Difference Between Stratified and Cluster Sampling

Last Updated : 08 Sep, 2023

The art of deducing information about large data and frequently challenging population from the analysis of a smaller sample is a the foundation of statical reasoning in the vast field of data analysis and research. This practice is referred to as “Sampling“. For any type of market research study, probability sampling is a method of choosing samples from a large population. The theory behind this is to randomly select a sample for the purpose of survey research. Stratified Sampling and Cluster Sampling are the two type of probability sampling.

Stratified and Cluster Sampling are statistical sampling techniques used to efficiently gather data from large populations. Stratified sampling divides the population into distinct subgroups based on characteristics or variables, ensuring homogeneity and variation. A random sample is selected from each stratum, reducing potential bias and ensuring accurate estimates. Cluster sampling divides the population into naturally occurring groups, such as geographical regions or organizational units, and randomly selects clusters to capture variability within them.

Both stratified and cluster sampling have several benefits, but they also have some drawbacks. We will see the difference between them in brief in this article.

What is Stratified Sampling ?

Stratified sampling divides a population into distinct and homogenous divisions, or strata. The categorization is based on certain characteristics or important factors. Stratified sampling divides the population into different subgroups or strata based on particular characteristics or criteria. Due to the strata’s design, individuals within each subgroup are more homogeneous, however certain subgroups are different. Then, based on size or significance, a random sample is taken from each stratum.

Advantages of Stratified Sampling:

Improved Representativeness: Stratified sampling makes sure that each strata or subgroup within the population is fairly represented in the sample. It leads to more accurate reflection of population’s diversity and traits.
Reduced sampling error: It reduces variability within each stratum by segmenting population into homogenous strata. Less variability reduces sampling error and estimations are more accurate.
Effective subgroup analysis: It facilitates in-depth analysis of these subgroups, as they are fairly represented in the sample.
Increased Precision: It typically produces estimates compared to simple random sampling. When working with limited resources and requirements for the precise results, this precision is especially beneficial.

What is Cluster Sampling?

Cluster sampling is a statistical sampling approach that divides a vast population into naturally occurring groups called clusters before collecting data from each cluster. These clusters, which are subsets of the population that are representative of it, can be geographical areas, organizational units, or any other logical groupings. Instead than choosing specific elements, researchers choose a small number of clusters at random and include all of the items found in those clusters in the sample. When it is more possible and affordable to gather data from clusters rather than individuals, this strategy is especially helpful. It helps in capturing the variation within clusters as well.

Advantages of Cluster Sampling:

Simple Sampling Design: Cluster sampling simplifies the sampling process by grouping elements into clusters which makes it easier to manage and collect data from small clusters.
Cost and Resource Efficiency: It is more cost effective than other methods because it reduces the logistical and operational expenses associated with reaching dispersed individuals.
Practical for Large Populations: With large number of population, Cluster sampling allow researchers to efficiently collect data from selected clusters reducing time and effort.
Facilitates Access: When in some situations individual elements are difficult to access, cluster sampling helps researchers to reach clusters that are more accessible.