Sampling methods

Introduction to Sampling Methods

Imagine you want to know what the favorite music genre of every teenager in the UK is. There are millions of teenagers! You couldn't possibly ask every single one—it would take years and cost a fortune. Instead, psychologists pick a smaller group to study. This process is called sampling.

In this chapter, we will learn how psychologists choose their participants and why the method they pick can change the results of their research. This is a vital part of Topic 11: Research Methods and will help you in both Paper 1 and Paper 2 exams.

Key Terms: Population vs. Sample

Before looking at the methods, we need to understand two basic ideas:

1. Target Population: This is the entire group of people that the researcher is interested in. For example, "all primary school teachers in Manchester" or "all people with a diagnosis of depression."

2. Sample: This is the smaller group of people who are actually taken from the target population to take part in the research. The goal is usually to have a sample that is representative—meaning it looks and acts like a "mini-version" of the whole target population.

Quick Review: If your target population is "students at your school," your sample might be "20 students chosen from the canteen."

The Four Sampling Methods

The Edexcel syllabus requires you to know four specific sampling methods. For the exam, you need to be able to describe them and explain their strengths and weaknesses.

1. Random Sampling

In a random sample, every member of the target population has an equal chance of being chosen. It is like putting every name from the population into a giant hat and pulling out the number you need.

How it works: Researchers often use a computer program or a random number generator to pick participants from a list of the target population.

Strengths:
• It is unbiased. Because everyone has an equal chance, the researcher cannot accidentally pick people they "prefer."
• It is likely to be representative if the sample size is large enough.

Weaknesses:
• It is time-consuming and difficult to get a full list of everyone in the target population.
• By pure "luck of the draw," you might still end up with an unrepresentative sample (e.g., accidentally picking all boys even though the population is mixed).

2. Stratified Sampling

This is the "proportional" method. The researcher identifies the different subgroups (called strata) within the target population and picks participants so that the sample matches the population proportions.

Example: If a school is \(60\%\) girls and \(40\%\) boys, a stratified sample of 10 students would contain exactly 6 girls and 4 boys.

Strengths:
• It is the most representative method. It ensures that specific groups (like age or gender) are perfectly balanced.
• It allows for high generalisability (you can apply the results to the whole population with confidence).

Weaknesses:
• It is extremely complex and time-consuming to calculate the proportions and sort people into groups.
• You need to know the exact details of the population, which aren't always available.

3. Volunteer Sampling

Also known as self-selected sampling. This happens when the researcher advertises for participants (e.g., a poster in a shop or an ad on social media), and people choose to take part.

Strengths:
• It is easy and convenient for the researcher.
• Participants are usually highly motivated to take part because they chose to be there, so they are less likely to drop out.

Weaknesses:
• Volunteer Bias: This is a major problem. People who volunteer for studies are often different from those who don't (e.g., they might be more helpful, have more free time, or have a specific interest in the topic). This makes the sample unrepresentative.

4. Opportunity Sampling

This is simply using whoever is available at the time and fits the criteria. If you walked down the street and asked the first 10 people you saw to fill out a survey, that would be opportunity sampling.

Strengths:
• It is the quickest and cheapest method because you use the people who are already there.

Weaknesses:
• It is often unrepresentative. For example, if you sample people in a town center at 10 AM on a Wednesday, you won't get any people who work a 9-to-5 job.
• There is a risk of researcher bias, as the researcher might unconsciously avoid people who look "scary" or "unfriendly."

Reliability and Validity in Sampling

When evaluating research, you must consider how the sampling method affects the "truth" of the results.

Validity: This asks, "Does the sample represent the target population?" If a sample is representative, it has high external validity. This means we can generalise the findings—we can confidently say that what we found in our small group is probably true for the whole population.

Reliability: This refers to consistency. If another researcher used the same sampling method with the same target population, would they get a similar group of people and similar results? Standardised procedures in sampling (like using a computer for random sampling) make it more reliable.

Comparison Summary Table

Use this table as a quick reference for your revision!

Common Mistakes to Avoid

• Confusing Random and Opportunity: Students often think "Opportunity" is random because you're just picking people on the street. It’s NOT! In Random sampling, everyone in the whole population has an equal chance. In Opportunity sampling, only the people standing near you have a chance.

• Generalisability: Don't just say a sample is "bad." Use the term generalisability. If a sample is unrepresentative, you cannot generalise the results to the target population.

Quick Tip: If an exam question asks you to "Evaluate the sampling method," always aim for one strength and one weakness, and try to use the word representative in your answer!

Note: For more on how these samples are used in research, see the chapters on "Experimental and Research Designs" and "Reliability and Validity."