Cluster Sample

Definition

A cluster sample is a sampling method where the population is divided into separate groups, known as clusters, and a whole cluster is randomly selected to represent the entire population. This technique is often used when it is difficult or costly to conduct a simple random sample. By using clusters, researchers can obtain data from a more manageable subset while still aiming for representativeness.

5 Must Know Facts For Your Next Test

1. In cluster sampling, each cluster should ideally be heterogeneous within itself but similar to other clusters to ensure diversity in the sample.
2. This method is especially useful when the population is geographically dispersed, making it costly or impractical to sample individuals directly.
3. Cluster sampling can introduce sampling error if the selected clusters are not representative of the entire population.
4. Researchers may choose between one-stage or two-stage cluster sampling, depending on whether they select all individuals in a chosen cluster or take a random sample from within that cluster.
5. Cluster sampling can be more efficient than simple random sampling in terms of time and resources while still achieving valid results.

Review Questions

• How does cluster sampling differ from stratified sampling in terms of structure and purpose?
  • Cluster sampling differs from stratified sampling primarily in how the population is divided. In cluster sampling, the population is divided into groups or clusters, and entire clusters are selected randomly, which may include individuals with varying characteristics. In contrast, stratified sampling involves dividing the population into distinct subgroups (strata) based on specific traits and then taking random samples from each stratum. The purpose of cluster sampling is often practical, focusing on reducing costs and time, while stratified sampling aims to ensure representation across different subgroups.
• Discuss the advantages and disadvantages of using cluster sampling in data collection.
  • Cluster sampling offers several advantages such as reduced costs and time savings when gathering data from geographically dispersed populations. It allows researchers to work with entire clusters rather than individuals, making data collection more manageable. However, one major disadvantage is that if the selected clusters are not representative of the overall population, it can lead to biased results. Additionally, there may be increased variability within clusters compared to a truly random sample, potentially affecting the reliability of conclusions drawn from the data.
• Evaluate how cluster sampling can impact the validity of statistical results compared to other sampling methods.
  • Cluster sampling can significantly influence the validity of statistical results depending on how well the selected clusters represent the overall population. If clusters are homogenous internally but varied across clusters, this method may produce valid insights about the population. However, if there’s substantial variation within selected clusters and limited diversity between them, it can introduce bias and reduce validity. Compared to other methods like simple random or stratified sampling, where each individual has an equal chance of selection, cluster sampling may risk underrepresenting certain segments of the population, leading to potentially flawed conclusions.

Related terms

Stratified Sample: A stratified sample is obtained by dividing the population into subgroups, or strata, and then taking random samples from each stratum to ensure that specific characteristics are represented.

Random Sampling:

Random sampling is a method where each member of the population has an equal chance of being selected, ensuring that the sample represents the population without bias.

Systematic Sample: A systematic sample involves selecting every nth member of the population after randomly determining a starting point, which can provide a structured way to achieve randomness.