what is qualitative research sampling

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling.
Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results.
It’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique for your qualitative research.

Qualitative research seeks to understand social phenomena from the perspective of those experiencing them. It involves collecting non-numerical data such as interviews, observations, and written documents to gain insights into human experiences, attitudes, and behaviors. While qualitative research can provide rich and nuanced insights, the accuracy and generalizability of findings depend on the quality of the sampling process. Sampling techniques are a critical component of qualitative research as it involves selecting a group of participants who can provide valuable insights into the research questions.

This article explores different types of sampling techniques in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques in qualitative research. and then compare and contrast these techniques to provide guidance on choosing the most appropriate method for a particular study. Additionally, you’ll find best practices for sampling and learn about ethical considerations researchers need to consider in selecting a sample. Overall, this article aims to help researchers conduct effective and high-quality sampling in qualitative research.

Factors to Consider When Choosing a Sampling Technique

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4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We’ll break down the definition of each technique, when to use it, and its advantages and disadvantages.

1. Purposive Sampling

Purposive sampling, or judgmental sampling, is a non-probability sampling technique in qualitative research that’s commonly used. In purposive sampling, researchers intentionally select participants with specific characteristics or unique experiences related to the research question. The goal is to identify and recruit participants who can provide rich and diverse data to enhance the research findings.

Purposive sampling is used when researchers seek to identify individuals or groups with particular knowledge, skills, or experiences relevant to the research question. For instance, in a study examining the experiences of cancer patients undergoing chemotherapy, purposive sampling may be used to recruit participants who have undergone chemotherapy in the past year. Researchers can better understand the phenomenon under investigation by selecting individuals with relevant backgrounds.

Purposive Sampling: Strengths and Weaknesses

Purposive sampling is a powerful tool for researchers seeking to select participants who can provide valuable insight into their research question. This method is advantageous when studying groups with technical characteristics or experiences where a random selection of participants may yield different results.

One of the main advantages of purposive sampling is the ability to improve the quality and accuracy of data collected by selecting participants most relevant to the research question. This approach also enables researchers to collect data from diverse participants with unique perspectives and experiences related to the research question.

However, researchers should also be aware of potential bias when using purposive sampling. The researcher’s judgment may influence the selection of participants, resulting in a biased sample that does not accurately represent the broader population. Another disadvantage is that purposive sampling may not be representative of the more general population, which limits the generalizability of the findings. To guarantee the accuracy and dependability of data obtained through purposive sampling, researchers must provide a clear and transparent justification of their selection criteria and sampling approach. This entails outlining the specific characteristics or experiences required for participants to be included in the study and explaining the rationale behind these criteria. This level of transparency not only helps readers to evaluate the validity of the findings, but also enhances the replicability of the research.

2. Convenience Sampling

When time and resources are limited, researchers may opt for convenience sampling as a quick and cost-effective way to recruit participants. In this non-probability sampling technique, participants are selected based on their accessibility and willingness to participate rather than their suitability for the research question. Qualitative research often uses this approach to generate various perspectives and experiences.

During the COVID-19 pandemic, convenience sampling was a valuable method for researchers to collect data quickly and efficiently from participants who were easily accessible and willing to participate. For example, in a study examining the experiences of university students during the pandemic, convenience sampling allowed researchers to recruit students who were available and willing to share their experiences quickly. While the pandemic may be over, convenience sampling during this time highlights its value in urgent situations where time and resources are limited.

Convenience Sampling: Strengths and Weaknesses

Convenience sampling offers several advantages to researchers, including its ease of implementation and cost-effectiveness. This technique allows researchers to quickly and efficiently recruit participants without spending time and resources identifying and contacting potential participants. Furthermore, convenience sampling can result in a diverse pool of participants, as individuals from various backgrounds and experiences may be more likely to participate.

While convenience sampling has the advantage of being efficient, researchers need to acknowledge its limitations. One of the primary drawbacks of convenience sampling is that it is susceptible to selection bias. Participants who are more easily accessible may not be representative of the broader population, which can limit the generalizability of the findings. Furthermore, convenience sampling may lead to issues with the reliability of the results, as it may not be possible to replicate the study using the same sample or a similar one.

To mitigate these limitations, researchers should carefully define the population of interest and ensure the sample is drawn from that population. For instance, if a study is investigating the experiences of individuals with a particular medical condition, researchers can recruit participants from specialized clinics or support groups for that condition. Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.

3. Snowball Sampling

Snowball sampling, also called referral sampling, is a unique approach researchers use to recruit participants in qualitative research. The technique involves identifying a few initial participants who meet the eligibility criteria and asking them to refer others they know who also fit the requirements. The sample size grows as referrals are added, creating a chain-like structure.

Snowball sampling enables researchers to reach out to individuals who may be hard to locate through traditional sampling methods, such as members of marginalized or hidden communities. For instance, in a study examining the experiences of undocumented immigrants, snowball sampling may be used to identify and recruit participants through referrals from other undocumented immigrants.

Snowball Sampling: Strengths and Weaknesses

Snowball sampling can produce in-depth and detailed data from participants with common characteristics or experiences. Since referrals are made within a network of individuals who share similarities, researchers can gain deep insights into a specific group’s attitudes, behaviors, and perspectives.

4. Theoretical Sampling

Theoretical sampling is a sophisticated and strategic technique that can help researchers develop more in-depth and nuanced theories from their data. Instead of selecting participants based on convenience or accessibility, researchers using theoretical sampling choose participants based on their potential to contribute to the emerging themes and concepts in the data. This approach allows researchers to refine their research question and theory based on the data they collect rather than forcing their data to fit a preconceived idea.

Theoretical sampling is used when researchers conduct grounded theory research and have developed an initial theory or conceptual framework. In a study examining cancer survivors’ experiences, for example, theoretical sampling may be used to identify and recruit participants who can provide new insights into the coping strategies of survivors.

Theoretical Sampling: Strengths and Weaknesses

One of the significant advantages of theoretical sampling is that it allows researchers to refine their research question and theory based on emerging data. This means the research can be highly targeted and focused, leading to a deeper understanding of the phenomenon being studied. Additionally, theoretical sampling can generate rich and in-depth data, as participants are selected based on their potential to provide new insights into the research question.

Participants are selected based on their perceived ability to offer new perspectives on the research question. This means specific perspectives or experiences may be overrepresented in the sample, leading to an incomplete understanding of the phenomenon being studied. Additionally, theoretical sampling can be time-consuming and resource-intensive, as researchers must continuously analyze the data and recruit new participants.

To mitigate the potential for bias, researchers can take several steps. One way to reduce bias is to use a diverse team of researchers to analyze the data and make participant selection decisions. Having multiple perspectives and backgrounds can help prevent researchers from unconsciously selecting participants who fit their preconceived notions or biases.

Another solution would be to use reflexive sampling. Reflexive sampling involves selecting participants aware of the research process and provides insights into how their biases and experiences may influence their perspectives. By including participants who are reflexive about their subjectivity, researchers can generate more nuanced and self-aware findings.

Choosing the proper sampling technique in qualitative research is one of the most critical decisions a researcher makes when conducting a study. The preferred method can significantly impact the accuracy and reliability of the research results.

For instance, purposive sampling provides a more targeted and specific sample, which helps to answer research questions related to that particular population or phenomenon. However, this approach may also introduce bias by limiting the diversity of the sample.

Conversely, convenience sampling may offer a more diverse sample regarding demographics and backgrounds but may also introduce bias by selecting more willing or available participants.

Snowball sampling may help study hard-to-reach populations, but it can also limit the sample’s diversity as participants are selected based on their connections to existing participants.

Theoretical sampling may offer an opportunity to refine the research question and theory based on emerging data, but it can also be time-consuming and resource-intensive.

Additionally, the choice of sampling technique can impact the generalizability of the research findings. Therefore, it’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique. By doing so, researchers can select the most appropriate method for their research question and ensure the validity and reliability of their findings.

Tips for Selecting Participants

When selecting participants for a qualitative research study, it is crucial to consider the research question and the purpose of the study. In addition, researchers should identify the specific characteristics or criteria they seek in their sample and select participants accordingly.

One helpful tip for selecting participants is to use a pre-screening process to ensure potential participants meet the criteria for inclusion in the study. Another technique is using multiple recruitment methods to ensure the sample is diverse and representative of the studied population.

Ensuring Diversity in Samples

Diversity in the sample is important to ensure the study’s findings apply to a wide range of individuals and situations. One way to ensure diversity is to use stratified sampling, which involves dividing the population into subgroups and selecting participants from each subset. This helps establish that the sample is representative of the larger population.

Maintaining Ethical Considerations

When selecting participants for a qualitative research study, it is essential to ensure ethical considerations are taken into account. Researchers must ensure participants are fully informed about the study and provide their voluntary consent to participate. They must also ensure participants understand their rights and that their confidentiality and privacy will be protected.

A qualitative research study’s success hinges on its sampling technique’s effectiveness. The choice of sampling technique must be guided by the research question, the population being studied, and the purpose of the study. Whether purposive, convenience, snowball, or theoretical sampling, the primary goal is to ensure the validity and reliability of the study’s findings.

By thoughtfully weighing the pros and cons of each sampling technique in qualitative research, researchers can make informed decisions that lead to more reliable and accurate results. In conclusion, carefully selecting a sampling technique is integral to the success of a qualitative research study, and a thorough understanding of the available options can make all the difference in achieving high-quality research outcomes.

If you’re interested in improving your research and sampling methods, Sago offers a variety of solutions. Our qualitative research platforms, such as QualBoard and QualMeeting, can assist you in conducting research studies with precision and efficiency. Our robust global panel and recruitment options help you reach the right people. We also offer qualitative and quantitative research services to meet your research needs. Contact us today to learn more about how we can help improve your research outcomes.

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Chapter 5. Sampling

Introduction.

Most Americans will experience unemployment at some point in their lives. Sarah Damaske ( 2021 ) was interested in learning about how men and women experience unemployment differently. To answer this question, she interviewed unemployed people. After conducting a “pilot study” with twenty interviewees, she realized she was also interested in finding out how working-class and middle-class persons experienced unemployment differently. She found one hundred persons through local unemployment offices. She purposefully selected a roughly equal number of men and women and working-class and middle-class persons for the study. This would allow her to make the kinds of comparisons she was interested in. She further refined her selection of persons to interview:

I decided that I needed to be able to focus my attention on gender and class; therefore, I interviewed only people born between 1962 and 1987 (ages 28–52, the prime working and child-rearing years), those who worked full-time before their job loss, those who experienced an involuntary job loss during the past year, and those who did not lose a job for cause (e.g., were not fired because of their behavior at work). ( 244 )

The people she ultimately interviewed compose her sample. They represent (“sample”) the larger population of the involuntarily unemployed. This “theoretically informed stratified sampling design” allowed Damaske “to achieve relatively equal distribution of participation across gender and class,” but it came with some limitations. For one, the unemployment centers were located in primarily White areas of the country, so there were very few persons of color interviewed. Qualitative researchers must make these kinds of decisions all the time—who to include and who not to include. There is never an absolutely correct decision, as the choice is linked to the particular research question posed by the particular researcher, although some sampling choices are more compelling than others. In this case, Damaske made the choice to foreground both gender and class rather than compare all middle-class men and women or women of color from different class positions or just talk to White men. She leaves the door open for other researchers to sample differently. Because science is a collective enterprise, it is most likely someone will be inspired to conduct a similar study as Damaske’s but with an entirely different sample.

This chapter is all about sampling. After you have developed a research question and have a general idea of how you will collect data (observations or interviews), how do you go about actually finding people and sites to study? Although there is no “correct number” of people to interview, the sample should follow the research question and research design. You might remember studying sampling in a quantitative research course. Sampling is important here too, but it works a bit differently. Unlike quantitative research, qualitative research involves nonprobability sampling. This chapter explains why this is so and what qualities instead make a good sample for qualitative research.

Quick Terms Refresher

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.
Sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
Sample size is how many individuals (or units) are included in your sample.

The “Who” of Your Research Study

After you have turned your general research interest into an actual research question and identified an approach you want to take to answer that question, you will need to specify the people you will be interviewing or observing. In most qualitative research, the objects of your study will indeed be people. In some cases, however, your objects might be content left by people (e.g., diaries, yearbooks, photographs) or documents (official or unofficial) or even institutions (e.g., schools, medical centers) and locations (e.g., nation-states, cities). Chances are, whatever “people, places, or things” are the objects of your study, you will not really be able to talk to, observe, or follow every single individual/object of the entire population of interest. You will need to create a sample of the population . Sampling in qualitative research has different purposes and goals than sampling in quantitative research. Sampling in both allows you to say something of interest about a population without having to include the entire population in your sample.

We begin this chapter with the case of a population of interest composed of actual people. After we have a better understanding of populations and samples that involve real people, we’ll discuss sampling in other types of qualitative research, such as archival research, content analysis, and case studies. We’ll then move to a larger discussion about the difference between sampling in qualitative research generally versus quantitative research, then we’ll move on to the idea of “theoretical” generalizability, and finally, we’ll conclude with some practical tips on the correct “number” to include in one’s sample.

Sampling People

To help think through samples, let’s imagine we want to know more about “vaccine hesitancy.” We’ve all lived through 2020 and 2021, and we know that a sizable number of people in the United States (and elsewhere) were slow to accept vaccines, even when these were freely available. By some accounts, about one-third of Americans initially refused vaccination. Why is this so? Well, as I write this in the summer of 2021, we know that some people actively refused the vaccination, thinking it was harmful or part of a government plot. Others were simply lazy or dismissed the necessity. And still others were worried about harmful side effects. The general population of interest here (all adult Americans who were not vaccinated by August 2021) may be as many as eighty million people. We clearly cannot talk to all of them. So we will have to narrow the number to something manageable. How can we do this?

First, we have to think about our actual research question and the form of research we are conducting. I am going to begin with a quantitative research question. Quantitative research questions tend to be simpler to visualize, at least when we are first starting out doing social science research. So let us say we want to know what percentage of each kind of resistance is out there and how race or class or gender affects vaccine hesitancy. Again, we don’t have the ability to talk to everyone. But harnessing what we know about normal probability distributions (see quantitative methods for more on this), we can find this out through a sample that represents the general population. We can’t really address these particular questions if we only talk to White women who go to college with us. And if you are really trying to generalize the specific findings of your sample to the larger population, you will have to employ probability sampling , a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. Why randomly? If truly random, all the members have an equal opportunity to be a part of the sample, and thus we avoid the problem of having only our friends and neighbors (who may be very different from other people in the population) in the study. Mathematically, there is going to be a certain number that will be large enough to allow us to generalize our particular findings from our sample population to the population at large. It might surprise you how small that number can be. Election polls of no more than one thousand people are routinely used to predict actual election outcomes of millions of people. Below that number, however, you will not be able to make generalizations. Talking to five people at random is simply not enough people to predict a presidential election.

In order to answer quantitative research questions of causality, one must employ probability sampling. Quantitative researchers try to generalize their findings to a larger population. Samples are designed with that in mind. Qualitative researchers ask very different questions, though. Qualitative research questions are not about “how many” of a certain group do X (in this case, what percentage of the unvaccinated hesitate for concern about safety rather than reject vaccination on political grounds). Qualitative research employs nonprobability sampling . By definition, not everyone has an equal opportunity to be included in the sample. The researcher might select White women they go to college with to provide insight into racial and gender dynamics at play. Whatever is found by doing so will not be generalizable to everyone who has not been vaccinated, or even all White women who have not been vaccinated, or even all White women who have not been vaccinated who are in this particular college. That is not the point of qualitative research at all. This is a really important distinction, so I will repeat in bold: Qualitative researchers are not trying to statistically generalize specific findings to a larger population . They have not failed when their sample cannot be generalized, as that is not the point at all.

In the previous paragraph, I said it would be perfectly acceptable for a qualitative researcher to interview five White women with whom she goes to college about their vaccine hesitancy “to provide insight into racial and gender dynamics at play.” The key word here is “insight.” Rather than use a sample as a stand-in for the general population, as quantitative researchers do, the qualitative researcher uses the sample to gain insight into a process or phenomenon. The qualitative researcher is not going to be content with simply asking each of the women to state her reason for not being vaccinated and then draw conclusions that, because one in five of these women were concerned about their health, one in five of all people were also concerned about their health. That would be, frankly, a very poor study indeed. Rather, the qualitative researcher might sit down with each of the women and conduct a lengthy interview about what the vaccine means to her, why she is hesitant, how she manages her hesitancy (how she explains it to her friends), what she thinks about others who are unvaccinated, what she thinks of those who have been vaccinated, and what she knows or thinks she knows about COVID-19. The researcher might include specific interview questions about the college context, about their status as White women, about the political beliefs they hold about racism in the US, and about how their own political affiliations may or may not provide narrative scripts about “protective whiteness.” There are many interesting things to ask and learn about and many things to discover. Where a quantitative researcher begins with clear parameters to set their population and guide their sample selection process, the qualitative researcher is discovering new parameters, making it impossible to engage in probability sampling.

Looking at it this way, sampling for qualitative researchers needs to be more strategic. More theoretically informed. What persons can be interviewed or observed that would provide maximum insight into what is still unknown? In other words, qualitative researchers think through what cases they could learn the most from, and those are the cases selected to study: “What would be ‘bias’ in statistical sampling, and therefore a weakness, becomes intended focus in qualitative sampling, and therefore a strength. The logic and power of purposeful sampling like in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the inquiry, thus the term purposeful sampling” ( Patton 2002:230 ; emphases in the original).

Before selecting your sample, though, it is important to clearly identify the general population of interest. You need to know this before you can determine the sample. In our example case, it is “adult Americans who have not yet been vaccinated.” Depending on the specific qualitative research question, however, it might be “adult Americans who have been vaccinated for political reasons” or even “college students who have not been vaccinated.” What insights are you seeking? Do you want to know how politics is affecting vaccination? Or do you want to understand how people manage being an outlier in a particular setting (unvaccinated where vaccinations are heavily encouraged if not required)? More clearly stated, your population should align with your research question . Think back to the opening story about Damaske’s work studying the unemployed. She drew her sample narrowly to address the particular questions she was interested in pursuing. Knowing your questions or, at a minimum, why you are interested in the topic will allow you to draw the best sample possible to achieve insight.

Once you have your population in mind, how do you go about getting people to agree to be in your sample? In qualitative research, it is permissible to find people by convenience. Just ask for people who fit your sample criteria and see who shows up. Or reach out to friends and colleagues and see if they know anyone that fits. Don’t let the name convenience sampling mislead you; this is not exactly “easy,” and it is certainly a valid form of sampling in qualitative research. The more unknowns you have about what you will find, the more convenience sampling makes sense. If you don’t know how race or class or political affiliation might matter, and your population is unvaccinated college students, you can construct a sample of college students by placing an advertisement in the student paper or posting a flyer on a notice board. Whoever answers is your sample. That is what is meant by a convenience sample. A common variation of convenience sampling is snowball sampling . This is particularly useful if your target population is hard to find. Let’s say you posted a flyer about your study and only two college students responded. You could then ask those two students for referrals. They tell their friends, and those friends tell other friends, and, like a snowball, your sample gets bigger and bigger.

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

My early experience with qualitative research was rather unique. At that time, I needed to do a project that required me to interview first-generation college students, and my friends, with whom I had been sharing a dorm for two years, just perfectly fell into the sample category. Thus, I just asked them and easily “gained my access” to the research subject; I know them, we are friends, and I am part of them. I am an insider. I also thought, “Well, since I am part of the group, I can easily understand their language and norms, I can capture their honesty, read their nonverbal cues well, will get more information, as they will be more opened to me because they trust me.” All in all, easy access with rich information. But, gosh, I did not realize that my status as an insider came with a price! When structuring the interview questions, I began to realize that rather than focusing on the unique experiences of my friends, I mostly based the questions on my own experiences, assuming we have similar if not the same experiences. I began to struggle with my objectivity and even questioned my role; am I doing this as part of the group or as a researcher? I came to know later that my status as an insider or my “positionality” may impact my research. It not only shapes the process of data collection but might heavily influence my interpretation of the data. I came to realize that although my inside status came with a lot of benefits (especially for access), it could also bring some drawbacks.

—Dede Setiono, PhD student focusing on international development and environmental policy, Oregon State University

The more you know about what you might find, the more strategic you can be. If you wanted to compare how politically conservative and politically liberal college students explained their vaccine hesitancy, for example, you might construct a sample purposively, finding an equal number of both types of students so that you can make those comparisons in your analysis. This is what Damaske ( 2021 ) did. You could still use convenience or snowball sampling as a way of recruitment. Post a flyer at the conservative student club and then ask for referrals from the one student that agrees to be interviewed. As with convenience sampling, there are variations of purposive sampling as well as other names used (e.g., judgment, quota, stratified, criterion, theoretical). Try not to get bogged down in the nomenclature; instead, focus on identifying the general population that matches your research question and then using a sampling method that is most likely to provide insight, given the types of questions you have.

There are all kinds of ways of being strategic with sampling in qualitative research. Here are a few of my favorite techniques for maximizing insight:

Consider using “extreme” or “deviant” cases. Maybe your college houses a prominent anti-vaxxer who has written about and demonstrated against the college’s policy on vaccines. You could learn a lot from that single case (depending on your research question, of course).
Consider “intensity”: people and cases and circumstances where your questions are more likely to feature prominently (but not extremely or deviantly). For example, you could compare those who volunteer at local Republican and Democratic election headquarters during an election season in a study on why party matters. Those who volunteer are more likely to have something to say than those who are more apathetic.
Maximize variation, as with the case of “politically liberal” versus “politically conservative,” or include an array of social locations (young vs. old; Northwest vs. Southeast region). This kind of heterogeneity sampling can capture and describe the central themes that cut across the variations: any common patterns that emerge, even in this wildly mismatched sample, are probably important to note!
Rather than maximize the variation, you could select a small homogenous sample to describe some particular subgroup in depth. Focus groups are often the best form of data collection for homogeneity sampling.
Think about which cases are “critical” or politically important—ones that “if it happens here, it would happen anywhere” or a case that is politically sensitive, as with the single “blue” (Democratic) county in a “red” (Republican) state. In both, you are choosing a site that would yield the most information and have the greatest impact on the development of knowledge.
On the other hand, sometimes you want to select the “typical”—the typical college student, for example. You are trying to not generalize from the typical but illustrate aspects that may be typical of this case or group. When selecting for typicality, be clear with yourself about why the typical matches your research questions (and who might be excluded or marginalized in doing so).
Finally, it is often a good idea to look for disconfirming cases : if you are at the stage where you have a hypothesis (of sorts), you might select those who do not fit your hypothesis—you will surely learn something important there. They may be “exceptions that prove the rule” or exceptions that force you to alter your findings in order to make sense of these additional cases.

In addition to all these sampling variations, there is the theoretical approach taken by grounded theorists in which the researcher samples comparative people (or events) on the basis of their potential to represent important theoretical constructs. The sample, one can say, is by definition representative of the phenomenon of interest. It accompanies the constant comparative method of analysis. In the words of the funders of Grounded Theory , “Theoretical sampling is sampling on the basis of the emerging concepts, with the aim being to explore the dimensional range or varied conditions along which the properties of the concepts vary” ( Strauss and Corbin 1998:73 ).

When Your Population is Not Composed of People

I think it is easiest for most people to think of populations and samples in terms of people, but sometimes our units of analysis are not actually people. They could be places or institutions. Even so, you might still want to talk to people or observe the actions of people to understand those places or institutions. Or not! In the case of content analyses (see chapter 17), you won’t even have people involved at all but rather documents or films or photographs or news clippings. Everything we have covered about sampling applies to other units of analysis too. Let’s work through some examples.

Case Studies

When constructing a case study, it is helpful to think of your cases as sample populations in the same way that we considered people above. If, for example, you are comparing campus climates for diversity, your overall population may be “four-year college campuses in the US,” and from there you might decide to study three college campuses as your sample. Which three? Will you use purposeful sampling (perhaps [1] selecting three colleges in Oregon that are different sizes or [2] selecting three colleges across the US located in different political cultures or [3] varying the three colleges by racial makeup of the student body)? Or will you select three colleges at random, out of convenience? There are justifiable reasons for all approaches.

As with people, there are different ways of maximizing insight in your sample selection. Think about the following rationales: typical, diverse, extreme, deviant, influential, crucial, or even embodying a particular “pathway” ( Gerring 2008 ). When choosing a case or particular research site, Rubin ( 2021 ) suggests you bear in mind, first, what you are leaving out by selecting this particular case/site; second, what you might be overemphasizing by studying this case/site and not another; and, finally, whether you truly need to worry about either of those things—“that is, what are the sources of bias and how bad are they for what you are trying to do?” ( 89 ).

Once you have selected your cases, you may still want to include interviews with specific people or observations at particular sites within those cases. Then you go through possible sampling approaches all over again to determine which people will be contacted.

Content: Documents, Narrative Accounts, And So On

Although not often discussed as sampling, your selection of documents and other units to use in various content/historical analyses is subject to similar considerations. When you are asking quantitative-type questions (percentages and proportionalities of a general population), you will want to follow probabilistic sampling. For example, I created a random sample of accounts posted on the website studentloanjustice.org to delineate the types of problems people were having with student debt ( Hurst 2007 ). Even though my data was qualitative (narratives of student debt), I was actually asking a quantitative-type research question, so it was important that my sample was representative of the larger population (debtors who posted on the website). On the other hand, when you are asking qualitative-type questions, the selection process should be very different. In that case, use nonprobabilistic techniques, either convenience (where you are really new to this data and do not have the ability to set comparative criteria or even know what a deviant case would be) or some variant of purposive sampling. Let’s say you were interested in the visual representation of women in media published in the 1950s. You could select a national magazine like Time for a “typical” representation (and for its convenience, as all issues are freely available on the web and easy to search). Or you could compare one magazine known for its feminist content versus one antifeminist. The point is, sample selection is important even when you are not interviewing or observing people.

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

We have already discussed some of the differences in the goals of quantitative and qualitative sampling above, but it is worth further discussion. The quantitative researcher seeks a sample that is representative of the population of interest so that they may properly generalize the results (e.g., if 80 percent of first-gen students in the sample were concerned with costs of college, then we can say there is a strong likelihood that 80 percent of first-gen students nationally are concerned with costs of college). The qualitative researcher does not seek to generalize in this way . They may want a representative sample because they are interested in typical responses or behaviors of the population of interest, but they may very well not want a representative sample at all. They might want an “extreme” or deviant case to highlight what could go wrong with a particular situation, or maybe they want to examine just one case as a way of understanding what elements might be of interest in further research. When thinking of your sample, you will have to know why you are selecting the units, and this relates back to your research question or sets of questions. It has nothing to do with having a representative sample to generalize results. You may be tempted—or it may be suggested to you by a quantitatively minded member of your committee—to create as large and representative a sample as you possibly can to earn credibility from quantitative researchers. Ignore this temptation or suggestion. The only thing you should be considering is what sample will best bring insight into the questions guiding your research. This has implications for the number of people (or units) in your study as well, which is the topic of the next section.

What is the Correct “Number” to Sample?

Because we are not trying to create a generalizable representative sample, the guidelines for the “number” of people to interview or news stories to code are also a bit more nebulous. There are some brilliant insightful studies out there with an n of 1 (meaning one person or one account used as the entire set of data). This is particularly so in the case of autoethnography, a variation of ethnographic research that uses the researcher’s own subject position and experiences as the basis of data collection and analysis. But it is true for all forms of qualitative research. There are no hard-and-fast rules here. The number to include is what is relevant and insightful to your particular study.

That said, humans do not thrive well under such ambiguity, and there are a few helpful suggestions that can be made. First, many qualitative researchers talk about “saturation” as the end point for data collection. You stop adding participants when you are no longer getting any new information (or so very little that the cost of adding another interview subject or spending another day in the field exceeds any likely benefits to the research). The term saturation was first used here by Glaser and Strauss ( 1967 ), the founders of Grounded Theory. Here is their explanation: “The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation . Saturation means that no additional data are being found whereby the sociologist can develop properties of the category. As he [or she] sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. [They go] out of [their] way to look for groups that stretch diversity of data as far as possible, just to make certain that saturation is based on the widest possible range of data on the category” ( 61 ).

It makes sense that the term was developed by grounded theorists, since this approach is rather more open-ended than other approaches used by qualitative researchers. With so much left open, having a guideline of “stop collecting data when you don’t find anything new” is reasonable. However, saturation can’t help much when first setting out your sample. How do you know how many people to contact to interview? What number will you put down in your institutional review board (IRB) protocol (see chapter 8)? You may guess how many people or units it will take to reach saturation, but there really is no way to know in advance. The best you can do is think about your population and your questions and look at what others have done with similar populations and questions.

Here are some suggestions to use as a starting point: For phenomenological studies, try to interview at least ten people for each major category or group of people . If you are comparing male-identified, female-identified, and gender-neutral college students in a study on gender regimes in social clubs, that means you might want to design a sample of thirty students, ten from each group. This is the minimum suggested number. Damaske’s ( 2021 ) sample of one hundred allows room for up to twenty-five participants in each of four “buckets” (e.g., working-class*female, working-class*male, middle-class*female, middle-class*male). If there is more than one comparative group (e.g., you are comparing students attending three different colleges, and you are comparing White and Black students in each), you can sometimes reduce the number for each group in your sample to five for, in this case, thirty total students. But that is really a bare minimum you will want to go. A lot of people will not trust you with only “five” cases in a bucket. Lareau ( 2021:24 ) advises a minimum of seven or nine for each bucket (or “cell,” in her words). The point is to think about what your analyses might look like and how comfortable you will be with a certain number of persons fitting each category.

Because qualitative research takes so much time and effort, it is rare for a beginning researcher to include more than thirty to fifty people or units in the study. You may not be able to conduct all the comparisons you might want simply because you cannot manage a larger sample. In that case, the limits of who you can reach or what you can include may influence you to rethink an original overcomplicated research design. Rather than include students from every racial group on a campus, for example, you might want to sample strategically, thinking about the most contrast (insightful), possibly excluding majority-race (White) students entirely, and simply using previous literature to fill in gaps in our understanding. For example, one of my former students was interested in discovering how race and class worked at a predominantly White institution (PWI). Due to time constraints, she simplified her study from an original sample frame of middle-class and working-class domestic Black and international African students (four buckets) to a sample frame of domestic Black and international African students (two buckets), allowing the complexities of class to come through individual accounts rather than from part of the sample frame. She wisely decided not to include White students in the sample, as her focus was on how minoritized students navigated the PWI. She was able to successfully complete her project and develop insights from the data with fewer than twenty interviewees. [1]

But what if you had unlimited time and resources? Would it always be better to interview more people or include more accounts, documents, and units of analysis? No! Your sample size should reflect your research question and the goals you have set yourself. Larger numbers can sometimes work against your goals. If, for example, you want to help bring out individual stories of success against the odds, adding more people to the analysis can end up drowning out those individual stories. Sometimes, the perfect size really is one (or three, or five). It really depends on what you are trying to discover and achieve in your study. Furthermore, studies of one hundred or more (people, documents, accounts, etc.) can sometimes be mistaken for quantitative research. Inevitably, the large sample size will push the researcher into simplifying the data numerically. And readers will begin to expect generalizability from such a large sample.

To summarize, “There are no rules for sample size in qualitative inquiry. Sample size depends on what you want to know, the purpose of the inquiry, what’s at stake, what will be useful, what will have credibility, and what can be done with available time and resources” ( Patton 2002:244 ).

How did you find/construct a sample?

Since qualitative researchers work with comparatively small sample sizes, getting your sample right is rather important. Yet it is also difficult to accomplish. For instance, a key question you need to ask yourself is whether you want a homogeneous or heterogeneous sample. In other words, do you want to include people in your study who are by and large the same, or do you want to have diversity in your sample?

For many years, I have studied the experiences of students who were the first in their families to attend university. There is a rather large number of sampling decisions I need to consider before starting the study. (1) Should I only talk to first-in-family students, or should I have a comparison group of students who are not first-in-family? (2) Do I need to strive for a gender distribution that matches undergraduate enrollment patterns? (3) Should I include participants that reflect diversity in gender identity and sexuality? (4) How about racial diversity? First-in-family status is strongly related to some ethnic or racial identity. (5) And how about areas of study?

As you can see, if I wanted to accommodate all these differences and get enough study participants in each category, I would quickly end up with a sample size of hundreds, which is not feasible in most qualitative research. In the end, for me, the most important decision was to maximize the voices of first-in-family students, which meant that I only included them in my sample. As for the other categories, I figured it was going to be hard enough to find first-in-family students, so I started recruiting with an open mind and an understanding that I may have to accept a lack of gender, sexuality, or racial diversity and then not be able to say anything about these issues. But I would definitely be able to speak about the experiences of being first-in-family.

—Wolfgang Lehmann, author of “Habitus Transformation and Hidden Injuries”

Examples of “Sample” Sections in Journal Articles

Think about some of the studies you have read in college, especially those with rich stories and accounts about people’s lives. Do you know how the people were selected to be the focus of those stories? If the account was published by an academic press (e.g., University of California Press or Princeton University Press) or in an academic journal, chances are that the author included a description of their sample selection. You can usually find these in a methodological appendix (book) or a section on “research methods” (article).

Here are two examples from recent books and one example from a recent article:

Example 1 . In It’s Not like I’m Poor: How Working Families Make Ends Meet in a Post-welfare World , the research team employed a mixed methods approach to understand how parents use the earned income tax credit, a refundable tax credit designed to provide relief for low- to moderate-income working people ( Halpern-Meekin et al. 2015 ). At the end of their book, their first appendix is “Introduction to Boston and the Research Project.” After describing the context of the study, they include the following description of their sample selection:

In June 2007, we drew 120 names at random from the roughly 332 surveys we gathered between February and April. Within each racial and ethnic group, we aimed for one-third married couples with children and two-thirds unmarried parents. We sent each of these families a letter informing them of the opportunity to participate in the in-depth portion of our study and then began calling the home and cell phone numbers they provided us on the surveys and knocking on the doors of the addresses they provided.…In the end, we interviewed 115 of the 120 families originally selected for the in-depth interview sample (the remaining five families declined to participate). ( 22 )

Was their sample selection based on convenience or purpose? Why do you think it was important for them to tell you that five families declined to be interviewed? There is actually a trick here, as the names were pulled randomly from a survey whose sample design was probabilistic. Why is this important to know? What can we say about the representativeness or the uniqueness of whatever findings are reported here?

Example 2 . In When Diversity Drops , Park ( 2013 ) examines the impact of decreasing campus diversity on the lives of college students. She does this through a case study of one student club, the InterVarsity Christian Fellowship (IVCF), at one university (“California University,” a pseudonym). Here is her description:

I supplemented participant observation with individual in-depth interviews with sixty IVCF associates, including thirty-four current students, eight former and current staff members, eleven alumni, and seven regional or national staff members. The racial/ethnic breakdown was twenty-five Asian Americans (41.6 percent), one Armenian (1.6 percent), twelve people who were black (20.0 percent), eight Latino/as (13.3 percent), three South Asian Americans (5.0 percent), and eleven people who were white (18.3 percent). Twenty-nine were men, and thirty-one were women. Looking back, I note that the higher number of Asian Americans reflected both the group’s racial/ethnic composition and my relative ease about approaching them for interviews. ( 156 )

How can you tell this is a convenience sample? What else do you note about the sample selection from this description?

Example 3. The last example is taken from an article published in the journal Research in Higher Education . Published articles tend to be more formal than books, at least when it comes to the presentation of qualitative research. In this article, Lawson ( 2021 ) is seeking to understand why female-identified college students drop out of majors that are dominated by male-identified students (e.g., engineering, computer science, music theory). Here is the entire relevant section of the article:

Method Participants Data were collected as part of a larger study designed to better understand the daily experiences of women in MDMs [male-dominated majors].…Participants included 120 students from a midsize, Midwestern University. This sample included 40 women and 40 men from MDMs—defined as any major where at least 2/3 of students are men at both the university and nationally—and 40 women from GNMs—defined as any may where 40–60% of students are women at both the university and nationally.… Procedure A multi-faceted approach was used to recruit participants; participants were sent targeted emails (obtained based on participants’ reported gender and major listings), campus-wide emails sent through the University’s Communication Center, flyers, and in-class presentations. Recruitment materials stated that the research focused on the daily experiences of college students, including classroom experiences, stressors, positive experiences, departmental contexts, and career aspirations. Interested participants were directed to email the study coordinator to verify eligibility (at least 18 years old, man/woman in MDM or woman in GNM, access to a smartphone). Sixteen interested individuals were not eligible for the study due to the gender/major combination. ( 482ff .)

What method of sample selection was used by Lawson? Why is it important to define “MDM” at the outset? How does this definition relate to sampling? Why were interested participants directed to the study coordinator to verify eligibility?

Final Words

I have found that students often find it difficult to be specific enough when defining and choosing their sample. It might help to think about your sample design and sample recruitment like a cookbook. You want all the details there so that someone else can pick up your study and conduct it as you intended. That person could be yourself, but this analogy might work better if you have someone else in mind. When I am writing down recipes, I often think of my sister and try to convey the details she would need to duplicate the dish. We share a grandmother whose recipes are full of handwritten notes in the margins, in spidery ink, that tell us what bowl to use when or where things could go wrong. Describe your sample clearly, convey the steps required accurately, and then add any other details that will help keep you on track and remind you why you have chosen to limit possible interviewees to those of a certain age or class or location. Imagine actually going out and getting your sample (making your dish). Do you have all the necessary details to get started?

Table 5.1. Sampling Type and Strategies

Type	Used primarily in...	Strategies
Probabilistic	Quantitative research
		Simple random	Each member of the population has an equal chance at being selected
		Stratified	The sample is split into strata; members of each strata are selected in proportion to the population at large
Non-probabilistic	Qualitative research
		Convenience	Simply includes the individuals who happen to be most accessible to the researcher
		Snowball	Used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people
		Purposive	Involves the researcher using their expertise to select a sample that is most useful to the purposes of the research; An effective purposive sample must have clear criteria and rationale for inclusion (e.g., )
		Quota	Set quotas to ensure that the sample you get represents certain characteristics in proportion to their prevalence in the population

Sampling in Qualitative Research

In gerontology the most recognized and elaborate discourse about sampling is generally thought to be in quantitative research associated with survey research and medical research. But sampling has long been a central concern in the social and humanistic inquiry, albeit in a different guise suited to the different goals. There is a need for more explicit discussion of qualitative sampling issues. This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It then describes common questions about sampling in qualitative research. In conclusion it proposes the concept of qualitative clarity as a set of principles (analogous to statistical power) to guide assessments of qualitative sampling in a particular study or proposal.

Questions of what is an appropriate research sample are common across the many disciplines of gerontology, albeit in different guises. The basic questions concern what to observe and how many observations or cases are needed to assure that the findings will contribute useful information. Throughout the history of gerontology, the most recognized and elaborate discourse about sampling has been associated with quantitative research, including survey and medical research. But concerns about sampling have long been central to social and humanistic inquiry (e.g., Mead 1953 ). The authors argue such concerns remained less recognized by quantitative researchers because of differing focus, concepts, and language. Recently, an explicit discussion about concepts and procedures for qualitative sampling issues has emerged. Despite the growing numbers of textbooks on qualitative research, most offer only a brief discussion of sampling issues, and far less is presented in a critical fashion ( Gubrium and Sankar 1994 ; Werner and Schoepfle 1987 ; Spradley 1979 , 1980 ; Strauss and Corbin 1990 ; Trotter 1991 ; but cf. Denzin and Lincoln 1994 ; DePoy and Gitlin 1993 ; Miles and Huberman 1994 ; Pelto and Pelto 1978 ).

The goal of this article is to extend and further refine the explicit discussion of sampling issues and techniques for qualitative research in gerontology. Throughout the article, the discussion draws on a variety of examples in aging, disability, ethnicity as well as more general anthropology.

The significance of the need to understand qualitative sampling and its uses is increasing for several reasons. First, emerging from the normal march of scientific developments that builds on prior research, there is a growing consensus about the necessity of complementing standardized data with insights about the contexts and insiders' perspectives on aging and the elderly. These data are best provided by qualitative approaches. In gerontology, the historical focus on aging pathology obscured our view of the role of culture and personal meanings in shaping how individuals at every level of cognitive and physical functioning personally experience and shape their lives. The individual embodying a “case” or “symptoms” continues to make sense of, manage, and represent experiences to him- or herself and to others. A second significance to enhancing our appreciation of qualitative approaches to sampling is related to the societal contexts of the scientific enterprise. Shifts in public culture now endorse the inclusion of the experiences and beliefs of diverse and minority segments of the population. A reflection of these societal changes is the new institutional climate for federally funded research, which mandates the inclusion and analysis of data on minorities. Qualitative approaches are valuable because they are suited to assessing the validity of standardized measures and analytic techniques for use with racial and ethnic subpopulations. They also permit us to explore diversities in cultural and personal beliefs, values, ideals, and experiences.

This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It describes the scientific implications of the cultural embeddedness of sampling issues as a pervasive feature in wider society. It then describes common questions about sampling in qualitative research. It concludes by proposing an analog to statistical power, qualitative clarity , as a set of principles to guide assessments of the sampling techniques in a study report or research proposal. The term clarity was chosen to express the goal of making explicit the details of how the sample was assembled, the theoretical assumptions, and the practical constraints that influenced the sampling process. Qualitative clarity should include at least two components, theoretical grounding and sensitivity to context. The concept focuses on evaluating the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation. These can be evaluated even if the factors to be measured cannot be specified.

A wide range of opinions about sampling exists in the qualitative research community. The authors take issue with qualitative researchers who dismiss these as irrelevant or even as heretical concerns. The authors also disagree with those quantitative practitioners who dismiss concerns about qualitative sampling as irrelevant in general on the grounds that qualitative research provides no useful knowledge. It is suggested that such a position is untenable and uninformed.

This article focuses only on qualitative research; issues related to combined qualitative and quantitative methods are not discussed. The focus is on criteria for designing samples; qualitative issues related to suitability of any given person for research are not addressed. The criteria for designing samples constitute what Johnson (1990) labels as “Criteria One issues,” the construction and evaluation of theory and data-driven research designs. Criteria Two issues relate to the individual subjects in terms of cooperativeness, rapport, and suitability for qualitative study methods.

Although this article may appear to overly dichotomize qualitative and quantitative approaches, this was done strictly for the purposes of highlighting key issues in a brief space. The authors write here from the perspective of researchers who work extensively with both orientations, singly and in combination, in the conduct of major in-depth and longitudinal research grants that employ both methods. It is the authors' firm belief that good research requires an openness to multiple approaches to conceptualizing and measurement phenomena.

Contributions, Logic and Issues in Qualitative Sampling

Major contributions.

Attention to sampling issues has usually been at the heart of anthropology and of qualitative research since their inception. Much work was devoted to evaluating the appropriateness of theory, design strategies, and procedures for sampling. Important contributions have been made by research devoted to identifying and describing the nature of sample universes and the relevant analytic units for sampling. For example, the “universe of kinship” ( Goodenough 1956 ) has been a mainstay of cross-cultural anthropological study. Kinship studies aim to determine the fundamental culturally defined building blocks of social relationships of affiliation and descent (e.g., Bott 1971 ; Fortes 1969 ). Ethnographic investigations document the diversity of kinship structures, categories of kith and kin, and terminologies that give each culture across the globe its distinctive worldview, social structure, family organization, and patterns to individual experiences of the world.

Concerns with sampling in qualitative research focus on discovering the scope and the nature of the universe to be sampled. Qualitative researchers ask, “What are the components of the system or universe that must be included to provide a valid representation of it?” In contrast, quantitative designs focus on determining how many of what types of cases or observations are needed to reliably represent the whole system and to minimize both falsely identifying or missing existing relationships between factors. Thus the important contributions of qualitative work derived from concerns with validity and process may be seen as addressing core concerns of sampling, albeit in terms of issues less typically discussed by quantitative studies. Two examples may clarify this; one concerns time allocation studies of Peruvian farmers and the other addresses a census on Truk Island in the South Pacific.

The Andes mountains of Peru are home to communities of peasants who farm and tend small herds to garner a subsistence living. To help guide socioeconomic modernization and to improve living conditions, refined time allocation studies (see Gross 1984 ) were conducted in the 1970s to assess the rational efficiency of traditional patterns of labor, production, and reproduction. Seemingly irrational results were obtained. A systematic survey of how villagers allocated their time to various activities identified a few healthy adults who sat in the fields much of the day. Given the marginal food supplies, such “inactivity” seemed irrational and suggested a possible avenue for the desired interventions to improve village economic production. Only after interviewing the farmers to learn why the men sat in the fields and then calculating the kilocalories of foods gained by putting these men to productive work elsewhere was an explanation uncovered. It was discovered that crop yields and available calories would decline , not increase, due to foraging birds and animals. Because the farmers sat there, the events of animal foraging never occurred in the data universe. Here, judgments about the rationality of behaviors were guided by too narrow a definition of the behavioral universe, shaped by reliance on analytic factors external to the system (e.g., biases in industrial economies that equate “busyness” with production). An important message here is that discovery and definition of the sample universe and of relevant units of activity must precede sampling and analyses.

On Truk Island in the South Pacific, two anthropologists each conducted an independent census using the same methods. They surveyed every person in the community. Statistical analyses of these total universe samples were conducted to determine the incidence of types of residence arrangements for newlywed couples. The researchers reached opposite conclusions. Goodenough (1956) argued that his colleague's conclusion that there are no norms for where new couples locate their residence clearly erred by classifying households as patrilocal (near the father), matrilocal, or neolocal (not near either parent) at one time as if isolated from other social factors. Goodenough used the same residence typology as did his colleague in his analysis, but identified a strong matralineal pattern (wife's extended family). Evidence for this pattern becomes clear when the behaviors are viewed in relation to the extended family and over time. The newlyweds settle on whatever space is available but plan to move later to the more socially preferred (e.g., matralineal) sites. This later aspect was determined by combining survey-based observations of behavior with interviews to learn “what the devil they think they are doing” ( Geertz 1973 ). Thus different analytic definitions of domestic units led to opposite conclusions, despite the use of a sample of the total universe of people! Social constructions of the lived universe, subjectively important temporal factors have to be understood to identify valid units for analyses and interpretation of the data.

The Peruvian and the Truk Island examples illustrate some of the focal contributions of qualitative approaches to sampling. Altering the quantitatively oriented sampling interval, frequency, or duration would not have produced the necessary insights. The examples also suggest some of the dilemmas challenging sampling in qualitative research. These will be addressed in a later section. Both cases reveal the influence of deeply ingrained implicit cultural biases in the scientific construction of the sampling universe and the units for sampling.

The Cultural Embeddedness of the Concept of Sampling

Sampling issues are not exclusive to science. Widespread familiarity with sampling and related issues is indicated by the pervasive popular appetite for opinion and election polls, surveys of consumer product prices and quality, and brief reports of newsworthy scientific research in the mass media. Sampling issues are at the heart of jury selection, which aims to represent a cross section of the community; frequent debates erupt over how to define the universe of larger American society (e.g., by race and gender) to use for juror selection in a specific community. We can shop for sampler boxes of chocolates to get a tasty representation of the universe of all the candies from a company. Debates about the representativeness, size, and biases in survey results because of the people selected for study or the small size of samples are a part of everyday conversation. Newspapers frequently report on medical or social science research, with accounts of experts' challenging the composition or size of the sample or the wording of the survey questions. Critical skills in sampling are instilled during schooling and on-the-job training.

Such widespread familiarity with basic sampling issues suggests a deep cultural basis for the fascination and thus the need for a more critical understanding. The concept and practices of sampling resonate with fundamental cultural ideals and taboos. It is perhaps the case that sampling is linked, in American culture, to democratic ideals and notions of inclusion and representation.

What does that mean for qualitative researchers designing sampling strategies? We need to be aware that the language of science is ladened with cultural and moral categories. Thus gerontological research may potentially be shaped by both cultural themes masked as scientific principles. Basic terms for research standards can simultaneously apply to ideals for social life ( Luborsky 1994 ). We construct and are admonished by peers to carefully protect independent and dependent variables; we design studies to provide the greatest statistical power and speak of controlling variables. At the same time, psychosocial interventions are designed to enhance these same factors of individual independence and senses of power and control. We examine constructs and data to see if they are valid or invalid; the latter word also is defined in dictionaries as referring to someone who is not upright but physically deformed or sickly. Qualitative research, likewise, needs to recognize that we share with informants in the search for themes and coherence in life, and normatively judge the performance of others in these terms ( Luborsky 1994 , 1993b ).

The ideals of representativeness and proportionality are not, in practice, unambiguous or simple to achieve as is evidenced in the complex jury selection process. Indeed, there is often more than one way to achieve representativeness. Implicit cultural values may direct scientists to define some techniques as more desirable than others. Two current examples illustrate how sampling issues are the source of vitriolic debate outside the scientific community: voting procedures, and the construction or apportionment of voting districts to represent minority, ethnic, or racial groups. Representing “the voice of the people” in government is a core tenet of American democracy, embodied in the slogan “one person one vote.” Before women's suffrage, the universe was defined as “one man one vote.” A presidential nomination for U.S. Attorney General Dr. Lani Guinier, was withdrawn, in part, because she suggested the possibility of an alternative voting system (giving citizens more than one vote to cast) to achieve proportional representation for minorities. We see in these examples that to implement generalized democratic ideals of equal rights and representation can be problematic in the context of the democratic ideal of majority rule. Another example is the continuing debate in the U.S. Supreme Court over how to reapportion voting districts so as to include sufficient numbers of minority persons to give them a voice in local elections. These examples indicate the popular knowledge of sampling issues, the intensity of feelings about representativeness, and the deep dilemmas about proportional representation and biases arising within a democratic society. The democratic ideals produce multiple conflicts at the ideological level.

It is speculated that the association of sampling issues with such core American cultural dilemmas exacerbates the rancor between qualitative and quantitative gerontology; whereas in disciplines that do not deal with social systems, there is a tradition of interdependence instead of rancor. For example, the field of chemistry includes both qualitative and quantitative methods but is not beset by the tension found in gerontology. Qualitative chemistry is the set of methods specialized in identifying the types and entire range of elements and compounds present in materials or chemical reactions. A variety of discovery-oriented methods are used, including learning which elements are reacting with one another. Quantities of elements present may be described in general ranges as being from a trace to a substantial amount. Quantitative chemistry includes measurement-oriented methods attuned to determining the exact quantity of each constituent element present. Chemists use both methods as necessary to answer research problems. The differences in social contextual factors may contribute to the lower level of tension between quantitative and qualitative traditions within the European social sciences situated as they are within alternative systems for achieving democratic representation in government (e.g., direct plebiscites or multiparty governments rather than the American electoral college approach to a two-party system).

Ideals and Techniques of Qualitative Sampling

The preceding discussion highlighted the need to first identify the ideal or goal for sampling and second to examine the techniques and dilemmas for achieving the ideal. The following section describes several ideals, sampling techniques, and inherent dilemmas. Core ideals include the determination of the scope of the universe for study and the identification of appropriate analytic units when sampling for meaning

Defining the universe

This is simultaneously one of qualitative research's greatest contributions and greatest stumbling blocks to wider acceptance in the scientific community. As the examples of the Peruvian peasants and Trukese postmarital residence norms illustrated, qualitative approaches that can identify relevant units (e.g., of farming activity or cultural ideals for matralineal residence) are needed to complement behavioral or quantitative methods if we are to provide an internally valid definition of the scope of the universe to be sampled. Probability-based approaches do not capture these dimensions adequately.

The problem is that the very nature of such discovery-oriented techniques runs counter to customary quantitative design procedures. This needs to be clearly recognized. Because the nature of the units and their character cannot be specified ahead of time, but are to be discovered, the exact number and appropriate techniques for sampling cannot be stated at the design stage but must emerge during the process of conducting the research. One consequence is that research proposals and reports may appear incomplete or inadequate when in fact they are appropriately defined for qualitative purposes. One technique in writing research proposals has been to specify the likely or probable number of subjects to be interviewed.

Evidence that a researcher devoted sufficient attention to these issues can be observed in at least two dimensions. First, one finds a wealth of theoretical development of the concepts and topics. In qualitative research, these serve as the analytic tools for discovery and aid in anticipating new issues that emerge during the analyses of the materials. Second, because standardized measurement or diagnostic tests have not yet been developed for qualitative materials, a strong emphasis is placed on analytic or interpretive perspectives to the data collection and data analyses.

Expository styles, traditional in qualitative studies, present another dilemma for qualitative discussions of sampling. An impediment to wider recognition of what constitutes an adequate design is customary, implicit notions about the “proper” or traditional formats for writing research proposals and journal articles. The traditional format for grant applications places discussions of theory in the section devoted to the general significance of the research application separate from the methods and measures. However, theoretical issues and conceptual distinctions are the research tools and methods for qualitative researchers, equivalent to the quantitative researchers' standardized scales and measures. As the authors have observed it written reviews of grant applications over many years, reviewers want such “clutter” in qualitative documents placed where it belongs elsewhere in the proposal, not in the design section ( Rubinstein 1994 ). Qualitative researchers look for the analytic refinement, rigor, and breadth in conceptualization linked to the research procedures section as signs of a strong proposal or publication. Thus basic differences in scientific emphases, complicated by expectations for standardized scientific discourse, need to be more fully acknowledged.

Appropriate analytic units: Sampling for meaning

The logic or premises for qualitative sampling for meaning is incompletely understood in gerontology. Although it appears that, in the last decade, there has been an improved interdisciplinary acceptance and communication within gerontology, gerontology is largely driven by a sense of medicalization of social aging and a bias toward survey sampling and quantitative analysis based on “adequate numbers” for model testing and other procedures. At the same time, and partly in reaction to the dominance of the quantitative ethos, qualitative researchers have demurred from legitimating or addressing these issues in their own work.

Understanding the logic behind sampling for meaning in gerontological research requires an appreciation of how it differs from other approaches. By sampling for meaning, the authors indicate the selection of subjects in research that has as its goal the understanding of individuals' naturalistic perceptions of self, society, and the environment. Stated in another way, this is research that takes the insider's perspective. Meaning is defined as the process of reference and connotation, undertaken by individuals, to evoke key symbols, values, and ideas that shape, make coherent, and inform experience ( D'Andrade 1984 ; Good & Good 1982 ; Luborsky and Rubinstein 1987 ; Mishler 1986 ; Rubinstein 1990 ; Williams 1984 ). Clearly, the qualitative approach to meaning stands in marked contrast to other approaches to assessing meaning by virtue of its focus on naturalistic data and the discovery of the informant's own evaluations and categories. For example, one approach assesses meaning by using standardized lists of predefined adjectives or phrases (e.g., semantic differential scale methods, Osgood, Succi, and Tannenbaum 1957 ); another approach uses diagnostic markers to assign individuals to predefined general types (e.g., depressed, anxious) as a way to categorize people rather than describe personal meaning (e.g., the psychiatric diagnostic manual, DSMEI-R, APA 1987 ).

The difference between the me of that night and the me of tonight is the difference between the cadaver and the surgeon doing the cutting. (Flaubert, quoted in Crapanzano 1982 , p. 181)

It is important to understand that meanings and contexts (including an individual's sense of identity), the basic building blocks of qualitative research, are not fixed, constant objects with immutable traits. Rather, meanings and identities are fluid and changeable according to the situation and the persons involved. Gustave Flaubert precisely captures the sense of active personal meaning-making and remaking across time. Cohler (1991) describes such meaning-making and remaking as the personal life history self, a self that interprets, experiences, and marshals meanings as a means to manage adversity. A classic illustration of the fluidity of meanings is the case presented by Evans-Pritchard (1940) who explains the difficulty he had determining the names of his informants at the start of his fieldwork in Africa. He was repeatedly given entirely different names by the same people. In the kinship-based society, the name or identity one provides to another person depends on factors relative to each person's respective clan membership, age, and community. Now known as the principle of segmentary opposition, the situated and contextual nature of identities was illustrated once the fieldworker discovered the informants were indexing their names to provide an identity at an equal level of social organization. For example, to explain who we are when we travel outside the United States, we identify ourselves as Americans, not as someone from 1214 Oakdale Road. When we introduce ourselves to a new neighbor at a neighborhood block party, we identify ourselves by our apartment building or house on the block, not by reference to our identity as residents at the state or national level.

Themes and personal meanings are markers of processes not fixed structures. Life stories, whose narration is organized around a strongly held personal theme(s) as opposed to a chronology of events from birth to present day, have been linked with distress and clinical depression ( Luborsky 1993b ). Williams (1984) suggests that the experience of being ill from a chronic medical disease arises when the disease disrupts the expected trajectory of one's biography. Some researchers argue that a break in the sense of continuity in personal meaning ( Becker 1993 ), rather than any particular meaning (theme), precedes illness and depression ( Atchley 1988 ; Antonovsky 1987 ).

Another example of fluid meaning is ethnicity. Ethnic identity is a set of meanings that can be fluid and vary according to the social situation, historical time period, and its personal salience over the lifetime ( Luborsky and Rubinstein 1987 , 1990 ). Ethnic identity serves as a source of fixed, basic family values during child socialization; more fluidly, as an ascribed family identity to redefine or even reject as part of psychological processes of individuation in early adulthood; sometimes a source of social stigma in communities or in times of war with foreign countries (e.g., “being Italian” during World War II); and a source of continuity of meaning and pride in later life that may serve to help adapt to bereavement and losses.

From the qualitative perspective, there are a number of contrasts that emerge between sampling for meaning and more traditional, survey-style sampling, which has different goals. Those who are not familiar with the sampling-for-meaning approach often voice concerns over such aspects as size ( Lieberson 1992 ), adequacy and, most tellingly, purpose of the sampling. Why, for example, are sample sizes often relatively small? What is elicited and why? What is the relationship between meanings and other traditional categories of analyses, such as age, sex, class, social statuses, or particular diseases?

What is perhaps the most important contrast between the sampling-for-meaning approach and more standard survey sampling is found in the model of the person that underlies elicitation strategies. The model of the person in standard research suggests that important domains of life can be tapped by a relatively small number of standardized “one size fits all” questions, organized and presented in a scientific manner, and that most responses are relatively objective, capable of being treated as a decontextualized trait, and are quantifiable ( Mishler 1986 ; Trotter 1991 ). From this perspective, individuals are viewed as sets of fixed traits and not as carriers and makers of meaning.

Sampling for meaning, in contrast, is based on four very distinct notions. The first is that responses have contexts and carry referential meaning. Thus questions about events, activities, or other categories of experience cannot be understood without some consideration of how these events implicate other similar or contrasting events in a person's life ( Scheer and Luborsky 1991 ). This is particularly important for older people.

Second, individuals often actively interpret experience. That is to say, many people—but not all—actively work to consider their experience, put it in context, and understand it. Experience is not a fixed response. Further, the concern with meanings or of remaking meaning can be more emergent during some life stages and events or attention to certain kinds of meanings than others. Examples of this include bereavement, retirement, ethnic identity, and personal life themes in later life.

Third, certain categories of data do not have a separable existence apart from their occurrences embodied within routines and habits of the day and the body. Although certain categories of elicited data may have a relatively objective status and be relatively “at hand” for a person's stock of knowledge, other topics may never have been considered in a way that enables a person to have ready access to them ( Alexander, Rubinstein, Goodman, and Luborsky 1992 ). Consequently, qualitative research provides a context and facilitates a process of collaboration between researcher and informant.

Fourth, interpretation, either as natural for the informant or facilitated in the research interview, is basically an action of interpretation of experience that makes reference to both sociocultural standards, be they general cultural standards or local community ones, as well as the ongoing template or matrix of individual experience. Thus, for example, a person knows cultural ideals about a marriage, has some knowledge of other people's marriages, and has intimate knowledge of one's own. In the process of interpretation, all these levels come into play.

These issues occur over a variety of sampling frames and processing frameworks. There are three such sampling contexts. First, sampling for meaning occurs in relation to individuals as representatives of experiential types. Here, the goal is the elucidation of particular types of meaning or experience (personal, setting-based, sociocultural), through inquiry about, discussion of, and conversation concerning experiences and the interpretation of events and social occur-rences. The goal of sampling, in this case, is to produce collections of individuals from whom the nature of experience can be elicited through verbal descriptions and narrations.

Second, sampling for meaning can occur in the context of an individual in a defined social process. An example here could include understanding the entry of a person into a medical practice as a patient, for the treatment of a disorder. Qualitatively, we might wish to follow this person as she moves through medical channels, following referrals, tests, and the like. Even beginning this research at a single primary physician, or with a sample of individuals who have a certain disorder, the structure of passage through a processing system may vary widely and complexly. However, given a fixed point of entry (a medical practice or a single disease), sampling for meaning is nested in ongoing social processes. Researchers wish to understand not only the patient's experience of this setting as she moves through it (e.g., Esteroff 1982 ) but also the perspectives of the various social actors involved.

Finally, researchers may wish to consider sampling for meaning in a fixed social setting. In a certain way, sampling for meaning in a fixed social setting is what is meant, in anthropology and other social sciences, by “participant observation.” The social setting is more or less fixed, as is the population of research informants. An example might be a nursing home unit, with a more or less fixed number of residents, some stability but some change, and regular staff of several types representing distinctive organizational strata and interests (administration, medicine, nursing, social work, aides, volunteers, family, or environmental services).

It is important to note that even though qualitative research focuses on the individual, subjectivity or individuality is not the only goal of study. Qualitative research can focus on the macrolevel. One basic goal of qualitative research in aging is to describe the contents of people's experiences of life, health, and disability. It is true that much of the research to date treats the individual as the basic unit of analysis. Yet, the development of insights into the cultural construction of life experiences is an equal priority because cultural beliefs and values instill and shape powerful experiences, ideals, and motivations and shape how individuals make sense of and respond to events.

Studying how macrolevel cultural and community ideologies pattern the microlevel of individual life is part of a tradition stretching from Margaret Mead, Max Weber, Robert Merton, Talcott Parsons, to studies of physical and mental disabilities by Edgerton (1967) , Esteroff (1982) , and Murphy (1987) . For example, Stouffer's (1949) pioneering of survey methods revealed that American soldiers in World War II responded to the shared adversity of combat differently according to personal expectations based on sociocultural value patterns and lived experiences. These findings further illustrate Merton's theories of relative deprivation and reference groups, which point to the basis of individual well-being in basic processes of social comparison.

The notion of stigma illustrates the micro- and the macrolevels of analyses. For example, stigma theory's long reign in the social and political sciences and in clinical practice illustrates the micro- and macroqualitative perspectives. Stigma theory posits that individuals are socially marked or stigmatized by negative cultural evaluations because of visible differences or deformities, as defined by the community. Patterns of avoidance and denial of the disabled mark the socially conditioned feelings of revulsion, fear, or contagion. Personal experiences of low self-esteem result when negative messages are internalized by, for example, persons with visible impairments, or the elderly in an ageist setting. Management of social stigma by individuals and family is as much a focus as is management of impairments. Stigma is related significantly to compliance with prescribed adaptive devices ( Zola 1982 ; Luborsky 1993a ). A graphic case of this phenomenon are polio survivors who were homebound due to dependence on massive bedside artificial ventilators. With the recent advent of portable ventilators, polio survivors gained the opportunity to become mobile and travel outside the home, but they did not adopt the new equipment, because the new independence was far outweighed by the public stigma they experienced ( Kaufert and Locker 1990 ).

A final point is that sampling for meaning can also be examined in terms of sampling within the data collected. For example, the entire corpus of materials and observations with informants needs to be examined in the discovery and interpretive processes aimed at describing relevant units for analyses and dimensions of meaning. This is in contrast to reading the texts to describe and confirm a finding without then systematically rereading the texts for sections that may provide alternative or contradictory interpretations.

Techniques for selecting a sample

As discussed earlier, probability sampling techniques cannot be used for qualitative research by definition, because the members of the universe to be sampled are not known a priori, so it is not possible to draw elements for study in proportion to an as yet unknown distribution in the universe sampled. A review of the few qualitative research publications that treat sampling issues at greater length (e.g., Depoy and Gitlin 1993 ; Miles and Huberman 1994 ; Morse 1994 ; Ragin and Becker 1992 ) identify five major types of nonprobability sampling techniques for qualitative research. A consensus among these authors is found in the paramount importance they assign to theory to guide the design and selection of samples ( Platt 1992 ). These are briefly reviewed as follows.

First, convenience (or opportunistic) sampling is a technique that uses an open period of recruitment that continues until a set number of subjects, events, or institutions are enrolled. Here, selection is based on a first-come, first-served basis. This approach is used in studies drawing on predefined populations such as participants in support groups or medical clinics. Second, purposive sampling is a practice where subjects are intentionally selected to represent some explicit predefined traits or conditions. This is analogous to stratified samples in probability-based approaches. The goal here is to provide for relatively equal numbers of different elements or people to enable exploration and description of the conditions and meanings occurring within each of the study conditions. The objective, however, is not to determine prevalence, incidence, or causes. Third, snowballing or word-of-mouth techniques make use of participants as referral sources. Participants recommend others they know who may be eligible. Fourth, quota sampling is a method for selecting numbers of subjects to represent the conditions to be studied rather than to represent the proportion of people in the universe. The goal of quota sampling is to assure inclusion of people who may be underrepresented by convenience or purposeful sampling techniques. Fifth, case study ( Ragin and Becker 1992 ; Patton 1990 ) samples select a single individual, institution, or event as the total universe. A variant is the key-informant approach ( Spradley 1979 ), or intensity sampling ( Patton 1990 ) where a subject who is expert in the topic of study serves to provide expert information on the specialized topic. When qualitative perspectives are sought as part of clinical or survey studies, the purposive, quota, or case study sampling techniques are generally the most useful.

How many subjects is the perennial question. There is seldom a simple answer to the question of sample or cell size in qualitative research. There is no single formula or criterion to use. A “gold standard” that will calculate the number of people to interview is lacking (cf. Morse 1994 ). The question of sample size cannot be determined by prior knowledge of effect sizes, numbers of variables, or numbers of analyses—these will be reported as findings. Sample sizes in qualitative studies can only be set by reference to the specific aims and the methods of study, not in the abstract. The answer only emerges within a framework of clearly stated aims, methods, and goals and is conditioned by the availability of staff and economic resources.

Rough “rules of thumb” exist, but these derive from three sources: traditions within social science research studies of all kinds, commonsense ideas about how many will be enough, and practical concerns about how many people can be interviewed and analyzed in light of financial and personnel resources. In practice, from 12 to 26 people in each study cell seems just about right to most authors. In general, it should be noted that Americans have a propensity to define bigger as better and smaller as inferior. Quantitative researchers, in common with the general population, question such small sample sizes because they are habituated to opinion polls or epidemiology surveys based on hundreds or thousands of subjects. However, sample sizes of less than 10 are common in many quantitative clinical and medical studies where statistical power analyses are provided based on the existence of very large effect sizes for the experimental versus control conditions.

Other considerations in evaluating sample sizes are the resources, times, and reporting requirements. In anthropological field research, a customary formula is that of the one to seven: for every 1 year of fieldwork by one researcher, 7 years are required to conduct the analysis. Thus, in studies that use more than one interviewer, the ability to collect data also increases the burden for analyses.

An outstanding volume exploring the logic, contributions, and dilemmas of case study research ( Ragin and Becker 1992 ) reports that survey researchers resort to case examples to explain ambiguities in their data, whereas qualitative researchers reach for descriptive statistics when they do not have a clear explanation for their observations. Again, the choice of sample size and group design is guided by the qualitative goal of describing the nature and contents of cultural, social, and personal values and experiences within specific conditions or circumstances, rather than of determining incidence and prevalence.

Who and who not?

In the tradition of informant-based and of participatory research, it is assumed that all members of a community can provide useful information about the values, beliefs, or practices in question. Experts provide detailed, specialized information, whereas nonexperts do so about daily life. In some cases, the choice is obvious, dictated by the topic of study, for example, childless elderly, retirees, people with chronic diseases or new disabilities. In other cases, it is less obvious, as in studies of disease, for example, that require insights from sufferers but also from people not suffering to gain an understanding for comparison with the experiences and personal meanings of similar people without the condition. Comparisons can be either on a group basis or matched more closely on a one-to-one basis for many traits (e.g., age, sex, disease, severity), sometimes referred to as yoked pairs. However, given the labor-intensive nature of qualitative work, sometimes the rationale for including control groups of people who do not have the experiences is not justifiable.

Homogeneity or diversity

Currently, when constructing samples for single study groups, qualitative research appears to be about equally split in terms of seeking homogeneity or diversity. There is little debate or attention to these contrasting approaches. For example, some argue that it is more important to represent a wide range of different types of people and experiences in order to represent the similarities and diversity in human experience, beliefs, and conditions (e.g., Kaufman 1987 , 1989 ) than it is to include sufficient numbers of people sharing an experience or condition to permit evaluation of within-group similarities. In contrast, others select informants to be relatively homogeneous on several characteristics to strengthen comparability within the sample as an aid to identifying similarities and diversity.

Summary and Reformulation for Practice

To review, the authors suggest that explicit objective criteria to use for evaluating qualitative research designs do exist, but many of these focus on different issues and aspects of the research process, in comparison to issues for quantitative studies. This article has discussed the guiding principles, features, and practices of sampling in qualitative research. The guiding rationale is that of the discovery of the insider's view of cultural and personal meanings and experience. Major features of sampling in qualitative research concern the issues of identifying the scope of the universe for sampling and the discovery of valid units for analyses. The practices of sampling, in comparison to quantitative research, are rooted in the application of multiple conceptual perspectives and interpretive stances to data collection and analyses that allow the development and evaluation of a multitude of meanings and experiences.

This article noted that sampling concerns are widespread in American culture rather than in the esoteric specialized concern of scientific endeavors ( Luborsky and Sankar 1993 ). Core scientific research principles are also basic cultural ideals ( Luborsky 1994 ). For example, “control” (statistical, personal, machinery), dependence and independence (variables and individual), a reliable person with a valid driver's license matches reliability and validity concerns about assessment scales. Knowledge about the rudimentary principles of research sampling is widespread outside of the research laboratory, particularly with the relatively new popularity of economic, political, and community polls as a staple of news reporting and political process in democratic governance. Core questions about the size, sources, and features of participants are applied to construct research populations, courtroom juries, and districts to serve as electoral universes for politicians.

The cultural contexts and popular notions about sampling and sample size have an impact on scientific judgments. It is important to acknowledge the presence and influence of generalized social sensibilities or awareness about sampling issues. Such notions may have less direct impact on research in fields with long-established and formalized criteria and procedures for determining sample size and composition. The generalized social notions may come to exert a greater influence as one moves across the spectrum of knowledge-building strategies to more qualitative and humanistic approaches. Even though such studies also have a long history of clearly articulated traditions of formal critiques (e.g., in philosophy and literary criticism), they have not been amenable to operationalization and quantification.

The authors suggested that some of the rancor between qualitative and quantitative approaches is rooted in deeper cultural tensions. Prototypic questions posed to qualitative research in interdisciplinary settings derive from both the application of frameworks derived from other disciplines' approaches to sampling as well as those of the reviewers as persons socialized into the community where the study is conceived and conducted. Such concerns may be irrelevant or even counterproductive.

Qualitative Clarity as an Analog to Statistical Power

The guiding logic of qualitative research, by design, generally prevents it from being able to fulfill the assumptions underlying statistical power analyses of research designs. The discovery-oriented goals, use of meanings as units of analyses, and interpretive methods of qualitative research dictate that the exact factors, dimensions, and distribution of phenomena identified as important for analyses may not always be specified prior to data analyses activities. These emerge from the data analyses and are one of the major contributions of qualitative study. No standardized scales or tests exist yet to identify and describe new arenas of cultural, social, or personal meanings. Meaning does not conform to normative distributions by known factors. No probability models exist that would enable prediction of distributions of meanings needed to perform statistical power analyses.

Qualitative studies however can, and should, be judged in terms of how well they meet the explicit goals and purposes relevant to such research.

The authors have suggested that the concept of qualitative clarity be developed to guide evaluations of sampling as an analog to the concept of statistical power. Qualitative clarity refers to principles that are relevant to the concerns of this type of research. That is, the adequacy of the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation can be evaluated even if the factors to be measured cannot be specified. The term clarity conveys the aim of making explicit, for open discussion, the details of how the sample was assembled, the theoretical assumptions and the pragmatic constraints that influenced the sampling process. Qualitative clarity should include at least two components, theoretical grounding and sensitivity to context. These are briefly described next.

Rich and diverse theoretical grounding

In the absence of standardized measures for assessing meaning, the analogous qualitative research tools are theory and discovery processes. Strong and well-developed theoretical preparation is necessary to provide multiple and alternative interpretations of the data. Traditionally, in qualitative study, it is the richness and sophistication of the analytic perspectives or “lenses” focused on the data that lends richness, credibility, and validity to the analyses. The relative degree of theoretical development in a research proposal or manuscript is readily apparent in the text, for example, in terms of extended descriptions of different schools of thought and possible multiple contrasting of interpretive explanations for phenomena at hand. In brief, the authors argue that given the stated goal of sampling for meaning, qualitative research can be evaluated to assess if it has adequate numbers of conceptual perspectives that will enable the study to identify a variety of meanings and to critique multiple rich interpretations of the meanings.

Sampling within the data is another important design feature. The discovery of meaning should also include sampling within the data collected. The entire set of qualitative materials should be examined rather than selectively read after identifying certain parts of the text to describe and confirm a finding without reading for sections that may provide alternative or contradictory interpretations.

Sensitivity to contexts

As a second component of qualitative clarity, sensitivity to context refers to the contextual dimensions shaping the meanings studied. It also refers to the historical settings of the scientific concepts used to frame the research questions and the methods. Researchers need to be continually attentive to examining the meanings and categories discovered for elements from the researchers' own cultural and personal backgrounds. The first of these contexts is familiar to gerontologists: patterns constructed by the individual's life history; generation; cohort; psychological, developmental, and social structure; and health. Another more implicit contextual aspect to examine as part of the qualitative clarity analysis is evidence of a critical view of the methods and theories introduced by the investigators. Because discovery of the insiders' perspective on cultural and personal meanings is a goal of qualitative study, it is important to keep an eye to biases derived from the intrusion of the researcher's own scientific categories. Qualitative research requires a critical stance as to both the kinds of information and the meanings discovered, and to the analytic categories guiding the interpretations. One example is recent work that illustrates how traditional gerontological constructs for data collection and analyses do not correspond to the ways individuals themselves interpret their own activities, conditions, or label their identities (e.g., “caregiver,” Abel 1991 ; “disabled,” Murphy 1987 ; “old and alone,” Rubinstein, 1986 ; “Alzheimer's disease,” Gubrium 1992 ; “life themes,” Luborsky 1993b ). A second example is the growing awareness of the extent to which past research tended to define problems of disability or depression narrowly in terms of the individual's ability, or failure, to adjust, without giving adequate attention to the societal level sources of the individual's distress ( Cohen and Sokolovsky 1989 ). Thus researchers need to demonstrate an awareness of how the particular questions guiding qualitative research, the methods and styles of analyses, are influenced by cultural and historical settings of the research ( Luborsky and Sankar 1993 ) in order to keep clear whose meanings are being reported.

To conclude, our outline for the concept of qualitative clarity, which is intended to serve as the qualitatively appropriate analog to statistical power, is offered to gerontologists as a summary of the main points that need to be considered when evaluating samples for qualitative research. The descriptions of qualitative sampling in this article are meant to extend the discussion and to encourage the continued development of more explicit methods for qualitative research.

Acknowledgments

Support for the first author by the National Institute of Child Health and Human Development (#RO1 HD31526) and the National Institute on Aging (#RO1 AG09065) is gratefully acknowledged. Ongoing support for the second author from the National Institute of Aging is also gratefully acknowledged.

Biographies

Mark R. Luborsky, Ph.D., is a senior research anthropologist and assistant director of research at the Philadelphia Geriatric Center. Federal and foundation grants support his studies of sociocultural values and personal meanings in early and late adulthood, and how these relate to mental and physical health, and to disability and rehabilitation processes. He also consults and teaches on these topics.

Robert L. Rubinstein, Ph.D., is a senior research anthropologist and director of research at the Philadelphia Geriatric Center. He has conducted research in the United States and Vanuatu, South Pacific Islands. His gerontological research interests include social relations of the elderly, childlessness in later life, and the home environments of old people.

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In This Article Expand or collapse the "in this article" section Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

Introduction.

Sampling Strategies
Sample Size
Qualitative Design Considerations
Discipline Specific and Special Considerations
Sampling Strategies Unique to Mixed Methods Designs

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Forthcoming articles expand or collapse the "forthcoming articles" section.

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Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST REVIEWED: 26 February 2020 LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

Sampling is a critical, often overlooked aspect of the research process. The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e., quantitatively) or go into sufficient depth (i.e., qualitatively). While quantitative research is generally concerned with probability-based approaches, qualitative research typically uses nonprobability purposeful sampling approaches. Scholars generally focus on two major sampling topics: sampling strategies and sample sizes. Or simply, researchers should think about who to include and how many; both of these concerns are key. Mixed methods studies have both qualitative and quantitative sampling considerations. However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study.

Sampling in Qualitative Research

Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size. Sampling strategies refers to the process of sampling and how to design a sampling. Qualitative sampling typically follows a nonprobability-based approach, such as purposive or purposeful sampling where participants or other units of analysis are selected intentionally for their ability to provide information to address research questions. Sample size refers to how many participants or other units are needed to address research questions. The methodological literature about sampling tends to fall into these two broad categories, though some articles, chapters, and books cover both concepts. Others have connected sampling to the type of qualitative design that is employed. Additionally, researchers might consider discipline specific sampling issues as much research does tend to operate within disciplinary views and constraints. Scholars in many disciplines have examined sampling around specific topics, research problems, or disciplines and provide guidance to making sampling decisions, such as appropriate strategies and sample size.

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Qualitative Sampling Methods

Affiliation.

1 14742 School of Nursing, University of Texas Health Science Center, San Antonio, TX, USA.
PMID: 32813616
DOI: 10.1177/0890334420949218

Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of each. Sample size and data saturation are discussed.

Keywords: breastfeeding; qualitative methods; sampling; sampling methods.

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10.2 Sampling in qualitative research

Learning objectives.

Define nonprobability sampling, and describe instances in which a researcher might choose a nonprobability sampling technique
Describe the different types of nonprobability samples

Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of whatever phenomenon it is that they are studying. In this section, we’ll examine the techniques that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most likely to use in their work.

Nonprobability sampling

Nonprobability sampling refers to sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know with nonprobability samples whether a sample is truly representative of a larger population. But that’s okay. Generalizing to a larger population is not the goal with nonprobability samples or qualitative research. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind (that would mean committing one of the errors of informal inquiry discussed in Chapter 1). We’ll take a closer look at the process of selecting research elements when drawing a nonprobability sample. But first, let’s consider why a researcher might choose to use a nonprobability sample.

two people filling out a clipboard survey in a crowd of people

When are nonprobability samples ideal? One instance might be when we’re starting a big research project. For example, if we’re conducting survey research, we may want to administer a draft of our survey to a few people who seem to resemble the folks we’re interested in studying in order to help work out kinks in the survey. We might also use a nonprobability sample if we’re conducting a pilot study or some exploratory research. This can be a quick way to gather some initial data and help us get some idea of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples can be useful for setting up, framing, or beginning research, even quantitative research. But it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques. Researchers also use nonprobability samples in full-blown research projects. These projects are usually qualitative in nature, where the researcher’s goal is in-depth, idiographic understanding rather than more general, nomothetic understanding.

Types of nonprobability samples

There are several types of nonprobability samples that researchers use. These include purposive samples, snowball samples, quota samples, and convenience samples. 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, we include them in this section of the chapter.

To draw a purposive sample , a researcher selects participants from their sampling frame because they have characteristics that the researcher desires. A researcher begins with specific characteristics in mind that she wishes to examine and then seeks out research participants who cover that full range of characteristics. For example, if you are studying mental health supports on your campus, you may want to be sure to include not only students, but mental health practitioners and student affairs administrators. You might also select students who currently use mental health supports, those who dropped out of supports, and those who are waiting to receive supports. The purposive part of purposive sampling comes from selecting specific participants on purpose because you already know they have characteristics—being an administrator, dropping out of mental health supports—that you need in your sample.

Note that these are different than inclusion criteria, which are more general requirements a person must possess to be a part of your sample. For example, one of the inclusion criteria for a study of your campus’ mental health supports might be that participants had to have visited the mental health center in the past year. That is different than purposive sampling. In purposive sampling, you know characteristics of individuals and recruit them because of those characteristics. For example, I might recruit Jane because she stopped seeking supports this month, JD because she has worked at the center for many years, and so forth.

Also, it’s important to recognize that purposive sampling requires you to have prior information about your participants before recruiting them because you need to know their perspectives or experiences before you know whether you want them in your sample. This is a common mistake that many students make. What I often hear is, “I’m using purposive sampling because I’m recruiting people from the health center,” or something like that. That’s not purposive sampling. Purposive sampling is recruiting specific people because of the various characteristics and perspectives they bring to your sample. Imagine we were creating a focus group. A purposive sample might gather clinicians, patients, administrators, staff, and former patients together so they can talk as a group. Purposive sampling would seek out people that have each of those attributes.

Quota sampling is another nonprobability sampling strategy that takes purposive sampling one step further. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation. Subgroups are created based on each category, and the researcher decides how many people to include from each subgroup and collects data from that number for each subgroup. Let’s consider a study of student satisfaction with on-campus housing. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves and instead eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. It is possible as well that your review of literature on the topic suggests that campus housing experiences vary by gender. If that is that case, perhaps you’ll decide on four important subgroups: men who live in apartments, women who live in apartments, men who live in dorm rooms, and women who live in dorm rooms. Your quota sample would include five people from each of the four subgroups.

Qualitative researchers can also use snowball sampling techniques to identify study participants. In snowball sampling , a researcher identifies one or two people she’d like to include in her study but then relies on those initial participants to help identify additional study participants. Thus, the researcher’s sample builds and becomes larger as the study continues, much as a snowball builds and becomes larger as it rolls through the snow. Snowball sampling is an especially useful strategy when a researcher wishes to study a stigmatized group or behavior. For example, a researcher who wanted to study how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting a call for interviewees in the newspaper or making an announcement about the study at some large social gathering. Instead, the researcher might know someone with the condition, interview that person, and ask the person to refer others they may know with the genital herpes to contact you to participate in the study. Having a previous participant vouch for the researcher may help new potential participants feel more comfortable about being included in the study.

a person pictured next to a network of associates and their interrelationships noted through lines connecting the photos

Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. In addition to using this sampling strategy for potentially stigmatized populations, it is also a useful strategy to use when the researcher’s group of interest is likely to be difficult to find, not only because of some stigma associated with the group, but also because the group may be relatively rare. This was the case for Steven Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011) [4] who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers first relied on their own networks to identify study participants, but because members of the study’s target population were not easy to find, access to the networks of initial study participants was very important for identifying additional participants. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received $50 for participating in the study and an additional $20 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

Finally, convenience sampling is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from those people or other relevant elements to which she has most convenient access. This method, also sometimes referred to as availability sampling, is most useful in exploratory research or in student projects in which probability sampling is too costly or difficult. If you’ve ever been interviewed by a fellow student for a class project, you have likely been a part of a convenience sample. While convenience samples offer one major benefit—convenience—they do not offer the rigor needed to make conclusions about larger populations. That is the subject of our next section on probability sampling.

Table 10.1 Types of nonprobability samples

Purposive	Researcher seeks out participants with specific characteristics.
Snowball	Researcher relies on participant referrals to recruit new participants.
Quota	Researcher selects cases from within several different subgroups.
Convenience	Researcher gathers data from whatever cases happen to be convenient.

Key Takeaways

Nonprobability samples might be used when researchers are conducting qualitative (or idiographic) research, exploratory research, student projects, or pilot studies.
There are several types of nonprobability samples including purposive samples, snowball samples, quota samples, and convenience samples.
Convenience sample- researcher gathers data from whatever cases happen to be convenient
Nonprobability sampling- sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown
Purposive sample- when a researcher seeks out participants with specific characteristics
Quota sample- when a researcher selects cases from within several different subgroups
Snowball sample- when a researcher relies on participant referrals to recruit new participants

Image attributions

business by helpsg CC-0

network by geralt CC-0

For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “The first measured century” at http://www.pbs.org/fmc/timeline/e1948election.htm. ↵
Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson. ↵
If you are interested in the history of polling, I recommend reading Fried, A. (2011). Pathways to polling: Crisis, cooperation, and the making of public opinion professions . New York, NY: Routledge. ↵
Kogan, S. M., Wejnert, C., Chen, Y., Brody, G. H., & Slater, L. M. (2011). Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. Journal of Adolescent Research , 26 , 30–60. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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7.2 Sampling in Qualitative Research

Learning objectives.

Define nonprobability sampling, and describe instances in which a researcher might choose a nonprobability sampling technique.
Describe the different types of nonprobability samples.

Qualitative researchers typically make sampling choices that enable them to deepen understanding of whatever phenomenon it is that they are studying. In this section we’ll examine the strategies that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most likely to use in their work.

Nonprobability Sampling

Nonprobability sampling Sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. refers to sampling techniques for which a person’s (or event’s or researcher’s focus’s) likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know with nonprobability samples whether a sample represents a larger population or not. But that’s OK, because representing the population is not the goal with nonprobability samples. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind (once again, that would mean committing one of the errors of informal inquiry discussed in Chapter 1 "Introduction" ). In the following subsection, “Types of Nonprobability Samples,” we’ll take a closer look at the process of selecting research elements The individual unit that is the focus of a researcher’s investigation; possible elements in social science include people, documents, organizations, groups, beliefs, or behaviors. when drawing a nonprobability sample. But first, let’s consider why a researcher might choose to use a nonprobability sample.

So when are nonprobability samples ideal? One instance might be when we’re designing a research project. For example, if we’re conducting survey research, we may want to administer our survey to a few people who seem to resemble the folks we’re interested in studying in order to help work out kinks in the survey. We might also use a nonprobability sample at the early stages of a research project, if we’re conducting a pilot study or some exploratory research. This can be a quick way to gather some initial data and help us get some idea of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples can be useful for setting up, framing, or beginning research. But it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques.

Researchers also use nonprobability samples in full-blown research projects. These projects are usually qualitative in nature, where the researcher’s goal is in-depth, idiographic understanding rather than more general, nomothetic understanding. Evaluation researchers whose aim is to describe some very specific small group might use nonprobability sampling techniques, for example. Researchers interested in contributing to our theoretical understanding of some phenomenon might also collect data from nonprobability samples. Maren Klawiter (1999) Klawiter, M. (1999). Racing for the cure, walking women, and toxic touring: Mapping cultures of action within the Bay Area terrain of breast cancer. Social Problems, 46 , 104–126. relied on a nonprobability sample for her study of the role that culture plays in shaping social change. Klawiter conducted participant observation in three very different breast cancer organizations to understand “the bodily dimensions of cultural production and collective action.” Her intensive study of these three organizations allowed Klawiter to deeply understand each organization’s “culture of action” and, subsequently, to critique and contribute to broader theories of social change and social movement organization. Thus researchers interested in contributing to social theories, by either expanding on them, modifying them, or poking holes in their propositions, may use nonprobability sampling techniques to seek out cases that seem anomalous in order to understand how theories can be improved.

In sum, there are a number and variety of instances in which the use of nonprobability samples makes sense. We’ll examine several specific types of nonprobability samples in the next subsection.

Types of Nonprobability Samples

To draw a purposive sample A nonprobability sample type for which a researcher seeks out particular study elements that meet specific criteria that the researcher has identified. , a researcher begins with specific perspectives in mind that he or she wishes to examine and then seeks out research participants who cover that full range of perspectives. For example, if you are studying students’ satisfaction with their living quarters on campus, you’ll want to be sure to include students who stay in each of the different types or locations of on-campus housing in your study. If you only include students from 1 of 10 dorms on campus, you may miss important details about the experiences of students who live in the 9 dorms you didn’t include in your study. In my own interviews of young people about their workplace sexual harassment experiences, I and my coauthors used a purposive sampling strategy; we used participants’ prior responses on a survey to ensure that we included both men and women in the interviews and that we included participants who’d had a range of harassment experiences, from relatively minor experiences to much more severe harassment.

While purposive sampling is often used when one’s goal is to include participants who represent a broad range of perspectives, purposive sampling may also be used when a researcher wishes to include only people who meet very narrow or specific criteria. For example, in their study of Japanese women’s perceptions of intimate partner violence, Miyoko Nagae and Barbara L. Dancy (2010) Nagae, M., & Dancy, B. L. (2010). Japanese women’s perceptions of intimate partner violence (IPV). Journal of Interpersonal Violence, 25 , 753–766. limited their study only to participants who had experienced intimate partner violence themselves, were at least 18 years old, had been married and living with their spouse at the time that the violence occurred, were heterosexual, and were willing to be interviewed. In this case, the researchers’ goal was to find participants who had had very specific experiences rather than finding those who had had quite diverse experiences, as in the preceding example. In both cases, the researchers involved shared the goal of understanding the topic at hand in as much depth as possible.

Qualitative researchers sometimes rely on snowball sampling A nonprobability sample type for which a researcher recruits study participants by asking prior participants to refer others. techniques to identify study participants. In this case, a researcher might know of one or two people she’d like to include in her study but then relies on those initial participants to help identify additional study participants. Thus the researcher’s sample builds and becomes larger as the study continues, much as a snowball builds and becomes larger as it rolls through the snow.

Snowball sampling is an especially useful strategy when a researcher wishes to study some stigmatized group or behavior. For example, a researcher who wanted to study how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting a call for interviewees in the newspaper or making an announcement about the study at some large social gathering. Instead, the researcher might know someone with the condition, interview that person, and then be referred by the first interviewee to another potential subject. Having a previous participant vouch for the trustworthiness of the researcher may help new potential participants feel more comfortable about being included in the study.

Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. In addition to using this sampling strategy for potentially stigmatized populations, it is also a useful strategy to use when the researcher’s group of interest is likely to be difficult to find, not only because of some stigma associated with the group, but also because the group may be relatively rare. This was the case for Steven M. Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011) Kogan, S. M., Wejnert, C., Chen, Y., Brody, G. H., & Slater, L. M. (2011). Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. Journal of Adolescent Research, 26 , 30–60. who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers first relied on their own networks to identify study participants, but because members of the study’s target population were not easy to find, access to the networks of initial study participants was very important for identifying additional participants. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received not only $50.00 for participating in the study but also $20.00 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

Quota sampling A nonprobability sample type for which a researcher identifies subgroups within a population of interest and then selects some predetermined number of elements from within each subgroup. is another nonprobability sampling strategy. This type of sampling is actually employed by both qualitative and quantitative researchers, but because it is a nonprobability method, we’ll discuss it in this section. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation. Subgroups are created based on each category and the researcher decides how many people (or documents or whatever element happens to be the focus of the research) to include from each subgroup and collects data from that number for each subgroup.

Let’s go back to the example we considered previously of student satisfaction with on-campus housing. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves but eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. It is possible as well that your review of literature on the topic suggests that campus housing experiences vary by gender. If that is that case, perhaps you’ll decide on four important subgroups: men who live in apartments, women who live in apartments, men who live in dorm rooms, and women who live in dorm rooms. Your quota sample would include five people from each subgroup.

In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. The leading polling entity at the time, The Literary Digest , predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide. When Gallup’s prediction that Roosevelt would win, turned out to be correct, “the Gallup Poll was suddenly on the map” (Van Allen, 2011). Van Allen, S. (2011). Gallup corporate history. Retrieved from http://www.gallup.com/corporate/1357/Corporate-History.aspx#2 Gallup successfully predicted subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election. For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “The first measured century” at http://www.pbs.org/fmc/timeline/e1948election.htm . Among other problems, the fact that Gallup’s quota categories did not represent those who actually voted (Neuman, 2007) Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson. underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods. If you are interested in the history of polling, I recommend a recent book: Fried, A. (2011). Pathways to polling: Crisis, cooperation, and the making of public opinion professions . New York, NY: Routledge. While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings.

Finally, convenience sampling A nonprobability sample type for which a researcher gathers data from the elements that happen to be convenient; also referred to as haphazard sampling. is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from those people or other relevant elements to which he or she has most convenient access. This method, also sometimes referred to as haphazard sampling, is most useful in exploratory research. It is also often used by journalists who need quick and easy access to people from their population of interest. If you’ve ever seen brief interviews of people on the street on the news, you’ve probably seen a haphazard sample being interviewed. While convenience samples offer one major benefit—convenience—we should be cautious about generalizing from research that relies on convenience samples.

Table 7.1 Types of Nonprobability Samples

Sample type	Description
Purposive	Researcher seeks out elements that meet specific criteria.
Snowball	Researcher relies on participant referrals to recruit new participants.
Quota	Researcher selects cases from within several different subgroups.
Convenience	Researcher gathers data from whatever cases happen to be convenient.

Key Takeaways

Nonprobability samples might be used when researchers are conducting exploratory research, by evaluation researchers, or by researchers whose aim is to make some theoretical contribution.
There are several types of nonprobability samples including purposive samples, snowball samples, quota samples, and convenience samples.
Imagine you are about to conduct a study of people’s use of the public parks in your hometown. Explain how you could employ each of the nonprobability sampling techniques described previously to recruit a sample for your study.
Of the four nonprobability sample types described, which seems strongest to you? Which seems weakest? Explain.

Sampling in Qualitative Research: Rationale, Issues, and Methods

Research on Aging 17(1):89-113
17(1):89-113

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What is purposive sampling?

Last updated

5 February 2023

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Cathy Heath

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This type of sampling is often used in qualitative research , allowing the researcher to focus on specific areas of interest and gather in-depth data on those topics. In this article, we will explore the concept of purposive sampling in more detail and discuss the advantages and limitations of using this approach in research studies.

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Purposive sampling is a technique used in qualitative research to select a specific group of individuals or units for analysis. Participants are chosen “on purpose,” not randomly. It is also known as judgmental sampling or selective sampling.

In purposive sampling, the researcher has a specific purpose or objective in mind when selecting the sample. Therefore, the sample is selected based on the characteristics or attributes that the researcher is interested in studying.

For example, suppose a researcher is interested in studying the experiences of individuals living with chronic pain. In that case, they might use purposive sampling to select a sample of individuals who have been diagnosed with chronic pain.

Purposive sampling is often used in qualitative research , as it allows the researcher to focus on specific areas of interest and gather in-depth data on those topics. It is also commonly used in small-scale studies with limited sample size.

When to use purposive sampling

Purposive sampling should be used when you have a clear idea of the specific attributes you're interested in studying and want to select a sample that accurately represents those characteristics.

Purposive sampling can be particularly useful in the following situations:

When the population of interest is small

For interest in studying a specific subgroup within the population

To study a rare or unusual phenomenon

It's important to note that purposive sampling is not suitable for all research studies and should be used cautiously. As the sample is not selected randomly, the results of the study may not be generalizable to the larger population, and the researcher must consider the potential for bias in the sample selection.

Principles of purposeful sampling

There are several important principles of purposive sampling that you should consider when using this approach in your research studies:

Clearly defined purpose - The purpose of the study should be clearly defined, and the sample should be selected based on the characteristics or attributes that you're interested in studying.

Representative sample - The sample should be representative of the characteristics or attributes being studied.

Bias - Biases can come into play when anything other than random sampling is used, so be aware of any potential biases and take steps to minimize them.

Expertise - Having expertise in the topic being studied is an important part of sample selection. Without a solid understanding of the characteristics being selected, the population might not be as representative as it should be.

How is purposive sampling conducted?

The steps to conducting a study using purposive sampling will vary depending on the topic and preferences of the researchers involved. The five steps of purposive sampling as a general framework are:

Define the purpose of the study

Identify the sample of individuals or units

Obtain informed consent from individuals

Collect the data using appropriate research methods

Analyze the data

Purposive sampling examples

Researchers can use several different types of purposive sampling methods , depending on what they're interested in studying and the specific research question they are trying to answer. In the list below, we'll discuss the various types of purposive sampling methods and provide examples of when each method might be used in research.

Maximum variation sampling

Maximum variation sampling involves selecting a sample of individuals or units representing the maximum range of variation within the characteristics or attributes the researcher is interested in studying. This type of sampling is used to understand the widest possible diversity of experiences or viewpoints within the population.

Homogeneous sampling

Homogeneous sampling involves selecting what is often a more narrow sample of individuals or units that are similar or have the same characteristics or attributes. This type of sampling is used to study a specific subgroup within the population in depth.

Typical case sampling

Typical case sampling involves selecting a sample of individuals or units that are representative of the typical experiences or characteristics of the population. This type of sampling is used to understand the most common or average experiences or characteristics within the population.

Extreme/deviant case sampling

Extreme case sampling involves selecting a sample of individuals or units that are considered extreme or unusual in the characteristics or attributes the researcher is interested in studying. This type of sampling is used to understand unusual or exceptional experiences or characteristics within the population and are often viewed as outliers in a wider population.

Critical case sampling

Critical case sampling involves selecting a sample of individuals or units that are important or central to the research question or the population being studied. This type of sampling is used to understand key experiences or characteristics within the population.

Expert sampling

Expert sampling involves selecting a sample of individuals or units that have specialized knowledge or expertise in the topic or issue being studied. This type of sampling is used to gather insights and understanding from experts in the field, which can be used to develop follow-up studies.

Purposive sampling vs. convenience sampling

Purposive sampling and convenience sampling are similar in that both involve the selection of a sample based on the researcher's judgment rather than using a random sampling method. However, there are some key differences between the two approaches.

In purposive sampling, the sample is selected based on the defined purpose of the study and is intended to be representative of the characteristics or attributes in which the researcher is interested.

Convenience sampling, on the other hand, involves selecting a sample of individuals or units that are readily available or easily accessible to the researcher. The sample is not selected based on any particular characteristics or attributes, but rather in terms of convenience for the researcher.

Advantages of purposive sampling

There are several advantages to using purposive sampling in research studies, including:

Representative sample - allows the researcher to select a sample highly representative of the characteristics or attributes they are interested in studying, relatively quickly, This can be particularly useful when the population of interest is small or when the researcher is interested in studying a specific subgroup within the population.

In-depth data - often used in qualitative research, which allows the researcher to gather in-depth data on specific topics or issues. This can provide valuable insights and understanding of the research question.

Practicality - practical and efficient in comparison to other sampling methods, particularly in small-scale studies with limited sample sizes.

Flexibility - flexibility in the selection of the sample, which can be useful when the researcher is studying a rare or unusual phenomenon.

Cost - can be less expensive than other sampling methods, as it does not require a random selection process.

Disadvantages of purposive sampling

It's important to note that purposive sampling has limitations and should be used with caution. Some of the disadvantages of purposive sampling are listed below:

Limited generalizability - As the sample is not selected randomly, the study’s results may not be generalizable to the larger population. Other risk factors are producing lop-sided research, where some subgroups are omitted or excluded.

Bias - Purposive sampling is subjective and relies on the researcher's judgment, which can introduce bias into the study. The researcher may unconsciously select individuals or units that fit their expectations or preconceived notions, which can affect the study’s validity. Participants can also manipulate the insights they give.

Sampling error - Sampling error, or the difference between the sample and the population, is more likely to occur in purposive sampling because the sample is not selected randomly. This can affect the reliability and accuracy of the study.

Limited sample size - Purposive sampling is often used in small-scale studies with limited sample sizes. This can affect the statistical power of the study and make it more difficult to detect significant differences or relationships.

Ethical considerations - The researcher must ensure that the study is conducted ethically and that the rights of the participants are protected. This may require obtaining informed consent from the individuals in the sample and safeguarding their privacy.

Challenges to the use of purposeful sampling

One of the main challenges to the use of purposive sampling in research studies is the limited generalizability of the findings. Because the sample is not selected randomly, it may not be representative of the broader population, and study results may not be applicable to other groups or populations. This can limit the usefulness and impact of the study, making it more challenging to draw conclusions about the larger population.

Each of the disadvantages listed in the previous section contributes to this problem. Researchers who wish to use purposive sampling need to be aware of the method’s weaknesses and actively take steps to avoid or mitigate them.

Why is purposive sampling used?

Purposive sampling is used in research studies when the researcher has a clear idea of the characteristics or attributes they are interested in studying and wants to select a sample that is representative of those characteristics. It is often used in qualitative research to gather in-depth data on specific topics or issues.

What is an example of purposive sampling?

An example of purposive sampling might be a researcher studying the experiences of individuals living with chronic pain, and therefore selecting a sample of individuals who have been diagnosed with chronic pain.

What type of research uses purposive sampling?

Purposive sampling is often used in qualitative research, as it allows the researcher to gather in-depth data on specific topics or issues. It may also be used in small-scale studies with a limited sample size.

What is a good sample size for purposive sampling?

The sample size for purposive sampling will depend on the research question and the characteristics or attributes the researcher is interested in studying. Generally, a sample size of 30 individuals is often considered sufficient for qualitative research, although larger sample sizes of 100 or more may be needed in some cases.

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What Is Purposive Sampling? | Definition & Examples

What Is Purposive Sampling? | Definition & Examples

Published on August 11, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

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 methods and examples, maximum variation sampling, homogeneous sampling, typical case sampling, extreme (or deviant) case sampling, critical case sampling, expert sampling, example: step-by-step purposive sampling, advantages and disadvantages of purposive sampling, other interesting articles, frequently asked questions about 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.

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

Maximum variation (or heterogeneous) 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.

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.

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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.

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 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.

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.

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 .

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.

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.

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.

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.

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.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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.

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 .

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 .

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Sampling: An Overview

Importance of sampling in sociological research, types of sampling.

Challenges and Considerations in Sampling

Sampling is a critical concept in sociological research and forms the foundation for empirical investigation. It refers to the process of selecting a subset of individuals, groups, or cases from a larger population for the purpose of conducting research. Sociologists rely on sampling techniques to collect data that represent the broader social phenomena they are studying, making it essential to understand its different forms, advantages, and limitations. This article provides an overview of the major types of sampling methods, their importance, and how they contribute to sociological inquiry.

In sociological research, it is often impossible, impractical, or unnecessary to collect data from every member of the population being studied. Populations are often too large, geographically dispersed, or dynamic to make a census—a complete enumeration of all individuals—feasible. This is where sampling becomes essential. By selecting a representative subset of the population, sociologists can draw conclusions that are generalizable to the larger group.

Sampling also allows researchers to save time and resources while maintaining the validity and reliability of their findings. This efficiency is crucial, especially in large-scale studies or when working with limited budgets and time constraints. Proper sampling techniques ensure that the data collected reflects the diversity, complexity, and characteristics of the population under study. Moreover, effective sampling techniques reduce bias and improve the credibility of research findings, which are fundamental to advancing sociological knowledge.

Sampling can be broadly divided into two categories: probability sampling and non-probability sampling . Each of these categories contains various techniques, each suited for different research goals and conditions.

Probability Sampling

Probability sampling is a method that allows every member of the population to have a known, non-zero chance of being selected. This approach is often considered the gold standard in sociological research because it leads to greater representativeness and generalizability of findings. Some common forms of probability sampling include:

1. Simple Random Sampling

Simple random sampling is the most straightforward type of probability sampling. In this method, each member of the population has an equal chance of being selected. This could be achieved by randomly selecting individuals through methods such as drawing names from a hat or using a random number generator. Simple random sampling is advantageous because it reduces bias and allows for straightforward statistical analysis. However, it requires a complete list of the population, which may not always be available or practical.

2. Systematic Sampling

Systematic sampling involves selecting every nth individual from a list of the population after randomly choosing a starting point. For example, if a researcher wants to sample every 10th person from a list of 1,000 individuals, they might randomly select the 5th individual and then every 10th individual after that (15th, 25th, etc.). This method is simpler than simple random sampling and can be effective if the population list does not have a pattern that could introduce bias. However, if the list is arranged in a particular order, systematic sampling may inadvertently introduce bias.

3. Stratified Sampling

Stratified sampling involves dividing the population into different subgroups, or strata, based on a specific characteristic (e.g., gender, age, or income level) and then randomly selecting individuals from each stratum. This method ensures that specific subgroups are adequately represented in the sample. For example, if a population consists of 60% women and 40% men, a stratified sample would ensure that the sample reflects these proportions. Stratified sampling improves the precision of estimates for each subgroup and can lead to more accurate results, but it requires prior knowledge of the population structure.

4. Cluster Sampling

Cluster sampling involves dividing the population into clusters, such as geographic regions or schools, and then randomly selecting entire clusters to be part of the sample. Researchers then collect data from all individuals within the selected clusters. This method is often used when it is difficult or expensive to obtain a complete list of the population, such as in studies of rural populations or large urban areas. While cluster sampling can reduce logistical costs, it is generally less statistically efficient than other probability sampling methods because individuals within a cluster may be more similar to each other than to those in other clusters.

Non-Probability Sampling

Non-probability sampling methods do not provide every individual in the population with an equal chance of being selected. These methods are often used in qualitative research or in situations where probability sampling is impractical. While they may introduce bias, non-probability samples can still provide valuable insights, especially when the research is exploratory or seeks to understand complex social phenomena. Common non-probability sampling methods include:

1. Convenience Sampling

Convenience sampling is a method where the researcher selects individuals who are easiest to reach or who are available at the time of the study. For example, a researcher might survey students in their classroom because they are readily accessible. While this method is easy and cost-effective, it is prone to selection bias because it does not represent the broader population. The findings from a convenience sample cannot be generalized, but they can provide preliminary insights or be used in exploratory research.

2. Purposive Sampling

Purposive sampling, also known as judgmental or expert sampling, involves selecting individuals based on specific criteria or characteristics that are relevant to the research question. For example, if a study is focused on the experiences of single mothers, the researcher will intentionally select participants who fit that description. This method allows the researcher to focus on individuals who have particular knowledge or experience, making it well-suited for qualitative research. However, purposive sampling introduces subjectivity, as the researcher decides who is included, and this can limit the generalizability of the findings.

3. Snowball Sampling

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Published: 02 September 2024

“I am there just to get on with it”: a qualitative study on the labour of the patient and public involvement workforce

Stan Papoulias ORCID: orcid.org/0000-0002-7891-0923 1 &
Louca-Mai Brady 2

Health Research Policy and Systems volume 22 , Article number: 118 ( 2024 ) Cite this article

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Workers tasked with specific responsibilities around patient and public involvement (PPI) are now routinely part of the organizational landscape for applied health research in the United Kingdom. Even as the National Institute for Health and Care Research (NIHR) has had a pioneering role in developing a robust PPI infrastructure for publicly funded health research in the United Kingdom, considerable barriers remain to embedding substantive and sustainable public input in the design and delivery of research. Notably, researchers and clinicians report a tension between funders’ orientation towards deliverables and the resources and labour required to embed public involvement in research. These and other tensions require further investigation.

This was a qualitative study with participatory elements. Using purposive and snowball sampling and attending to regional and institutional diversity, we conducted 21 semi-structured interviews with individuals holding NIHR-funded formal PPI roles across England. Interviews were analysed through reflexive thematic analysis with coding and framing presented and adjusted through two workshops with study participants.

We generated five overarching themes which signal a growing tension between expectations put on staff in PPI roles and the structural limitations of these roles: (i) the instability of support; (ii) the production of invisible labour; (iii) PPI work as more than a job; (iv) accountability without control; and (v) delivering change without changing.

Conclusions

The NIHR PPI workforce has enabled considerable progress in embedding patient and public input in research activities. However, the role has led not to a resolution of the tension between performance management priorities and the labour of PPI, but rather to its displacement and – potentially – its intensification. We suggest that the expectation to “deliver” PPI hinges on a paradoxical demand to deliver a transformational intervention that is fundamentally divorced from any labour of transformation. We conclude that ongoing efforts to transform health research ecologies so as to better respond to the needs of patients will need to grapple with the force and consequences of this paradoxical demand.

Peer Review reports

Introduction – the labour of PPI

The inclusion of patients, service users and members of the public in the design, delivery and governance of health research is increasingly embedded in policy internationally, as partnerships with the beneficiaries of health research are seen to increase its relevance, acceptability and implementability. In this context, a growing number of studies have sought to evaluate the impact of public participation on research, including identifying the barriers and facilitators of good practice [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Some of this inquiry has centred on power, control and agency. Attention has been drawn, for example, to the scarcity of user or community-led research and to the low status of experiential knowledge in the hierarchies of knowledge production guiding evidence-based medicine [ 9 ]. Such hierarchies, authors have argued, constrain the legitimacy that the experiential knowledge of patients can achieve within academic-led research [ 10 ], may block the possibility of equitable partnerships such as those envisioned in co-production [ 11 ] and may function as a pull back against more participatory or emancipatory models of research [ 12 , 13 , 14 ]. In this way, patient and public inclusion in research may become less likely to aim towards inclusion of public and patient-led priorities, acting instead as kind of a “handmaiden” to research, servicing and validating institutionally pre-defined research goals [ 15 , 16 , 17 ].

Research on how public participation-related activities function as a form of labour within a research ecosystem, however, is scarce [ 18 ]. In this paper, we examine the labour of embedding such participation, with the aim of understanding how such labour fits within the regimes of performance management underpinning current research systems. We argue that considering this “fit” is crucial for a broader understanding of the implementation of public participation and therefore its potential impact on research delivery. To this end, we present findings from a UK study of the labour of an emerging professional cadre: “patient and public involvement” leads, managers and co-ordinators (henceforth PPI, the term routinely used for public participation in the United Kingdom). We concentrate specifically on staff working on research partnerships and centres funded by the National Institute for Health and Care Research (NIHR). This focus on the NIHR is motivated by the organization’s status as the centralized research and development arm of the National Health Service (NHS), with an important role in shaping health research systems in the United Kingdom since 2006. NIHR explicitly installed PPI in research as a foundational part of its mission and is currently considered a global leader in the field [ 19 ]. We contend that exploring the labour of this radically under-investigated workforce is crucial for understanding what we see as the shifting tensions – outlined in later sections – that underpin the key policy priority of embedding patients as collaborators in applied health research. To contextualize our study, we first consider how the requirement for PPI in research relates to the overall policy rationale underpinning the organizational mission of the NIHR as the NHS’s research arm, then consider existing research on tensions identified in efforts to embed PPI in a health system governed through regimes of performance management and finally articulate the ways in which dedicated PPI workers’ responsibilities have been developed as a way to address these tensions.

The NIHR as a site of “reformed managerialism”

The NIHR was founded in 2006 with the aim of centralizing and rationalizing NHS research and development activities. Its foundation instantiated the then Labour government’s efforts to strengthen and consolidate health research in the UK while also tackling some of the problems associated with the earlier introduction of new public management (NPM) principles in the governance of public services. NPM had been introduced in the UK public sector by Margaret Thatcher’s government, in line with similar trends in much of the Global North [ 20 ]. The aim was to curb what the Conservatives saw as saw as excesses in both public spending and professional autonomy. NPM consisted in management techniques adapted from the private sector: in the NHS this introduction was formalized via the 1990 National Health Service and Community Care Act, which created an internal market for services, with local authorities purchasing services from local health providers (NHS Trusts) [ 21 ]; top-down management control; an emphasis on cost-efficiency; a focus on targets and outputs over process; an intensification of metrics for performance management; and a positioning of patients and the public as consumers of health services with a right to choose [ 22 , 23 ]. In the context of the NHS, cost-efficiency meant concentrating on services and on research which would have the greatest positive impact on population health while preventing research waste [ 24 ]. By the mid-1990s, however, considerable criticism had been directed towards this model, including concerns that NPM techniques resulted in silo-like operations and public sector fragmentation, which limited the capacity for collaboration between services essential for effective policy. Importantly, there was also a sense that an excessive managerialism had resulted in a disconnection of public services from public and civic aims, that is, from the values, voices and interests of the public [ 25 , 26 ].

In this context, the emergence of the NIHR can be contextualized through the succeeding Labour government’s much publicized reformed managerialism, announced in their 1997 white paper “The New NHS: Modern, Dependable” [ 27 ]. Here, the reworking of NPM towards “network governance” meant that the silo-like effects of competition and marketization were to be attenuated through a turn to cross-sector partnerships and a renewed attention to quality standards and to patients’ voices [ 28 ]. It has been argued, however, that the new emphasis on partnerships did not undermine the dominance of performance management, while the investment in national standards for quality and safety resulted in an intensified metricization, with the result that this reform may have been more apparent than real, amounting to “NPM with a human face” [ 29 , 30 , 31 ]. Indeed, the NIHR can be seen as an exemplary instantiation of this model: as a centralized commissioner of research for the NHS, the NIHR put in place reporting mechanisms and performance indicators to ensure transparent and cost-efficient use of funds, with outputs and impact measured, managed and ranked [ 24 ]. At the same time, the founding document of the NIHR, Best Research for Best Health, articulates the redirection of such market-oriented principles towards a horizon of public good and patient benefit. The document firmly and explicitly positioned patients and the public as both primary beneficiaries of and important partners in the delivery of health research. People (patients) were to be placed “at the centre of a research system that focuses on quality, transparency and value for money” [ 32 ], a mission implemented through the installation of “structures and mechanisms to facilitate increased involvement of patients and the public in all stages of NHS Research & Development” [ 33 ]. This involvement would be supported by the advisory group INVOLVE, a key part of the new centralized health research system. INVOLVE, which had started life in 1996 as Consumers in NHS Research, funded by the Department of Health, testified to the Labour administration’s investment in championing “consumer” involvement in NHS research as a means of increasing research relevance [ 34 ]. The foundation of the NIHR then exemplified the beneficent alignment of NPM with public benefit, represented through the imaginary of a patient-centred NHS, performing accountability to the consumers/taxpayers through embedding PPI in all its activities. In this context, “public involvement” functioned as the lynchpin through which such alignment could be effected.

PPI work and the “logic of deliverables”: a site of tension

Existing research on the challenges of embedding PPI has typically focussed on the experiences of academics tasked with doing so within university research processes. For example, Pollard and Evans, in a 2013 paper, argue that undertaking PPI work in mental health research can be arduous, emotionally taxing and time consuming, and as such, can be in tension with expectations for cost-efficient and streamlined delivery of research outputs [ 35 ]. Similarly, Papoulias and Callard found that the “logic of deliverables” governing research funding can militate against undertaking PPI or even constitute PPI as “out of sync” with research timelines [ 36 ]. While recent years have seen a deepening operationalization of PPI in the NIHR and beyond, there are indications that this process, rather than removing these tensions, may have recast them in a different form. For example, when PPI is itself set up as performance-based obligation, researchers, faced with the requirement to satisfy an increasing number of such obligations, may either engage in “surface-level spectacles” to impress the funder while eschewing the long-term commitment necessary for substantive and ongoing PPI, or altogether refuse to undertake PPI, relegating the responsibility to others [ 37 , 38 ]. Such refusals may then contribute to a sharpening of workplace inequalities: insofar as PPI work is seen as “low priority” for more established academic staff, it can be unevenly distributed within research organizations, with precariously employed junior researchers and women typically assigned PPI responsibilities with the assumption that they possess the “soft skills” necessary for these roles [ 39 ].

Notably, the emergence of a dedicated PPI workforce is intended as a remedy for this tension by providing support, expertise and ways of negotiating the challenges associated with undertaking PPI responsibilities. In the NIHR, this workforce is part of a burgeoning infrastructure for public involvement which includes national standards, training programmes, payment guidelines, reporting frameworks and impact assessments [ 40 , 41 , 42 , 43 , 44 , 45 ]. By 2015, an INVOLVE review of PPI activities during the first 10 years of the NIHR attested to “a frenzy of involvement activity…across the system”, including more than 200 staff in PPI-related roles [ 40 ]. As NIHR expectations regarding PPI have become more extensive, responsibilities of PPI workers have proliferated, with INVOLVE organizing surveys and national workshops to identify their skills and support needs [ 41 , 42 ]. In 2019, the NIHR mandated the inclusion of a “designated PPI lead” in all funding applications, listing an extensive and complex roster of responsibilities. These now included delivery and implementation of long-term institutional strategies and objectives, thus testifying to the assimilation of involvement activities within the roster of “performance-based obligations” within research delivery systems [ 43 ]. Notably however, this formalization of PPI responsibilities is ambiguous: the website states that the role “should be a budgeted and resourced team member” and that they should have “the relevant skills, experience and authority”, but it does not specify whether this should be a researcher with skills in undertaking PPI or indeed someone hired specifically for their skills in PPI, that is, a member of the PPI workforce. Equally, the specifications, skills and support needs, which have been brought together into a distinct role, have yet to crystallize into a distinct career trajectory.

Case studies and evaluations of PPI practice often reference the skills and expertise required in leading and managing PPI. Chief among them are relational and communication skills: PPI workers have been described as “brokers” who mediate and enable learning between research and lay spaces [ 44 , 45 ]; skilled facilitators enabling inclusive practice [ 46 , 47 , 48 ]; “boundary spanners” navigating the complexities of bridging researchers with public contributors and undertaking community engagement through ongoing relational work [ 49 ]. While enumerating the skillset required for PPI work, some of these studies have identified a broader organizational devaluation of PPI workers: Brady and colleagues write of PPI roles as typically underfunded with poor job security, which undermines the continuity necessary for generating trust in PPI work [ 46 ], while Mathie and colleagues report that many PPI workers describe their work as “invisible”, a term which the authors relate to the sociological work on women’s labour (particularly housework and care labour) which is unpaid and rendered invisible insofar as it is naturalized as “care” [ 50 ]. Research on the neighbouring role of public engagement professionals in UK universities, which has been more extensive than that on PPI roles, can be instructive in fleshing out some of these points: public engagement professionals (PEPs) are tasked with mediating between academics and various publics in the service of a publicly accountable university. In a series of papers on the status of PEPs in university workplaces, Watermeyer and colleagues argue that, since public engagement labour is relegated to non-academic forms of expertise which lack recognition, PEPs’ efforts in boundary spanning do not confer prestige. This lack of prestige can, in effect, function as a “boundary block” obstructing PEPs’ work [ 51 , 52 ]. Furthermore, like Mathie and Brady, Watermeyer and colleagues also argue that the relational and facilitative nature of engagement labour constitutes such labour as feminized and devalued, with PEPs also reporting that their work remains invisible to colleagues and institutional audit instruments alike [ 50 , 53 ].

The present study seeks to explore further these suggestions that PPI labour, like that of public engagement professionals, lacks recognition and is constituted as invisible. However, we maintain that there are significant differences between the purpose and moral implications of involvement and engagement activities. PPI constitutes an amplification of the moral underpinnings of engagement policies: while public engagement seeks to showcase the public utility of academic research, public involvement aims to directly contribute to optimizing and personalizing healthcare provision by minimizing research waste, ensuring that treatments and services tap into the needs of patient groups, and delivering the vision of a patient-centred NHS. Therefore, even as PPI work may be peripheral to other auditable research activities, it is nevertheless central to the current rationale for publicly funded research ecosystems: by suturing performance management and efficiency metrics onto a discourse of public benefit, such work constitutes the moral underpinnings of performance management in health research systems. Therefore, an analysis of the labour of the dedicated PPI workforce is crucial for understanding how this suturing of performance management and “public benefit” works over the conjured figures of patients in need of benefit. This issue lies at the heart of our research study.

Our interview study formed the first phase of a multi-method qualitative inquiry into the working practices of NIHR-funded PPI leads. While PPI lead posts are in evidence in most NIHR-funded research, we decided to focus on NIHR infrastructure funding specifically: these are 5-year grants absorbing a major tranche of NIHR funds (over £600 million annually in 2024). They function as “strategic investments” embodying the principles outlined in Best Research for Best Health: they are awarded to research organizations and NHS Trusts for the purposes of developing and consolidating capacious environments for early stage and applied clinical research, including building a research delivery workforce and embedding a regional infrastructure of partnerships with industry, the third sector and patients and communities [ 55 ]. We believe that understanding the experience of the PPI workforce funded by these grants may give better insights into NIHR’s ecosystem and priorities, since they are specifically set up to support the development of sustainable partnerships and embed the translational pipeline into clinical practice.

The study used purposive sampling with snowball elements. In 2020–2021, we mapped all 72 NIHR infrastructure grants, identified the PPI teams working in each of these using publicly available information (found on the NIHR website and the websites and PPI pages of every organization awarded infrastructure grants) and sent out invitation emails to all teams. Where applicable, we also sent invitations to mailing lists of PPI-lead national networks connected to these grants. Inclusion criteria were that potential participants should have oversight roles, and/or be tasked with cross-programme/centre responsibilities, meaning that their facilitative and strategy building roles should cover the entirety of activities funded by one (and sometimes more than one) NIHR infrastructure grant or centres including advisory roles over most or all research projects associated with the centre of grant, and that they had worked in this or a comparable environment for 2 years.

The individuals who showed interest received detailed information sheets. Once they agreed to participate, they were sent a consent form and a convenient interview time was agreed. We conducted 21 semi-structured interviews online, between March and June 2021, lasting 60–90 min. The interview topic guide was developed in part through a review of organizational documents outlining the role and through a consideration of existing research on the labour of PPI within health research environments. It focussed on how PPI workers fit within the organization relationship between the actual work undertaken and the way this work is represented to both the organization and the funder. Interview questions included how participants understand their role; how they fit in the organization; how their actual work relates to the job description; how their work is understood by both colleagues and public contributors; the relationship between the work they undertake and how this is represented in reports to funder and presentations; and what they find challenging about their work. Information about participants’ background and what brought them to their present role was also gathered. Audio files were checked, transcribed and the transcripts fully de-identified. All participants were given the opportunity to check transcripts and withdraw them at any point until December 2021. None withdrew.

We analysed the interviews using reflexive thematic analysis with participatory elements [ 54 , 55 ]. Reflexive thematic analysis emphasizes the interpretative aspects of the analytical process, including the data “collection” process itself, which this approach recognizes as a generative act, where meaning is co-created between interviewer and participant and the discussion may be guided by the participant rather than strictly adhering to the topic guide [ 56 ]. We identified patterns of meaning through sustained and immersive engagement with the data. NVivo 12 was used for coding, while additional notes and memos on the Word documents themselves mitigated the over-fragmentation that might potentially limit NVivo as a tool for qualitative analysis. Once we had developed themes which gave a thorough interpretation of the data, we presented these to participants in two separate workshops to test for credibility and ensure that participants felt ownership of the process [ 57 ].

As the population from which the sample was taken is quite small, with some teams working across different infrastructure grants, confidentiality and anonymity were important concerns for participants. We therefore decided neither to collect nor to present extensive demographic information to preserve confidentiality and avoid deductive disclosure [ 58 ]. Out of our 21 participants 20 were women; there was some diversity in age, ethnicity and heritage, with a significant majority identifying as white (British or other European). Participants had diverse employment histories: many had come from other university or NHS posts, often in communications, programme management or human resources; a significant minority had come from the voluntary sector; and a small minority from the private sector. As there was no accredited qualification in PPI at the time this study was undertaken, participants had all learned their skills on their present or previous jobs. A total of 13 participants were on full-time contracts, although in several cases funding for these posts was finite and fragmented, often coming from different budgets.

In this paper we present five inter-related themes drawing on the conceptual architecture we outlined in the first half of this paper to explore how PPI workers navigate a research ecosystem of interlocking institutional spaces that is governed by “NPM with a human face”, while striving to align patients and the public with the imaginary of the patient-centred NHS that mobilizes the NIHR mission. These five themes are: (i) the instability of support; (ii) the production of invisible labour; (iii) PPI as moral imperative; (iv) accountability without control; and (v) delivering change without changing.

“There to grease the cogs rather than be the cogs”: the instability of “support”

Infrastructure grants act as a hub for large numbers of studies, often in diverse health fields, most of which should, ideally, include PPI activities. Here, dedicated PPI staff typically fulfil a cross-cutting role: they are meant to oversee, provide training and advise on embedding PPI activities across the grant and, in so doing, support researchers in undertaking PPI. On paper, support towards the institution in the form of training, delivering strategy for and evaluating PPI is associated with more senior roles (designated manager or lead) whereas support towards so-called public contributors is the remit of more junior roles (designated co-ordinator or officer) and can include doing outreach, facilitating, attending to access needs and developing payment and compensation procedures. However, these distinctions rarely applied in practice: participants typically reported that their work did not neatly fit into these categories and that they often had to fulfil both roles regardless of their title. Some were the only person in the team specifically tasked with PPI, and so their “lead” or “manager” designation was more symbolic than actual:

I have no person to manage, although sometimes I do get a little bit of admin support, but I don’t have any line management responsibility. It is really about managing my workload, working with people and managing the volunteers that I work with and administrating those groups and supporting them (P11).

P11’s title was manager but, as they essentially worked alone, shuttling between junior and senior role responsibilities, they justified and made sense of their title by reframing their support work with public contributors as “management”. Furthermore, other participants reported that researchers often misunderstood PPI workers’ cross-cutting role and expected them to both advise on and deliver PPI activities themselves, even in the context of multiple projects, thus altogether releasing researchers of such responsibility.

As a PPI lead, it is very difficult to define what your role is in different projects….and tasks … So, for example, I would imagine in [some cases] we are seen as the go-to if they have questions. [..] whereas, in [other cases], it is like, “Well, that’s your job because you’re the PPI lead” […] there is not a real understanding that PPI is everyone’s responsibility and that the theme leads are there to facilitate and to grease the cogs rather than be the cogs (P20).

Furthermore, participants reported that the NIHR requirement for a PPI lead in all funding applications might in fact have facilitated this slippage. As already mentioned, the NIHR requirement does not differentiate between someone hired specifically to undertake PPI and a researcher tasked with PPI activities. The presence of a member of staff with a “PPI lead” title thus meant that PPI responsibilities in individual research studies could continue to accrue on that worker:

The people who have been left with the burden of implementing [the NIHR specified PPI lead role] are almost exclusively people like me, though, because now researchers expect me to allow myself to be listed on their project as the PPI lead, and I actually wrote a document about what they can do for the PPI lead that more or less says, “Please don’t list me as your PPI lead. Please put aside funds to buy a PPI lead and I will train them, because there is only one me; I can’t be the PPI lead for everyone” (P10).

This expectation that core members of staff with responsibilities for PPI would also be able to act as PPI leads for numerous research projects suggests that this role lacks firm organizational co-ordinates and boundaries. Here, the presence of a PPI workforce does not, in fact, constitute an appropriate allocation of PPI labour but rather testifies to a continuing institutional misapprehension of the nature of such labour particularly in terms of its duration, location and value.

Conjuring PPI: the production of invisible labour

Participants consistently emphasized the invisibility of the kinds of labour, both administrative and relational, specific to public involvement as a process, confirming the findings of Mathie and colleagues [ 50 ]. This invisibility took different forms and had different justifications. Some argued that key aspects of their work, which are foundational to involvement, such as the process of relationship building, do not lend themselves to recognition as a performance indicator: “ There is absolutely no measure for that because how long is a piece of string” (P11). In addition, relationship building necessitated a considerably greater time investment than was institutionally acceptable, and this was particularly evident when it came to outreach. Participants who did their work in community spaces told stories of uncomprehending line-managers, or annoyed colleagues who wondered where the PPI worker goes and what they do all day:

There is very little understanding from colleagues about what I do on a day-to-day basis, and it has led to considerable conflict …. I would arrive at the office and then I would be disappearing quite promptly out into the community, because that is where I belong […] So, it is actually quite easy to become an absent person (P3).

Once again, the NIHR requirement for designated PPI leads in funding applications, intended to raise the visibility of PPI work by formalizing it as costed labour, could instead further consolidate its invisibility:

I am constantly shoved onto bids as 2% of my full-time equivalent and I think I worked out for a year that would be about 39 hours a year. For a researcher, popping the statistician down and all these different people on that bid, “Everyone is 2% and we need the money to run the trial, so 2% is fine”. And if I said to them, “Well, what do you think I would do in those 39 hours?” they wouldn’t have a clue, not a clue (P17).

The 2% of a full-time allocation is accorded to the PPI worker because 2–5% is the time typically costed for leadership roles or for roles with a circumscribed remit (e.g. statisticians). However, this allocation, in making PPI workers’ labour visible either as oversight (what project leads do) or as methodological expertise (what statisticians do), ends up producing the wrong kind of visibility: the 39 h mentioned here might make sense when the role mainly involves chairing weekly meetings or delivering statistical models but are in no way sufficient for the intense and ongoing labour of trust-building and alignment between institutions and public contributors in PPI.

Indeed, such costings, by eliding the complexity and duration of involvement, may reinforce expectations that PPI can be simply conjured up at will and delivered on demand:

A researcher will say to us, “I would really like you to help me to find some people with lived experience, run a focus group and then I’ll be away”. To them, that is the half-hour meeting to talk about this request, maybe 10 minutes to draft a tweet and an email to a charity that represents people with that condition […] the reality is it is astronomically more than that, because there is all this hidden back and forth. […] [researchers] expect to be able to hand over their protocol and then I will find them patients and those patients will be … representative and I will be able to talk to all of those patients and … write them up a report and …send it all back and they will be able to be like, “Thanks for the PPI”, and be on their merry way (P13).

What P13 communicates in this story is the researcher’s failure to perceive the difference between PPI work and institutional norms for project delivery: the researcher who asks for “some people with lived experience” is not simply underestimating how long this process will take. Rather, involvement work is perceived as homologous to metricized and institutionally recognizable activities (for example, recruitment to trials or producing project reports) for which there already exist standard procedures. Here, the relational complexity and improvised dynamic of involvement is turned into a deliverable (“the PPI”) that can be produced through following an appropriate procedure. When PPI workers are expected to instantly deliver the right contributors to fit the project needs, PPI labour is essentially black boxed and in its place sits “the PPI”, a kind of magical object seemingly conjured out of nowhere.

Such invisibility, however, may also be purposefully produced by the PPI workers themselves. One participant spoke of this at length, when detailing how they worked behind the scenes to ensure public contributors have input into research documents:

When we get a plain English summary from a researcher, we rewrite them completely. If the advisory group [see] … a really bad plain English summary, they are just going to go, “I don’t understand anything”. I might as well do the translation straight away so that they can actually review something they understand. [Researchers then] think, “Oh, [the public advisory group] are so good at writing” … and I am thinking, “Well, they don’t … write, they review, and they will say to me, ‘Maybe move this up there and that up there, and I don’t understand these’”, … They are great, don’t get me wrong, but they don’t write it. And it is the same with a lot of things. They think that [the group] are the ones that do it when it is actually the team (P7).

Here, the invisibility of the PPI worker’s labour is purposefully wrought to create good will and lubricate collaboration. Several participants said that they chose to engage in such purposeful invisibility because they knew that resources were not available to train researchers in plain writing and public contributors in academic writing. PPI workers, in ghost-writing accessible texts, thus effect a shortcut in the institutional labour required to generate alignment between researchers and public contributors. However, this shortcut comes at a price: in effecting it, PPI workers may collude in conjuring “the PPI” – they may themselves make their own work disappear.

“Not a 9 to 5”: PPI work as more than a job

Most participants reported that overtime working was common for themselves and their teammates, whether they were on a fractional or full-time contract. Overall, participants saw undertaking extra work as a necessary consequence of their commitment towards public contributors, a commitment which made it difficult to turn work down:

Everyone loses if you say no: the public contributors aren’t involved in a meaningful way, the project won’t be as good because it doesn’t have meaningful PPI involvement (P20).

While overwork was a common result of this commitment, some participants described such overwork as the feature that distinguished PPI work from what one commonly understands as a “job”, because, in this case, over-work was seen as freely chosen rather than externally imposed:

It is me pushing myself or wanting to get things done because I started it and I think I would get less done if I worked less and that would bother me, but I don’t think it is a pressure necessarily from [line manager] or [the institution] or anyone to be like, “No, do more” (P13).

Participants presented relationship building not only as the most time-consuming but also the most enjoyable aspect of PPI work. Community engagement was a key site for this and once again participants tended to represent this type of work as freely chosen:

I did most of the work in my free time in the end because you have to go into communities and you spend a lot longer there. […] So, all of that kind of thing I was just doing in my spare time and I didn’t really notice at the time because I really enjoyed it (P6).

Thus, time spent in relationship building was constituted as both work and not work. It did not lend itself to metricization via workplace time management and additionally, was not perceived by participants themselves as labour (“I didn’t really notice it at the time”). At the same time, out-of-hours work was rationalized as necessary for inclusivity, set up to enable collaboration with public contributors in so far as these do not have a contractual relationship to the employer:

That is not a 9–5. That is a weekends and holidays sort of job, because our job is to reduce the barriers to involvement and some of those barriers are hours – 9–5 is a barrier for some people (P17).

If working overtime allows PPI workers to reduce barriers and enable collaboration with those who are not employed by the institution, that same overtime work also serves to conceal the contractual nature of the PPI workers’ own labour, which now becomes absorbed into the moral requirements of PPI.

“Caught in the middle”: accountability without control

Participants repeatedly emphasized that their ability to contribute to research delivery was stymied by their lack of control over specific projects and over broader institutional priority setting:

… as a PPI lead we are not full member of staff, we are not responsible for choosing the research topics. We […] can only guide researchers who come to us and tell us what they are doing … we don’t have any power to define what the public involvement looks like in a research project (P6).

Tasked with creating alignments and partnerships between the publics and institutions, participants argued that they did not have the power to make them “stick” because they are not “really” part of the team. However, even as PPI workers lacked the power to cement partnerships, any failure in the partnership could be ascribed to them, perceived as a failure of the PPI worker by both funder and public contributors:

Often you have to hand over responsibility and the researcher [who] can let the panel down and … I feel like I have let the panel member down because … I am the one who said, “Oh yes, this person wants to talk to you”, and I find that really challenging, getting caught in the middle like that (P21).

This pairing of accountability with lack of control became more pronounced in grant applications or reports to the funder:

It is also quite frustrating in the sense that, just because I advise something, it doesn’t necessarily mean that it gets implemented or even included in the final grant. [even so] whatever the feedback is still reflects on us, not necessarily on the people who were making the wider decisions […] As PPI leads, we are still usually the ones that get the blame (P10).

Several participants testified to this double frustration: having to witness their PPI plans being rewritten to fit the constraints (financial, pragmatic) of the funding application, they then often found themselves held accountable if the PPI plans fail to carry favour with the funder. PPI workers then become the site where institutional accountability to both its public partners and to the funder gathers – it is as though, while located outside most decision-making, they nevertheless become the attractors for the institution’s missing accountability, which they experience, in the words of P21, as “ being caught in the middle ” or, as another participant put it, as “ the worry you carry around ” (P16).

“There to just get on with it”: delivering change without changing

Participants recognized that effective collaboration between research institutions and various publics requires fundamental institutional changes. Yet they also argued that while PPI workers are not themselves capable of effecting such change, there is nevertheless considerable institutional pressure to deliver on promises made in grant applications and build PPI strategies on this basis:

So, there is that tension about […] pushing this agenda and encouraging people to do more [….] rather than just accepting the status quo. But actually, the reality is that it is very, very hard to get everybody in [grant name] to change what they do and I can’t make that happen, [senior PPI staff] can’t make that happen, nobody can. The whole systemic issue … But you have got, somehow in the strategy and what you say you are going to do, that tension between aspiration and reality (P4).

This tension between aspiration and reality identified here could not be spelled out in reports for fear of reputational damage. In fact, the expectation to have delivered meaningful PPI, now routinely set up in NIHR applications, could itself militate against such change. For example, a frequently voiced concern was that PPI was being progressively under-resourced:

I feel the bar is getting higher and higher and higher and expectations are higher and we have got no extra resource (P16).

However, annual reports, the mechanism through which the doing of PPI is evidenced, made it difficult to be open about any such under-resourcing.

We will allude to [the lack of resources]. So, we will say things like, “We punch above our weight”, but I am not sure that message gets home to the NIHR very clearly. It is not like the annual report is used to say, “Hey, you’re underfunding this systematically, but here’s all the good stuff we do”, because the annual report is, by essence, a process of saying how great you are, isn’t it? (P3).

The inclusion of PPI as a “deliverable” meant that, in a competitive ecosystem, the pressure is on to report that PPI has always already been delivered. As another participant put it, “ no one is going to report the bad stuff ” (P17). Hence reporting, in setting up PPI as a deliverable, reinforced new zones of invisibility for PPI labour and made it harder to surface any under-resourcing for such labour. Furthermore, such reporting also played down any association between successful PPI and system transformation. Another participant described the resistance they encountered after arguing the organization should move away from “last-minute” PPI:

I think it is really hard when […] these people are essentially paying your pay cheque, to then try to push back on certain things that I don’t think are truly PPI ….[A]s somebody who I felt my role was really to show best practice, for then [to be] seen as this difficult person for raising issues or pushing back rather than just getting things done, is really hard [….] I get the impression, at least within the [organization] … that I am not there to really point out any of the issues. I am there just to get on with it (P14).

This opposition between pointing out the issues and “getting on with it” is telling. It names a contradiction at the heart of PPI labour: here, the very act of pushing back – in this case asking for a commitment to more meaningful and ongoing PPI – can be perceived as going against the PPI worker’s responsibilities, insofar as it delays and undoes team expectations for getting things done, for delivering PPI. Here, then, we find an exemplary instance of the incommensurability between the temporal demands of research and those of meaningful PPI practice.

How do the five themes we have presented help open out how policies around public participation are put into practice—as well as the contradictions that this practice navigates – in health systems organized by the rhetorical suturing of performance management onto public benefit? We have argued that the development of a dedicated workforce represents an attempt to “repair” the tension experienced by researchers between the administrative, facilitative and emotional work of PPI and the kinds of deliverables that the institution requires them to prioritize. We argue that our findings indicate that insofar as PPI workers’ role then becomes one of “delivering” PPI, this tension is reproduced and at times intensified within their work. This is because, as actors in the health research ecosystem, PPI staff are tethered to the very regimes of performance management, which give rise to an institutional misapprehension of the actual labour associated with delivering PPI.

This misapprehension surfaces in the instruments through which the funder costs, measures and generates accountability for PPI – namely, the requirement for a costed PPI lead and the mandatory inclusion of a PPI section in applications and regular reports to funder. The NIHR requirement for a costed PPI lead, intended to legitimize the undertaking of PPI as an integral part of a research team’s responsibilities, may instead continue to position the PPI worker as a site for the research team’s wholesale outsourcing of responsibility for PPI, since this responsibility, while in tension with other institutional priorities, cannot nevertheless be refused by the team. Furthermore, the use of titles such as lead, manager or co-ordinator not only signal an orderly distinction between junior and senior roles, which often does not apply in practice, but also reframes the extra-institutional work of PPI (the forging of relationships and administrative support with public contributors), through the intra-institutional functions of performance/project management. This reframing elides an important difference between the two: public and patient partners, for the most part, do not have a formal contractual relationship with the institution and are not subject to performance management in the way that contracted researchers and healthcare professionals are. Indeed, framing the relationship between PPI workers and public contributors through the language of “management” fundamentally misrecognizes the kinds of relationalities produced in the interactions between PPI workers and public contributors and elides the externality of PPI to the “logic of deliverables” [ 36 ].

The inclusion of a detailed PPI section in grant applications and annual reports to funder further consolidates this misapprehension by also representing public involvement as if it is already enrolled within organizational normative procedures and therefore compels those in receipt of funding to evidence such delivery through annual reports [ 37 ]. This demand puts PPI workers under increasing pressure, since their function is to essentially present PPI objectives as not only achievable but already achieved, thus essentially bracketing out the process of organizational transformation which is a necessary prerequisite to establishing enduring partnerships with patients and the public. This bracketing out is at work in the organizational expectation to “just get on with it”, which structures the labour of delivering PPI in NIHR-funded research. Here, the demand to just get on, to do the work one is paid to do, forecloses the possibility of engaging with the structural obstacles that militate against that work being done. To the extent that both role designation and reporting expectations function to conceal the disjuncture that the establishment of public partnerships represents for regimes of performance management, they generate new invisibilities for PPI workers. These invisibilities radically constrain how such labour can be adequately undertaken, recognized and resourced.

In suggesting that much of the labour of staff in public involvement roles is institutionally invisible, and that organizational structures may obstruct or block their efforts, we concur with the arguments made by Watermeyer, Mathie and colleagues about the position of staff in public engagement and public involvement roles, respectively. However, our account diverges from theirs in our interpretation of how and why this labour is experienced as invisible and how that invisibility could be remedied. Mathie and colleagues in particular attribute this invisibility to a lack of parity and an institutional devaluation of what are perceived as “soft skills” – facilitation and relationship building in particular [ 50 ]. They therefore seek to raise PPI work to visibility by emphasizing the complexity of PPI activities and by calling for a ring-fencing of resources and a development of infrastructures capable of sustaining such work. While we concur that the invisibility of PPI labour is connected to its devaluation within research institutions, we also suggest that, in addition, this invisibility is a symptom of a radical misalignment between regimes of performance management and the establishment of sustainable public partnerships. Establishing such partnerships requires, as a number of researchers have demonstrated [ 18 , 59 , 60 ], considerable institutional transformation, yet those tasked with delivering PPI are not only not in a position to effect such transformation, they are also compelled to conceal its absence.

Recognizing and addressing the misalignment between regimes of performance management and the establishment of sustainable public partnerships becomes particularly pressing given the increasing recognition, in many countries, that public participation in health research and intervention development is an important step to effectively identifying and addressing health inequalities [ 19 , 61 , 62 ]. Calls for widening participation, for the inclusion of under-served populations and for co-designing and co-producing health research, which have been gathering force in the last 20 years, have gained renewed urgency in the wake of the coronavirus disease 2019 (COVID-19) pandemic [ 63 , 64 , 65 , 66 , 67 ]. In the United Kingdom, Best Research for Best Health: The Next Chapter, published by the NIHR in 2021 to define the direction and priorities for NHS Research for the coming decade, exemplifies this urgency. The document asserts that a radical broadening of the scope of PPI (now renamed “public partnerships”) is essential for combatting health inequalities: it explicitly amplifies the ambitions of its 2006 predecessor by setting up as a key objective “close and equitable partnerships with communities and groups, including those who have previously not had a voice in research” [ 68 ]. Here, as in other comparable policy documents, emphasis on extending partnerships to so-called underserved communities rests on the assumption that, to some degree at least, PPI has already become the norm for undertaking research. This assumption, we argue, closes down in advance any engagement with the tensions we have been discussing in this paper, and in so doing risks exacerbating them. The document does recognize that for such inclusive partnerships to be established institutions must “work differently, taking research closer to people [..] and building relationships of trust over time” – though, we would suggest, it is far from clear how ready or able institutions are really to take on what working differently might mean.

Our study engages with and emphasizes this need to “work differently” while also arguing that the demands and expectations set up through regimes of performance management and their “logic of deliverables” are not favourable to an opening of a space in which “working differently” could be explored. In health research systems organized through these regimes, “working differently” is constrained by the application of the very templates, instruments and techniques which constitute and manage “business as usual”. Any ongoing effort to transform health research systems so as better to respond to growing health inequalities, our study implies, needs to combat, both materially and procedurally, the ease with which the disjuncture between embedding public partnerships and normative ways of undertaking research comes to disappear.

Limitations

We focus on the labour of the PPI workforce and their negotiation of performance management regimes, which means that we have not discussed relationships between PPI staff and public contributors nor presented examples of good practice. While these are important domains for study if we are to understand the labour of the PPI workforce, they lie outside the scope of this article. Furthermore, our focus on the UK health research system means that our conclusions may have limited generalizability. However, both the consolidation of NPM principles in public sector institutions and the turn to public and patient participation in the design and delivery of health research are shared developments across countries in the Global North in the last 40 years. Therefore, the tensions we discuss are likely to also manifest in health systems outside the United Kingdom, even as they may take somewhat different forms, given differences in how research and grants are costed, and roles structured. Finally, this project has elements of “insider” research since both authors, while working primarily as researchers, have also had experience of embedding PPI in research studies and programmes. Insider research has specific strengths, which include familiarity with the field and a sense of shared identity with participants which may enhance trust, facilitate disclosure and generate rich data. In common with other insider research endeavours, we have sought to reflexively navigate risks of bias and of interpretative blind spots resulting from over-familiarity with the domain under research [ 69 ] by discussing our findings and interpretations with “non-insider” colleagues while writing up this research.

Our qualitative study is one of the first to investigate how the UK PPI workforce is negotiating the current health research landscape. In doing so, we have focused on the UK’s NIHR since this institution embodied the redirection of performance management regimes towards public benefit by means of public participation. If PPI is set up as both the means of enabling this redirection and an outcome of its success, then the PPI workforce, the professional cadre evolving to support PPI, becomes, we argue, the site where the tensions of attempting this alignment are most keenly experienced.

We suggest that, while such alignment would demand a wholesale transformation of organizational norms, the regimes of performance management underpinning research ecologies may also work to foreclose such transformation, thus hollowing out the promise of patient-centred research policies and systems. Recognizing and attending to this foreclosure is urgent, especially given the current policy emphasis in many countries on broadening the scope, ambition and inclusivity of public participation as a means of increasing the reach, relevance and potential positive impact of health research.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

S.P. presented earlier versions of this paper at the 8th annual conference of the Centre for Public Engagement Kingston University, December 2021; at the Medical Sociology conference of the British Sociological Association, September 2022; and at the annual Health Services Research UK Conference, July 2023. They are grateful to the audiences of these presentations for their helpful comments. Both authors are also grateful to the generous participants and to the NIHR Applied Research Collaboration Public Involvement Community for their sustaining support and encouragement during this time. S.P. also wishes to thank Felicity Callard for her comments, advice and suggestions throughout this process: this paper would not have been completed without her.

S.P. is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration (ARC) South London at King’s College Hospital NHS Foundation Trust. The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

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S.P. developed the original idea for this article through earlier collaborations with L.M.B. whose long-term experience as a PPI practitioner has been central to both the project and the article. L.M.B. contributed to conceptualization, wrote the first draft of the background and undertook revisions after the first draft including reconceptualization of results. S.P. contributed to conceptualization, undertook data analysis, wrote the first draft of findings and discussion and revised the first draft in its entirety in consultation with L.M.B. Both authors read and approved the final manuscript.

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Papoulias, S., Brady, LM. “I am there just to get on with it”: a qualitative study on the labour of the patient and public involvement workforce. Health Res Policy Sys 22 , 118 (2024). https://doi.org/10.1186/s12961-024-01197-5

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Open access
Published: 04 September 2024

Perspectives on a peer-driven intervention to promote pre-exposure prophylaxis (PrEP) uptake among men who have sex with men in southern New England: a qualitative study

Jun Tao 1 , 2 , 3 ,
Hannah Parent 2 ,
Ishu Karki 4 ,
Harrison Martin 2 ,
Sarah Alexandra Marshall 4 ,
Jhanavi Kapadia 5 ,
Amy S. Nunn 6 ,
Brandon D. L. Marshall 3 ,
Henry F. Raymond 7 ,
Leandro Mena 8 &
Philip A. Chan 1 , 2

BMC Health Services Research volume 24 , Article number: 1023 ( 2024 ) Cite this article

Metrics details

Pre-exposure prophylaxis (PrEP) is a highly effective pharmaceutical intervention that prevents HIV infection, but PrEP uptake across the US has been slow among men who have sex with men (MSM), especially among Black/African American (B/AA) and Hispanic /Latino (H/L) MSM. This study investigates the acceptability and essential components of a peer-driven intervention (PDI) for promoting PrEP uptake among MSM, with a specific focus on B/AA and H/L communities.

We conducted 28 semi-structured, qualitative interviews with MSM in southern New England to explore the components of a PDI, including attitudes, content, and effective communication methods. A purposive sampling strategy was used to recruit diverse participants who reflect the communities with the highest burden of HIV infection.

Of 28 study participants, the median age was 28 years (interquartile range [IQR]: 25, 35). The sample comprised B/AA (39%, n = 11) and H/L (50%, n = 14) individuals. Notably, nearly half of the participants (46%) were current PrEP users. We found that many participants were in favor of using a PDI approach for promoting PrEP. Additionally, several participants showed interest in becoming peer educators themselves. They emphasized the need for strong communication skills to effectively teach others about PrEP. Moreover, participants noted that peer education should cover key topics like how PrEP works, how effective it is, and any possible side effects.

Conclusions

Our study shows that effective PDIs, facilitated by well-trained peers knowledgeable about PrEP, could enhance PrEP uptake among MSM, addressing health disparities and potentially reducing HIV transmission in B/AA and H/L communities.

Peer Review reports

Introduction

In 2021, gay, bisexual, and other men who have sex with men (MSM) accounted for 67% of all new HIV infections in the United States (US) [ 1 ]. Black/African American (B/AA) MSM are the most affected subpopulation, followed by Hispanic/Latino (H/L) MSM [ 1 ]. B/AA and H/L MSM each represent less than 1% of the population [ 2 ], but account for 25% and 22% of all new HIV diagnoses in 2021, respectively [ 1 ]. Given this concentrated epidemic, the 2025 National HIV/AIDS Strategy lists MSM as one of the high-priority populations for HIV initiatives, in particular Black, Latino, and American Indian/Alaska Native men, as focusing resources on these populations would reduce HIV incidence disparities among MSM and achieve greater impact in reducing HIV incidence overall [ 3 ].

Pre-exposure prophylaxis (PrEP) — a highly effective pharmaceutical intervention against new HIV infection [ 4 , 5 , 6 ]— has the potential to dramatically reduce HIV incidence among MSM in the US [ 7 , 8 ]. Despite the proven efficacy and recommendation by the Centers for Disease Control and Prevention (CDC) that all sexually active persons at risk of HIV acquisition could benefit from PrEP, PrEP uptake has been slow across the US, especially among B/AA and H/L MSM [ 9 , 10 , 11 ]. In 2021, B/AA individuals comprised 14%, H/L individuals made up 17%, while White individuals accounted for 65% of all persons prescribed PrEP in the US [ 12 ]. Other studies have found that B/AA and H/L MSM who initiate PrEP are significantly less likely to be retained in care at three months relative to white MSM [ 13 , 14 ]. B/AA and H/L MSM are also more likely to have limited awareness about PrEP [ 15 ], low perceived HIV risk [ 16 ], medical mistrust [ 17 ], and experience stigma [ 18 ] and financial burdens [ 19 ]; all of which contribute to suboptimal PrEP uptake and retention in PrEP care [ 20 , 21 , 22 , 23 , 24 , 25 ].

Community-based outreach approaches and peer-driven interventions (PDI) have the potential to mitigate these barriers and enhance PrEP uptake among MSM, especially B/AA and H/L MSM. PDIs involve recruiting peer educators and then encouraging them to educate and motivate members of their social network(s) for PrEP uptake. Research demonstrates that PDIs are both cost-effective for engaging hard-to-reach populations [ 26 , 27 ], and efficient for disseminating HIV education, promoting condom use, and expanding HIV testing among MSM [ 28 , 29 ]. However, there is limited research on the feasibility of a PDI for PrEP promotion, apart from a pilot study conducted by the authors from 2018 to 2019 [ 30 ]. Most of the 15 participants in the pilot study viewed positively a PDI to promote PrEP. The current study investigated the acceptability and effectiveness of PDIs for PrEP uptake among MSM, focusing on B/AA and H/L subgroups. Acknowledging their distinct needs, the study aimed to identify PDI components tailored to each group, addressing specific barriers in PrEP adoption. Findings from this study can inform the development and implementation of future PDIs for PrEP promotion in the US.

Study population and recruitment

Our research staff conducted 28 in-depth interviews with MSM between October 6th, 2020, and September 2nd, 2022. Participants were recruited from multiple venues, including clinical outreach at The Miriam Hospital (TMH) Immunology Center; lesbian, gay, bisexual, transgender, and LGBTQ + bars and community-based organizations in Providence, Rhode Island; and LGBTQ + email-based listservs in southern New England (Rhode Island, Connecticut, and Massachusetts). Participants were eligible if they: (a) were 18 years of age or older, (b) were assigned male at birth and identified as a man, (c) had sex with men in the past three months, and (d) had a HIV negative status. Our sampling approach was purposive, aiming to encompass a wide range of experiences among B/AA and H/L MSM with different PrEP statuses, including both current users and non-users [ 31 , 32 , 33 ].

Interviews and data collection

Twenty-eight eligible study participants were invited to participate in a 45–60-minute one-on-one interview with a trained researcher. Study staff decided to conduct all interviews via Zoom, a HIPPA-compliant online meeting platform, due to the coronavirus disease 2019 (COVID-19) pandemic. Interviewers referred to an interview guide that they developed specifically for the purpose of the current study (Supplementary File 1 ). Interview questions covered the following topics: (1) awareness and acceptability of PrEP; (2) social network characteristics (including both physical and virtual interactions) and acceptability of promoting PrEP through these networks; (3) facilitators and barriers to PrEP uptake; (4) potential peer-delivered intervention components and related content to support PrEP initiation; (5) characteristics of ideal PrEP educators (e.g., leadership, responsibility, and passion and commitment to HIV prevention); and (6) cultural nuances about PrEP. As PDIs are based on existing social networks to reach individuals who may be at high risk of HIV infection, it is crucial to understand the dynamics of these networks and provide insights into how individuals are connected both directly and indirectly. Enrollment ended when preliminary data analysis reflected thematic saturation [ 34 ]. Specifically, we continued enrolling participants until data saturation was achieved, a point at which additional interviews ceased to yield novel insights or significantly alter our understanding of the research topic. Each participant was compensated $50 for their time. The Institutional Review Board at The Miriam Hospital approved the study (IRB number:1594759).

Data analysis

Interviews were digitally recorded and then transcribed verbatim by an external HIPAA-certified transcription company. Following transcription, research staff meticulously reviewed the transcripts to ensure they accurately reflected the recorded content, paying close attention to linguistic nuances and context-specific details. In addition to conducting interviews, research staff took notes, completed standardized debriefing forms immediately following the interview, and reviewed these forms with study investigators during weekly team meetings. Qualitative data were analyzed iteratively during data collection by a trained research assistant, and the interview guides were adapted as needed to address any unanticipated, emergent themes. The primary themes were organized and distilled into the primary findings presented here. This method allowed us to determine when we reached saturation in data collection. Qualitative and mixed methods data analysis was conducted using Dedoose, a versatile software platform designed for analyzing qualitative and mixed methods research data. Dedoose facilitates the organization, coding, and interpretation of complex datasets, enhancing the efficiency and depth of analysis [ 35 ]. The study team developed a coding scheme based on the interview protocol and transcripts of 28 interviews. After an initial round of coding, the research team met to discuss the coding and revise the codebook. Three independent qualitative analysts coded the transcripts. Discrepancies in the coding between any two analysts were resolved by the third analyst who assigned the final code. We used a thematic analysis strategy to analyze the data [ 36 ]. The research team reviewed and analyzed data to identify themes within the domains from the interview guide.

Demographic characteristics

Of the 28 study participants, the median age was 28 years old (interquartile range [IQR]: 25, 35). The majority reported having a college education or above (78%), having health insurance (93%), identifying same-sex sexual orientation (79%), and being single (57%). B/AA and H/L individuals accounted for 39% ( N = 11) and 50% ( N = 14) of all participants, respectively. Thirteen individuals (46%) reported currently taking PrEP; five individuals (18%) reported previous PrEP use; nine individuals (32%) had never taken PrEP before; and one individual (4%) had never heard of PrEP (Table 1 ).

Themes identified during interviews

Thematic analysis suggested several key themes for informing the development and implementation of a PDI to promote PrEP uptake among MSM, especially B/AA and H/L individuals. Table 2 presents a list of the themes generated from participant interviews, including: (1) characteristics of social networks; (2) the role of peers in increasing PrEP awareness and knowledge; (3) attitudes towards a PrEP PDI; (4) ideal characteristics of PrEP peer educators; (5) key components of PrEP education; (6) suggested approaches for initiating conversations about PrEP; (7) cultural barriers to initiating PrEP; (8) barriers to initiating PrEP for young adults; (9) suggestions for PrEP education content for peer educators; and (10) the impact of COVID-19 on social and sexual behaviors. These themes are described below along with illustrative quotes.

Social network characteristics

Most participants reported having racially and ethnically diverse social networks. They usually considered less than ten people to be very important in their lives; among those they considered important, they mentioned family members, people with whom they had grown up, or more recent connections made at college, work, or through mutual friends. The majority of participants saw or communicated frequently with two to three individuals.

“It really is a mix. I have white friends. My best friend is actually Palestinian , and , yeah , Asian friends , Mexican friends. It’s a mix…That’s the overall ethnic diagnosis. [Laughter] Well , yeah , I just meet friends , and they come in all flavors.” - Asian/non-Hispanic male , mid-twenties .

Most participants had several friends who self-identified as gay or bisexual men.

“[I have] quite a few [gay and bisexual friends]. I don’t know in terms of numbers…but I would probably say probably around 20 or 30 people. This is all from goin’ to college and some people from back home. In terms of my close-knit circle of friends that identify as gay and bisexual , I would probably say around five or six… Maybe ten max.” - Black/Hispanic male , early-twenties .

The role of peers in raising PrEP awareness

Of 28 participants, seven reported that they first heard about PrEP from their peers. Their interest in PrEP was sparked by the knowledge and experience their peers shared, motivating them to explore and learn more about PrEP.

“There was a friend of mine who told me that he was taking it. I asked what it was , and he told me… I was like , “Oh , okay.” Then , I looked it up on the internet and that’s when I saw and learned a little more about it.” – Black/non-Hispanic male , late-twenties .

Of thirteen individuals who were currently taking PrEP, eight reported already playing the role of a peer educator within their social network. This included having conversations about PrEP with LGBTQ friends, straight friends, and sexual partners.

“From my friends’ circle , three of my college friends have gone into PrEP after I have talked to them. The three of them identify as African Americans…Every knowledge that I gain that will affect [my friend group] , I will try to share that [knowledge with them] as much as I can.” - Black/Hispanic male , late-twenties . “I am an advocate for PrEP…I talk to people about it. All of my gay friends I’ve spoken to about it , partners that I have hooked up with in the past I’ve spoken to about it…I think it’s something we should all know is available as an option.” – White/Hispanic male , mid-thirties .

Attitude toward a PDI approach

All participants had a positive attitude toward using a PDI approach for PrEP promotion. Many thought that members of their own communities would be a reliable source for disseminating PrEP knowledge and capable of motivating their peers to seek PrEP counseling and care.

“I think [a community member] is helpful because you would trust that person , so I think it’s important ‘cause—yeah. I think it’s more effective and more impactful than just reading something online.” – Asian/Hispanic male , mid-twenties .

Most interviewees reported that they would be willing to be a peer educator to improve PrEP awareness and reduce rates of HIV infection in their communities.

“I’d love to take action within the queer community and help support HIV prevention…that would be something I’d be very interested in. Do I have enough education about it? No. But it’s definitely something that I feel like I’d love to learn more about and be able to pass that on.” – White/non-Hispanic male , early-twenties .

Characteristics of peer educators

Most participants stated that peer educators should have strong communication skills to effectively approach members of their social network for PrEP education. In addition, participants expected PrEP educators to be friendly, outgoing, respectful towards others, and willing to listen. According to participants, PrEP educators should also be able to ensure members in their social network are comfortable, be trustworthy, and highly knowledgeable about HIV and PrEP.

“I think characteristics they should have is that they should be , first of all , social. Obviously , they have to be able to communicate to people , be willing to give people the information , be patient with their questions…and treat people with respect when answering the questions.” - Black/non-Hispanic male , late-twenties . “I think a PrEP promoter should be outgoing , engaging. They should also be willing to listen because there are a number of people , especially in the African American community , who are going to be distrustful of someone who’s pushing a medicine on them. I think that a PrEP promoter should also be willing to hear a ‘no’ and not want to immediately get defensive or get argumentative.” - Black/non-Hispanic male , mid-thirties . “I think [peer educators] should know the effectiveness of [PrEP]. They should know who is considered high risk and who are the ones that should probably be taking it. Definitely should know what some of the side effects of it can be. I think [a peer educator] should be someone who’s actually taking it themselves , because then they have the firsthand—I get that everyone’s body is different , but it’s a little bit more comforting hearing it from someone who’s actually taking it. I think that they should just know general things about HIV.” – Black/non-Hispanic male , late-twenties .

Critical components for PrEP education

Participants identified components that are critical to include when providing PrEP education. The efficacy of PrEP, how it works in the body, and PrEP side effects were common suggestions for PrEP education content. Since there are several forms of PrEP available, including multiple daily oral formulations and long-acting injectable formulations, participants wanted to learn more about these different options. A few participants also mentioned that PrEP education should underscore the high risk of HIV acquisition for MSM, especially B/AA and H/L individuals, as well as emphasize the importance of taking PrEP as prescribed.

“I think you know the things that you probably wanna cover. It’s like , the risk—people knowin’ their risk , one; two , the options that they can take. There’s Truvada and there’s Descovy. You mentioned those two things. What’s the difference between them and how they both basically benefit the same thing.” – Black/Hispanic male , early-twenties . “I should definitely know about both the good and the bad cases of people who have taken it. People who have experienced negative side effects , what they were and what the percentage of that is if they have those statistics. Then , like I said , just the effectiveness of if you take it every day like you’re supposed to.” - Black/non-Hispanic male , late-twenties .

Some participants stated that they would like to learn more information about PrEP’s efficacy and that being equipped with this knowledge would help them feel more comfortable in playing a role as a PrEP peer educator.

“I think the best way to help someone like me to promote this would be to give me the information. If I had all the information that I’m curious about , it’d be a lot easier for me to spread the message of what PrEP is and why it’s important…For me , again , like I mentioned , I’m all about the facts. I need to know the science behind it. I need to know the truth. I don’t wanna know your opinion , how you feel about it. Show me the paperwork. If it’s on paper , it’s good to go for me.” - White/Hispanic male , mid-thirties .

Since many participants noted that cost was a major barrier to PrEP uptake, some suggested including information about how to pay for PrEP in the intervention, including available financial assistance programs.

“The training that I think I would need is I would want to have more information on the how it can be affected by insurance , ‘cause that’s another big question , is how can people afford it? I’m not gonna lie , I know about my personal insurance , but I know everyone’s different and there’s different things , we’ve got different tiers. I think that that’s a big one , because that’s gonna be a question , I feel like , everyone’s gonna ask is , ‘Financially , is it realistic for me to do this?’” – Black/non-Hispanic male , late-twenties .

Approaches to initiate PrEP conversation

We asked participants about how best to initiate a conversation about PrEP with peers in their social network. A range of approaches were identified such as: letting the conversation occur naturally, asking their peers directly if they have heard about PrEP and then providing more information if they have not, sharing personal experiences related to PrEP, providing an informational sheet about PrEP without first discussing it at-length, and only providing information if someone asks about it first.

“I think it just comes up casually , I don’t think that there is an intended approach. If I’m talking to my friends—for example , yesterday…my friends asked , “Hey , so what are you up to today?” I said , “Well , I have my PrEP appointment at this time.” Then , the conversation organically flows from that…I don’t go out of my way to say , “Wait , what about PrEP?” It doesn’t come like that. It’s usually more like a casual conversation.” – Hispanic male , mid-thirties . “I would probably just ask them ‘have they heard of it.’ Then , from there , gauge what they knew of PrEP. Then , probably in that , hear any concerns they would have , and just try to use my experience to help them gain access to it.” - Black/non-Hispanic male , early-thirties . “As opposed to having a long conversation [with someone you don’t know] , when you don’t know if someone’s interested in that conversation , you can very easily give them a card and just say , “Think about this.” I think it’s a good way to do it.” – Black/non-Hispanic male , early-twenties .

Participants identified text messaging, social media, and in-person meetings as preferred methods to deliver a PrEP PDI. Some suggested text messaging as an easier and more convenient delivery method of information than meeting in-person and identified social media as being able to reach a large audience quickly.

“I think , in this day and age , social media and texting is probably the best way to get in contact with people. That’s definitely how I would approach it , just a friendly little message in text or social media.” - Black/non-Hispanic male , early-thirties . “Maybe a social network would be a really good way to spread that message because you can spread one thing—you can share one thing on , let’s say Facebook. Within one hours that one message you’ve just shared could reach the other side of the world. Hundreds and thousands of people on the other side of the world.” – White/Hispanic male , mid-thirties .

However, some participants demonstrated a strong preference for in-person approaches, stating they believed it would be more effective in establishing a connection.

“The best way would be in-person. I think all the best conversations are face-to-face conversations.” - White/non-Hispanic male , late-seventies . “I’m old fashioned. I do the in-person just ‘cause I think that does capture everything. Sometimes via text or calling , you do lose some of that situational reaction. You lose a lot , you lose facial expression , you lose a lot of different things that can eventually help an individual make the right step in their life.” – White/Hispanic male , mid-twenties .

Cultural beliefs impact on PrEP uptake

Participants generally agreed that cultural beliefs may act as barriers to initiating PrEP. For example, some cultures do not accept same-sex relationships, and therefore those who belong to these cultures may find it uncomfortable to talk about sexual health and be unable to pursue PrEP.

“I definitely think there are some cultures that are not open to same-sex relationships or sex before marriage and things like that. That shame and stuff , I’m sure , can trickle in for individuals when it comes to taking something like PrEP.” - Black/non-Hispanic male , early-thirties .

One participant who identified as B/AA discussed how internalized stigma and low self-esteem, resulting from structural racism within our society, can act as a barrier to initiating PrEP. In addition, according to this participant there may be a misconception among some B/AA MSM that PrEP is not as applicable to them as it is for White MSM.

“I think we live in a culture that still is reckoning with racism. I think one of the effects of that is that if someone has a hard time seeing themselves as valuable or worthy of being treated well and being healthy , then they’re also less likely to practice—have good sex practices. That strikes me as a pretty big cultural barrier. …I also think , just in terms of how segregated people are , I think there is still an image of PrEP as something that mostly white gay men do. I think that’s a piece of it too.” - Black/non-Hispanic male , early-thirties .

Some H/L participants shared their experiences on being from a community where conversations about sexual health may not be normalized, and how that creates a barrier to initiating PrEP.

“I think sex sometimes is one of those where , in a lot of Hispanic countries , or not countries itself , but little communities…You don’t talk about sex at a dinner table. You don’t talk about sex with your friends. You only talk about sex with your spouse , or whatever the case is. In some cases , your parents won’t even talk to you about sex because it’s taboo , and you shouldn’t. They don’t even refer to genitalia as penis and vagina. They just go , like—they call it by other cutesy names , because they’re embarrassed to talk about sex. I think , culturally , there is a lot of barriers , and I think that could be a struggle when it comes to PrEP knowledge. Because a lot of—whether it’s a Hispanic gay man , or a Hispanic straight male , they may not be willing to talk about it. They even may think they don’t need it.”- Hispanic male , mid-thirties .

Unique barriers for young adults interested in PrEP

One young adult participant mentioned that being on their parents’ insurance was a key barrier to initiating PrEP. They started to take PrEP only once they were on their own health plan.

“I wanted to make sure that I was on my own insurance , ‘cause I’m not out to my parents. I don’t think we had that insurance where I would get on somethin’ and they would notify them , but I’m a very private person when it comes to a lot of things in my life. That was one of the main factors for me [to start PrEP] , that I was off my parent’s insurance and I had my own insurance and I was able to afford it.”- Black/Hispanic male , early-twenties .

Another young adult, recently graduated from college, reported a lack of comprehensive sexual health education and PrEP resources available through their school.

I think a lot of [sexual health education] came from , honestly , off-campus resources , once I started going to a clinic outside of my college campus….my college campus was the one who recommended [PrEP] for me , but they told me to go off campus to supply it. – White/non-Hispanic male , early twenties .

The impact of COVID on PrEP use and access

All participants reported that the COVID-19 pandemic drastically changed the way they socialize and communicate, and their sexual activity. During the COVID-19 pandemic, most individuals had minimal in-person social activities and reduced sexual activity as well.

“Pre-COVID…I had more sexual partners for sure. Now , during COVID , if I chat with someone—you add the question on top of like , “Hey , what’s your status?” …You’re not necessarily just asking these days for what’s your HIV status , you’re also asking what’s your COVID status? Are you negative? Are you safe? Some people will just give you different answers. I have chosen not to engage in too many sexual encounters in 2020.”- Hispanic male , mid-thirties .

Due to reduced sexual activity and lower perceived risk of HIV acquisition during the pandemic, some participants chose to temporarily discontinue PrEP.

“At the beginning of the pandemic , I had stopped taking it , ’cause there was no need. I wasn’t having any sexual activity for a few months…Once restriction and things were loosening up a bit , that’s when I was being sexually active again. That’s when I started [PrEP] back up.” –Black/non-Hispanic male , early-thirties .

The COVID-19 pandemic often negatively impacted participants’ access to medical care, including PrEP appointments and routine testing.

“There was interruptions…at the height of COVID , so the very early stages where a lot of medical or health organizations or providers were going teletherapy. You were able to do your consulting or your check-in appointment , but unless you had symptoms of something , they were like , “Okay , don’t go to the doctor. Don’t do the three-month blood work of whether it’s your kidney or your STD bloodwork , ” or whatever. It became very like , okay…so that’s kinda why I got off of it.”- White/Hispanic male , mid-thirties .

Several participants reported having prescription refills delivered at-home to mitigate COVID-related barriers to PrEP access.

“The pills usually get delivered to my…so it’s usually pretty straight forward. I’m home all the time , so I can’t really miss a dose.” – Hispanic male , mid-thirties .

However, a couple participants who received their PrEP refills via at-home delivery reported major issues with the delivery service that negatively affected their adherence to PrEP.

“There was two weeks where I just wasn’t getting it delivered. I called the people and said , “Hey , I signed up for delivery. Why haven’t you sent it?” They eventually just signed me up for delivery permanently. Now I get it delivered automatically , which is good…I did have a two-week gap that one time. Since then , I’ve been covered.” – Black/non-Hispanic male , early-twenties . “The process in which I can reach and get [PrEP] is a bit hard. Because they are not found in the nearby stores , I need to order online and delivery sometimes is delayed , so that’s a big challenge to me.” – Black/non-Hispanic male , late-twenties .

This study is one of the first to explore the acceptability and potential components of a PDI for PrEP among MSM. Our findings highlighted a positive perception of a PDI for PrEP among participants, with a willingness to both educate and learn from their peers. This is consistent with previous studies that have explored the effectiveness of PDIs in promoting various HIV prevention measures, such as adherence to antiretroviral therapy (ART) and increased condom use, particularly within populations disproportionately affected by HIV [ 27 , 37 , 38 , 39 ]. These findings are also consistent with the pilot study conducted by the authors on a PDI for PrEP uptake among MSM [ 30 ]. This current study had nearly double the participants of the pilot, with no participants that were involved in both studies, and was conducted during and post-COVID, which has allowed us to capture a broader range of perspectives and analyze how the COVID-19 pandemic has changed our study population’s social interactions, sexual behaviors, and PrEP access. Given the alignment of our study’s findings with existing literature and the urgent need for PrEP promotion, especially among B/AA and H/L MSM who face a disproportionate burden of HIV infection, a PDI approach holds significant promise to reducing this burden among the most affected populations [ 25 , 29 ]. Our study suggests that PDIs have the potential to address disparities in PrEP uptake and contribute to reducing HIV transmission within these communities.

We conducted an in-depth exploration of the essential components necessary for an effective implementation of a PDI to promote uptake of PrEP. This exploration encompassed various topics, including shared characteristics of social networks among MSM, the role of peers in raising PrEP awareness, general attitudes toward a PDI approach, desired attributes and formal trainings of peer educators, approaches to initiate conversations about PrEP, and cultural barriers to PrEP initiation. Through identifying and investigating these themes, our results have important implications for understanding the various elements that should be in place for a PDI tailored to PrEP promotion to succeed.

Most study participants reported having a diverse group of friends with varied racial and ethnic backgrounds. They also mentioned having a close-knit circle of friends who identified as MSM. This diverse network facilitated open and comfortable discussions about sex, HIV, and drug-related topics among their social connections. Participants’ ability to engage in such conversations was influenced by characteristics of their social network, and if they had access to an environment where important health-related discussions could take place without fear of judgment or stigma. However, it’s important to recognize that this sample may not reflect the wider Black and Hispanic MSM populations, even in our study setting. Caution is warranted when extrapolating that such supportive discussions are uniformly prevalent in diverse networks across these communities. Additionally, as indicated by responses in the study, many Hispanic and Black communities face significant sex- and racism-related stigma. This stigma poses a substantial, yet not unassailable, obstacle to the effectiveness of interventions. The findings underscore the need for nuanced approaches to health promotion that consider the complex interplay of cultural, social, and individual factors. Our participants highlighted the importance of selecting peer educators who are outgoing, personable, and skilled in effective communication. Moreover, peer educators should exhibit a strong knowledge base and enthusiasm for PrEP and be willing to educate individuals within their own social circles. Furthermore, peer educators who share experiences or backgrounds with participants were seen as having an advantage in building trust and rapport. This aligns with existing research demonstrating that individuals at the center of social networks exert greater influence over the health behavior of others within their network [ 40 ].

Participants identified several critical components to be included in PrEP education content. They stressed the need to emphasize the higher HIV risk among MSM compared to heterosexuals and suggested presenting current HIV data during sessions to underscore this. They also wanted to see evidence from previous studies demonstrating PrEP’s effectiveness in HIV prevention. Additionally, they emphasized the importance of discussing adherence to maximize PrEP’s effectiveness and desired more information on how PrEP functions in the body, available PrEP options, and candid discussions about potential side effects. Lastly, many participants suggested that it would be beneficial for peers providing PrEP education to include information about financial assistance programs for PrEP and how to navigate insurance when initiating PrEP. Given that neither of the two uninsured participants were currently on PrEP, it is likely that the existence of PrEP financial assistance programs for uninsured individuals is not widespread knowledge. Therefore, to fill this gap in PrEP coverage of uninsured patients, future peer-based interventions should include information about these programs and assist patients in applying for them.

It is crucial that PDI approaches include culturally appropriate content and address the cultural barriers mentioned by B/A and H/L participants. Given that several participants voiced they would be more comfortable with a peer educator with whom they shared an identity or cultural background, it may be highly beneficial for a PrEP PDI to select peer educators who are B/AA or H/L MSM currently on PrEP or with prior PrEP experience. Training for peer educators should also include how to approach conversations about PrEP with a focus on navigating cultural barriers that may inhibit sexual health conversations as well as the effects of internalized stigma. Such cultural competence will ensure that the intervention resonates with the targeted population.

Many participants suggested and preferred in-person meetings for PDIs. Some participants expressed a strong preference for face-to-face interactions, emphasizing the efficacy of personal engagement in providing information and addressing concerns. Conversely, other participants exhibited a greater openness to digital modes of communication. Nevertheless, it’s worth noting that the evolving landscape of social interactions influenced by the COVID-19 pandemic has prompted a shift in preferences, with some participants preferring virtual modes of engagement.

We also found that young adults face unique barriers when considering PrEP initiation, as highlighted by some participants in our study. For one individual, a key obstacle was enrollment on their parent’s insurance plan. For young adults still on a parental insurance plan, navigating sensitive conversations with their parents regarding their sexual health choices can pose a daunting challenge, in particular for those with parents unaware or unsupportive of their sexual orientation. Another participant disclosed that their university health services were not equipped to prescribe or provide PrEP, and that sexual health education and resources through their university were lacking. These experiences underscore the need for tailored interventions and support systems to address the unique concerns of young adults, including college students, interested in PrEP.

The COVID-19 pandemic caused significant changes in participants’ social and sexual behaviors. Lockdowns and social distancing measures led to reduced in-person social activities and fewer sexual encounters. Consequently, some participants temporarily stopped taking PrEP as they perceived their risk of HIV acquisition to be low. The pandemic posed challenges to consistent PrEP access and adherence due to disruptions in healthcare services and personal circumstances. Fear of COVID-19 infection also deterred participants from visiting healthcare facilities for PrEP prescriptions. Despite efforts like at-home medication delivery, some participants faced issues that hindered their adherence. HIV prevention initiatives should enhance PrEP accessibility through measures such as affordability without insurance and improved delivery options.

In our study, we employed purposive sampling based on PrEP status and race/ethnicity to explore attitudes towards a PDI on PrEP promotion among individuals with varying PrEP experiences and racial/ethnic backgrounds. This sampling methodology may have influenced our observation that B/AA MSM had a higher likelihood of PrEP usage in this study, a finding that diverges from the predominant trends reported in existing literature [ 41 ]. It is reasonable to consider that B/AA MSM who are currently taking PrEP could be more motivated to participate in a study focusing on PrEP-related interventions. Given that the primary aim of our study was to explore knowledge of and perspectives on PrEP, this potential sampling bias is unlikely to significantly detract from the validity of our findings. Indeed, capturing the insights of active PrEP users is integral to understanding the nuances of PrEP promotion strategies within diverse communities.

Nonetheless, it is important to consider the limitations of this study. The insights presented here are based on interviews conducted in the state of Rhode Island and variations in perspectives may exist in different geographical and cultural settings. While we employed purposive sampling to capture diverse viewpoints, the findings may not fully represent the broader MSM population, especially B/AA and H/L MSM. Additionally, the study was conducted during the COVID-19 pandemic, which may have impacted preferences for in-person meetings and social interactions. Aware of the potential for social desirability bias, we employed a non-judgmental and supportive interview approach, reassuring participants that all responses were valid without any ‘correct’ answers, to mitigate this effect.

In conclusion, this study suggests ways to develop a tailored PDI approach for promoting PrEP uptake among MSM populations, in particular B/AA and H/L MSM. Our findings suggest that the success of PDIs will be feasible and acceptable but their success will depend on careful selection of peer educators, comprehensive training, culturally-sensitive content, and the acknowledgment of unique challenges related to HIV and PrEP within these communities.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to risk of compromising privacy of the qualitative interview participants but are available from the corresponding author on reasonable request.

Abbreviations

Pre-exposure prophylaxis

Peer-driven intervention
Black/African American
Hispanic/Latino

Men who have sex with men

Centers for Disease Control and Prevention

Human immunodeficiency virus

Lesbian, gay, bisexual, transgender, queer

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Acknowledgements

I extend my deepest gratitude to Dr. Nancy Barnett whose mentorship has been invaluable throughout the course of this research. Dr. Barnett’s guidance, support, and expert insights have not only shaped this work but have also significantly contributed to my professional growth and development as a researcher. Dr. Barnett’s dedication to excellence and her unwavering commitment to nurturing my potential were pivotal in the successful completion of this manuscript. I am profoundly thankful for the opportunity to work with her in this study.

This research was funded by the National Institute of Mental Health (K01MH19660).

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JT designed the study and conducted the participant interviews, with assistance and guidance from PC. SAM acted as our qualitative research expert and led the training of research assistants for interview coding and analysis. JK, IK, HP, and HM coded the interviews. IK led the thematic analysis, with support from SAM and JT. JT, HP, HM, and IK majorly contributed to the writing and revision of the article. HP prepared Tables 1 and 2. BM, HR, LM, SAM and AN contributed to further revisions. All authors read and approved the final manuscript.

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Tao, J., Parent, H., Karki, I. et al. Perspectives on a peer-driven intervention to promote pre-exposure prophylaxis (PrEP) uptake among men who have sex with men in southern New England: a qualitative study. BMC Health Serv Res 24 , 1023 (2024). https://doi.org/10.1186/s12913-024-11461-7

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Experiences of Turkish mothers of children with autism: a phenomenological study

Published: 04 September 2024

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Eda Aktaş ORCID: orcid.org/0000-0003-1424-9678 1 ,
Fadime Ustuner Top ORCID: orcid.org/0000-0002-7341-5704 2 , 4 &
Sevda Uzun ORCID: orcid.org/0000-0002-5954-717X 3

The study aimed to explore mothers’ experiences of children with autism using a phenomenological approach. Semi-structured, in-depth interviews were conducted with 18 parents with children attending a special education centre in a northern province of Turkey. Criterion sampling, a form of purposive sampling, was employed to select participants. Interviews continued until data saturation was achieved. All interviews were audio-recorded and transcribed verbatim. Thematic analysis was utilized to analyze the data, and the study adhered to the COREQ checklist for reporting qualitative research. As a result of analyses, three main themes (the process of diagnosis, the effect of the child’s illness on parents and their challenges over time, and considerations regarding the child’s educational journey) and eight sub-themes (feelings and thoughts, reflections on the causes, reactions encountered, emotional effects, social effects, physical effects, perception of social support, and expectations from the educator and the education process) emerged. The study concluded that mothers of children with autism are emotionally, physically, and socially impacted by their child’s condition, facing considerable challenges in coping. In light of these findings, it is recommended that policymakers in our country develop plans that address the difficulties expressed by participants, review and potentially enhance financial and social support for families, and consider increasing special education hours.

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Published: 07 September 2024

Balancing risk and reward: exploring women’s transactional sexual relationships with Blessers in South Africa

Gavin George ORCID: orcid.org/0000-0001-7258-8470 1 , 2 ,
Leena Maqsood 3 &
Courtenay Sprague 3 , 4 , 5

Humanities and Social Sciences Communications volume 11 , Article number: 1158 ( 2024 ) Cite this article

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Development studies

Transactional sexual relationships (TSRs) take varied forms, with research emphasizing TSRs’ inherent risks, primarily to women. In South Africa, Blessed relationships have emerged as a prominent type of TSR. Blessers have become coveted male partners due to their relative wealth and willingness to ‘bless’ female partners with financial and material gifts, as well as the improved social status that accompanies such partnerships. Given the limited literature on Blesser partnerships, we investigated women’s implicit and explicit negotiated rewards and their perceived risk and ability to employ risk mitigation strategies, utilizing risk as a sociological concept to enhance understanding of risks women assume, and subsequent gendered implications. This qualitative study was conducted in 2017-2018 in KwaZulu-Natal (KZN) province, South Africa, a high HIV prevalence setting, with 22 women, using purposive and snowball sampling methods. We found that women in relationships with a Blesser recognize and navigate a number of risks, including: the threat of HIV acquisition, falling pregnant, experiencing an emotional void following a TSR, and expressed feelings of guilt and shame, as segments of South African society remain judgmental of TSRs. These risks are then weighed against the pursuit of sustained financial and social rewards achieved through such relationships.

Introduction

Transactional sexual relationships (TSRs) have been linked to adverse outcomes for women, such as unplanned pregnancy, and increased health risks, including acquiring sexually transmitted infections and experiencing physical or sexual violence and mental and emotional distress, particularly in low- and middle-income country settings (Choudhry et al., 2014 ; Cluver et al., 2011 ; Dunkle et al., 2007 ; Jewkes et al., 2006 ; Stoebenau et al., 2016 ; UNAIDS, 2018 ; Wamoyi et al., 2016 ). Research findings support the notion that women are victims of these relationships, especially in the African region, within a context of oppressive gender norms: including toxic masculinities, which are often associated with physical and psychological abuse (Fielding-Miller et al., 2016 ; Wamoyi et al., 2011 ) and substantial power imbalances (Pulerwitz et al., 2018 ). At the same time, literature also recognizes the growing economic autonomy exerted by women which has altered both the dynamics and transactional nature of these relationships (Hunter, 2007 ; Kabeer, 2005 ; Shefer and Strebel, 2012 ). The extant literature also presents a departure from the notion that TSRs are solely exploitative (Maganja et al., 2007 ), suggesting that sex-for-money relationships are viewed within much of the African context as an indicator of the male’s commitment to their female partner (Hunter, 2002 ; Wojcicki, 2002 ). Notwithstanding the commonplace practice of material or monetary exchange within sexual relationships, economic and social conditions can also result in women expressly selling sex, motivated either by survival or improved social status (Stoebenau et al., 2011 ).

In South Africa, blessed relationships are an increasingly prominent form of TSRs, and regarded by some scholars (Moodley and Ebrahim, 2019 ) as a contextual evolution of sugar dating—a pseudo-romantic TSR between a wealthy man and younger woman (Pardiwalla, 2016 )—as viewed in the western world. Blessed relationships have grown in prominence through the use of social media, which is widely leveraged as the platform to link women with a prospective Blesser. Blessers have been characterized as relatively wealthy males willing to satiate the material and financial needs of a female partner in exchange for sex and companionship (Ranganathan et al., 2017 ). Research has already distinguished types of TSRs within a number of contexts. Scholars (Stoebenau et al., 2023 , Moodley and Ebrahim, 2019 and Bougard and Matsi-Madolo, 2017 ) have concluded that TSRs in South Africa are not confined to prostitution but exist along a continuum. Among other characteristics that make these TSRs distinct from concubinage and prostitution, is the manner by which negotiation precipitates the Blessed relationship, commitment of both parties and the nature of the exchange that characterizes the partnership.

Within the South African social-structural context, the economic and social conditions exist for TSRs to be motivated by both “survival” and in pursuit of social status or “consumption”. With respect to the latter, the “Blessed relationship” has emerged: a convergence of sexual agency, economics and a consumerist culture (Moodley and Ebrahim, 2019 ). Whilst blessed relationships are situated within the “sex for improved social status” paradigm, wherein, as Stoebenau, Heise, Wamoyi et al. ( 2016 ) note, women are sexual agents who engage in this form of transactional sex for lifestyle attainment, their agency remains compromised (see Sprague et al., 2021 for more discussion). Within the South African context, Blesser relationships are associated with maladaptive outcomes for women, evidenced by data revealing elevated HIV risk for women engaged in such relationships (George et al., 2022 ; Doyisa et al., 2023 ). Studies have also documented that some women experience negative psychological consequences and infringement of sexual rights, with these relationships potentially undermining their independence and often failing to meet their emotional needs (Kyegombe et al., 2020 ; Sprague et al, 2021 ; UNAIDS, 2018 ). The evidence of adverse health effects of TSRs on women reflects the role of gender as a social determinant of health—a determinant widely acknowledged to shape both opportunities to be healthy and health outcomes positively or negatively via exposures to intermediary determinants (such as employment, education and housing) (Solar and Irwin, 2010 ; Miani et al., 2021 ; Phillips, 2005 ). Within sociology, gender is regarded as, not just an individual identity or trait, but a social system that allocates differential resources and positions to individuals according to their gender, a perspective we adopt here (Connell, 2012 ). Given that TSRs, and by extension, Blessed relationships, are predicated on an exchange, they provide some women an opportunity to negotiate the conditions of the relationship (Choudhry et al., 2015 ). Whether and how women adopt risk reduction strategies, whilst attempting to pursue their expressed needs and desires from these relationships, has been understudied. Despite the ubiquitous practice of transactional sex, there remains a dearth of literature exploring how women seek to navigate such risk and maximize the benefits of transactional relationships, especially within the African context (Stoebenau et al., 2016 ). Additionally, there are few qualitative studies adopting a narrative approach capturing black South African women’s in-depth experiences of relationships with Blessers, and direct perceptions of how risk is mitigated, and rewards pursued, given the potential adverse health outcomes described. This qualitative inquiry is positioned within this opening. We explore the motivation for women to seek out relatively wealthy sexual partners, the perceived risks posed by these relationships and the strategies women adopted to reduce potential harm whilst seeking to sustain the financial benefits accruing from these relationships. As TSRs present in different forms, it is important to understand the nuanced way in which risk, motivation and agency manifest, in an effort to improve our ability to mitigate risk and improve health outcomes for affected populations.

In previous literature on Blessers, scholars (Doyisa et al., 2023 ; Moodley and Ebrahim, 2019 ; Palfreman, 2020 ) have suggested that these relationships are motivated by a desire for improved social status or financial gain—fitting into Stoebenau et al. ( 2016 ) continuum of instrumentality (Fig. 1 ).

Stoebenau et al. ( 2016 )’s Continuum of Instrumentality.

Motivation is, however, countered by the perceived presence of risk (Douglas, 1986 ). Literature suggests that individuals not only evaluate the existence of risk, but consider their own ability to appropriately manage or navigate through it (Douglas, 1986 ). Risk is not simply the outcome of “individual choices”, but of “negotiated actions” in context (Rhodes, 1997 ). This empirical work aims to elucidate some of the complexity around South African women’s motivations to engage in a relationship with a Blesser, how they seek to pursue their own ends, and how they view and negotiate the risks associated with these relationships. Specifically, we consider the degree of agency perceived by women engaged in sexual relationships with Blessers, and the balancing of risk and reward by applying a sociological concept of risk and considering the gendered implications of the findings.

Design and methods

We adopted a qualitative design and methods for this study, drawing on grounded theory and semi-structured interviews to capture the experiences of women engaging in relationships with Blessers (Charmaz, 2006 ). A qualitative paradigm allowed for the identification of latent social patterns and structures which characterized these relationships; and required the establishment of trust, essential in ensuring the reliability of qualitative data (Ritchie et al., 2013 ). We probed the complex and diverse realities associated with relationships with Blessers, exploring both implicit and explicit negotiation processes, agency, gender roles, power and norms within participants’ socio-cultural contexts. Next, we applied two conceptual frameworks to guide the data analyses. Firstly, Stoebenau et al. ( 2016 ) developed a framework that sought to capture the nuance and complexity of TSRs using three paradigms to describe motivations for transactional sex. This entails that women opt for sex for the fulfillment of basic needs; improving their social status; and the acquisition of love from Blessers, tied to the receiving of material items (Doyisa et al., 2023 ). Secondly, this study views risk as a sociological concept, which is both objective but also defined by the individual who is influenced by their social context, as conceived by Sanders ( 2004 ). Context and social circumstances, including gender dimensions, need to be understood when evaluating an individual’s recognition of risk and their subsequent response to it (Sanders, 2004 ).

The study was conducted in the province of KwaZulu-Natal (KZN), in South Africa, using purposive and snowball sampling (referral of participants to others) to enroll participants matching the eligibility criteria: women, aged 18 or older, who were currently or had previously engaged in a relationship with a Blesser (Tong et al., 2007 ). The sample (22) of participants were all black South African women aged between 21 and 49. Some participants were recruited via social media: they were informed and openly invited through Facebook groups established to connect women to Blessers (within KZN). Additionally, students at the University of KZN were informed of the study through the University’s notice system and invited to contact the study lead (first author) if interested in participating. The nature of this recruitment strategy meant we were likely to engage with participants based in urban areas and from higher socio-economic strata. This aligned with the characteristics describing these relationships and specifically the women seeking Blessers, i.e., that they occur predominantly in urban areas and are connected to Blessers via social media (Zawu, 2022 ). The University was mobilized as a recruitment site, with previous research emphasizing that Blessed relationships were forged within university settings (Frieslaar and Masango, 2021 ; Doyisa et al., 2023 ). Additionally, we elected snowball sampling, given that TSRs can be pervasive within peer groups (Leclerc-Madlala, 2003 ). We employed a semi-structured questionnaire that was pilot-tested, then refined to add appropriate prompts and ensure clarity prior to use. Interview questions were organized into four parts, with consistent probes (semi-structured questions) to enable each participant to share their unique perceptions and experiences, including: (i) introduction to and motivation to engage in a relationship with a Blesser; (ii) experience of the relationship (nature, duration, activities, dynamics, perceptions); (iii) navigating risk and reward, including violence, HIV and related health risks, and negotiation of material exchange and sexual expectation; and (iv) self-reflections and insights.

We undertook 19 in-depth interviews and one group interview with three participants (these women knew each other and preferred to be interviewed together) between July 2017 and September 2018. Interviews took place in multiple locations in Pietermaritzburg and Durban, including coffee shops, restaurants, a private room on the university campus, and telephonically. Participants were given a voucher to the value of ZAR100 (USD5.31 Footnote 1 ) for redemption at a retail pharmacy.

Data coding and analysis

We managed data collection, coding and analyses in Microsoft Word and Excel. All interviews were recorded and transcribed verbatim (mean = 41 min). Three researchers coded the transcripts independently. Based on trends grounded in the narrative descriptions, we established broad codes and developed a coding framework, with attention to participants’ motivations and navigation of the risks of engaging in these relationships, along with the expected and realized rewards of Blesser partnerships. We refined the coding to capture the primary themes and sub-themes emerging across the transcriptions. We adopted key reliability strategies to minimize the risk of bias in data collection and reporting, including the use of purposive sampling, protracted engagement in the field, and utilizing independent data coders who applied a code-recode approach to confirm which dominant themes aligned with the transcriptions (Tong et al., 2007 ).

Table 1 below details participants’ key socio-demographic indicators. These data indicate some broad ranges with respect to the ages of the interviewees and their Blesser partners, whilst also quantifying the monetary value attained by the respondents in these relationships. These amounts do not capture the non-monetary gifts provided by Blessers which took the form of clothing and cosmetics, along with consumables and travel expenses associated with social functions and trips (domestic and international). All interviewees were relatively well-educated: most had at least some college education whilst many also engaged in full or part-time employment. The majority were also in a simultaneous sexual relationship (in addition to their Blesser), with the majority of Blessers known to have partners (e.g., spouses) of their own. Of the 22 participants, the majority, 73%, reported using condoms ‘always,’ while only nine percent used condoms ‘sometimes.’ A further nine percent reported ‘never’ using condoms, with the remaining nine percent not providing a response. All names are pseudonyms.

We focus on two emergent themes: participants’ motivations to engage in a relationship with a Blesser and their reported perception of risk and ability to navigate the risks within these relationships. The sub-themes allowed further unpacking and ordering of these main themes, which collectively supported and extended the sociological concept of risk guiding this work, with implications for gender drawn (discussed later). The focus for participants was therefore on managing the balance between the rewards which motivated women to enter into relationships with Blessers, their ability to negotiate these rewards and their perception of and ability to mitigate the recognized risks.

Theme: motivation to engage in a relationship with a Blesser

Money and the pursuit of elevated social status remained a central tenet for women who had previously or who were currently engaged in a relationship with a Blesser. The ability to acquire money from Blessers was a strong motivation to seek out a Blesser, with interviewees motivated to attain a desirable lifestyle, whilst some were looking for Blessers to cover everyday expenses.

Sub-theme: seeking out a partner who can provide is normalized

Interviewees affirmed the social-cultural and gender norm that men were viewed as providers with women recipients of money and gifts.

A man has to look after a woman. It’s something that we grew up being told and believing in that. Now it’s just that, in life, not just older men, sometimes it’s younger boys and usually it’s like you know you’re going into a relationship knowing very well that the person is married and that you can’t see him whenever you want to you and you have to expect the lines with his family and all of that. I think that’s the only difference. But other than that, it has always been known that a man has to work and provide for his partner. ( Nandi, 30)

Participants were often seduced by the rewards gleaned from peers who had engaged in a relationship with a Blesser. Knowing a peer involved with a Blesser, and witnessing the manifold financial and social benefits, ignited women’s desires to acquire a Blesser for themselves. Further suggesting that these desires are socially acceptable:

If some people have it then why can’t I have it as well? Why can’t I have the money and the happiness? (Busisiwe, 24)

We found participants continued engaging in Blessed relationships, despite the majority of the sample being well educated and employed. Strikingly for some, gainful employment (or the prospect of) did not deter women from seeking or retaining their Blessers. The attainment of a better life remained paramount and an ambition shared amongst many of the interviewees. As demonstrated here, a growing materialism in society, reflected by participants’ aspirations for greater wealth as a marker of social status, is evident:

But for now, If I could get a job that pays me like 20,000 [$1,064], I’d still keep him [her blesser] because I’d need him to pay for the place that I’ll be staying in. Maybe groceries and whatsoever while I’m paying for my car, while I’m trying to get myself a house. (Nozipho, 25)

Sub-theme: the rewards of being in a relationship with a Blesser

Rewards were often predicated on the needs and desires of women and the financial capabilities of the Blesser. Participant narratives revealed that rewards varied: from financial support to the purchase of material items, including clothing, electronics and other items. Women consistently communicated their perception of Blessers as a source of financial support, illustrated by the following participant:

He gives me the money; he takes care of me, he buys what I want. If I want a new phone, he buys it for me. So that is why I am rating him a level seven [referring to Blessers being rated according to how much or what they provide to their partners] because what’s left is for him to buy me a house and a car. (Nozipho)

The money provided by Blessers was considered significant by many and afforded women in these relationships the opportunity to upgrade their lifestyle choices, in many cases, promising a tantalizing freedom from economic constraints:

It was that time I decided to buy a second-hand car and he was able to give me so I can buy a better car. He always called to make sure I had enough money for petrol, to change the license, deed, what not, and I didn’t have to ask money for home and I was able to help out with my nephews or take my nieces for lunch or whatnot. Things I couldn’t do before and I could take them out for ice cream. So, I was able to do all of those things that I wanted to do for them. Like I would go and buy them movie tickets and whatever they wanted. At home, if they needed something, I was able to say okay, I do have this amount of money. (Nandi)

Blessers encouraged lavish expenditure, as captured in the following quote:

He told me every month I must get the card, buy clothes… disapproved of everything cheap I would buy with my own money. He bought everything, my fridge my clothes. An entire makeover. Linen, kettle… everything. (Hope, 26)

Not all the money acquired from Blessers were used for luxury items. Some participants used the money to pay for their studies, family and other everyday expenses, with some having to rely on their Blessers to meet more basic financial needs:

He also took care of my daily needs. He also sent of money. It was roughly around; my apartment was around 4.5 (thousand) [$239], and R2000 [$106] was for my other living necessities and 1000 rand [$53] for myself. He didn’t send the money for tuition to me instead he sent directly to the school. That was like a lot like R30K [$1596], for the whole year, like just one time. I just received the message that payment is done. (Pearl, 30)

The funds received from Blessers would augment any monies received from family members, as reported here:

You find that you come from a family where you have family members who are able to give you money. But let’s say they give you 500 rands [$27] per month and with that 500 rand I have to do my groceries; I have to buy my toiletries; I have to buy, have to get money for printing my assignments and all of that. And honestly that money is not enough. (Nandi)

Blessers also proved to be an economic safety net, with participants able to call on them if they needed money, in keeping with men’s roles as providers:

When I’m broke and I don’t have money. When I need transport, he’s there. When I’m shopping for groceries, he’s there to fill up for me. When I need something for my kids, he offers. (Mpho, 30)

Sub-theme: the process of negotiating with Blessers

The relationship was at times implicitly underpinned by an understanding of a set of agreed obligations by both parties. The ability of women to negotiate what they needed and wanted was determined by the power dynamic within the relationship and could resemble commercial sex activities. However, monetary and material gifts were received throughout the relationship, and not necessarily following a sexual act. The provision of money or other material items were not always prompted by women, with Blessers oftentimes satiating the perceived needs and desires of their partners spontaneously.

Some participants described the implicit nature of the transactional arrangement:

We didn’t negotiate, like I think there are signs like you see in every relationship. I mean every time they see you, they offer you money. In my case, the person was loaded with money. (Kaya, 23)

One participant followed a more subtle form of negotiation, using her social media account to signal what she wanted:

I don’t ask for things you know, I mean like, unless like maybe I saw a shoe you know that I kind of like, I put it on my status and I’m like I want that, and that’s how he sends me money. Even with my hair you know, I kind of like take a picture and I’m like, oh I need to do my hair, and then I get some money from, you know. (Unathi, 23)

Explicit requests for money and specific items remained prevalent, with women seeking to dictate the terms of engagement with Blessers, as evident here:

You know, you want to be with me. Then it’s cool, we gonna have lunch. Then maybe you start telling how interested you are in me, and whatsoever, and then we have sort of like a relationship but in this relationship, what are you doing for me? First of all, for me to actually see that you can be my “Blesser”, you have to provide for me. I have to see that oh, okay, maybe he flushes me with gifts…I need to go for lunch, can you give me money for lunch? I can rate him from there. If he gives me this much, maybe he gave me 1500 [$80] or 2000 [$106]. Oh okay, then he can be a “Blesser” but if you can’t do that, then I’m like it’s not even negotiable. (Nozipho)

Some participants proved to be artful in negotiating their ‘wants’ from Blessers. One participant recounted her negotiation tactics:

The trick with these blessers, is you can’t say that I need 5000 [$266] for you to sleep with me. So, the trick is, you actually have to say the least amount and then they give you more. If you start with the high amount you best believe that’s the last day of seeing him. (Duduzile, 28)

She continued:

So, what you do is, you don’t ask for, the trick is that you don’t ask for a lot of money. Let’s say my goal is that I need 6000 [$319.10] to buy a weave. I’m not going to ask for 6000. I’m going to break it down and say oh I need 3000 [$160] for, I don’t say weave, I’ll say something having to do with school or something that sounds convincing. Or rent. That I need to pay it. Knowing that he won’t just put 3, he’ll put 5 (ZAR5000or $266). So I’m closer to my mark. So it’s kind of like, they never put exact amounts. It’s always like the extra. (Duduzile)

Theme: risks associated with Blesser relationships, and the ability to navigate them

Many participants recognized that there were risks associated with Blessed relationships. More than half of the interviewees (thirteen), felt that the relationship was overtly transactional and therefore devoid of any emotional connection. This resulted in palpable tensions with participants’ value systems, which presented a moral dilemma – expressed by participants who had and didn’t have other partners (see Sprague et al., 2023 ).

Some participants were aware of the power imbalance, expressing feelings of emotional suppression and mental exhaustion. These adverse effects combined with the other health risks many were concerned about, including the possibility of contracting HIV or falling pregnant.

Sub-theme: risk perception and mitigation

Some participants instituted risk mitigating strategies, despite the prospect of losing out financially:

So, because of multiple partners, I honestly feel like there’s high risk because… I find, there was this other guy who was an architect because I said, “let’s get tested” I was actually willing to give him sex because he took me to Zimbali [An upmarket exclusive resort on the north coast of KwaZulu-Natal]. I was like, you know what, it’s fine. He has taken me to Zimbali so many times for this and that and what not. So, I said, “no we should get tested” That’s the time he ran for his life like, “no he hasn’t been tested” I don’t know years and what’s not. So, I was like there’s no way I’m gonna die and leave my kids. For what? For money, hell no! (Busisiwe)

Busisiwe continued to emphasize that condom use was essential in preventing HIV and pregnancy and described her fears about both:

So, if they don’t want to use protection… because I’ll tell them we have to use protection. I don’t want HIV, I don’t want a child either. I’ve got two already, I don’t want another one… you’ll find that what would happen is, maybe you’d have sex once with protection and the next time he’s like “you not HIV positive, right”. And obviously if you say no, who in their right mind will just say “Yes, I am” during that time, you know. That’s why I’m thinking… that’s why I’ve become so scared of this whole deal. (Busisiwe)

A participant here relays how Blessers themselves were conscious of the risk of HIV and how she was manipulated into non-reciprocal HIV testing:

But eventually I ended up sleeping with him. Initially with a condom, but then I knew that eventually it was going to get to that point where it’s like…but what these guys do…it’s an interesting trick. They’ll tell you oh let’s go and get tested. So, it’s like to get your confidence [trust]. And you’re like “wow he wants us to get tested so he probably wants to practice safe sex.” so you go and get tested ahead of him because you want to make sure that when you both get tested, you know, it’s like you already know your status. And he’ll want to go get tested together. So, he’ll make you test in front of him. And then when your time comes it’s like “oh phone call, oh.” And when it’s his time he’s like “oh, I forgot I need to do…”

She continued to communicate her experience of Blessers manipulating HIV testing:

So, they drag the testing, their [HIV] testing. It’s like they want to know that they test through you. Like if you’re okay [HIV-negative] it’s like, oh I’m okay. And sometimes you don’t pick up on it. (Duduzile)

Sub-theme: compromised autonomy making navigating risk difficult

The power scales appear tilted in favor of men through a consequence of the basic economic principle of demand and supply, and gender roles still affording greater economic power to men in South Africa. Yet, fewer men have the financial capacity to fulfill the material desires of all women, relative to the number of women seeking Blessers who were willing and able to provide for them. These gendered opportunities and risks can lead to women ceding control and women’s inability to maintain certain barriers that limit risk, evident in this excerpt:

Men who have control over you because of money, they feel they can just… [flash] it in your face like…it’s just like…one day you can have it, one day you will lose it. Like he’s just flying it to me. Like if you want this, you have to….it’s like…how do I say this? Like you are in it…I was in it for the money. I was enjoying it for the money and he was willing to give it to me. (Nosisa, 22)

A different respondent reflected on the financial power dynamics that mirrored the behavioral dynamics underpinning her relationship with a Blesser:

The person who has the means, holds the power because if you refuse, there is another one he can take most of the time, it is them who have the power in this relationship and most of the time it is he who ends the relationship. (Kaya)

Others also echoed that women were easily replaceable, ostensibly placing them in a weaker bargaining position:

They want someone who looks nice because most of the time these people who have money feel like they can replace you if you have an attitude, or do not want to be submissive. They have this whole notion of… err, you can be replaced. There’s another ten thousand of you, I will not beg you. (Busisiwe)

Participants conceded that the promise of money often overshadowed the presence of any risk. Women were therefore willing to expose themselves to risks in pursuit of the monetary rewards reaped from a Blesser:

Money makes you dizzy, you just forget about the risks. (Sizani, 35)

The dominant motivating factor for the study population engaging in a relationship with a Blesser was financial, material and/or improved social status – reflecting Stoebenau et al.’s ( 2016 ) continuum of instrumentality (Fig. 1 ). Findings here are congruent with a review of the literature on transactional sex and other studies of Blessers (Mampane, 2018 ; Stoebenau et al., 2016 ). Social norms, including a quest for greater affluence were influencing and facilitating women’s search for a Blesser, and their effort to acquire money, material items and/or a desired lifestyle to keep pace with peers (Sprague et al., 2023 ). Previous research (Pulerwitz et al., 2021 ); Wamoyi et al., 2019 ) recognizes the role of social and gender norms, particularly the expectation that men provide for women’s material needs who, in turn, reciprocate with sex. Additionally, there is increasing influence from peer groups to attain a Blesser and the status that accompanies these relationships.

The nature of the rewards, and the manner in which these rewards were negotiated, provide additional insight into the form and nature of the relationships that women have with Blessers. Study participants were not destitute, with almost all college educated and a majority deriving an income from formal employment. The financial gain from Blessers served to largely satiate a consumerist lifestyle for many. However, a number participants also channeled money to purchase basic needs such as groceries, paying tuition fees and rental payments.

Some participants retained expectations of their Blessers, which were determined after an initial vetting process, based on a Blesser’s ability and willingness to meet their financial expectations. Many participants negotiated, whether explicitly or implicitly, with the aim of achieving their goals (i.e., extracting maximum value) from Blessers. Although women remained cognizant of the fact that what women offered was not a scarce commodity, underscoring a ‘competitive market’ for Blessers. Some women did not negotiate, whilst others did so overtly. The majority of women adopted a subtle negotiating technique with the aim of achieving their intended goals, though many women found that, over the course of the partnership, they did not achieve exactly what they desired, as disillusionment or disappointment followed (Sprague et al., 2021 ; Sprague et al., 2023 ).

Obtaining Blessers and the nature of these relationships have become normalized, resulting in a societal acceptance of the obligations of both parties, leading to an implicit exchange of sex for money and material goods. The material items and lifestyle acquired by women in their relationships with Blessers, was on constant display, this being part of the appeal. The lifestyle was not only seductive to those who had it, but was also appealing to their peers, some of whom would be prompted to actively attempt to attain a Blesser for themselves, indicating this social phenomenon could become even more entrenched in society.

These relationships are, however, being forged within a context of high risk. Risk can manifest in both social, psychological and physical forms (Fielding-Miller et al., 2016 ), with physical risk presenting as the potential for contracting a sexually transmitted infection like HIV, abuse or falling pregnant. Emotional risk manifested in women ceding power to Blessers in an effort to sustain the pipeline of financial and material rewards and the lifestyle acquired through these relationships. Critically, the ability to mitigate risk was influenced by the level of autonomy retained by women in the relationship. The existence or retention of this autonomy was variable and played a significant role in parametrizing the relationship and the ability to navigate risk. Importantly, it should be recognized that it is not only women who are conscious of the risk factors, with Blessers also aware of the risk posed to them, oftentimes insisting on risk mitigating measures themselves, e.g., HIV testing or condom use.

Studies undertaken in sub-Saharan and South Africa focusing on transactional sex are replete with evidence revealing elevated HIV risk, pregnancy and reduced sexual agency for women (Brouard and Crewe, 2012 ; Dunkle et al., 2004 ), whilst studies focusing specifically on Blessed relationships have also been characterized by violence and abuse (Frieslaar and Masango, 2021 ). In our study, the level of agency, control and comfort varied for participants. These women were required to actively balance the risk and benefits of partnering with a Blesser, followed by making calculated decisions and judgements in an effort to minimize risk and maximize their financial gain. The probability of encountering risks was not only determined by varying dispositions to risk taking and risk avoidance (Sanders, 2004 ), but in the case of this study, by the motivation of the rewards associated with sustaining a relationship with a Blesser.

Whilst most participants in this study were highly educated, with many having jobs, together with agentic examples around negotiating, there was the general acceptance of a pervasive gendered power imbalance. Some of the women had to relinquish their agency and cede to the demands of Blessers out of fear of losing the financial support on offer. These power imbalances along gender lines characterize TSRs (Pulerwitz et al., 2018 ) with women recognizing that they have to, at times, play a ‘submissive’ role. By implication, gendered expectations, roles and beliefs influenced the assumption of risk by women—risks that are gendered because of women’s ability to fall pregnant and greater risk of HIV acquisition. In this exploratory investigation, we posit that women take on greater adverse health risks because of their gender (gender roles and expectations and lower economic power), underscoring the gender penalty that is paid by many participants, i.e., they are more likely to assume risks potentially leading to adverse health outcomes due to their gender.

In an analysis of these data published elsewhere (Sprague et al, 2021 ), some women in the sample were able to extricate themselves from these relationships once they were able to meet their own lifestyle needs independent of Blessers, or after deciding to seek out a more emotionally fulfilling relationship. The surrender of agency could therefore be temporary, and a concession to sustain the relationship and the benefits derived from it.

Much of the extent literature (Brouard and Crewe, 2012 ; Duby et al., 2022 ; Dunkle et al., 2004 ; Wamoyi et al., 2019 ) in relation to the risk facing adolescent girls and young women (AGYW) engaging in TSRs is accompanied by a clarion call for risk mitigating interventions. Some scholars (Plourde et al., 2016 ) stress that interventions aimed at changing behaviors alone will have limited efficacy, calling for focus on the social-structural factors and norms that mediate behaviors, such as peers, communities in which these young women live, and their access to economic and academic opportunities. This study, however, reveals that despite access to economic and academic opportunities, the motivation to engage in TSRs remains robust. Strikingly, these data therefore indicate that women remain aspirational relative to their own socio-economic circumstances, and further influenced by their peer group, underscoring the persistent nature of TSRs even within a highly educated and income generating study population.

The cohort of participants in this study fits within Stoebenau et al. ( 2016 ) “The powerful agent and sex for improved social status” paradigm, in that they seek out the “commodities of modernity” (Leclerc-Madlala, 2003 ) and exhibit agency on a continuum. Whilst these authors (Stoebenau et al., 2016 ) and others (Jewkes and Morrell, 2012 ) recognize that this agency can be tenuous, it does not extend to the deliberate concession of agency pursuant of a sustained relationship with a Blesser and the continued acquisition of financial and material benefits. Blessed relationships are heterogenous in the way power and agency present, as such women have varied ability, both with different Blessers and over the course of a Blessed relationship, to exercise any measure of control over their exposure to risk. Those women in our study who recognized the potential risk posed to themselves, were cognizant that Blessers were a scarce commodity and therefore willingly sacrificed agency in an effort to prolong the relationship, with potential implications for their reproductive and sexual health, psychological health and wellbeing, overall—illustrating the role of gender as an adverse social determinant of health (Solar and Irwin, 2010 ). The examination of Blessed relationships against existing frameworks are important as we seek to understand their distinctive elements within the extent literature on TSRs. There is a concerted and deliberate effort to balance the economic and social rewards on offer against the potential risk that these relationships present. The framework offered by Stoebenau et al. ( 2016 ) remains relevant in its recognition of what motivates women to engage in TSRs, with this work acknowledging that the level of risk faced by women, in the case of this study, can be negotiated (Rhodes, 1997 ) or even avoided, depending on agentic capacity and the desire to sustain the rewards derived from these relationships.

Study limitations and future research

Study participants were university-going women in KZN—a high HIV prevalence setting—and hence, not representative of all South African women, including risks. Women from different socio-economic contexts may therefore have different experiences with Blessers. Future studies should engage with Blessers and other stakeholders, such as community members, to provide a balanced perspective, advancing our understanding of this type of TSR as a social phenomenon and of the social norms contained within. Further research is needed to understand how women with differing social-demographics and socio-economic statuses understand and negotiate risk-reward to inform future interventions.

The study provides novel insights into the complexities of the Blessed relationship. Participants were motivated to engage in a relationship with a Blesser with the expectation of financial and or material rewards. For most, this motivation was not rooted within a context of deprivation but rather within a social context where seeking a Blesser was a normative goal. The exchange of sex for money was largely implicit within these relationships, with women aiming to maximize the value from this arrangement and achieve their goals. There was also the recognition that women outnumbered the available Blesser men, contributing to the ‘scarcity’ and desirability of Blessers as benefactors. Whilst women recognize the potential risks these relationships present, agency was at times sacrificed out of fear that any insisted mitigation strategy could result in losing the Blesser, also highlighting the existence of gendered power imbalances. We posit the assumption of risks is higher for women than for men because of gendered power, and that women pay a gender penalty as a result. Insights captured here extend the current paradigms in which we view TSRs, where women balance risk and reward, often leading to a trade-off and the acceptance of risk—with implications for sexual and reproductive health. Findings further highlight the challenge of designing interventions aimed at reducing the associated risk posed by TSRs, given that structural drivers such as poverty and education did not mediate these relationships among the study population. The goal of obtaining a Blesser is generally accepted and even facilitated by peers, which indicates that greater understanding of social norms and how to disrupt them is essential to obviate this practice.

Data availability

The datasets of interview transcripts generated and analyzed during the current study are available from the corresponding author on reasonable request.

This is the exchange rate calculated as of July 1, 2023 from OANDA Currency Converter accessible here . On this date USD1 = ZAR18.80.

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We are grateful to the participants for sharing their intimate experiences with us.

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Gavin George

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GG carried out the funding acquisition, the conceptualization, the fieldwork, data analysis and writing of the manuscript. CS co- conceptualized the study and along with LM, contributed to the formal analysis and exposition of results and draft preparation. All authors read and agreed to the submitted version of the manuscript.

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George, G., Maqsood, L. & Sprague, C. Balancing risk and reward: exploring women’s transactional sexual relationships with Blessers in South Africa. Humanit Soc Sci Commun 11 , 1158 (2024). https://doi.org/10.1057/s41599-024-03629-7

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Sampling For Qualitative Data Analysis

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SAMPLING PROCEDURE AND SAMPLE (QUALITATIVE RESEARCH)
Sampling in Qualitative Research
Description of Sampling and Sample
Qualitative and quantitative Sampling in Research
QUANTITATIVE METHODOLOGY (Part 2 of 3):
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Series: Practical guidance to qualitative research. Part 3: Sampling
What is a sampling plan? A sampling plan is a formal plan specifying a sampling method, a sample size, and procedure for recruiting participants (Box 1) [].A qualitative sampling plan describes how many observations, interviews, focus-group discussions or cases are needed to ensure that the findings will contribute rich data.
Different Types of Sampling Techniques in Qualitative Research
Different Types of Sampling Techniques in Qualitative Research
(PDF) Sampling in Qualitative Research
(PDF) Sampling in Qualitative Research
Big enough? Sampling in qualitative inquiry
Any senior researcher, or seasoned mentor, has a practiced response to the 'how many' question. Mine tends to start with a reminder about the different philosophical assumptions undergirding qualitative and quantitative research projects (Staller, 2013). As Abrams (2010) points out, this difference leads to "major differences in sampling ...
Chapter 5. Sampling
Qualitative researchers ask very different questions, though. Qualitative research questions are not about "how many" of a certain group do X (in this case, what percentage of the unvaccinated hesitate for concern about safety rather than reject vaccination on political grounds). Qualitative research employs nonprobability sampling. By ...
Sampling Methods
This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research. It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or ...
Sampling Techniques for Qualitative Research
Sampling Techniques for Qualitative Research
PDF Sampling Strategies in Qualitative Research
Sampling Strategies in Qualitative Research. In this context, in part as a reaction against the positioning of qualitative research as less vital and relevant given its refusal to undertake random sampling with large numbers - due to a fundamental asymmetry in goals (e.g. Lincoln and Guba, 1985) and inability in
Purposeful sampling for qualitative data collection and analysis in
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, 2002).This involves identifying and selecting individuals or groups of individuals that are especially knowledgeable about or experienced with a phenomenon of interest ...
Qualitative Sampling Methods
Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of ...
Sampling in Qualitative Research
Go to: In gerontology the most recognized and elaborate discourse about sampling is generally thought to be in quantitative research associated with survey research and medical research. But sampling has long been a central concern in the social and humanistic inquiry, albeit in a different guise suited to the different goals.
PDF Sampling Techniques for Qualitative Research
Qualitative studies use specific tools and techniques (methods) to sample people, organizations, or whatever is to be examined. The methodology guides the selection of tools and techniques for sampling, data analysis, quality assurance, etc. These all vary according to the purpose and design of the study and the RQ.
Qualitative, Quantitative, and Mixed Methods Research Sampling
The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e ...
Sampling in qualitative interview research: criteria, considerations
However, in studies based on qualitative research interviews, sampling issues are rarely discussed. Possible reasons include a lack of universal 'rules of thumb' governing sampling considerations and the diversity of approaches to qualitative inquiry. Some misconceptions lead to poor practices and higher likelihood of rejection.
Qualitative Sampling Methods
Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of ...
10.2 Sampling in qualitative research
Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of whatever phenomenon it is that they are studying. In this section, we'll examine the techniques that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most ...
Sampling in Qualitative Research: Rationale, Issues, and Methods
There is a need for more explicit discussion of qualitative sampling issues. This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It then describes common questions about sampling in qualitative research.
Sampling in Interview-Based Qualitative Research: A Theoretical and
A four-point approach to sampling in qualitative interview-based research is presented and critically discussed in this article, which integrates theory and process for the following: (1) defining a sample universe, by way of specifying inclusion and exclusion criteria for potential participation; (2) deciding upon a sample size, through the ...
7.2 Sampling in Qualitative Research
Qualitative researchers sometimes rely on snowball sampling techniques to identify study participants. In this case, a researcher might know of one or two people she'd like to include in her study but then relies on those initial participants to help identify additional study participants. Thus the researcher's sample builds and becomes ...
Sampling in Qualitative Research: Rationale, Issues, and Methods
The focus is on criteria for designing samples; qualitative issues related to suitability of any given person for research are not addressed. The criteria for designing samples constitute what ...
What Is Purposive Sampling? Technique, Examples, and FAQs
What Is Purposive Sampling? Technique, Examples, and ...
What Is Purposive Sampling?
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: An Overview
Table of Contents. Importance of Sampling in Sociological Research; Types of Sampling; Challenges and Considerations in Sampling; Conclusion; Sampling is a critical concept in sociological research and forms the foundation for empirical investigation. It refers to the process of selecting a subset of individuals, groups, or cases from a larger population for the purpose of conducting research.
"I am there just to get on with it": a qualitative study on the labour
These and other tensions require further investigation. This was a qualitative study with participatory elements. Using purposive and snowball sampling and attending to regional and institutional diversity, we conducted 21 semi-structured interviews with individuals holding NIHR-funded formal PPI roles across England.
Sampling in Quantitative Research (docx)
Sampling in qualitative research has a different meaning than it does in quantitative research. Qualitative sampling, you are looking to find a group of individuals or a culture or a social organization in which you can get rich description of the load experience of either the question under inquiry or the culture or social organization ...
Planning Qualitative Research: Design and Decision Making for New
Qualitative research, conducted thoughtfully, is internally consistent, rigorous, and helps us answer important questions about people and their lives (Lincoln & Guba, 1985). These fundamental epistemological foundations are key for developing the right research mindset before designing and conducting qualitative research. ... Sampling: People ...
Perspectives on a peer-driven intervention to promote pre-exposure
Qualitative and mixed methods data analysis was conducted using Dedoose, a versatile software platform designed for analyzing qualitative and mixed methods research data. Dedoose facilitates the organization, coding, and interpretation of complex datasets, enhancing the efficiency and depth of analysis [ 35 ].
Full article: Generic and specific competencies: analysis and
Research instruments. For the qualitative stage, focus groups were conducted with key actors who, in a co-creation space, provided their opinions, mainly regarding the skills and competencies that a professional should have to work in the tourism sector. ... Sampling and data collection. Regarding the quantitative phase, in the sampling process ...
Experiences of Turkish mothers of children with autism: a
Criterion sampling, a form of purposive sampling, was employed to select participants. Interviews continued until data saturation was achieved. All interviews were audio-recorded and transcribed verbatim. Thematic analysis was utilized to analyze the data, and the study adhered to the COREQ checklist for reporting qualitative research.
Balancing risk and reward: exploring women's transactional sexual
This qualitative study was conducted in 2017-2018 in KwaZulu-Natal (KZN) province, South Africa, a high HIV prevalence setting, with 22 women, using purposive and snowball sampling methods.

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Different Types of Sampling Techniques in Qualitative Research

Key Takeaways:

In this Article:

Factors to Consider When Choosing a Sampling Technique

4 Types of Sampling Techniques and Their Applications

1. Purposive Sampling

Purposive Sampling: Strengths and Weaknesses

2. Convenience Sampling

Convenience Sampling: Strengths and Weaknesses

3. Snowball Sampling

Snowball Sampling: Strengths and Weaknesses

4. Theoretical Sampling

Theoretical Sampling: Strengths and Weaknesses

Tips for Selecting Participants

Ensuring Diversity in Samples

Maintaining Ethical Considerations

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BEFORE YOU GO

Chapter 5. Sampling

The “Who” of Your Research Study

Sampling People

When Your Population is Not Composed of People

Case Studies

Content: Documents, Narrative Accounts, And So On

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

What is the Correct “Number” to Sample?

Examples of “Sample” Sections in Journal Articles

Final Words

Further Readings

Sampling in Qualitative Research

Contributions, Logic and Issues in Qualitative Sampling

The Cultural Embeddedness of the Concept of Sampling

Ideals and Techniques of Qualitative Sampling

Defining the universe

Appropriate analytic units: Sampling for meaning

Techniques for selecting a sample

Who and who not?

Homogeneity or diversity

Summary and Reformulation for Practice

Qualitative Clarity as an Analog to Statistical Power

Rich and diverse theoretical grounding

Sensitivity to contexts

Acknowledgments

Biographies

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Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST REVIEWED: 26 February 2020 LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

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10.2 Sampling in qualitative research

Nonprobability sampling

Types of nonprobability samples

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7.2 Sampling in Qualitative Research

Nonprobability Sampling