Selecting a sample is the process of finding and choosing the people who are going to be the target of your research.
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.
Positivist researchers want to make sure their research is representative – 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.
NB – The people who are the targets of social research are also known as the ‘respondents’
Five sampling methods used in sociology
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 carries the same risk of being unrepresentative as random sampling as, for example, every 10th person could be a girl.
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.