Sampling Methods in Psychology: 5 Techniques With Examples

In six leading psychology journals, 96% of studies relied on samples from Western industrialised countries, which hold about 12% of the world’s population (Arnett, 2008). Sampling methods decide who psychology is really about.

No psychologist can test everyone. A study of memory cannot recruit every adult in Britain, and a study of attachment cannot observe every baby. So researchers study a smaller group and hope that what they find is true of the larger group too. The way they choose that smaller group is the sampling method, and it decides how far the results can be trusted outside the room where they were collected.

Sampling is one of the most examined topics in A level research methods. It appears in short “identify the sampling technique” questions, in “explain one limitation” questions, and in almost every evaluation of a classic study, from Milgram’s obedience experiment to the Stanford Prison Experiment. It also sits alongside experimental design: sampling decides who takes part in a study, and design decides which condition each of those people goes into.

This guide explains each sampling method in plain language, with real studies, step-by-step instructions, strengths and limitations, a stratified sample calculator, and a practice quiz at the end.

What Are Sampling Methods in Psychology?

Sampling methods in psychology are the techniques researchers use to select participants from a target population. Also called sampling techniques, they range from fully random selection, where chance alone decides who takes part, to simply using whoever is available or whoever volunteers. The method chosen affects how representative the sample is, and so how far the results can be generalised.

Four terms come up again and again, and each has a precise meaning in an exam answer.

Target population and sample

The target population is the group a study is about; the sample is the group a study actually tests. The target population is defined by the research question. A study of exam stress in Year 13 students has a target population of Year 13 students, not all teenagers and not all students. A study of how children form attachments has a target population of infants and their carers.

A common exam mistake is to describe the target population as everyone in the world, or to confuse it with the sample. A good answer names the group precisely: “Year 13 students in UK schools” is a target population; “the 40 Year 13 students who completed the questionnaire” is a sample.

Why sampling matters: bias and generalisation

Sampling matters because the whole point of studying a sample is to learn about the population it came from. The AQA A level specification puts it in exactly those terms, asking students to understand the “implications of sampling techniques, including bias and generalisation” (AQA, 2015).

• Sampling bias happens when some members of the target population are more likely to end up in the sample than others, so the sample is skewed. A study of sleep that recruits only through a gym will over-represent people who exercise.
• Generalisation is applying findings from the sample to the wider target population. The more representative the sample, the more confidently results can be generalised. A biased sample means the results may only be true of people like those who took part.

Even a perfectly chosen sample will not match its population exactly, simply by chance. That unavoidable wobble is sampling error, and statisticians measure it with the standard error. Sampling bias is different: it is a systematic skew built into the way the sample was chosen, and collecting more participants the same way does not fix it.

Which Sampling Methods Do You Need for A Level Psychology?

The sampling methods you need for A level psychology depend on your exam board. AQA names five techniques: random, systematic, stratified, opportunity and volunteer (AQA, 2015). Edexcel names four, leaving out systematic sampling (Pearson Edexcel, 2015). OCR names four, but swaps stratified and systematic for snowball sampling and calls volunteer sampling “self-selected” (OCR, 2015).

AQA also lists sampling under its mathematical skills, with the example of “explaining how a random or stratified sample could be obtained from a target population” (AQA, 2015). That makes the step-by-step procedures below worth learning word for word, not just the definitions. OCR, for its part, asks students to evaluate its core studies for “sampling bias” (OCR, 2015), so the evaluation points matter as much as the descriptions.

The Five Sampling Techniques Compared

The five A level sampling techniques differ in one key way: who decides who takes part. In random, systematic and stratified sampling a random or fixed rule decides, so the researcher cannot pick people who suit the hypothesis. In opportunity sampling the researcher decides, and in volunteer sampling the participants decide. That single difference explains most of their strengths and weaknesses.

Random, systematic and stratified sampling are sometimes grouped together as probability sampling, because each member of the population has a known chance of being selected. Opportunity and volunteer sampling are non-probability sampling. You do not need these labels for most A level exams, but they appear in university textbooks and research papers, and they are a useful way to remember which methods can claim to be unbiased.

Random Sampling in Psychology

Random sampling is a technique in which every member of the target population has an equal chance of being selected. The choice is made by a random process, such as drawing names from a hat or using a random number generator, so the researcher has no say in who is picked. It is the method most people think of first, and the one most often described wrongly in exams.

How to take a random sample, step by step

1. Define the target population precisely, for example all 600 students at one college.
2. Obtain a complete list of everyone in it, the sampling frame, such as the college register.
3. Give every person on the list a number, from 1 to 600.
4. Use a random number generator (or draw numbered slips from a container) to pick the required number of different numbers, for example 30.
5. The people with those numbers are the sample. If someone declines, pick another random number rather than choosing a replacement yourself.

Drawing names from a hat is called the lottery method. It is perfectly acceptable in an exam answer, as long as every member of the target population goes into the hat, the slips are the same size and they are mixed properly.

Strengths of random sampling

• No researcher bias. The researcher cannot choose participants who seem friendly, able or likely to support the hypothesis, because chance makes every choice.
• Likely to be representative with large samples. The bigger the random sample, the more closely it tends to mirror the population, which makes generalisation more justified.
• Allows statistics to be used properly. Inferential tests are built on the logic of samples drawn by chance, so random sampling fits their assumptions better than any other method.

Limitations of random sampling

• It needs a full list of the population. For many target populations, such as “adults with anxiety” or “children in the UK”, no complete list exists or it is not available to researchers.
• It is time-consuming. Contacting randomly chosen people, who may be spread across a large area, takes far longer than asking whoever is nearby.
• People can refuse. Once some of the chosen people decline, the sample that remains is partly self-selected, which brings back some of the bias random sampling was meant to remove.
• It can still be unrepresentative by chance, especially with small samples. The next section shows how often.

Does a random sample guarantee a representative one?

A random sample does not guarantee a representative one, especially when it is small. Random sampling guarantees fairness in how people are chosen, not balance in who ends up chosen. To see how big the effect is, imagine a sixth form of 1,000 students, 600 of them girls. The figures below are exact probabilities for random samples of different sizes, calculated for this article with the hypergeometric distribution (the rule for drawing without replacement).

With a random sample of 10, there is more than a one-in-three chance that boys make up half or more of the sample, even though girls outnumber boys three to two in the population. Even with 100 students, the chance of getting exactly 60 girls is under one in ten, although by then the sample will almost always land within ten percentage points. The lesson is that random sampling is reliable in the long run and with large numbers, and much less reliable with the small samples typical of A level practicals. It is the main reason stratified sampling exists.

Systematic Sampling in Psychology

Systematic sampling is a technique in which every nth member of the target population is selected from a list. The gap between selections, the sampling interval, is found by dividing the population size by the sample size. It is quick and objective once a list exists, and AQA is the only A level board that names it.

How to take a systematic sample

1. Obtain a list of the whole target population, for example an alphabetical register of 600 pupils.
2. Work out the sampling interval: population size divided by sample size. For a sample of 30 from 600, the interval is 600 ÷ 30 = 20.
3. Choose a starting point at random between 1 and 20, for example 7.
4. Select that person and then every 20th person after them: the 7th, 27th, 47th, 67th and so on, until you have 30.

Choosing the starting point at random matters. If the researcher simply starts at the first name every time, the people at the top of the list always have a chance and the others never do. With a random start, every person on the list has the same one-in-20 chance of selection, which is why some textbooks treat systematic sampling as a form of random sampling. It is not a simple random sample, though: two people next to each other on the list can never both be chosen.

Strengths and limitations of systematic sampling

• Strength: objective. Once the interval and start are set, the researcher has no influence over who is chosen, so researcher bias is avoided.
• Strength: simple and quick. Counting through a list is easier than generating dozens of random numbers, and it spreads the sample evenly through the list.
• Limitation: it needs a list. Like random sampling, it only works when a complete sampling frame exists.
• Limitation: hidden patterns. If the list has a repeating pattern that happens to match the interval, the sample will be badly skewed. If a register lists pupils in tutor groups of 20 with the form captain always first, an interval of 20 and a start of 1 would pick only form captains.

Stratified Sampling in Psychology

Stratified sampling is a technique in which the target population is divided into subgroups, called strata, and participants are randomly selected from each stratum in proportion to its size in the population. If 60% of the population are women, 60% of the sample will be women. It is the most representative of the A level techniques, because the proportions are built in rather than left to chance.

The strata should be characteristics that might affect the thing being measured. For a study of attitudes to technology, age groups might be the obvious strata. For a study of stress at work, job role might matter more. Choosing strata that are irrelevant adds work without making the sample any more useful.

How to calculate a stratified sample

To calculate a stratified sample, divide the number in each stratum by the total population, then multiply by the sample size you want. Take a company of 800 employees: 400 office staff, 240 warehouse staff and 160 drivers. The researcher wants a sample of 40.

The final step is the one students most often forget. Having worked out that 20 office staff are needed, the researcher must choose which 20 by random sampling within that stratum, for example by numbering all 400 office staff and using a random number generator. Stratified sampling is proportions first, random selection second. If the 20 office staff are simply the first 20 who answer an email, the sample is no longer stratified in the A level sense, it is closer to a quota sample.

Real numbers rarely divide so neatly. If a stratum works out at 7.4 people, round to whole people and check that the strata still add up to the total sample size. The calculator below does this automatically, rounding down and then giving any spare places to the strata with the largest remainders, and it tells you when rounding has happened.

Strengths of stratified sampling

• Highly representative. The sample reflects the population’s make-up on the chosen characteristics by design, not by luck, so generalisation is more justified than with any other A level technique.
• No researcher bias. Once the numbers are set, random selection within each stratum means the researcher cannot pick favourites.
• Works with small samples. Unlike simple random sampling, even a small stratified sample is guaranteed to include every important subgroup in the right proportion.

Limitations of stratified sampling

• Time-consuming and complex. The researcher must know the exact size of every stratum and have a list of the people in each one.
• Only as good as the strata chosen. The sample matches the population only on the characteristics used to stratify. A sample stratified by age and gender can still be unrepresentative on social class, ethnicity or anything else not accounted for.
• Refusals still creep in. People selected within each stratum can decline, so the final sample may not match the planned proportions.

Opportunity Sampling in Psychology

Opportunity sampling is a technique in which the researcher selects whoever is available and willing to take part at the time and place of the study. Asking people in a school canteen, a library or a shopping centre are all opportunity samples. It is the same thing as convenience sampling: A level boards say “opportunity”, while university textbooks and research papers usually say “convenience”.

University psychology departments have long relied on the students around them. The classic Loftus and Palmer car crash study tested 45 students in its first experiment and 150 in its second (Loftus and Palmer, 1974). The paper says they were students, but not how they were recruited, which is a useful exam point in itself: if a study does not describe its sampling method, you cannot confidently name it, and you should say what the description does tell you. A sample drawn entirely from one student population is limited however it was recruited.

Godden and Baddeley’s (1975) famous diving study of context-dependent memory tested 18 divers, 13 men and five women, all members of a university diving club. For a study that needed people who could learn word lists underwater, drawing on one club was the practical choice. It also means the sample was small, young and drawn from a single, unusual group.

Strengths and limitations of opportunity sampling

• Strength: quick, cheap and convenient. No list of the population is needed and no advertising, so a sample can be gathered in an hour. This is why it is the most common method in student practicals.
• Strength: practical for field studies. In a field experiment the researcher often has to use whoever passes, because the setting chooses the participants.
• Limitation: unrepresentative. The sample reflects only the people in one place at one time. Shoppers on a weekday morning are not typical of all adults.
• Limitation: researcher bias. The researcher decides whom to approach and may, even unconsciously, choose people who look friendly, similar to themselves or likely to cooperate.

Volunteer Sampling in Psychology

Volunteer sampling is a technique in which participants select themselves by responding to an advert or request, such as a poster, newspaper advertisement or online post. It is also called self-selected sampling, the term OCR uses. The key feature is that the participant makes the decision to take part, not the researcher.

Two of the most famous studies in psychology used volunteer samples. Milgram recruited 40 men aged 20 to 50 from New Haven and the surrounding area “by a newspaper advertisement and direct mail solicitation”, paying each $4.50 for coming to what they believed was a study of memory and learning at Yale (Milgram, 1963). The men came from a wide spread of occupations, from postal clerks and labourers to teachers and engineers: 37.5% were skilled or unskilled workers, 40% worked in sales, business or white-collar jobs, and 22.5% were professionals. They were still all men, all local, and all people who chose to answer an advert.

The Stanford Prison Experiment began with a newspaper advert asking for male volunteers for a psychological study of “prison life”, paying $15 a day. Seventy-five men replied. After questionnaires and interviews, the 24 judged most stable, most mature and least involved in anti-social behaviour were selected and randomly assigned to be guards or prisoners (Haney et al., 1973). They were male college students, largely middle class and, with one exception, white.

Volunteer bias: the Stanford prison advert tested

Volunteer bias is the risk that people who volunteer differ in important ways from people who do not. It is the main limitation of volunteer sampling, and it has been tested directly. Carnahan and McFarland (2007) placed two almost identical newspaper adverts. One copied the Stanford Prison Experiment’s wording, asking for volunteers for “a psychological study of prison life”. The other was identical except that it left out the words “of prison life”.

The people who volunteered for the prison study scored significantly higher on aggressiveness, authoritarianism, Machiavellianism, narcissism and social dominance, and lower on empathy and altruism, than those who volunteered for the plain psychological study (Carnahan and McFarland, 2007). The wording of an advert, in other words, can attract a particular kind of person. That does not prove the Stanford guards were cruel because of who volunteered, and the authors describe the implications for the original study as “conjecture”. But it shows exactly why examiners reward volunteer bias as an evaluation point: the sample may carry the very traits the study claims to be caused by the situation.

Strengths and limitations of volunteer sampling

• Strength: easy and reaches many people. One advert, online post or poster can recruit large numbers with little effort from the researcher.
• Strength: willing participants. Volunteers have chosen to take part, so they are more likely to engage properly and less likely to drop out.
• Strength: useful for specific groups. An advert can target a particular group, such as people with a phobia, who could not be found by asking passers-by.
• Limitation: volunteer bias. Volunteers may be more curious, confident, helpful or interested in the topic than people who ignore the advert, so the sample is unlikely to be representative.
• Limitation: demand characteristics. Eager volunteers may try to work out the aim of the study and behave in the way they think the researcher wants.

Snowball Sampling in Psychology

Snowball sampling is a technique in which existing participants recruit further participants from among people they know. The researcher finds a few members of the target population, and each one passes the study on to others like them, so the sample grows like a snowball rolling downhill. OCR is the only A level board that names it.

Snowball sampling is used for hidden or hard-to-reach groups, where no list exists and an advert would not work, such as young carers, people with a rare condition or members of a closed community. Its strength is access: trust passes through personal contacts, so people who would never answer a stranger may agree to help a friend’s researcher. Its limitation is that everyone in the sample comes from the same social networks, so they are likely to be similar to each other and unrepresentative of the wider group. People with no connections to the first participants have no chance of being included.

Other Sampling Methods You May Meet

University courses, the IB and research papers use a few more sampling methods that are not on the A level specifications. You will not be asked to name them in an A level exam, but recognising them helps when reading real studies.

• Quota sampling sets the proportions for each subgroup, like stratified sampling, but fills them with whoever is available rather than by random selection. Market researchers with clipboards often work to quotas.
• Cluster sampling randomly selects whole groups, such as schools, and then tests everyone in them. It saves travel and time, but people within one cluster tend to be alike.
• Purposive sampling deliberately chooses people with a particular characteristic or experience, and is common in qualitative research and case studies where depth matters more than breadth.

Sampling Bias in Psychology: What It Is and Why It Matters

Sampling bias in psychology is a systematic difference between the people in a sample and the target population, caused by the way the sample was chosen. Because it is built into the method, it does not shrink as the sample gets bigger. A large biased sample simply gives a more confident wrong answer, and the most famous example in the history of surveys shows just how wrong.

The poll that sampled millions and still got it wrong

In 1936 the American magazine the Literary Digest mailed about ten million ballots to predict the presidential election, and more than two million were returned. It forecast a clear win for Alf Landon, with about 57% of the vote. Franklin Roosevelt won with about 61% and carried all but two states. Squire (1988) used a 1937 survey to find out why, and concluded that both the sample and the response were biased, and that the two together produced the wildly wrong estimate. If everyone the magazine polled had replied, it would at least have picked the right winner.

The same two problems sit behind the A level techniques. A skewed list is the risk in random and systematic sampling when the sampling frame does not cover the whole population. Who chooses to reply is the risk in volunteer sampling. Two million responses could not rescue a biased method, which is the clearest possible answer to the idea that a big enough sample fixes everything.

WEIRD samples: psychology’s biggest sampling problem

A WEIRD sample is one drawn from Western, Educated, Industrialised, Rich and Democratic societies, and psychology relies on them heavily. Arnett (2008) found that in six leading American Psychological Association journals, 68% of studies used samples from the United States and 96% used samples from Western industrialised countries. Those countries hold about 12% of the world’s population (Rad et al., 2018).

The problem had barely shifted a decade later. Rad and colleagues checked every study published in 2014 in Psychological Science, one of the field’s top journals. Of the studies that said where their participants came from, 58% sampled Americans and 94% sampled Western countries. More than 72% of abstracts gave no information about the population studied at all (Rad et al., 2018).

This matters because WEIRD people are not a neutral stand-in for everyone. Henrich et al. (2010) reviewed research on visual perception, fairness, cooperation, spatial reasoning, moral reasoning and self-concept, and concluded that people from WEIRD societies are “among the least representative populations one could find for generalizing about humans”. The wider issues of gender and cultural bias in psychology start here, with who is sampled.

Are student samples a problem?

Student samples can be a problem when results are generalised to the general public. Hanel and Vione (2016) compared students with the general population across 59 countries on 12 personality and attitude measures. The differences were “partly substantial” and inconsistent from country to country, so there was no simple correction that could turn student results into public ones. Students were not just a slightly younger version of everyone else; they differed unpredictably.

For A level evaluation, the lesson is to be specific. “The sample was students, so it cannot be generalised” is weak. “The sample was 150 university students, who may differ from older adults in driving experience and in how they judge speed, so the findings may not apply to real eyewitnesses” is the kind of point that earns marks.

Generalisation: How Far Can Results Be Applied?

Generalisation is the extent to which findings from a sample can be applied to the target population and beyond. It depends mainly on how representative the sample is, and a good evaluation connects the sampling method to a specific limit on generalisation rather than stating that “it cannot be generalised”.

Three questions make generalisation points precise:

1. Who was in the sample? Name the characteristics: age, gender, culture, occupation, where they were recruited.
2. Who was left out? Milgram’s sample had no women. The Stanford Prison Experiment excluded anyone who was not judged stable and mature. The Schaffer and Emerson attachment study was carried out with families in one area of Glasgow in the 1960s.
3. Could that matter for this behaviour? This is the step that earns the marks. Would women respond differently to an authority figure? Would people from a collectivist culture show the same conformity as the students in Asch’s line study? If there is a plausible reason the missing group might behave differently, generalisation is limited.

Generalisation is not only about samples. It also depends on the setting and the task, which is ecological validity, and on whether findings from one era still hold today, which is temporal validity. Sampling is the part that asks about the people.

How to Choose a Sampling Method

The right sampling method depends on the target population, the time and money available, and how far the results need to be generalised. There is no single best technique, only the best one for a particular study. Researchers weigh representativeness against practicality, and most accept some bias in exchange for being able to run the study at all.

Ethics also shape the choice. Whatever the technique, every participant must give informed consent and be free to withdraw, so no method can force a randomly chosen person to take part. Research with sensitive groups, which is covered in the guide to social sensitivity and research ethics, may rule out some methods altogether: snowball sampling, for example, can expose participants to others in their network.

Sampling also connects to the analysis. A sample’s size affects the chance of detecting a real effect, which is the idea of statistical power explained in the guide to statistical significance, p-values and errors. And once the data are in, the choice of statistical test depends on the design and level of measurement, not the sampling method, so the two decisions should not be confused.

How to Answer Sampling Questions in A Level Exams

Sampling questions in A level exams usually ask you to identify a technique, explain a strength or limitation in context, or describe how a sample could be obtained. The key to all three is applying the point to the study in the question rather than writing a general definition.

Identifying the technique

Ask one question: who decided who took part? If chance decided, it is random. If a rule such as “every tenth person” decided, it is systematic. If proportions were set and then filled randomly, it is stratified. If the researcher approached whoever was around, it is opportunity. If people responded to an advert, it is volunteer. If participants recruited their friends, it is snowball.

Describing how to obtain a sample

When asked how a researcher could obtain a random or stratified sample, write the steps in order and name the target population. For a stratified sample from a college of 1,000 students with 400 in Year 12 and 600 in Year 13, and a sample of 50, a full answer is:

1. Obtain a list of all 1,000 students, divided into Year 12 and Year 13.
2. Work out the proportions: 400 ÷ 1,000 × 50 = 20 from Year 12, and 600 ÷ 1,000 × 50 = 30 from Year 13.
3. Number the Year 12 students from 1 to 400 and use a random number generator to choose 20.
4. Number the Year 13 students from 1 to 600 and use a random number generator to choose 30.

Common mistakes that lose marks

• Calling an opportunity sample “random”. Asking people at random in the street is not random sampling. The people in the street had a chance of selection; everyone else in the target population had none.
• Confusing opportunity and volunteer. If the researcher asked people directly, it is opportunity, even though they agreed. If people responded to a request they found for themselves, it is volunteer.
• Forgetting the random step in stratified sampling. Setting the proportions is only half the method.
• Saying a random sample “is representative”. It is more likely to be representative, especially if large, but it is not guaranteed.
• Generic evaluation. “It can’t be generalised” earns little. Say who was in the sample, who was missing, and why it matters for this behaviour.

Test yourself on the ten scenarios below. Several are drawn from real studies discussed in this guide.

Conclusion

Sampling methods decide who a psychology study is really about. Random, systematic and stratified sampling take the choice away from the researcher and give the best chance of a representative sample, at the cost of time and a full list of the population. Opportunity and volunteer sampling are quick and practical, which is why so many classic studies used them, but they bring researcher bias and volunteer bias. Snowball sampling reaches groups no other method can, within the limits of one social network.

For A level, learn the definitions, the step-by-step procedures and one strength and one limitation of each technique, then practise applying them to real studies. The best evaluation always names who was in the sample, who was left out, and why that might change the results.

Frequently Asked Questions

What are the 5 sampling methods in psychology?

The five sampling methods on the AQA A level psychology specification are random, systematic, stratified, opportunity and volunteer sampling. Random, systematic and stratified sampling use chance or a fixed rule to choose participants. Opportunity sampling uses whoever is available, and volunteer sampling uses people who respond to an advert. OCR students also need snowball sampling.

What is the difference between opportunity and convenience sampling?

There is no difference: opportunity sampling and convenience sampling are two names for the same technique. Both mean recruiting whoever is available at the time and place of the research. A level psychology specifications use “opportunity sampling”, while university textbooks, statistics courses and research papers usually use “convenience sampling”.

What is a representative sample in psychology?

A representative sample in psychology is one whose characteristics match those of the target population in the ways that could affect the results, such as age, gender, culture or social background. Findings from a representative sample can be generalised to the population with confidence. Stratified sampling is designed to produce one; large random samples usually come close.

What is a target population in psychology?

A target population in psychology is the entire group of people a researcher wants their conclusions to apply to, such as all UK primary school teachers or all adults with a phobia of spiders. The sample is drawn from the target population. Defining it precisely matters, because a sample can only be judged representative of a clearly named group.

What is sampling bias in psychology?

Sampling bias in psychology occurs when the method of choosing participants makes some members of the target population more likely to be included than others, so the sample differs systematically from the population. Recruiting only university students, or only people who answer an advert, are common causes. Unlike chance error, sampling bias is not reduced by recruiting more people the same way.

What is a WEIRD sample in psychology?

A WEIRD sample in psychology is one drawn from Western, Educated, Industrialised, Rich and Democratic societies. The term was coined by Henrich, Heine and Norenzayan in 2010 to describe the narrow populations most psychology research relies on. WEIRD participants often behave differently from people in other societies, so findings based on them may not apply to humans in general.

What is snowball sampling in psychology?

Snowball sampling in psychology is a recruitment technique where each participant introduces the researcher to further participants from their own contacts. It is used to reach hidden or hard-to-reach groups for which no list exists. Its main weakness is that participants share the same social networks and tend to be similar, so the sample is rarely representative.

How do you work out a stratified sample in psychology?

1. Divide the target population into strata, such as age groups, and count how many people are in each.
2. For each stratum, divide its size by the total population and multiply by the sample size.
3. Round to whole people, checking the numbers still add up to the sample size.
4. Randomly select that many people from each stratum.

References

• AQA. (2015). AS and A-level Psychology specification (7181, 7182). AQA.
• Arnett, J. J. (2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist, 63(7), 602-614.
• Carnahan, T., and McFarland, S. (2007). Revisiting the Stanford prison experiment: Could participant self-selection have led to the cruelty? Personality and Social Psychology Bulletin, 33(5), 603-614.
• Godden, D. R., and Baddeley, A. D. (1975). Context-dependent memory in two natural environments: On land and underwater. British Journal of Psychology, 66(3), 325-331.
• Hanel, P. H. P., and Vione, K. C. (2016). Do student samples provide an accurate estimate of the general public? PLoS ONE, 11(12), e0168354.
• Haney, C., Banks, C., and Zimbardo, P. (1973). A study of prisoners and guards in a simulated prison. Naval Research Reviews, 9, 1-17.
• Henrich, J., Heine, S. J., and Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2-3), 61-83.
• Loftus, E. F., and Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13(5), 585-589.
• Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67(4), 371-378.
• OCR. (2015). A Level Psychology H567 specification. OCR.
• Pearson Edexcel. (2015). Pearson Edexcel Level 3 Advanced GCE in Psychology (9PS0) specification. Pearson.
• Rad, M. S., Martingano, A. J., and Ginges, J. (2018). Toward a psychology of Homo sapiens: Making psychological science more representative of the human population. Proceedings of the National Academy of Sciences, 115(45), 11401-11405.
• Squire, P. (1988). Why the 1936 Literary Digest poll failed. Public Opinion Quarterly, 52(1), 125-133.

Further Reading and Research

Recommended Articles

• Experimental Design in Psychology: 3 Types With Examples
• Gender and Cultural Bias in Psychology
• Quantitative vs Qualitative Analysis

Suggested Books

• Coolican, H. (2019). Research Methods and Statistics in Psychology (7th ed.). Routledge.
  • The standard A level and undergraduate reference, with a full chapter on samples, sampling bias and the trade-offs between probability and non-probability methods.
• Henrich, J. (2020). The WEIRDest People in the World. Farrar, Straus and Giroux.
  • An accessible book-length account of why Western populations are psychologically unusual, by the lead author of the original WEIRD paper.
• Lohr, S. L. (2021). Sampling: Design and Analysis (3rd ed.). CRC Press.
  • A university-level textbook on how random, stratified, systematic and cluster samples work, for anyone who wants the statistics behind the A level definitions.

Recommended Websites

• AQA A-level Psychology (7182)
  • The specification, past papers and mark schemes, including the research methods content on sampling techniques, bias and generalisation.
• PubMed Central
  • Free full text of Rad, Martingano and Ginges (2018), with the complete breakdown of where participants in one leading psychology journal came from.
• PLoS ONE
  • Free full text of Hanel and Vione (2016), comparing student samples with the general public across 59 countries.

Kathy Brodie

Kathy Brodie is an Early Years Professional, Trainer and Author of multiple books on Early Years Education and Child Development. She is the founder of Early Years TV and the Early Years Summit.

Kathy’s Author Profile

To cite this article please use:

Early Years TV Sampling Methods in Psychology: 5 Techniques With Examples. Available at: https://www.earlyyears.tv/sampling-methods-psychology-techniques-examples/ (Accessed: 2 October 2026).