What Is Non-Probability Sampling? | Types & Examples
Non-probability sampling is a sampling method that uses non-random criteria like the availability, geographical proximity, or expert knowledge of the individuals you want to research in order to answer a research question.
Non-probability sampling is used when the population parameters are either unknown or not possible to individually identify. For example, visitors to a website that doesn’t require users to create an account could form part of a non-probability sample.
Types of non-probability sampling
There are five common types of non-probability sampling:
- Convenience sampling
- Quota sampling
- Self-selection (volunteer) sampling
- Snowball sampling
- Purposive (judgmental) sampling
Convenience sampling is primarily determined by convenience to the researcher.
This can include factors like:
- Ease of access
- Geographical proximity
- Existing contact within the population of interest
Convenience samples are sometimes called “accidental samples,” because participants can be selected for the sample simply because they happen to be nearby when the researcher is conducting the data collection.
In quota sampling, you select a predetermined number or proportion of units, called a quota. Your quota should comprise subgroups with specific characteristics (e.g., individuals, cases, or organisations) and should be selected in a non-random manner.
Your subgroups, called strata, should be mutually exclusive. Your estimation can be based on previous studies or on other existing data, if there are any. This helps you determine how many units should be chosen from each subgroup. In the data collection phase, you continue to recruit units until you reach your quota.
There are two types of quota sampling:
- Proportional quota sampling is used when the size of the population is known. This allows you to determine the quota of individuals that you need to include in your sample in order to be representative of your population.
- Non-proportional quota sampling is used when the size of the population is unknown. Here, it’s up to you to determine the quota of individuals that you are going to include in your sample in advance.
Note that quota sampling may sound similar to stratified sampling, a probability sampling method where you divide your population into subgroups that share a common characteristic.
The key difference here is that in stratified sampling, you take a random sample from each subgroup, while in quota sampling, the sample selection is non-random, usually via convenience sampling. In other words, who is included in the sample is left up to the subjective judgment of the researcher.
Self-selection (volunteer) sampling
Self-selection sampling (also called volunteer sampling) relies on participants who voluntarily agree to be part of your research. This is common for samples that need people who meet specific criteria, as is often the case for medical or psychological research.
In self-selection sampling, volunteers are usually invited to participate through advertisements asking those who meet the requirements to sign up. Volunteers are recruited until a predetermined sample size is reached.
Self-selection or volunteer sampling involves two steps:
- Publicizing your need for subjects
- Checking the suitability of each subject and either inviting or rejecting them
Keep in mind that not all people who apply will be eligible for your research. There is a high chance that many applicants will not fully read or understand what your study is about, or may possess disqualifying factors. It’s important to double-check eligibility carefully before inviting any volunteers to form part of your sample.
Snowball sampling is used when the population you want to research is hard to reach, or there is no existing database or other sampling frame to help you find them. Research about socially marginalised groups such as drug addicts, homeless people, or sex workers often uses snowball sampling.
To conduct a snowball sample, you start by finding one person who is willing to participate in your research. You then ask them to introduce you to others.
Alternatively, your research may involve finding people who use a certain product or have experience in the area you are interested in. In these cases, you can also use networks of people to gain access to your population of interest.
Purposive (judgmental) sampling
Purposive sampling is a blanket term for several sampling techniques that choose participants deliberately due to qualities they possess. It is also called judgmental sampling, because it relies on the judgment of the researcher to select the units (e.g., people, cases, or organizations studied).
Common purposive sampling techniques include:
- Maximum variation (heterogeneous) sampling
- Homogeneous sampling
- Typical case sampling
- Extreme (or deviant) case sampling
- Critical case sampling
- Expert sampling
These can either be used on their own or in combination with other purposive sampling techniques.
Maximum variation sampling
The idea behind maximum variation sampling is to look at a subject from all possible angles in order to achieve greater understanding. Also known as heterogeneous sampling, it involves selecting candidates across a broad spectrum relating to the topic of study. This helps you capture a wide range of perspectives and identifies common themes evident across the sample.
Homogeneous sampling, unlike maximum variation sampling, aims to achieve a sample whose units share characteristics, such as a group of people that are similar in terms of age, culture, or job. The idea here is to focus on this similarity, investigating how it relates to the topic you are researching.
Typical case sampling
A typical case sample is composed of people who can be regarded as “typical” for a community or phenomenon. A typical case sample allows you to develop a profile of what would generally be agreed as being “average” or “normal.”
Typical case samples are often used when large communities or complex problems are investigated. In this way, you can gain an understanding in a relatively short time, even if you are not familiar with what’s going on yourself.
Note that the purpose of typical case sampling is to describe and illustrate what is typical to those unfamiliar with the setting or situation. The purpose is not to make generalised statements about the experiences of all participants. In other words, typical case sampling allows you to compare samples, not generalise samples to populations.
Extreme (deviant) case sampling
Extreme (or deviant) case sampling uses extreme cases of a particular phenomenon (outliers). This can mean remarkable failures, successes, or crises, as well as any event, organisation, or individual that appears to be the “exception to the rule.” Extreme case sampling is most often used when researchers are developing best-practice guidelines.
Note that extreme case sampling usually occurs in combination with other sampling strategies. The process of identifying extreme or deviant cases usually occurs after some portion of data collection and analysis has already been completed.
Critical case sampling
Critical case sampling is used where a single case (or a small number of cases) can be critical or decisive in explaining the phenomenon of interest. It is often used in exploratory research, or in research with limited resources.
There are a few cues that can help show you whether or not a case is critical, such as:
- “If it happens here, it will happen anywhere”
- “If that group is having problems, then all groups are having problems”
It is critical to ensure that your cases fit these criteria prior to proceeding with this sampling method.
Expert sampling involves selecting a sample based on demonstrable experience, knowledge, or expertise of participants. This expertise may be a good way to compensate for a lack of observational evidence or to gather information during the exploratory phase of your research.
Alternatively, your research may be focused on individuals who possess exactly this expertise, similar to ethnographic research.
Non-probability sampling examples
There are a few methods you can use to draw a non-probability sample, such as:
Suppose you are researching the motivations of digital nomads (young professionals working solely in an online environment). You are curious what led them to adopt this lifestyle.
Since your population of interest is located all over the globe, it clearly isn’t feasible to conduct your study in person. Instead, you decide to use social media, finding your participants through snowball sampling.
You start by identifying social media sites that cater to digital nomads, such as Facebook groups, blogs, or freelance job sites. You ask the administrators for permission to post a call for participants with information about your research, encouraging readers to share the call with peers.
You are part of a research group investigating online behavior and cyberbullying, in particular among users aged 15 to 30 in your state. You are collecting data in two ways, using an online survey.
You first place a link to your survey in an online news article about cyber-hate published by local media. Second, you create an advertising campaign through social media, targeted at users aged 15 to 30 and linking back to your survey. To entice users to participate, a prize draw (movie tickets) is mentioned in all ads. The survey and the campaign are active for the same length of time.
These two data collection methods are river samples. The name refers to the idea of researchers dipping into the traffic flow of a website, catching some of the users floating by.
You are interested in the level of knowledge about myocardial infarction symptoms among the general population.
For a week, you stand in a shopping mall and stop passersby, asking them whether they would be willing to take part in your research. To try to allow as broad a range of respondents as possible to be included, you interview equal numbers of people from Monday to Friday during working hours.
Probability vs. non-probability sampling
Sampling methods can be broadly divided into two types:
- Probability sampling: When the sample is drawn in such a way that each unit in the population has an equal chance of selection
- Non-probability sampling: When you select the units for your sample with other considerations in mind, such as convenience or geographical proximity
For many types of analysis, it is important that the statistical analysis is conducted from a random probability sample from the population of interest. For the sample to qualify as random, each unit must have an equal chance (i.e., equal probability) of being selected.
When you use a random selection method (e.g., a drawing) and ensure that you have a sufficiently large sample, your sample is more likely to be representative, and the results generalisable.
Non-probability sampling designs are used when the sample needs to be collected based on a specific characteristic of the population (e.g., people with diabetes).
Unlike probability sampling, the goal is not to achieve objectivity in the selection of samples, or to make statistical inferences. Rather, the goal is to apply the results only to a certain subsection or organisation. These are used in both quantitative and qualitative research.
Advantages and disadvantages of non-probability sampling
It is important to be aware of the advantages and disadvantages of non-probability sampling and to understand how they can play a role in your study design.
Advantages of non-probability sampling
Depending on your research design, there are advantages to choosing non-probability sampling.
- Non-probability sampling does not require a sampling frame, so your subjects are often readily available. This can make non-probability sampling quicker and easier to carry out.
- Non-probability sampling allows you to target particular groups within your population. In certain types of research, it is vital that certain units be included in your sample. For example, many kinds of medical research rely on people with a specific health issue.
- Although it is not possible to make statistical inferences from the sample to the population, non-probability sampling methods can provide researchers with the data to make other types of generalisations from the sample being studied.
Disadvantages of non-probability sampling
Non-probability sampling has some downsides as well. These include the following:
- Non-probability samples are extremely unlikely to be representative of the population studied. This undermines the generalisability of your results.
- Non-probability samples are at risk of several kinds of research bias:
- As some units in the population have no chance of being included in the sample, undercoverage bias is likely.
- Furthermore, since the selection of units included in the sample is often based on ease of access, sampling bias is common as well.
- While the subjective judgment of the researcher in choosing who makes up the sample can be an advantage, it also increases the risk of researcher bias.
You can mitigate the disadvantages of non-probability sampling by describing your choices in the methodology section of your dissertation. Specifically, explain how your sample would differ from one that was randomly selected and mention any subjects who might be excluded or overrepresented in your sample.
Frequently asked questions about non-probability sampling
- What is a sampling method?
This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling, convenience sampling, and snowball sampling.
- What is a sampling frame?
- What is sampling?
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.
- What is stratified sampling?
- What is the difference between stratified and cluster sampling?
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous, so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous, as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population.
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