What Is Purposive Sampling? | Definition & Examples

Purposive sampling refers to a group of non-probability sampling techniques in which units are selected because they have characteristics that you need in your sample. In other words, units are selected “on purpose” in purposive sampling.

Also called judgmental sampling, this sampling method relies on the researcher’s judgment when identifying and selecting the individuals, cases, or events that can provide the best information to achieve the study’s objectives.

Purposive sampling is common in qualitative research and mixed methods research. It is particularly useful if you need to find information-rich cases or make the most out of limited resources, but is at high risk for research biases like observer bias.

When to use purposive sampling

Purposive sampling is best used when you want to focus in depth on relatively small samples. Perhaps you would like to access a particular subset of the population that shares certain characteristics, or you are researching issues likely to have unique cases.

The main goal of purposive sampling is to identify the cases, individuals, or communities best suited to helping you answer your research question. For this reason, purposive sampling works best when you have a lot of background information about your research topic. The more information you have, the higher the quality of your sample.

Prevent plagiarism. Run a free check.

Try for free

Purposive sampling methods and examples

Depending on your research objectives, there are several purposive sampling methods you can use:

Maximum variation sampling

Maximum variation sampling, also known as heterogeneous sampling, is used to capture the widest range of perspectives possible.

To ensure maximum variation, researchers include both cases, organizations, or events that are considered typical or average and those that are more extreme in nature. This helps researchers to examine a subject from different angles, identifying important common patterns that are true across variations.

Example: Maximum variation sampling
Suppose you are researching the challenges of mental health services programs in your state. Using maximum variation sampling, you select programs in urban and rural areas in different parts of the state, in order to capture maximum variation in location.

In this way, you can document unique or diverse variations that have emerged in different locations.

Homogeneous sampling

Homogeneous sampling, unlike maximum variation sampling, aims to reduce variation, simplifying the analysis and describing a particular subgroup in depth.

Units in a homogeneous sample share similar traits or specific characteristics—e.g., life experiences, jobs, or cultures. The idea is to focus on this precise similarity, analyzing how it relates to your research topic. Homogeneous sampling is often used for selecting focus group participants.

Example: Homogeneous sampling
Continuing your research on mental health services programs in your state, you are now interested in illuminating the experiences of different ethnicities through group interviewing.

Using homogeneous sampling, you select Latinx directors of mental health services agencies, interviewing them about the challenges of implementing evidence-based treatments for mental health problems.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Typical case sampling

Typical case sampling is used when you want to highlight what is considered a normal or average instance of a phenomenon to those who are unfamiliar with it. Participants are generally chosen based on their likelihood of behaving like everyone else sharing the same characteristics or experiences.

Keep in mind that the goal of typical case sampling is to illustrate a phenomenon, not to make generalized statements about the experiences of all participants. For this reason, typical case sampling allows you to compare samples, not generalize samples to populations.

Example: Typical-case sampling 
You are researching the reactions of 9th grade students to a job placement program. To develop a typical case sample, you select participants with similar socioeconomic backgrounds from five different cities.

You collect the students’ experiences via surveys or interviews and create a profile of a “typical” 9th grader who followed a job placement program. This can offer useful insights to employers who want to offer job placements to students in the future.

Extreme (or deviant) case sampling

The idea behind extreme case sampling is to illuminate unusual cases or outliers. This can involve notable successes or failures, “top of the class vs. bottom of the class” scenarios, or any unusual manifestation of a phenomenon of interest.

This form of sampling, also called deviant case sampling, is often used when researchers are developing best practice guidelines or are looking into “what not to do.”

Example: Extreme (or deviant) case sampling
You are researching heart surgery patients who recovered significantly faster or slower than average. Since these are unusual cases, you’re looking for variation in these cases to explain why their recoveries were atypical.

Critical case sampling

Critical case sampling is used when a single or very small number of cases can be used to explain other similar cases.  Researchers determine whether a case is critical by using this maxim: “if it happens here, it will happen anywhere.” In other words, a case is critical if what is true for one case is likely to be true for all other cases.

Although you cannot make statistical inferences with critical case sampling, you can apply your findings to similar cases. Researchers use critical case sampling in the initial phases of their research, in order to establish whether a more in-depth study is needed.

Example: Critical case sampling 
You are researching how to involve local communities in local government decision-making processes, but you are not sure whether the communities will understand the regulations.

If you first ask local government officials and they do not understand them, then probably no one will. Alternatively, if you ask random passersby, and they do understand them, then it’s safe to assume most people will.

In this way, your critical cases could either be those with relevant expertise or those who have no relevant expertise.

Expert sampling

Expert sampling is used when your research requires individuals with a high level of knowledge about a particular subject. Your experts are thus selected based on a demonstrable skill set, or level of experience possessed.

This type of sampling is useful when there is a lack of observational evidence, when you are investigating new areas of research, or when you are conducting exploratory research.

Example: Expert sampling
You are investigating the barriers to reduced meat consumption among consumers in the US. In addition to focus groups with consumers, you decide to contact a number of experts and interview them. In the context of your research, food scientists are the experts who can provide valuable insights into the root of the problem, as well as any successes, failures, or future trends to watch.

Example: Step-by-step purposive sampling

Purposive sampling is widely used in qualitative research, when you want to focus in depth on a certain phenomenon. There are five key steps involved in drawing a purposive sample.

Step 1: Define your research problem

Start by deciding your research problem: a specific issue, challenge, or gap in knowledge you aim to address in your research. The way you formulate your problem determines your next steps in your  research design, as well as the sampling method and the type of analysis you undertake.

Example: Research problem
Suppose you are researching the outcomes of a six-month cognitive behavioral therapy (CBT) intervention on youth aged 10–16 who experienced behavioral difficulties due to exposure to a specific traumatic event.

Step 2: Determine your population

You should begin by clearly defining the population from which your sample will be taken, since this is where you will draw your conclusions from.

Example: Target population 
Here, your target population are the youth aged 10–16 who experienced similar traumatic events and received CBT due to difficulties they were experiencing afterwards.

Step 3: Define the characteristics of your sample

In purposive sampling, you set out to identify members of the population who are likely to possess certain characteristics or experiences (and to be willing to share them with you). In this way, you can select the individuals or cases that fit your study, focusing on a relatively small sample.

Example: Purposive sampling designs
You may choose to focus on the youth who responded better than the average to the CBT intervention and try to explain why they responded in this manner. In this case, you can use extreme case sampling, only focusing on the few cases that stand out.

Alternatively, you may be interested in identifying common patterns, despite the variations in how the youth responded to the intervention. You can draw a maximum variation sample by including a range of outcomes:

  • Youth who reported no effects after the intervention
  • Youth who had an average response to the intervention
  • Youth who reported significantly better outcomes than the average after the intervention

Step 4: Collect your data using an appropriate method

Depending on your research question and the type of data you want to collect, you can now decide which data collection method is best for you.

Example: Data collection in purposive sampling
You interview the mental health professionals who delivered the intervention, as well as a number of youth (after getting their parents’ permission).

Regardless of the purposive sampling technique you choose, you recruit cases until you reach a saturation point.

Step 5: Analyze and interpret your results

Purposive sampling is an effective method when dealing with small samples, but it is also an inherently biased method. For this reason, you need to document the research bias in the methodology section of your paper and avoid applying any interpretations beyond the sampled population.

Advantages and disadvantages of purposive sampling

Knowing the advantages and disadvantages of purposive sampling can help you decide if this approach fits your research design.

Advantages of purposive sampling

There are several advantages to using purposive sampling in your research.

  • Although it is not possible to make statistical inferences from the sample to the population, purposive sampling techniques can provide researchers with the data to make other types of generalizations from the sample being studied. Remember that these generalizations must be logical, analytical, or theoretical in nature to be valid.
  • Purposive sampling techniques work well in qualitative research designs that involve multiple phases, where each phase builds on the previous one. Purposive sampling provides a wide range of techniques for the researcher to draw on and can be used to investigate whether a phenomenon is worth investigating further.

Disadvantages of purposive sampling

However, purposive sampling can have a number of drawbacks, too.

  • As with other non-probability sampling techniques, purposive sampling is prone to research bias. Because the selection of the sample units depends on the researcher’s subjective judgment, results have a high risk of bias, particularly observer bias.
  • If you are not aware of the variations in attitudes, opinions, or manifestations of the phenomenon of interest in your target population, identifying and selecting the units that can give you the best information is extremely difficult.
You can use an iterative approach to draw an appropriate sample. Here, you sample and re-sample until you reach a saturation point (when you no longer receive new responses to your questions).

Other interesting articles

If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about purposive sampling

What is the difference between purposive sampling and convenience sampling?

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

What is non-probability sampling?

In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

What is a sampling method?

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use 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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Nikolopoulou, K. (2023, June 22). What Is Purposive Sampling? | Definition & Examples. Scribbr. Retrieved June 11, 2024, from https://www.scribbr.com/methodology/purposive-sampling/

Is this article helpful?
Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students. She specializes in writing about research methods and research bias.