Difference Between Stratified and Cluster Sampling
Cluster sampling and stratified sampling are two different statistical sampling techniques, each with a unique methodology and aim. Let’s see how they differ from each other.
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Parameters |
Stratified Sampling |
Cluster Sampling |
|---|---|---|
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Definition |
Stratified sampling divides a population into homogeneous strata based on variables of interest. A random sample is selected from each stratum proportionally, ensuring each subgroup is adequately represented in the final sample, reducing potential bias and allowing for more precise estimates. |
Cluster sampling is a statistical technique that divides a population into naturally occurring groups, such as geographical regions or organizational units. Researchers randomly select clusters and include all elements within them in the sample, reducing costs and logistical challenges. This method provides insights into variability within selected clusters. |
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Precision |
It enables more precise estimates within each stratum. |
Precision of cluster sampling may vary within clusters. |
|
Homogeneity |
Stratified sampling has homogeneity within groups. |
Cluster sampling has homogeneity between groups. |
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Heterogeneity |
In stratified sampling, there can be heterogeneity between the groups or subgroups (called strata). |
Heterogeneity within groups is shown by cluster sampling, allowing for variations among chosen elements. |
|
Groups Formation |
Population divided into homogeneous subgroups known as strata. |
Population divided into naturally occurring clusters. |
| In terms of Cost | Stratified Sampling may involve in higher cost then the Cluster Sampling. | Cluster Sampling is cost effective. |
Difference Between Stratified and Cluster Sampling
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.
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