Cluster Sampling Explained: What Is Cluster Sampling?

The cluster sampling technique is a sampling method in which statisticians break a large population into a number of clusters or sampling units. Each of these selected clusters will ideally have similar demographic characteristics as the overall population. Cluster sampling is a probability sampling method, which means that any member of the population could theoretically be part of the sampling frame.

An example of cluster sampling is creating subgroups based upon geographical areas. For instance, if a large retailer wanted to conduct market research for sales purposes, it could examine four locations—say one store each in Boston, Memphis, Omaha, and San Francisco—to see how its stores performed. The market researchers would extrapolate data from these sample selections to explain trends at all of the retailer's stores. This form of data collection is more cost-efficient than many other methods of sampling, which makes it popular with commercial clients.

Consider the differences between these two types of cluster sampling methods.

• One-stage cluster sampling: Also known as single-stage cluster sampling, this method involves dividing a total population into clusters, each of which has a similar demographic breakdown. A few of these clusters are chosen by random selection, and then researchers collect data from the clusters that were randomly selected.
• Multi-stage cluster sampling: In two-stage cluster sampling, researchers can create clusters within clusters, and study both the broader cluster and the smaller cluster. In multi-stage sampling, you can further divide the small clusters.

Cluster sampling typically comes into use to meet one of the following conditions.

• Speed is a priority. When sampling and analysis must be done quickly and efficiently, cluster sampling is a strong choice.
• Geographic disparity complicates systematic sampling. Cluster sampling helps when the initial sampling frame includes large populations that cannot easily be sampled at the same time.
• A large group needs to be broken into manageable pieces. In many cases, smaller sample groups make a researcher's job manageable. Cluster sampling creates these smaller groups.
• Pinpoint accuracy is not the top priority. To embrace cluster sampling, there must be some degree of tolerance for sampling error.
• Cost matters. When budget constraints prevent more exacting types of sampling like stratified sampling, some researchers turn to cluster sampling.

Both cluster sampling and stratified sampling break large sample groups into smaller ones. Consider the differences between these sampling methods.

• Homogeneity vs. heterogeneity: The stratified sampling method creates homogeneous strata where all members share a common trait. The cluster sampling method creates clusters of equal size, but these groups are heterogeneous and not selected for a common trait.
• Accuracy vs. cost: Stratified sampling creates precision by limiting the size of the stratum and by ensuring the demographic similarity of people within that stratum. Cluster sampling is popular for market research, particularly when researchers must cover multiple geographical areas. Some cluster sampling goes into great depth. Single-stage cluster sampling only takes one sample of a population, but two-stage cluster sampling and multi-stage cluster sampling go even further. Yet all of these methods can be more cost-effective than stratified sampling or random probability sampling of large populations.

Cluster sampling appeals to market research firms because it is more cost-efficient than random systematic sampling or stratified sampling. It can be quite useful for taking large samples of disparate people, such as those located long distances from each other.

There are also disadvantages to cluster sampling. It often produces a higher sampling error and weaker confidence intervals than other methods of sampling. This high sampling error owes to the fact that it can be difficult to create clusters that are truly representative of the broader population. What's more, despite statisticians' best efforts, some clusters that are ostensibly similar turn out to have differences that lead to very different sampling results. As such, cluster sampling is not considered as accurate as alternative sampling methods such as stratified sampling.

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