Sampling methods - Selecting appropriate sampling techniques

Learning Outcomes

After reading this article, you will be able to distinguish between main sampling methods used in business analysis, including random, systematic, stratified, cluster, and quota sampling. You will be able to evaluate advantages and limitations of each sampling technique, identify which method is most suitable in given situations, and explain how sampling supports effective and efficient data collection for management accounting and business decisions.

ACCA Management Accounting (MA) Syllabus

For ACCA Management Accounting (MA), you are required to understand the role of sampling when collecting and analysing business data. You must recognize the different types of sampling, describe their application, and select the most appropriate method in various business scenarios.

• Explain key sampling techniques: random, systematic, stratified, cluster, multistage, and quota
• Recognize strengths and weaknesses of major sampling methods
• Select appropriate sampling methods given a data collection scenario
• Understand the impact of sampling method on reliability of conclusions

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. Which sampling technique divides a population into groups and then selects a random sample from each group in proportion to size?

   • a) Random
   • b) Systematic
   • c) Stratified
   • d) Cluster

2. True or false? Systematic sampling can introduce bias if there is a hidden pattern in the population list.

3. In which scenario would quota sampling be more practical than random sampling?

   • a) The entire population list is available and small
   • b) Respondents must meet specific demographic targets, and no complete list exists
   • c) Sampling is based on machine readings
   • d) Selecting every 50th invoice from a sequential list

4. What are the main differences between random and cluster sampling? Briefly explain.

Introduction

When gathering data to support management decisions, examining every member of a population is often impractical due to time, cost, or feasibility constraints. Sampling methods allow you to select a representative subset so that you can reach valid conclusions efficiently. Choosing the correct sampling method is essential for ensuring that your data is reliable and unbiased.

Key Term: population
The entire group or set of items, events, or people relevant to a specific question or study.

Key Term: sample
A subset of the population chosen for analysis, intended to reflect the characteristics of the whole.

OVERVIEW OF SAMPLING METHODS

There are multiple ways to draw a sample from a population. The method selected affects both how representative your results are and the validity of your conclusions.

RANDOM SAMPLING

Random sampling is the process where each member of the population has an equal chance of being selected.

Key Term: random sampling
A method where every member of the population has an equal probability of being included in the sample.

This technique minimizes bias and is suitable when a full and accessible list of the population exists. Random numbers (generated by a computer or a table) guide the selection.

SYSTEMATIC SAMPLING

Systematic sampling involves selecting every nth item from a list after starting at a random point.

Key Term: systematic sampling
A technique involving selection of every kth item from a population list, after a random starting point.

This method is straightforward to implement, especially with ordered lists such as invoices, staff ID numbers, or production batches. Care is needed if the population order has cyclical patterns, which could lead to bias.

STRATIFIED SAMPLING

Stratified sampling separates the population into subgroups (strata), then samples from each proportionally.

Key Term: stratified sampling
A sampling approach where the population is divided into strata (groups) based on shared characteristics, with random samples drawn from each group in proportion to their size.

Stratified sampling helps ensure meaningful representation of key subgroups in the sample (e.g., by department, location, or age), making estimates more accurate for heterogeneous populations.

CLUSTER AND MULTISTAGE SAMPLING

Cluster sampling divides the population into groups (clusters), then randomly selects clusters and studies all members within them or takes a subsample.

Key Term: cluster sampling
A sampling technique where the population is split into clusters (usually geographically), and then all (or a random selection) of items within chosen clusters are sampled.

Key Term: multistage sampling
A complex form of cluster sampling where selection happens in stages, sampling clusters first, then sub-units within those clusters.

Cluster and multistage sampling are practical when populations are widespread or a full list is not available.

QUOTA SAMPLING

Quota sampling is a non-random approach, frequently used in market research and opinion poll surveys.

Key Term: quota sampling
A non-random sampling method where the interviewer fills pre-set quotas for different population subgroups.

This method is fast and cost-effective when precise lists are unavailable or the sampling frame is incomplete. However, its subjectivity may introduce bias.

STRENGTHS AND LIMITATIONS

SELECTING THE RIGHT SAMPLING METHOD

Selecting an appropriate sampling technique depends on:

• The availability of a complete population list
• Time and resource constraints
• The need for representation of subgroups
• The structure or dispersion of the population
• Risk of sampling bias

Worked Example 1.1

A university wants to survey graduate employment outcomes. There are 2,000 graduates, with 1,600 undergraduates and 400 postgraduates. Management wants results to reflect both groups. Which sampling method is most suitable?

Answer:
Stratified sampling is appropriate. Dividing into strata (undergraduate and postgraduate), then sampling from each in proportion, ensures both groups are represented as in the total population.

Worked Example 1.2

A manufacturer examines warranty claims by reviewing every 100th warranty card processed. However, claims show seasonal variation by month. What could be a problem with systematic sampling in this context?

Answer:
If seasonal trends repeat at fixed intervals, systematic sampling could either miss or over-represent claims from particular months, introducing bias. Random or stratified sampling by season may be preferable.

Worked Example 1.3

A research firm plans to survey opinions in a country with thousands of towns and cities. A full population list is not available. Which sampling method should be used?

Answer:
Cluster or multistage sampling is suited. The country can be divided by regions, towns, then streets, and clusters sampled at each stage. This practical approach avoids the need for a single comprehensive list.

Revision Tip

If details of the population are incomplete or fieldwork is required, cluster or quota sampling is usually quicker than random approaches—but be alert to possible bias.

Exam Warning

Systematic sampling is not truly random—be wary of hidden patterns. You may be asked to recommend against this method if the population is cyclical.

Summary

Selecting the correct sampling method is essential for efficient, unbiased data collection. Always match the sampling technique to the business problem, available resources, and data structure. Random and stratified methods are preferred for accuracy, but systematic, cluster, and quota methods provide practicality where lists are incomplete or populations are large.

Key Point Checklist

This article has covered the following key knowledge points:

• Distinguish between population and sample
• Explain and define random, systematic, stratified, cluster, multistage, and quota sampling
• Evaluate strengths and limitations of each sampling method
• Select a suitable sampling technique for a given context
• Recognize the risks of bias and importance of representativeness in sampling

Key Terms and Concepts

• population
• sample
• random sampling
• systematic sampling
• stratified sampling
• cluster sampling
• multistage sampling
• quota sampling