This lesson will introduce cluster samples and focus on:
When using a cluster sample, the population is divided into groups. These groups are called “clusters”.
It’s important to note that these groups are natural groupings. They don't necessarily have anything in common, other than say, geography, typically. So we're going to take a random sample of clusters instead of a random sample of individuals.
Each individual in the cluster is going to be part of the sample, if we select that cluster. So unlike the groups in a stratified random sample, the groups in a cluster sample aren't based on a characteristic or variable.
The individuals in the cluster just happen to be near each other.
You work at a potato chip company and it’s your job to implement some quality control in the manufacturing department. Maybe you stand at the start of the assembly line and take a simple random sample of individual chips. That would work just fine.
However, it might be easier for you to sample some bags of chips. The bags of chips are clusters. You would then take a bag of chips off the assembly line and sample every chip in that bag for quality control. That’s cluster sampling.
Just like every sampling method, cluster sampling has pros and cons.
What would a simple random sample look like? How might a cluster sample be different from a stratified random sample
Suppose the spinner landed on three, as displayed above. That means that every apartment on the third floor would receive carpeting. You wouldn’t need the carpet installers traveling to different rooms on different floors. It sure would be easier for the carpet installer and just as cost effective. So that would be a cluster sample, as opposed to some other type of sample. But what if all the floors were NOT heterogeneous? What if apartments on the third floor allowed pets? Carpet might not hold up as well. That’s one of the disadvantages of cluster sampling in action. But typically, the clusters are pretty representative and it's very similar to a simple random sample.
Cluster sampling is done by taking naturally-occurring-- typically geographically-- similar groups, and taking a simple random sample of the clusters. And then each member in the cluster becomes part of the sample.
A couple of advantages are that they are more cost effective,and usually achieve the same results as a simple random sample. The disadvantage is that sometimes the cluster may not be heterogeneous, as we saw in the landlord example with pets allowed on carpet.
Source: This work is adapted from Sophia author Jonathan Osters.
A sampling method where the population is separated into groups, typically geographically, and a random selection of clusters is made. Each individual in the cluster becomes part of the sample.
Smaller subgroups of the population, not necessarily similar in any way besides all being together in one place, making the individuals easier to sample together.