Non-Probability Sampling Techniques

While probability sampling is the gold standard for research that aims to generalize findings to a broader population, there are many situations where it is not feasible, practical, or necessary. In these instances, researchers turn to non-probability sampling techniques. The defining characteristic of these methods is that the probability of any given individual from the population being selected is unknown. This is because selection is not random. Consequently, while these methods are often more convenient and less expensive, they come with a significant caveat: one cannot statistically estimate the margin of error or be confident that the sample accurately reflects the population. The risk of selection bias is high. However, these techniques are invaluable for exploratory research, pilot studies, qualitative research, and studying hard-to-reach populations

Convenience Sampling

This is the most straightforward of all sampling techniques, involving the selection of participants simply because they are easy to reach and available to participate. The researcher makes little to no attempt to ensure the sample is representative of a larger population. Examples include interviewing shoppers at a single mall, surveying students in an introductory psychology class, or using employees at one’s own company as subjects. While this method is fast and inexpensive, it is highly susceptible to bias. The sample may over-represent certain groups and under-represent others, making it inappropriate for drawing general conclusions. Its primary utility is in pilot testing survey instruments or generating initial hypotheses

Purposive Sampling

Also known as judgmental or selective sampling, this technique involves the researcher using their own expertise and judgment to select participants who are most relevant to the study’s purpose. The goal is not to create a representative sample but to find individuals with specific knowledge, experience, or characteristics. For example, if a study aims to understand the challenges of starting a small business, a researcher might purposively seek out and interview individuals who have successfully launched a startup in the last five years. Similarly, a study on the effectiveness of a medical treatment would purposively sample patients who have undergone that specific treatment. This method is highly effective for gathering in-depth information from a targeted group but is subject to researcher bias

Snowball Sampling

This technique, also called chain-referral sampling, is used when the population of interest is hard to locate, hidden, or rare. The process begins with the researcher identifying and recruiting one or a few initial participants who meet the study criteria. After collecting their data, the researcher asks these participants to refer other individuals they know who also fit the criteria. The sample grows like a snowball rolling downhill as referrals lead to more referrals. Snowball sampling is particularly useful for studying populations such as undocumented immigrants, members of a specific subculture, or individuals with a rare disease. A major limitation is that the sample is unlikely to be diverse; participants will tend to know each other and share similar characteristics, which can bias the results

Quota Sampling

Quota sampling can be seen as the non-probability equivalent of stratified sampling. The researcher first identifies relevant subgroups in the population (e.g., based on age, gender, ethnicity, or income level) and determines the proportion of each subgroup in that population. The researcher then sets a quota—a target number of participants to recruit from each subgroup. The key difference from stratified sampling is that the actual selection of participants within each subgroup is non-random; it is typically done using convenience or purposive methods until the quotas are filled. While this method ensures that the final sample’s composition mirrors the population on the chosen characteristics, the non-random selection within each quota still allows for significant potential bias

Voluntary Response Sampling

In this method, individuals self-select to participate in the study. The researcher does not select participants but rather invites a broad audience to take part, and those who are interested opt in. Classic examples include online polls, call-in radio or television surveys, or QR codes on restaurant receipts that lead to a feedback survey. This technique is extremely biased because the people who choose to participate are often not representative of the general population. They typically have very strong opinions—either positive or negative—and are more motivated to share them. While easy to implement, data from voluntary response samples should be interpreted with extreme caution