Sampling methods - Cluster, multistage, and quota sampling

Learning Outcomes

After reviewing this article, you will be able to explain and distinguish cluster, multistage, and quota sampling methods. You will understand how and when each technique is applied in business and accounting contexts, their main features, the differences between probability and non-probability sampling, and the relevant strengths and weaknesses. You will also be able to select the most appropriate sampling approach in various real-world exam scenarios.

ACCA Management Accounting (MA) Syllabus

For ACCA Management Accounting (MA), you are required to understand several sampling methods, how to select the appropriate method, and to recognize their strengths and weaknesses—especially for data collection, analytics, and audit contexts.

• Explain and describe sampling techniques: cluster, multistage, and quota sampling
• Identify when each sampling method is suitable
• Compare advantages and limitations of each method
• Differentiate between probability and non-probability sampling
• Select an appropriate sampling method for a specific scenario

Test Your Knowledge

Attempt these questions before reading this article. If you find some difficult or cannot remember the answers, remember to look more closely at that area during your revision.

1. State one main difference between cluster sampling and stratified sampling.
2. Which sampling method would be most practical for a nationwide survey where a full list of individuals is not available? a) Simple random sampling
   b) Multistage sampling
   c) Systematic sampling
   d) Quota sampling
3. True or false? Quota sampling guarantees a truly representative sample.
4. In which situation would cluster sampling be preferable over random sampling?

Introduction

Selecting a suitable sampling method is essential for drawing reliable conclusions from data without examining every item in a large population. In accounting, management, and audit, efficient sampling saves time and cost, while valid results support sound decision-making. Cluster, multistage, and quota sampling are commonly used when random sampling is impractical or when other constraints exist.

Key Term: Sampling
The process of selecting a subset of items (sample) from a larger group (population) to estimate characteristics of the whole group.

Cluster Sampling

Cluster sampling involves dividing the population into groups (clusters), usually based on geography or another natural grouping. Then, either all items in certain clusters or a sample of items within selected clusters are included.

Key Term: Cluster Sampling
A probability sampling technique where the population is split into groups (clusters) and a sample of clusters is chosen at random. All or selected items within those clusters are surveyed.

Cluster sampling is especially practical when a complete list of individual items is not available, but natural clusters can be identified. For example, if a company needs to audit retail outlets across a country, it can sample entire cities or regions (clusters), then examine all or some outlets within those clusters.

Advantages of Cluster Sampling

• Cost-effective for large or widely-dispersed populations.
• More practical when population lists are incomplete but clusters are accessible.
• Reduces travel and administrative effort.

Limitations of Cluster Sampling

• Clusters may be more similar internally than the population as a whole, which can reduce statistical accuracy.
• Risk of unrepresentative samples if clusters differ greatly.

Worked Example 1.1

A food manufacturer wants to check quality control in 1,000 supermarkets nationwide. A list of all supermarkets is not available, but there is a list of 50 cities. The auditor randomly selects 5 cities and audits all supermarkets in those cities.

Answer:
This is cluster sampling, as whole cities (clusters) are selected and all units in those cities are included.

Multistage Sampling

Multistage sampling extends cluster sampling by adding layers of selection. After choosing clusters, you further select subgroups or items within them.

Key Term: Multistage Sampling
A probability sampling technique where sampling takes place in several steps: first clusters are randomly selected, then further sampling is done within those clusters, often at multiple levels.

Multistage sampling is suitable when populations are large and complex, such as national surveys or audits across many organisational levels.

Steps

1. Divide the population into first-stage clusters (e.g., regions).
2. Randomly select some clusters.
3. Divide each cluster into sub-clusters (e.g., towns), and sample again.
4. Continue as needed, then select final units (e.g., individual stores).

Advantages of Multistage Sampling

• Feasible for complex populations.
• Reduces costs even further by narrowing down sampling at each stage.
• Can combine various sampling techniques at different stages.

Limitations of Multistage Sampling

• Complexity increases with each stage.
• If not well-designed, can suffer from the same issues as cluster sampling.

Worked Example 1.2

A researcher wants to survey working habits of employees in a country. First, 4 provinces are selected at random (first stage). Then, within each province, 5 towns are selected (second stage). Within each town, 3 companies are randomly chosen, and finally, employees are sampled from each company. What sampling method is used?

Answer:
Multistage sampling, as sampling occurs at multiple levels (province → town → company → employee).

Quota Sampling

Quota sampling is a non-probability method commonly used in market research. The researcher specifies quotas for certain population subgroups and selects sample members until each quota is met. For example, a survey might require 20 female respondents aged 18–35 and 15 male respondents aged 36–50.

Key Term: Quota Sampling
A non-probability sampling method where sample quotas are set for different population segments, and the interviewer selects respondents to fill each quota by convenience or judgement.

Quota sampling does not rely on random selection. The researcher may use personal judgement or convenience to pick individuals who meet each criterion.

Advantages of Quota Sampling

• Fast and cost-effective for gaining understanding.
• Useful when random sampling is unfeasible or time is limited.

Limitations of Quota Sampling

• No guarantee of true randomness—risk of selection bias.
• May not fully represent the population, especially if the interviewer’s beliefs affect choices.

Worked Example 1.3

A marketing team aims to interview 50 shop customers: 25 female and 25 male. Interviewers stop people at a shopping mall until each quota is filled. What sampling method is this?

Answer:
Quota sampling, as quotas based on gender are filled by meeting the targets, not random selection.

Comparing the Methods

Exam Warning

Be careful: Quota sampling is not random. ACCA exams may test whether you can identify when a sample is not representative due to method choice.

Revision Tip

In exams, look for evidence of random selection. If none is mentioned and quotas are used, you are likely dealing with non-probability quota sampling.

Summary

Cluster sampling selects whole groups, multistage sampling adds extra sampling layers, and quota sampling fills pre-set groups without randomness. Each method has distinct uses, advantages, and limitations. Understanding when to apply each method and recognising sources of bias are key for ACCA assessment and practical data analysis.

Key Point Checklist

This article has covered the following key knowledge points:

• Define and describe cluster, multistage, and quota sampling
• Identify situations where each method is suitable
• List strengths and weaknesses for each method
• Distinguish between probability and non-probability sampling
• Detect risks of bias and common exam pitfalls

Key Terms and Concepts

• Sampling
• Cluster Sampling
• Multistage Sampling
• Quota Sampling