In one line: Nobody can study everyone. So researchers study a sample, and how it is chosen decides who the findings apply to.
A cook tastes one spoonful to judge a whole pot of soup.
That works if the soup has been stirred. If all the salt has sunk to the bottom, a spoonful from the top gives the wrong idea of the pot.
A sample is the spoonful. It only tells you about the pot if it is like the pot.
Three words to start with
Target population
Everyone the study is about.
For a study of stress at your school: all the students in the school.
Sample
The people who actually take part, called participants.
Here: the 60 students who fill in the questionnaire.
Representative
A sample with the same mix of people as the target population: the same share of each year group, of boys and girls, of stressed and relaxed students.
Everyone · Some · Alike
If the sample is representative, the findings can be generalised: applied to the whole target population.
Sampling is the way the sample is chosen.
Remember: The sample stands in for the population. Choose it badly, and it stands in for nobody.
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Key idea: The techniques that give a representative sample are hard to carry out. The easy ones risk bias.
The five techniques
Random sampling
Every member of the target population has an equal chance of being chosen: names from a hat, or picked by a computer.
Strength: the sample tends to be representative.
Limit: it needs a full list of the population (a sampling frame), and the people picked may refuse.
Stratified sampling
The population is split into subgroups (strata), such as year groups, and people are picked at random from each, in the same proportions as in the population.
Strength: every subgroup is represented.
Limit: it takes time and detailed knowledge of the population.
Opportunity sampling
Whoever is available and willing at the time: the students in the library this lunchtime. Also called convenience sampling.
Strength: quick and easy.
Limit: the people who happen to be there are often not typical.
Self-selected sampling
People choose to take part, for example by answering a poster or an advert. Also called volunteer sampling.
Strength: easy to set up, and everyone is willing.
Limit: volunteers tend to be keener or to hold stronger views than other people.
Snowball sampling
A few participants are found, and each one brings in others they know. The sample grows like a rolling snowball.
Strength: it reaches hidden or hard-to-find groups.
Limit: the participants know each other and tend to be alike.
Random · Stratified · Opportunity · Self-selected · Snowball
In the first two, chance decides who takes part. In the other three it does not, so they are called non-probability techniques: people are not chosen at random from the target population.
One more you will meet: purposive sampling: The researcher chooses people on purpose, because they have the experience the study is about: eight students who care for a relative.
It is common in interviews and focus groups. It is also a non-probability technique.
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Key idea: Sampling bias means the sample does not represent the target population. It gets in by three routes.
Three routes
People select themselves
Volunteers share traits: they are more motivated, or have stronger opinions.
A sign-up sheet for a stress study attracts the students who want to talk about stress.
The researcher picks
In opportunity sampling the researcher decides who to approach, and tends to choose people who look friendly or who are their own friends, without meaning to.
Groups are left out
An online advert never reaches people who are offline.
And when people who are asked say no, those who refuse may differ from those who agree: non-response bias.
They choose · You choose · Some cannot be chosen
One sample has been used far more than any other: university students. An estimated 60 to 80% of Western psychology studies rely on them.
More widely, most research uses WEIRD samples: people from Western, Educated, Industrialised, Rich and Democratic societies. They are a small and unusual part of the world's population.
Exam tip: For any sample, ask: whose voice is missing?
Then say how the missing people might have answered differently.
Key idea: A finding can only be applied as far as the sample is like the people you want to apply it to.
External validity: three questions
Population validity
Do the results apply to the target population?
This is the one the sampling technique decides.
Ecological validity
Would the results be the same in a real-life setting?
Temporal validity
Would the results still hold at another time?
Who · Where · When
Studies with numbers and large samples talk about generalising to a population.
Studies with a few people studied in depth, such as interviews, talk about transferability: the findings may apply to people or places that are similar.
Bigger is not the same as better: A thousand volunteers are still volunteers.
A large sample helps only if it was chosen in a way that makes it representative.
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How this is asked: Every class practical has a sample, so sampling belongs in any answer that describes or designs a study. It is asked most directly through the concept of bias: explain bias in relation to your class practical, for 4 marks.
Your class practicals
- Your class runs four small studies of its own, called class practicals.
- They are an experiment, an interview, an observation and a survey.
- Paper 2 asks about the ones your class ran.
- Keep a short note of each: aim, what you did, who took part, what you found, and the ethics.
The answer pattern
- Say what bias is, in one sentence.
- Name the sampling technique and show how it was used.
- Say who was left out of the sample.
- Say what that does to the findings.
The trap: "the sample was too small": Size alone is rarely the problem.
The point that counts is who was in the sample, and who was not.
Your class practical was a survey about stress. You gave a questionnaire to the 25 students who were in the library during your free period. Explain the concept of bias in relation to the survey in your class practical.
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