What is the Difference Between Stratified Sampling and Cluster Sampling?

Stratified sampling and cluster sampling are both probability sampling methods used to ensure that a sample is representative of the target population. However, they differ in how the sample is selected and the characteristics of the groups being sampled. Here are the main differences between the two methods:

1. Group Characteristics: In cluster sampling, the groups created are heterogeneous, meaning the individual characteristics in the cluster vary. In contrast, the groups created in stratified sampling are homogeneous, meaning that units share characteristics.
2. Sampling Process: In stratified sampling, you select some units of all groups and include them in your sample. This ensures equal representation of the diverse group. In cluster sampling, you randomly select entire groups and include all units of each group in your sample.
3. Group Formation: In stratified sampling, you divide the subjects of your research into sub-groups called strata, based on shared characteristics such as race, income, education, gender, or country. In cluster sampling, the population is divided into groups, or clusters, based on natural breaks between groups, like voting districts or blocks of a city.
4. Cost-effectiveness and Operational Efficiency: Cluster sampling improves cost-effectiveness and operational efficiency by selecting entire clusters, making every member eligible to participate. Stratified sampling typically requires more effort to ensure equal representation by dividing the target population into specific categories.
5. Accuracy: Stratified sampling is generally more accurate because the researcher chooses specific categories for equal representation. Cluster sampling is best when the diversity within a cluster shouldn't vary from each other.

In summary, the main differences between stratified and cluster sampling are the characteristics of the groups being sampled, the sampling process, group formation, cost-effectiveness and operational efficiency, and accuracy. Both methods ensure that the sample is representative of the target population, but the choice between the two depends on the study's goals, data relevance, and the population's natural differences.

Comparative Table: Stratified Sampling vs Cluster Sampling

Here is a table comparing the differences between stratified sampling and cluster sampling:

In summary, stratified sampling is used when the researcher wants to ensure that specific subgroups within the population are represented in the sample, while cluster sampling is used when the researcher wants to improve the efficiency and cost-effectiveness of the sampling process.