Sampling 04: Cluster Sampling
Chapter 1: Introduction to Cluster Sampling
Cluster sampling is a cost-effective sampling method.
It simplifies the sampling process, avoiding direct collection from an entire population.
Example: A study on medical students correlating board scores with match results.
Ideal approach: Visit every medical school in the country.
Challenges include time, expense, and logistical hurdles.
Solution: Use cluster sampling to group medical schools and select a few to sample from.
Chapter 2: Understanding Clusters
Definition of a cluster: A group of subjects with something in common, representative of the overall population.
Use case: Medical schools serve as clusters for medical students.
Example: Selecting a medical school like UT Houston or Baylor.
Assumptions about the population: Students are likely similar in demographics (intelligence, gender ratio, economic status).
Process involves selecting representative clusters from the larger population.
Chapter 3: Implementing Cluster Sampling
Steps in cluster sampling:
Identify clusters (e.g., medical schools).
Randomly select clusters.
Collect data from all individuals in selected clusters.
Process described as taking a census of the chosen clusters.
Benefits:
Saves time and costs compared to a simple random sample of every medical student.
Avoids the need for comprehensive rosters from each medical school.
Chapter 4: Alternatives Within Clusters
Not necessary to evaluate every member of a selected cluster; a simple random sample can be chosen instead.
This method becomes known as multi-stage sampling (first select clusters, then sample within those clusters).
Cautionary example: Two selected schools that lack representation (
Example: University of Smarty Pants (above average) vs. University of Dummies (below average)).
These examples highlight potential bias in clustering.
Chapter 5: Creating Effective Clusters
Representation is key: Clusters should reflect the diversity of the entire population.
Adjust cluster definitions:
Instead of individual schools, use geographic regions (e.g., West, Northeast).
This ensures representation across different schools, encompassing a variety of student capabilities.
Chapter 6: Conclusion of Cluster Sampling
Summary: Cluster sampling involves forming representative clusters based on commonality.
Steps: Identify clusters, randomly select clusters, and either conduct a census or sample within the cluster.
Importance of randomness in cluster selection for valid results.