Cluster sampling exists because of the complexities that come from dealing with a large population. A target population is an important variable that makes or mars any research effort. If you’re dealing with a small target population, you can easily collect data from everyone to help you arrive at a valid result. However, this isn’t always the case.
Originally a statistical terminology, cluster sampling has become one of the most common ways to collect representative data from a vast target audience for a systematic investigation. In this guide, we’d explore different types of cluster sampling and show you how to apply this technique to market research.
Cluster sampling is a survey research approach where the researcher splits the target audience into smaller naturally occurring groups or clusters and randomly chooses sets for the systematic investigation. These random selections are from the research sample for data collection and analysis.
Cluster sampling helps researchers to study large populations. Instead of administering a questionnaire to every member of your target audience, you simply group them and collect representative data from each group. This way, the researcher speeds up the systematic investigation. By splitting the target audience into smaller, homogeneous groups, researchers achieve reliable outcomes while saving time and cost. Each group is defined by distinct characteristics—for example, gender, religion, age, and income levels.
Cluster sampling is commonly classified by stages, although some researchers prefer a classification method based on group representation in each subset. Against this background, we can identify three distinct types of cluster sampling:
For one-stage cluster sampling, the researcher allows every member of the selected clusters to participate in the systematic investigation. In other words, sample selection is only made once before the research takes off.
One-stage cluster sampling is also referred to as single-stage sampling.
For a two-stage cluster sampling, the researcher selects the research sample twice. First, they conduct single-stage sampling where subgroups are chosen randomly. Next, they narrow down the sample by selecting a few research participants from the selected clusters.
Most times, the final survey sample is a fair representation of distinct characteristics and elements of the single-stage clusters.
Multi-stage cluster sampling allows the researcher to filter the target audience and select a particular sample for the systematic investigation. After choosing the two-stage sample, the researcher further selects the research sample based on standardized criteria.
Cluster sampling is an intelligent way to approach data collection in research. However, the success of this method depends on how well you identify homogeneous subsets within your target audience and group them accordingly.
To help you pull through with this, here’s a simple step-by-step guide on performing cluster sampling.
Step 1: Identify the target audience for your systematic investigation. Here, make sure the target population has adequate knowledge of the subject matter and is accessible.
Step 2: Next, create possible sampling frames for your research. You can also adopt an existing framework for clustering and coverage.
Step 3: Decide on the number of clusters in your target population. When you’ve done this, split the target population into clusters based on distinct characteristics. Members of the same group should have homogeneous qualities differentiating them from the others.
Step 4: Select the clusters for your systematic investigation using any of the methods earlier mentioned. If you opt for the multi-stage method, you need to create sub-groups within each group
Cluster sampling is very effective in areas where researchers can’t collect data from the entire population. This makes it a very practical sampling method for statisticians undergoing research. For example, during a natural disaster, it is impractical to collect data from every single person affected by the disaster.
In market research, cluster sampling allows organizations to collect relevant responses from a vast target audience spread across multiple geographical locations.
When creating market research surveys for geographical areas, it can get pretty expensive and time-consuming to attempt to survey a broad region.
So, cluster sampling is used to survey geographical clusters divided by their respective regions.
Stratified sampling is closely related to cluster sampling, so it’s easy to confuse one for the other. To help you, we’ve outlined four key differences between these two types of probability sampling.
Cluster sampling is a type of probability sampling where the researcher randomly selects a sample from naturally occurring clusters. On the other hand, stratified sampling involves dividing the target population into homogeneous groups or strata and selecting a random sample from the segments.
In stratified sampling, the research sample comprises a random selection from all strata, while for cluster sampling, the research sample comes from randomly selected clusters.
In stratified sampling, the researcher splits the target population into homogeneous groups. On the other hand, the sub-groups occur naturally in cluster sampling.
Stratified sampling achieves homogeneity within the strata, while cluster sampling achieves uniformity between the clusters.
Read: What is Stratified Sampling? Definition, Examples, Types
So, why is cluster sampling a big deal in data collection? Frankly, there are several reasons. When dealing with a large target population and a strict time frame, it’s impossible to gather all the data you need from every target audience member. By adopting cluster sampling, researchers can gather quality responses from their target audience while saving time and resources.
Common advantages of cluster sampling include:
Although cluster sampling isn’t always the answer to data collection in a systematic investigation despite its many advantages, specifically, it has the following disadvantages:
The hack to cluster sampling is identifying the fine lines between subgroups in your research population. This means that the parameters used must create research groups that are similar yet internally diverse. You can break your target audience into naturally-occurring clusters when you get this right and collect the information you need.
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