Stratified Sampling vs Cluster Sampling

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Stratified Sampling vs Cluster Sampling Availability Bias
Table of Contents

What is sampling?

Survey sampling is a process of selecting respondents for your research that represent your target population. The survey will lead to great results if the selected sample represents the target audience very closely. These attributes can be age, occupation, location, or any other variable.

Researchers use market research tools like Voxco to form survey research panels for their survey research, which consists of sending them a questionnaire to measure attitudes or opinions and derive insights that might be generalized for their target population. Researchers use sampling to reduce their cost of research and improve the quality of insights.

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Stratified Sampling and Cluster Sampling are both a part of probability sampling in statistical analysis. Probability Sampling is a procedure of selecting samples from a broad population for any kind of market research study.

The theory behind probability sampling is to randomly select a sample for the purpose of survey research.

What is Stratified Sampling?

Stratified Sampling vs Cluster Sampling Availability Bias

Stratified Sampling is a category under probability sampling which is based on dividing a population into strata, and members of the sample are selected randomly from these strata. In stratified sampling, the strata must be homogenous and also collectively exhaustive, and mutually exclusive as well. The strata must define a part of the population. 

Moreover, the members of the sample must be distinct, that is, every element must be a part of one and only one stratum in the population. This implies that the entire population requires to be a part of the samples. To ensure precision and reduce sampling error, simple random sampling is employed in each stratum. Calculate your margin of error using the margin of error calculator.

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Examples of Stratified Sampling

Stratified sampling is a better representative of the population. Stratified sampling is a better choice of sampling method when you anticipate that the subgroup you are studying will have different mean values for variables.  

Let’s look through a stratified sampling example to see when it’s used.

Example 1: In the case of a political survey or say, a socio-economic class survey, you can use stratified sampling.

As a researcher, you would need to include participants of various groups like race, religion, or economic class in the case of the overall political survey. In the socio-economic survey, you can make a stratified sample for each category, lower class, middle class, and upper class. 

However, you need to ensure that the stratified samples are based on their proportionality to the total population. This will guarantee a total representation of the entire population while also reducing any sampling errors. 

Example 2: You want to understand what effect an MBA degree can have on the income gap between different gender identities in your company. Relying on the employee list you figure only a small proportion of company employees are MBA graduates. So, you use stratified sampling to compare the difference between men, women, and other gender identities with an MBA degree against those without one.

What is Cluster Sampling?

Stratified Sampling vs Cluster Sampling Availability Bias

Often used in market research tools, cluster sampling is a technique used when homogeneity is external but heterogeneity is internal within clusters/groupings. It is a process of dividing a population into multiple groups/clusters.

Cluster sampling is generally used to reduce the number of interviews and the cost, to reach the desired accuracy. When the majority of heterogeneity is internal within the group in a fixed sample size, the chance of random error is reduced. The total population is divided into clusters and a simple random sample of the cluster is chosen and then the elements in each of these clusters are then sampled.

There are single-stage, two-stage, or multiple-stage sampling methods in cluster sampling. These methods depend upon the number of steps required to create the desired sample.

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Examples of Cluster Sampling

As mentioned cluster sampling is advantageous when a large population is in need of a survey because it is less costly. Hence, area sampling is one the examples of cluster

sampling. Also, it is used when high mortality cases like wars, famines, and natural

diseases are required for estimation.

In the process of cluster sampling, respondents are grouped within a local area into several clusters. However, it is also essential to achieve precision in the estimate

for which the sample size must be increased.

1. One-stage cluster sampling example: 

Let’s say the retail shop you work for wants to determine how many people from the town buy their product. 

The shop splits the town into several neighborhoods and randomly selects people to form cluster samples. Every member chosen from the neighborhood can participate in the research. 

2. Two-stage cluster sampling example: 

Let’s say a restaurant owner wants to know how all its restaurant branches are performing. The owner clusters the branches based on their location and then randomly selects samples from the clusters for studying the performance. 

Related: In-depth sampling guide.

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What are the Characteristics of Cluster Sampling?

  1. Cluster sampling is cost-effective in contrast to other sampling techniques.
  2. Surveys on large populations are handled with help of cluster sampling.
  3. In cluster sampling, the population should be heterogeneous but there must be homogeneity between clusters/groups 
  4. The clusters must be representative of the total population.

What is the difference between Stratified Sampling and Cluster Sampling?

Stratified Sampling vs Cluster Sampling Availability Bias

All the above information highlights the difference between the two categories of Sampling. Underneath are some key differences to clear any lingering doubts:

  1. In Cluster Sampling, the sampling is done on a population of clusters therefore, cluster/group is considered a sampling unit.
  2. In Stratified Sampling, elements within each stratum are sampled.
  3. In Cluster Sampling, only selected clusters are sampled.
  4. In Stratified Sampling, from each stratum, a random sample is selected.
  5. In Cluster Sampling, the aim is to reduce cost and increase the efficiency of sampling.
  6. In Stratified Sampling, the motive is to increase precision to reduce error.

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Probability sampling implies that every person of the target audience has a chance of being selected as a research sample. 

Probability sampling includes the following sampling methods: 

  • Simple random sampling
  • Systematic sampling
  • Stratified random sampling
  • Cluster sampling

Stratified random sampling is when you divide the subjects of your research into sub-groups. These sub-groups are called strata. The strata in stratified random sampling are based on shared characteristics such as race, income, education, gender, country, etc.

Cluster sampling is of three types. In all three cluster sampling, you need to start by dividing the population into clusters and then selecting clusters for your sample in random order. 


One-stage cluster sampling: Every member within the selected cluster can participate in the research. 

Two-stage cluster sampling: For the research, you select the research sample twice. 

1st – select random sub-groups 

2nd – narrow down the sample by selecting a few participants within selected clusters. 

Multi-stage cluster sampling: This allows you to filter your target population & select a specific sample for research. After performing two-stage cluster sampling, you can further select a sample for multi-stage cluster sampling. 

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