Purposive sampling method pdf

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.

Refers to sequential structure; refers to simultaneous structure. 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.

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. Fourth, if we assume that probability sampling will be the preferred strategy for the quantitative components of most implementation research, the selection of a single or multistage purposeful sampling strategy should be based, in part, on how it relates to the probability sample, either for the purpose of answering the same question in which case a strategy emphasizing variation and dispersion is preferred or the for answering related questions in which case, a strategy emphasizing similarity and central tendencies is preferred.

Fifth, 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. Selecting a strategy that gives emphasis to one does not mean that it cannot be used for the other. Having said that, our analysis has assumed at least some degree of concordance between breadth of understanding associated with quantitative probability sampling and purposeful sampling strategies that emphasize variation on the one hand, and between the depth of understanding and purposeful sampling strategies that emphasize similarity on the other hand.

While there may be some merit to that assumption, depth of understanding requires both an understanding of variation and common elements. Finally, it should also be kept in mind that quantitative data can be generated from a purposeful sampling strategy and qualitative data can be generated from a probability sampling strategy. Each set of data is suited to a specific objective and each must adhere to a specific set of assumptions and requirements.

Nevertheless, the promise of mixed methods, like the promise of implementation science, lies in its ability to move beyond the confines of existing methodological approaches and develop innovative solutions to important and complex problems. For states engaged in EBP implementation, the need for these solutions is urgent.

Hoagwood, PI. National Center for Biotechnology Information , U. Adm Policy Ment Health. Author manuscript; available in PMC Sep 1. Lawrence A. Palinkas , Ph. Horwitz , Ph. Green , Ph. Wisdom , Ph. Sarah M. Carla A. Jennifer P. Author information Copyright and License information Disclaimer. Palinkas, Ph. Albert G. Copyright notice. The publisher's final edited version of this article is available at Adm Policy Ment Health. See other articles in PMC that cite the published article.

Abstract Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Keywords: mental health services, children and adolescents, mixed methods, qualitative methods implementation, state systems.

Principles of Purposeful Sampling Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources Patton, Types of purposeful sampling designs There exist numerous purposeful sampling designs. Table 1 Purposeful sampling strategies in implementation research. Strategy Objective Example Considerations Emphasis on similarity Criterion-i To identify and select all cases that meet some predetermined criterion of importance Selection of consultant trainers and program leaders at study sites to facilitators and barriers to EBP implementation Marshall et al.

Can be used to identify cases from standardized questionnaires for in- depth follow-up Patton, Criterion-e To identify and select all cases that exceed or fall outside a specified criterion Selection of directors of agencies that failed to move to the next stage of implementation within expected period of time.

Typical case To illustrate or highlight what is typical, normal or average A child undergoing treatment for trauma Hoagwood et al. Often used for selecting focus group participants Snowball To identify cases of interest from sampling people who know people that generally have similar characteristics who, in turn know people, also with similar characteristics.

Critical case sampling22 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? If that group is having problems, then we can be sure all the groups are having problems?. 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 sampling23 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 sampling24 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. 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.

Proper care will be taken in selecting the sample. At times, this method is less expensive and less time consuming. It is very useful when some of the units are very important and must be included. Purposive samples 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? The knowledge of population may not always be available. If this happens then the researcher cannot fully use the method. Hence, research is undertaken with the purpose to arrive at a state of generality.

Thus, sampling method is employed due to which the researcher is able to provide a valid analysis even in this vast universe. Among various types of sampling method, purposive sampling is also one of them. Some types of research design necessitate researchers taking a decision about the individual participants who would be most likely to contribute appropriate data, both in terms of relevance and depth.

For example, in life history research, some potential participants may be willing to be interviewed, but may not be able to provide sufficiently rich data. In sampling, a small, but carefully chosen sample can be used to represent the population.

Purposive sampling seems to be more appropriate when the universe happens to be small and a known characteristic of it is to be studied intensively. It starts with a purpose in mind and the sample is thus selected to include people of interest and exclude those who do not suit the purpose. Hence, despite having some limitations, purposive sampling is the only possible solution when some of the units are very important cannot be missed out.

Kothari, C. Myneni, S. What Is Qualitative Research? These include purposive samples, snowball samples, quota samples, and convenience samples. Rigor of qualitative research continues to be challenged even now in the 21st century—from the very idea that qualitative research alone is open to Transferability was enhanced by using purposive sampling method and providing a thick description and a robust data with a wide possible range of. Chapter 8 Sampling Fundamentals.

Qualitative evaluation and research methods. Purposive sampling. Intensity - Information-rich cases that. Sampling In Research. Sampling For Qualitative Research The sampling plan in qualitative research is appropriate when the selected participants and settings are sufficient to provide the information needed for a Sampling in Qualitative Research -JU Medicine In qualitative research, sampling method and sample size. Keywords Sampling, purposive sampling, random sampling, theoretical sampling, case study, stratified sampling, quota sampling, sample Sampling is an important component of qualitative research design that has been given less attention in methodological textbooks and journals than its.

For example, a list of homeowners would not be. What are some qualitative research methods? It focuses on understanding a research question by involving people's beliefs, experiences, and attitudes. This is based on the intention or the purpose of study. Sampling to Achieve Representativeness or Comparability B. Guidelines for purposive sampling are often provided in the context of selecting sites or data sources for pre-dominately qualitative, interpretivist research e. Purposive sampling, one of the most common sampling strategies, groups participants according to preselected criteria relevant to a particular research question for.

Should I use qualitative research? This study employed a combination of two purposive sampling strategies: critical case and stratified sampling. Open Access. Purposive sampling is useful in these instances because it provides a wide range of non-probability. Conversely, the criteria for sample size in qualitative research are not based on probability computations but represent expert opinion. In Qualitative research , non-numerical data is used to study elements in their natural settings.

Heterogeneous or maximum variation sampling relies on researcher's judgment to select participants with diverse characteristics. Miles et al. With probability sampling, a researcher can specify In addition, such lists may provide very biased samples for some research questions we ask.

The two most popular sampling techniques are purposeful and convenience sampling because they align the best across nearly all qualitative research designs.

In most research contexts, sample size specification and justification is required by funders, ethics committees, and other reviewers before a study is implemented [14, 15]. Stakeholder Sampling: Particularly useful in the con- text of evaluation research and policy analysis, this strategy involves identifying who the major stakehold They did their research at the objectives that qualitative researchers might have, the.

Learn more about it. 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. Qualitative research When the paramount objective is understanding When variables cannot be quantified When variables are best understood in their natural settings When studying intimate details of roles, processes, and groups.

In purposive sampling, items are selected according to some logic or strategy, carefully but not randomly Patton, Researchers are able to draw upon a wide range of qualitative research designs when their focus is on Details: Qualitative Research in Psychology, in press 2 Sampling is an important component of qualitative research.

Judgmental or purposive sampling: Judgemental or purposive samples are formed by the Exploratory research: Researchers use this sampling technique widely when conducting qualitative. In qualitative work, rigor is pursued differently, because what counts as a "complete" dataset, or analysis completeness, differs when you are working with smaller samples and Iteration means incorporating what you learn at one point in the research process into the remainder of the research. PDF Over the last decades, the quality of research has taken a prominent role within the broader academic discourse on qualitative methodology.

This expertise may be required during the exploratory phase of qualitative research, highlighting potential new areas of interest or opening.

In medical research, a qualitative sample might include people suffering from a particular condition. Qualitative Sampling Techniques - Statistics Solutions. Purposive sampling is when researchers thoroughly think through how they will establish a sample population, even if it is not statistically representative of the greater population at hand. Be it statistical sampling in terms of quantitative or , as it is referred to in this paper, purposive selection in te rms of qualitative research, the structure.

While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research, and because they are both nonprobability methods. It looks into a very specific cohort of people with very defined characteristics. In this sense, qualitative research differs slightly from scientific research in general. In such instances, different types of sampling technique may be required at each phase.

Qualitative researchers report their research to reflect the situatedness of their research in a number of ways. Purposeful Sampling: Also known as purposive and selective sampling, purposeful sampling is a sampling technique that qualitative researchers use to recruit participants.

A purposive sample is one that is selected based on characteristics of a population and the purpose of the study. Elements are selected until exact proportions of certain types of data is obtained or sufficient data in different categories is collected.

Qualitative research is important because it measures things that numbers might not be able to define, qualitative methods sometimes identify trends before This differs from the aggregate information that would be the outcome in quantitative research. Return articles published in. In purposive sampling, we sample with a purpose or one or more specific predefined groups in mind. Thus, the number of people in various categories of the sample is fixed.

Purposive sampling in a qualitative evidence synthesis: a worked example from a synthesis on parental perceptions Conclusions: Our approach to purposive sampling helped ensure that we included studies representing a wide geographic spread, rich data.

Purposive or Judgmental Sample i. Purposive Sampling. The use of quantitative methods Life histories Documentary research. An effective purposive sample must have clear criteria and rationale for inclusion.