Sampling Techniques in Social Research

Last Updated on October 2, 2026 by Karl Thompson

Selecting a sample is the process of finding and choosing the people who are going to be the target of your research.

Sampling techniques are named in the AQA specification as part of research design within Theory and Methods, which is assessed on Paper 1 and Paper 3 (mostly Paper 3). You may also need to apply them in a methods in context question on Paper 1, where you have to say how a sampling method would work for a particular piece of education research.

Six sampling methods used in sociology are:

1. Random sampling – pick at random
2. Systematic sampling – every nth person from a list
3. Stratified sampling – 50% males 50% females, for example
4. Multistage sampling – select the sample in stages, using a different method at each stage
5. Snowball sampling – start with one person, ask them to suggest someone else to interview
6. Quota sampling – selecting people with particular characteristics

Choosing a sampling method

The sampling method researchers choose will depend on a variety of factors including theoretical perspective (whether Positivist or Interpretivist), as well as practical and ethical factors.

Most researchers will have a ‘target population’ in mind before conducting research. The target population consists of those people who have the characteristics of the sample you wish to study. If you’re interested in conducting primary research on the experiences of working class school children in 2017 (or whatever year we’re currently in!), then your target population would be all working class school children.

Many researchers use a sampling frame to choose a sample, which is simply a list from which a sample is chosen – this might be a register of all pupils in a school, if you are conducting research in a school, for example.

NB – The people who are the targets of social research are also known as the ‘respondents’

Positivist researchers are interested in large scale research and so are especially concerned to make sure their samples are representative of wider populations – research is representative if the characteristics of the people in the sample (the people who are actually researched) reflect the characteristics of the target population.

Interpretivists generally prefer smaller scale research and are generally more interested in getting niche samples of deviant groups and don’t need to worry about generalising to wider populations.

Random sampling

An example of random sampling would be picking names out of a hat. In random sampling everyone in the population has the same chance of getting chosen. This is easy because it is quick and can even be performed by a computer. However, because it is down to chance you could end up with an unrepresentative sample, perhaps with one demographic being missed out.

Systematic sampling

An example of a systematic sample would be picking every 10th person on a list or register. This is a very convenient method when you have a list of people on a database that could be used as a sampling frame, and it’s also a method computers can use to automatically generate samples.

However, this method may also be unrepresentative, depending on how the list is organised.

Stratified sampling

This method attempts to make the sample as representative as possible, avoiding the problems that could be caused by using a completely random sample. To do this the sample frame will be divided into a number of smaller groups, such as social class, age, gender, ethnicity etc. Individuals are then drawn at random from these groups. If you are observing doctors and you had split the sample frame into ethnic groups you would draw the same proportion of participants from each group as that group makes up of the sampling frame. For example, if 10% of the doctors on your list were Asian, you would draw 10% of your participants from the Asian group.

The strength of stratified sampling is that the groups you care about are guaranteed to appear in the right proportions. The weakness is that you need a sampling frame which already records those characteristics, and building one can be slow and expensive.

Quota sampling

In this method researchers will be told to ensure the sample fits with certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed. The researcher might then find these 30 by going to a job centre. The problem of representativeness is again a problem with the quota sampling method.

Quota sampling looks a lot like stratified sampling, and the difference is worth learning. In a stratified sample the individuals in each group are picked at random from a sampling frame. In a quota sample the interviewer simply keeps approaching people until each quota is full, so who ends up in the sample depends on who the interviewer chooses to stop. That makes it cheaper and quicker, which is why market researchers use it, but it is not a random method and the researcher’s own choices can bias it.

Multistage sampling

With multistage sampling, a researcher selects a sample by using combinations of different sampling methods. For example, in Stage 1, a researcher might use systematic sampling, and in Stage 2, they might use random sampling to select a subset for the final sample.

Multistage sampling is most useful when the population is large and spread out, because it narrows the task down in steps rather than requiring one huge sampling frame. The price is that each stage can introduce its own bias.

Snowball sampling

With this method, researchers might find a few participants, and then ask them to find participants themselves and so on. This is useful when a sample is difficult to obtain. For example Laurie Taylor used this method when investigating criminals. It would be difficult for him to find a sample as he didn’t know many criminals; however these criminals know a lot of people who would be willing to participate, so it is more efficient to use the snowball method.

The weakness is that the sample depends entirely on who the first participants know. People tend to refer others like themselves, so the sample is unlikely to be representative and you cannot safely generalise from it.

Evaluating sampling methods: representativeness and generalisation

The main evaluation point for any sampling method is how far the sample is representative of the target population, because that decides whether the findings can be generalised.

Random, systematic and stratified sampling are the techniques most closely tied to Positivist research, because they give every member of the population, or every member of each group, a known chance of being selected. That is what lets the researcher generalise from the sample to the wider population and produce the statistics that Positivists want. Quota and snowball sampling are not random, so they cannot support generalisation in the same way. HOWEVER, that matters much less to Interpretivists, who are after depth and understanding of a particular group rather than statistical generalisation, and who often cannot get a sampling frame for the group they want to study anyway.

Two further points are worth making in an answer. First, every method that uses a sampling frame is only as good as that frame: if the list is incomplete or out of date, some people have no chance of being selected, however carefully the sample is drawn. Second, even a perfect sample is weakened if many of the people chosen do not take part. This is called non-response, and if the people who refuse differ from those who agree, the sample ends up unrepresentative.

Contemporary evidence to evaluate sampling (2026 update)

Response rates on the Crime Survey for England and Wales

The Crime Survey for England and Wales (CSEW) is a national face-to-face victimisation survey, and it gives a live example of non-response. The ONS reports that the response rate for the year ending March 2026 was 48% (a preliminary figure), a little up on the previous year but well below the 64% recorded for the year ending March 2020, before the coronavirus pandemic. The CSEW estimates for the year ending March 2026 are based on 31,350 face-to-face interviews.

This is useful evidence on both sides. A falling response rate raises the worry that the people who agree to be interviewed are no longer a fair reflection of the population. HOWEVER, the ONS’s own data quality review concluded that the fall had not affected the representativeness of the survey. So a low response rate is a risk to representativeness rather than proof that a sample is unrepresentative.

Using sampling in the exam

In a methods in context question, don’t just name a technique. Say how it would work in the study you have been given: what the sampling frame would be (a school register, say), who might be left out of it, and whether the researcher needs a representative sample or is happy with a small, specialised one. Then evaluate it by linking it to representativeness and generalisation, and to whether the researcher is taking a Positivist or an Interpretivist approach.

Theory and Methods A Level Sociology Revision Bundle

If you like this sort of thing, then you might like my Theory and Methods revision notes bundle – specifically designed to get students through the theory and methods sections of A level sociology papers 1 and 3.

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• 74 pages of revision notes
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Signposting

This post has been written primarily for students studying theory and methods, as part of the AQA’s A-Level Sociology course. This material is fundamental to the research methods topic!

Sampling is one stage in the wider research process. It also links closely to validity and reliability, to the other key terms in research methods, and to how social surveys are carried out.

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