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.
IN CONTEXT
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.
Advantages:
Disadvantages:
What would a simple random sample look like? How might a cluster sample be different from a stratified 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.
Good luck!
Source: This work is adapted from Sophia author Jonathan Osters.