Difference between stratified sampling and cluster sampling
Introduction
Worldwide presence In statistics, it is common to use sampling techniques to obtain representative data of a population. Two common techniques are stratified sampling and cluster sampling. Both have their particularities and are used in different situations. Next, we will see what each technique consists of and what the differences are between them.
Stratified sampling
Stratified sampling consists of dividing the population into different strata or subgroups, and then applying the simple random sampling technique to each of those strata. In this way, it is ensured that each stratum is represented in the sample, and a more precise and homogeneous sample can be obtained.
Example
Suppose we want to do a study on the preference for sports in a city. We can divide the population into different strata based on age and gender. Then, we apply simple random sampling in each of these strata to obtain a representative sample of the entire population.
Cluster sampling
In cluster sampling, population units are divided into groups or clusters, and a specified number of these groups are randomly selected for inclusion in the sample. Then, all the units from those selected groups are taken to form the sample. This technique is useful when data is not available for each individual in the population, but is available for each group.
Example
Suppose we want to do a study on the quality of education in a province. Instead of individually sampling students, we can randomly select a set number of schools in the province and then take a sample of students from those schools.
Differences between stratified sampling and cluster sampling
- Stratified sampling divides the population into different strata, while cluster sampling divides the population into groups or clusters.
- Stratified sampling uses the simple random sampling technique in each stratum, while cluster sampling randomly selects groups to include in the sample.
- Stratified sampling is used when it is desired to obtain a homogeneous and representative sample of each stratum, while cluster sampling is useful when the data are not available individually, but are available by group.
Conclusion
In summary, stratified sampling and cluster sampling are useful techniques in the world of statistics to obtain representative samples of a population. Both have their particularities and are used in different situations, so it is important to understand what each technique consists of and what the differences are between them.