Cluster sampling

Definition

Cluster sampling is a sampling technique where the population is divided into separate groups, known as clusters, and a random sample of these clusters is selected for study. This method is particularly useful when a population is widespread and hard to access, making it easier and more cost-effective to gather data by focusing on selected clusters rather than attempting to sample individuals from the entire population.

5 Must Know Facts For Your Next Test

1. Cluster sampling is advantageous when populations are large and dispersed geographically, making it impractical to conduct simple random sampling.
2. In cluster sampling, researchers first identify all the clusters in the population before randomly selecting a few of these clusters for detailed study.
3. This method can introduce higher sampling error compared to simple random sampling because individuals within a cluster may be more similar to each other than to those in other clusters.
4. Cluster sampling is often used in fields like education and public health, where it's more feasible to sample entire schools or neighborhoods rather than individual students or residents.
5. Researchers can use one-stage or two-stage cluster sampling; in one-stage, all individuals in selected clusters are sampled, while in two-stage, a random sample of individuals is taken from each selected cluster.

Review Questions

• How does cluster sampling differ from simple random sampling in terms of methodology and efficiency?
  • Cluster sampling differs from simple random sampling primarily in its approach to selecting participants. While simple random sampling involves randomly selecting individuals from the entire population, cluster sampling first divides the population into groups or clusters and then randomly selects entire clusters. This method can be more efficient and cost-effective, especially when dealing with large or geographically dispersed populations, as it reduces the need for extensive travel and individual data collection.
• What are some potential drawbacks of using cluster sampling compared to other probability sampling methods?
  • One major drawback of cluster sampling is that it can lead to higher levels of sampling error. Since individuals within a chosen cluster may share similar characteristics, the results may not be as generalizable as those obtained from methods like simple random or stratified sampling. Additionally, if the clusters are not homogeneous, this could result in biased estimates. It's important for researchers to carefully consider their clustering strategy to minimize these issues.
• Evaluate the effectiveness of cluster sampling in research design when studying diverse populations with geographical barriers.
  • Cluster sampling can be highly effective in research design when studying diverse populations that face geographical barriers. By focusing on specific clusters, researchers can gather rich data without the logistical difficulties of reaching every individual across wide areas. This approach allows for more manageable data collection while still capturing variability within the population. However, researchers must also be cautious about the potential bias that may arise if selected clusters do not accurately reflect the diversity of the overall population. The effectiveness ultimately hinges on careful planning and execution of the clustering process.

Related terms

Population: The entire group of individuals or instances about whom we are trying to gather information.

Sampling Frame: A list or other device used to define a researcher's population of interest, which includes all elements that can be sampled.

Stratified Sampling:

A sampling method where the population is divided into strata, or subgroups, and random samples are taken from each stratum to ensure representation.