Trying to survey an entire population of people is hard. How could you possibly reacheverywoman between the ages of 20-29? Every student at your college? Every person in Germany? It takes a lot of time and money to get data from every single person, and when you finally do, that data might have changed! To combat this problem researchers might use methods like cluster sampling or stratified sampling to collect data from groups or individuals that represent the larger population. These two are often confused, so this page offers insight on cluster sampling vs. stratified sampling.

Note that these are not the only two sampling methods available. Other sampling methods include:

Differences Between Cluster Sampling vs. Stratified Sampling?What Is Cluster Sampling?Types of Cluster SamplingRules of Cluster SamplingExamples of Cluster SamplingWhat Is Stratified Sampling?Rules and Principles of Stratified SamplingExamples of Stratified Sampling

Differences Between Cluster Sampling vs. Stratified Sampling?

What Is Cluster Sampling?

Types of Cluster Sampling

Rules of Cluster Sampling

Examples of Cluster Sampling

What Is Stratified Sampling?

Rules and Principles of Stratified Sampling

Examples of Stratified Sampling

Cluster sampling and stratified sampling are two sampling methods that break up populations into smaller groups and take samples based on those groups. In cluster sampling, natural “clusters” are groups that are selected for the sample. In stratified samples, individuals within chosen groups are selected for the sample.

Cluster sampling is a type of sampling in which a larger population is naturally divided up into different clusters, or groups. Clusters, rather than individuals, are randomly selected as the sample. Certain rules and principles of cluster sampling ensure that researchers still get an accurate sample.

Depending on the resources available to the researchers, clusters may undergo a series of random selections before the final sample is chosen. Researchers may put the clusters together themselvesoruse natural borders or groupings that divide clusters (state borders, age, homeroom, etc.)

Maybe this creates too large of a sample. In this case, the researchers will enter a second stage and randomly choose a group of people within each cluster to use in the sample.Double-stage cluster samplingwill produce a less accurate sample but is more convenient if resources are limited.

To ensure that clusters can represent the entire population, researchers follow a few rules and principles of cluster sampling:

Clusters must be similar.The peoplewithineach cluster can be very different from each other, but the clusters as a whole should look very similar across the population. When this happens, you are more likely to choose a cluster that represents the whole population.

Clusters should be mutually exclusive.Individuals should only belong to one cluster.

Clusters must represent the whole population.This is the ultimate goal. Cluster sampling is usually chosen due to limited resources or convenience. The goal should not change: you want to understand the whole population better.

As you read through these examples of cluster sampling, you may be asking yourself, “Can’t I just choose the groups I want to sample to ensure I represent the whole population?” You can, but then you might not be using cluster sampling to put together your sample. Stratified sampling is very similar to cluster sampling, but the small differences between them could be the difference in terms of how accurate or biased your sample becomes.

Stratified random sampling is a sampling method that intentionally divides the population into different strata, then randomly selects individuals from each stratum to ensure that all groups are accounted for in the sample. Strata can be anything from race to age to zip code.

Stratified random sampling can prevent the problems that come with cluster sampling when clusters are imbalanced. Remember the example with the neighborhoods that looked very different from one another? Researchers could still consider each neighborhood as an individual stratum, but then select individuals from each neighborhood as a way to get a set of data that reflects the population as a whole.

Your sample must represent the whole population.Again, this is the ultimate goal. If your sampling method seems to leave out or disproportionately represent certain facets of the population, you may want to evaluate your method and try again.

Sampling willnever be exactly perfect. The only way to gather data that accurately reflects a whole population is to gather data on the whole population. When this isn’t possible, cluster sampling, stratified sampling, and other sampling methods may get the job done.

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Reference this article:Practical Psychology. (2022, November).Cluster Sampling vs Stratified Sampling.Retrieved from https://practicalpie.com/cluster-sampling-vs-stratified-sampling/.Practical Psychology. (2022, November). Cluster Sampling vs Stratified Sampling. Retrieved from https://practicalpie.com/cluster-sampling-vs-stratified-sampling/.Copy

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Practical Psychology. (2022, November).Cluster Sampling vs Stratified Sampling.Retrieved from https://practicalpie.com/cluster-sampling-vs-stratified-sampling/.Practical Psychology. (2022, November). Cluster Sampling vs Stratified Sampling. Retrieved from https://practicalpie.com/cluster-sampling-vs-stratified-sampling/.Copy

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