Cluster Sampling

Definition

Cluster sampling is a probability sampling technique where the entire population is divided into groups or clusters, and a random sample of these clusters is selected to represent the whole population. This method is often used when the population is geographically dispersed or when a complete list of all individual members is not available.

5 Must Know Facts For Your Next Test

1. Cluster sampling is particularly useful when the population is geographically dispersed or when a complete list of all individual members is not available.
2. In cluster sampling, the population is first divided into mutually exclusive and exhaustive groups or clusters, and then a random sample of these clusters is selected.
3. Cluster sampling can reduce the cost and time required for data collection compared to other probability sampling techniques, as the researcher only needs to visit the selected clusters.
4. The accuracy of cluster sampling depends on the homogeneity within the clusters and the heterogeneity between the clusters.
5. Cluster sampling can be used in combination with other sampling techniques, such as stratified sampling or systematic sampling, to improve the representativeness of the sample.

Review Questions

• Explain how cluster sampling can be used in the context of data, sampling, and variation in data and sampling.
  • In the context of data, sampling, and variation in data and sampling, cluster sampling can be used to collect data from a geographically dispersed population. By dividing the population into clusters, such as neighborhoods or schools, and then randomly selecting a sample of these clusters, the researcher can reduce the cost and time required for data collection while still maintaining the representativeness of the sample. This method can help address issues related to variation in data and sampling, as the selected clusters can provide a diverse representation of the population.
• Describe how the use of cluster sampling can impact the experimental design and ethical considerations in a study.
  • The use of cluster sampling can influence the experimental design and ethical considerations in a study. From an experimental design perspective, cluster sampling can introduce additional sources of variation that need to be accounted for, such as the differences between clusters. Researchers may need to adjust their sample size or use statistical techniques like multilevel modeling to address the hierarchical structure of the data. Ethically, the use of cluster sampling may raise concerns about the equitable representation of different geographic or demographic groups within the sample, and researchers must ensure that the selection of clusters does not inadvertently exclude or marginalize certain populations.
• Analyze how the finite population correction factor (FPCF) can be applied in the context of cluster sampling.
  • In the context of cluster sampling, the finite population correction factor (FPCF) can be used to adjust the variance of the sample estimate when the population size is finite. The FPCF accounts for the fact that the sample is drawn from a finite population, which can affect the precision of the estimate. When using cluster sampling, the FPCF should be applied at the cluster level, as the clusters themselves represent a finite population. This adjustment can be particularly important when the number of clusters is small, as the finite population effect can be more pronounced. By incorporating the FPCF, researchers can obtain more accurate estimates of the population parameters and make informed decisions about the representativeness and generalizability of their findings.

Related terms

Probability Sampling: Probability sampling is a sampling technique where each member of the population has a known, non-zero chance of being selected as part of the sample.

Simple Random Sampling:

Simple random sampling is a probability sampling technique where each member of the population has an equal chance of being selected as part of the sample.

Stratified Sampling:

Stratified sampling is a probability sampling technique where the population is divided into mutually exclusive groups (strata), and a random sample is selected from each stratum.