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Researchers conduct online surveys to understand opinions that are relevant to their target audience (the population that interacts with the product or offerings). But not everyone is a part of the target audience. Therefore, to improve the quality of the insights generated from the survey, it is important for the researchers to understand who to include in the survey research.Before conducting surveys using online survey software, researchers can use market research tools that have survey panel managers to create samples to ensure high survey response rates
Cluster sampling is a type of probability sampling. But before we learn about that, let’s look at what probability sampling is.
In probability sampling, you need to select a sample from the target population randomly. By randomly selecting a sample, you ensure that every person or organization in your population has an equal possibility of being selected.
For example, in gameshows, you must have seen participants randomly selected when the host picks their name from a jar.
Cluster sampling, when used, gives every unit/person in the population an equal and known chance of being selected in the sample group.
For this method of sampling, researchers divide the population into internally heterogeneous and externally homogeneous subpopulations known as clusters. The clusters are externally homogeneous as they appear to be grouped together by a shared characteristic/criteria but are internally heterogeneous because the subpopulations within the clusters have different compositions.
Clusters may be divided by different cities in a country, different areas in a city, different organizations, different universities, different industrial estates, etc. After these clusters have been decided, researchers select certain clusters and eliminate the rest.
For example, if you’re conducting a study across all cities in the United States, you can use cluster sampling to eliminate certain cities or clusters in order to select your final sample group
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Typically, there are three types of cluster sampling:
When we talk about “stages” in sampling, it indicates the number of steps taken toward selecting the desired sample group.
Let’s go over the three main types of this sampling method:
One-stage sampling, also known as single-stage cluster sampling, is a method where every element within the selected clusters will become a part of the sample group. This is oftentimes not feasible if the target population is vast and the clusters are too large to include fully.
For example, if you were to conduct a study on the consumption of soda in a particular city, you could use area sampling to divide the city into different areas, called clusters, and then select certain clusters to be a part of the sample group.
Two-stage sampling is a more feasible and realistic method of sampling in cases where the population is too large or is scattered over a large geographical area.
In this method, simple random sampling (sometimes other methods like systematic sampling are also used) is used to select elements from the selected clusters, further narrowing down to the desired sample size.
Carrying forward the previous example, if your sample is too large even after eliminating the clusters that weren’t selected, you may use two-stage sampling to narrow the sample further.
With two-stage sampling, you can use simple random sampling to select elements from each one of the selected clusters. The units of the narrowed-down sample group will be the selected respondents for the study on soda consumption.
Multistage sampling takes two-stage sampling further by adding a step, or a few more steps, to obtain the desired sample group. This means that you need to use multiple steps to obtain the desired sample, and at each stage, you are left with a smaller and smaller sample group.
This is the most complex of the three cluster sampling methods but is also the most advantageous for very large populations and geographically dispersed populations.
To further build on the example of the study of soda consumption, let’s assume the city you are researching is highly populous, like New York. In such a case, it’s probable that even after implementing two-stage sampling, you may not reach your desired sample size. You can then take further steps to obtain your desired sample size using multistage sampling.
These are three ways this sampling method is categorized. Now let’s look at the steps to cluster sample.
There are four steps to conducting cluster sampling:
This sampling method is mostly used by market researchers to gather information about the clusters of the entire target population when they are unable to survey the entire population.
These are the following steps used to perform single-stage cluster sampling:
✔ Clearly define the population you want to gather data from.
✔ Make sure each cluster you create represents the entire target population.
✔ The clusters should also be diverse, i.e., mutually exclusive. Each cluster should represent
every possible characteristic of the target population.
✔ Ensure that the clusters don’t occur at the same time. They shouldn’t overlap.
✔ When the clusters represent the target population, you can imitate simple random sampling. This will ensure the validity of the research result.
✔ Select the way you want to collect data and conduct your research.
Further steps may be taken using two-stage or multistage sampling to achieve desired sample size if it cannot be achieved through one-stage sampling.
We have defined the steps and requirements in each step to cluster the sample. Now, let’s see the advantages and disadvantages of this sampling method.
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This sampling is used when it is impossible to gather information from the entire population.
Along with this simple benefit, there are a few more advantages to this sampling method.
Cluster sampling is relatively easy to implement.
The cluster sampling method is more cost-effective and time-efficient in contrast to some other forms of probability sampling, such as simple random sampling.
If clusters of the population are made properly, this sampling method can create highly reliable/valid results as the selected sample group will mirror similar characteristics of the population.
Like advantages, there are also quite a few disadvantages of using cluster sampling, such as
The results of this sampling method can be imprecise if clusters aren’t created properly. Results are usually not as valid as those that are resulted from simple random sampling.
The results of cluster sampling are usually also difficult to compute and interpret.
This method of sampling also tends to be difficult to plan and execute in comparison to some other forms of sampling.
It is also relatively more prone to high sampling error. You can find your margin of error using the margin of error calculator.
Cluster sampling is more useful when a survey needs to be conducted over a larger population. When the population is larger for you to survey it, that’s where cluster sampling comes in.
Creating cluster samples that represent the target population helps reduce bias in survey results.
Area sampling is one example of this sampling method.
A bakery owner is planning to expand her business. Before that, she wants to know how many people from the neighborhood buy her bakery products. She splits the neighborhood into several areas and randomly selects customers to form cluster samples. Then she surveys every member chosen from the neighborhood for her research.
Let’s say the management of a toy company wants to examine how all of its outlets are performing in the market. The management divides the outlets based on location and randomly selects samples to form clusters. Then they used the cluster sample to study the performance of all the outlets.
[Related read: Guide to Sampling Methods]
Cluster sampling and stratified sampling both divide the population into subgroups.
So what is the difference between the two? Here’s what:
In stratified sampling, the objective is to accurately represent the population and obtain results that aptly represent the population.
In stratified random sampling, on the other hand, elements are picked from each subgroup (also known as strata) so that each stratum is equally represented in the sample group.
Whereas in cluster samples, whole clusters are chosen to be a part of the sample group.
In contrast, each cluster has a sub-population that is heterogeneous.
While cluster sampling only requires selected clusters.
[Related read: Stratified Sampling Vs. Cluster Sampling]
This sampling method is typically used in market and geographical research. In both cases, the population is large, so the researchers divide the population into clusters to get the information they need.
It is the most reliable and affordable sampling method. Once you make your clusters, you can use online survey software to gather data from randomly selected respondents. This way, you can focus more on your research and less on worrying about how to collect the data.
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