Stratified Sampling and Cluster Sampling

Stratified Sampling:

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Stratified sampling is a sampling technique where the population is divided into distinct subgroups or strata based on certain characteristics that are relevant to the research. Random samples are then independently taken from each stratum. This method ensures that each subgroup is adequately represented in the final sample.

• Process:

1. Stratification: Divide the population into homogeneous subgroups (strata) based on specific characteristics (e.g., age, income, geographic location).
2. Random Sampling: Independently take random samples from each stratum. This can be done using simple random sampling or other sampling methods within each stratum.
3. Combine Samples: Combine the samples from each stratum to form the final representative sample.

• Advantages:
• Ensures representation from each subgroup, leading to more precise and reliable results.
• Suitable when there is considerable variability within the population.
• Disadvantages:
• Requires knowledge of the population characteristics to create meaningful strata.
• Can be more complex and time-consuming than simple random sampling.
• Example:
• In a survey about a product, stratify the population by age groups (e.g., 18-24, 25-34, 35-44) and then randomly sample within each age group.

Cluster Sampling:

Cluster sampling involves dividing the population into clusters or groups and then randomly selecting entire clusters for the sample. The key difference from stratified sampling is that in cluster sampling, all individuals within the selected clusters are included in the sample.

• Process:

1. Cluster Formation: Divide the population into clusters, where each cluster is a group of individuals.
2. Random Cluster Selection: Randomly select a few clusters from the population.
3. Inclusion of All Members: Include all individuals within the selected clusters in the sample.

• Advantages:
• More cost-effective than some other sampling methods.
• Suitable for large and geographically dispersed populations.
• Disadvantages:
• May introduce additional variability within clusters.
• Less precise compared to simple random or stratified sampling.
• Example:
• In a study on school performance, divide schools into clusters and randomly select a few schools. Include all students from the selected schools in the study.

Comparison:

• Units Selected:
• In stratified sampling, individuals are randomly selected from each stratum.
• In cluster sampling, entire clusters are randomly selected, and all individuals within those clusters are included.
• Application:
• Stratified sampling is suitable when the population has distinct subgroups with variations in characteristics.
• Cluster sampling is useful when the population can be naturally grouped into clusters, and it is more practical to sample entire clusters.

Both stratified and cluster sampling are methods of improving the efficiency of the sampling process by taking advantage of the structure within the population. The choice between them depends on the characteristics of the population and the research objectives.