The What And Why of “Cluster Sampling”

First, let’s be honest. Although it is now a commonly used term, if you’re not familiar with “cluster sampling”, it can come across as a bad episode from the Twilight Zone. There’s something eerie about the word to make one feel like he/she is about to get probed by aliens. Ok, perhaps it is just me and my wild imagination.

Unless you’re 70 years old, cranking stats in a windowless basement office, market research terminologies can make even the brightest of the bunch take a mulligan or spurt an occasional “what the heck?”. Ironically, the term cluster is probably one of the most simplistic terminology in the research dictionary.  A cluster, as defined by Webster, is “a group of similar things or people positioned or occuring closely together”. The main idea behind creating clusters is to group a set of objects in order to create relationships and similarities among each group. In a nutshell, it is simply a form of classification or distributing groups.


In an ideal world, research practitioners would love to survey the entire population and select their respondents randomly to make sure everyone is accounted for and therefore ensure their research results are as accurate as possible. This is referred to as random sampling. Unfortunately, there are two issues associated with this approach – cost and feasibility. However, by dividing and classifying the population into groups (cluster sampling), this provides the researcher the ability to account for individuals with common interest, relative to the larger population. By using the cluster sampling technique, the sample data set is smaller, which helps keep research costs reasonable.

When using cluster sampling methods, it is critical to keep in mind that only one variable (element) can be assigned to a cluster. In most cases, clusters are created by geography. For example, if Apple wanted to gauge the performance of the iPad in Spain, the researcher would create clusters by all cities in Spain. The larger cities would be accounted for and cluster analysis would determine usage of iPad by each city.

While there are other complexities to using cluster sampling – stages, sample selection, sample size, etc., in comparison to other sampling methods, cluster sampling can be a very effective technique to determine the characteristics of a group and can be implemented without the need of other elements of the population. Most importantly, cluster sampling provides 4 key advantages that other methods fall short on:


-Takes less time and cost less

-Easy to implement

-Higher margin on data accuracy

The next time you’re limited by budget and don’t have time to run around the country to conduct interviews, consider using cluster sampling. Getting probed is the least of your worries.