Stratified sampling is a probability sampling method that divides the population into different strata or subgroups based on shared characteristics, such as age, gender, or socio-economic status. Cluster sampling, on the other hand, is a method that divides the population into clusters or groups based on geographic proximity or other convenient criteria. The main difference between these two methods lies in the way the population is divided and the selection of the sample. Stratified sampling aims to ensure that the sample is representative of the different strata within the population, while cluster sampling aims to obtain a sample that is representative of the clusters within the population.
Stratified vs. Cluster Sampling: Key Differences
Stratified and cluster sampling are two non-probability sampling techniques used in research to select a sample that represents a larger population. They both have their own advantages and disadvantages.
Stratified Sampling
- In stratified sampling, the population is divided into subgroups (strata) based on a known characteristic.
- The researcher then selects a sample from each stratum based on its size in the population.
- This method ensures that each stratum is adequately represented in the sample.
Cluster Sampling
- In cluster sampling, the population is divided into groups (clusters) that are geographically dispersed.
- The researcher then randomly selects a few clusters and surveys all members within those clusters.
- This method is often used when the population is widely spread out.
Key Differences
1. Basis of Selection
- Stratified: Divides population into strata based on known characteristic.
- Cluster: Divides population into geographically dispersed clusters.
2. Representation
- Stratified: Ensures adequate representation of subgroups in sample.
- Cluster: May not accurately represent subgroups if clusters are homogeneous.
3. Cost and Convenience
- Stratified: Can be more time-consuming and expensive due to subgroup identification.
- Cluster: More convenient and cost-effective, especially for large populations.
4. Precision
- Stratified: Generally more precise than cluster sampling.
- Cluster: Less precise, as it relies on a smaller number of clusters.
5. Sampling Error
- Stratified: Lower sampling error due to proportionate representation.
- Cluster: Higher sampling error due to within-cluster homogeneity.
Comparison Table
| Feature | Stratified Sampling | Cluster Sampling |
|---|---|---|
| Basis of selection | Subgroups (strata) | Clusters |
| Representation | Ensures subgroup representation | May not represent all subgroups |
| Cost and convenience | More time-consuming and expensive | More convenient and cost-effective |
| Precision | Generally more precise | Less precise |
| Sampling error | Lower | Higher |
Question 1:
What is the fundamental distinction between stratified and cluster sampling?
Answer:
Stratified sampling divides a population into distinct subgroups (strata) based on shared characteristics, ensuring representation of each stratum in the sample. In contrast, cluster sampling randomly selects groups (clusters) of individuals from the population, resulting in a sample that represents the geographic or organizational structure of the population.
Question 2:
How do the sampling procedures of stratified and cluster sampling differ?
Answer:
Stratified sampling involves identifying and sampling from each stratum independently, aiming for proportional representation. Cluster sampling, on the other hand, involves selecting a random sample of clusters and then sampling individuals within those clusters.
Question 3:
What are the key advantages and disadvantages of using stratified sampling versus cluster sampling?
Answer:
Stratified sampling ensures representativeness of subgroups and reduces sampling error, but can be more difficult and time-consuming to implement. Cluster sampling is more efficient and cost-effective, but may result in less precise estimates due to within-cluster homogeneity.
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