Cluster Sampling

Definition

Cluster sampling is a sampling technique where the population is divided into groups, or clusters, and a random sample of these clusters is selected to represent the entire population. This method is particularly useful when the population is large and spread out, allowing researchers to save time and resources by collecting data from selected clusters instead of the whole population.

5 Must Know Facts For Your Next Test

1. Cluster sampling can reduce costs and improve efficiency when working with large populations by limiting data collection to only selected groups.
2. It is often used in educational research, health studies, and market research where populations may be geographically dispersed.
3. This method can introduce a higher level of sampling error compared to simple random sampling since entire clusters are chosen rather than individuals.
4. Cluster sampling can be single-stage, where entire clusters are sampled, or two-stage, where individual elements within selected clusters are sampled.
5. Care must be taken to ensure that the clusters are representative of the entire population to avoid bias in the results.

Review Questions

• How does cluster sampling differ from stratified sampling in terms of sample selection?
  • Cluster sampling involves dividing the population into clusters and then randomly selecting whole clusters to study, while stratified sampling divides the population into strata based on specific characteristics and samples from each stratum. In cluster sampling, entire groups are chosen, which can lead to higher variability among samples, whereas stratified sampling aims for equal representation of various subgroups within the overall population. This fundamental difference affects how researchers analyze and interpret data gathered through these methods.
• What are some advantages and disadvantages of using cluster sampling in research?
  • One significant advantage of cluster sampling is its cost-effectiveness and efficiency, especially when dealing with geographically dispersed populations. By focusing on specific clusters, researchers can save time and resources. However, a notable disadvantage is that it may increase sampling error compared to other methods like simple random sampling, as the sampled clusters might not accurately reflect the diversity of the entire population. This could lead to biased results if the selected clusters share common characteristics not present in the broader population.
• Evaluate how cluster sampling can impact the reliability and validity of research findings.
  • Cluster sampling can impact the reliability and validity of research findings significantly based on how well the selected clusters represent the entire population. If the chosen clusters are homogeneous and do not reflect the wider diversity of the population, this could skew results and reduce validity. Additionally, because cluster sampling may introduce greater variability within clusters compared to individual random samples, it could affect reliability if not managed carefully. Researchers must ensure proper cluster selection and consider potential biases when interpreting findings to maintain both reliability and validity.

Related terms

Stratified Sampling: A sampling method where the population is divided into subgroups or strata that share similar characteristics, and samples are drawn from each stratum.

Simple Random Sampling: A basic sampling technique where every individual in the population has an equal chance of being selected.

Sampling Frame: A complete list of individuals or units in the population from which a sample is drawn, ensuring that all members have a chance of being selected.