Sampling techniques

The four techniques

• Random: every member of the target population has an equal chance of selection (names drawn from a hat, random number generator). Free from researcher bias, but needs a full list of the population and may still be unrepresentative by chance.
• Stratified: the population is divided into subgroups (strata, such as gender or age), and people are randomly selected from each in the same proportions as the population. Very representative, but needs detailed knowledge of the population and is time-consuming.
• Volunteer (self-selected): people choose to take part, for example by answering an advert. Easy and ethical, but volunteers may be a particular type of person (keen, helpful), giving volunteer bias. Milgram's newspaper advert produced a volunteer sample.
• Opportunity: whoever is available at the time. Quick and cheap, but likely to be biased and unrepresentative.

Calculating a stratified sample

If a school has 300 boys and 200 girls and a researcher wants a sample of 20:

• boys: 300 / 500 x 20 = 12
• girls: 200 / 500 x 20 = 8

Then pick each subgroup's members at random.

Why it matters

A representative sample allows findings to be generalised to the target population. Pearson's guidance for the practical: at least 10 per group in most cases, and opportunity sampling should be justified by more than 'it was quick'.

Worked example

A researcher wants 30 participants from a college of 600 students: 360 study sciences and 240 study arts. How many science students should a stratified sample include?

1. Proportion of science students = 360 / 600 = 0.6.
2. 0.6 x 30 = 18.

Answer: 18 science students (and 12 arts students).

Check yourself

1. A company has 80 managers and 320 other staff. A stratified sample of 25 is needed. How many managers should be included?
   Show answer

   5 managers. 80 / 400 x 25 = 5 managers.

2. Milgram recruited participants through a newspaper advert. Which technique is this?
   • Random sampling
   • Stratified sampling
   • Opportunity sampling
   • Volunteer sampling
   Show answer

   Volunteer sampling. Participants chose to respond to the advert.

3. What defines a random sample?
   • The researcher picks people who look suitable
   • Every member of the target population has an equal chance of being chosen
   • People choose whether to take part
   • Subgroups are represented in proportion
   Show answer

   Every member of the target population has an equal chance of being chosen. Equal chance of selection is the key feature.

4. A student hands out questionnaires to people in the canteen at lunchtime. What is the main weakness of this sample?
   • It requires a full list of the population
   • It is too time-consuming
   • It is likely to be unrepresentative, as only people in the canteen at that time could be chosen
   • It removes the right to withdraw
   Show answer

   It is likely to be unrepresentative, as only people in the canteen at that time could be chosen. Opportunity samples reflect who is available, which introduces bias.

5. Which sampling technique is most likely to give a sample that reflects the gender balance of a school?
   • Stratified sampling
   • Volunteer sampling
   • Opportunity sampling
   • Snowball sampling
   Show answer

   Stratified sampling. Stratified samples match the population's proportions.

6. Explain why a volunteer sample may not be representative.
   Show answer

   Volunteers are a particular type of person, for example more motivated, helpful or interested in the topic, so they may differ from the rest of the population and results cannot be generalised.. Self-selection means a certain type of person takes part.

Practise with instant feedback Print this lesson

Sources

• Pearson Edexcel International Advanced Subsidiary/Advanced Level in Psychology specification (issue January 2026)
• Pearson International A Level Psychology component guide: WPS01
• IAL Psychology revision site: Sampling techniques

Last updated 8 November 2026. Spotted a mistake? Tell us.