What sampling methods do psychologists use?
Sampling: the difference between population and sample; sampling techniques including random, systematic, stratified, opportunity and volunteer; implications of sampling techniques, including bias and generalisation.
Covers AQA 4.7 sampling: population versus sample, random, systematic, stratified, opportunity and volunteer sampling, and the implications of bias and generalisation.
Reviewed by: AI editorial process; not yet individually human-reviewed
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What this dot point is asking
AQA wants you to describe the five sampling techniques and the implications of each for bias and generalisation. The exam skill is to describe how each sample is obtained, to calculate a stratified sample, and to link sample bias to limited generalisation.
The five techniques
A target population is the whole group the researcher is interested in, and because studying everyone is impractical they study a sample drawn from it. The five techniques differ in how that sample is selected, which determines how representative it is. A random sample gives every member of the population an equal chance of being chosen, usually by obtaining a complete list and selecting names by a lottery or random number generator; it is free of researcher bias but can still produce an unrepresentative sample by chance and needs a full list. A systematic sample selects every nth person from a list (for example every 10th name); it is objective and simple but is not truly random. A stratified sample first divides the population into strata (sub-groups such as year groups or genders) and then samples from each in proportion to its size in the population, which makes it the most representative method, though it is the most time-consuming. An opportunity sample simply uses whoever is available and willing at the time; it is quick and convenient but prone to researcher bias and unrepresentative. A volunteer sample relies on people putting themselves forward, for example by answering an advert; it is convenient and gathers willing participants but suffers from volunteer bias.
Implications
The implication that ties the whole topic together is generalisation: the more representative the sample, the more confidently the findings can be generalised to the target population. Random, systematic and stratified sampling tend to produce more representative samples (stratified most of all, because it deliberately mirrors the population's structure), so they support stronger generalisation, at the cost of being more time-consuming and, for random and stratified methods, requiring a full list of the population. Opportunity and volunteer sampling are easier and quicker but introduce bias. Opportunity samples reflect researcher bias and the narrow slice of people available in one place at one time, while volunteer samples suffer from volunteer bias, because people who choose to take part may differ systematically from the rest of the population (for example being more motivated or having more free time). The greater the bias, the more limited the generalisation, which is why methods sections and evaluations so often turn on the sampling technique used.
Exam-style practice questions
Practice questions written in the style of AQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
AQA 20194 marksA school has 800 students. A researcher wants a stratified sample of 40 students, and 25% of the school are in Year 7. Calculate how many Year 7 students should be in the sample, and explain why stratified sampling is representative.Show worked answer →
A 4-mark item (2 AO2 calculation, 2 AO3 explanation).
Calculation: the sample should contain the same proportion of each stratum as the population. 25% of the sample of 40 should be Year 7, so Year 7 students. (Equivalently, of is .) The researcher would then randomly select 10 students from Year 7, and fill the rest of the sample by applying the same proportion logic to the other year groups.
Explanation: stratified sampling is representative because the important sub-groups (strata) of the population appear in the sample in the same proportions as in the population, so the sample reflects the make-up of the whole population and findings can be generalised more confidently. A full-mark answer shows the calculation () and explains the proportional representation point.
AQA 20213 marksExplain one limitation of opportunity sampling and one limitation of volunteer sampling.Show worked answer →
A 3-mark item. Markers want a developed limitation specific to each technique.
Opportunity sampling (using whoever is available) is prone to researcher bias and is unrepresentative, because the sample is drawn from one place at one time (for example one street), so it may over-represent certain types of people and findings cannot be generalised to the wider population.
Volunteer sampling (people self-select, for example by answering an advert) suffers from volunteer bias: people who volunteer may share characteristics, such as being more motivated, more helpful or having more free time, which differ from the general population, again limiting generalisation. A full-mark answer gives one developed, technique-specific limitation for each.
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Sources & how we know this
- AQA A-level Psychology (7182) specification — AQA (2015)