-effective as it requires the least effort and is convenient.

Cluster sampling can be broken down into three distinct categories, each based on the number of required steps to obtain an optimal sample: 

• Single-stage cluster sampling
• Two-stage cluster sampling
• Multistage cluster sampling

A cluster sampling example situation would be as follows: 

Consider a company that wants to survey its employees. It may use cluster sampling by selecting departments or teams as clusters and then randomly selecting some of these clusters for the survey. 

The employees within the selected clusters would then be surveyed.

Key Points:

• Naturally visible groups are added to the final sample set
• Primarily used in market research. Also used in public health, educational, environmental, and social sciences research
• This method is used when the clusters are mutually exclusive and can only be applied within a cluster whose elements showcase distinctions

What is Stratified Sampling?

Stratified sampling is a technique where the population is first divided into unique, homogenous sub-groups or strata. Then, you randomly select individuals from each stratum to add to your sample. 

Stratified sampling comes in handy when you need to separate your audience according to their age, gender, income, race, religion, education level, or other characteristics. 

Types of stratified sampling include proportionate stratified sampling, where the sample size from each stratum is equivalent to the size of the stratum in the population, and disproportionate stratified sampling, where the sample size from each stratum is inconsistent with the size of the stratum in the population.

An example of stratified sampling would be as follows:

Imagine you’re conducting a survey on customer satisfaction in a retail store with customers of different ages. 

Customers can be separated into strata based on age, and then a proportional or disproportionate sample can be drawn from each stratum to represent the whole customer population.

Now that you know what cluster and stratified sampling are, let’s understand how they differ.

Key Points:

• Here, the population is divided into groups or strata
• The strata should be mutually exclusive, as is the case with cluster sampling
• This method aims to improve precision by ensuring each stratum is distinctive

Cluster Sampling Vs. Stratified Sampling: Key Differences

The following table mentions the key differences between the two sampling techniques: 

Comparison Factors	Cluster Sampling	Stratified Sampling
Definition	Elements of this sample are randomly selected from naturally divided groups called clusters	Elements of this sample are randomly selected from unique, mutually exclusive, and homogeneous strata (groups)
Selection format	You randomly pick clusters and all the elements within it	You randomly choose members from the multiple strata
Purpose	To reduce cost and improve efficiency	To increase precision and accurately represent the population
Sample element selection	Collectively	Distinctively
Population segmentation type	Naturally	Depends on the researcher
Heterogeneity	Internally, within clusters	Externally, between multiple strata
Homogeneity	Externally, between multiple clusters	Internally, within strata

Cluster Sampling Vs. Stratified Sampling: Similarities

Despite their many differences, cluster sampling and stratified sampling share a bunch of similarities, which are explained below: 

• Both techniques are a type of probability sampling method. This means there is an equal chance for each member of the population to be included in the sample.
• Both techniques segment their population into distinct groups (be it clusters or strata).
• Regarding sample collection, both techniques are much more efficient and cost-effective than simple random sampling.

When To Use Each Sampling Technique

Understanding cluster and stratified sampling is the first step to getting started. As for the next step, you should know when to use each technique so you can make the most of it.

Follow this simple rule if you’re unsure of whether to use cluster sampling or stratified sampling:

If the population is heterogeneous (i.e., there are natural, visible distinctions between the population members), go with stratified sampling to collect a random sample.

On the other hand, if the population is homogeneous (i.e., there are no visible distinctions between the population members), your best bet is cluster sampling.

Conclusion

You can use various sampling methods to study your subjects. 

However, when choosing a sampling method, you should focus on these two significant aspects: 

• The sampling method should help you randomly select a sample, 
• And the sample should accurately represent the population. 

Cluster sampling and stratified sampling do just that!

Both methods are time and cost-efficient and flawlessly segment the population into smaller groups, thus making it easy for you to analyze the data.

Use cluster sampling when you have a large population with little to no distinctions, and use stratified sampling when you have a large population with visible varying characteristics.