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 judgemental sampling, this sampling method relies on the researcher’s judgement 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.

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.

Purposive sampling methods and examples

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

• Maximum variation (or heterogeneous) sampling
• Homogeneous sampling
• Typical case sampling
• Extreme (or deviant) case sampling
• Critical case sampling
• Expert sampling

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, organisations, 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.

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.

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 generalised statements about the experiences of all participants. For this reason, typical case sampling allows you to compare samples, not generalise samples to populations.

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’.

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.

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: 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.

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.

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.

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.

Step 5: Analyse 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 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 generalisations from the sample being studied. Remember that these generalisations 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, such as sampling bias or selection bias. Because the selection of the sample units depends on the researcher’s subjective judgement, results have a high risk of 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.

Frequently asked questions about purposive sampling

What is the difference between purposive sampling and convenience sampling?

Purposive sampling 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 research findings based on either convenience or purposive sampling can only be generalised 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.