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IB DP Psychology — Research Methodology | SL & HL | Paper 3 (HL) / Research Methods
🎯 Learning Intentions By the end of this note, you will be able to:
- Define the terms population, sample, and sampling technique using IB-aligned terminology.
- Describe the key features of opportunity, purposive, and snowball sampling techniques.
- Explain how the choice of sampling technique affects the generalisability and validity of research findings.
- Evaluate the strengths and limitations of each sampling technique using the concept areas of Bias, Measurement, and Responsibility.
- Apply knowledge of sampling techniques to a given research scenario or case study.
- Discuss how sampling decisions relate to ethical considerations and the protection of participants.
Introduction & Context
Imagine a researcher wants to understand how social media use affects the self-esteem of teenagers worldwide. There are hundreds of millions of teenagers on the planet — it is obviously impossible to study all of them. So the researcher must make a decision: which teenagers will actually take part in the study? This seemingly practical question turns out to be one of the most consequential decisions in the entire research process.
The group of people a researcher ultimately studies is called a sample, and the method used to select that group is called a sampling technique. The choice of technique shapes who is included, who is excluded, and — critically — whether the findings can be applied to anyone beyond the people who actually participated. In psychology, where the goal is often to understand human behaviour in general, this matters enormously.
Different research questions, contexts, and practical constraints call for different approaches to sampling. A clinical researcher studying a rare condition cannot simply walk into a shopping centre and recruit participants. A sociologist studying underground communities cannot post an advertisement in a newspaper. Understanding why different techniques exist — and what trade-offs each involves — is essential for both conducting and critically evaluating psychological research.
🔍 Analogy — The Taste Test Imagine you are making a large pot of soup and want to know if it tastes good. You do not eat the entire pot — you take a spoonful. That spoonful is your sample . If you stir the soup well before tasting, your spoonful is likely to represent the whole pot accurately. But if all the salt has sunk to the bottom and you only taste from the top, your sample is misleading. Sampling in psychology works the same way: the method you use to select your "spoonful" determines how well it represents the whole "pot" — the population you want to understand.
Core Concepts
Before examining specific techniques, it is important to establish two foundational ideas: the distinction between a population and a sample, and the concept of generalisability. These ideas underpin every sampling decision a researcher makes.
📖 Key Definition — Population and Sample A population refers to the entire group of individuals that a researcher is interested in studying — for example, all university students in a country, or all adults diagnosed with depression. A sample is the subset of that population who actually participate in the research. The goal of sampling is to select a sample that is sufficiently representative of the population so that findings can be meaningfully applied