what sampling methods are used in qualitative research

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what sampling methods are used in qualitative research

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

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

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

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

Factors to Consider When Choosing a Sampling Technique

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4 Types of Sampling Techniques and Their Applications

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

1. Purposive Sampling

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

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

Purposive Sampling: Strengths and Weaknesses

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

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

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

2. Convenience Sampling

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

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

Convenience Sampling: Strengths and Weaknesses

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

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

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

3. Snowball Sampling

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

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

Snowball Sampling: Strengths and Weaknesses

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

4. Theoretical Sampling

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

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

Theoretical Sampling: Strengths and Weaknesses

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

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

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

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

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

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

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

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

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

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

Tips for Selecting Participants

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

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

Ensuring Diversity in Samples

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

Maintaining Ethical Considerations

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

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

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

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

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Chapter 5. Sampling

Introduction.

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

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

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

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

Quick Terms Refresher

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

The “Who” of Your Research Study

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

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

Sampling People

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

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

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

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

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

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

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

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

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

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

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

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

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

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

When Your Population is Not Composed of People

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

Case Studies

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

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

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

Content: Documents, Narrative Accounts, And So On

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

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

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

What is the Correct “Number” to Sample?

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

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

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

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

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

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

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

How did you find/construct a sample?

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

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

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

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

Examples of “Sample” Sections in Journal Articles

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

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

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

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

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

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

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

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

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

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

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

Final Words

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

Table 5.1. Sampling Type and Strategies

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

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Methodology

Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

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.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The 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

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

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

Research bias

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

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A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Sampling in Qualitative Research

January 2019
In book: Qualitative Techniques for Workplace Data Analysis (pp.25-51)

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

Introduction.

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

Other Subject Areas

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

Cyber Safety in Schools
Girls' Education in the Developing World
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Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST REVIEWED: 26 February 2020 LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

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

Sampling in Qualitative Research

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

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Sampling Methods & Strategies 101

Everything you need to know (including examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to research, sooner or later you’re bound to wander into the intimidating world of sampling methods and strategies. If you find yourself on this page, chances are you’re feeling a little overwhelmed or confused. Fear not – in this post we’ll unpack sampling in straightforward language , along with loads of examples .

Overview: Sampling Methods & Strategies

What is sampling in a research context?
The two overarching approaches

Simple random sampling

Stratified random sampling, cluster sampling, systematic sampling, purposive sampling, convenience sampling, snowball sampling.

How to choose the right sampling method

What (exactly) is sampling?

At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group . For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent (good luck with that!) – but you could potentially collect data from a smaller subset of this group.

In technical terms, the larger group is referred to as the population , and the subset (the group you’ll actually engage with in your research) is called the sample . Put another way, you can look at the population as a full cake and the sample as a single slice of that cake. In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. In other words, you’d want to cut a perfect cross-sectional slice of cake, such that the slice reflects every layer of the cake in perfect proportion.

Achieving a truly representative sample is, unfortunately, a little trickier than slicing a cake, as there are many practical challenges and obstacles to achieving this in a real-world setting. Thankfully though, you don’t always need to have a perfectly representative sample – it all depends on the specific research aims of each study – so don’t stress yourself out about that just yet!

With the concept of sampling broadly defined, let’s look at the different approaches to sampling to get a better understanding of what it all looks like in practice.

The two overarching sampling approaches

At the highest level, there are two approaches to sampling: probability sampling and non-probability sampling . Within each of these, there are a variety of sampling methods , which we’ll explore a little later.

Probability sampling involves selecting participants (or any unit of interest) on a statistically random basis , which is why it’s also called “random sampling”. In other words, the selection of each individual participant is based on a pre-determined process (not the discretion of the researcher). As a result, this approach achieves a random sample.

Probability-based sampling methods are most commonly used in quantitative research , especially when it’s important to achieve a representative sample that allows the researcher to generalise their findings.

Non-probability sampling , on the other hand, refers to sampling methods in which the selection of participants is not statistically random . In other words, the selection of individual participants is based on the discretion and judgment of the researcher, rather than on a pre-determined process.

Non-probability sampling methods are commonly used in qualitative research , where the richness and depth of the data are more important than the generalisability of the findings.

If that all sounds a little too conceptual and fluffy, don’t worry. Let’s take a look at some actual sampling methods to make it more tangible.

Need a helping hand?

Probability-based sampling methods

First, we’ll look at four common probability-based (random) sampling methods:

Importantly, this is not a comprehensive list of all the probability sampling methods – these are just four of the most common ones. So, if you’re interested in adopting a probability-based sampling approach, be sure to explore all the options.

Simple random sampling involves selecting participants in a completely random fashion , where each participant has an equal chance of being selected. Basically, this sampling method is the equivalent of pulling names out of a hat , except that you can do it digitally. For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 numbers (each number, reflecting a participant) and then use that dataset as your sample.

Thanks to its simplicity, simple random sampling is easy to implement , and as a consequence, is typically quite cheap and efficient . Given that the selection process is completely random, the results can be generalised fairly reliably. However, this also means it can hide the impact of large subgroups within the data, which can result in minority subgroups having little representation in the results – if any at all. To address this, one needs to take a slightly different approach, which we’ll look at next.

Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly , but from within certain pre-defined subgroups (i.e., strata) that share a common trait . For example, you might divide the population into strata based on gender, ethnicity, age range or level of education, and then select randomly from each group.

The benefit of this sampling method is that it gives you more control over the impact of large subgroups (strata) within the population. For example, if a population comprises 80% males and 20% females, you may want to “balance” this skew out by selecting a random sample from an equal number of males and females. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So, depending on your research aims, the stratified approach could work well.

Next on the list is cluster sampling. As the name suggests, this sampling method involves sampling from naturally occurring, mutually exclusive clusters within a population – for example, area codes within a city or cities within a country. Once the clusters are defined, a set of clusters are randomly selected and then a set of participants are randomly selected from each cluster.

Now, you’re probably wondering, “how is cluster sampling different from stratified random sampling?”. Well, let’s look at the previous example where each cluster reflects an area code in a given city.

With cluster sampling, you would collect data from clusters of participants in a handful of area codes (let’s say 5 neighbourhoods). Conversely, with stratified random sampling, you would need to collect data from all over the city (i.e., many more neighbourhoods). You’d still achieve the same sample size either way (let’s say 200 people, for example), but with stratified sampling, you’d need to do a lot more running around, as participants would be scattered across a vast geographic area. As a result, cluster sampling is often the more practical and economical option.

If that all sounds a little mind-bending, you can use the following general rule of thumb. If a population is relatively homogeneous , cluster sampling will often be adequate. Conversely, if a population is quite heterogeneous (i.e., diverse), stratified sampling will generally be more appropriate.

The last probability sampling method we’ll look at is systematic sampling. This method simply involves selecting participants at a set interval , starting from a random point .

For example, if you have a list of students that reflects the population of a university, you could systematically sample that population by selecting participants at an interval of 8 . In other words, you would randomly select a starting point – let’s say student number 40 – followed by student 48, 56, 64, etc.

What’s important with systematic sampling is that the population list you select from needs to be randomly ordered . If there are underlying patterns in the list (for example, if the list is ordered by gender, IQ, age, etc.), this will result in a non-random sample, which would defeat the purpose of adopting this sampling method. Of course, you could safeguard against this by “shuffling” your population list using a random number generator or similar tool.

Systematic sampling simply involves selecting participants at a set interval (e.g., every 10th person), starting from a random point.

Non-probability-based sampling methods

Right, now that we’ve looked at a few probability-based sampling methods, let’s look at three non-probability methods :

Again, this is not an exhaustive list of all possible sampling methods, so be sure to explore further if you’re interested in adopting a non-probability sampling approach.

First up, we’ve got purposive sampling – also known as judgment , selective or subjective sampling. Again, the name provides some clues, as this method involves the researcher selecting participants using his or her own judgement , based on the purpose of the study (i.e., the research aims).

For example, suppose your research aims were to understand the perceptions of hyper-loyal customers of a particular retail store. In that case, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes .

Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience . Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad .

Purposive sampling involves the researcher selecting participants using their own judgement, based on the purpose of the study.

Next up, we have convenience sampling. As the name suggests, with this method, participants are selected based on their availability or accessibility . In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.

Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. However, as you’d expect, this sampling method is unlikely to produce a representative sample and will of course be vulnerable to researcher bias , so it’s important to approach it with caution.

Last but not least, we have the snowball sampling method. This method relies on referrals from initial participants to recruit additional participants. In other words, the initial subjects form the first (small) snowball and each additional subject recruited through referral is added to the snowball, making it larger as it rolls along .

Snowball sampling is often used in research contexts where it’s difficult to identify and access a particular population. For example, people with a rare medical condition or members of an exclusive group. It can also be useful in cases where the research topic is sensitive or taboo and people are unlikely to open up unless they’re referred by someone they trust.

Simply put, snowball sampling is ideal for research that involves reaching hard-to-access populations . But, keep in mind that, once again, it’s a sampling method that’s highly prone to researcher bias and is unlikely to produce a representative sample. So, make sure that it aligns with your research aims and questions before adopting this method.

How to choose a sampling method

Now that we’ve looked at a few popular sampling methods (both probability and non-probability based), the obvious question is, “ how do I choose the right sampling method for my study?”. When selecting a sampling method for your research project, you’ll need to consider two important factors: your research aims and your resources .

As with all research design and methodology choices, your sampling approach needs to be guided by and aligned with your research aims, objectives and research questions – in other words, your golden thread. Specifically, you need to consider whether your research aims are primarily concerned with producing generalisable findings (in which case, you’ll likely opt for a probability-based sampling method) or with achieving rich , deep insights (in which case, a non-probability-based approach could be more practical). Typically, quantitative studies lean toward the former, while qualitative studies aim for the latter, so be sure to consider your broader methodology as well.

The second factor you need to consider is your resources and, more generally, the practical constraints at play. If, for example, you have easy, free access to a large sample at your workplace or university and a healthy budget to help you attract participants, that will open up multiple options in terms of sampling methods. Conversely, if you’re cash-strapped, short on time and don’t have unfettered access to your population of interest, you may be restricted to convenience or referral-based methods.

In short, be ready for trade-offs – you won’t always be able to utilise the “perfect” sampling method for your study, and that’s okay. Much like all the other methodological choices you’ll make as part of your study, you’ll often need to compromise and accept practical trade-offs when it comes to sampling. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.

Let’s recap…

In this post, we’ve covered the basics of sampling within the context of a typical research project.

Sampling refers to the process of defining a subgroup (sample) from the larger group of interest (population).
The two overarching approaches to sampling are probability sampling (random) and non-probability sampling .
Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling.
Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
When choosing a sampling method, you need to consider your research aims , objectives and questions, as well as your resources and other practical constraints .

If you’d like to see an example of a sampling strategy in action, be sure to check out our research methodology chapter sample .

Last but not least, if you need hands-on help with your sampling (or any other aspect of your research), take a look at our 1-on-1 coaching service , where we guide you through each step of the research process, at your own pace.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Excellent and helpful. Best site to get a full understanding of Research methodology. I’m nolonger as “clueless “..😉

Excellent and helpful for junior researcher!

Grad Coach tutorials are excellent – I recommend them to everyone doing research. I will be working with a sample of imprisoned women and now have a much clearer idea concerning sampling. Thank you to all at Grad Coach for generously sharing your expertise with students.

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Annals of Tourism Research

Research note sampling in qualitative interview research: criteria, considerations and guidelines for success.

• Outlines sampling considerations for qualitative research interviews.
• Links sampling to research integrity and rigor.
• Discusses issues of sample size in qualitative interviews.
• Suggests best practice for research reporting.
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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.

Table 10.1 Types of nonprobability samples

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

Key Takeaways

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

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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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What are sampling methods and how do you choose the best one?

Posted on 18th November 2020 by Mohamed Khalifa

This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.

Introduction to sampling methods

Examples of different sampling methods, choosing the best sampling method.

It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding .

However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique.

What is a sampling frame?

A sampling frame is a record of the target population containing all participants of interest. In other words, it is a list from which we can extract a sample.

What makes a good sample?

A good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study.

We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. For the purposes of this blog we will be focusing on random sampling methods .

Example: We want to conduct an experimental trial in a small population such as: employees in a company, or students in a college. We include everyone in a list and use a random number generator to select the participants

Advantages: Generalisable results possible, random sampling, the sampling frame is the whole population, every participant has an equal probability of being selected

Disadvantages: Less precise than stratified method, less representative than the systematic method

Simple sampling method example in stick men.

Example: Every nth patient entering the out-patient clinic is selected and included in our sample

Advantages: More feasible than simple or stratified methods, sampling frame is not always required

Disadvantages: Generalisability may decrease if baseline characteristics repeat across every nth participant

Systematic sampling method example in stick men

Example: We have a big population (a city) and we want to ensure representativeness of all groups with a pre-determined characteristic such as: age groups, ethnic origin, and gender

Advantages: Inclusive of strata (subgroups), reliable and generalisable results

Disadvantages: Does not work well with multiple variables

Stratified sampling method example stick men

Example: 10 schools have the same number of students across the county. We can randomly select 3 out of 10 schools as our clusters

Advantages: Readily doable with most budgets, does not require a sampling frame

Disadvantages: Results may not be reliable nor generalisable

Cluster sampling method example with stick men

How can you identify sampling errors?

Non-random selection increases the probability of sampling (selection) bias if the sample does not represent the population we want to study. We could avoid this by random sampling and ensuring representativeness of our sample with regards to sample size.

An inadequate sample size decreases the confidence in our results as we may think there is no significant difference when actually there is. This type two error results from having a small sample size, or from participants dropping out of the sample.

In medical research of disease, if we select people with certain diseases while strictly excluding participants with other co-morbidities, we run the risk of diagnostic purity bias where important sub-groups of the population are not represented.

Furthermore, measurement bias may occur during re-collection of risk factors by participants (recall bias) or assessment of outcome where people who live longer are associated with treatment success, when in fact people who died were not included in the sample or data analysis (survivors bias).

By following the steps below we could choose the best sampling method for our study in an orderly fashion.

Research objectiveness

Firstly, a refined research question and goal would help us define our population of interest. If our calculated sample size is small then it would be easier to get a random sample. If, however, the sample size is large, then we should check if our budget and resources can handle a random sampling method.

Sampling frame availability

Secondly, we need to check for availability of a sampling frame (Simple), if not, could we make a list of our own (Stratified). If neither option is possible, we could still use other random sampling methods, for instance, systematic or cluster sampling.

Study design

Moreover, we could consider the prevalence of the topic (exposure or outcome) in the population, and what would be the suitable study design. In addition, checking if our target population is widely varied in its baseline characteristics. For example, a population with large ethnic subgroups could best be studied using a stratified sampling method.

Random sampling

Finally, the best sampling method is always the one that could best answer our research question while also allowing for others to make use of our results (generalisability of results). When we cannot afford a random sampling method, we can always choose from the non-random sampling methods.

To sum up, we now understand that choosing between random or non-random sampling methods is multifactorial. We might often be tempted to choose a convenience sample from the start, but that would not only decrease precision of our results, and would make us miss out on producing research that is more robust and reliable.

References (pdf)

Mohamed Khalifa

No Comments on What are sampling methods and how do you choose the best one?

Thank you for this overview. A concise approach for research.

really helps! am an ecology student preparing to write my lab report for sampling.

I learned a lot to the given presentation.. It’s very comprehensive… Thanks for sharing…

Very informative and useful for my study. Thank you

Oversimplified info on sampling methods. Probabilistic of the sampling and sampling of samples by chance does rest solely on the random methods. Factors such as the random visits or presentation of the potential participants at clinics or sites could be sufficiently random in nature and should be used for the sake of efficiency and feasibility. Nevertheless, this approach has to be taken only after careful thoughts. Representativeness of the study samples have to be checked at the end or during reporting by comparing it to the published larger studies or register of some kind in/from the local population.

Thank you so much Mr.mohamed very useful and informative article

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Qualitative Sampling Methods

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

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

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

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Qualitative Research: Methods

Getting Started
Focus Groups
Observation
Case Studies
Data Collection
Cleaning Text
Analysis Tools
Institutional Review

Table of Contents: Methods Section

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Assessment Resources (qualitative and quantitative)

Qualitative Research Methods: A Data Collector's Field Guide Downloadable how-to guide covers the mechanics of data collection for applied qualitative research; appropriate for novice and experienced researchers.

In qualitative research, only a sample (subset) of a population is selected for any given study.Three of the most common sampling methods are:

Purposive sampling Participants are grouped according to preselected criteria relevant to a particular research question; sample sizes often determined by theoretical saturation (new data doesn't bring additional insights)
Quota sampling While designing a study, it is determined how many people with which characteristics need to be included as participants
Snowball sampling Participants or informants use their social networks to refer the researcher to other people who could potentially participate in the study, often used to find and recruit “hidden populations"

Choosing a Method for Collecting Qualitative Data


Surveys
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Table was adapted from the Basic Guide to Program Evaluation, http://www.managementhelp.org/evaluatn/fnl_eval.htm#anchor1585345

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Introduction

Sampling, for the purposes of this guide, refers to any process by which members of a population are selected to participate in research. There are many methods for sampling, each with a slightly different purpose. In the box below you can learn more about these common sampling techniques:

simple random sampling
stratified sampling
cluster sampling
systematic sampling
non-probability sampling

Before you can obtain a sample, you must first identify a target population . The target population refers to all of the people who are the focus of a study. For example, a study about elementary school teacher burnout would include all elementary school teachers in the population. In some cases, you may need to consider an accessible population . This is a subset of the target population that can reasonably be accessed by the researcher for sampling. Oftentimes, researchers will use a sampling frame to facilitate their sampling methods. A sampling frame is a list of all of the members of the population.

Sampling Techniques

Simple Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling

Through simple random sampling (SRS), all members of the population have an equal chance of being selected. Therefore, this is a type of probability sampling . A rudimentary method of SRS is drawing names out of a hat. Each slip of paper has the same chance of being chosen on every draw. You could also use a random number generator to facilitate random selection from the population.

Simple random sampling assumes that all members of the population are accessible. If your population is "people in the United States" and you are attempting to sample via the Internet, members of the population without Internet access do not have a chance to be selected. This would not be an appropriate use of simple random sampling.

Researchers use SRS when the intention is to obtain a representative sample that can provide data for generalizing to the population. If members are chosen randomly, the sample is less subject to bias that may exist by non-random sampling methods.

Stratified sampling is a two-step sampling procedure. First, the population is divided into groups or strata . How this is done will depend on your specific population. Using the example of elementary school teachers, we could divide the teachers up based on state or school district, with each state (or school district) representing one strata. Next, members of each strata are selected for participation. When they are selected randomly from within each strata, it is called stratified random sampling .

population divided into four strata with members of each strata being selected via simple random sampling to make up the sample

In the above figure, the population was divided into four strata. Members of each strata were selected to participate using simple random sampling. Thus all four strata are represented in the final sample of participants. This method is effective in ensuring all strata are included in the sample. For example, making sure teachers from all 50 states are included in the sample.

You can also adjust the proportion of the sample that comes from each strata to maintain proportional alignment with the population. For example, if 50% of the population is in Group Three , 50% of the sample can be randomly selected from that strata, thus ensuring the final makeup of the sample aligns with the makeup of the population.

Like stratified sampling, cluster sampling is a two-step sampling procedure that also starts with dividing the population into groups called clusters . As with stratified sampling, how groups are divided will depend on your population. For example, teachers could be divided into clusters based on school district or grade level taught. The primary difference between stratified sampling and cluster sampling is that whole clusters are randomly selected and everyone in that cluster is included in the sample.

population divided into four clusters with two clusters being randomly selected to make up the sample

In the above figure, the population was divided into four clusters. Two of these clusters were randomly chosen. All members of Group One and Group Four will participate in the study, making up the sample.

Cluster sampling is ideal when there are not major differences between the clusters. Consider dividing teachers based on school district versus grade level taught. Each school district will include the same grade levels, though may have some variability in factors like school size and geographic location. Selecting school districts at random can help create a representative sample that covers an array of factors. If the clusters are grade level, randomly selecting grade levels mean entire grades are not included in the sample. These grades may have meaningful differences from the included grades. So, cluster sampling may not be as effective in this situation.

Systematic sampling occurs when participants are selected at set intervals. For example, choosing every third person from a list. To ensure this method aligns with probability sampling conditions, the starting point is randomly selected. Consider the following visual that shows systematic selection, beginning with the second person in the line (the randomly selected starting point).

line of 11 people depicting systematic selection of every third person starting with the second person

Systematic sampling offers benefits similar to simple random sampling but is often perceived as being simpler to carry out. It also combats the potential problem of clusters that can occur with random sampling. While random sampling aims to select a variety from the population, there is also no way to regulate who it selects. So, clusters of individuals could be selected at random, thus potentially biasing the research. Systematic sampling ensures and even distribution across the population.

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Sampling in research

This page outlines key information around sampling methods in both quantitative and qualitative research..

Including a succinct justification for your chosen sample size is important for the Research Ethics Committee to understand that a credible plan is in place, and importantly that participant involvement is required. A detailed academic defence of your approach is not necessary. You may wish to read this paper to find out more.

In research, a sample is a group of people, items or objects taken from a larger population.

There are two major types of sampling, that is, the process and method of selecting your sample: probabilistic and non-probabilistic.

Probabilistic sampling

Probabilistic sampling refers to any sampling method which involves researchers using some form of random selection of items or individuals, rather than deliberate choice. Examples of probabilistic sampling include random sampling, stratified sampling, and cluster sampling.

Non-probabilistic sampling

Non-probabilistic sampling methods involve researchers deliberately choosing or selecting items or individuals for the sample, based on specific criteria and/or the specific research objectives. Examples of non-probabilistic sampling include convenience sampling, purposive sampling, and snowball sampling.

Quantitative research

In quantitative research protocols, the rationale for your sample size should be explained. If appropriate, you should also explain whether you have conducted a formal sample size calculation. This information should be included in your ethics application.

Quantitative sample size calculations should demonstrate exactly what statistical test the calculation is powered for (usually relating to the primary hypothesis/research questions), power, alpha, and effect size.

Qualitative research

Sample size in qualitative research is very different from quantitative research – there are no power calculations. However, it is important to justify your sample size as part of planning your research and to be transparent when you are reporting the results of the study. Good practice is to justify your sample size with reference to your particular study rather than citing an external norm.

Further information
Further information about sampling strategies can be found here
Further information on sample size calculations can be found here - Sage Research Methods provide various resources which can be accessed via your University of Bath login.
Information about sampling for survey research can be found here
For further information and guidance on sample size in qualitative research springer and sage journals
Related guides
Ethics Review Process
A-Z of research ethics guidance

Find out more about research integrity and ethics

A qualitative exploration of disseminating research findings among public health researchers in China

Yiluan Hu 1 ,
Xuejun Yin 1 , 2 ,
Yachen Wang 1 ,
Enying Gong 1 ,
Xin Xin 3 ,
Jing Liu 4 ,
Xia Liu 4 ,
Ruitai Shao 1 ,
Juan Zhang 1 , 5 &
Ross C. Brownson 6 , 7

BMC Public Health volume 24 , Article number: 2518 ( 2024 ) Cite this article

106 Accesses

Metrics details

Research dissemination is essential to accelerate the translating of evidence into practice. Little is known about dissemination among Chinese public health researchers. This study aimed to explore the understanding and practices of disseminating research findings and to identify barriers and facilitators that influence dissemination activities to non-research audiences.

This study deployed an exploratory qualitative design with purposive and snowball sampling. One focus group with 5 participants and 12 in-depth interviews were conducted with participants working in diverse fields from universities ( n = 10), the National Chinese Center for Disease Control and Prevention ( n = 4), the Chinese National Cancer Center ( n = 1), the Chinese National Center for Cardiovascular Disease ( n = 1), and China office of a global research institute ( n = 1) from May to December 2021 to reach saturation. Data were initially analyzed using inductive thematic analysis. The designing for dissemination (D4D) logic model was then used to organize themes and subthemes. Two coders independently coded all transcripts and discussed disparities to reach a consensus.

Out of 17 participants, 12 misunderstood the concept of dissemination; 14 had disseminated to non-research audiences: 10 to the public, 10 to practitioners, and 9 to policymakers. We identified multiple barriers to dissemination to non-research audiences across four phases of the D4D logic model, including low priority of dissemination, limited application of D4D strategies, insufficient support from the research organizations, practice settings, and health systems, and overemphasis on academic publications.

Conclusions

There was a lack of understanding and experience of dissemination, indicating a lack of emphasis on active dissemination in China. We provide implications for raising awareness, building capacity, facilitating multidisciplinary collaboration, providing incentives and infrastructure, changing climate and culture, establishing communication and executive networks, and accelerating systematic shifts in impact focus.

Peer Review reports

Introduction

The gap between research and practice is well documented [ 1 , 2 , 3 , 4 ]. Dissemination refers to the active approach of spreading evidence-based interventions to the target audience via predetermined channels using planned strategies [ 3 , 5 ] and is a prerequisite for bridging the gap between research and practice. The concept of dissemination has some overlap with other related concepts including science popularization and knowledge translation. Although both use communication techniques as useful strategies, science popularization is mainly about propagating general knowledge to the public with the aim of improving citizens’ science literacy [ 6 ], whereas dissemination involves wider audiences and aims to maximize the impact of research and promote the uptake of evidence. On the other hand, although sharing a similar goal with dissemination of bridging the research-practice gap, knowledge translation refers to the dynamic and iterative process involving synthesis, dissemination, exchange, and ethically-sound application of knowledge, which considers dissemination a component of translation [ 7 , 8 ].

Despite the importance of dissemination, dissemination is often not a priority for researchers and their organization [ 9 ] and is largely missed. For example, in a study of US public health researchers, 78% reported dissemination as important to their research, while only 27% spent over 10% of their time on dissemination [ 3 ] and 28% rated their dissemination efforts as excellent or good [ 10 ]. In addition, there are inconsistencies in preferred sources of information between researchers and non-researchers. Almost all researchers disseminated their research through academic publications [ 11 , 12 , 13 , 14 ], yet practitioners and policymakers may find them inaccessible, difficult to understand, or time-consuming [ 11 , 15 , 16 , 17 ].

To effectively disseminate the evidence, dissemination and implementation (D&I) science has thrived and designing for dissemination (D4D) has emerged as a promising direction within D&I science. The D4D perspective highlights the responsibility of researchers to actively disseminate and the need to plan from the outset to fit the adopters’ needs, assets, and time frames [ 3 ]. Useful D4D strategies include stakeholder involvement, application of D&I science theories and frameworks, incorporation of marketing, business, communication, systems approaches and professionals, and related disciplines [ 3 , 18 , 19 ]. Despite the availability of D4D, the application remains insufficient. For example, only 17% of US public health researchers used a framework or theory to plan their dissemination activities and only 34% typically involved stakeholders in the research process in 2012; 55% of US and Canadian D&I scientists typically involved stakeholders in the research process in 2018. While there is a growing body of evidence on D4D in some regions of the world, there are limited data on D4D from China.

Evidence from high-income countries has revealed individual-level barriers such as lack of capacity and reluctance to disseminate findings of a single study, and organizational-level barriers such as lack of financial resources, staff time, and academic incentives [ 14 , 20 ]. Yet, little is known about dissemination in China, where the D&I science is still in its infancy. With progresses in China’s health reform, science popularization and knowledge translation has received increasing attention, but dissemination received little attention in the field of public health. In addition, the large population, high disease burden, shortage of healthcare providers, and relatively centralized health system further exacerbate the complexity of dissemination in China [ 16 , 21 ]. A quantitative study conducted by the current team among Chinese public health researchers suggested that only 58.1% had disseminated their research findings, and that main barriers included a lack of financial resources, platforms, and collaboration mechanisms at the organizational level, as well as a lack of time, knowledge, and skills at the individual level [ 22 ].

Hence, there is urgency to explore factors underlying the dissemination in China from the perspective of researchers. We aimed to explore researchers’ understanding of the concept of dissemination and current dissemination activities, further to identify barriers and facilitators that influence dissemination to non-research audiences guided by the D4D logic model.

A qualitative study design was deployed to explore public health researchers’ perspectives on contextual factors affecting the dissemination of research findings in China. The study was reported according to the Consolidated criteria for reporting qualitative research (COREQ) guidelines (see Additional file 1) [ 23 ].

Theoretical framework

With the aim to gain insight into the barriers and facilitators for researchers to design for dissemination, this study adopted the D4D logic model as an analytical framework. The D4D logic model was published by Kwan and colleagues [ 19 ] in 2022 and included four phases: (1) the initial conceptualization phase identifying need and demand, and establishing evidence base of health issues; (2) the design phase using multiple strategies to determine the design of dissemination product as well as the packaging, messaging, and distribution plan; (3) the subsequent dissemination phase based on the push-pull-capacity model and situating the push of research, pull of practice, and capacity of health systems to support dissemination; and (4) the impact phase ensuring adoption, sustainment, and equity benefits [ 19 ].

Participants and sampling

Study participants were public health researchers working in universities, the National Chinese Center for Disease Control and Prevention (briefly as China CDC), the Chinese National Cancer Center, the Chinese National Center for Cardiovascular Disease, or China Offices of global research institutes. Universities are the most important producers of evidence in China, followed by healthcare institutions, research institutions, and companies [ 24 ].Teaching and researching are core activities for university researchers, and academic publication is one of the key tenure and promotion criteria. The China CDC is a governmental and national-level technical institution affiliated with the National Health Commission of China, and shoulders the responsibilities of focusing on the key tasks of national disease prevention and control and of instructing the provincial-, prefecture-, city-, and county-level CDC. Also under the leadership of the National Health Commission of China and shoulder responsibilities of evidence generation and implementation, the Chinese National Cancer Center and the Chinese National Center for Cardiovascular Disease are based in two big specialized hospitals in China. Given that university researchers are the biggest community for evidence generation in China, most of the participants were university researchers.

Purposive and snowball sampling methods were applied to reach less accessible target participants. First, participants were purposively selected on the basis that they had rich experience in public health research and took an active part in academia. Second, interviewees were asked to nominate other researchers who might be willing to provide information for in-depth interviews, particularly those with expertise in dissemination and implementation science. All potential participants were contacted directly by telephone by a senior member (JZ) of the research team to seek their participation. Participants were informed of the study’s purpose, process, confidentiality, and right to withdraw at any time. They were then asked to give informed oral consent to participate in the study and to be audio-recorded prior to the formal interview. In total, 18 researchers received the invitation; one declined due to unavailability during the time of this study.

Data collection

Data were collected from May 2021 to December 2021 through a focus group and in-depth interviews. Given that participants may be unfamiliar with the concept of dissemination and the experience of dissemination may be limited, we initially conducted a focus group of five participants to stimulate discussion. During the discussion, participants were actively involved and contributed a lot to the topic, so we later conducted individual interviews to gather a rich and detailed understanding of the participants’ perspectives. The focus group of five participants and the first two individual in-depth interviews were conducted face-to-face, while later ten individual in-depth interviews were conducted via Tencent Meeting (Chinese online meeting software, similar to Zoom) because of the COVID-19-related physical distancing restrictions. During the interviews, participants were alone in their office or a private space to ensure confidentiality so that they could share freely.

A multidisciplinary team of researchers and students in dissemination and implementation science, behavior science, psychology, and qualitative methods contributed to developing the interview guide. The interview guide was pilot tested and refined prior to the formal interview. As dissemination is a relatively new concept in China, participants entered interviews with a discussion about their understanding of this concept. To ensure participants have consistent understanding of dissemination, the interviewer then clarified the concept as the active approach of spreading evidence-based interventions to the target audience via predetermined channels using planned strategies [ 3 , 5 ]. Then, participants were encouraged to have a deep, detailed discussion on their dissemination experience and barriers and facilitators of dissemination to non-research audiences. Participants’ demographic information, which was pre-collected, was confirmed with participants at the end of the interview. The interview guide can be found in supplementary file 2.

All interviews were conducted in Mandarin Chinese by an interviewer experienced in qualitative research (JZ, professor, Ph.D., female) with a note-taker (YH, master’s student, female). No repeat interviews were conducted. The researchers collected participants’ demographic information, research interests, and research projects online before the formal interview to have a deep understanding of their perspectives. All interviews were audio-recorded and transcribed after obtaining oral consent from the interviewees. Transcripts were not returned to participants for comment or correction. Following qualitative research best practices [ 25 , 26 , 27 ], data collection ended when information saturation occurred and no new information was observed.

Data analysis

Data analysis occurred concurrently with data collection. Verbatim transcripts were coded using the inductive thematic analysis approach in NVivo 11 software. First, a coder (YH) reviewed transcripts to generate initial codes and aggregated them into categories to form early themes and subthemes. The D4D logic model [ 19 ] was then used to organize and map the relationships between themes and subthemes. Then, another coder (YW) independently applied codes to transcripts using the same coding framework. The codebook was constantly checked against the transcripts and was finally determined by comparison until no new information was identified. All coding results were compared and discussed between the two coders to reach a consensus. Unsolved discrepancies were resolved through discussion with a senior researcher (JZ) and at research team meetings. Data analysis was conducted in Chinese. All themes, subthemes, and typical verbatim quotes used to illustrate the main themes, were translated into English. Quotes are identified by participants’ ID to guarantee anonymity. Participants did not provide feedback on the findings.

Information saturation was reached after completing a focus group of 5 participants and 12 in-depth individual interviews with public health researchers in China. The interviews took 41.9 ± 10.9 min on average. Participants aged between 32 and 65 years, with an average of 46.5 ± 8.3 years, were primarily female (70.6%), and had a Ph.D. degree (88.2%). They worked in the universities in the field of health policy, behavioral science, global health, and implementation science ( n = 10), the China CDC in the field of tobacco control, AIDS/STD control, tuberculosis control, and environmental health ( n = 4), the Chinese National Cancer Center ( n = 1), the Chinese National Center for Cardiovascular Disease ( n = 1), and the China office of a global research institute ( n = 1).

Theme 1: understanding of the concept of dissemination

Five out of 17 participants had no difficulty understanding the concept of dissemination as the active approach of spreading evidence-based interventions to the target audience via predetermined channels using planned strategies, while 12 participants misunderstood dissemination to some extent. Eight participants did not differentiate dissemination of research findings from science popularization of general knowledge when discussing their dissemination activities.

Dissemination means that I share some knowledge with others… I have always paid close attention to new media , and I have written and post some health science articles in Zhihu (Chinese online question-and-answer social media , similar to Quora) … Some online magazines often invite me and my colleagues to write some science articles , for example , I recently wrote an article to share some psychological and behavioral techniques for smoking cessation (Participant 01).

One participant viewed dissemination as knowledge translation, saying that dissemination referred to the process of translating and applying research, especially interventional research, into practice and policy.

I feel that dissemination in Chinese would be easily understood as science popularization , but it actually highlights the translation to the practice and policy , so translating it as ‘knowledge translation’ in Chinese may be more appropriate (participant 16).

Three participants argued that dissemination was similar to health communication, which refers to the communication and sharing of information.

The government is now promoting the awareness of knowledge translation , but I feel that knowledge translation in Chinese emphasizes the process of translating and applying our research , which is more about health technology , and sometimes there may be some commercial elements in knowledge translation. Dissemination is more similar to health communication (participant 14).

Theme 2: experience of dissemination

Subtheme 2.1: dissemination within academia.

Three participants working in the universities mainly published their research findings in peer-reviewed journals or through academic conferences for different reasons: one expressed a lack of resources in reaching non-research audiences, while two showed a lack of motivation, saying that dissemination to non-research audiences was not their priority.

I mainly published my research on peer-reviewed journals… for ordinary researchers like me , access and resources were limited (participant 07). As a researcher , I am very competent when disseminating within academia. Even if I encounter difficulties , I will face them. But for dissemination to practitioners or policymakers , the main disseminator is not me and should not be me… I am a teacher , and my priorities for the next five to ten years include publishing textbooks , participating in academic activities , working with young students , and conducting research (participant 17).

Subtheme 2.2: dissemination beyond academia

Fourteen participants described their experiences disseminating research findings to non-research audiences: 10 had disseminated to the public, 10 to practitioners, and 9 to policymakers. Participants disseminated to the public through social media and mass media. They cited social media as an accessible channel for every individual researcher. However, they felt their personal influence was limited in reaching a wide population, and they needed more resources to use mass media for dissemination. In addition, researchers were worried about possible misinformation and disinformation when disseminating on social media and mass media.

Our impact as a researcher to disseminate is so weak that our research findings posted on WeChat (Chinese social media , similar to WhatsApp and Snapchat) Moments can only be noticed by a few hundred people at most (participant 02). We are not required to add references , and sometimes the already added ones may even be deleted… and because our target audience is the public , we need to translate academic language into plain language… sometimes I am afraid of making scientific mistakes or causing misinformation (participant 01).

Dissemination to policymakers was considered impactful but with a high threshold. A participant indicated that in such cases, dissemination to practitioners was an alternative strategy to influence practice since it was more accessible. Of nine participants who have ever disseminated to policymakers, three worked in China CDC, and five engaged in health policy research.

My organization (China CDC) is a technical support organization for administrative decisions and policy-making , so a lot of our work is done for dissemination (participant 15). For researchers conducting health policy research like me , it is a must to disseminate to our government (participant 08).

Some participants felt the issuance of standards and guidelines ( n = 4) and publication of patents ( n = 5) as their dissemination routes. In contrast, some participants thought standards, guidelines, and patents were dissemination products that needed further disseminated, and the issuance of these products did not mean successful dissemination.

The implementation of patents is limited… now patents are mainly used by my peer researchers. Publishing patents does not mean dissemination , and patents themselves actually need to be further disseminated and implemented (participant 15).

Theme 3: facilitators and barriers of dissemination based on the D4D logic model

Factors influencing dissemination to non-research audiences emerged across four phases of the D4D logic model [ 19 ], and seven subthemes were identified: (1) motivation; (2) design processes; (3) packaging and distribution design; (4) push of research; (5) pull of practice; (6) capacity of health systems; and (7) impact of research. The subthemes are discussed in detail below and in Table 1 .

Subtheme 3.1: motivation

Most participants expressed their willingness to disseminate to non-research audiences out of a sense of social responsibility and social recognition, with the exception of two participants who did not consider dissemination to be their priority. Social climate was mentioned as another facilitator of dissemination.

The ultimate goal of scientific research is to change the public’s cognition and behavior , and the government’s decision-making process. If you do not consider dissemination , your research has no value , and it is hard to get recognition from our peers and the public (participant 12).

Subtheme 3.2: design processes

Subtheme 3.2.1: stakeholder involvement and context analysis.

Some participants indicated difficulties building relationships and reaching consensus with stakeholders (e.g., the public, media, practitioners, and policymakers) because of potential conflicts of interest between stakeholders and researchers. Involving stakeholders from the outset, building contacts based on previous relationships, and matching stakeholders’ needs were recommended by participants as helpful for stakeholder involvement. In addition, involving stakeholders from all sectors of society, not only within the health system but also outside of it (e.g., education system, non-governmental organizations, non-profit organizations, and commercial organizations), was thought to have the potential to make a greater influence.

This was based on previous collaboration between their organization and ours , and we have a long-term collaboration with them , so it was quite natural and easy to involve them… We got in touch with them when the research is being formulated. The sooner you can get in touch with stakeholders and get their support , the better… and if we can connect with people and organizations outside the health system , our dissemination efforts may have a greater impact and be more sustainable (participant 13).

Subtheme 3.2.2: application of D&I methodologies

The application of D&I methodologies was stressed as a facilitator of dissemination. However, some participants indicated that D&I science was still an emerging field in China, the limited understanding of D&I methodologies impeded the dissemination and implementation of research.

Currently , there is limited knowledge of methodologies including research design , theoretical frameworks , and qualitative methods for D&I science in China , which hinders the dissemination and implementation of research (participant 16).

Subtheme 3.2.3: marketing and business approaches

Some participants mentioned that the field of marketing was quite relevant to dissemination design and that marketing and communication approaches were promising for dissemination to non-research audiences, especially to the general public.

Take food marketing in food policy as an example , I feel that Coke’s advertising is so good that I also want to drink it; on the contrary , if you simply tell me not to eat food high in sugar and salt , then I will just not listen , let alone the ordinary consumers (participant 06).

Subtheme 3.2.4: context and situation analysis

Conducting context and situation analysis was cited as the foundation for understanding context and tailoring dissemination efforts.

Health communication always emphasizes needs assessment and audience segmentation , and it is important to understand the audiences’ needs. In many cases , what we were doing did not meet the needs of our audiences , and they did not accept (participant 04).

Subtheme 3.2.5: complexity of social, health, organizational, and political systems

Participants perceived policy resistance and low confidence in disseminating research with negative, politically or economically sensitive findings in complex social, health, organizational, and political systems. In addition, some participants noted that the COVID-19 pandemic increased the uncertainty of research findings and the vulnerability of collaboration networks.

For example , research involving the control of the tobacco industry , which is related to the economy , is very sensitive (participant 06). At first , everything went well , and they were very supportive. But because of the COVID-19 pandemic , the organization changed leadership , so we had to communicate with them again (participant 13).

Subtheme 3.3: packaging and distribution design

Subtheme 3.3.1: capability of packaging.

Participants indicated that integrating and packaging for non-research audiences was difficult and time-consuming and could be irregular and misleading, which calls for special competencies that differ from usual academic training.

It is demanding , requiring a high level of processing , summarizing , writing , and packaging skills. These are huge challenges that our daily training does not teach us (participant 12).

Subtheme 3.3.2: availability of distribution channels and platforms

The availability of channels and platforms was highlighted as an important contextual factor affecting dissemination. Those in the early stages of their careers, who had not yet established academic influence, expressed a lack of access to channels to interact with policymakers who were beyond the reach of individual researchers. Leveraging existing channels, platforms, and programs was recommended to facilitate dissemination to intended audiences.

Especially , we young researchers actually have many ideas and know a lot , but we do not have channels to share (participant 01). It is important to consider taking advantage of existing platforms or programs and hitching a ride whenever possible. Otherwise , dissemination involves a lot of financial and personnel input (participant 13).

Subtheme 3.4: push of research

Subtheme 3.4.1: incentives.

Academic publications were cited as the chief yardstick of performance evaluation, promotion requirements, and grant obligations. Some participants stated that the extent of dissemination to policymakers would also influence performance evaluation but were not given the same importance as academic publications. This was attributed by some participants to the difficulty in quantifiably evaluating dissemination activities. Although the China CDC participants expressed less pressure for academic publication than their university counterparts, they also complained about the academic incentive systems.

Dissemination to policymakers is now considered in performance evaluation , but still not as much as publishing papers on peer-reviewed journals… they may never regard dissemination as the most important criterion (participant 06). Currently , the value of science is still limited to publication and ‘Impact Factor’… Another problem is that it is difficult to define our dissemination efforts. For example , I cannot say how many people are using my APP and how much impact it burst , but I can say how many papers I have published in top journals (participant 11).

Subtheme 3.4.2: infrastructure

Seven participants reported having a dedicated person or team responsible for dissemination-related activities in their organization. These persons or teams served mainly for patent applications, communication, and publicity.

We have a Development Office dedicated for knowledge translation. They would organize seminars on dissemination like how to apply for patents (participant 14). The attitude of the communication platform in our school is very clear , and its purpose is to build prestige for our school. If we have proper research to disseminate , they will help with propaganda (participant 17).

Some participants mentioned that their organization would provide additional support, such as administrative facilitation, to help them disseminate more smoothly.

In addition to providing administrative costs , our university also provides intangible support for the development of D&I science and for the coordination of different departments (participant 16).

Subtheme 3.5: pull of practice

Participants noted a lack of climate or culture to support dissemination mainly because of the lack of priority given to some health issues themselves and the dissemination activities among leaders and practitioners.

The national government is advocating the dissemination and implementation of many innovations , but the local government may find it difficult to understand the value of (disseminating) these innovations and may not be unwilling to provide financial or personnel support (participant 10). We introduced our research and why we wanted to work with them to disseminate it , but they said that was not their focus. Then what was their focus at that time? All they wanted to do was help village doctors to pass a qualification exam and select the ‘most beautiful village doctor’. They were not interested in our dissemination of chronic diseases (participant 17).

Subtheme 3.6: capacity of health systems

Subtheme 3.6.1: communication networks.

The lack of networks between researchers and non-research audiences was cited as a barrier. Some researchers expected the health systems to build mechanisms for bidirectional communication networks between researchers and non-research audiences.

There is no mechanism to collaborate us with non-research audience… some researchers may have such relationships with non-research audiences , but that is out of their personal impact and efforts rather than the mechanisms in the health system (participant 02). There is a gap between researchers and policymakers in the academic system… maybe our organization could help bridge the gap. For example , the organization could build a system to collect our research findings regularly and disseminate to policymakers because universities have this kind of relationship with the government (participant 07).

Subtheme 3.6.2: executive networks

Executive network in the health system was considered necessary for dissemination on a large scale but difficult for ordinary university researchers to have. A participant in the China CDC pointed out that although the top-down CDC system in China, including CDCs at national, provincial, city, and county levels, could facilitate wide dissemination, their dissemination impact was still limited by the lack of human resources for public health.

Our dissemination success has benefited greatly from the solid executive network built before. For example , under the Chinese National Cancer Center , we have Cancer Prevention Offices at the provincial level. They could help us disseminate our research findings , like our evidence and apps. However , most researchers , especially university researchers , do not have such an objective support network (participant 11). The lack of human resources in public health is one of the most common problems in our country. For example , we have 40 staff working on tuberculosis at the China CDC , but only 10 at each provincial CDC , and 2 at each county CDC. In many cases , there are even half a person in counties working on tuberculosis (participant 10).

Subtheme 3.7: impact of research

Participants noted a chasm between overemphasis on academic publications and ignorance of long-term impact in the current academic system. Despite a series of national policies designed to break the undesirable orientation of “academic publications only” issued by the Chinese government, participants were pessimistic about them. They stated that the interpretation and implementation of these policies need to be further reviewed and improved.

Dissemination to non-research audiences is not expected by my organization , which does not care about these activities. However , it is the government that holds the baron , and there is nothing my organization can do about it. (participant 09). At present , national policies are developing and changing fast , but how to interpret and implement these policies needs to be gradually improved… our government is paying more and more attention to dissemination , but when it comes to the implementation level , there are still many shortcomings (participant 14).

This qualitative study explored the understanding and practices of dissemination, and further identified the barriers and facilitators of dissemination, which may be the first of this type in China. We found a lack of understanding of the concept and inadequate practices of dissemination to non-research audiences among Chinese public health researchers. We also identified barriers and facilitators in the conceptualization, design, dissemination, and impact phases of the D4D logic model [ 19 ], suggesting considerable room for improvement in the application of D4D strategies and the development of systematic resources. Our findings begin to provide a roadmap of ideas and actions to improve the active dissemination of research in China.

Dissemination was poorly understood by Chinese public health researchers, who confused it with some related concepts such as communication, science popularization, and knowledge translation, indicating a lag in the development and advocacy of dissemination in China. The lag in development and the lack of understanding of dissemination may hinder the dissemination practice and the uptake of evidence. Hence, dissemination, which highlights taking an active approach, identifying target audience, selecting predetermined channels, and using planned strategies to disseminate, should be deeply rooted in researchers’ mind to facilitate research uptake and understanding.

The public, practitioners, and policymakers were identified as three key non-research audiences for dissemination, yet most only gave a brief description when asked about their dissemination practices. While the internet and media are promising for large-scale dissemination, there is a need to strengthen the capacity of researchers to address misinformation and disinformation [ 28 , 29 ] and to facilitate collaboration between researchers and the media to achieve wide dissemination in China. Dissemination to the public and practitioners is considered as feasible and direct, while dissemination to policymakers as crucial for long-term impact. Indeed, the Chinese government holds accountability for the health of people, and proactively disseminating research findings to policymakers and government officials helps make a a greater public health impact. Nevertheless, the participants faced the dilemma of lacking personal relationships and access to channel to interact with policymakers. Although some academic associations (e.g., the Chinese Preventive Medicine Association) bring together researchers and practitioners in China, their potential to connect researchers and policymakers needs to be further strengthened to lead to dissemination success. Most of the participants with experience of dissemination in policy dissemination were those working in the China CDC or engaged in health policy research: the former stressed the mission of the China CDC to provide technical support for policy-making, and the latter stated that influencing policy was the fundamental goal of health policy research. This also suggests that organizations and researchers with stronger missions and resources to influence policy may have greater opportunities to disseminate to policymakers.

Although few in this study explicitly stated that dissemination to non-research audiences was not their priority, a lack of design capacity and distribution channels among researchers, insufficient support in organizations and the health systems, and an overemphasis on academic publications hindered dissemination to non-research audiences. First, there was a limited application of D4D strategies in the design of dissemination products, packaging and distribution plans. This is consistent with other studies suggesting that the lack of capacity was a common barrier to dissemination practice in low- and middle-income countries [ 30 ]. A good news was that Chinese researchers were actively involved diverse stakeholders at multiple stages of their research, which is consistent with the international trend of increasing emphasis on stakeholder engagement [ 31 , 32 ]. A survey of US and Canadian researchers in 2018 also revealed increases in stakeholder involvement compared to a survey of US researchers in 2012 [ 3 , 33 ]. However, there was a need to build multisectoral partnerships and improve stakeholder involvement’s depth and quality [ 32 ]. In addition, some researchers were aware of the potential for leveraging methods and frameworks from D&I science, marketing and business, communications and visual arts, and systems science to achieve dissemination success, yet the practical application needed to be improved. These disciplines (e.g., D&I science, marketing, systems science, and complexity science) originated from abroad and may not seem familiar to the Chinese public health researchers, it may require a lengthy learning and adaptation process. There are some simple tools and principles for guidance [ 34 ]. Notably, not all research finding should be disseminated to all audiences, the ability of deciding what to disseminate and to whom to disseminate should be strengthened in initial stage. Therefore, it is necessary to build capacity in the D4D principles and skills and to promote teaming across disciplines, as it may be unrealistic for public health researchers to develop all the D4D skills [ 13 ].

In addition to the need to improve researchers’ capacity and partnership across disciplines, there remained substantial room for improvement in the resources and structures that support dissemination. Specifically, there was a lack of incentives and infrastructure in research organizations (the push), a lack of climate and culture in practice or policy settings (the pull), and a lack of dissemination networks in the health system (the capacity). The persistent push–pull disconnect between researchers and practitioners was reported in other study [ 35 , 36 ]. As might have been expected, academic publications were the main criteria for performance evaluation, which may also be true in many other countries [ 10 , 14 , 33 , 37 , 38 , 39 ]. Furthermore, although some participants reported having a dedicated person or team for dissemination-related activities, the responsibilities of these dedicated persons or teams need to be further clarified and their capacity needs to be further enhanced. On the other hand, previous research points out that attention to dissemination tends to focus more on the push side than the pull and capacity sides [ 11 , 19 ]. For example, studies in the US suggested that 53% of researchers reported having a designated individual or team for dissemination [ 3 ] while only 20% of practitioners reported so [ 40 ]. Thus, changing the climate and culture in practice or policy settings to be receptive and prepared for dissemination, providing infrastructure to enhance communication between researchers and non-research audiences, and building executive networks to support wide dissemination are needed as a lack of platforms and collaboration mechanisms is also a common barrier to dissemination [ 30 ].

Problems with the lack of push, pull, and capacity for dissemination may be partly attributed to overemphasizing academic metrics rather than the long-term health and equity impacts. Several government funding agencies in developed countries have adopted policies to support or even require dissemination efforts [ 12 , 19 , 41 , 42 , 43 ]. Yet most funding agencies in China still focus on academic impact, existing fundings for dissemination in China are small in terms of its scale and are competitive to apply for. To address this issue, the Chinese government has adopted a series of national policies to reduce the overemphasis on academic publications and improve the evaluation system [ 44 , 45 , 46 , 47 ]. However, policy interpretation and grassroots implementation need to be further improved to accelerate the system shift to focus on the long-term impact of research. Frameworks such as the Research Excellence Framework (REF) [ 48 ] and the Translational Science Benefits Model (TSBM) [ 49 ] provide an outline and benchmarks by which researchers can measure the impact of scientific discoveries beyond traditional academic metrics.

This study revealed important aspects regarding research dissemination in China from the perspective of researchers with some limitations. First, 17 interview participants may not fully reflect the full spectrum in China although data saturation was reached. Given that dissemination is in its infancy in China, this study plays an initial study and future studies may need to involve more and more diversified participants to reveal dissemination of the whole research system in China. Second, some interviews were conducted online due to the COVID-19 pandemic, which limited the ability to gain information from contextual details and nonverbal expressions during the interviews. Third, the study is a qualitative exploratory study, additional large-scale quantitative studies are needed to triangulate the findings across the broader population. Indeed, the research team has run a large-scale survey to examine the attitudes and practices of Chinese public health researchers towards dissemination.

This study highlights a lack of emphasis on active dissemination in China and identifies multiple barriers to dissemination. There is a need to advance the field to promote understanding and raise awareness of dissemination—with the goal of ultimately more rapidly and equitably moving evidence to practice and policy. There is also a need to build capacity in D4D and to collaborate with experts from multiple disciplines (e.g., marketing, systems science, complexity science) to break down disciplinary silos. The findings also provide implications for promoting training programs, providing incentives and infrastructure for diverse dissemination activities, creating a climate and culture of readiness for dissemination, establishing bidirectional communication networks and efficient executive networks, and accelerating systematic shifts in policy orientation. Otherwise, dissemination is likely to sink to low priority in the already over-stretched system.

Data availability

All the data and materials of this qualitative study are available from the corresponding author on reasonable request.

Abbreviations

designing for dissemination

dissemination and implementation

National Chinese Center for Disease Control and Prevention

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Acknowledgements

We would like to acknowledge the support of all participants.

This work was supported in part by Disciplines Construction Project: Population Medicine (number WH10022022010) and Disciplines construction project: Multimorbidity (number WH10022022034). RCB is supported by the US National Cancer Institute (number P50CA244431), the National Institute of Diabetes and Digestive and Kidney Diseases (numbers P30DK092950, P30DK056341), and the Centers for Disease Control and Prevention (number U48DP006395), and the Foundation for Barnes-Jewish Hospital.

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Yiluan Hu, Xuejun Yin, Yachen Wang, Enying Gong, Ruitai Shao & Juan Zhang

The George Institute for Global Health, University of New South Wales, Newtown, NSW, Australia

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JZ, RS, and RCB obtained funding. JZ, RS, RCB, and YH were responsible for the conceptualization and design of the study. JZ, RS, YH, XY, EG, and XX developed the interview guide. JZ, YH, JL, and XL collected data. YH and YW analyzed the data. YH wrote the first draft. JZ, RCB, RS, YH, and YW edited the manuscript. All authors approved the final version for submission.

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Experiences and attitudes of psychiatric nurses in caring for patients with repeated non-suicidal self-injury in China: a qualitative study

Leiyu Yue 1 , 2 ,
Rui Zhao 3 ,
Yu Zhuo 1 ,
Xiaomin Kou 1 &
Jianying Yu 1

BMC Psychiatry volume 24 , Article number: 629 ( 2024 ) Cite this article

Metrics details

The incidence of non-suicidal self-injury (NSSI) is high and often occurs repeatedly. Psychiatric nurses play a vital role in the care and treatment of NSSI patients, as they have the most frequent contact with patients. The experiences and attitudes of nurses has a direct affect on the quality of care they provide to patients. Negative care experiences and attitudes of patient aversion on behalf of nurses may delay the observation and treatment of changes in the patient’s condition, leading to irreversible risks. Although cross-sectional studies have investigated the attitudes of medical staff toward NSSI patients, quantitative research results cannot comprehensively reflect the emotional experiences and complex psychological changes of the study subjects. A few studies have focused on the psychiatric nurses’ care experiences and attitudes toward patients with repeated NSSI.

This study aimed to explore psychiatric nurses’ care experiences and attitudes toward patients during repeated NSSI.

A thematic analysis qualitative study was used. Using purposive sampling, 18 psychiatric nurses were recruited from a mental health center in Chengdu, China. Semi-structured interviews were conducted and audio-recorded. Audio-recordings were transcribed verbatim and analyzed using six-phase thematic analysis.

Four themes emerged from the analysis: psychiatric nurses’ care experiences, perceptions, care attitudes and coping style toward repeated NSSI patients. Psychiatric nurses have experienced negative care experiences and severe career burnout during the patient’s repeated NSSI. Nurses’ attitudes toward NSSI patients changed during repeated NSSI, from understanding to indifference to anger and resentment. At the same time, it was found that nurses’ coping style with NSSI patients could be divided into three stages, namely, active coping, neglect and perfunctory, and criticism and punishment.

Conclusions

The findings have implications for health care systems regarding interventions to improve nurses’ care experiences and attitudes toward repeated NSSI patients. These findings suggest that enhancing nurses’ understanding of NSSI, establishing standardized emergency response and intervention programs, guiding positive professional values and responsibility, and improving nurses’ caring attitudes can promote the early detection and timely intervention of NSSI.

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Introduction

NSSI (non-suicidal self-injury) refers to the behavior of directly damaging one’s body tissue, such as self-harming, skin scratching, and self-burning [ 1 ]. NSSI usually starts in early adolescence with an estimated global prevalence of 17.6% in community samples [ 2 ], while it is as high as 40 ∼ 61% in clinical samples [ 2 , 3 ]. NSSI often occurs repeatedly [ 4 , 5 ]. A study with a community sample of Chinese adolescents, assessed every three months over two years with a self-report measure showed consistently NSSI of up to 69.2% [ 6 ]. Multiple episodes of NSSI can lead to serious health problems such as infection and even autoamputation of body tissues [ 7 ]. Additionally, the suicide risk of patients who engage in NSSI is hundreds of times higher than that of the general population [ 8 ]. This is concerning, as self-injury is the single most reliable predictor of suicidal ideation and attempts, with one-fourth of suicides preceded by acts of NSSI within the prior year [ 9 ]. NSSI has become an important issue affecting the health of adolescents. At present, NSSI has been included as an independent mental health issue in the 5th edition of the Diagnostic and Statistical Manual for Mental Disorders and is a major global public health issue [ 10 ].

A Canadian study with large sample ( n = 2038) of hospitalized children and adolescents showed that about 29% attempted to self-injury even when under care in a mental health setting [ 11 ]. Research shows that more than half of people who have experienced NSSI often experience repeated self-injury [ 5 ]. However, from clinical practice, these data may be higher. Due to its high detection rate, high risk, and high repeatability, this behavior has become one of the most important public health problems in the world [ 10 ]. The effective treatment of NSSI remains a prominent research focus within the field of psychiatry. No specific pharmacological intervention has demonstrated consistent efficacy in addressing NSSI [ 12 ]. Consequently, the current consensus among researchers emphasizes the primacy of psychological interventions in the treatment of this condition [ 13 ]. A systematic review and meta-analysis identified potential benefits of Dialectical Behavior Therapy (DBT) [ 14 ] in reducing the occurrence of NSSI in adolescents. However, the extended duration of this therapeutic approach precludes its application as an immediate crisis intervention strategy for hospitalized patients facing acute emergencies. An effective crisis intervention methods for self-harm are developed and evaluated on individuals with borderline personality disorder (BPD), such as Patient-Initiated Brief Admissions(PIBA) [ 15 , 16 ]. To date, no studies have been found on the application of PIBA to NSSI patients. The dearth of evidence-based practices for crisis intervention in NSSI cases is noteworthy. Healthcare professionals have expressed that they often feel powerless and helpless due to the lack of standardized, timely, and effective intervention programs for NSSI patients [ 17 ].

Psychiatric nurses play a vital role in the care and treatment of NSSI patients, as they have the most frequent contact with patients. They play a crucial role in distinguishing whether patients have attempted self-injury, preventing self-injury, guiding patients in psychotherapy, or providing other assistance within their capabilities. The incidence of repeated NSSI is high, and only about 50% of NSSI adolescents have self-disclosure behavior [ 18 ], which is difficult to identify and prevent, and poses great risks to patient safety. This has become an increasingly difficult point in nursing safety management, causing great distress and pressure to psychiatric nurses [ 19 ]. Besides, the behavior of patients with NSSI—including manipulation, self-mutilation, aggression, and noncompliance with treatment recommendations can challenge the therapeutic relationship [ 16 ]. Such patient behavior can impede the efforts of psychiatric nurses and give rise to feelings of frustration and anger as they try to understand the destructive behavior and emotional outbursts of these patients [ 15 ]. Although psychiatric nurses expect to maintain professionalism and provide more support to patients, regardless of the emotional weight of this situation, nurses caring for NSSI patients face the risk of empathy fatigue and even a change in attitudes toward patients, which can have a negative impact on their empathy and thus affect the patients’ experiences with care [ 20 ].

Attitude can be defined as a response to a stimulus that involves cognitive, affective and behavioral components, extending to all aspects of intelligence and behavior [ 21 ]. This response is an interior disposition that affects the selection of an action or behavior to be adopted toward persons, events or objectives [ 21 ]. The Knowledge-Attitude-Behavior (KAB) model also explained that knowledge is the foundation of behavior change, and belief and attitude are the driving force of behavior change [ 22 ]. The attitude of nurses seems to have a positive or negative impact on the care provided to NSSI patients [ 23 ]. Studies have shown that negative attitudes toward self-injury are associated with a lack of preparation among professionals and may reinforce self-injury-related stigma and discrimination and decrease the level of care provided to patients who attempt self-injury [ 24 ]. Research reports that NSSI patients describe their experiences with healthcare professionals during hospitalization as judgmental, non-listening, and lacking sufficient knowledge [ 25 ]. Nurses’ negative attitudes toward NSSI patients, such as indifference and antipathy, are key factors that hinder patients from continuing treatment and seeking further help. Negative attitudes, which are reported to significantly influence outcomes and are even connected to adverse events such as patient falls and medication errors, are especially problematic [ 26 ]. Therefore, the negative care experiences and aversion attitudes of nurses may delay the observation and treatment of changes in the patient’s condition, which may lead to irreversible risks.

Although cross-sectional studies have investigated the attitudes of medical staff toward suicide or NSSI patients [ 27 ], quantitative research results cannot comprehensively reflect the emotional experiences of the study subjects, and psychological measurement scales have difficulty measuring complex psychological changes. Furthermore, the use of self-report questionnaire could produce overly optimistic scores because negative attitudes are not in accordance with nurses’ professional self-images and social expectations [ 28 ]. Previous qualitative studies have mostly focused on interviews with NSSI patients [ 29 ], parents [ 30 ], and teachers [ 31 ]. It is currently unclear how Chinese psychiatric nurses’ care experiences and attitudes toward repeated NSSI patients, as well as how they affect coping style toward repeated NSSI patients. Understanding the care experiences and attitudes of psychiatric nurses toward NSSI patients plays an important role in preventing and reducing repeated self-injury. This study explores the care experiences and attitudes of psychiatric nurses toward patients with repeated NSSI. The study is conducted in the context of Chinese culture using qualitative research methods, and analyzes the difficulties and challenges in nursing practice for NSSI patients. The goal is to provide a research basis for further improving and enhancing nurses’ care attitudes, coping style, and support systems toward NSSI patients and to provide a more theoretical basis for the management of NSSI patients.

Study design

This qualitative study took a thematic analysis, which attempts to understand human experience and uncover meanings [ 32 ]. This approach is an appropriate and powerful method to use when seeking to understand a set of experiences, thoughts, or behaviors across a data set [ 33 ].

Research setting

The study was conducted from March 2023 to June 2023 at the mental health center of a tertiary hospital in Chengdu, Sichuan Province, China. The tertiary hospital we chose is the largest regional medical center in the city, and approximately 600 NSSI patients were hospitalized each year in the last 3 years. The mental health center has 5 wards with a total of 300 beds and approximately 120 nurses. Patients with NSSI may be admitted to any of these wards where the participants were selected and interviewed.

Participants and recruitment

This study employed a purposive sampling method with a maximum variation strategy to ensure a diverse range of perspectives. Participants eligible for this study were nurses who (a) possessed a professional nursing qualification certificate; (b) were able to independently execute overall responsibility-based nursing care; (c) had a primary role as a staff nurse (bedside) position; (d) had at least 1 year of clinical experience in psychiatry; and (e) had cared for NSSI patients at least once in the past year. Nurses who were on sick leave, personal leave, maternity leave, rotation, and continuing education during the study period were excluded. The sampling criteria were carefully designed to capture a broad spectrum of experiences and backgrounds among psychiatric nurses. Specifically, we sought to include participants across the following dimensions: age (ranged from 22 to 55 years old), educational background (e.g., bachelor’s, master’s, and doctoral degrees in nursing or related fields), professional title (junior nurses, senior nurses, and nurse-in-charge to capture varying levels of expertise and responsibility). The sample size was based on the principle of saturation of interview information, and a total of 18 psychiatric nurses who had previous experience in handling NSSI cases were ultimately interviewed.

Ethical considerations

This study was approved by the Ethics Review Committee of West China Hospital of Sichuan University. The research subjects all provided informed consent and voluntarily participated in this study. Participants were assured that their demographic data and interview responses would be confidential and anonymized. Addressing sensitive topics requires attention to the potential emotional impact the interview may have on the participant and the researcher [ 34 ]. During the interview, if participants have any discomfort (anxiety, irritability, etc.), they can terminate the interview at any time or even withdraw from the study. Additional measures included providing contact information for emotional and mental health resource follow-up.

Data collection

We conducted 18 one-on-one semi-structured interviews with participants in a psychotherapy room. Before the formal interview, the researcher first fully communicated with the participants, told them to record and record the conversation on the spot under the premise of protecting privacy, explained the purpose, method and content of the study in detail, and assured the participants that the interview contents were confidential and could be terminated at any time. The interview guide was developed based on our interests of study and research gaps identified from the literature review and was adjusted after analysis of the first three interviews. The interviews used open-ended questions and focused on 4 topics: (1) nurses’ responses and behaviors to patient NSSI; (2) nurses’ care experiences for repeated NSSI; (3) nurses’ perceptions about repeated NSSI; and (4) changes in nurses’ attitudes and behaviors during repeated NSSI. The interviews were semi-structured, following an interview guide (Table 1 ). Each interview lasted 30 to 45 min. No repeat interviews were conducted. Interviews were conducted in Mandarin or Sichuan dialect according to participants’ preferences.

Data analysis

Two researchers transcribed the speech data into text, and confirmed the transcribed interview contents with the participants. Nvivo 11 [ 35 ] software was used for classification and coding analysis. Thematic analysis was used, following the six-phase process described by Kiger [ 36 ]. The approach is inductive without a prior coding frame or the authors’ “analytical preconceptions” [ 32 ]. From the transcript, a number of themes emerged, along with relevant quotations from the interviews, in an inductive process using a realist epistemological perspective. This perspective assumes that the truthful experiences and attitudes of psychiatric nurses in caring for patients with repeated non-suicidal self-injury can be obtained. Themes and subthemes were compared between cases to determine if they needed to be refined according to their supportive data. The research team, together with advisory experts read through all data, scrutinized coding, and defined and redefined themes until the scope and content of each theme could be clearly and succinctly described and consensus was reached.

To ensure rigor and trustworthiness that the findings reflected participants’ words and perspectives, Lincoln and Guba’s criteria of credibility, dependability, confirmability and transferability were used in this study [ 37 ]. Strategies included two investigator discussions of coding decisions, maintaining field notes, and an audit trail (confirmability), participant checking and peer debriefing (credibility), an audit trail of coding decisions and theme development (dependability), and providing rich descriptions of participants, data collection process, and context (transferability) [ 38 ]. Considering the mental health counseling background of the two authors responsible for interviews, reflective journals were written for audit in order to reduce researcher bias.

Characteristics of the 18 participants are shown in Table 2 . 4 themes and 14 sub-themes emerged from the analysis (Table 3 ).

Care experiences of repeated NSSI

This theme describes some of the negative experiences experienced by psychiatric nurses in the course of caring for patients with repeated NSSI, including: empathetic fatigue, excessive psychological pressure, struggling on caring them, feeling confused and helpless, deteriorating nurse patient relationship and career frustration and burnout.

Feeling powerless and helpless

The nurses in this study clearly show that caring for patients with NSSI is more challenging than caring for other patients. They all work very hard to help patients but often feel powerless and helpless due to a lack of understanding, team support, and standardized intervention protocols.

A10: “Without an exact theoretical model and standard intervention program to guide us on how to properly deal with such patients , we lack confidence in the intervention process , and the intervention effect is far from ideal. ”

Respondents expressed the need for support from colleagues and leaders and wanted to talk with colleagues or leaders after patient incidents of self-injury. However; a lack of empathy from fellow staff members during these conversations made nurses feel even more isolated and criticized, causing them not to reach out and to distance themselves from potential sources of support, and thereby perpetuating the lack of emotional support.

A13: “When I reported this to colleagues and leaders or asked for help , I was often questioned or criticized by them , and thereafter I refused to report or communicate with them when no serious adverse events occurred.”

Excessive psychological pressure

Psychiatric nurses described high concealment of NSSI, that is, they were not able to predict when patients would commit NSSI, and the unpredictable consequences of NSSI, and nurses felt stressed during work. In addition, most of the nurses lost confidence in the potential for patients’ conditions to improve, and experienced feelings of pessimism and despair.

A4: “Since the behavior of NSSI patients is hidden and not easy to prevent , especially in the night shift , the manpower is relatively weak , and I always worry that NSSI patients will show NSSI behavior again when I am busy dealing with other things (which is always the case) , which makes me feel particularly stressed and in a state of mental tension.” A8: “My job is to give patients help and hope , but right now I’m the most desperate person on the ward.”

Empathetic fatigue

Almost all interviewees reported empathy fatigue after long-term care of patients with repeated NSSI. Psychiatric nurses described themselves have faced the pain and suffering of NSSI patients for a long time, may gradually lose their emotional empathy and care for them, and feel that their emotional resources are exhausted and emotionally exhausted. The nurses recounted spending a lot of time and energy to help NSSI patients solve problems or relieve painful emotions, while the patients’ self-injury behavior did not decrease, and even suffered from apathy and aggressive behavior. Over time, they felt that their sensory sensitivity gradually decreased, became numb to the patient’s pain or self-injury behavior, and felt that they became apathetic, and indifferent.

A1: “My child is 14 years old , which is similar to the age of these patients. At first , when I saw them hurt themselves , I would feel very distressed. I would patiently comfort and help them , just like helping my own children. However , not long after each incident , they will hurt themselves in different ways. I have become indifferent to them for a long time” . A2: “Well , what’s the point? No matter how hard you try , she will still respond the same way , which is to continue NSSI.”

Struggling on caring them

Most participants mentioned that although after facing the patient’s long-term repeated self-injury, they lost confidence in the patient’s treatment and became numb and indifferent to self-injury behavior. But they had a responsibility to help patients get better, so they struggling on caring them.

A7: “I know I should actively respond to patients’ self-injury and needs , but I am struggling internally.” A14: “I sometimes feel like I’m wasting my time dealing with repeated self-injury , but I still have to deal with it!”

Deteriorating nurse patient relationship

Some interviewed nurses stated that they were embarrassed to admit that they had difficulty establishing a collaborative relationship with NSSI patients. Others stated directly that their tolerance for repeated NSSI patients is low, and that they hold a moral critical attitude toward patients with repeated NSSI. They may blame the patients and are less willing to sympathize and understand them, which will distance the nurse from the patient. In addition, patients gave nurses feedback that they thought nurses did not understand why they hurt themselves, and even blamed and criticized them, which led to patients’ reluctance to maintain communication with nurses, resulting in increasingly distant nurse-patient relationship.

A9: “I know for sure that one’s patience is limited , even though I am a nurse (maintaining a high degree of professional responsibility). When I tried to help a NSSI patient , he still repeatedly demonstrated self-injury behavior. In anger I said to the patient: I am too disappointed in you! He could also sense that I was losing patience with him and began to distance himself from me.”

Career frustration and burnout

Respondents reported that when some repeated NSSI patients are admitted to the ward, they required too much time to prevent, control, and deal with their self-injury. However, even if nurses spent a lot of time and effort helping them, the results were often disappointing. Most respondents said that the long-term impact of patients’ unreasonable demands, destructive behavior, repeated NSSI, and repeated treatment led to a lack of confidence and frustration for nurses, resulting in more serious burnout.

A7: “In the face of NSSI patients , we tried everything we could think of , including the application of some psychological therapy techniques , but later , it was still to no avail , and I felt my energy was exhausted , and I gradually lost my passion for this job.”

Perceptions of repeated NSSI

Nurses have a vague perception of NSSI, meaning that nurses are not sure what the real reason and purpose behind each patient’s self-injury is. However, in clinical practice, they often treat NSSI as a way for patients to reduce mental distress and/or meet needs.

Coping strategy for reducing mental distress

A common feature of the interviews was that psychiatric nurse related NSSI to mental health and portrayed it as a maladaptive coping strategy for reducing mental distress.

A2: “ Although I’m not sure why they hurt themselves, I have heard from patients who say that NSSI makes them feel better , or that the physical pain caused by NSSI drives away or relieves their mental pain. ” A17: “When adolescent patients are emotional or depressed , they may choose to relieve their emotions through self-injury , because they lack other coping methods.”

A way to meet a need

Some psychiatric nurses also see NSSI as a way or tool for adolescents to meet needs such as getting attention, eliciting sympathy, or manipulating caregivers (parents or medical staff). In the hospital, some adolescents commit self-injury when medical personnel refuse their request, and some NSSI become more serious or dangerous.

A6: “They threaten parents with self-harm at home , and they threaten staff in the same way at hospitals to meet their demands.” A17: “It is common to encounter NSSI patients who ask to smoke , call their parents or even leave the hospital , and if their requests are not met , they will go to NSSI.”

Care attitudes of repeated NSSI

The care attitudes of psychiatric nurses toward NSSI patients changes with the recurrence of NSSI. The attitudes described by the nurses toward patients with NSSI fluctuated between understanding, sympathy, and anger. This change in attitude may stem from the experience of caring for patients with NSSI as well as perceptions of NSSI.

Understanding and sympathy

Some respondents said that for NSSI patients, self-injury is not only a way to express bad feelings but also a strategy to cope with negative feelings, which is why they choose hospitalization. Patients who commit NSSI have also told nurses that it made them feel better or that the physical pain caused by NSSI drove away or lessened their mental pain. Psychiatric nurses express empathy for the patients’ self-injury, and actively try to help patients.

A1: “When I saw their wounds , I was very shocked by how much pain they must have in their hearts to cause them to treat themselves in such a way , it was painful to watch.” A15: “At the moment of their self-injury, they may be very desperate and uncomfortable.”

Apathy and escape

Most respondents mentioned that they have the responsibility to help patients cope with adverse emotions or meet their reasonable requirements, but with the increasing frequency of repeated NSSI or the increasing severity of NSSI results, nurses gradually lose confidence and patience in the treatment of patients and become numb and indifferent to their self-injury.

A11: “When I tried to stop them from hurting themselves , they spoke harshly to me and even attacked me. At this time , I would feel very sad because good intentions would not be rewarded. So that in the later stage , I would ask my colleagues to deal with it , as I did not want to face such patients.” A16: “I know it was not a suicide attempt. Otherwise , the scratch would not be so shallow. I do not even want to care why she continues to do this.”

Antipathy and resentment

Some interviewed nurses described that their tolerance for repeated NSSI patients is low, and that they hold a moral critical attitude toward patients with repeated NSSI. They may blame the patients and are less willing to sympathize and understand them, which can play a role in recurrent attacks or lack of improvement in the patient’s condition. At the same time, interviewed nurses believe that repeated NSSI behavior is a manifestation of intentional manipulation or excessive dependence, causing a serious loss of empathy for patients and resentment among medical staff.

A7: “There are plenty of alternative ways in our ward to help her relieve her mood , but she still repeatedly took the way we forbid , which made me very unacceptable and feel frustrated and even caused me to show antipathy.” A10: “They threaten their parents with NSSI at home , and they threaten their staff in the same way at the hospital. This behavior and motivation make me very resentful.”

Psychiatric nurses stated that NSSI seems to demonstrate a phenomenon of human-to-human transmission. This may be related to the tendency of teenagers to accept negative information that promotes NSSI and imitates others’ NSSI behavior [ 39 ]. In addition, it is not difficult to find in clinical practice that patients with long-term repeated NSSI often try to persuade more adolescents to participate in self-injury. Nurses resents these types of initiators because they make it difficult to manage the entire ward.

A6: “When there is a repeatedly hospitalized NSSI patient in the ward , a group of new NSSI patients will emerge at this time. I will be very angry when I find out through tracking that the NSSI behavior of the newly admitted patients is learned from the veteran NSSI patients.”

Coping style of repeated NSSI

Through interviews, we found that psychiatric nurses’ responses to repeated NSSI can be divided into three stages: actively respond, neglect and perfunctory, criticism and discipline. Behind the variations in coping styles at each stage are changes in nurses’ cognition and attitudes toward NSSI patients.

Actively respond

Some interviewed nurses believe that for teenagers, NSSI is not only a way to express negative emotions but also a strategy to deal with negative emotions. Psychiatric nurses express understanding of their behavior, actively explore alternative ways to help alleviate negative emotions, and provide suggestions on how to deal with triggering factors.

A11: “I will ask them about their feelings and reasons behind NSSI , express concerns about potential health issues that may arise later , and provide advice on how to respond to negative emotions and alternative approaches , as well as how to deal with triggering factors.” A9: “Although I sometimes feel disappointed and resentful about patients’ long-term and repeated NSSI behaviors , my responsibility is still to actively handle patients’ problems.”

Neglect and perfunctory

Most respondents mentioned that with the increasing frequency of repeated NSSI or the increasing severity of NSSI results, they gradually lose confidence and patience in the treatment of patients and become numb and indifferent to their self-injury, and sometimes ignored and perfunctory NSSI behaviors of patients. Besides, when nurses believe that NSSI is a way for teenagers to meet their needs or receive attention, and think that NSSI are not causing serious health problems, they will choose to ignore the patient’s self-injurious behavior and believe that reducing attention can help the regression of NSSI.

A2: “I expressed my powerlessness and helplessness toward his performance , and I have become emotionally numb. Sometimes when I hear NSSI patients calling for help , I delay the response.” A15: “I believe that any method is not helpful in preventing NSSI. In contrast , excessive attention will only reinforce their self-injury.”

Criticism and punishment

Almost all respondents mentioned that repeated NSSI of patients results in a serious loss of empathy among psychiatric nurses toward patients, and causes aversion and resentment among psychiatric nurses. When nurses believe that teenagers’ NSSI is intentional, they may try other approaches to preventing teenagers from engaging in NSSI. One approach is criticism, and the other is punishment. Criticizing and punishing this behavior is to prevent it from happening again. In traditional Chinese culture, criticism and punishment are also very common and practical educational methods.

A17: “ If a patient attempts to manipulate us using NSSI (such as requesting discharge) , I will tell them which approach is incorrect and express my anger. In addition , warn them that if similar behavior occurs again , I will restrain them.”

Some participants also expressed that when they learned that the patient had experienced NSSI again, they found it difficult to control their emotions and expressed anger toward the patient, as they had invested a lot of energy and emotions in exchange for repeated NSSI and aggressive behavior.

A3: “ Do you know? After spending a lot of time and energy guiding her on how to deal with negative emotions , shortly after the conversation ended , she hurt herself again , which made me very angry. I blamed and criticized her behavior.”

Some interviewed nurses feel strongly that punishment is a very effective way of discipline. From the perspective of immediately stopping bad behavior, punishment is often effective. For psychiatric nurses, ensuring patient safety is the primary task of their work. Protective restraint is a coping method that can have an immediate effect on preventing patients from engaging in NSSI. Another reason why psychiatric nurses use punitive measures is that they are concerned that not punishing them will only pamper adolescent patients and cause them to worry about losing control of the patients. Moreover, punishment is easy and often a “reactive” response.

A9: “When my patients engage in NSSI , I will intervene with protective restraints because they are violating the basic principles of mutual respect and trust. “ . A18: “I know that adopting protective renstraints on patients with repeated NSSI is often a disguised punishment. However , when faced with patients’ bad behavior , I still fall into the old habit of punishment.”

Punishment is often effective at stopping bad behavior in the short term, but the problem is that the nurses are not aware of the long-term effects of punishment. Nevertheless, the primary reason psychiatric nurses insist on using punitive measures is that they do not know what else to do.

A12: “I do not think restraints are a very effective intervention measure , but we have tried various ways and methods , but still have not effectively solved the problem of NSSI , and I do not know how to deal with it.”

It is necessary to discuss the experiences and attitude of psychiatric nurses who care for patients with repeated NSSI, because the nurse’s attitude toward patients seems to have an impact (positive or negative) on the quality of patient care. For the first time, this study explored the care experience and attitude of Chinese psychiatric nurses toward repeated NSSI patients through qualitative research methods. Four themes were identified through interviews: care experience, perceptions, care attitude and coping style toward repeated NSSI. Six kinds of care experience were identified as: feeling powerless and helpless, excessive psychological pressure, empathetic fatigue, struggling on caring them, deteriorating nurse patient relationship and career frustration and burnout.

In this study, we found that psychiatric nurse felt excessive psychological pressure in the face of NSSI. Hadfield et al. [ 40 ] similarly show that doctors view treating people with self-injury as a futile task that causes feelings of despair and frustration. In recent years, the proportion of NSSI patients within psychiatric inpatient populations has been increasing [ 41 ]. However, due to the incidence of repeated NSSI is high, and only about 50% of NSSI adolescents have self-disclosure behavior [ 18 ] which is difficult to identify and prevent, and poses great distress and pressure to psychiatric nurses, and strong negative emotions such as anxiety and despair. These outcomes have been widely reported in the literature [ 42 , 43 ]. In addition, although many of the nurses in this study had cared for a large number of NSSI patients, they indicated that they did not have an in-depth understanding of NSSI and sometimes had to improvise patient guidance and interventions based on their own experience, with little effect. Nurses make it clear that they work very hard to help NSSI patients but often feel powerless and helpless by the lack of standardized, timely and effective intervention programs. Without a theoretical model to guide this work, the effect of the intervention is very limited, similarly to reports by Kickan [ 43 ]. To address this issue, a novel intervention known as patient-initiated brief admission (PIBA) has been developed. PIBA is a psychiatric nursing intervention on the basis of the theoretical concepts of patient participation, shared decision-making and patient autonomy [ 15 , 44 ]. The aim of PIBA is to promote constructive coping strategies when increased anxiety and thoughts of self-harm become unmanageable [ 45 ]. At present, PIBA is mainly used in BPD patients with emotional instability and self-injury, and the effect is significant [ 46 ]. Furthermore, a study suggested that BA may reduce work-related stress experienced by nurses while caring for persons with emotional instability and self-harm [ 46 ]. Therefore, future studies can be combined with PIBA to provide a constructive crisis management approach for NSSI. During a crisis, this easily accessible care option has the potential to prevent harm to patients and reduce the stress on nurses caring for patients with NSSI patients.

A noteworthy finding of this study was that psychiatric nurses experienced empathy fatigue and career burnout in the process of caring for repeated NSSI patients. Psychiatric nurses recurrently witness repeated self-injury, unreasonable demands, destructive behaviors and repeated hospitalization of NSSI patients. In response, nurses gradually lose patience with patients, become frustrated, and lack confidence in their ability to treat patients. Psychiatric nurses recounted spending a lot of time and energy to help NSSI patients solve problems or relieve painful emotions, while the patients’ self-injury behavior did not decrease, and even suffered from apathy and aggressive behavior [ 47 ]. Such patient behavior can impede the efforts of the nurses and give rise to feelings of frustration and anger in psychiatric nurses who try to understand the destructive behavior and emotional outbursts of such patients [ 15 ]. This enormous and lasting psychological gap leads to the empathy fatigue of nurses for patients. A meta-analysis shows that mental health nurses face a sense of low value, heavy pressure and hopelessness for a long time, which can easily lead to career burnout [ 48 ]. Burnout not only deteriorates nurses’ work performance but also adversely influences their health and well-being [ 49 ]. Therefore, it is suggested that clinical managers should introduce effective strategies and courses(such as mindfulness-based stress reduction [ 50 ])to reduce nurse burnout while paying attention to staff mental health and stress management. In addition, support for nurses is essential because nurses often encounter distressing situations when caring for NSSI patients. It is recommended that nurses have access to mental health training, mental health liaison teams in acute trusts, and managerial support [ 51 ].

Research has also shown that although psychiatric nurses expect and strive to maintain professionalism and provide as much support as possible to patients, the specific experiences (empathy fatigue, anxiety, hopelessness, etc.) of nurses working in high-stress environments for NSSI patients negatively affects their empathy and willingness to care. However, driven by responsibility and leadership, so they struggling on caring repeated NSSI patients. However, it is worth noting that most participants mentioned that although after facing the patient’s long-term repeated self-injury, they lost confidence in the patient’s treatment and became numb and indifferent to self-injury behavior. In other words, psychiatric nurses are at risk of reduced or lack of sense of responsibility when caring for patients with repeated NSSI. Responsibility guides nurses to pay attention to patients’ emotional changes, seek information, prioritize care, and develop responses [ 52 ]. Reduced or absent accountability can delay the observation and recognition of changes in a patient’s condition, with serious consequences [ 53 ]. People who self-harm present at healthcare services during times of crisis, with the potential intention of suicide. It is, therefore, imperative that services for this vulnerable group are delivered with compassion and in a non-judgmental manner. This is an ethical issue in nursing practice, particularly as maintaining a non-judgmental and positive attitude is a core nursing value [ 54 ]. This finding highlights the need to support the development of nurses’ professional values and sense of responsibility throughout their education and career.

Psychiatric nurses have a vague perception of NSSI, meaning that nurses are not sure what the real reason and purpose behind each patient’s self-injury is. However, in clinical practice, they often treat NSSI as a way for patients to reduce mental distress and/or meet needs (such as getting attention, eliciting sympathy, or manipulating caregivers). This is in line with multiple studies and theories on NSSI [ 40 , 42 , 43 ]. One of the things we must note is that when psychiatric nurses also see NSSI as a way or tool for adolescents to meet needs, it appears to label patients. Labels can carry harmful, implicit biases that negatively affect clinical outcomes for the people they describe [ 55 , 56 ]. For example, our study found some psychiatric nurses will choose to ignore the patient’s self-injury and believe that reducing attention can help the regression of NSSI when they believe that NSSI is a way to meet needs or get attention. Similarly, a previous study found that medical staff see NSSI as an attention-seeking behaviors in emergency department, which is why they refrained from giving too much attention to NSSI patients [ 40 ]. Owens et al. [ 57 ] study found that when care providers withheld sufficient attention to self-injuries patients, it can lead to a tendency to avoid seeking help in the future. Refusing to seek help may miss the best time to receive psychological intervention, with irreversible consequences. In some cases, hold a moral critical attitude toward NSSI among nurse may delayed NSSI patients for proper treatment. Therefore, improving psychiatric nurses’ understanding of NSSI may avoid labeling patients and reduce biased statements.

The study found that the nurse-patient relationship gradually deteriorated in the process of patients with repeated NSSI. The nurses in this study stated that their tolerance for repeated NSSI patients is low, and that they hold a moral critical attitude toward patients with repeated NSSI. They may blame the patients and are less willing to sympathize and understand them, which will distance the nurse from the patient. Research reports that NSSI patients describe their experiences with healthcare professionals during hospitalization as judgmental, non-listening, and lacking sufficient knowledge [ 25 ]. In fact, the relationship between psychiatric care providers and patients is often described as challenging [ 17 ]. Psychiatric nurses often encounter patients experiencing acute crises, a phase in which patients may express anger, self-harm, and have suicidal ideations. In response to such conflict-prone situations, coercive measures such as physical restraint, forced medication, and seclusion are often applied against the patient’s will to ensure the safety of both patients and staff [ 58 ]. However, the use of coercive measures may make patients hostile to nurses and destroy the nurse-patient relationship. From this, we can infer that the reason for the gradual deterioration of the nurse-patient relationship is that the negative feelings and misunderstandings toward each other remain from both sides. Therefore, the authors of this study suggest that future quantitative studies could be used to investigate how common this phenomenon is and to explore how to develop a more collaborative nurse-patient relationship.

In terms of attribution, this study showed that the caring attitude of psychiatric nurses toward NSSI patients changes with the recurrence of NSSI. As NSSI repeats more frequently or NSSI results become increasingly severe, the attitude of nurses toward NSSI patients changes from understanding to indifference to anger and resentment. However; in previous studies, the dynamic changes and depth of this care attitude may have been masked [ 27 ]. After the nurses invested a great deal of emotion in exchange for the patient’s repeated self-injury, they consumed the nurse’s patience and compassion, accompanied by a change in attitude. Although nurses acknowledge that ‘understanding can generate empathy’ and describe the importance of understanding patients to gain more empathy, they also acknowledge that it is difficult to truly understand NSSI because they do not easily accept this coping style. In clinical practice, nurses’ capacity for empathy is also easily affected by patients’ negative emotions and aggressive behaviors, resulting in antipathy and aversion toward patients [ 59 ]. Although nurses are well aware that NSSI is a disorder that requires psychological assistance, many staff members may inadvertently revert to indifference, anger, or resentment when they encounter repeated NSSI if they do not fully understand the patterns and mechanisms of repeated NSSI. Therefore, this finding emphasizes the importance and urgency of studying the mechanism of NSSI and the pattern of repeated NSSI. Further research on the educational intervention that are most effective in improving the attitudes of psychiatric nurses towards patients with NSSI would be valuable.

Our study also highlights that how nurses respond to NSSI patients is not simply determined by perceptions of NSSI, and that nurses’ attitudes towards patients are another strong predictor. An attitude is not specifically a behavior but instead an inclination toward actions, modes or ways to address, react or face a situation or problem in a variety of circumstances [ 21 ]. The KAB model elaborates that a person’s knowledge directly affects their attitude and indirectly affects their behavior through their attitude [ 22 ]. With this model, it can be assumed that the knowledge of psychiatric nurses about NSSI affects their attitudes toward it, and that their attitudes affect the actions (behaviors) they take. In this study, nurses who understood and sympathized with patients’ self-injurious behavior in the past were more likely to mediate, avoid conflict, and cooperate with patients. Nurses who had expressed indifference and resentment to patients’ self-injurious behavior in the past were more likely to deny, be perfunctory, or criticize and punish them. Social psychology believes that attitude is the most important breakthrough point in influencing strategies [ 60 ]. The ultimate goal of social influence may be behavioral change, but reaching this ultimate goal requires a complex channel of attitude. Only by mobilizing a person’s real and implicit attitude and exerting his subjective initiative can he affect his behavior [ 61 ]. Therefore, relying solely on training and guidelines might prove inadequate in enhancing the care provided to individuals with NSSI. Ideally, comprehensive competence development should encompass not only the acquisition of skills but also the cultivation of awareness regarding attitudes and the promotion of reflection on the nurses’ influence on the patients’ trajectory.

Strengths and limitations

This is the first qualitative study to explore the experiences and attitudes of psychiatric nurses in caring for patients with repeated NSSI in China. Despite the promising findings of this study, there were several limitations. As with all qualitative research, the findings are limited by self-reporting and are subjected to social desirability bias. This was a relatively small local study; therefore, the findings are not necessarily representative of a broader cross section of psychiatric nurses. The findings may not be transferable to nurses working in other types of hospitals, cultures or countries. In addition, this study explored nurses’ caregiving experiences and attitudes towards repeated NSSI patients from the perspective of nurses, and did not explore other factors that might affect nurses’ caregiving experience and attitude towards patients, such as their own personality traits and coping styles. One limitation of this study is that nurses’ caregiving experiences and attitudes toward NSSI patients with different diagnoses and characteristics were not separately discussed. It is hoped that future studies can make up for the above limitations. Despite this, the recurring nature of key phrases and words lends weight to the strength and credibility of the findings. Several factors add to the trustworthiness of the study’s findings. The investigators used an established method of data analysis and several strategies (included two investigator discussions of coding decisions, maintaining field notes, and an audit trail, participant checking and peer debriefing, an audit trail of coding decisions and theme development, and providing rich descriptions of participants, data collection process, and context) to enhance the trustworthiness of the findings. Data collection continued until saturation was reached and confirmed by two investigators. Analysis of the findings was validated by three investigators and by several participants of the study.

Through interviews with psychiatric nurses, this study found that psychiatric nurses have experienced negative care experiences and severe career burnout during the patient’s repeated NSSI. It is of concern that psychiatric nurses are not sure what the real reason and purpose behind each patient’s self-injury is. Nurses’ attitudes toward NSSI patients changed during repeated NSSI, from understanding to indifference to anger and resentment. At the same time, it was found that psychiatric nurses’ coping style with NSSI patients could be divided into three stages. Behind the changes in coping style at each stage were the changes in nurses’ attitudes toward repeated NSSI patients. These findings suggest that enhancing nurses’ understanding of NSSI behavior, establishing standardized emergency response and intervention programs, guiding positive professional values and responsibility, and improving nurses’ caring attitudes can promote the early detection and timely intervention of NSSI.

Data availability

The primary author is willing to share all of the transcribed interview data upon request.

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Yue and Yu were responsible for study design, interviewing participants face-to-face. Yue and Zhao were responsible for data analysis. Zhuo and Kou were responsible for recruiting and screening participants and transcribing interviews verbatim. Yue wrote the main manuscript and all authors reviewed the manuscript. The author(s) read and approved the final manuscript.

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Yue, L., Zhao, R., Zhuo, Y. et al. Experiences and attitudes of psychiatric nurses in caring for patients with repeated non-suicidal self-injury in China: a qualitative study. BMC Psychiatry 24 , 629 (2024). https://doi.org/10.1186/s12888-024-06064-9

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1 School of Social Work, University of Southern California, Los Angeles, CA 90089-0411

Sarah M. Horwitz

2 Department of Child and Adolescent Psychiatry, New York University, New York, NY

Carla A. Green

3 Center for Health Research, Kaiser Permanente Northwest, Portland, OR

Jennifer P. Wisdom

4 George Washington University, Washington DC

Naihua Duan

5 New York State Neuropsychiatric Institute and Department of Psychiatry, Columbia University, New York, NY

Kimberly Hoagwood

Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods. This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.

Recently there have been several calls for the use of mixed method designs in implementation research ( Proctor et al., 2009 ; Landsverk et al., 2012 ; Palinkas et al. 2011 ; Aarons et al., 2012). This has been precipitated by the realization that the challenges of implementing evidence-based and other innovative practices, treatments, interventions and programs are sufficiently complex that a single methodological approach is often inadequate. This is particularly true of efforts to implement evidence-based practices (EBPs) in statewide systems where relationships among key stakeholders extend both vertically (from state to local organizations) and horizontally (between organizations located in different parts of a state). As in other areas of research, mixed method designs are viewed as preferable in implementation research because they provide a better understanding of research issues than either qualitative or quantitative approaches alone ( Palinkas et al., 2011 ). In such designs, qualitative methods are used to explore and obtain depth of understanding as to the reasons for success or failure to implement evidence-based practice or to identify strategies for facilitating implementation while quantitative methods are used to test and confirm hypotheses based on an existing conceptual model and obtain breadth of understanding of predictors of successful implementation ( Teddlie & Tashakkori, 2003 ).

Sampling strategies for quantitative methods used in mixed methods designs in implementation research are generally well-established and based on probability theory. In contrast, sampling strategies for qualitative methods in implementation studies are less explicit and often less evident. Although the samples for qualitative inquiry are generally assumed to be selected purposefully to yield cases that are “information rich” (Patton, 2001), there are no clear guidelines for conducting purposeful sampling in mixed methods implementation studies, particularly when studies have more than one specific objective. Moreover, it is not entirely clear what forms of purposeful sampling are most appropriate for the challenges of using both quantitative and qualitative methods in the mixed methods designs used in implementation research. Such a consideration requires a determination of the objectives of each methodology and the potential impact of selecting one strategy to achieve one objective on the selection of other strategies to achieve additional objectives.

In this paper, we present different approaches to the use of purposeful sampling strategies in implementation research. We begin with a review of the principles and practice of purposeful sampling in implementation research, a summary of the types and categories of purposeful sampling strategies, and a set of recommendations for matching the appropriate single strategy or multistage strategy to study aims and quantitative method designs.

Principles of Purposeful Sampling

Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources ( Patton, 2002 ). This involves identifying and selecting individuals or groups of individuals that are especially knowledgeable about or experienced with a phenomenon of interest ( Cresswell & Plano Clark, 2011 ). In addition to knowledge and experience, Bernard (2002) and Spradley (1979) note the importance of availability and willingness to participate, and the ability to communicate experiences and opinions in an articulate, expressive, and reflective manner. In contrast, probabilistic or random sampling is used to ensure the generalizability of findings by minimizing the potential for bias in selection and to control for the potential influence of known and unknown confounders.

As Morse and Niehaus (2009) observe, whether the methodology employed is quantitative or qualitative, sampling methods are intended to maximize efficiency and validity. Nevertheless, sampling must be consistent with the aims and assumptions inherent in the use of either method. Qualitative methods are, for the most part, intended to achieve depth of understanding while quantitative methods are intended to achieve breadth of understanding ( Patton, 2002 ). Qualitative methods place primary emphasis on saturation (i.e., obtaining a comprehensive understanding by continuing to sample until no new substantive information is acquired) ( Miles & Huberman, 1994 ). Quantitative methods place primary emphasis on generalizability (i.e., ensuring that the knowledge gained is representative of the population from which the sample was drawn). Each methodology, in turn, has different expectations and standards for determining the number of participants required to achieve its aims. Quantitative methods rely on established formulae for avoiding Type I and Type II errors, while qualitative methods often rely on precedents for determining number of participants based on type of analysis proposed (e.g., 3-6 participants interviewed multiple times in a phenomenological study versus 20-30 participants interviewed once or twice in a grounded theory study), level of detail required, and emphasis of homogeneity (requiring smaller samples) versus heterogeneity (requiring larger samples) ( Guest, Bunce & Johnson., 2006 ; Morse & Niehaus, 2009 ; Padgett, 2008 ).

Types of purposeful sampling designs

There exist numerous purposeful sampling designs. Examples include the selection of extreme or deviant (outlier) cases for the purpose of learning from an unusual manifestations of phenomena of interest; the selection of cases with maximum variation for the purpose of documenting unique or diverse variations that have emerged in adapting to different conditions, and to identify important common patterns that cut across variations; and the selection of homogeneous cases for the purpose of reducing variation, simplifying analysis, and facilitating group interviewing. A list of some of these strategies and examples of their use in implementation research is provided in Table 1 .

Purposeful sampling strategies in implementation research

Strategy	Objective	Example	Considerations
Emphasis on similarity
Criterion-i	To identify and select all cases that meet some predetermined criterion of importance	Selection of consultant trainers and program leaders at study sites to facilitators and barriers to EBP implementation ( ).	Can be used to identify cases from standardized questionnaires for in- depth follow-up ( )
Criterion-e	To identify and select all cases that exceed or fall outside a specified criterion	Selection of directors of agencies that failed to move to the next stage of implementation within expected period of time.
Typical case	To illustrate or highlight what is typical, normal or average	A child undergoing treatment for trauma ( )	The purpose is to describe and illustrate what is typical to those unfamiliar with the setting, not to make generalized statements about the experiences of all participants ( ).
Homogeneity	To describe a particular subgroup in depth, to reduce variation, simplify analysis and facilitate group interviewing	Selecting Latino/a directors of mental health services agencies to discuss challenges of implementing evidence- based treatments for mental health problems with Latino/a clients.	Often used for selecting focus group participants
Snowball	To identify cases of interest from sampling people who know people that generally have similar characteristics who, in turn know people, also with similar characteristics.	Asking recruited program managers to identify clinicians, administrative support staff, and consumers for project recruitment ( ).	Begins by asking key informants or well- situated people “Who knows a lot about…” (Patton, 2001)
Extreme or deviant case	To illuminate both the unusual and the typical	Selecting clinicians from state agencies or mental health with best and worst performance records or implementation outcomes	Extreme successes or failures may be discredited as being too extreme or unusual to yield useful information, leading one to select cases that manifest sufficient intensity to illuminate the nature of success or failure, but not in the extreme.
Emphasis on variation
Intensity	Same objective as extreme case sampling but with less emphasis on extremes	Clinicians providing usual care and clinicians who dropped out of a study prior to consent to contrast with clinicians who provided the intervention under investigation. ( )	Requires the researcher to do some exploratory work to determine the nature of the variation of the situation under study, then sampling intense examples of the phenomenon of interest.
Maximum variation	Important shared patterns that cut across cases and derived their significance from having emerged out of heterogeneity.	Sampling mental health services programs in urban and rural areas in different parts of the state (north, central, south) to capture maximum variation in location ( ).	Can be used to document unique or diverse variations that have emerged in adapting to different conditions ( ).
Critical case	To permit logical generalization and maximum application of information because if it is true in this one case, it’s likely to be true of all other cases	Investigation of a group of agencies that decided to stop using an evidence-based practice to identify reasons for lack of EBP sustainment.	Depends on recognition of key dimensions that make for a critical case. Particularly important when resources may limit the study of only one site (program, community, population) ( )
Theory-based	To find manifestations of a theoretical construct so as to elaborate and examine the construct and its variations	Sampling therapists based on academic training to understand the impact of CBT training versus psychodynamic training in graduate school of acceptance of EBPs	Sample on the basis of potential manifestation or representation of important theoretical constructs. Sampling on the basis of emerging concepts with the aim being to explore the dimensional range or varied conditions along which the properties of concepts vary.
Confirming and disconfirming case	To confirm the importance and meaning of possible patterns and checking out the viability of emergent findings with new data and additional cases.	Once trends are identified, deliberately seeking examples that are counter to the trend.	Usually employed in later phases of data collection. Confirmatory cases are additional examples that fit already emergent patterns to add richness, depth and credibility. Disconfirming cases are a source of rival interpretations as well as a means for placing boundaries around confirmed findings
Stratified purposeful	To capture major variations rather than to identify a common core, although the latter may emerge in the analysis	Combining typical case sampling with maximum variation sampling by taking a stratified purposeful sample of above average, average, and below average cases of health care expenditures for a particular problem.	This represents less than the full maximum variation sample, but more than simple typical case sampling.
Purposeful random	To increase the credibility of results	Selecting for interviews a random sample of providers to describe experiences with EBP implementation.	Not as representative of the population as a probability random sample.
Nonspecific emphasis
Opportunistic or emergent	To take advantage of circumstances, events and opportunities for additional data collection as they arise.		Usually employed when it is impossible to identify sample or the population from which a sample should be drawn at the outset of a study. Used primarily in conducting ethnographic fieldwork
Convenience	To collect information from participants who are easily accessible to the researcher	Recruiting providers attending a staff meeting for study participation.	Although commonly used, it is neither purposeful nor strategic

Embedded in each strategy is the ability to compare and contrast, to identify similarities and differences in the phenomenon of interest. Nevertheless, some of these strategies (e.g., maximum variation sampling, extreme case sampling, intensity sampling, and purposeful random sampling) are used to identify and expand the range of variation or differences, similar to the use of quantitative measures to describe the variability or dispersion of values for a particular variable or variables, while other strategies (e.g., homogeneous sampling, typical case sampling, criterion sampling, and snowball sampling) are used to narrow the range of variation and focus on similarities. The latter are similar to the use of quantitative central tendency measures (e.g., mean, median, and mode). Moreover, certain strategies, like stratified purposeful sampling or opportunistic or emergent sampling, are designed to achieve both goals. As Patton (2002 , p. 240) explains, “the purpose of a stratified purposeful sample is to capture major variations rather than to identify a common core, although the latter may also emerge in the analysis. Each of the strata would constitute a fairly homogeneous sample.”

Challenges to use of purposeful sampling

Despite its wide use, there are numerous challenges in identifying and applying the appropriate purposeful sampling strategy in any study. For instance, the range of variation in a sample from which purposive sample is to be taken is often not really known at the outset of a study. To set as the goal the sampling of information-rich informants that cover the range of variation assumes one knows that range of variation. Consequently, an iterative approach of sampling and re-sampling to draw an appropriate sample is usually recommended to make certain the theoretical saturation occurs ( Miles & Huberman, 1994 ). However, that saturation may be determined a-priori on the basis of an existing theory or conceptual framework, or it may emerge from the data themselves, as in a grounded theory approach ( Glaser & Strauss, 1967 ). Second, there are a not insignificant number in the qualitative methods field who resist or refuse systematic sampling of any kind and reject the limiting nature of such realist, systematic, or positivist approaches. This includes critics of interventions and “bottom up” case studies and critiques. However, even those who equate purposeful sampling with systematic sampling must offer a rationale for selecting study participants that is linked with the aims of the investigation (i.e., why recruit these individuals for this particular study? What qualifies them to address the aims of the study?). While systematic sampling may be associated with a post-positivist tradition of qualitative data collection and analysis, such sampling is not inherently limited to such analyses and the need for such sampling is not inherently limited to post-positivist qualitative approaches ( Patton, 2002 ).

Purposeful Sampling in Implementation Research

Characteristics of implementation research.

In implementation research, quantitative and qualitative methods often play important roles, either simultaneously or sequentially, for the purpose of answering the same question through convergence of results from different sources, answering related questions in a complementary fashion, using one set of methods to expand or explain the results obtained from use of the other set of methods, using one set of methods to develop questionnaires or conceptual models that inform the use of the other set, and using one set of methods to identify the sample for analysis using the other set of methods ( Palinkas et al., 2011 ). A review of mixed method designs in implementation research conducted by Palinkas and colleagues (2011) revealed seven different sequential and simultaneous structural arrangements, five different functions of mixed methods, and three different ways of linking quantitative and qualitative data together. However, this review did not consider the sampling strategies involved in the types of quantitative and qualitative methods common to implementation research, nor did it consider the consequences of the sampling strategy selected for one method or set of methods on the choice of sampling strategy for the other method or set of methods. For instance, one of the most significant challenges to sampling in sequential mixed method designs lies in the limitations the initial method may place on sampling for the subsequent method. As Morse and Neihaus (2009) observe, when the initial method is qualitative, the sample selected may be too small and lack randomization necessary to fulfill the assumptions for a subsequent quantitative analysis. On the other hand, when the initial method is quantitative, the sample selected may be too large for each individual to be included in qualitative inquiry and lack purposeful selection to reduce the sample size to one more appropriate for qualitative research. The fact that potential participants were recruited and selected at random does not necessarily make them information rich.

A re-examination of the 22 studies and an additional 6 studies published since 2009 revealed that only 5 studies ( Aarons & Palinkas, 2007 ; Bachman et al., 2009 ; Palinkas et al., 2011 ; Palinkas et al., 2012 ; Slade et al., 2003) made a specific reference to purposeful sampling. An additional three studies ( Henke et al., 2008 ; Proctor et al., 2007 ; Swain et al., 2010 ) did not make explicit reference to purposeful sampling but did provide a rationale for sample selection. The remaining 20 studies provided no description of the sampling strategy used to identify participants for qualitative data collection and analysis; however, a rationale could be inferred based on a description of who were recruited and selected for participation. Of the 28 studies, 3 used more than one sampling strategy. Twenty-one of the 28 studies (75%) used some form of criterion sampling. In most instances, the criterion used is related to the individual’s role, either in the research project (i.e., trainer, team leader), or the agency (program director, clinical supervisor, clinician); in other words, criterion of inclusion in a certain category (criterion-i), in contrast to cases that are external to a specific criterion (criterion-e). For instance, in a series of studies based on the National Implementing Evidence-Based Practices Project, participants included semi-structured interviews with consultant trainers and program leaders at each study site ( Brunette et al., 2008 ; Marshall et al., 2008 ; Marty et al., 2007; Rapp et al., 2010 ; Woltmann et al., 2008 ). Six studies used some form of maximum variation sampling to ensure representativeness and diversity of organizations and individual practitioners. Two studies used intensity sampling to make contrasts. Aarons and Palinkas (2007) , for example, purposefully selected 15 child welfare case managers representing those having the most positive and those having the most negative views of SafeCare, an evidence-based prevention intervention, based on results of a web-based quantitative survey asking about the perceived value and usefulness of SafeCare. Kramer and Burns (2008) recruited and interviewed clinicians providing usual care and clinicians who dropped out of a study prior to consent to contrast with clinicians who provided the intervention under investigation. One study ( Hoagwood et al., 2007 ), used a typical case approach to identify participants for a qualitative assessment of the challenges faced in implementing a trauma-focused intervention for youth. One study ( Green & Aarons, 2011 ) used a combined snowball sampling/criterion-i strategy by asking recruited program managers to identify clinicians, administrative support staff, and consumers for project recruitment. County mental directors, agency directors, and program managers were recruited to represent the policy interests of implementation while clinicians, administrative support staff and consumers were recruited to represent the direct practice perspectives of EBP implementation.

Table 2 below provides a description of the use of different purposeful sampling strategies in mixed methods implementation studies. Criterion-i sampling was most frequently used in mixed methods implementation studies that employed a simultaneous design where the qualitative method was secondary to the quantitative method or studies that employed a simultaneous structure where the qualitative and quantitative methods were assigned equal priority. These mixed method designs were used to complement the depth of understanding afforded by the qualitative methods with the breadth of understanding afforded by the quantitative methods (n = 13), to explain or elaborate upon the findings of one set of methods (usually quantitative) with the findings from the other set of methods (n = 10), or to seek convergence through triangulation of results or quantifying qualitative data (n = 8). The process of mixing methods in the large majority (n = 18) of these studies involved embedding the qualitative study within the larger quantitative study. In one study (Goia & Dziadosz, 2008), criterion sampling was used in a simultaneous design where quantitative and qualitative data were merged together in a complementary fashion, and in two studies (Aarons et al., 2012; Zazelli et al., 2008 ), quantitative and qualitative data were connected together, one in sequential design for the purpose of developing a conceptual model ( Zazelli et al., 2008 ), and one in a simultaneous design for the purpose of complementing one another (Aarons et al., 2012). Three of the six studies that used maximum variation sampling used a simultaneous structure with quantitative methods taking priority over qualitative methods and a process of embedding the qualitative methods in a larger quantitative study ( Henke et al., 2008 ; Palinkas et al., 2010; Slade et al., 2008 ). Two of the six studies used maximum variation sampling in a sequential design ( Aarons et al., 2009 ; Zazelli et al., 2008 ) and one in a simultaneous design (Henke et al., 2010) for the purpose of development, and three used it in a simultaneous design for complementarity ( Bachman et al., 2009 ; Henke et al., 2008; Palinkas, Ell, Hansen, Cabassa, & Wells, 2011 ). The two studies relying upon intensity sampling used a simultaneous structure for the purpose of either convergence or expansion, and both studies involved a qualitative study embedded in a larger quantitative study ( Aarons & Palinkas, 2007 ; Kramer & Burns, 2008 ). The single typical case study involved a simultaneous design where the qualitative study was embedded in a larger quantitative study for the purpose of complementarity ( Hoagwood et al., 2007 ). The snowball/maximum variation study involved a sequential design where the qualitative study was merged into the quantitative data for the purpose of convergence and conceptual model development ( Green & Aarons, 2011 ). Although not used in any of the 28 implementation studies examined here, another common sequential sampling strategy is using criteria sampling of the larger quantitative sample to produce a second-stage qualitative sample in a manner similar to maximum variation sampling, except that the former narrows the range of variation while the latter expands the range.

Purposeful sampling strategies and mixed method designs in implementation research

Sampling strategy	Structure	Design	Function
Single stage sampling (n = 22)
Criterion (n = 18)	Simultaneous (n = 17) Sequential (n = 6)	Merged (n = 9) Connected (n = 9) Embedded (n = 14)	Convergence (n = 6) Complementarity (n = 12) Expansion (n = 10) Development (n = 3) Sampling (n = 4)
Maximum variation (n = 4)	Simultaneous (n = 3) Sequential (n = 1)	Merged (n = 1) Connected (n = 1) Embedded (n = 2)	Convergence (n = 1) Complementarity (n = 2) Expansion (n = 1) Development (n = 2)
Intensity (n = 1)	Simultaneous Sequential	Merged Connected Embedded	Convergence Complementarity Expansion Development
Typical case Study (n = 1)	Simultaneous	Embedded	Complementarity
Multistage sampling (n = 4)
Criterion/maximum variation (n = 2)	Simultaneous Sequential	Embedded Connected	Complementarity Development
Criterion/intensity (n = 1)	Simultaneous	Embedded	Convergence Complementarity Expansion
Criterion/snowball (n = 1)	Sequential	Connected	Convergence Development

Criterion-i sampling as a purposeful sampling strategy shares many characteristics with random probability sampling, despite having different aims and different procedures for identifying and selecting potential participants. In both instances, study participants are drawn from agencies, organizations or systems involved in the implementation process. Individuals are selected based on the assumption that they possess knowledge and experience with the phenomenon of interest (i.e., the implementation of an EBP) and thus will be able to provide information that is both detailed (depth) and generalizable (breadth). Participants for a qualitative study, usually service providers, consumers, agency directors, or state policy-makers, are drawn from the larger sample of participants in the quantitative study. They are selected from the larger sample because they meet the same criteria, in this case, playing a specific role in the organization and/or implementation process. To some extent, they are assumed to be “representative” of that role, although implementation studies rarely explain the rationale for selecting only some and not all of the available role representatives (i.e., recruiting 15 providers from an agency for semi-structured interviews out of an available sample of 25 providers). From the perspective of qualitative methodology, participants who meet or exceed a specific criterion or criteria possess intimate (or, at the very least, greater) knowledge of the phenomenon of interest by virtue of their experience, making them information-rich cases.

However, criterion sampling may not be the most appropriate strategy for implementation research because by attempting to capture both breadth and depth of understanding, it may actually be inadequate to the task of accomplishing either. Although qualitative methods are often contrasted with quantitative methods on the basis of depth versus breadth, they actually require elements of both in order to provide a comprehensive understanding of the phenomenon of interest. Ideally, the goal of achieving theoretical saturation by providing as much detail as possible involves selection of individuals or cases that can ensure all aspects of that phenomenon are included in the examination and that any one aspect is thoroughly examined. This goal, therefore, requires an approach that sequentially or simultaneously expands and narrows the field of view, respectively. By selecting only individuals who meet a specific criterion defined on the basis of their role in the implementation process or who have a specific experience (e.g., engaged only in an implementation defined as successful or only in one defined as unsuccessful), one may fail to capture the experiences or activities of other groups playing other roles in the process. For instance, a focus only on practitioners may fail to capture the insights, experiences, and activities of consumers, family members, agency directors, administrative staff, or state policy leaders in the implementation process, thus limiting the breadth of understanding of that process. On the other hand, selecting participants on the basis of whether they were a practitioner, consumer, director, staff, or any of the above, may fail to identify those with the greatest experience or most knowledgeable or most able to communicate what they know and/or have experienced, thus limiting the depth of understanding of the implementation process.

To address the potential limitations of criterion sampling, other purposeful sampling strategies should be considered and possibly adopted in implementation research ( Figure 1 ). For instance, strategies placing greater emphasis on breadth and variation such as maximum variation, extreme case, confirming and disconfirming case sampling are better suited for an examination of differences, while strategies placing greater emphasis on depth and similarity such as homogeneous, snowball, and typical case sampling are better suited for an examination of commonalities or similarities, even though both types of sampling strategies include a focus on both differences and similarities. Alternatives to criterion sampling may be more appropriate to the specific functions of mixed methods, however. For instance, using qualitative methods for the purpose of complementarity may require that a sampling strategy emphasize similarity if it is to achieve depth of understanding or explore and develop hypotheses that complement a quantitative probability sampling strategy achieving breadth of understanding and testing hypotheses ( Kemper et al., 2003 ). Similarly, mixed methods that address related questions for the purpose of expanding or explaining results or developing new measures or conceptual models may require a purposeful sampling strategy aiming for similarity that complements probability sampling aiming for variation or dispersion. A narrowly focused purposeful sampling strategy for qualitative analysis that “complements” a broader focused probability sample for quantitative analysis may help to achieve a balance between increasing inference quality/trustworthiness (internal validity) and generalizability/transferability (external validity). A single method that focuses only on a broad view may decrease internal validity at the expense of external validity ( Kemper et al., 2003 ). On the other hand, the aim of convergence (answering the same question with either method) may suggest use of a purposeful sampling strategy that aims for breadth that parallels the quantitative probability sampling strategy.

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Purposeful and Random Sampling Strategies for Mixed Method Implementation Studies

(1) Priority and sequencing of Qualitative (QUAL) and Quantitative (QUAN) can be reversed.
(2) Refers to emphasis of sampling strategy.

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Furthermore, the specific nature of implementation research suggests that a multistage purposeful sampling strategy be used. Three different multistage sampling strategies are illustrated in Figure 1 below. Several qualitative methodologists recommend sampling for variation (breadth) before sampling for commonalities (depth) ( Glaser, 1978 ; Bernard, 2002 ) (Multistage I). Also known as a “funnel approach”, this strategy is often recommended when conducting semi-structured interviews ( Spradley, 1979 ) or focus groups ( Morgan, 1997 ). This approach begins with a broad view of the topic and then proceeds to narrow down the conversation to very specific components of the topic. However, as noted earlier, the lack of a clear understanding of the nature of the range may require an iterative approach where each stage of data analysis helps to determine subsequent means of data collection and analysis ( Denzen, 1978 ; Patton, 2001) (Multistage II). Similarly, multistage purposeful sampling designs like opportunistic or emergent sampling, allow the option of adding to a sample to take advantage of unforeseen opportunities after data collection has been initiated (Patton, 2001, p. 240) (Multistage III). Multistage I models generally involve two stages, while a Multistage II model requires a minimum of 3 stages, alternating from sampling for variation to sampling for similarity. A Multistage III model begins with sampling for variation and ends with sampling for similarity, but may involve one or more intervening stages of sampling for variation or similarity as the need or opportunity arises.

Multistage purposeful sampling is also consistent with the use of hybrid designs to simultaneously examine intervention effectiveness and implementation. An extension of the concept of “practical clinical trials” ( Tunis, Stryer & Clancey, 2003 ), effectiveness-implementation hybrid designs provide benefits such as more rapid translational gains in clinical intervention uptake, more effective implementation strategies, and more useful information for researchers and decision makers ( Curran et al., 2012 ). Such designs may give equal priority to the testing of clinical treatments and implementation strategies (Hybrid Type 2) or give priority to the testing of treatment effectiveness (Hybrid Type 1) or implementation strategy (Hybrid Type 3). Curran and colleagues (2012) suggest that evaluation of the intervention’s effectiveness will require or involve use of quantitative measures while evaluation of the implementation process will require or involve use of mixed methods. When conducting a Hybrid Type 1 design (conducting a process evaluation of implementation in the context of a clinical effectiveness trial), the qualitative data could be used to inform the findings of the effectiveness trial. Thus, an effectiveness trial that finds substantial variation might purposefully select participants using a broader strategy like sampling for disconfirming cases to account for the variation. For instance, group randomized trials require knowledge of the contexts and circumstances similar and different across sites to account for inevitable site differences in interventions and assist local implementations of an intervention ( Bloom & Michalopoulos, 2013 ; Raudenbush & Liu, 2000 ). Alternatively, a narrow strategy may be used to account for the lack of variation. In either instance, the choice of a purposeful sampling strategy is determined by the outcomes of the quantitative analysis that is based on a probability sampling strategy. In Hybrid Type 2 and Type 3 designs where the implementation process is given equal or greater priority than the effectiveness trial, the purposeful sampling strategy must be first and foremost consistent with the aims of the implementation study, which may be to understand variation, central tendencies, or both. In all three instances, the sampling strategy employed for the implementation study may vary based on the priority assigned to that study relative to the effectiveness trial. For instance, purposeful sampling for a Hybrid Type 1 design may give higher priority to variation and comparison to understand the parameters of implementation processes or context as a contribution to an understanding of effectiveness outcomes (i.e., using qualitative data to expand upon or explain the results of the effectiveness trial), In effect, these process measures could be seen as modifiers of innovation/EBP outcome. In contrast, purposeful sampling for a Hybrid Type 3 design may give higher priority to similarity and depth to understand the core features of successful outcomes only.

Finally, multistage sampling strategies may be more consistent with innovations in experimental designs representing alternatives to the classic randomized controlled trial in community-based settings that have greater feasibility, acceptability, and external validity. While RCT designs provide the highest level of evidence, “in many clinical and community settings, and especially in studies with underserved populations and low resource settings, randomization may not be feasible or acceptable” ( Glasgow, et al., 2005 , p. 554). Randomized trials are also “relatively poor in assessing the benefit from complex public health or medical interventions that account for individual preferences for or against certain interventions, differential adherence or attrition, or varying dosage or tailoring of an intervention to individual needs” ( Brown et al., 2009 , p. 2). Several alternatives to the randomized design have been proposed, such as “interrupted time series,” “multiple baseline across settings” or “regression-discontinuity” designs. Optimal designs represent one such alternative to the classic RCT and are addressed in detail by Duan and colleagues (this issue) . Like purposeful sampling, optimal designs are intended to capture information-rich cases, usually identified as individuals most likely to benefit from the experimental intervention. The goal here is not to identify the typical or average patient, but patients who represent one end of the variation in an extreme case, intensity sampling, or criterion sampling strategy. Hence, a sampling strategy that begins by sampling for variation at the first stage and then sampling for homogeneity within a specific parameter of that variation (i.e., one end or the other of the distribution) at the second stage would seem the best approach for identifying an “optimal” sample for the clinical trial.

Another alternative to the classic RCT are the adaptive designs proposed by Brown and colleagues ( Brown et al, 2006 ; Brown et al., 2008 ; Brown et al., 2009 ). Adaptive designs are a sequence of trials that draw on the results of existing studies to determine the next stage of evaluation research. They use cumulative knowledge of current treatment successes or failures to change qualities of the ongoing trial. An adaptive intervention modifies what an individual subject (or community for a group-based trial) receives in response to his or her preferences or initial responses to an intervention. Consistent with multistage sampling in qualitative research, the design is somewhat iterative in nature in the sense that information gained from analysis of data collected at the first stage influences the nature of the data collected, and the way they are collected, at subsequent stages ( Denzen, 1978 ). Furthermore, many of these adaptive designs may benefit from a multistage purposeful sampling strategy at early phases of the clinical trial to identify the range of variation and core characteristics of study participants. This information can then be used for the purposes of identifying optimal dose of treatment, limiting sample size, randomizing participants into different enrollment procedures, determining who should be eligible for random assignment (as in the optimal design) to maximize treatment adherence and minimize dropout, or identifying incentives and motives that may be used to encourage participation in the trial itself.

Alternatives to the classic RCT design may also be desirable in studies that adopt a community-based participatory research framework ( Minkler & Wallerstein, 2003 ), considered to be an important tool on conducting implementation research ( Palinkas & Soydan, 2012 ). Such frameworks suggest that identification and recruitment of potential study participants will place greater emphasis on the priorities and “local knowledge” of community partners than on the need to sample for variation or uniformity. In this instance, the first stage of sampling may approximate the strategy of sampling politically important cases ( Patton, 2002 ) at the first stage, followed by other sampling strategies intended to maximize variations in stakeholder opinions or experience.

On the basis of this review, the following recommendations are offered for the use of purposeful sampling in mixed method implementation research. First, many mixed methods studies in health services research and implementation science do not clearly identify or provide a rationale for the sampling procedure for either quantitative or qualitative components of the study ( Wisdom et al., 2011 ), so a primary recommendation is for researchers to clearly describe their sampling strategies and provide the rationale for the strategy.

Second, use of a single stage strategy for purposeful sampling for qualitative portions of a mixed methods implementation study should adhere to the same general principles that govern all forms of sampling, qualitative or quantitative. Kemper and colleagues (2003) identify seven such principles: 1) the sampling strategy should stem logically from the conceptual framework as well as the research questions being addressed by the study; 2) the sample should be able to generate a thorough database on the type of phenomenon under study; 3) the sample should at least allow the possibility of drawing clear inferences and credible explanations from the data; 4) the sampling strategy must be ethical; 5) the sampling plan should be feasible; 6) the sampling plan should allow the researcher to transfer/generalize the conclusions of the study to other settings or populations; and 7) the sampling scheme should be as efficient as practical.

Third, the field of implementation research is at a stage itself where qualitative methods are intended primarily to explore the barriers and facilitators of EBP implementation and to develop new conceptual models of implementation process and outcomes. This is especially important in state implementation research, where fiscal necessities are driving policy reforms for which knowledge about EBP implementation barriers and facilitators are urgently needed. Thus a multistage strategy for purposeful sampling should begin first with a broader view with an emphasis on variation or dispersion and move to a narrow view with an emphasis on similarity or central tendencies. Such a strategy is necessary for the task of finding the optimal balance between internal and external validity.

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

Fifth, it should be kept in mind that all sampling procedures, whether purposeful or probability, are designed to capture elements of both similarity and differences, of both centrality and dispersion, because both elements are essential to the task of generating new knowledge through the processes of comparison and contrast. Selecting a strategy that gives emphasis to one does not mean that it cannot be used for the other. Having said that, our analysis has assumed at least some degree of concordance between breadth of understanding associated with quantitative probability sampling and purposeful sampling strategies that emphasize variation on the one hand, and between the depth of understanding and purposeful sampling strategies that emphasize similarity on the other hand. While there may be some merit to that assumption, depth of understanding requires both an understanding of variation and common elements.

Finally, it should also be kept in mind that quantitative data can be generated from a purposeful sampling strategy and qualitative data can be generated from a probability sampling strategy. Each set of data is suited to a specific objective and each must adhere to a specific set of assumptions and requirements. Nevertheless, the promise of mixed methods, like the promise of implementation science, lies in its ability to move beyond the confines of existing methodological approaches and develop innovative solutions to important and complex problems. For states engaged in EBP implementation, the need for these solutions is urgent.

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Multistage Purposeful Sampling Strategies

Acknowledgments

This study was funded through a grant from the National Institute of Mental Health (P30-MH090322: K. Hoagwood, PI).

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Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis

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https://doi.org/10.1080/13814788.2017.1375091

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In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.

General practice/family medicine
general qualitative designs and methods
data collection

Key points on sampling, data collection and analysis

The data collection plan needs to be broadly defined and open during data collection.

Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used.

Data saturation determines sample size and is different for each study.

The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions.

Analyses of ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory or a descriptive summary, respectively.

This article is the third paper in a series of four articles aiming to provide practical guidance to qualitative research. In an introductory paper, we have described the objective, nature and outline of the Series [ Citation 1 ]. Part 2 of the series focused on context, research questions and design of qualitative research [ Citation 2 ]. In this paper, Part 3, we address frequently asked questions (FAQs) about sampling, data collection and analysis.

What is a sampling plan?

Box 1. Sampling strategies in qualitative research. Based on Polit & Beck [ Citation 3 ].

Some practicalities: a critical first step is to select settings and situations where you have access to potential participants. Subsequently, the best strategy to apply is to recruit participants who can provide the richest information. Such participants have to be knowledgeable on the phenomenon and can articulate and reflect, and are motivated to communicate at length and in depth with you. Finally, you should review the sampling plan regularly and adapt when necessary.

What sampling strategies can I use?

Sampling is the process of selecting or searching for situations, context and/or participants who provide rich data of the phenomenon of interest [ Citation 3 ]. In qualitative research, you sample deliberately, not at random. The most commonly used deliberate sampling strategies are purposive sampling, criterion sampling, theoretical sampling, convenience sampling and snowball sampling. Occasionally, the ‘maximum variation,’ ‘typical cases’ and ‘confirming and disconfirming’ sampling strategies are used. Key informants need to be carefully chosen. Key informants hold special and expert knowledge about the phenomenon to be studied and are willing to share information and insights with you as the researcher [ Citation 3 ]. They also help to gain access to participants, especially when groups are studied. In addition, as researcher, you can validate your ideas and perceptions with those of the key informants.

What is the connection between sampling types and qualitative designs?

The ‘big three’ approaches of ethnography, phenomenology, and grounded theory use different types of sampling.

In ethnography, the main strategy is purposive sampling of a variety of key informants, who are most knowledgeable about a culture and are able and willing to act as representatives in revealing and interpreting the culture. For example, an ethnographic study on the cultural influences of communication in maternity care will recruit key informants from among a variety of parents-to-be, midwives and obstetricians in midwifery care practices and hospitals.

Phenomenology uses criterion sampling, in which participants meet predefined criteria. The most prominent criterion is the participant’s experience with the phenomenon under study. The researchers look for participants who have shared an experience, but vary in characteristics and in their individual experiences. For example, a phenomenological study on the lived experiences of pregnant women with psychosocial support from primary care midwives will recruit pregnant women varying in age, parity and educational level in primary midwifery practices.

Grounded theory usually starts with purposive sampling and later uses theoretical sampling to select participants who can best contribute to the developing theory. As theory construction takes place concurrently with data collection and analyses, the theoretical sampling of new participants also occurs along with the emerging theoretical concepts. For example, one grounded theory study tested several theoretical constructs to build a theory on autonomy in diabetes patients [ Citation 4 ]. In developing the theory, the researchers started by purposefully sampling participants with diabetes differing in age, onset of diabetes and social roles, for example, employees, housewives, and retired people. After the first analysis, researchers continued with theoretically sampling, for example, participants who differed in the treatment they received, with different degrees of care dependency, and participants who receive care from a general practitioner (GP), at a hospital or from a specialist nurse, etc.

In addition to the ‘big three’ approaches, content analysis is frequently applied in primary care research, and very often uses purposive, convenience, or snowball sampling. For instance, a study on peoples’ choice of a hospital for elective orthopaedic surgery used snowball sampling [ Citation 5 ]. One elderly person in the private network of one researcher personally approached potential respondents in her social network by means of personal invitations (including letters). In turn, respondents were asked to pass on the invitation to other eligible candidates.

Sampling is also dependent on the characteristics of the setting, e.g., access, time, vulnerability of participants, and different types of stakeholders. The setting, where sampling is carried out, is described in detail to provide thick description of the context, thereby, enabling the reader to make a transferability judgement (see Part 3: transferability). Sampling also affects the data analysis, where you continue decision-making about whom or what situations to sample next. This is based on what you consider as still missing to get the necessary information for rich findings (see Part 1: emergent design). Another point of attention is the sampling of ‘invisible groups’ or vulnerable people. Sampling of these participants would require applying multiple sampling strategies, and more time calculated in the project planning stage for sampling and recruitment [ Citation 6 ].

How do sample size and data saturation interact?

A guiding principle in qualitative research is to sample only until data saturation has been achieved. Data saturation means the collection of qualitative data to the point where a sense of closure is attained because new data yield redundant information [ Citation 3 ].

Data saturation is reached when no new analytical information arises anymore, and the study provides maximum information on the phenomenon. In quantitative research, by contrast, the sample size is determined by a power calculation. The usually small sample size in qualitative research depends on the information richness of the data, the variety of participants (or other units), the broadness of the research question and the phenomenon, the data collection method (e.g., individual or group interviews) and the type of sampling strategy. Mostly, you and your research team will jointly decide when data saturation has been reached, and hence whether the sampling can be ended and the sample size is sufficient. The most important criterion is the availability of enough in-depth data showing the patterns, categories and variety of the phenomenon under study. You review the analysis, findings, and the quality of the participant quotes you have collected, and then decide whether sampling might be ended because of data saturation. In many cases, you will choose to carry out two or three more observations or interviews or an additional focus group discussion to confirm that data saturation has been reached.

When designing a qualitative sampling plan, we (the authors) work with estimates. We estimate that ethnographic research should require 25–50 interviews and observations, including about four-to-six focus group discussions, while phenomenological studies require fewer than 10 interviews, grounded theory studies 20–30 interviews and content analysis 15–20 interviews or three-to-four focus group discussions. However, these numbers are very tentative and should be very carefully considered before using them. Furthermore, qualitative designs do not always mean small sample numbers. Bigger sample sizes might occur, for example, in content analysis, employing rapid qualitative approaches, and in large or longitudinal qualitative studies.

What methods of data collection are appropriate?

Box 2. Qualitative data collection methods.

What role should I adopt when conducting participant observations?

What is important is to immerse yourself in the research setting, to enable you to study it from the inside. There are four types of researcher involvement in observations, and in your qualitative study, you may apply all four. In the first type, as ‘complete participant’, you become part of the setting and play an insider role, just as you do in your own work setting. This role might be appropriate when studying persons who are difficult to access. The second type is ‘active participation’. You have gained access to a particular setting and observed the group under study. You can move around at will and can observe in detail and depth and in different situations. The third role is ‘moderate participation’. You do not actually work in the setting you wish to study but are located there as a researcher. You might adopt this role when you are not affiliated to the care setting you wish to study. The fourth role is that of the ‘complete observer’, in which you merely observe (bystander role) and do not participate in the setting at all. However, you cannot perform any observations without access to the care setting. Such access might be easily obtained when you collect data by observations in your own primary care setting. In some cases, you might observe other care settings, which are relevant to primary care, for instance observing the discharge procedure for vulnerable elderly people from hospital to primary care.

How do I perform observations?

Box 3. Further reading on interviews and focus group discussion.

Box 4. Qualitative data analysis.

What are the general features of an interview?

Interviews involve interactions between the interviewer(s) and the respondent(s) based on interview questions. Individual, or face-to-face, interviews should be distinguished from focus group discussions. The interview questions are written down in an interview guide [ Citation 7 ] for individual interviews or a questioning route [ Citation 8 ] for focus group discussions, with questions focusing on the phenomenon under study. The sequence of the questions is pre-determined. In individual interviews, the sequence depends on the respondents and how the interviews unfold. During the interview, as the conversation evolves, you go back and forth through the sequence of questions. It should be a dialogue, not a strict question–answer interview. In a focus group discussion, the sequence is intended to facilitate the interaction between the participants, and you might adapt the sequence depending on how their discussion evolves. Working with an interview guide or questioning route enables you to collect information on specific topics from all participants. You are in control in the sense that you give direction to the interview, while the participants are in control of their answers. However, you need to be open-minded to recognize that some relevant topics for participants may not have been covered in your interview guide or questioning route, and need to be added. During the data collection process, you develop the interview guide or questioning route further and revise it based on the analysis.

Box 5. Further reading on qualitative analysis.

What is a face-to-face interview?

A face-to-face interview is an individual interview, that is, a conversation between participant and interviewer. Interviews can focus on past or present situations, and on personal issues. Most qualitative studies start with open interviews to get a broad ‘picture’ of what is going on. You should not provide a great deal of guidance and avoid influencing the answers to fit ‘your’ point of view, as you want to obtain the participant’s own experiences, perceptions, thoughts, and feelings. You should encourage the participants to speak freely. As the interview evolves, your subsequent major and subordinate questions become more focused. A face-to-face or individual interview might last between 30 and 90 min.

Most interviews are semi-structured [ Citation 3 ]. To prepare an interview guide to enhance that a set of topics will be covered by every participant, you might use a framework for constructing a semi-structured interview guide [ Citation 10 ]: (1) identify the prerequisites to use a semi-structured interview and evaluate if a semi-structured interview is the appropriate data collection method; (2) retrieve and utilize previous knowledge to gain a comprehensive and adequate understanding of the phenomenon under study; (3) formulate a preliminary interview guide by operationalizing the previous knowledge; (4) pilot-test the preliminary interview guide to confirm the coverage and relevance of the content and to identify the need for reformulation of questions; (5) complete the interview guide to collect rich data with a clear and logical guide.

The first few minutes of an interview are decisive. The participant wants to feel at ease before sharing his or her experiences. In a semi-structured interview, you would start with open questions related to the topic, which invite the participant to talk freely. The questions aim to encourage participants to tell their personal experiences, including feelings and emotions and often focus on a particular experience or specific events. As you want to get as much detail as possible, you also ask follow-up questions or encourage telling more details by using probes and prompts or keeping a short period of silence [ Citation 6 ]. You first ask what and why questions and then how questions.

You need to be prepared for handling problems you might encounter, such as gaining access, dealing with multiple formal and informal gatekeepers, negotiating space and privacy for recording data, socially desirable answers from participants, reluctance of participants to tell their story, deciding on the appropriate role (emotional involvement), and exiting from fieldwork prematurely.

What is a focus group discussion and when can I use it?

A focus group discussion is a way to gather together people to discuss a specific topic of interest. The people participating in the focus group discussion share certain characteristics, e.g., professional background, or share similar experiences, e.g., having diabetes. You use their interaction to collect the information you need on a particular topic. To what depth of information the discussion goes depends on the extent to which focus group participants can stimulate each other in discussing and sharing their views and experiences. Focus group participants respond to you and to each other. Focus group discussions are often used to explore patients’ experiences of their condition and interactions with health professionals, to evaluate programmes and treatment, to gain an understanding of health professionals’ roles and identities, to examine the perception of professional education, or to obtain perspectives on primary care issues. A focus group discussion usually lasts 90–120 mins.

You might use guidelines for developing a questioning route [ Citation 9 ]: (1) brainstorm about possible topics you want to cover; (2) sequence the questioning: arrange general questions first, and then, more specific questions, and ask positive questions before negative questions; (3) phrase the questions: use open-ended questions, ask participants to think back and reflect on their personal experiences, avoid asking ‘why’ questions, keep questions simple and make your questions sound conversational, be careful about giving examples; (4) estimate the time for each question and consider: the complexity of the question, the category of the question, level of participant’s expertise, the size of the focus group discussion, and the amount of discussion you want related to the question; (5) obtain feedback from others (peers); (6) revise the questions based on the feedback; and (7) test the questions by doing a mock focus group discussion. All questions need to provide an answer to the phenomenon under study.

You need to be prepared to manage difficulties as they arise, for example, dominant participants during the discussion, little or no interaction and discussion between participants, participants who have difficulties sharing their real feelings about sensitive topics with others, and participants who behave differently when they are observed.

How should I compose a focus group and how many participants are needed?

The purpose of the focus group discussion determines the composition. Smaller groups might be more suitable for complex (and sometimes controversial) topics. Also, smaller focus groups give the participants more time to voice their views and provide more detailed information, while participants in larger focus groups might generate greater variety of information. In composing a smaller or larger focus group, you need to ensure that the participants are likely to have different viewpoints that stimulate the discussion. For example, if you want to discuss the management of obesity in a primary care district, you might want to have a group composed of professionals who work with these patients but also have a variety of backgrounds, e.g. GPs, community nurses, practice nurses in general practice, school nurses, midwives or dieticians.

Focus groups generally consist of 6–12 participants. Careful time management is important, since you have to determine how much time you want to devote to answering each question, and how much time is available for each individual participant. For example, if you have planned a focus group discussion lasting 90 min. with eight participants, you might need 15 min. for the introduction and the concluding summary. This means you have 75 min. for asking questions, and if you have four questions, this allows a total of 18 min. of speaking time for each question. If all eight respondents participate in the discussion, this boils down to about two minutes of speaking time per respondent per question.

How can I use new media to collect qualitative data?

New media are increasingly used for collecting qualitative data, for example, through online observations, online interviews and focus group discussions, and in analysis of online sources. Data can be collected synchronously or asynchronously, with text messaging, video conferences, video calls or immersive virtual worlds or games, etcetera. Qualitative research moves from ‘virtual’ to ‘digital’. Virtual means those approaches that import traditional data collection methods into the online environment and digital means those approaches take advantage of the unique characteristics and capabilities of the Internet for research [ Citation 10 ]. New media can also be applied. See Box 3 for further reading on interview and focus group discussion.

Can I wait with my analysis until all data have been collected?

You cannot wait with the analysis, because an iterative approach and emerging design are at the heart of qualitative research. This involves a process whereby you move back and forth between sampling, data collection and data analysis to accumulate rich data and interesting findings. The principle is that what emerges from data analysis will shape subsequent sampling decisions. Immediately after the very first observation, interview or focus group discussion, you have to start the analysis and prepare your field notes.

Why is a good transcript so important?

First, transcripts of audiotaped interviews and focus group discussions and your field notes constitute your major data sources. Trained and well-instructed transcribers preferably make transcripts. Usually, e.g., in ethnography, phenomenology, grounded theory, and content analysis, data are transcribed verbatim, which means that recordings are fully typed out, and the transcripts are accurate and reflect the interview or focus group discussion experience. Most important aspects of transcribing are the focus on the participants’ words, transcribing all parts of the audiotape, and carefully revisiting the tape and rereading the transcript. In conversation analysis non-verbal actions such as coughing, the lengths of pausing and emphasizing, tone of voice need to be described in detail using a formal transcription system (best known are G. Jefferson’s symbols).

To facilitate analysis, it is essential that you ensure and check that transcripts are accurate and reflect the totality of the interview, including pauses, punctuation and non-verbal data. To be able to make sense of qualitative data, you need to immerse yourself in the data and ‘live’ the data. In this process of incubation, you search the transcripts for meaning and essential patterns, and you try to collect legitimate and insightful findings. You familiarize yourself with the data by reading and rereading transcripts carefully and conscientiously, in search for deeper understanding.

Are there differences between the analyses in ethnography, phenomenology, grounded theory, and content analysis?

Ethnography, phenomenology, and grounded theory each have different analytical approaches, and you should be aware that each of these approaches has different schools of thought, which may also have integrated the analytical methods from other schools ( Box 4 ). When you opt for a particular approach, it is best to use a handbook describing its analytical methods, as it is better to use one approach consistently than to ‘mix up’ different schools.

In general, qualitative analysis begins with organizing data. Large amounts of data need to be stored in smaller and manageable units, which can be retrieved and reviewed easily. To obtain a sense of the whole, analysis starts with reading and rereading the data, looking at themes, emotions and the unexpected, taking into account the overall picture. You immerse yourself in the data. The most widely used procedure is to develop an inductive coding scheme based on actual data [ Citation 11 ]. This is a process of open coding, creating categories and abstraction. In most cases, you do not start with a predefined coding scheme. You describe what is going on in the data. You ask yourself, what is this? What does it stand for? What else is like this? What is this distinct from? Based on this close examination of what emerges from the data you make as many labels as needed. Then, you make a coding sheet, in which you collect the labels and, based on your interpretation, cluster them in preliminary categories. The next step is to order similar or dissimilar categories into broader higher order categories. Each category is named using content-characteristic words. Then, you use abstraction by formulating a general description of the phenomenon under study: subcategories with similar events and information are grouped together as categories and categories are grouped as main categories. During the analysis process, you identify ‘missing analytical information’ and you continue data collection. You reread, recode, re-analyse and re-collect data until your findings provide breadth and depth.

Throughout the qualitative study, you reflect on what you see or do not see in the data. It is common to write ‘analytic memos’ [ Citation 3 ], write-ups or mini-analyses about what you think you are learning during the course of your study, from designing to publishing. They can be a few sentences or pages, whatever is needed to reflect upon: open codes, categories, concepts, and patterns that might be emerging in the data. Memos can contain summaries of major findings and comments and reflections on particular aspects.

In ethnography, analysis begins from the moment that the researcher sets foot in the field. The analysis involves continually looking for patterns in the behaviours and thoughts of the participants in everyday life, in order to obtain an understanding of the culture under study. When comparing one pattern with another and analysing many patterns simultaneously, you may use maps, flow charts, organizational charts and matrices to illustrate the comparisons graphically. The outcome of an ethnographic study is a narrative description of a culture.

In phenomenology, analysis aims to describe and interpret the meaning of an experience, often by identifying essential subordinate and major themes. You search for common themes featuring within an interview and across interviews, sometimes involving the study participants or other experts in the analysis process. The outcome of a phenomenological study is a detailed description of themes that capture the essential meaning of a ‘lived’ experience.

Grounded theory generates a theory that explains how a basic social problem that emerged from the data is processed in a social setting. Grounded theory uses the ‘constant comparison’ method, which involves comparing elements that are present in one data source (e.g., an interview) with elements in another source, to identify commonalities. The steps in the analysis are known as open, axial and selective coding. Throughout the analysis, you document your ideas about the data in methodological and theoretical memos. The outcome of a grounded theory study is a theory.

Descriptive generic qualitative research is defined as research designed to produce a low inference description of a phenomenon [ Citation 12 ]. 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 [ Citation 12 ]. Descriptive generic qualitative research often applies content analysis. Descriptive content analysis studies are not based on a specific qualitative tradition and are varied in their methods of analysis. The analysis of the content aims to identify themes, and patterns within and among these themes. An inductive content analysis [ Citation 11 ] involves breaking down the data into smaller units, coding and naming the units according to the content they present, and grouping the coded material based on shared concepts. They can be represented by clustering in treelike diagrams. A deductive content analysis [ Citation 11 ] uses a theory, theoretical framework or conceptual model to analyse the data by operationalizing them in a coding matrix. An inductive content analysis might use several techniques from grounded theory, such as open and axial coding and constant comparison. However, note that your findings are merely a summary of categories, not a grounded theory.

Analysis software can support you to manage your data, for example by helping to store, annotate and retrieve texts, to locate words, phrases and segments of data, to name and label, to sort and organize, to identify data units, to prepare diagrams and to extract quotes. Still, as a researcher you would do the analytical work by looking at what is in the data, and making decisions about assigning codes, and identifying categories, concepts and patterns. The computer assisted qualitative data analysis (CAQDAS) website provides support to make informed choices between analytical software and courses: http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing . See Box 5 for further reading on qualitative analysis.

The next and final article in this series, Part 4, will focus on trustworthiness and publishing qualitative research [ Citation 13 ].

The authors thank the following junior researchers who have been participating for the last few years in the so-called ‘Think tank on qualitative research’ project, a collaborative project between Zuyd University of Applied Sciences and Maastricht University, for their pertinent questions: Erica Baarends, Jerome van Dongen, Jolanda Friesen-Storms, Steffy Lenzen, Ankie Hoefnagels, Barbara Piskur, Claudia van Putten-Gamel, Wilma Savelberg, Steffy Stans, and Anita Stevens. The authors are grateful to Isabel van Helmond, Joyce Molenaar and Darcy Ummels for proofreading our manuscripts and providing valuable feedback from the ‘novice perspective’.

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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Assessing the impact of the indigenous farmers' food literacy on millet production: evidence from Eastern India

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Published: 26 September 2024

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Partha Sarathi Swain ORCID: orcid.org/0000-0003-3788-5239 1 ,
Ashis Kumar Pradhan 2 &
Provash Kumer Sarker 3

This study investigates the relationship between food literacy and farmer’s intent to produce millets for their subsistence. To do so, we used a structured questionnaire highlighting three aspects of food literacy by drawing data from 100 millet producers from a remote tribal region in the Koraput district of Odisha and employed the least absolute shrinkage and selection operator regression technique and other regression methods for robustness. The result from the regression methods has revealed that variables associated with food literacy, such as knowledge about the farmland type, farm mechanization, and nutritional uses influence millet production. However, the cultural importance of millet had a significant impact on millet production as millet is culturally associated with tribal culture. Overall findings underscore the effective use of technology that supports the feminization and protection of indigenous millet crops and endorse the implementation of special schemes for the promotion and marketing of millets.

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The Programme is operated through four key components i.e., production, processing, marketing and consumption. The main purpose of this programme is to revive millet and bring it back in the farm of producers to the plate of consumers. Further, the state has introduced a first-ever flagship method of direct incentive to farmers (DBT) to the registered millet growers who ever participated under the OMM programmer. The state is the first ever to introduce ragi (finger millet) ladoo in Integrated Child Development Service (ICDS) programmes to provide better health and nutrition security to the child, pregnant women, and adolescent girls. Thus, the state has now included millet in the Mid-day Meal Scheme (MDM), and the Public Distribution Scheme (PDS). With all these recent developments the state has been declared a “Best Millet Promoting State” and awarded with “Poshak Anaj Awards” by the Indian Council of Agricultural Research (ICAR), Indian Institute of Millet Research (IIMR) and the Food and Agriculture Organization of the United Nation (FAO).

We use the variable CULUSES as endogenous instrumented variable while performing the 2SLS IV regression. Cultural values are embedded within the community in the form of social customs, beliefs, and tradition. Therefore, we consider this variable as endogenous instrumented variable.

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Acknowledgements

We thank anonymous reviewers for their insightful comments and useful suggestions. We also thank Dr Vivek Singh, Dept. of Civil, Structure and Environment Engineering, Trinity College, Dublin and Dr Avinash Ranjan, Dept. of Humanities and Social Sciences, IIT(ISM)Dhanbad for their suggestions on the earlier version of the paper. The usual disclaimer applies.

The current research is a part of Doctoral thesis for which the corresponding author has recieved fellowship from the institute. However, our research did not recieve any funding from external sources.

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Swain, P.S., Pradhan, A.K. & Sarker, P.K. Assessing the impact of the indigenous farmers' food literacy on millet production: evidence from Eastern India. Socio Ecol Pract Res (2024). https://doi.org/10.1007/s42532-024-00201-0

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Sampling frame

Sample size

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1. Simple random sampling

2. Systematic sampling

3. Stratified sampling

4. Cluster sampling

1. Convenience sampling

2. Voluntary response sampling

3. Purposive sampling

4. Snowball sampling

5. Quota sampling

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Sampling in Qualitative Research

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

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

Sampling in Qualitative Research

Sampling Methods & Strategies 101

Overview: Sampling Methods & Strategies

Simple random sampling

What (exactly) is sampling?

The two overarching sampling approaches

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Probability-based sampling methods

Non-probability-based sampling methods

How to choose a sampling method