Skip Nav

Purposive Sampling

Principles of Purposeful Sampling

❶Fifth, it should be kept in mind that all sampling procedures, whether purposeful or probability, are designed to capture elements of both similarity and differences, of both centrality and dispersion, because both elements are essential to the task of generating new knowledge through the processes of comparison and contrast.

Reader's Guide

Looks like you do not have access to this content.
Purposive sampling

Moreover, certain strategies, like stratified purposeful sampling or opportunistic or emergent sampling, are designed to achieve both goals. As Patton , p. Each of the strata would constitute a fairly homogeneous sample. Despite its wide use, there are numerous challenges in identifying and applying the appropriate purposeful sampling strategy in any study.

For instance, the range of variation in a sample from which purposive sample is to be taken is often not really known at the outset of a study.

To set as the goal the sampling of information-rich informants that cover the range of variation assumes one knows that range of variation. Second, there are a not insignificant number in the qualitative methods field who resist or refuse systematic sampling of any kind and reject the limiting nature of such realist, systematic, or positivist approaches.

However, even those who equate purposeful sampling with systematic sampling must offer a rationale for selecting study participants that is linked with the aims of the investigation i. What qualifies them to address the aims of the study? While systematic sampling may be associated with a post-positivist tradition of qualitative data collection and analysis, such sampling is not inherently limited to such analyses and the need for such sampling is not inherently limited to post-positivist qualitative approaches Patton, In implementation research, quantitative and qualitative methods often play important roles, either simultaneously or sequentially, for the purpose of answering the same question through convergence of results from different sources, answering related questions in a complementary fashion, using one set of methods to expand or explain the results obtained from use of the other set of methods, using one set of methods to develop questionnaires or conceptual models that inform the use of the other set, and using one set of methods to identify the sample for analysis using the other set of methods Palinkas et al.

A review of mixed method designs in implementation research conducted by Palinkas and colleagues revealed seven different sequential and simultaneous structural arrangements, five different functions of mixed methods, and three different ways of linking quantitative and qualitative data together.

However, this review did not consider the sampling strategies involved in the types of quantitative and qualitative methods common to implementation research, nor did it consider the consequences of the sampling strategy selected for one method or set of methods on the choice of sampling strategy for the other method or set of methods.

For instance, one of the most significant challenges to sampling in sequential mixed method designs lies in the limitations the initial method may place on sampling for the subsequent method. As Morse and Neihaus observe, when the initial method is qualitative, the sample selected may be too small and lack randomization necessary to fulfill the assumptions for a subsequent quantitative analysis.

On the other hand, when the initial method is quantitative, the sample selected may be too large for each individual to be included in qualitative inquiry and lack purposeful selection to reduce the sample size to one more appropriate for qualitative research.

The fact that potential participants were recruited and selected at random does not necessarily make them information rich. An additional three studies Henke et al. The remaining 20 studies provided no description of the sampling strategy used to identify participants for qualitative data collection and analysis; however, a rationale could be inferred based on a description of who were recruited and selected for participation.

Of the 28 studies, 3 used more than one sampling strategy. For instance, in a series of studies based on the National Implementing Evidence-Based Practices Project, participants included semi-structured interviews with consultant trainers and program leaders at each study site Brunette et al.

Six studies used some form of maximum variation sampling to ensure representativeness and diversity of organizations and individual practitioners.

Two studies used intensity sampling to make contrasts. Aarons and Palinkas , for example, purposefully selected 15 child welfare case managers representing those having the most positive and those having the most negative views of SafeCare, an evidence-based prevention intervention, based on results of a web-based quantitative survey asking about the perceived value and usefulness of SafeCare.

Kramer and Burns recruited and interviewed clinicians providing usual care and clinicians who dropped out of a study prior to consent to contrast with clinicians who provided the intervention under investigation. One study Hoagwood et al. County mental directors, agency directors, and program managers were recruited to represent the policy interests of implementation while clinicians, administrative support staff and consumers were recruited to represent the direct practice perspectives of EBP implementation.

Table 2 below provides a description of the use of different purposeful sampling strategies in mixed methods implementation studies. Criterion-i sampling was most frequently used in mixed methods implementation studies that employed a simultaneous design where the qualitative method was secondary to the quantitative method or studies that employed a simultaneous structure where the qualitative and quantitative methods were assigned equal priority.

Three of the six studies that used maximum variation sampling used a simultaneous structure with quantitative methods taking priority over qualitative methods and a process of embedding the qualitative methods in a larger quantitative study Henke et al.

Two of the six studies used maximum variation sampling in a sequential design Aarons et al. The single typical case study involved a simultaneous design where the qualitative study was embedded in a larger quantitative study for the purpose of complementarity Hoagwood et al. Although not used in any of the 28 implementation studies examined here, another common sequential sampling strategy is using criteria sampling of the larger quantitative sample to produce a second-stage qualitative sample in a manner similar to maximum variation sampling, except that the former narrows the range of variation while the latter expands the range.

Criterion-i sampling as a purposeful sampling strategy shares many characteristics with random probability sampling, despite having different aims and different procedures for identifying and selecting potential participants.

In both instances, study participants are drawn from agencies, organizations or systems involved in the implementation process. Individuals are selected based on the assumption that they possess knowledge and experience with the phenomenon of interest i. Participants for a qualitative study, usually service providers, consumers, agency directors, or state policy-makers, are drawn from the larger sample of participants in the quantitative study.

From the perspective of qualitative methodology, participants who meet or exceed a specific criterion or criteria possess intimate or, at the very least, greater knowledge of the phenomenon of interest by virtue of their experience, making them information-rich cases. However, criterion sampling may not be the most appropriate strategy for implementation research because by attempting to capture both breadth and depth of understanding, it may actually be inadequate to the task of accomplishing either.

Although qualitative methods are often contrasted with quantitative methods on the basis of depth versus breadth, they actually require elements of both in order to provide a comprehensive understanding of the phenomenon of interest.

Ideally, the goal of achieving theoretical saturation by providing as much detail as possible involves selection of individuals or cases that can ensure all aspects of that phenomenon are included in the examination and that any one aspect is thoroughly examined.

This goal, therefore, requires an approach that sequentially or simultaneously expands and narrows the field of view, respectively. By selecting only individuals who meet a specific criterion defined on the basis of their role in the implementation process or who have a specific experience e. For instance, a focus only on practitioners may fail to capture the insights, experiences, and activities of consumers, family members, agency directors, administrative staff, or state policy leaders in the implementation process, thus limiting the breadth of understanding of that process.

To address the potential limitations of criterion sampling, other purposeful sampling strategies should be considered and possibly adopted in implementation research Figure 1. For instance, strategies placing greater emphasis on breadth and variation such as maximum variation, extreme case, confirming and disconfirming case sampling are better suited for an examination of differences, while strategies placing greater emphasis on depth and similarity such as homogeneous, snowball, and typical case sampling are better suited for an examination of commonalities or similarities, even though both types of sampling strategies include a focus on both differences and similarities.

Alternatives to criterion sampling may be more appropriate to the specific functions of mixed methods, however. For instance, using qualitative methods for the purpose of complementarity may require that a sampling strategy emphasize similarity if it is to achieve depth of understanding or explore and develop hypotheses that complement a quantitative probability sampling strategy achieving breadth of understanding and testing hypotheses Kemper et al.

Similarly, mixed methods that address related questions for the purpose of expanding or explaining results or developing new measures or conceptual models may require a purposeful sampling strategy aiming for similarity that complements probability sampling aiming for variation or dispersion. A single method that focuses only on a broad view may decrease internal validity at the expense of external validity Kemper et al. On the other hand, the aim of convergence answering the same question with either method may suggest use of a purposeful sampling strategy that aims for breadth that parallels the quantitative probability sampling strategy.

Furthermore, the specific nature of implementation research suggests that a multistage purposeful sampling strategy be used. Three different multistage sampling strategies are illustrated in Figure 1 below. Several qualitative methodologists recommend sampling for variation breadth before sampling for commonalities depth Glaser, ; Bernard, Multistage I. This approach begins with a broad view of the topic and then proceeds to narrow down the conversation to very specific components of the topic.

However, as noted earlier, the lack of a clear understanding of the nature of the range may require an iterative approach where each stage of data analysis helps to determine subsequent means of data collection and analysis Denzen, ; Patton, Multistage II.

Similarly, multistage purposeful sampling designs like opportunistic or emergent sampling, allow the option of adding to a sample to take advantage of unforeseen opportunities after data collection has been initiated Patton, , p. Multistage I models generally involve two stages, while a Multistage II model requires a minimum of 3 stages, alternating from sampling for variation to sampling for similarity. A Multistage III model begins with sampling for variation and ends with sampling for similarity, but may involve one or more intervening stages of sampling for variation or similarity as the need or opportunity arises.

Multistage purposeful sampling is also consistent with the use of hybrid designs to simultaneously examine intervention effectiveness and implementation. Such designs may give equal priority to the testing of clinical treatments and implementation strategies Hybrid Type 2 or give priority to the testing of treatment effectiveness Hybrid Type 1 or implementation strategy Hybrid Type 3.

When conducting a Hybrid Type 1 design conducting a process evaluation of implementation in the context of a clinical effectiveness trial , the qualitative data could be used to inform the findings of the effectiveness trial.

Thus, an effectiveness trial that finds substantial variation might purposefully select participants using a broader strategy like sampling for disconfirming cases to account for the variation. Alternatively, a narrow strategy may be used to account for the lack of variation. In either instance, the choice of a purposeful sampling strategy is determined by the outcomes of the quantitative analysis that is based on a probability sampling strategy.

In Hybrid Type 2 and Type 3 designs where the implementation process is given equal or greater priority than the effectiveness trial, the purposeful sampling strategy must be first and foremost consistent with the aims of the implementation study, which may be to understand variation, central tendencies, or both.

In all three instances, the sampling strategy employed for the implementation study may vary based on the priority assigned to that study relative to the effectiveness trial. For instance, purposeful sampling for a Hybrid Type 1 design may give higher priority to variation and comparison to understand the parameters of implementation processes or context as a contribution to an understanding of effectiveness outcomes i.

In contrast, purposeful sampling for a Hybrid Type 3 design may give higher priority to similarity and depth to understand the core features of successful outcomes only.

Finally, multistage sampling strategies may be more consistent with innovations in experimental designs representing alternatives to the classic randomized controlled trial in community-based settings that have greater feasibility, acceptability, and external validity. Optimal designs represent one such alternative to the classic RCT and are addressed in detail by Duan and colleagues this issue.

Like purposeful sampling, optimal designs are intended to capture information-rich cases, usually identified as individuals most likely to benefit from the experimental intervention. The goal here is not to identify the typical or average patient, but patients who represent one end of the variation in an extreme case, intensity sampling, or criterion sampling strategy. Hence, a sampling strategy that begins by sampling for variation at the first stage and then sampling for homogeneity within a specific parameter of that variation i.

Another alternative to the classic RCT are the adaptive designs proposed by Brown and colleagues Brown et al, ; Brown et al. Adaptive designs are a sequence of trials that draw on the results of existing studies to determine the next stage of evaluation research.

They use cumulative knowledge of current treatment successes or failures to change qualities of the ongoing trial. An adaptive intervention modifies what an individual subject or community for a group-based trial receives in response to his or her preferences or initial responses to an intervention.

Consistent with multistage sampling in qualitative research, the design is somewhat iterative in nature in the sense that information gained from analysis of data collected at the first stage influences the nature of the data collected, and the way they are collected, at subsequent stages Denzen, Furthermore, many of these adaptive designs may benefit from a multistage purposeful sampling strategy at early phases of the clinical trial to identify the range of variation and core characteristics of study participants.

This information can then be used for the purposes of identifying optimal dose of treatment, limiting sample size, randomizing participants into different enrollment procedures, determining who should be eligible for random assignment as in the optimal design to maximize treatment adherence and minimize dropout, or identifying incentives and motives that may be used to encourage participation in the trial itself.

In this instance, the first stage of sampling may approximate the strategy of sampling politically important cases Patton, at the first stage, followed by other sampling strategies intended to maximize variations in stakeholder opinions or experience. On the basis of this review, the following recommendations are offered for the use of purposeful sampling in mixed method implementation research.

First, many mixed methods studies in health services research and implementation science do not clearly identify or provide a rationale for the sampling procedure for either quantitative or qualitative components of the study Wisdom et al. Second, use of a single stage strategy for purposeful sampling for qualitative portions of a mixed methods implementation study should adhere to the same general principles that govern all forms of sampling, qualitative or quantitative.

Kemper and colleagues identify seven such principles: Third, the field of implementation research is at a stage itself where qualitative methods are intended primarily to explore the barriers and facilitators of EBP implementation and to develop new conceptual models of implementation process and outcomes.

This is especially important in state implementation research, where fiscal necessities are driving policy reforms for which knowledge about EBP implementation barriers and facilitators are urgently needed. Thus a multistage strategy for purposeful sampling should begin first with a broader view with an emphasis on variation or dispersion and move to a narrow view with an emphasis on similarity or central tendencies.

Such a strategy is necessary for the task of finding the optimal balance between internal and external validity. 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. 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. 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. 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. In other words, it can be difficult to convince the reader that the judgement you used to select units to study was appropriate. After all, if different units had been selected, would the results and any generalisations have been the same? Purposive sampling Purposive sampling, also known as judgmental , selective or subjective sampling, is a type of non-probability sampling technique. Purposive sampling explained Types of purposive sampling Advantages and disadvantages of purposive sampling.

These categories are provided only for additional information for EPSY students. Patton has proposed the following cases of purposive sampling.

Purposive sampling is popular in qualitative research. Qualitative evaluation and research methods 2nd ed. Maximum Variation — Purposefully picking a wide range of variation on dimensions of interest…documents unique or diverse variations that have emerged in adapting to different conditions.

Identifies important common patterns that cut across variations. Homogeneous — Focuses, reduces variation, simplifies analysis, facilitates group interviewing. Typical Case — Illustrates or highlights what is typical, normal, average.

Main Topics

Privacy Policy

Purposive sampling (also known as judgment, selective or subjective sampling) is a sampling technique in which researcher relies on his or her own judgment when choosing members of population to participate in the study. Purposive sampling is a non-probability sampling method and it .

Privacy FAQs

Critical case sampling. 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.

About Our Ads

Bringing together the work of over eighty leading academics and researchers worldwide to produce the definitive reference and research tool for the social sc. Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be .

Cookie Info

Note: These categories are provided only for additional information for EPSY students. PURPOSIVE SAMPLING - Subjects are selected because of so. research, as people are constantly looked upon for knowl- The purposive sampling technique is a type of non-probability sampling that is most effective when one needs to study a certain cultural domain with knowledgeable experts within. Purposive sampling may also be used with both qualitative and quantitative re-search techniques. The.