Cluster sampling in statistics allows you to collect the right representative data from a large target audience. Collecting accurate data from a large population is hard, and this is where cluster sampling comes in to ease things up.
Here’s what we’ll cover about the topic. Feel free to jump to a relevant section:
- What is cluster sampling?
- How to conduct cluster sampling
- The three different types
- Applications or Uses
- Advantages of cluster samples
What is Cluster Sampling?
Cluster sampling divides a large target group into multiple smaller groups or clusters for research purposes. Researchers then form a sample by randomly selecting these clusters.
The random selection gives every group in that target population an equal chance to be a part of the sample group. However, only a few relevant groups are selected and the rest are eliminated.
We use a cluster sample to study large populations. Typically, clusters are obtained from pre-existing groups such as schools or cities.
Then we group the samples together by certain shared characteristics or attributes. Samples include multiple attributes such as demographics, goals, habits, backgrounds, etc.
Providing more attributes helps you accurately target the right group that could give you accurate feedback. Instead of selecting the entire population, researchers pick a smaller, more productive group within that population to research and collect data.
Cluster sampling example
If you’re looking to conduct a survey on the performance of smartphones in the United States, you can divide America’s population into certain popular cities such as New York or Los Angeles.
You can then target those individuals who own smartphones in the respective cities and who use a certain kind of mobile OS. All of the above are valid attributes that would help you target the right group of people to research.
How to Conduct Cluster Sampling in 4 Simple Steps
Here’s how to conduct single-stage cluster sampling and find the correct representative sample:
Step 1: Define Your Audience
Decide on your target population and desired sample size.
Step 2: Create Clusters or Subgroups
Now divide your target population into smaller subgroups or clusters based on a specific criteria. Getting this step right is crucial as it affects the quality of your segment or cluster and how well it represents your target population.
Step 3: Randomly Select Your Clusters
Pick a cluster or group that closely resembles the audience that you’re looking to research. You can pick a cluster based on a method of random selection. Make sure to keep the sample size in mind while you select one.
Step 4: Collect Data from the Sample
Finally, conduct your research and collect data from your selected clusters.
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3 Types of Cluster Sampling
Here are the three types:
1. Single-stage Cluster Sampling
In single-stage, you collect data from all the units within the selected clusters.
2. Double-stage Cluster Sampling
In double-stage, you collect data from a random sample of units in the selected clusters.
3. Multi-stage Cluster Sampling
In multi-stage, you simply repeat the double-stage sampling technique until you have reached your desired sample size
Applications of Cluster Sampling
Here are the two common applications:
Cluster sampling in market research
It is typically used in market research. It is especially useful when you cannot collect data from the entire population.
When you’re targeting a huge audience that’s spread across multiple geographic locations, it can get pretty expensive and time-consuming. In this case, you use cluster samples to keep things more economical and time-efficient.
Cluster sampling in statistics
Statisticians use clusters as a practical sampling method for research. For instance, you use it during a natural disaster as it is totally impractical to collect data from every single person affected by the disaster.
Advantages of Cluster Sampling
Time and cost-efficient
Collecting data from a smaller number of people is more time and cost-efficient than collecting data from the target population as a whole.
Easy to implement
Compared to other probability sampling methods, a cluster sample is relatively easy to implement in practical situations.
It can get you reliable, valid results when your target population is clustered properly.
Disadvantages of Cluster Sampling
When you fail to put together groups that closely resemble your desired population, you’ll get results that aren’t accurate.
Difficult to analyze
It can become quite difficult to analyze when you’re dealing with different segments of people or clusters and their characteristics or attributes.
High sampling error
Cluster samples can be prone to high sampling error when not done right.
Frequently Asked Questions (FAQs) about Cluster Sampling
What is cluster sampling?
It is a research approach that splits the target audience into multiple smaller groups or clusters to better target and collect accurate data.
What are the different types of cluster sampling?
There are three distinct types: single-stage, two-stage, and multi-stage. You can collect data from every unit (single-stage) or a random sample of units (multi-stage) within the selected clusters.
What are some advantages and disadvantages of cluster sampling?
It is time and cost-efficient, especially when targeting large samples. However, it provides average results as it is difficult to ensure that your clusters closely represent your target population as a whole.
In order to conduct an effective one, you need to come up with characteristics or attributes that accurately represent your target audience. Once you’ve nailed this part, it gets a lot easier to collect data that’s accurate and resembles the opinions of your target population.
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Have you got any questions on cluster analysis? Any interesting tips or hacks for conducting effective cluster analysis? Let us know in the comment section below.
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