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Achieving a representative sample

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The accuracy of your questionnaire research is also determined by who you survey.

It’s important to try and get a full range of people to conduct questionnaires so that your research is representative of the full range of people that took part in your project.

There are various techniques that researchers use in order to achieve a ‘representative sample’.

For example, instead of standing in the same place at an event and handing out questionnaires, you could vary where you stand so that you capture the different sets of people who have chosen to visit specific areas of the event. Another technique (when surveying groups of people) is to select randomly from the group (to avoid only surveying the most assertive / outgoing people in any group). You could do this, for example, by always handing your questionnaire to the person in the group whose birthday is next.

At activities (that go on over a period of time), you could select people to complete the questionnaires at random from a list. So, for example, if you're aiming to get 89 activity participants to complete the questionnaire (out of 200 participants) then you could generate 89 numbers (between 1-200) using a random number generator and then pick your sample of 89 accordingly from the list or 'register' of all participants. For this to work, your list of participants should be numbered (in this example - from 1-200) so that every person has a unique number. There are a number of free random number generators on the web such as this one.

This may all sound like hard work, but time spent planning your sample is definitely time well spent. Achieving a good, representative sample will mean that you will be able to 'defend' your sample against criticism. Put simply, people are more likely to believe what you are saying about the impact of your project if you can show that your sample is big enough and representative enough. Worst case scenario - if you haven't managed to achieve a decent sample - is that people will simply say that your conclusions can't be trusted.