Before you choose the method that you’ll use to collect data, you need to think about the size of population you are studying (e.g. the group of visitors to your museum or online exhibition), the characteristics of that population and the amount of data (the sample) that will be representative enough so you can report what you learn with confidence.
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1. Define the population.
Population = the whole group at the centre of the research question.
Who are you trying to learn from? This is your population. You’ve already done this in Phase one - you should know who your key stakeholder(s) are. Ask yourself: can - or should - you survey the whole population?
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4. Calculate your sample.
Sample = a small part of the whole population intended to show what the group is like or experiences
Sampling = gathering information or data from a subset of a larger population, rather than from everyone
You now need to work out how you will sample the (target) population/stakeholder. This is based on an understanding that you can’t hear from everyone, so you have to try to get a sample that is representative enough so that you can report confidently on what you have learned. How can the sample be representative of the whole population?
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The sample you need will define what method you use, and each method has different considerations for agreeing your sample. Below we think about how you can work out the sample you need based on two of the most commonly used data collection methods.
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Questionnaires 10% is the minimum you should aim for for a representative survey sample. This is the case when you are collecting data up to 1,000 responses. For example, if you only have 600 visitors, try to collect at least 60 responses. After you collect 1,000 responses, no matter how big your population size, you should normally have a good sample. For example, if you have 60,000 visitors, you don’t need to collect 6,000 responses - 1000 should normally give you representative perspective (see more in https://tools4dev.org/resources/how-to-choose-a-sample-size/). |
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Tip. Look at different sample size calculators to help you determine what sample size will work for you. Such tools ask you to consider:
The calculator then works out your margin of error, which you should ideally report with your findings. Based on observation in the non-academic cultural sector, such margins of error are rarely reported. Examples |
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Types of sampling
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Randomly generate a list of the people who you will survey out of a bigger population. For example, out of 4,000 Europeana Network Association (ENA) members, we might use a random sampling tool to decide who we will survey instead of surveying the whole membership.
Different groups with the same characteristics in one population are divided into separate groups or ‘strata’ (the target population). Then these groups are randomly sampled. For example, we add all educators in ENA into one group and then randomly select members to survey, and do the same for researchers.
Somewhat the same as stratified sampling, but the whole population is broken up into clusters (randomly selected). All, or a certain number, of these groups are then randomly sampled and you can compare the results. For example, we group all ENA members into ten clusters, and we randomly sample members in five of these ten groups.
Select members of the population at a regular interval that you agree in advance. For example, we would survey every 10th person in a list of ENA members arranged alphabetically. |
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