Cluster Sampling
Definition
Cluster sampling is a statistical method used to select a sample from a larger population by dividing it into clusters or groups. Once the population is divided, a random sample of clusters is chosen, and all items within selected clusters are examined in detail. This technique allows for efficient sampling when dealing with large populations and is often utilized in fields such as auditing, market research, and social sciences.
Examples
Auditing Invoices:
- An auditor wishes to audit a company’s invoices over a year. Instead of examining every invoice, the auditor divides the invoices into monthly clusters. Then, a few months are randomly selected, and every invoice within those months is thoroughly examined.
Market Research:
- A market research firm wants to conduct a survey on customer satisfaction. They may divide a city into different neighborhoods (clusters). Some neighborhoods are then randomly chosen, and surveys are conducted with every household within the selected neighborhoods.
Educational Studies:
- Researchers studying student performance might divide a large school district into individual schools (clusters). They randomly select several schools and assess all students within those schools.
Frequently Asked Questions
What is the difference between cluster sampling and stratified sampling?
In cluster sampling, the population is divided into clusters and a random sample of clusters is selected, while in stratified sampling, the population is divided into strata (subgroups) and a random sample is taken from each stratum.
Why is cluster sampling used?
Cluster sampling is used for its efficiency, especially when the population is large and dispersed. It reduces travel costs and logistical challenges, and it is useful when complete population lists are unavailable.
What are the advantages of cluster sampling?
- Cost-efficient
- Effective for large, widespread populations
- Easier to implement when a comprehensive list of the population is not available
What are the disadvantages of cluster sampling?
- Higher sampling error compared to simple random sampling
- Can lead to biased results if clusters are not homogenous
How is cluster sampling different from simple random sampling?
In simple random sampling, every member of the population has an equal chance of being selected. In cluster sampling, groups or clusters are randomly chosen first, and then all members within the selected clusters are sampled.
Related Terms
- Simple Random Sampling: A sampling method where every individual in the population has an equal chance of being selected.
- Stratified Sampling: A technique where the population is divided into subgroups (strata) that share similar characteristics, and a random sample is taken from each stratum.
- Systematic Sampling: A method where samples are selected at regular intervals from an ordered population.
- Multistage Sampling: A complex form of cluster sampling that involves multiple levels of random sampling, often starting with large units and progressively sampling smaller units.
Online References
Suggested Books for Further Studies
- “Survey Sampling” by Leslie Kish
- “Sampling: Design and Analysis” by Sharon L. Lohr
- “Sampling Techniques” by William G. Cochran
- “Introduction to the Practice of Statistics” by David S. Moore, George P. McCabe, and Bruce A. Craig
Accounting Basics: “Cluster Sampling” Fundamentals Quiz
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