Model Answer
0 min readIntroduction
Sampling is a crucial aspect of research methodology, enabling inferences about a population based on a smaller subset. Both cluster sampling and stratified sampling are probability sampling methods, aiming to achieve representative samples. However, they differ significantly in their approach to group formation and selection. While stratified sampling divides the population into homogeneous subgroups (strata) and then randomly selects from each, cluster sampling divides the population into heterogeneous groups (clusters) and randomly selects entire clusters for study. Understanding these nuances is vital for researchers to choose the most appropriate technique for their specific research objectives.
Cluster Sampling
Cluster sampling involves dividing the population into groups, or clusters, typically based on geographical proximity. These clusters are then randomly selected, and all individuals within the selected clusters are included in the sample. It’s often used when the population is geographically dispersed and creating a complete list of individuals is difficult or costly.
- Group Formation: Based on naturally occurring groups (e.g., schools, villages, hospitals).
- Homogeneity: Clusters are generally heterogeneous, meaning they contain a diverse range of characteristics.
- Selection: Random selection of clusters, followed by inclusion of all units within the selected clusters.
Stratified Sampling
Stratified sampling involves dividing the population into homogeneous subgroups, called strata, based on shared characteristics (e.g., age, gender, income). A random sample is then drawn from each stratum, ensuring representation from all subgroups. This technique is used when researchers want to ensure that specific subgroups are adequately represented in the sample.
- Group Formation: Based on characteristics like age, gender, income, education level.
- Homogeneity: Strata are homogeneous, meaning individuals within each stratum share similar characteristics.
- Selection: Random selection of units from each stratum, often proportionally to the stratum’s size in the population.
Comparison: Cluster vs. Stratified Sampling
The key differences between cluster and stratified sampling can be summarized in the following table:
| Feature | Cluster Sampling | Stratified Sampling |
|---|---|---|
| Group Formation | Naturally occurring, heterogeneous groups | Artificially created, homogeneous groups (strata) |
| Homogeneity within Groups | Low | High |
| Selection of Units | All units within selected clusters | Random sample from each stratum |
| Cost & Efficiency | Generally less expensive and more efficient, especially for geographically dispersed populations | Generally more expensive and time-consuming |
| Precision | Lower precision compared to stratified sampling | Higher precision, especially when strata are well-defined |
Examples
Cluster Sampling Example: A researcher wants to study the health habits of high school students in a large city. Instead of randomly selecting students from all high schools, they randomly select 10 high schools (clusters) and survey all students within those schools. This is more efficient than creating a list of all high school students in the city.
Stratified Sampling Example: A political pollster wants to estimate the proportion of voters who support a particular candidate. They divide the population into strata based on age groups (e.g., 18-29, 30-49, 50+). They then randomly sample voters from each age group, ensuring that each age group is represented in the sample proportionally to its size in the population. This ensures a more accurate representation of the electorate’s preferences.
Conclusion
In conclusion, both cluster and stratified sampling are valuable techniques for obtaining representative samples. Cluster sampling is particularly useful when dealing with geographically dispersed populations and limited resources, while stratified sampling is preferred when ensuring representation from specific subgroups is crucial. The choice between the two depends on the research objectives, the characteristics of the population, and the available resources. A careful consideration of these factors is essential for selecting the most appropriate sampling method and ensuring the validity of research findings.
Answer Length
This is a comprehensive model answer for learning purposes and may exceed the word limit. In the exam, always adhere to the prescribed word count.