Sampling Techniques in Social Research

Five sampling techniques are random, systematic, stratified, quota, multistage and snowball.

Last Updated on November 23, 2022 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.

Five 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 – 50% males, 50% females then within both male and female groups 50% young, 50% old.
  5. snowball sampling – start with one person, ask them to suggest someone else to interview.
  6. quota sample – selecting people with particular characteristics

Choosing a sampling method

The sampling method researchers choose will depend on a variety of factors including theoretical perspective (wether 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 8% of the participants from the Asian group, as you know that 8% of doctors in Britain are Asian.

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.

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, he might use random sampling to select a subset for the final sample

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.


This material is fundamental to the research methods topic!

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