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 whole population. This method is particularly useful when dealing with large populations that are spread out geographically, as it simplifies the data collection process by focusing on specific clusters rather than the entire population.

5 Must Know Facts For Your Next Test

1. Cluster sampling can be more cost-effective and efficient than other sampling methods, especially when populations are dispersed over a wide area.
2. In cluster sampling, entire clusters are selected randomly, and all members of the chosen clusters are included in the sample, rather than randomly selecting individuals from the entire population.
3. This method can introduce higher variability compared to simple random sampling if the clusters are not homogeneous, meaning differences within clusters can affect results.
4. Cluster sampling is often used in public health studies and educational research where researchers might focus on specific schools or communities instead of trying to sample individuals from the entire population.
5. Researchers need to carefully define what constitutes a cluster to ensure that the clusters selected are representative of the broader population.

Review Questions

• How does cluster sampling differ from stratified sampling in terms of selection and representation?
  • Cluster sampling differs from stratified sampling primarily in how groups are formed and sampled. In cluster sampling, entire groups or clusters are selected randomly, and every member of those clusters is included in the sample. In contrast, stratified sampling involves dividing the population into distinct strata based on specific characteristics and then randomly selecting individuals from each stratum. This ensures that each subgroup is represented within the sample, whereas cluster sampling may focus on entire areas or communities without individual representation.
• Discuss the advantages and disadvantages of using cluster sampling for survey research.
  • One major advantage of cluster sampling is its cost-effectiveness and efficiency in large populations where gathering data can be logistically challenging. It allows researchers to collect data from a concentrated area rather than scattering efforts across a wide region. However, a disadvantage is that if clusters are not homogenous, this can lead to increased variability in the results and potentially skew findings. This means careful consideration must be given to how clusters are defined to maintain representativeness.
• Evaluate how cluster sampling might impact the generalizability of survey results in social psychology research.
  • The use of cluster sampling can significantly influence the generalizability of survey results in social psychology research. If the selected clusters reflect specific characteristics that do not represent the broader population, findings may not be applicable outside those clusters. For instance, if researchers study attitudes toward a social issue within one community without considering wider demographics, their conclusions may not hold true for other groups. Thus, while cluster sampling can simplify logistics and reduce costs, researchers must critically assess whether their chosen clusters adequately represent the diversity of perspectives within the larger population to draw valid conclusions.

Related terms

Stratified sampling: A sampling method that involves dividing the population into subgroups, or strata, and randomly sampling from each stratum to ensure representation across key characteristics.

Random sampling: A basic sampling technique where every individual in the population has an equal chance of being selected, ensuring that the sample is representative.

Sampling frame: A list or database from which a sample is drawn, representing all the elements in the population that are eligible for selection.