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
### When is cluster sampling particularly useful?
- [x] When dealing with large, dispersed populations.
- [ ] When the population is homogenous.
- [ ] When a list of all individuals in the population is available.
- [ ] When conducting time-sensitive studies.
> **Explanation:** Cluster sampling is particularly useful when dealing with large, dispersed populations, as it reduces travel costs and logistical challenges.
### What is the first step in cluster sampling?
- [ ] Choosing a random sample of individuals.
- [ ] Dividing the population into strata.
- [x] Dividing the population into clusters.
- [ ] Selecting all clusters for sampling.
> **Explanation:** The first step in cluster sampling is to divide the population into clusters.
### How does cluster sampling differ from stratified sampling?
- [x] Clusters are sampled as whole groups while strata have individual members sampled.
- [ ] Clusters and strata are fundamentally the same.
- [ ] Cluster sampling requires a smaller sample size.
- [ ] Stratified sampling only applies to small populations.
> **Explanation:** In cluster sampling, entire groups (clusters) are sampled, whereas in stratified sampling, individual members are randomly selected from each subgroup (stratum).
### Which of the following is a potential disadvantage of cluster sampling?
- [ ] Exhaustive data collection
- [ ] Reduced sampling error
- [x] Higher sampling error
- [ ] Inefficiency in handling large populations
> **Explanation:** One potential disadvantage of cluster sampling is higher sampling error compared to simple random sampling.
### Why might an auditor choose to use cluster sampling?
- [ ] To ensure every invoice is audited.
- [x] To reduce the volume of data while maintaining representativeness.
- [ ] To dismiss non-relevant clusters.
- [ ] To introduce bias in sample selection.
> **Explanation:** An auditor might use cluster sampling to reduce the volume of data that needs to be examined while still maintaining a representative sample.
### In which scenario is cluster sampling **not** ideal?
- [x] When population members are extremely heterogeneous within clusters.
- [ ] When assessing geographically dispersed populations.
- [ ] When population listings are unreliable.
- [ ] When aiming to reduce logistical complexities.
> **Explanation:** Cluster sampling is not ideal when population members within clusters are extremely heterogeneous, as this can lead to biased results.
### What is the main goal of using cluster sampling in market research?
- [ ] To segment the market into non-overlapping areas.
- [ ] To ensure every individual in the population is contacted.
- [x] To efficiently obtain a representative sample from large populations.
- [ ] To conduct detailed analysis of every single product feature.
> **Explanation:** The main goal of using cluster sampling in market research is to efficiently obtain a representative sample from large populations.
### Which step follows after clusters have been randomly selected in cluster sampling?
- [ ] Discarding all non-selected clusters.
- [ ] Dividing the clusters into smaller sub-groups.
- [x] Examining or collecting data from every item within the selected clusters.
- [ ] Merging data from non-cluster members.
> **Explanation:** After clusters have been randomly selected, the next step is to examine or collect data from every item within the selected clusters.
### Can cluster sampling lead to biased results?
- [x] Yes, if the clusters are not homogenous.
- [ ] No, cluster sampling completely avoids bias.
- [ ] Only in populations equal to the sample size.
- [ ] Bias is impossible if clusters are randomly selected.
> **Explanation:** Cluster sampling can lead to biased results if the clusters are not homogenous.
### Which type of study typically uses cluster sampling?
- [ ] Detailed case studies
- [ ] Experimental designs
- [x] Large-scale surveys
- [ ] Time-series analyses
> **Explanation:** Large-scale surveys typically use cluster sampling due to its efficiency in handling large and widespread populations.
Thank you for exploring the fundamentals of cluster sampling and working through our insightful quiz questions. Continue to enhance your understanding of statistical and auditing principles!
