qualitative research population mean

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

Developing the Qualitative Research Design

Qualitative research: target population, sampling frame, and sample.

Qualitative Descriptive Design
Design and Development Research (DDR) For Instructional Design
Qualitative Narrative Inquiry Research
Action Research Resource
Case Study Design in an Applied Doctorate
SAGE Research Methods
Research Examples (SAGE) This link opens in a new window
Dataset Examples (SAGE) This link opens in a new window
IRB Resource Center This link opens in a new window

There are various qualitative designs: case study, action research, ethnography, evaluative, interpretive description, grounded theory, narrative, phenomenology, and photovoice/visual.

Qualitative Research Approaches
Qualitative Study Alignment Table of Research Questions and Interview Questions
Case Study Planning Matrix

The target population is the population that the sample will be drawn from. It is all individuals who possess the desired characteristics (inclusion criteria) to participate in the doctoral project or dissertation-in-practice.

The sampling design represents the plan for obtaining a sample from the target population. A sampling frame can be employed to identify participants and can provide access to the population for recruitment of the sample.

To identify all individuals in the doctoral project or dissertation-in-practice population a sampling frame is identified and provides access to the population for recruitment of sample. Review Trochim's Knowledge Base at http://www.socialresearchmethods.net/kb/ for more information.

Exercise #1

Use the script below by replacing the italicized text with the appropriate information to state the study population.

"The study population for the proposed study is comprised of all (individuals with relevant characteristics), within (describe the sampling frame)."

The study sample is a subset of the target population, which possesses the appropriate characteristics for the proposed study. Potential participants are then screened based on inclusion/exclusion criteria, and if met are then recruited into the sample. Review Trochim's Knowledge Base at http://www.socialresearchmethods.net/kb/ for more information.

Exercise #2

Use the script below to state the sample.

"(sampling method) will be used to obtain (sample number) participants that meet the following inclusion criteria (list relevant characteristics needed to participate)."

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Qualitative Research Using R: A Systematic Approach pp 1–19 Cite as

Qualitative Research: An Overview

Yanto Chandra 3 &
Liang Shang 4
First Online: 24 April 2019

3685 Accesses

5 Citations

Qualitative research is one of the most commonly used types of research and methodology in the social sciences. Unfortunately, qualitative research is commonly misunderstood. In this chapter, we describe and explain the misconceptions surrounding qualitative research enterprise, why researchers need to care about when using qualitative research, the characteristics of qualitative research, and review the paradigms in qualitative research.

Qualitative research
Gioia approach
Yin-Eisenhardt approach
Langley approach
Interpretivism

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Qualitative research is defined as the practice used to study things –– individuals and organizations and their reasons, opinions, and motivations, beliefs in their natural settings. It involves an observer (a researcher) who is located in the field , who transforms the world into a series of representations such as fieldnotes, interviews, conversations, photographs, recordings and memos (Denzin and Lincoln 2011 ). Many researchers employ qualitative research for exploratory purpose while others use it for ‘quasi’ theory testing approach. Qualitative research is a broad umbrella of research methodologies that encompasses grounded theory (Glaser and Strauss 2017 ; Strauss and Corbin 1990 ), case study (Flyvbjerg 2006 ; Yin 2003 ), phenomenology (Sanders 1982 ), discourse analysis (Fairclough 2003 ; Wodak and Meyer 2009 ), ethnography (Geertz 1973 ; Garfinkel 1967 ), and netnography (Kozinets 2002 ), among others. Qualitative research is often synonymous with ‘case study research’ because ‘case study’ primarily uses (but not always) qualitative data.

The quality standards or evaluation criteria of qualitative research comprises: (1) credibility (that a researcher can provide confidence in his/her findings), (2) transferability (that results are more plausible when transported to a highly similar contexts), (3) dependability (that errors have been minimized, proper documentation is provided), and (4) confirmability (that conclusions are internally consistent and supported by data) (see Lincoln and Guba 1985 ).

We classify research into a continuum of theory building — > theory elaboration — > theory testing . Theory building is also known as theory exploration. Theory elaboration refers to the use of qualitative data and a method to seek “confirmation” of the relationships among variables or processes or mechanisms of a social reality (Bartunek and Rynes 2015 ).

In the context of qualitative research, theory/ies usually refer(s) to conceptual model(s) or framework(s) that explain the relationships among a set of variables or processes that explain a social phenomenon. Theory or theories could also refer to general ideas or frameworks (e.g., institutional theory, emancipation theory, or identity theory) that are reviewed as background knowledge prior to the commencement of a qualitative research project.

For example, a qualitative research can ask the following question: “How can institutional change succeed in social contexts that are dominated by organized crime?” (Vaccaro and Palazzo 2015 ).

We have witnessed numerous cases in which committed positivist methodologists were asked to review qualitative papers, and they used a survey approach to assess the quality of an interpretivist work. This reviewers’ fallacy is dangerous and hampers the progress of a field of research. Editors must be cognizant of such fallacy and avoid it.

A social enterprises (SE) is an organization that combines social welfare and commercial logics (Doherty et al. 2014 ), or that uses business principles to address social problems (Mair and Marti 2006 ); thus, qualitative research that reports that ‘social impact’ is important for SEs is too descriptive and, arguably, tautological. It is not uncommon to see authors submitting purely descriptive papers to scholarly journals.

Some qualitative researchers have conducted qualitative work using primarily a checklist (ticking the boxes) to show the presence or absence of variables, as if it were a survey-based study. This is utterly inappropriate for a qualitative work. A qualitative work needs to show the richness and depth of qualitative findings. Nevertheless, it is acceptable to use such checklists as supplementary data if a study involves too many informants or variables of interest, or the data is too complex due to its longitudinal nature (e.g., a study that involves 15 cases observed and involving 59 interviews with 33 informants within a 7-year fieldwork used an excel sheet to tabulate the number of events that occurred as supplementary data to the main analysis; see Chandra 2017a , b ).

As mentioned earlier, there are different types of qualitative research. Thus, a qualitative researcher will customize the data collection process to fit the type of research being conducted. For example, for researchers using ethnography, the primary data will be in the form of photos and/or videos and interviews; for those using netnography, the primary data will be internet-based textual data. Interview data is perhaps the most common type of data used across all types of qualitative research designs and is often synonymous with qualitative research.

The purpose of qualitative research is to provide an explanation , not merely a description and certainly not a prediction (which is the realm of quantitative research). However, description is needed to illustrate qualitative data collected, and usually researchers describe their qualitative data by inserting a number of important “informant quotes” in the body of a qualitative research report.

We advise qualitative researchers to adhere to one approach to avoid any epistemological and ontological mismatch that may arise among different camps in qualitative research. For instance, mixing a positivist with a constructivist approach in qualitative research frequently leads to unnecessary criticism and even rejection from journal editors and reviewers; it shows a lack of methodological competence or awareness of one’s epistemological position.

Analytical generalization is not generalization to some defined population that has been sampled, but to a “theory” of the phenomenon being studied, a theory that may have much wider applicability than the particular case studied (Yin 2003 ).

There are different types of contributions. Typically, a researcher is expected to clearly articulate the theoretical contributions for a qualitative work submitted to a scholarly journal. Other types of contributions are practical (or managerial ), common for business/management journals, and policy , common for policy related journals.

There is ongoing debate on whether a template for qualitative research is desirable or necessary, with one camp of scholars (the pluralistic critical realists) that advocates a pluralistic approaches to qualitative research (“qualitative research should not follow a particular template or be prescriptive in its process”) and the other camps are advocating for some form of consensus via the use of particular approaches (e.g., the Eisenhardt or Gioia Approach, etc.). However, as shown in Table 1.1 , even the pluralistic critical realism in itself is a template and advocates an alternative form of consensus through the use of diverse and pluralistic approaches in doing qualitative research.

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Chandra, Y., Shang, L. (2019). Qualitative Research: An Overview. In: Qualitative Research Using R: A Systematic Approach. Springer, Singapore. https://doi.org/10.1007/978-981-13-3170-1_1

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Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”.

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.”

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection:

Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.

Criterion sampling-selection based on pre-identified factors.

Convenience sampling- selection based on availability.

Snowball sampling- the selection is by referral from other participants or people who know potential participants.

Extreme case sampling- targeted selection of rare cases.

Typical case sampling-selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo.

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research.

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others.

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Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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.

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, but Gallup’s polling disagreed. Gallup successfully predicted Roosevelt’s win and subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election. [1] Among other problems, the fact that Gallup’s quota categories did not represent those who actually voted (Neuman, 2007) [2] underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods. [3] 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. For that, you need probability sampling, which we will discuss in the next section.

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.

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

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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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Published: 25 March 2024

Long-acting injectable depot buprenorphine from a harm reduction perspective in patients with ongoing substance use and multiple psychiatric comorbidities: a qualitative interview study

Björn Johnson 1 ,
Bodil Monwell 2 , 3 &
Andrea Johansson Capusan 4 , 5

Harm Reduction Journal volume 21 , Article number: 68 ( 2024 ) Cite this article

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Long-acting injectable depot buprenorphine may increase access to opioid agonist treatment (OAT) for patients with opioid use disorder in different treatment phases. The aim of this study was to explore the experiences of depot buprenorphine among Swedish patients with ongoing substance use and multiple psychiatric comorbidities.

Semi-structured qualitative interviews were conducted with OAT patients with experience of depot buprenorphine. Recruitment took place at two OAT clinics with a harm reduction focus, specializing in the treatment of patients with ongoing substance use and multiple comorbidities. Nineteen participants were included, 12 men and seven women, with a mean age of 41 years (range 24–56 years), and a mean of 21 years (5–35 years) of experience with illicit substance use. All participants had ongoing substance use and psychiatric comorbidities such as ADHD, anxiety, mood, psychotic and eating disorders. Interviews were transcribed verbatim. Thematic content analysis was conducted both manually and using qualitative data analysis software.

Participants reported social benefits and positive changes in self-perception and identity. In particular, depot buprenorphine contributed to a realization that it was possible to make life changes and engage in activities not related to substance use. Another positive aspect that emerged from the interviews was a noticeable relief from perceived pressure to divert OAT medication, while some expressed the lack of income from diverted oral/sublingual OAT medication as a negative, but still acceptable, consequence of the depot buprenorphine. Many participants considered that the information provided prior to starting depot buprenorphine was insufficient. Also, not all patients found depot buprenorphine suitable, and those who experienced coercion exhibited particularly negative attitudes towards the medication.

Conclusions

OAT patients with ongoing substance use and multiple psychiatric comorbidities reported clear benefits of depot buprenorphine, including changes in self-perception which has been theorized to play an important role in recovery. Clinicians should consider the specific information needs of this population and the extensive diversion of traditional OAT medications in this population to improve the treatment experience and outcomes. Overall, depot buprenorphine is a valuable treatment option for a population in need of harm reduction and may also contribute to psychological changes that may facilitate recovery in those with the greatest need.

Novel treatment options with long-acting injectable depot buprenorphine (hereafter: depot buprenorphine) for opioid use disorder have increased access to opioid agonist treatment (OAT) in recent years. In pivotal trials, depot injections have shown similar efficacy to sublingual buprenorphine/naloxone [ 1 ] and superior efficacy to placebo [ 2 ]. The first depot injection was approved in the USA in 2017 [ 3 ] and since 2019, weekly [ 1 ] and/or monthly [ 1 , 2 ] subcutaneous buprenorphine injections are available for the treatment of moderate to severe opioid use disorder (OUD) in the EU, UK, Canada, Australia, New Zealand and several countries in the Middle East and North Africa. Patient-reported outcomes from RCTs and observational studies indicate long-term safety and increased patient satisfaction with depot injections [ 4 , 5 ].

Patient perspectives on depot buprenorphine

Interestingly, the introduction of depot buprenorphine has also led to a renewed research interest in patient perceptions and attitudes towards OAT in general and depot buprenorphine in particular.In recent years, several studies have investigated how depot buprenorphine affects patients’ lives, their treatment, and their relationships with treatment staff [ 6 , 7 , 8 , 9 ]. Our research group conducted a stratified qualitative interview study [ 7 ] to explore reasons for choosing depot buprenorphine and reasons for discontinuing or declining this treatment. Qualitative studies describe benefits in terms of practical factors, an increased sense of freedom, psychological benefits such as a reshaping of self-identity to feel “normal” [ 6 , 7 ], and a reduction in the stigma associated with daily supervised OAT [ 8 ]. Loss of contact with staff, the need for a daily routine, and concerns about medication effects and side effects are reasons for choosing to discontinue depot buprenorphine or continue with sublingual treatment [ 9 ]. A trusting relationship with treatment staff and adequate information are important for successful induction of depot buprenorphine [ 7 ]. In contrast, mistrust and coercion could lead to a “polluted pharmaceutical atmosphere”, similar to that described during the clinical introduction of sublingual buprenorphine/naloxone combinations, which negatively affected patients’ perceptions of medication effects and side effects [ 10 ].

In the early stages of clinical implementation, depot buprenorphine was predominantly offered to more stable patients, which explains why early qualitative studies [ 6 , 7 , 8 ] did not capture how depot buprenorphine affected patients at the most severe end of the OUD spectrum with ongoing polysubstance use and multiple comorbidities. This perspective is important for the use of buprenorphine depot injections. Firstly, research from other medical fields such as the treatment of schizophrenia, with decades of experience in depot injections, suggests that the most unstable patients may benefit most from these formulations [ 11 ]. In addition, unstable patients may face several barriers to accessing care, including poor treatment adherence and clinicians’ concerns that treatment may be harmful [ 12 , 13 ] which must be balanced against the considerable harms of untreated OUD. It is therefore important to explore how unstable patients themselves experience the potential benefits and disadvantages associated with depot buprenorphine.

The setting

In Sweden, national regulations require OAT to be provided in a specialised psychiatric or addiction care setting, registered with the Health and Social Care Inspectorate [ 14 , 15 ]. The treatment must include both medication and, for those patients who need it, psychological or psychosocial treatment and support, either provided by the unit or in collaboration with the municipal social services or other care providers [ 15 ].

The availability of OAT is low compared to many Western European countries. The contrast with neighbouring Denmark and Norway, with more than twice as many OAT patients per 100 000 inhabitants than Sweden, is stark [ 16 , 17 ]. Access to treatment also varies considerably across the country [ 16 ]. There are no current estimates of the prevalence of opioid dependence in the Swedish population, but drug-related deaths increased steadily over the period 2000–2017 and are among the highest in Europe [ 18 ]. However, there has been a slight decrease in deaths since 2017, in parallel with an expansion of harm reduction interventions such as naloxone distribution, needle exchange programs and an increased access to OAT.

The number of OAT patients in Sweden has increased continuously over the last decades, from about 1000 patients in 2000 to about 7500 patients in 2022 [ 16 ]. The most common OAT medication is still methadone, followed by sublingual buprenorphine and sublingual buprenorphine-naloxone combination. Since 2007, the Swedish National Board of Health and Welfare has recommended buprenorphine-naloxone as the first-line medication in OAT, but in many local settings, implementation of these recommendations has been hampered by local traditions and patient resistance. Depot buprenorphine was introduced in 2019. In 2022 about 10% of Swedish OAT patients were prescribed depot formulations.

For many years OAT was a controversial treatment modality in Sweden. Access was strictly regulated, with high thresholds for entry, a strong focus on abstinence and rehabilitation [ 19 ], and an emphasis on the potential harms caused by the treatment itself, while disregarding the harms of untreated OUD. Harm reduction-oriented OAT did not exist, and patients with repeated relapses into illicit substance use were discharged from treatment according to earlier national guidelines [ 20 ]. This led, among other things, to the emergence of a significant illicit market for OAT medicines [ 21 ].

Since 2015, however, the national regulatory framework for OAT has been brought in line with modern research, with lower thresholds for entering treatment [ 19 ] and no longer recommending involuntary discharge, while emphasizing harm reduction measures such as naloxone distribution [ 22 ]. The metropolitan areas now have clinics with a strong harm-reduction profile, with units specialising in clients with ongoing substance use and multiple somatic and psychiatric co-morbidities. However, there are considerable variations across the country, and in some healthcare regions involuntary discharge is still a common practice [ 16 ].

Swedish OAT has traditionally had a strong focus on control and medical safety. According to the current national regulations, which have not yet been adapted to the depot formulations, the prescribed medication must be taken daily under clinic supervision for the first three months. After that, if the treatment outcome is stable, the doctor can gradually allow the patient to manage their own medication [ 23 , 24 ].

In this study, we aimed to investigate the experiences of patients with ongoing, severe substance use and multiple comorbidities who are receiving long-acting injectable depot buprenorphine within a harm reduction setting in Sweden. Our goal was to generate new insights into the potential benefits and challenges associated with depot buprenorphine in this specific patient population. Specifically, we explored how the adoption of depot buprenorphine impacts various aspects of patients’ lives, including their treatment experiences, relationships with treatment staff, and perspectives on their future opioid agonist treatment (OAT). By focusing on this subgroup, we aimed to contribute valuable knowledge to enhance the understanding and optimization of depot buprenorphine use in the context of harm reduction for patients with complex needs.

The study is based on qualitative, semi-structured interviews with OAT patients with ongoing substance use and multiple comorbidities.

Sampling and recruitment

Participants were recruited between December 2021 and May 2022 from two harm reduction units specializing in the treatment of patients with ongoing, severe polysubstance use and multiple comorbidities. The units were part of two larger OAT clinics, each serving approximately 600–700 patients, located in two large cities (> 300,000 inhabitants). In the first clinic, around 100 patients, of whom just under half were treated with depot buprenorphine, had contact with the harm reduction unit. In the second clinic, about 150 patients had contact with the harm reduction unit, but only about ten of them were treated with depot buprenorphine. The treatment staff included doctors specialized in psychiatry or addiction medicine, nurses, and mental health workers. Additionally, the clinics could provide access to medical workups and hepatitis C treatment, as well as services from psychologists and occupational therapists.

Inclusion criteria for the study were having (1) OAT at one of the two units with harm reduction profile included in the study, (2) ongoing substance use and (3) multiple comorbidities (such as, but not limited to, substance use disorders other than OUD, psychiatric disorders including affective, anxiety or psychotic disorder, somatic comorbidities including hepatitis C); and (4) willingness to participate. Exclusion criteria were the inability to give informed consent, either because of poor language skills or because they were too impaired by substances and/or mental illness at the time of the information. Excluded participants were given the opportunity to return at a later date if they wished to participate in the study.

Posters, flyers and information to clinical staff were used to inform patients who were part of the target group about the opportunity to participate. Interested patients were given a date and time for the interview, which usually coincided with a visit for their depot injection or other medication collection. As patients with ongoing substance use and/or mental illness may find it difficult to attend scheduled appointments, the interviewer was also available in the reception area of the clinics, without prior booking, allowing for the opportunity to meet with respondents who missed their scheduled appointments.

All interviews were conducted by BM, a clinical researcher with extensive experience in conducting qualitative interviews with patients who use psychoactive substances and have severe psychiatric comorbidities. BM had previously worked as healthcare counsellor at another OAT clinic but had no previous relationship with the clinics or patients in question. Recruitment and interviews continued until BM deemed that data saturation had been achieved.

Ethical considerations

Interviewing patients with active use and comorbidities poses several ethical challenges. Patients may be too affected by substances or by mental illness to give informed consent or to participate in the interview at a given time. This could limit the possibility of the most vulnerable patients to have their voices heard. We chose to handle this dilemma by conducting an individual clinical assessment of potential participants prior to the interviews. BM conducted a clinical assessment to evaluate the participants’ degree of influence of psychoactive substances, potential cognitive impairment, and current psychiatric status. Assessments were recorded in field notes.

Another aspect was that many patients had experience of involuntary discharge and it was important to ensure confidentiality. Participants received both verbal and written information about the study. They were informed that the interviews would be confidential, that they could discontinue the interview at any time, that all data would be pseudonymized before publication, and that their participation would not affect their treatment in any way. Subsequently, the patients signed an informed consent form. In the case of patients interviewed by telephone, staff at the clinic provided written information and obtained written consent before the interview. Participant characteristics are reported only at the group level, to avoid the risk of individual participants being identified.

The study was reviewed and approved by the Swedish Ethical Review Authority (Ref. No. 2020 − 00796).

Interview procedure

Semi-structured interviews were conducted using an interview guide covering the following themes: (a) background and history of substance use, (b) previous treatment experiences, (c) experiences with and views on OAT, (d) relationships with treatment staff, (e) views on control and support in ongoing treatment, (f) thoughts on the choice of drug formulation, (g) perceptions of the information provided by staff about depot buprenorphine, and (h) thoughts about the future.

The data consist of nineteen interviews. Eighteen were conducted face-to-face, in a secluded room at the clinic in question, and one was conducted by telephone [ 7 , 25 ]. Seven participants were perceived to be affected by substance intake (six of them confirmed this) and a further two were experiencing withdrawal symptoms. None of them were disoriented or considered so substance-impaired that they were unable to give informed consent or that it would be impossible to conduct an interview. However, four other people were excluded from the study: three did not meet the inclusion criteria (two were stable and in remission, one did not speak sufficient Swedish) and one was excluded due to agitated, aggressive behavior at the clinic. After the interviews, the participants received a shopping voucher worth SEK 200 (about €20).

The interviews lasted on average 37 min (range 21–55 min). They were recorded on a digital voice recorder and then transcribed verbatim by BM.

Thematic analysis [ 26 ] was carried out in two ways. The material was thoroughly read and coded by BM and AJC based on the themes outlined in the interview guide. This was followed by a detailed coding, in which different patterns in the interview responses were identified. The themes and sub-themes were then compiled in an Excel spreadsheet. In parallel, and blinded to the above findings, BJ conducted a computer-assisted thematic analysis using NVivo (Release 1.7, QSR International 2022). Initially, an inductive coding was carried out manually in NVivo. This coding was then carefully reviewed, with some codes modified and others merged. Subsequently, more general categories and subcategories were created. The parallel categorizations in Excel and NVivo were then compared and found to be largely consistent. In the final step of the analysis, the categories and codes were reviewed once more in NVivo and illustrative quotes were selected from the relevant text passages. The selection was made by BJ, who also wrote the first draft of the results section. The quotes were translated into English using ChatGPT 3.5 and then proofread by a native English translator.

The participants

Nineteen participants, 12 men and 7 women, mean age 41 years (range 24–56 years) were included. Eighteen had ongoing depot buprenorphine treatment, 12 with weekly injections and 5 with monthly injections. One participant had recently discontinued depot buprenorphine treatment and switched back to sublingual mono-buprenorphine.

Study participants had a diagnosed opioid use disorder and extensive experience of polydrug use. The mean duration of illicit drug use was 21 years (5–35 years). Before starting OAT, nine participants had used heroin as their primary drug, two had mainly used other opioids, and six had switched between heroin and other opioids. Two reported primary drugs other than opioids. Many had started using drug in their early teens and spoke of a childhood spent in difficult circumstances, including parents with drug problems, lack of care, and traumatic experiences.

All participants had received some treatment prior to starting OAT. Nine had extensive treatment experience for substance use disorders, including compulsory treatment, while the rest had more limited treatment experience prior to OAT. Most had been in OAT for a relatively short time, three years or less, but some had long experience and had been involuntarily discharged from OAT several times. As mentioned above, involuntary discharge used to be common practice in Sweden.

As the interviews were conducted in clinics for unstable patients, almost all participants had ongoing illicit substance use. At the time of the interview, nine participants reported extensive use, eight had more limited use, and one reported short-time abstinence, of less than three months. In one case, the participant did not provide information on drug status. There was a high level of psychiatric comorbidity in the group, with ADHD or other neurodevelopmental disorders, current or lifetime anxiety disorders, mood disorders, trauma and/or psychosis. Experiences of eating disorders and intentional self-harm were also reported.

Freedom of choice and information

Participants were offered depot injections due to their ongoing instability, with the intention of improving adherence and/or reducing the risk of overdose. Several patients reported that they had been advised by staff to try depot buprenorphine, because of inadequate effectiveness or side effects from previous medication. Some had asked to try depot buprenorphine themselves, having been recommended to do so by peers or having otherwise come across positive information. Most participants reported that the decision to try depot buprenorphine was voluntary. However, some reported feeling that the staff had effectively forced them to choose between depot buprenorphine and involuntary discharge from OAT, either because they were suspected of selling medication, or because they missed scheduled appointments or otherwise mismanaged their treatment. One participant reported being presented with a fait accompli:

“When I came [to the clinic] before Christmas, there was only one bottle and one syringe here when I was supposed to get my dose. […] They just said I would get this [a depot injection] instead, something about it being Christmas and New Year and they couldn’t dispense tablets daily, so I got this instead. They said it was the same dose as the tablets.” (Male participant #3) .

In addition to exploring participants’ opinions about voluntariness, we also inquired about the adequacy of the information they had received before starting depot buprenorphine treatment. While some participants felt that they had been adequately informed, others had gained knowledge by observing friends or partners who had tried depot buprenorphine. Nevertheless, a significant number of participants, including some who were enthusiastic about depot buprenorphine, reported that they had not received enough information. For instance, one participant stated:

“No, I didn’t get so much information other than that it [the dose] would last for a week. That it was in injection form, I wouldn’t have to come every day. And that it works just like usual.” (Female participant #6) .

Effects and side effects of depot buprenorphine

The perceived effects and side effects of the medication were a topic that was addressed in all interviews. A clear majority of the participants reported being satisfied with depot buprenorphine and described the depot effect as more even and stable than treatment with sublingual tablets.

“On Subs [buprenorphine tablets], I felt worse in the evenings and in the morning, when you wake up and so on. It feels like the “sub” wears off when you sleep. Then it takes a while before it starts working again. You dip quickly. Now it’s even.” (Male participant #14) .

Participants described feeling good, harmonious, and/or more “normal”. Some who had previously supplemented their medication with heroin or other illicit opioids, reported that they no longer needed to do so. The craving was gone, and so were the thoughts of heroin.

“I have no craving for heroin anymore, it’s completely insane. Because I had it on the tablets all the time, for all those years. I had to work with the craving all the time. But the depot buprenorphine, they kind of just cut it off.” (Female participant #19) .

Some participants said that they felt that the medication was right for them from the start, while others reported that the effect had varied during the first few weeks. The latter reported decreasing effects and withdrawal symptoms that became noticeable or significant towards the end of the week. Some said that they sometimes bought illicit buprenorphine to balance the effect. During the titration phase, many patients reported being offered earlier refills or extra doses in the form of sublingual tablets to counter withdrawal symptoms.

“I think it’s going great. Except on the weekends… I have it weekly, so on Saturday afternoon it starts to run out. You get cold sweats. (…) I have to run out to buy Subs on the street. I just have to.” (#15, Woman, 56 years) .

Use of illicit substances – mainly benzodiazepines or other sedatives or hypnotics – was common among the patients but notably the participants did not relate this to the depot buprenorphine. Instead, they described it as something they chose to do because they enjoyed it, or to cope with anxiety and poor mental health, or as a habit they had had for a long time and did not think they could stop. However, most participants reported that their use of illicit substances decreased when they started with the depot buprenorphine.

There were also some participants who were dissatisfied with the depot buprenorphine. They reported that the medication was not effective enough against drug cravings, or that the effect wore off after a few days, resulting in cravings and gradually increasing withdrawal symptoms. All of these people had an extensive use of illicit substances, which they described as a way of boosting their medication. Several of the dissatisfied participants stated that they had been negative towards depot buprenorphine from the beginning and had felt coerced by the staff. One person had switched back to sublingual tablets and two others said that they wanted to do so.

Just over half of the participants reported side effects, mostly described as temporary or mild. Pain, tenderness or “lumps” at the injection site were the most common. Some also described side effects such as tingling, numbness, dry mouth, brief nausea and headache after the injection which they related to temporary too high dose exposure. These side effects resolved over time or after dose adjustment. Several participants mentioned typical opioid-related side effects such as constipation, stomach problems, and sweating, but these problems were described as milder than with sublingual buprenorphine or methadone. Overall, participants described more side effects and negative experiences with other formulations than with depot buprenorphine.

Social benefits of depot buprenorphine

All the participants who had a positive view of depot buprenorphine talked about various social benefits that the injections had given them. The most commonly reported benefit was that depot buprenorphine meant that the patients no longer had to follow the “traditional” Swedish OAT structure (see background section), which was perceived as time-consuming and/or uncomfortable. Often, this was about avoiding the stress and anxiety that could result from having to get up early and go to the clinic every day.

“You don’t have to rush and feel anxiety about coming here. Otherwise, you must get up every morning, feel bad [due to early withdrawal symptoms] and take the bus all the way here. Meet a load of people everywhere to get your dose. With all that anxiety the whole time, which starts the night before. Damn. But when I got [the depot buprenorphine], I felt good, was just… healthy all the time… you know, I could wake up at 8 in the morning and feel that everything was fine… and then I could go back to sleep for a while.” (Male participant #3) .

Avoiding meeting other patients who were under the influence of drugs, or from whom they had other reasons to stay away, was also mentioned as a benefit by several people.

“Before, when I picked up my [buprenorphine tablets], I picked them up in the afternoon because it affected me a lot to come in the morning and see everything that was going on here… yes, how they [the other patients] are. It’s tough seeing them when they’re under the influence.” (Female participant #22) .

As well as highlighting what they did not have to do, some participants emphasized the increased freedom that depot buprenorphine gave them – the freedom to travel and see relatives, and to have more control over their own time. “No, but still, my ambition is to get monthly injections. To get this higher degree of freedom. I think it’s not only beneficial for me, it’s beneficial for all individuals after a time.” (Male participant #7) .

Positive changes in self-perception and identity

In addition to the social benefits of depot buprenorphine, many patients also reported more profound changes in perspective and daily life – that depot buprenorphine could help you to “shift the focus in life […], to self-realization instead of destructiveness” (Male participant #12) as one participant put it. This type of benefit was described mainly by patients who did not have an extensive use of illicit substances. “You don’t have to think about the fact that you are, like, a former junkie. You don’t have to think about your life as a drug addict. That’s not what you are, you’re a human being.” (Male participant #12) .

Participants reported that depot buprenorphine had helped them realize that they could make positive changes in their lives. “I can do things in my life. I’m not tied down anymore. I’m tied to this place [the clinic], but not in the same way. Not tied to addiction… and not tied to medication either.” (Male participant #7) Several also mentioned engaging in other activities to fill their day, such as dating, working out, or cooking, after starting depot buprenorphine treatment.

Some participants described the opportunity to shift focus as an almost life-changing transformation of their self-image and identity. They no longer lived as people with addiction, and therefore did not need to identify as such.

“The biggest lifestyle difference between the tablets and [depot buprenorphine], I think, is that I can feel more like a normal… um… normal person. I don’t have to identify as a… as the addicted person in the same way now that I get the injections (…) This is the person I want to be. The person I am today, who can stand for their decisions and be a good fellow human being. Make good decisions for myself and others.” (Male participant #21) .

However, changing one’s identity could also be a challenging or even frightening experience. One person, who had experienced severe drug problems since her early teens, described it as a strange feeling to suddenly be able to be someone else, but at the same time not knowing what to do with the rest of one’s life.

“I have a disability pension. I don’t know if I’ll get a job or something… I don’t have anyone [to talk to]. But I thought I would start talking to my contact person. So maybe I could start with some kind of activity. To pass the time. (…) Because I am alone during the days now. I just sit, sit at home.” (Female participant #6) .

Diversion and the illicit buprenorphine market

One of the obvious advantages of depot buprenorphine is that it cannot be diverted, i.e., sold to or shared with people outside of treatment. In the interviews, we asked questions about the illicit buprenorphine market and what depot buprenorphine could mean for this market.

Many patients testified to a relatively extensive illicit trade in buprenorphine tablets associated with OAT programs. There was often “a damn pestering ” (Male participant #16) from people wanting to buy. “As soon as I walk out of this door here, if you go to the regular [clinic] and pick up [tablets] during the day, then there are at least thirty people asking to buy.” (Female participant #22) The costumers are other people with opioid dependence, “those who have dropped out of [treatment] or who were our friends when we were still using.” (Female participant #15) .

Several participants said that it was nice to be able to avoid the hassle by referring to receiving depot buprenorphine. In fact, some of them described this as one of the greatest benefits of this treatment. “It’s great! I’m so happy to be able to say it: ‘You can’t suck out my Subutex because it’s in my arm’” [laughs and taps his arm]. (Male participant #15) Another participant stated: “Even today, people who don’t know I’m on depot call me. But it [the medication] is in my stomach, it’s not on the table, I have nothing to sell [chuckles].” (Female participant #6) .

That patients who receive sublingual tablets often sell part of their dose was a common perception among the patients we interviewed. An eight-milligram tablet can be sold for 150–300 kronor [approx. 13–26 €] in the city where we conducted most of the interviews. Such sales can therefore provide a significant extra income. “It’s very common. […] If you think about it, three hundred kronor a day… that’s 9,000 [800 €] a month.” (Male participant #13) .

Several participants suggested that economic motives often played a decisive role for patients who declined or discontinued depot buprenorphine treatment.

“[They would say], ‘No, this [depot buprenorphine] doesn’t work for me, it’s crap.’ But I think that’s bullshit. 99% of it has to do with either using other substances and wanting to keep the option to do so, or to sell a part of their medication.” (Male participant #21) .

Participants also shared their own experiences of selling tablets. One person said that he had previously sold a part of his dose for economic reasons, but that he did not regret starting depot buprenorphine treatment.

“It certainly changed my financial situation a little bit. But based on the stability and well-being I get from [depot buprenorphine], it’s priceless. So [depot buprenorphine], for me, it’s the holy grail. There’s nothing I would choose over [depot buprenorphine], I wouldn’t even choose heroin.” (Male participant #21) .

In this study we explored the experiences of depot buprenorphine treatment in unstable OAT patients with severe ongoing polysubstance use and multiple psychiatric comorbidities.

While the positive and negative aspects reported by this group of patients were similar to those reported in previous Australian and Swedish studies of more stable OAT patients [ 6 , 7 , 8 ], several treatment aspects emerged that were more specific to this treatment group.

It is particularly interesting that this group of unstable patients describe similar positive changes in self-perception and identity as shown in studies with more unselected groups of OAT patients [ 6 , 7 ]. People with long-term problems with illicit drugs often develop a lifestyle with particular values, skills and livelihoods associated with drug use. Over time, many lose their networks and anchorage in “normal” society and develop an identity as a “deviant” or “outsider” [ 27 , 28 , 29 ]. It is in the drug subculture that they are rooted, have most of their social relationships and feel a sense of belonging. Many studies emphasise the importance of changing one’s identity in order to move away from drug use and bring about lasting change [ 27 , 28 , 30 , 31 ]. For this to be successful, individuals need to break with their previous lifestyle and resume or establish social relationships outside the drug-using subculture. The stories of the participants in this study suggest that depot buprenorphine can be an important facilitator in such a process of change. It can free up time, allow a change of focus in life, and reduce exposure to people and environments associated with the drug subculture.

Diversion to the illicit market is a well-documented problem in OAT [ 21 , 32 ] and has been a primary motivation for the development of buprenorphine-naloxone combinations. The practical impossibility of diversion has been emphasized as a major advantage of depot buprenorphine. It is therefore rather surprising that previous studies on depot buprenorphine have not explored patients’ views on this issue. Our study is the first to explicitly examine the importance of the illicit buprenorphine market in patients’ decision-making regarding sublingual versus depot buprenorphine.

As in previous research [ 21 ], our interviews revealed a significant illicit market for buprenorphine tablets. Many participants recounted their own experiences of using illicit buprenorphine prior to starting OAT. This use usually took the form of low doses administered intranasally or intravenously, although sublingual use of illicit buprenorphine also occurred. Such use often had pseudo-therapeutic motives, for example when patients had difficulty obtaining or retaining a place in regular treatment [ 33 ].

It can be difficult to obtain reliable information about sensitive topics through interviews, particularly in relation to prohibited or stigmatizing behaviors that the participants may have engaged in themselves [ 21 ]. However, the participants in this study were unexpectedly candid about diversion, including the negative impact of decreased diversion on their income. Pressure to sell their medication appeared to be part and parcel of their daily lives. Several participants also shared their own experiences of selling medication before starting depot buprenorphine treatment. This is consistent with previous research describing patients with ongoing use, whose social contacts include others with active use, as most likely to engage in diversion [ 21 ].

Drug subcultures often develop what the anthropologist Philippe Bourgois [ 34 ] has called a “moral economy of sharing”, i.e. a system of norms in which it is considered unethical not to share drugs with friends who are “drug sick”. In this moral economy, economic and altruistic motives often go hand in hand [ 35 , 36 ]. Breaking with such a norm system is difficult and does not happen automatically simply by starting treatment where different rules are supposed to apply. As noted above, successful disengagement often requires breaking away from your old network in the drug culture and creating a new, drug-free social network. The accounts of participants in this study suggest that depot buprenorphine may facilitate such disengagement.

Although they may continue to use other drugs, these unstable patients clearly experienced reduced opioid craving and increased stability. When treated with sublingual formulations, both missing doses and diversion are common, potentially leading to suboptimal medication levels. The positive effects of depot buprenorphine may in fact reflect unstable patients receiving a sufficient dose of buprenorphine, which is necessary for effective treatment retention [ 37 ]. Additionally, depot buprenorphine may increase access to treatment, which is particularly important given that this group of patients may not be offered OAT to the same extent as more stable patients with better adherence [ 12 , 13 ].

Conversely, insufficient medication effects towards the end of the dose periods (in our population mostly weekly injections) may contribute to relapse and continued substance use among unstable patients, who are close to the illicit market. Insufficient effects were particularly evident during titration, but the problem may persist in some patients. It is important to take patients’ experiences into account and make appropriate dose adjustments or use monthly formulations to increase stability.

Like previous research [ 7 , 9 ], this study suggests that depot buprenorphine is not suitable for all patients. People who are skeptical before trying depot buprenorphine often remain so, and many in this group seem to discontinue the treatment. It is therefore necessary and appropriate to offer patients a choice of different formulations. New patients and unstable patients can be offered depot buprenorphine or a buprenorphine-naloxone combination as alternatives for buprenorphine treatment. Both earlier findings [ 21 ] and the findings of this study indicate that mono-buprenorphine tablets entail a higher risk of diversion.

Changing medications can cause frustration and anxiety for patients in OAT, particularly when information about the new medication is insufficient or when patients feel coerced to make the change [ 7 , 10 ]. Although patients expressed trust in treatment staff and reported receiving information from both staff and peers, overall we found that patients perceived information about depot buprenorphine to be insufficient. One possible explanation may be that the information provided might not reflect patients’ experiences at different stages of their treatment. A recent study of patients’ early experiences of treatment highlighted shifting negative and positive states [ 38 ] and emphasized the need for staff to inform patients about this and to help them manage their emotions and anxiety during the induction phase. Another factor to consider is the potential cognitive impairment due to ongoing substance use and comorbidities, suggesting that information may need to be adapted and repeated to meet the needs of this unstable patient population.

In conclusion, this study delves into the experiences of depot buprenorphine treatment among unstable patients with severe polysubstance use and psychiatric comorbidities. While echoing both positive and negative aspects observed in stable and unselected groups of patients, it highlights the potential of depot buprenorphine in facilitating identity change, decreasing diversion to the illicit market, and enhancing treatment retention. However, challenges such as insufficient medication effects and inadequate information dissemination warrant careful consideration, emphasizing the importance of individualized treatment options and targeted communication for this patient population.

Data availability

Permission to share data is controlled by the ethics permission. Queries regarding the permission to obtain data can be made to the Swedish Ethics Authority, +46 10 475 0800 (email: [email protected]).

Abbreviations

Opioid agonist treatment

Opioid use disorder

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Acknowledgements

We would like to thank the participants who generously shared their experiences in the interviews. We also thank the clinical staff at the two participating OAT clinics for their help in informing participants about the study and providing logistical help to facilitate the interviews.

Open access funding provided by Lund University. (1) Swedish Research Council for Health, Working Life and Welfare, project number: FORTE 2022 − 228 (2) Medical Research Council of Southeast Sweden, project number: FORSS-931904, -940502, -969130, -982042. Funding providers had no role in the actual work with any part of this study or the current manuscript.

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Björn Johnson

Department of Psychiatry, County Hospital Jönköping, Jönköping, Sweden

Bodil Monwell

Department of Social Work, Jönköping University, Jönköping, Sweden

Department of Psychiatry in Linköping, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

Andrea Johansson Capusan

Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

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Conceptualization: AJC, BJ, BMData collection: BM. Funding acquisition: AJC, BJ, BM. Methodology: AJC, BJ, BM. Project administration: AJC. Qualitative analysis: AJC, BJ, BM. Resources: AJC. Writing – original draft: AJC, BJ. Writing – review & editing: AJC, BJ, BM.

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This study is part of a research project studying the introduction of depot buprenorphine in Sweden, approved by the Swedish Ethical Review Authority (reference no. 2020 − 00796).

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Andrea J Capusan has received speaker’s fees, and/or scientific advisory board compensation from Lundbeck, Indivior, Camurus, and DNE Pharma, all outside the scope of the current project.Björn Johnson has no competing interests to declare.Bodil Monwell has no competing interests to declare.

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Johnson, B., Monwell, B. & Capusan, A.J. Long-acting injectable depot buprenorphine from a harm reduction perspective in patients with ongoing substance use and multiple psychiatric comorbidities: a qualitative interview study. Harm Reduct J 21 , 68 (2024). https://doi.org/10.1186/s12954-024-00984-1

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Saturation in qualitative research: exploring its conceptualization and operationalization

Benjamin saunders.

1 Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire ST5 5BG UK

Tom Kingstone

Shula baker, jackie waterfield.

2 School of Health Sciences, Queen Margaret University, Edinburgh, EH21 6UU UK

Bernadette Bartlam

Heather burroughs, clare jinks.

Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation—as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

Introduction

In broad terms, saturation is used in qualitative research as a criterion for discontinuing data collection and/or analysis. 1 Its origins lie in grounded theory (Glaser and Strauss 1967 ), but in one form or another it now commands acceptance across a range of approaches to qualitative research. Indeed, saturation is often proposed as an essential methodological element within such work. Fusch and Ness ( 2015 : p. 1408) claim categorically that ‘failure to reach saturation has an impact on the quality of the research conducted’; 2 Morse ( 2015 : p. 587) notes that saturation is ‘the most frequently touted guarantee of qualitative rigor offered by authors’; and Guest et al. ( 2006 : p. 60) refer to it as having become ‘the gold standard by which purposive sample sizes are determined in health science research.’ A number of authors refer to saturation as a ‘rule’ (Denny 2009 ; Sparkes et al. 2011 ), or an ‘edict’ (Morse 1995 ), of qualitative research, and it features in a number of generic quality criteria for qualitative methods (Leininger 1994 ; Morse et al. 2002 ).

However, despite having apparently attained something of the status of orthodoxy, saturation is defined within the literature in varying ways—or is sometimes undefined—and raises a number of problematic conceptual and methodological issues (Dey 1999 ; Bowen 2008 ; O’Reilly and Parker 2013 ). Drawing on a number of examples in the literature, this paper seeks to explore some of these issues in relation to three core questions:

‘What?’—in what way(s) is saturation defined?

‘where and why’—in what types of qualitative research, and for what purpose, should saturation be sought, ‘when and how’—at what stage in the research is saturation sought, and how can we assess if it has been achieved.

In addressing these questions, we will explore the implications of different models of saturation—and the theoretical and methodological assumptions that underpin them—for the varying purposes saturation may serve across different qualitative approaches. In doing so, the paper will contribute to the small but growing literature that has critically examined the concept of saturation (e.g. Bowen 2008 ; O’Reilly and Parker 2013 ; Walker 2012 ; Morse 2015 ; Nelson 2016 ), aiming to extend the discussion around its conceptualization and use. We will argue not only for greater transparency in the reporting of saturation, as others have done (Bowen 2008 ; Francis et al. 2010 ), but also for a more thorough consideration on the part of qualitative researchers regarding how saturation relates to the research question(s) they are addressing, in addition to the theoretical and analytical approach they have adopted, with due recognition of potential inconsistencies and contradictions in its use.

In their original treatise on grounded theory, Glaser and Strauss ( 1967 : p. 61) defined saturation in these terms:

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 sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. He goes out of his 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.

Here, the decision to be made relates to further sampling, and the determinant of adequate sampling has to do with the degree of development of a theoretical category in the process of analysis. Saturation is therefore closely related to the notion of theoretical sampling—the idea that sampling is guided by ‘the necessary similarities and contrasts required by the emerging theory’ (Dey 1999 : p. 30)—and causes the researcher to ‘combine sampling, data collection and data analysis, rather than treating them as separate stages in a linear process’ (Bryman 2012 : p. 18).

Also writing from a grounded theory standpoint, Urquhart ( 2013 : p. 194) defines saturation as: ‘the point in coding when you find that no new codes occur in the data. There are mounting instances of the same codes, but no new ones’, whilst Given ( 2016 : p. 135) considers saturation as the point at which ‘additional data do not lead to any new emergent themes’. A similar position regarding the (non)emergence of new codes or themes has been taken by others (e.g. Birks and Mills 2015 ; Olshansky 2015 ). 3 These definitions show a change of emphasis, and suggest a second model of saturation. Whilst the focus remains at the level of analysis, the decision to be made appears to relate to the emergence of new codes or themes, rather than the degree of development of those already identified. Moreover, Urqhart ( 2013 ) and Birks and Mills ( 2015 ) relate saturation primarily to the termination of analysis, rather than to the collection of new data.

According to Starks and Trinidad ( 2007 : p. 1375), however, theoretical saturation occurs ‘when the complete range of constructs that make up the theory is fully represented by the data’. Whilst not wholly explicit, this definition suggests a third model of saturation with a different directional logic: not ‘given the data, do we have analytical or theoretical adequacy?’, but ‘given the theory, do we have sufficient data to illustrate it?’ 4

If we move outside the grounded theory literature, 5 a fourth perspective becomes apparent in which there are references to data saturation, rather than theoretical saturation (e.g. Fusch and Ness 2015 ). 6 This view of saturation seems to centre on the question of how much data (usually number of interviews) is needed until nothing new is apparent, or what Sandelowski ( 2008 : p. 875) calls ‘informational redundancy’ (e.g. Francis et al. 2010 ; Guest et al. 2006 ). Grady ( 1998 : p. 26) provides a similar description of data saturation as the point at which:

New data tend to be redundant of data already collected. In interviews, when the researcher begins to hear the same comments again and again, data saturation is being reached… It is then time to stop collecting information and to start analysing what has been collected.

Whilst several others have defined data saturation in a similar way (e.g. Hill et al. 2014 : p. 2; Middlemiss et al. 2015 ; Jackson et al. 2015 ), Legard et al. ( 2003 ) seem to adopt a narrower, more individual-oriented perspective on data saturation, whereby saturation operates not at the level of the dataset as a whole, but in relation to the data provided by an individual participant; i.e. it is achieved at a particular point within a specific interview:

Probing needs to continue until the researcher feels they have reached saturation, a full understanding of the participant’s perspective (Legard et al. 2003 : p. 152).

From this perspective, the researcher’s response to the data—through which decisions are made about whether or not any new ‘information’ is being generated—is not necessarily perceived as forming part of the analysis itself. Thus, in this model, the process of saturation is located principally at the level of data collection and is thereby separated from a fuller process of data analysis, and hence from theory.

Four different models of saturation seem therefore to exist (Table 1 ). The first of these, rooted in traditional grounded theory, uses the development of categories and the emerging theory in the analysis process as the criterion for additional data collection, driven by the notion of theoretical sampling; using a term in common use, but with a more specific definitional focus, this model could thus be labelled as theoretical saturation . The second model takes a similar approach, but saturation focuses on the identification of new codes or themes, and is based on the number of such codes or themes rather than the completeness of existing theoretical categories. This can be termed inductive thematic saturation . In this model, saturation appears confined to the level of analysis; its implication for data collection is at best implicit. In the third model, a reversal of the preceding logic is suggested, whereby data is collected so as to exemplify theory, at the level of lower-order codes or themes, rather than to develop or refine theory. This model can be termed a priori thematic saturation , as it points to the idea of pre-determined theoretical categories and leads us away from the inductive logic characteristic of grounded theory. Finally, the fourth model—which, again aligning with the term already in common use, we will refer to as data saturation —sees saturation as a matter of identifying redundancy in the data, with no necessary reference to the theory linked to these data; saturation appears to be distinct from formal data analysis.

Table 1

Models of saturation and their principal foci in the research process

‘Hybrid’ forms of saturation

Some authors appear to espouse interpretations of saturation that combine two or more of the models defined above, making its conceptualization less distinct. For example, Goulding ( 2005 ) suggests that both data and theory should be saturated within grounded theory, and Drisko ( 1997 : p. 192) defines saturation in terms of ‘the comprehensiveness of both the data collection and analysis’. Similarly, Morse’s view of saturation seems to embody elements of both theoretical and data saturation. She links saturation with the idea of replication, in a way that suggests a process of data saturation:

However, when the domain has been fully sampled – when all data have been collected – then replication of data occurs and, with this replication… the signal of saturation (Morse 1995 : p. 148).

Morse notes elsewhere that she is able to tell when her students have achieved saturation, as they begin to talk about the data in more generalized terms and ‘can readily supply examples when asked. These students know their data’ (Morse 2015 : p. 588). This too suggests a form of data saturation. However, Morse also proposes that saturation is lacking when ‘there are too few examples in each category to identify the characteristics of concepts, and to develop theory’ (Morse 2015 : p. 588). This perspective seems to be located firmly in the idea of theory development (as other parts of the quoted papers by Morse make clear), though a heavy emphasis is placed at the level of the data and the way in which the data exemplify theory, thereby seeming to evoke both data and theoretical saturation.

Hennink et al. ( 2017 ) go further, appearing to combine elements of all four models of saturation. They firstly identify ‘code saturation’, the point at which ‘no additional issues are identified and the codebook begins to stabilize’ ( 2017 : p. 4), which seems to combine elements of both inductive thematic saturation and data saturation. However, within this approach saturation is discussed as relating not only to codes developed inductively, but also to a priori codes, which echoes the third model: a priori thematic saturation. They go on to distinguish ‘code saturation’ from ‘meaning saturation’; in the latter, the analyst attempts to ‘fully understand conceptual codes or the conceptual dimensions of… concrete codes’ ( 2017 : p. 14). This focus on saturating the dimensions of codes seems more akin to theoretical saturation; however, their analysis remains at the level of codes, rather than theoretical categories developed from these codes, and Hennink et al. explicitly position their approach outside grounded theory methods.

Morse ( 2015 : p. 587) takes the view that saturation is ‘present in all qualitative research’ and as previously noted, it is commonly considered as the ‘gold standard’ for determining sample size in qualitative research, with little distinction between different types of qualitative research. We question this perspective, and would instead argue—as is suggested by the different models of saturation considered in the previous section—that saturation has differing relevance, and a different meaning, depending on the role of theory, a viewpoint somewhat supported by other commentators who have questioned its application across the spectrum of qualitative methods (Walker 2012 ; O’Reilly and Parker 2013 ; van Manen et al. 2016 ).

In a largely deductive approach (i.e. one that relies wholly or predominantly on applying pre-identified codes, themes or other analytical categories to the data, rather than allowing these to emerge inductively) saturation may refer to the extent to which pre-determined codes or themes are adequately represented in the data—rather like the idea of the categories being sufficiently replete with instances, or ‘examples’, of data, as suggested in the a priori thematic saturation model outlined above. Thus, in their attempt to establish an adequate sample size for saturation, Francis et al. ( 2010 ) refer explicitly to research in which conceptual categories have been pre-established through existing theory, and it is significant in this respect that they link saturation with the notion of content validity. In contrast, within a more inductive approach (e.g. the inductive thematic saturation and theoretical saturation models outlined above), saturation suggests the extent to which ‘new’ codes or themes are identified within the data, and/or the extent to which new theoretical insights are gained from the data via this process.

In both the deductive and the inductive approach, we can make sense of the role of saturation, however much it differs in each case, because the underlying approach to analysis is essentially thematic, and usually occurs in the context of interview or focus group studies involving a number of informants. It is less straightforward to identify a role for saturation in qualitative approaches that are based on a biographical or narrative approach to analysis, or that, more generally, include a specific focus on accounts of individual informants (e.g. interpretative phenomenological analysis). In such studies, analysis tends to focus more on strands within individual accounts rather than on analytical themes ; these strands are essentially continuous, whereas themes are essentially recurrent. Accordingly, Marshall and Long ( 2010 ) suggest that saturation was not appropriate in their study of maternal coping processes, based on narrative methods. Elsewhere, however, a less straightforward picture emerges. Hawkins and Abrams ( 2007 ) utilized saturation in the context of a study based on life-history interviews with 39 formerly homeless mentally ill men and women. The authors state: ‘Of the 39 participants, six did not complete a second interview because they were unavailable, impaired, or the research team felt the first interview had achieved saturation’ (p. 2035), suggesting that judgments of saturation were made within each participant’s account. Power et al. ( 2015 ) adopted a story-telling approach to women’s experience of post-partum hospitalization, and recruitment continued until data saturation, which was established through ‘the repetition of responses’ (p. 372). Analysis was thematic, and it is not clear whether saturation was determined in relation to themes across participants’ stories, or within individual stories. Similarly, in a study of osteoarthritis in footballers, based on interpretative phenomenological analysis, Turner et al. ( 2002 ) employed saturation, which was defined both in terms of the emergence of themes from the analysis and a ‘consensus across views expressed’ (p. 298), which suggests that, notwithstanding the interpretive phenomenological analysis perspective adopted, saturation was sought more across than within cases. Hale et al. ( 2007 : p. 91) argue, however, that saturation is not normally an aim in interpretative phenomenological analysis, owing to the concern to obtain ‘full and rich personal accounts’, which highlights the particular analytical focus within individual accounts in this approach, and van Manen dissociates saturation from phenomenological research more generally (van Manen et al. 2016 ).

Considering the various types of research in which saturation might feature helps to clarify the purposes it is intended to fulfil. When used in a deductive approach to analysis, saturation serves to demonstrate the extent to which the data instantiate previously determined conceptual categories, whereas in more inductive approaches, and grounded theory in particular, it says something about the adequacy of sampling in relation to theory development (although we have seen that there are differing accounts of how specifically this should be achieved). In narrative research, a role for saturation is harder to discern. Rather than the sufficient development of theory, it might be seen to indicate the ‘completeness’ of a biographical account. However, one could question whether the point at which a participant’s story is interpreted as being ‘complete’—having presumably conveyed everything seen to be relevant to the focus of the study—is, in fact, usefully described by the concept of saturation, given the distance that this moves us away from the operationalization of saturation in broadly thematic approaches. This might, furthermore, lead us to ask whether there is the risk of saturation losing its coherence and utility if its potential conceptualization and uses are stretched too widely.

The same issue is relevant with regard to a number of other, less obvious, purposes that have been proposed for saturation. For example, it has been claimed to demonstrate the trustworthiness of coding (Damschroder et al. 2007 )—but as saturation will be a direct and automatic consequence of one’s coding decisions, it is not clear how it can be an independent measure of their quality. Dubé et al. ( 2016 ) suggest that saturation says something about (though not conclusively) the ability to extrapolate findings, and Boddy ( 2016 : p. 428) claims that ‘once saturation is reached, the results must be capable of some degree of generalisation’; this seems to move us away from the notion of the theoretical adequacy of an analysis, and the explanatory scope of a theory, toward a much more empirical sense of generalizability. The use of saturation in these two cases could perhaps indicate a degree of confusion in some studies about the meaning of saturation and its purpose, even when taking into account the differing models of saturation outlined earlier. Therefore, we would suggest that for saturation to be conceptually meaningful and practically useful there should be some limit to the purposes to which it can be applied.

Perspectives taken on saturation

The perspective taken on what is meant by saturation within a given study will have implications for when it will be sought. Taking the fourth model of saturation identified earlier—the data saturation approach, as based on the notion of informational redundancy—it is clear that saturation can be identified at an early stage in the process, as from this perspective saturation is often seen as separate from, and preceding, formal analysis. Decisions about when further data collection is unnecessary are commonly based on the researcher’s sense of what they are hearing within interviews, and this decision can therefore be made prior to coding and category development. In a focus group study of HIV perceptions in Ghana, Ganle ( 2016 ) used the notion of saturation to determine when each focus group discussion should terminate. Such a decision would seem, however, to relate to only a very preliminary stage of analysis and is likely to be driven by only a rudimentary sense of any emergent theory. A similar point can be made in relation to Hancock el al.’s ( 2016 ) study of male nurses’ views on selecting a nursing speciality. They talk of logging each instance in which their focus group participants ‘discussed a theme’, with saturation then judged in relation to the number of times themes were discussed. Though not elaborated upon, this appears to imply a very narrow definition of a theme as something that can be somehow ‘observed’ during the course of a focus group. However, interpretations at this stage regarding what might constitute a theme, before even beginning to consider whether identified themes are saturated, will be superficial at best. Moreover, conclusions reached at this stage may not be particularly informative as regards subsequent theory development—pieces of data that appear to be very similar when first considered may be found to exemplify different theoretical constructs on detailed analysis, and correspondingly, data that are empirically dissimilar may turn out to have much in common theoretically. Judgments at this stage will also relate to a framework of themes and categories that is theoretically immature, and that may be subject to considerable modification; for example, the changes that may occur during the successive stages of open, selective and theoretical coding in grounded theory (Glaser 1978 ).

With regard to the second model identified, inductive thematic saturation, the fact that the focus is more explicitly on reaching saturation at the level of analysis—i.e. in relation to the (non-)emergence of new codes or themes—might suggest it will be achieved at a later stage than in data saturation approaches (notwithstanding the concurrent nature of data-collection and analysis in many qualitative approaches). However, focusing on the emergence or otherwise of codes rather than on their theoretical development still points us towards saturation being achieved at a relatively early stage. Hennink et al. ( 2017 ) highlight this in a study on patient retention in HIV care, in which they found that saturation of codes was achieved at an earlier point than saturation of the ‘dimensions, nuances, or insights’ related to codes. Hennink et al. argue that an approach to saturation relying only on the number of codes ‘misses the point of saturation’ ( 2017 : p. 15) owing to a lack of understanding of the ‘meaning’ of these codes.

In contrast to data saturation and inductive thematic saturation, the first model of saturation considered, theoretical saturation—as based on the grounded theory notion of determining when the properties of theoretical categories are adequately developed—indicates that the process of analysis is at a more advanced stage and at a higher level of theoretical generality. Accordingly, Zhao and Davey ( 2015 : p. 1178) refer to a form of saturation determined by ‘theoretical completeness’ and ceased sampling ‘when dimensions and gaps of each category of the grounded theory had been explicated,’ and Bowen ( 2008 ) gives a detailed account of how evidence of saturation emerged at the level of thematic categories and the broader process of theory construction.

Saturation as event or process

A key issue underlying the identification of saturation is the extent to which it is viewed as an event or a process. Commonly, saturation is referred to as a ‘point’ (e.g. Otmar et al. 2011 ; Jassim and Whitford 2014 ; Kazley et al. 2015 ), suggesting that it should be thought of as a discrete event that can be recognized as such by the analyst. Strauss and Corbin ( 1998 : p. 136), however, talk about saturation as a ‘matter of degree’, arguing that there will always be the potential for ‘the “new” to emerge’. They suggest that saturation should be more concerned with reaching the point where further data collection becomes ‘counter-productive’, and where the ‘new’ does not necessarily add anything to the overall story or theory. Mason ( 2010 ) makes a similar argument, talking of the point at which there are ‘diminishing returns’ from further data-collection, and a number of researchers seem to take this more incremental approach to saturation. Aiken et al. ( 2015 : p. 154), for example, refer in their interview study of unintended pregnancy to being ‘confident of having achieved or at least closely approached thematic saturation.’ Nelson ( 2016 ), echoing Dey’s ( 1999 ) earlier view, argues that the term ‘saturation’ is itself problematic, as it intuitively lends itself to thinking in terms of a fixed point and a sense of ‘completeness’. He thus argues that ‘conceptual depth’ may be a more appropriate term—at least from a grounded theory perspective—whereby the researcher considers whether sufficient depth of understanding has been achieved in relation to emergent theoretical categories.

On this incremental reading of saturation, the analysis does not suddenly become ‘rich’ or ‘insightful’ after that one additional interview, but presumably becomes rich er or more insightful. The question will then be ‘how much saturation is enough?’, rather than ‘has saturation occurred?’ 7 This is a less straightforward question, but one that much better highlights the fact that this can only be a matter of the analyst’s decision—saturation is an ongoing, cumulative judgment that one makes, and perhaps never completes, 8 rather than something that can be pinpointed at a specific juncture.

Uncertainty and equivocation

A desire to identify a specific point in time at which saturation is achieved seems often to give rise to a degree of uncertainty or equivocation. In a number of studies, saturation is claimed, but further data collection takes place in an apparent attempt to ‘confirm’ (Jassim and Whitford 2014 : p. 191; Forsberg et al. 2000 : p. 328) or ‘validate’ (Vandecasteele et al. 2015 : p. 2789) this claim; for example:

After the 10th interview, there were no new themes generated from the interviews. Therefore, it was deemed that the data collection had reached a saturation point. We continued data collection for two more interviews to ensure and confirm that there are no new themes emerging (Jassim and Whitford ( 2014 : pp. 190–191).

Furthermore, a reluctance to rely on evidence of saturation sometimes indicates that saturation is being used in at best an unclear, or at worst an inconsistent or incoherent, fashion. For example, Hill et al. ( 2014 : p. 2), whilst espousing the principle of saturation, seem not fully to trust it:

Saturation was monitored continuously throughout recruitment. For completeness we chose to fully recruit to all participant groups to reduce the chance of missed themes.

Similarly, Jackson et al. ( 2000 : p. 1406) claim that saturation had been established, but then appear to retreat somewhat from this conclusion:

Following analysis of eight sets of data, data saturation was established… however, two additional participants were recruited to ensure data saturation was achieved.

Constantinou et al. ( 2017 ) propose that, given the potential for uncertainty about the point at which saturation is reached, attention should focus more on providing evidence that saturation has been reached, than on concerns about the point at which this occurred. Thus, rather curiously, they propose that it ‘does not hurt to include all interviews from the initial sampling’ ( 2017 : p. 13). This view is inherently problematic, however, as not only does it imply that saturation is a retrospective consideration following the completion of data collection, rather than as guiding ongoing sampling decisions, but one could also argue that saturation loses its relevance if all data are included regardless of whether or not they contribute further insights or add to conceptual understanding. This approach appears to indicate a preoccupation with having enough data to show evidence of saturation, i.e. not too few interviews, rather than saturation aiding decisions about the adequacy of the sample.

Whilst the above suggests ambivalence towards assessing the point at which saturation is achieved, others report having made the conscious decision to continue sampling beyond saturation, appearing to seek additional objective evidence to bolster their sampling decisions. For instance, in investigating staff and patient views on a stroke unit, Tutton et al. ( 2012 : p. 2063) talk of how, despite having achieved saturation, ‘increased observation may have increased the degree of immersion in the lives of those on the unit’, whilst Naegeli et al. ( 2013 : p. 3) look to gain ‘more in-depth understanding… beyond the saturation point’. Similar points are made by Kennedy et al. ( 2012 : p. 859), who talk of looking for ‘novel aspects’ after the achievement of saturation, and Poletti et al. ( 2007 : p. 511), who propose the need to ‘fill gaps in the data’ following saturation. These examples suggest a view that there is something of theoretical importance that is not captured by saturation, though it is unclear from the explanations given as to exactly what this is. 9

Another indication of an ambivalent view taken on saturation is suggested by Mason’s ( 2010 ) observation that sample sizes in studies based on interviews are commonly multiples of ten. This suggests that, in practice, rules of thumb or other a priori guidelines are commonly used in preference to an adaptive approach such as saturation. Quite frequently, studies that adopt the criterion of saturation propose at the same time a prior sample size (e.g. McNulty et al. 2015 ; Long-Sutehall et al. 2011 ). In a similar way, Niccolai et al. ( 2016 ) sought saturation during their analysis, but also state (p. 843) that:

An a priori sample size of 30 to 40 was selected based on recommendations for qualitative studies of this nature… and the anticipated complexity and desired level of depth for our research questions.

Fusch and Ness ( 2015 : p. 1409) appear to endorse this somewhat inconsistent approach when advocating that the researcher should choose a sample size that has ‘the best opportunity for the researcher to reach data saturation’. 10

This tentative and equivocal commitment to saturation may reflect a practical response to the demands of funding bodies and ethics committees for a clear statement of sample size prior to starting a study (O’Reilly and Parker 2013 )—perceived obligations that, in practice, may be given priority over methodological considerations. However, it may also arise from the specific but somewhat uncertain logic that underlies saturation. Determining that further data collection or analysis is unnecessary on the basis of what has been concluded from data gathered hitherto is essentially a statement about the unobserved (what would have happened if the process of data collection and/or analysis had proceeded) based on the observed (the data collection and/or analysis that has taken place hitherto). Furthermore, if saturation is used in relation to negative case analysis in grounded theory (i.e. sources of data that may question or disconfirm aspects of the emergent theory) the logic becomes more tenuous—a statement about the unobserved based on the unobserved. 11 In either case, an uncertain predictive claim is made about the nature of data yet to be collected, and furthermore a claim that could only be tested if the decision to halt data collection were to be overturned. Additionally, the underlying reasoning makes specific assumptions about the way in which the analysis will generate theory, and the earlier in the process of theory development that this occurs the less warranted such assumptions may be. Accordingly, researchers who confidently propose saturation as a criterion for sampling at the outset of a study may become less certain as to how it should be operationalized once the study is in progress, and may therefore be reluctant to abide by it.

This paper has offered a critical reflection on the concept of saturation and its use in qualitative research, contributing to the small body of literature that has examined the complexities of the concept and its underlying assumptions. Drawing on recent examples of its use, saturation has been discussed in relation to three key sets of questions: What? Where and why? When and how?

Extending previous literature that has highlighted the variability in the use of saturation (O’Reilly and Parker 2013 ; Walker 2012 ), we have scrutinized the different ways in which it has been operationalized in the research literature, identifying four models of saturation, each of which appears to make different core assumptions about what saturation is, and about what exactly is being saturated. These have been labelled as: theoretical saturation, inductive thematic saturation, a priori thematic saturation, and data saturation. Moving forward, the identification and recognition of these different models of saturation may aid qualitative researchers in untangling some of the inconsistencies and contradictions that characterize its use.

Saturation’s apparent position as a ‘gold standard’ in assessing quality and its near universal application in qualitative research have been previously questioned (Guest et al. 2006 ; O’Reilly and Parker 2013 ; Malterud et al. 2016 ). Similarly, doubts have been raised regarding its common adoption as a sole criterion of the adequacy of data collection and analysis (Charmaz 2005 ), or of the adequacy of theory development: ‘Elegance, precision, coherence, and clarity are traditional criteria for evaluating theory, somewhat swamped by the metaphorical emphasis on saturation’ (Dey 2007 : p. 186). On the basis of such critiques, we have examined how saturation might be considered in relation to different theoretical and analytical approaches. Whilst we concur with the argument that saturation should not be afforded unquestioned status, polarization of saturation as either applicable or non-applicable to different approaches, as has been suggested (Walker 2012 ), may be too simplistic. Instead we propose that saturation has differing relevance, and a different meaning, depending on the role of theory, the analytic approach adopted, and so forth, and thus may usefully serve different purposes for different types of research—purposes that need to be clearly articulated by the researcher.

Whilst arguing for flexibility in terms of the purpose and use of saturation, we also suggest that there must be some limit to this range of purposes. Some of the ways in which saturation has been operationalized, we would suggest, risk stretching or diluting its meaning to the point where it becomes too widely encompassing, thereby undermining its coherence and utility.

When and how saturation may be judged to have been reached will differ depending on the type of study, as well as assumptions about whether it represents a distinct event or an ongoing process. The view of saturation as an event has been problematized by others (Strauss and Corbin 1998 ; Dey 1999 ; Nelson 2016 ), and we have explored the implications of conceptualizing saturation in this way, arguing that it appears to give rise to a degree of uncertainty and equivocation, in part driven by the uncertain logic of the concept itself—as a statement about the unobserved based on the observed. This uncertainty appears to give rise to inconsistencies and contradictions in its use, which we would argue could be resolved, at least in part, if saturation were to be considered as a matter of degree, rather than simply as something either attained or unattained. However, whilst considering saturation in incremental terms may increase researchers’ confidence in making claims to it, we suggest it is only through due consideration of the specific purpose for which saturation is being used, and what one is hoping to saturate, that the uncertainty around the concept can be resolved.

In highlighting and examining these areas of complexity, this paper has extended previous discussions of saturation in the literature. Whilst consideration of the concept has led some commentators to argue for the need for qualitative researchers to provide a more thorough and transparent reporting of how they achieved saturation in their research, thus allowing readers to assess the validity of this claim (Bowen 2008 ; Francis et al. 2010 ), our arguments go beyond this. We contend that there is a need not only for more transparent reporting, but also for a more thorough re-evaluation of how saturation is conceptualized and operationalized, including recognition of potential inconsistencies and contradictions in the use of the concept—this re-evaluation can be guided through attending to the four approaches we have identified and their implications for the purposes and uses of saturation. This may lead to a more consistent use of saturation, not in terms of its always being used in the same way, but in relation to consistency between the theoretical position and analytic framework adopted, allowing saturation to be used in such a way as to best meet the aims and objectives of the research. It is through consideration of such complexities in the context of specific approaches that saturation can have most value, enabling it to move away from its increasingly elevated yet uneasy position as a taken-for-granted convention of qualitative research.

Acknowledgements

This paper has been informed by discussions with members of the social sciences group of the Institute for Primary Care and Health Sciences at Keele University. TK is funded by South Staffordshire and Shropshire NHS Foundation Trust. CJ is partly funded by NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands (CLAHRC, West Midlands); the views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Compliance with ethical standards

Conflicts of interest.

All authors declare to have no conflicts of interest.

1 Although primarily employed in primary research, principles of saturation have also been applied to qualitative synthesis (Garrett et al. 2012 ; Lipworth et al. 2013 ). However, our focus here is on its use in primary studies.

2 These authors proceed to make the more extreme claim that saturation ‘is important in any study, whether quantitative, qualitative, or mixed methods’ (Fusch and Ness 2015 : p. 1411).

3 It should be noted that Birks and Mills ( 2015 ) also state that, as part of theoretical saturation, ‘Categories are clearly articulated with sharply defined and dimensionalized properties’, suggesting a somewhat broader view of saturation, in which the nature of emerging themes is important, rather than just the fact of their (non)emergence.

4 This evokes Glaser’s criticism of Strauss’s approach to sampling, which he regards as conventional, rather than theoretical, sampling: ‘In conventional sampling the analyst questions, guesses and uses experience to go where he thinks he will have the data to test his hypotheses and find the theory that he has preconceived. Discovery to Strauss does not mean induction and emergence, it means finding his theory in data so that it can be tested’ (Glaser 1992 : p. 103).

5 Charmaz ( 2008 , 2014 ) is critical of the extension of the notion of saturation beyond the context of grounded theory, and in particular of its extension into what we here refer to as data saturation.

6 Few authors draw an explicit distinction between data and theoretical saturation—among the exceptions are Bowen ( 2008 ), Sandelowski ( 2008 ), O’Reilly and Parker ( 2013 ), and Hennink et al. ( 2017 ).

7 Hence, Dey ( 1999 : p. 117) suggests the term ‘sufficiency’ in preference to ‘saturation’.

8 This reflects Glaser and Strauss’s ( 1967 : p. 40) view of theory generation: ‘one is constantly alert to emergent perspectives that will change and help develop his theory. These perspectives can easily occur even on the final day of study or even when the manuscript is reviewed in page proof; so the published word is not the final one, but only a pause in the never-ending process of generating theory’.

9 On occasions, a reason for going beyond saturation appears to be ethical rather than methodological. Despite reaching saturation, France et al. ( 2008 : p. 22) note that owing to their ‘commitment to and respect for all the women who wanted to participate in the study, data collection did not end until all had been interviewed.’ Similarly, Kennedy et al. ( 2012 : p. 858) report that they exceeded saturation as this appeared to be ‘more ethical than purposefully choosing individuals to re-interview, or only interviewing until saturation’.

10 Bloor and Wood ( 2006 : p. 165) suggest that this tendency may stem from researchers feeling obliged to abide by sample sizes previously declared to funding bodies or ethics committees, whilst making claims to saturation in order to retain a sense of methodological credibility. Some authors—e.g. Guest et al. ( 2006 ), Francis et al. ( 2010 ), Hennink et al. ( 2017 )—have attempted for formulate procedures whereby the specific number of participants required to achieve saturation is calculated in advance.

11 The first logic is counter-inductive—future non-occurrences of data, codes or theoretical insights are posited on the basis of prior occurrences. In relation to negative case analysis, however, the logic becomes inductive—future non-occurrences are posited on the basis of prior non-occurrences.

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IMAGES

Population vs. Sample
Population vs. Sample: Understanding the Difference
3:1 Introduction to Qualitative Research: Definition and context
Qualitative vs Quantitative Research: What's the Difference?
Population vs. Sample
Probability Sampling: How to Represent Large Populations

VIDEO

Exploring Qualitative and Quantitative Research Methods and why you should use them
Comparison of Quantitative & Qualitative Research
Concept of Value & Population Mean Part I
Difference between Qualitative research and Quantitative research
Quantitative & Qualitative Research Methods (Science)
CLASSIFICATION OF DATA

COMMENTS

What Is the Big Deal About Populations in Research?
In research, there are 2 kinds of populations: the target population and the accessible population. The accessible population is exactly what it sounds like, the subset of the target population that we can easily get our hands on to conduct our research. While our target population may be Caucasian females with a GFR of 20 or less who are ...
Defining and Identifying Members of a Research Study Population: CTSA
An early step in research projects that involve humans consists of composing a clear and detailed definition of the study population. All experimental, observational, and qualitative research designs involving human subjects should define the study population in order to determine the eligibility of individuals for a study.
(PDF) CONCEPT OF POPULATION AND SAMPLE
The population refers to an entire set of units that exhibit a variable characteristic under investigation and for which research findings can be generalized (Shukla, 2020). Meanwhile, a sample is ...
Qualitative Study
Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants ...
How to use and assess qualitative research methods
Qualitative research is defined as "the study of the nature of phenomena", including "their ... This formal definition can be complemented with a more pragmatic rule of ... Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity ...
Planning Qualitative Research: Design and Decision Making for New
Qualitative research draws from interpretivist and constructivist paradigms, seeking to deeply understand a research subject rather than predict outcomes, as in the positivist paradigm (Denzin & Lincoln, 2011).Interpretivism seeks to build knowledge from understanding individuals' unique viewpoints and the meaning attached to those viewpoints (Creswell & Poth, 2018).
Chapter 5. Sampling
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.
LibGuides: Section 2: Developing the Qualitative Research Design
Qualitative Research: Target Population, Sampling Frame, and Sample. The target population is the population that the sample will be drawn from. It is all individuals who possess the desired characteristics (inclusion criteria) to participate in the doctoral project or dissertation-in-practice. The sampling design represents the plan for ...
(PDF) General, Target, and Accessible Population: Demystifying the
population definition. Baškarada (2014) has observed that the qualitative research paradigm has increasingly . served as a unique option for knowledge sharing and academic debate over the years.
Population vs. Sample
A population is the entire group that you want to draw conclusions about.. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. In research, a population doesn't always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries ...
What Is Qualitative Research?
Revised on 30 January 2023. Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which ...
What Is Qualitative Research?
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...
Qualitative Research: An Overview
Qualitative research Footnote 1 —research that primarily or exclusively uses non-numerical data—is one of the most commonly used types of research and methodology in the social sciences. Unfortunately, qualitative research is commonly misunderstood. It is often considered "easy to do" (thus anyone can do it with no training), an "anything goes approach" (lacks rigor, validity and ...
What is Qualitative in Qualitative Research
What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being "qualitative," the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term ...
Differentiating Between Population and Target Population in Research
The mean difference of 9.83 is significant at 0.001 levels. ... concentration of the target and accessible population in qualitative inquiry. ... between the population and the target population ...
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 goals and strategies."(p.537).
Qualitative Study
Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data.
Qualitative Approaches to Studying Marginalized Communities
There are a wide range of qualitative research approaches, such as participatory research, autoethnographic research, narrative and biographical research, or traditional qualitative research based on interviews with representatives of marginalized groups. ... declare that the main principle is respect and dignity for the researched population ...
Statistics without tears: Populations and samples
In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied, in terms of location and restriction to a particular age group, sex or occupation. ... Focus group discussions (qualitative study) with local people, especially those residing away from the ...
Qualitative Research
Qualitative Research. Qualitative research is a type of research methodology that focuses on exploring and understanding people's beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus ...
10.2 Sampling in qualitative research
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 ...
Long-acting injectable depot buprenorphine from a harm reduction
Long-acting injectable depot buprenorphine may increase access to opioid agonist treatment (OAT) for patients with opioid use disorder in different treatment phases. The aim of this study was to explore the experiences of depot buprenorphine among Swedish patients with ongoing substance use and multiple psychiatric comorbidities. Semi-structured qualitative interviews were conducted with OAT ...
Series: Practical guidance to qualitative research. Part 3: Sampling
Descriptive generic qualitative research is defined as research designed to produce a low inference description of a phenomenon . Although Sandelowski maintains that all research involves interpretation, she has also suggested that qualitative description attempts to minimize inferences made in order to remain 'closer' to the original data ...
Saturation in qualitative research: exploring its conceptualization and
In broad terms, saturation is used in qualitative research as a criterion for discontinuing data collection and/or analysis. 1 Its origins lie in grounded theory (Glaser and Strauss ), but in one form or another it now commands acceptance across a range of approaches to qualitative research. Indeed, saturation is often proposed as an essential ...

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

Developing the Qualitative Research Design

Exercise #1

Exercise #2

Qualitative Research: An Overview

Buying options

Author information

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Copyright information

About this chapter

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Qualitative Study

Publication types

Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research

Qualitative Research Methods

One-to-One Interview

Focus Groups

Ethnographic Studies

Text Analysis

Process of Observation

Record Keeping

Qualitative Research Analysis Methods

Thematic Analysis

Content Analysis

Discourse Analysis

Grounded Theory Analysis

Narrative Analysis

Phenomenological Analysis

Comparative Analysis

Applications of Qualitative Research

How to Conduct Qualitative Research

Examples of Qualitative Research

Purpose of Qualitative Research

When to use Qualitative Research

Characteristics of Qualitative Research

Advantages of Qualitative Research

Limitations of Qualitative Research

About the author

Muhammad Hassan

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10.2 Sampling in qualitative research

Nonprobability sampling

Types of nonprobability samples

Key Takeaways

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Long-acting injectable depot buprenorphine from a harm reduction perspective in patients with ongoing substance use and multiple psychiatric comorbidities: a qualitative interview study

Conclusions

Patient perspectives on depot buprenorphine

The setting

Sampling and recruitment

Ethical considerations

Interview procedure

The participants

Freedom of choice and information

Effects and side effects of depot buprenorphine

Social benefits of depot buprenorphine

Positive changes in self-perception and identity

Diversion and the illicit buprenorphine market

Data availability

Abbreviations

Acknowledgements