With this post dedicated to cluster sampling, we conclude our first block of posts on random sampling. With our next post, we will launch into nonrandom sampling methods, which are used most commonly in online research.
Cluster sampling is a method that makes the most of groups or clusters in the population that correctly represent the total population in relation to the characteristic that we wish to measure. In other words, all of the variability that exists in a population is contained within the population. When this is the case, we can select just a few of these clusters to conduct our study.
Let’s look at this method from another point of view. In most of the methods we’ve seen so far, the sampling units have coincided with the units to be studied (individuals). With cluster sampling, however, the sampling units are groups of units to be studied, which can be very beneficial when it comes to minimizing the cost of the sampling process. Of course, there’s a trade-off: this technique usually entails less precision, since there is a lack of heterogeneity among the clusters.