Purposive sampling, also known as judgmental, selective or subjective sampling, is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques where the units that are investigated are based on the judgement of the researcher [see our articles: Non-probability sampling to learn more about non-probability sampling, and Sampling: The basics, for an introduction to terms such as units, cases and sampling]. There are a number of different types of purposive sampling, each with different goals. This article explains (a) what purposive sampling is, (b) the eight of the different types of purposive sampling, (c) how to create a purposive sample, and (d) the broad advantages and disadvantages of purposive sampling.
Purposive sampling represents a group of different non-probability sampling techniques. Also known as judgmental, selective or subjective sampling, purposive sampling relies on the judgement of the researcher when it comes to selecting the units (e.g., people, cases/organisations, events, pieces of data) that are to be studied. Usually, the sample being investigated is quite small, especially when compared with probability sampling techniques.
Unlike the various sampling techniques that can be used under probability sampling (e.g., simple random sampling, stratified random sampling, etc.), the goal of purposive sampling is not to randomly select units from a population to create a sample with the intention of making generalisations (i.e., statistical inferences) from that sample to the population of interest [see the article: Probability sampling]. This is the general intent of research that is guided by a quantitative research design.
The main goal of purposive sampling is to focus on particular characteristics of a population that are of interest, which will best enable you to answer your research questions. The sample being studied is not representative of the population, but for researchers pursuing qualitative or mixed methods research designs, this is not considered to be a weakness. Rather, it is a choice, the purpose of which varies depending on the type of purposing sampling technique that is used. For example, in homogeneous sampling, units are selected based on their having similar characteristics because such characteristics are of particular interested to the researcher. By contrast, critical case sampling is frequently used in exploratory, qualitative research in order to assess whether the phenomenon of interest even exists (amongst other reasons).
During the course of a qualitative or mixed methods research design, more than one type of purposive sampling technique may be used. For example, critical case sampling may be used to investigate whether a phenomenon is worth investigating further, before adopting a maximum variation sampling technique is used to develop a wider picture of the phenomenon. We explain the different goals of these types of purposive sampling technique in the next section.
There are a wide range of purposive sampling techniques that you can use (see Patton, 1990, 2002; Kuzel, 1999, for a complete list). Each of these types of purposive sampling technique is discussed in turn:
Maximum variation sampling, also known as heterogeneous sampling, is a purposive sampling technique used to capture a wide range of perspectives relating to the thing that you are interested in studying; that is, maximum variation sampling is a search for variation in perspectives, ranging from those conditions that are view to be typical through to those that are more extreme in nature. By conditions, we mean the units (i.e., people, cases/organisations, events, pieces of data) that are of interest to the researcher. These units may exhibit a wide range of attributes, behaviours, experiences, incidents, qualities, situations, and so forth. The basic principle behind maximum variation sampling is to gain greater insights into a phenomenon by looking at it from all angles. This can often help the researcher to identify common themes that are evident across the sample.
Homogeneous sampling is a purposive sampling technique that aims to achieve a homogeneous sample; that is, a sample whose units (e.g., people, cases, etc.) share the same (or very similar) characteristics or traits (e.g., a group of people that are similar in terms of age, gender, background, occupation, etc.). In this respect, homogeneous sampling is the opposite of maximum variation sampling. A homogeneous sample is often chosen when the research question that is being address is specific to the characteristics of the particular group of interest, which is subsequently examined in detail.
Typical case sampling is a purposive sampling technique used when you are interested in the normality/typicality of the units (e.g., people, cases, events, settings/contexts, places/sites) you are interested, because they are normal/typical. The word typical does not mean that the sample is representative in the sense of probability sampling (i.e., that the sample shares the same/similar characteristics of the population being studied). Rather, the word typical means that the researcher has the ability to compare the findings from a study using typical case sampling with other similar samples (i.e., comparing samples, not generalising a sample to a population). Therefore, with typical case sampling, you cannot use the sample to make generalisations to a population, but the sample could be illustrative of other similar samples. Whilst typical case sampling can be used exclusively, it may also follow another type of purposive sampling technique, such as maximum variation sampling, which can help to act as an exploratory sampling strategy to identify the typical cases that are subsequently selected.
Extreme (or deviant) case sampling is a type of purposive sampling that is used to focus on cases that are special or unusual, typically in the sense that the cases highlight notable outcomes, failures or successes. These extreme (or deviant) cases are useful because they often provide significant insight into a particular phenomenon, which can act as lessons (or cases of best practice) that guide future research and practice. In some cases, extreme (or deviant) case sampling is thought to reflect the purest form of insight into the phenomenon being studied.
Critical case sampling is a type of purposive sampling technique that is particularly useful in exploratory qualitative research, research with limited resources, as well as research where a single case (or small number of cases) can be decisive in explaining the phenomenon of interest. It is this decisive aspect of critical case sampling that is arguably the most important. To know if a case is decisive, think about the following statements: ?If it happens there, it will happen anywhere?; or ?if it doesn?t happen there, it won?t happen anywhere?; and ?If that group is having problems, then we can be sure all the groups are having problems? (Patton, 202, p.237). Whilst such critical cases should not be used to make statistical generalisations, it can be argued that they can help in making logical generalisations. However, such logical generalisations should be made carefully.
Total population sampling is a type of purposive sampling technique where you choose to examine the entire population (i.e., the total population) that have a particular set of characteristics (e.g., specific experience, knowledge, skills, exposure to an event, etc.). In such cases, the entire population is often chosen because the size of the population that has the particular set of characteristics that you are interest in is very small. Therefore, if a small number of units (i.e., people, cases/organisations, etc.) were not included in the sample that is investigated, it may be felt that a significant piece of the puzzle was missing [see the article, Total population sampling, to learn more].
Expert sampling is a type of purposive sampling technique that is used when your research needs to glean knowledge from individuals that have particular expertise. This expertise may be required during the exploratory phase of qualitative research, highlighting potential new areas of interest or opening doors to other participants. Alternately, the particular expertise that is being investigated may form the basis of your research, requiring a focus only on individuals with such specific expertise. Expert sampling is particularly useful where there is a lack of empirical evidence in an area and high levels of uncertainty, as well as situations where it may take a long period of time before the findings from research can be uncovered. Therefore, expert sampling is a cornerstone of a research design known as expert elicitation.
Whilst each of the different types of purposive sampling has its own advantages and disadvantages, there are some broad advantages and disadvantages to using purposive sampling, which are discussed below.
There are a wide range of qualitative research designs that researchers can draw on. Achieving the goals of such qualitative research designs requires different types of sampling strategy and sampling technique. One of the major benefits of purposive sampling is the wide range of sampling techniques that can be used across such qualitative research designs; purposive sampling techniques that range from homogeneous sampling through to critical case sampling, expert sampling, and more.
Whilst the various purposive sampling techniques each have different goals, they can provide researchers with the justification to make generalisations from the sample that is being studied, whether such generalisations are theoretical, analytic and/or logical in nature. However, since each of these types of purposive sampling differs in terms of the nature and ability to make generalisations, you should read the articles on each of these purposive sampling techniques to understand their relative advantages.
Qualitative research designs can involve multiple phases, with each phase building on the previous one. In such instances, different types of sampling technique may be required at each phase. Purposive sampling is useful in these instances because it provides a wide range of non-probability sampling techniques for the researcher to draw on. For example, critical case sampling may be used to investigate whether a phenomenon is worth investigating further, before adopting an expert sampling approach to examine specific issues further.
Purposive samples, irrespective of the type of purposive sampling used, can be highly prone to researcher bias. The idea that a purposive sample has been created based on the judgement of the researcher is not a good defence when it comes to alleviating possible researcher biases, especially when compared with probability sampling techniques that are designed to reduce such biases. However, this judgemental, subjective component of purpose sampling is only a major disadvantage when such judgements are ill-conceived or poorly considered; that is, where judgements have not been based on clear criteria, whether a theoretical framework, expert elicitation, or some other accepted criteria.
The subjectivity and non-probability based nature of unit selection (i.e., selecting people, cases/organisations, etc.) in purposive sampling means that it can be difficult to defend the representativeness of the sample. In other words, it can be difficult to convince the reader that the judgement you used to select units to study was appropriate. For this reason, it can also be difficult to convince the reader that research using purposive sampling achieved theoretical/analytic/logical generalisation. After all, if different units had been selected, would the results and any generalisations have been the same?