What is Purposive Sampling?
Purposive sampling is a non-probability method for obtaining a sample where researchers use their expertise to choose specific participants that will help the study meet its goals. These subjects have particular characteristics that the researchers need to evaluate their research question. In other words, the researchers pick the participants “on purpose.”
Statisticians also refer to this approach as judgmental sampling because it involves using judgment to determine which subjects can best help the study answer its research question.
Use purposive sampling when you need to learn a lot from a small sample, and you understand the subject area well enough to know which subjects are vital to your project. This approach can help you focus on a narrowly defined subpopulation, critical people in a process, typical cases, or unusual cases. Purposive sampling is a set of methods that use different approaches to answer the research question.
In this post, learn about the types of purposive sampling that can help you answer various research questions.
Learn more about Types of Sampling Methods in Research.
Types of Purposive Sampling
Purposive sampling is a set of different methods that all involve using the researchers’ judgment to choose a few subjects that can best answer their research question. The methods vary depending on the research goals.
This form of purposive sampling recruits a very narrowly defined subpopulation, producing a sample with uniform traits.
Researchers use homogeneous sampling when they need to understand a particular group. They can define the group based on life experiences, traits, background, job type, etc. Studies frequently use this approach to recruit for focus groups.
For example, I’ve personally used this method to recruit college professors who routinely use digital images for teaching to participate in focus groups.
Maximum Variation Sampling
This type of purposive sampling aims to maximize the differences between subjects, unlike the previous method, which minimizes them. This form is also known as heterogeneous sampling.
Researchers use maximum variation sampling to gather the full range of participants, from the most extreme to the most common. This process collects a small sample that intentionally covers the full spectrum of possibilities, allowing the project to understand all viewpoints. The variability in this type of sample is greater than what you’d obtain with random selection because you’re intentionally maximizing it.
For example, suppose you want to understand the issues important to a community. In that case, you might collect a sample that contains all income and education levels, races, and genders. This broad perspective will help you understand the full array of issues that various subgroups are experiencing.
Related post: Heterogeneity in Data and Samples
This type of purposive sampling recruits subjects who are typical or average.
Researchers use this method to understand a population’s most ordinary members. This process aims to paint a picture of the average case, outcome, or beliefs.
For example, if you want to describe the typical impact of new hunting regulations on the average hunter, you’ll need to recruit average hunters.
Deviant (or Extreme) Cases
This form of purposive sampling finds unusual cases. These are not the typical outcomes or population members. They might be the upper and lower performers of an activity. In short, you’re looking for the outliers.
Researchers use this method when they need to understand the variation that causes different outcomes. Using this approach, the researchers can learn about factors that cause subjects to do better and worse than most. These studies can help formulate guidelines for improvements.
For example, if you’re studying a reading comprehension program, you might use deviant case sampling to choose the best and worst performing students in the program. Assessing these students might reveal the factors that improve or decrease reading comprehension.
This form of purposive sampling finds the few subjects that can predict or explain many other cases. Using their subject-area knowledge, researchers expect that they can apply the insights from the sample case to others. Consequently, they must have the information necessary to know which subjects are critical. Critical cases are often defined by having or not having specific knowledge or experience.
Researchers use this method to determine whether more rigorous studies are warranted. While you can expect that the results from critical case sampling studies can apply broadly to similar cases, you can’t perform inferential statistics on these data or fully generalize them to a population.
Suppose you’re studying the awareness of new health guidelines, and your critical case sample contains nurses in a community health center. If your sample of nurses is unaware of the new guidelines, it’s unlikely the typical community member will know about them.
Expert sampling is a kind of purposive sampling that finds experts in an area.
Researchers use this method in the early phases of a study to become better informed. Experts can help them identify the critical factors, constraints, and issues in a research area. Using this information, they can more effectively develop follow-up studies.
For example, researchers can ask educational experts to identify factors that influence reading comprehension. Using this information, they can develop studies that assess these factors in greater detail.
Advantages and Disadvantages of Purposive Sampling
As a non-probability method, purposive sampling has various advantages and disadvantages compared to probability methods, such as random sampling.
Advantages of Purposive Sampling
Purposive sampling can provide data during the exploratory research phase to inform more in-depth, follow-up research. It can offer this information quickly and easily using relatively small samples that don’t require having a complete population list or the other complications associated with random sampling.
Disadvantages of Purposive Sampling
Purposive sampling is a non-probability method that does not use random selection. Consequently, the sample you obtain is likely to be biased and not represent the population, preventing generalizations from the sample to the population. Learn more about Representative Samples and Sampling Bias.