What is sampling?
In survey research, sampling is the process of using a subset of a population to represent the whole population.
Let’s say you wanted to do some research on everyone in Europe. To ask every person would be almost impossible. Even if everyone said “yes”, carrying out a survey across different states, in different languages and timezones, and then collecting and processing all the results, would take a long time and be very costly.
Sampling allows large-scale research to be carried out with a more realistic cost and time-frame because it uses a smaller number of individuals in the population to stand in for the whole.
However, when you decide to sample, you take on a new task. You have to decide who is part of your sample and how to choose the people who will best represent the whole population. How you go about that is what the practice of sampling is all about.
The total number of people or things you are interested in
A smaller number within your population that will represent the whole
The process and method of selecting your sample
Why is sampling important?
Although the idea of sampling is easiest to understand when you think about a very large population, it makes sense to use sampling methods in studies of all types and sizes. After all, if you can reduce the effort and cost of doing a study, why wouldn’t you? And because sampling allows you to research larger target populations using the same resources as you would smaller ones, it dramatically opens up the possibilities for research.
Sampling is a little like having gears on a car or bicycle. Instead of always turning a set of wheels of a specific size and being constrained by their physical properties, it allows you to translate your effort to the wheels via the different gears, so you’re effectively choosing bigger or smaller wheels depending on the terrain you’re on and how much work you’re able to do.
Sampling allows you to “gear” your research so you’re less limited by the constraints of cost, time, and complexity that come with different population sizes.
It allows us to do things like carrying out exit polls during elections, map the spread and effects rates of epidemics across geographical areas, and carry out nationwide census research that provides a snapshot of society and culture.
Probability and non-probability sampling
Sampling strategies vary widely across different disciplines and research areas, and from study to study.
- Probability sampling, also known as random sampling, is a kind of sample selection where randomisation is used instead of deliberate choice.
- Non-probability sampling techniques are where the researcher deliberately picks items or individuals for the sample based on their research goals or knowledge.
Probability sampling methods
There’s a wide range of probability sampling methods to explore and consider. Here are some of the best-known options.
1. Simple random sampling
With simple random sampling, every element in the population has an equal chance of being selected as part of the sample. It’s something like picking a name out of a hat. Simple random sampling can be done by anonymising the population – e.g. by assigning each item or person in the population a number and then picking numbers at random.
Simple random sampling is easy to do and cheap, and it removes all risk of bias from the sampling process. However, it also offers no control for the researcher and may lead to unrepresentative groupings being picked by chance.
2. Systematic sampling
With systematic sampling, also known as systematic clustering, the random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked.
Although there’s randomness involved, the researcher can choose the interval at which items are picked, which allows them to make sure the selections won’t be accidentally clustered together.
3. Stratified sampling
Stratified sampling involves random selection within predefined groups. It’s useful when researchers know something about the target population and can decide how to subdivide it (stratify it) in a way that makes sense for the research.
For example, if you were researching travel behaviours in a group of people, it might be helpful to separate those who own or have use of a car from those who are dependent on public transport.
Stratified sampling has benefits but it also introduces the question of how to stratify a population, which adds in more risk of bias.
4. Cluster sampling
With cluster sampling, groups rather than individual units of the target population are selected at random. These might be pre-existing groups, such as people in certain zip codes or students belonging to an academic year.
Cluster sampling can be done by selecting the entire cluster, or in the case of two-stage cluster sampling, by randomly selecting the cluster itself, then selecting at random again within the cluster.
Non-probability sampling methods
Non-probability sampling methods don’t offer the same bias-removal benefits as probability sampling, but there are times when these types of sampling are chosen for expediency or simplicity. Here are some forms of non-probability sampling and how they work.
1. Convenience sampling
People or elements in a sample are selected on the basis of their availability. If you are doing a research survey and you work at a university, for example, a convenience sample might consist of students or co-workers who happen to be on campus with free time who are willing to take your questionnaire.
This kind of sample can have value, especially if it’s done as an early or preliminary step, but significant bias will be introduced.
2. Quota sampling
Like the probability-based stratified sampling method, this approach aims to achieve a spread across the target population by specifying who should be recruited for a survey according to certain groups or criteria. For example, your quota might include a certain number of males and a certain number of females, or people in certain age brackets or ethnic groups.
Bias may be introduced during the selection itself – for example, volunteer bias might skew the sample towards people with free time who are interested in taking part. Or bias may be part and parcel of the way categories for the quotas are selected by researchers.
3. Purposive sampling
Participants for the sample are chosen consciously by researchers based on their knowledge and understanding of the research question at hand or their goals. Also known as judgment sampling, this technique is unlikely to result in a representative sample, but it is a quick and fairly easy way to get a range of results or responses.
4. Snowball or referral sampling
With this approach, people recruited to be part of a sample are asked to invite those they know to take part, who are then asked to invite their friends and family and so on. The participation radiates through a community of connected individuals like a snowball rolling downhill.
This method can be helpful when the researcher doesn’t know very much about the target population and has no easy way to contact or access them. However it will introduce bias, for example by missing out isolated members of a community or skewing towards certain age or interest groups who recruit amongst themselves.
Avoid or reduce sampling errors and bias
Using a sample is a kind of short-cut. If you could ask every single person in a population to take part in your study and have each of them reply, you’d have a highly accurate (and very labor-intensive) project on your hands.
But since that’s not realistic, sampling offers a “good-enough” solution that sacrifices some accuracy for the sake of practicality and ease. How much accuracy you lose out on depends on how well you control for sampling error, non-sampling error, and bias in your survey design. Our blog post helps you to steer clear of some of these issues.
How to choose the correct sample size
Finding the best sample size for your target population is something you’ll need to do again and again, as it’s different for every study.
To make life easier, we’ve provided a sample size calculator. To use it, you need to know your
- Population size
- Confidence level
- Margin of error (confidence interval)
If any of those terms are unfamiliar, have a look at our blog post on determining sample size for details of what they mean and how to find them.