Non-Probability Sampling: Definition, Types, Examples, and More

Introduction

When collecting feedback or conducting other forms of survey research, it’s important to consider not only the questions you ask respondents, but who you are asking. This article will briefly compare probability sampling and non-probability sampling but focus mostly on the latter. Thinking about your sampling methods approach to research is critical to understand any confounding effects or bias that might influence the answers you receive and ensure you’ve got the right data to make the right decisions.

What is Non-Probability Sampling?

Non-probability sampling is defined as a methodological approach in research where the selection of participants (sample) of a population is not based on random selection. This is an important distinction because it means every member of the population does not have an equal chance of being chosen to participate. 

It’s helpful to compare non-probability sampling to probability sampling when thinking about the two. For probability sampling, consider a group of 100 participants. You might have them line up randomly and then choose every other person. Or, you might assign each person a number and then use a random method, such as using software, to randomize the numbers and pick your respondents based on the results.

With non-probability sampling, your goal tends to be just to find participants to help gather data to inform decisions, understanding that you might not get a good representation of the population you are interested in. For example, you might accept every participant who applies to the research, or recruit in a way that will result in very similar participants.

Knowing if you are using probability or non-probability sampling should help you think about generalizing your findings to a larger population.

Types of Non-Probability Sampling

Convenience Sampling

Convenience sampling, as the name suggests, involves selecting samples that are easy to access. It is the go-to method when time, resources, or both are limited.

However, this ease comes with trade-offs. While convenience sampling can provide immediate insights, its reliance on accessibility over randomness can introduce biases, limiting the generalizability of the findings. 

Some examples of convenience sampling could be standing outside of a grocery store to talk to customers entering and leaving the store, or talking to students studying in a library. These approaches would make finding participants easier, but runs a large risk of significant bias by missing larger parts of population of interest. For example, people who buy groceries online or at other stores, or students who study at home.

Quota Sampling

Quota sampling might initially not feel like a non-probability sampling approach, since initial participants that come into your research might feel random, but ultimately will not be. As your targeting criteria gets more and more specific, you move further and further away from anyone in your target population from having a chance to be included in your sample. For example, researching the population of a country, but including quotas of age and income to ensure a representative sample is inherently a counter to everyone in the population having an equal probability of being chosen.

Snowball Sampling

Snowball sampling is particularly useful for reaching populations that are difficult to access or identify. In this method, existing study participants recruit future participants from among their acquaintances. This approach is akin to referral programs in marketing, where initial contacts lead to a network of participants, and is similar to convenience sampling.

For example, snowball sampling could be instrumental in studying the experiences of individuals with a rare disease, as initial participants can help researchers connect with others who share the same condition. Or, fans of a sports team in another country might share an invitation to participant in a survey through forums or group-chats used within the community. While snowball sampling can open doors to niche populations, its reliance on personal networks almost certainly will introduce biases related to the social circles of the initial participants (similar age, social-demographic profiles, etc.).

Judgmental or Purposive Sampling

Judgmental or purposive sampling is a nuanced form of non-probability sampling where researchers rely on their expertise and judgment to select participants who are deemed most suitable for the study. 

This method is particularly useful when the research aims to gather in-depth information from a very specific group of people. Researchers use their knowledge of the population to make informed decisions about who should be included to provide the most valuable insights. 

For example, a market research firm looking to understand high-end consumer preferences might specifically target individuals known for their luxury spending habits. While judgmental sampling allows for a focused exploration of particular phenomena, it carries the risk of bias, as the selection is inherently subjective. 

Non-Probability Sampling Examples

Convenience Sampling with Student Volunteers

A university researcher aiming to study stress levels may opt for convenience sampling by surveying students who are readily available, such as those in a psychology class. This method is quick and efficient but may not fully represent the diversity of stress factors among all students and almost certainly wouldn’t provide results that could reliably be assumed to be true for all adults of college age.

Quota Sampling for Career Goals Research

In a study exploring career goals across different majors, a researcher might use quota sampling to ensure equal representation from each department. By setting quotas for students from the sciences, arts, business, and other faculties, the study can achieve a balanced view that mirrors the university's student body composition.

Using Snowball Sampling for Studying Rare Diseases

Researchers interested in studying a rare genetic disorder might start with a small group of diagnosed individuals and use snowball sampling to reach others with the same condition. This method allows access to a specific, hard-to-find population, providing valuable insights into the experiences and needs of those affected by the disorder.

When to Use Non-Probability Sampling?

Most market research these days is conducted using non-probability sampling techniques. It is common for researchers to use lists provided by customers or to use panel providers that recruit using their managed lists of willing participants or through channels such as social media, website ads, etc. It is also extremely common for market research surveys to use quotas so that the sample collected mirrors demographic variables (for example, around 11% of the population in the US is aged 20-24, so in a survey you want around 11% of your total respondents to be aged 20-24).

“Agile” research, or initial research efforts are also likely to use non-probability sampling in order to recruit participants quickly and cheaply.

Advantages of Non-Probability Sampling

Non-probability sampling offers several key advantages that make it an attractive choice for many research projects:

1. Cost-Effectiveness: This approach often requires fewer resources in terms of time and money, making it suitable for studies with limited budgets.
2. Convenience: It allows researchers to gather data more quickly and easily, especially when samples are readily accessible.
3. Specificity: Non-probability sampling is ideal for studies focusing on specific segments of a population, providing deep insights into particular groups.
4. Feasibility: It is particularly useful when the population frame is unknown or when it's challenging to apply random sampling methods.
5. Richness of Data: This method often yields rich, qualitative insights that can be invaluable for exploratory research or when a deep understanding of complex issues is required.

Despite these advantages, researchers should be aware of the limitations and biases inherent in non-probability sampling and consider how these may impact the validity and generalizability of their findings.

Disadvantages of Non-Probability Sampling

While non-probability sampling has its benefits, it also comes with several notable disadvantages:

1. Lack of Representativeness: Since samples are not randomly selected, the results may not be generalizable to the broader population.
2. Sampling Bias: The selection process may introduce biases, as the choice of participants is influenced by accessibility, referral, or the researcher's judgment.
3. Limited Scalability: The insights gained from non-probability sampling may not be applicable or scalable to larger populations.
4. Potential for Overlooking Subgroups: There's a risk of missing important segments of the population, which can lead to incomplete or skewed findings.

Additionally, determining the right sample size can be challenging in non-probability sampling, as the lack of randomness may affect the reliability of the sample size calculations.

These disadvantages highlight the need for careful consideration when choosing to use non-probability sampling, ensuring that the method aligns with the research objectives and the limitations are acknowledged and addressed in the study's findings.

Conclusion

Non-probability sampling plays a vital role in research, offering flexibility, efficiency, and depth of insight for studies focusing on specific population segments or when resources are limited. For example, many primary and secondary research efforts rely on these techniques to gather initial insights quickly.

While it offers several advantages, including cost-effectiveness and speed, researchers must also be mindful of its limitations, such as potential survey biases and the challenges of generalizing findings. Employing robust data analysis methods and considering tools like conjoint analysis tools or MaxDiff studies can help mitigate some of these issues.