sampling procedure example 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.

In this Article:

• Purposive Sampling
• Convenience Sampling
• Snowball Sampling
• Theoretical Sampling

Factors to Consider When Choosing a Sampling Technique

Practical approaches to sampling: recommended practices, final thoughts, get expert guidance on your sample needs.

Want expert input on the best sampling technique for your qualitative research project? Book a consultation for trusted advice.

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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

Further Readings

Fusch, Patricia I., and Lawrence R. Ness. 2015. “Are We There Yet? Data Saturation in Qualitative Research.” Qualitative Report 20(9):1408–1416.

Saunders, Benjamin, Julius Sim, Tom Kinstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. “Saturation in Qualitative Research: Exploring Its Conceptualization and Operationalization.”  Quality & Quantity  52(4):1893–1907.

• Rubin ( 2021 ) suggests a minimum of twenty interviews (but safer with thirty) for an interview-based study and a minimum of three to six months in the field for ethnographic studies. For a content-based study, she suggests between five hundred and one thousand documents, although some will be “very small” ( 243–244 ). ↵

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

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).  Sampling frames can differ from the larger population when specific exclusions are inherent, as in the case of pulling names randomly from voter registration rolls where not everyone is a registered voter.  This difference in frame and population can undercut the generalizability of quantitative results.

The specific group of individuals that you will collect data from.  Contrast population.

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A sampling strategy in which the sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the sample.  This is often done through a lottery or other chance mechanisms (e.g., a random selection of every twelfth name on an alphabetical list of voters).  Also known as random sampling .

The selection of research participants or other data sources based on availability or accessibility, in contrast to purposive sampling .

A sample generated non-randomly by asking participants to help recruit more participants the idea being that a person who fits your sampling criteria probably knows other people with similar criteria.

Broad codes that are assigned to the main issues emerging in the data; identifying themes is often part of initial coding . 

A form of case selection focusing on examples that do not fit the emerging patterns. This allows the researcher to evaluate rival explanations or to define the limitations of their research findings. While disconfirming cases are found (not sought out), researchers should expand their analysis or rethink their theories to include/explain them.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

The result of probability sampling, in which a sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the random sample.  This is often done through a lottery or other chance mechanisms (e.g., the random selection of every twelfth name on an alphabetical list of voters).  This is typically not required in qualitative research but rather essential for the generalizability of quantitative research.

A form of case selection or purposeful sampling in which cases that are unusual or special in some way are chosen to highlight processes or to illuminate gaps in our knowledge of a phenomenon.   See also extreme case .

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

The accuracy with which results or findings can be transferred to situations or people other than those originally studied.  Qualitative studies generally are unable to use (and are uninterested in) statistical generalizability where the sample population is said to be able to predict or stand in for a larger population of interest.  Instead, qualitative researchers often discuss “theoretical generalizability,” in which the findings of a particular study can shed light on processes and mechanisms that may be at play in other settings.  See also statistical generalization and theoretical generalization .

A term used by IRBs to denote all materials aimed at recruiting participants into a research study (including printed advertisements, scripts, audio or video tapes, or websites).  Copies of this material are required in research protocols submitted to IRB.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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• 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.

Table of contents

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 Techniques for Qualitative Research

• First Online: 27 October 2022

Cite this chapter

• Heather Douglas 4  

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This chapter explains how to design suitable sampling strategies for qualitative research. The focus of this chapter is purposive (or theoretical) sampling to produce credible and trustworthy explanations of a phenomenon (a specific aspect of society). A specific research question (RQ) guides the methodology (the study design or approach ). It defines the participants, location, and actions to be used to answer the question. Qualitative studies use specific tools and techniques ( methods ) to sample people, organizations, or whatever is to be examined. The methodology guides the selection of tools and techniques for sampling, data analysis, quality assurance, etc. These all vary according to the purpose and design of the study and the RQ. In this chapter, a fake example is used to demonstrate how to apply your sampling strategy in a developing country.

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Douglas, H. (2022). Sampling Techniques for Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_29

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Sampling in Qualitative Research

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

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

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

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

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

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

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

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

Contributions, Logic and Issues in Qualitative Sampling

Major contributions.

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

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

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

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

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

The Cultural Embeddedness of the Concept of Sampling

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

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

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

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

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

Ideals and Techniques of Qualitative Sampling

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

Defining the universe

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

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

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

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

Appropriate analytic units: Sampling for meaning

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Techniques for selecting a sample

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

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

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

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

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

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

Who and who not?

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

Homogeneity or diversity

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

Summary and Reformulation for Practice

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

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

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

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

Qualitative Clarity as an Analog to Statistical Power

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

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

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

Rich and diverse theoretical grounding

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

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

Sensitivity to contexts

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

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

Acknowledgments

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

Biographies

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

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

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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.

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 .

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.

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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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Qualitative Sampling Methods

Affiliation.

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

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

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

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10.2 Sampling in qualitative research

Learning objectives.

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

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

Nonprobability sampling

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

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.

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

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

Key Takeaways

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

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• 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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Sampling Methods in Qualitative Research: Definition, Types with Examples

What is Sampling in Qualitative Research?

Sampling in qualitative research is defined as an initial stage process involving the deliberate selection of individuals or cases from a broader population to participate in a study. 

Unlike quantitative research, where the emphasis is often on achieving statistical generalizability, qualitative research seeks to obtain depth and richness of information. 

In qualitative research sampling, the focus is not on achieving statistical representation of population but rather on gaining a profound understanding of the subject under investigation. Researchers carefully consider the appropriateness of each sampling method based on the research question, objectives, and the nature of the study population, ensuring alignment with the qualitative approach and the desired richness of data.

Key Methods for Qualitative Research Sampling

Various sampling methods are employed to select participants or cases that can provide meaningful insights and contribute to a rich understanding of the research question. Here, we’ll explore four common types of sampling methods in qualitative research, along with explanations and examples:

• Purposeful Sampling:

Purposeful sampling involves intentionally selecting participants or cases based on specific criteria relevant to the research question. The goal is to gather in-depth information from individuals who can provide rich insights into the phenomenon under investigation. Researchers may use different purposeful sampling strategies, such as maximum variation (selecting diverse cases) or typical case (choosing a representative example).

Example: In a study exploring the experiences of cancer survivors, purposeful sampling might involve selecting participants with a variety of cancer types, treatment histories, and socio-demographic backgrounds to capture diverse perspectives.

• Snowball Sampling:

Snowball sampling, or chain referral sampling, is used when studying populations that are challenging to reach through traditional methods. The researcher starts with a small number of participants and asks them to refer others who share similar characteristics or experiences. This method is particularly useful for studying hidden populations or subcultures.

Example: When researching illicit drug users, a researcher might start by interviewing a few individuals and then ask them to refer others in their social network who have similar experiences with drug use.

• Theoretical Sampling:

Theoretical sampling is associated with grounded theory methodology. Unlike other sampling methods, theoretical sampling involves an ongoing and iterative process. Sampling decisions are made based on emerging themes and theoretical insights uncovered during data analysis. The goal is to gather data that help develop and refine emerging theories.

Example: In a study exploring the experiences of individuals transitioning between careers, theoretical sampling might involve selecting participants who can provide insights into specific aspects of the transition process as the study progresses.

• Quota Sampling:

Quota sampling involves setting specific quotas based on predetermined characteristics such as age, gender, or socio-economic status. The researcher aims to ensure that the sample reflects the diversity present in the larger population. Quota sampling provides a structured way to achieve a balanced sample.

Example: In a study on consumer preferences for a new product, quota sampling might involve ensuring that the sample includes a proportional representation of different age groups and income levels to capture a range of perspectives.

These sampling methods are selected based on the nature of the research question, the goals of the study, and the characteristics of the population under investigation. Researchers often choose a method that aligns with the qualitative approach and allows for the collection of rich, context-specific data.

• Convenience Sampling:

Convenience sampling involves selecting participants who are readily available and easily accessible to the researcher. This method is often pragmatic and efficient, but it may introduce bias since participants are not chosen based on specific criteria related to the research question. Convenience sampling is common in exploratory or pilot studies.

Example: If a researcher is studying the use of mobile banking apps, they might approach individuals in a public space, such as a coffee shop, and ask them about their experiences with mobile banking for a quick and accessible sample.

• Criterion Sampling:

Criterion sampling involves selecting participants who meet specific criteria relevant to the research question. The criteria are predetermined and guide the researcher in choosing individuals who possess certain characteristics or have experienced particular events. This method ensures that the sample aligns closely with the study’s objectives.

Example: In a study on the impact of a specific educational intervention, criterion sampling might involve selecting participants who have completed the intervention program, ensuring that the sample includes individuals directly affected by the educational initiative.

Each of these qualitative sampling methods has its advantages and limitations. Researchers carefully consider the appropriateness of the method based on the research question, the study’s objectives, and the characteristics of the population being studied. The goal is to select a sampling strategy that aligns with the qualitative research approach, allowing for a nuanced exploration of the phenomenon under investigation.

Qualitative Research Sampling: Key Best Practices 

Using sampling methods in qualitative research requires thoughtful consideration and adherence to best practices to ensure the study’s validity, reliability, and relevance. Here are some best practices for employing sampling methods in qualitative research:

1. Clearly Define Research Objectives:

Begin by clearly defining the research objectives and the specific goals of the study. This clarity will guide the selection of an appropriate sampling method aligned with the research questions.

2. Select a Sampling Method Aligned with Research Goals:

Choose a sampling method that aligns with the nature of the research question and the study’s objectives. Consider the strengths and limitations of each method, and select the one that best serves the research purpose.

3. Use Multiple Sampling Strategies:

Consider employing multiple sampling strategies within the same study. This can enhance the richness and diversity of the data by capturing various perspectives and experiences related to the research question.

4. Establish Inclusion and Exclusion Criteria:

Clearly define inclusion and exclusion criteria based on the study’s objectives. This helps ensure that participants or cases selected contribute directly to the research question and provide relevant insights.

5. Document Sampling Decisions:

Document the rationale behind sampling decisions, including the criteria used and any adjustments made during the study. Transparent documentation enhances the study’s transparency, replicability, and credibility.

6. Consider Saturation:

Monitor data saturation throughout the study. Once saturation is reached, which means that no new data is available, data collection can cease, ensuring that the study has sufficiently explored the research question.

7. Strive for Diversity within the Sample:

Aim for diversity within the sample to capture a range of perspectives. Diversity can include variations in age, gender, socio-economic status, or other relevant characteristics, depending on the research question.

8. Ethical Considerations:

Prioritize ethical considerations in participant selection. Obtain informed consent, safeguard participant confidentiality, and ensure that vulnerable populations are treated with sensitivity and respect.

9. Adapt Sampling Strategies as Needed:

Be open to adapting sampling strategies based on emerging insights. Theoretical sampling, in particular, allows for adjustments in the sampling plan as the study progresses and new themes emerge.

10. Member Checking:

Consider implementing member checking, where preliminary findings are shared with participants to validate or refine the interpretations. This enhances the trustworthiness and credibility of the study.

11. Reflect on Researcher Bias:

Acknowledge and reflect on the potential biases introduced by the researcher during the sampling process. Reflexivity ensures transparency and helps mitigate bias in participant selection and interpretation of data.

By adhering to these best practices, researchers can enhance the rigor and quality of qualitative research. These practices contribute to the trustworthiness of the study and ensure that the selected sampling method aligns effectively with the research objectives.

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Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

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7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

• How it works

Qualitative Research Questionnaire – Types & Examples

Published by Alvin Nicolas at August 19th, 2024 , Revised On August 20, 2024

Before you start your research, the first thing you need to identify is the research method . Depending on different factors, you will either choose a quantitative or qualitative study.

Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences. Unlike quantitative research, which focuses on numerical data , qualitative research allows exploring and interpreting the experiences of the subject. Questionnaires, although mostly associated with quantitative research, can also be a valuable instrument in qualitative studies. Let’s explore what qualitative research questionnaires are and how you can create one.

What Is A Qualitative Research Questionnaire

Qualitative research questionnaires are a structured or semi-structured set of questions designed to gather detailed, open-ended participant responses. It allows you to uncover underlying reasons and opinions and provides insights into a particular phenomenon.

While quantitative questionnaires often have closed-ended questions and numerical responses, a qualitative questionnaire encourages participants to express themselves freely. Before you design your questionnaire, you should know exactly what you need so you can keep your questions specific enough for the participants to understand.

For example:

• Describe your experience using our product.
• How has technology impacted your work-life balance?

Types of Qualitative Research Questions With Examples

Now that you are familiar with what qualitative research questions are, let’s look at the different types of questions you can use in your survey .

Descriptive Questions

These are used to explore and describe a phenomenon in detail. It helps answer the “what” part of the research, and the questions are mostly foundational.

Example: How do students experience online learning?

Comparative Questions

This type allows you to compare and contrast different groups or situations. You can explore the differences and similarities to highlight the impact of specific variables.

Example: How do the study habits of first-year and fourth-year university students differ?

Interpretive Questions

These questions help you understand the meanings people attach to experiences or phenomena by answering the “how” and “why”.

Example: What does “success” mean to entrepreneurs?

Evaluative Questions

You can use these to assess the quality or value of something. These allow you to understand the outcomes of various situations.

Example: How effective is the new customer service training program?

Process-Oriented Questions

To understand how something happens or develops over time, researchers often use process-oriented questions.

Example: How do individuals develop their career goals?

Exploratory Questions

These allow you to discover new perspectives on a topic. However, you have to be careful that there must be no preconceived notions or research biases to it.

Example: What are the emerging trends in the mobile gaming industry?

How To Write Qualitative Research Questions?

For your study to be successful, it is important to consider designing a questionnaire for qualitative research critically, as it will shape your research and data collection. Here is an easy guide to writing your qualitative research questions perfectly.

Tip 1: Understand Your Research Goals

Many students start their research without clear goals, and they have to make substantial changes to their study in the middle of the research. This wastes time and resources.

Before you start crafting your questions, it is important to know your research objectives. You should know what you aim to discover through your research, or what specific knowledge gaps you are going to fill. With the help of a well-defined research focus, you can develop relevant and meaningful information.

Tip 2: Choose The Structure For Research Questions

There are mostly open-ended questionnaires in qualitative research. They begin with words like “how,” “what,” and “why.” However, the structure of your research questions depends on your research design . You have to consider using broad, overarching questions to explore the main research focus, and then add some specific probes to further research the particular aspects of the topic.

Tip 3: Use Clear Language

The more clear and concise your research questions are, the more effective and free from ambiguity they will be. Do not use complex terminology that might confuse participants. Try using simple and direct language that accurately conveys your intended meaning.

Here is a table to explain the wrong and right ways of writing your qualitative research questions.

Tip 4: Check Relevance With Research Goals

Once you have developed some questions, check if they align with your research objectives. You must ensure that each question contributes to your overall research questions. After this, you can eliminate any questions that do not serve a clear purpose in your study.

Tip 5: Concentrate On A Single Theme

While it is tempting to cover multiple aspects of a topic in one question, it is best to focus on a single theme per question. This helps to elicit focused responses from participants. Moreover, you have to avoid combining unrelated concepts into a single question.

If your main research question is complicated, you can create sub-questions with a “ladder structure”. These allow you to understand the attributes, consequences, and core values of your research. For example, let’s say your main broad research question is:

• How do you feel about your overall experience with our company?

The intermediate questions may be:

• What aspects of your experience were positive?
• What aspects of your experience were negative?
• How likely are you to recommend our company to a friend or colleague?

Types Of Survey Questionnaires In Qualitative Research

It is important to consider your research objectives, target population, resources and needed depth of research when selecting a survey method. The main types of qualitative surveys are discussed below.

Face To Face Surveys

Face-to-face surveys involve direct interaction between the researcher and the participant. This method allows observers to capture non-verbal cues, body language, and facial expressions, and helps adapt questions based on participant responses. They also let you clarify any misunderstandings. Moreover, there is a higher response rate because of personal interaction.

Example: A researcher conducting a study on consumer experiences with a new product might visit participants’ homes to conduct a detailed interview.

Telephone Surveys

These type of qualitative research survey questionnaires provide a less intrusive method for collecting qualitative data. The benefits of telephone surveys include, that it allows you to collect data from a wider population. Moreover, it is generally less expensive than face-to-face interviews and interviews can be conducted efficiently.

Example: A market research firm might conduct telephone surveys to understand customer satisfaction with a telecommunication service.

Online Surveys

Online survey questionnaires are a convenient and cost-effective way to gather qualitative data. You can reach a wide audience quickly, and participants may feel more comfortable sharing sensitive information because of anonymity. Additionally, there are no travel or printing expenses.

Example: A university might use online surveys to explore students’ perceptions of online learning experiences.

Strengths & Limitations Of Questionnaires In Qualitative Research

Questionnaires are undoubtedly a great data collection tool. However, it comes with its fair share of advantages and disadvantages. Let’s discuss the benefits of questionnaires in qualitative research and their cons as well.

Qualitative Research Questionnaire Example

Here is a concise qualitative research questionnaire sample for research papers to give you a better idea of its format and how it is presented.

Thank you for participating in our survey. We value your feedback on our new mobile app. Your responses will help us improve the applications and better meet your needs.

Demographic Information

• Occupation:
• How long have you been using smartphones:
• How would you describe your overall experience with the new mobile app?
• What do you like most about the app?
• What do you dislike most about the app?
• Are there any specific features you find particularly useful or helpful? Please explain.
• Are there any features you think are missing or could be improved? Please elaborate.
• How easy is the app to navigate? Please explain any difficulties you encountered.
• How does this app compare to other similar apps you have used?
• What are your expectations for future updates or improvements to the app?
• Is there anything else you would like to share about your experience with the app?

Are questionnaires quantitative or qualitative research?

A survey research questionnaire can have both qualitative and quantitative questions. The qualitative questions are mostly open-ended, and quantitative questions take the form of yes/no, or Likert scale rating. 

Can we use questionnaires in qualitative research?

Yes, survey questionnaires can be used in qualitative research for data collection. However, instead of a Likert scale or rating, you can post open-ended questions to your respondents. The participants can provide detailed responses to the questions asked.

Why are questionnaires good for qualitative research?

In qualitative research, questionnaires allow you to collect qualitative data. The open-ended and unstructured questions help respondents present their ideas freely and provide insights. 

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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• Open access
• Published: 25 August 2024

Navigating sexual minority identity in sport: a qualitative exploration of sexual minority student-athletes in China

• Meng Xiang 1 , 2 ,
• Kim Geok Soh 2 ,
• Yingying Xu 3 ,
• Seyedali Ahrari 4 &
• Noor Syamilah Zakaria 5  

BMC Public Health volume  24 , Article number:  2304 ( 2024 ) Cite this article

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Sexual minority student-athletes (SMSAs) face discrimination and identity conflicts in intercollegiate sport, impacting their participation and mental health. This study explores the perceptions of Chinese SMSAs regarding their sexual minority identities, aiming to fill the current gap in research related to non-Western countries.

A qualitative methodology was adopted, utilising the Interpretive Phenomenological Analysis (IPA) approach with self-categorization theory as the theoretical framework. Participants were recruited through purposive and snowball sampling, and data were collected via semi-structured interviews, documents, and field notes. Sixteen former and current Chinese SMSAs participated in this study.

The study reveals four themes: hidden truths, prioritisation of athlete identity, self-stereotyping, and attempt. The results revealed that while SMSAs were common in intercollegiate sport, their identities were often concealed and not openly discussed. The predominant focus on athlete identity in sport overshadowed their sexual minority identities. Additionally, SMSAs developed self-stereotypes that influenced their thoughts and behaviours. The non-heterosexual team atmosphere in women’s teams led to the development of intimate relationships among teammates.

Conclusions

The findings from this study could be incorporated into existing sport policies to ensure the safe participation of SMSAs in Chinese intercollegiate sports. This research offers valuable insights for the development and implementation of inclusive policies. Future research in China could investigate the attitudes of coaches and heterosexual student-athletes toward sexual minority identities to inform targeted interventions.

Peer Review reports

Collegiate sport serves as a conduit for hope, competition, learning, success, and enhanced well-being for students [ 1 , 2 ]. Within this context, situated at the intersection of student-athlete and sexual minority identities [ 3 ], sexual minority student-athletes (SMSAs) experience more challenges than their heterosexual counterparts. Sexual minority constitutes a group of individuals whose sexual and affectual orientation, romantic attraction, or sexual characteristics differ from that of heterosexuals. Sexual minority persons are inclusive of lesbian, gay, bi+, and asexual-identified individuals [ 4 ].

In an effort to enhance the support of SMSAs in sport, Team DC, the association of sexual minorities sport club, awarded seven SMSAs the 2023 Team DC College Scholarship [ 5 ]. Besides the Team DC scholarship, there are the Rambler Scholarship, US Lacrosse SMSAs Inclusion Scholarship, NCAA Women’s Athletics Scholarship and Ryan O’Callaghan Foundation [ 6 , 7 , 8 ]. These scholarships were set up to make sport a more welcoming and safer environment for SMSAs. In particular, the Sexual Minority Scholarship echoes the International Olympic Committee’s framework of equity, inclusion, and non-discrimination, which states that everyone has the right to participate in sport without discrimination and in a manner that respects their health, safety and dignity [ 9 , 10 ].

Despite efforts by educational and sport organisations to foster inclusivity, research shows that the sport environment remains hostile to sexual minority individuals [ 11 , 12 ]. In intercollegiate sport, empirical evidence points to persistent negative attitudes [ 13 , 14 , 15 , 16 , 17 ], which are expressed through marginalisation, exclusion, use of homophobic language, discrimination, and harassment [ 17 , 18 , 19 , 20 ]. SMSAs frequently confront the difficult choice of disclosing their identity, often opting for concealment. Denison et al. found that SMSAs who disclose their identity to their teams may face increased discrimination [ 21 ]. Pariera et al. also observed deep-rooted fears among SMSAs of being marginalised by their teams upon revealing their sexual orientation [ 22 ]. Consequently, the hostile environment led to lower participation rates among sexual minority youth compared to their heterosexual counterparts [ 23 ].

In China, there is a lack of clear public policies related to the sexual minority population [ 24 ]. Despite homosexuality being removed from the Chinese Classification of Mental Disorders-3 in 2001 [ 25 ]. China’s stance towards sexual minority issues remains ambiguous. Many scholars describe this attitude as “no approval, no disapproval, and no promotion” [ 26 , 27 , 28 , 29 ]. Due to the lack of legal protection, sexual minorities frequently encounter discrimination. A Chinese national survey revealed that only 5.1% of sexual minority individuals felt comfortable being open about their gender and sexual identity in China [ 30 ]. This discrimination is particularly severe among Chinese sexual minority youth, who are at higher risk of bullying in school and college [ 31 , 32 ]. These youths face childhood victimisation [ 33 , 34 , 35 ], which heightens their risk of mental and behavioural health issues [ 36 , 37 , 38 ], including non-medical use of prescription drugs [ 39 ], depression [ 40 , 41 ], and suicide [ 42 ].

While sports participation is crucial for the well-being of sexual minority individuals, research on the sports participation of sexual minority youth in China is limited. The literature highlights a significant gap in understanding the status and circumstances of SMSAs in China. Most existing studies focus on Western populations [ 43 , 44 , 45 ], overlooking the unique sociocultural interactions affecting SMSAs in non-Western contexts, making it challenging for China to apply these findings. Furthermore, the lack of reliable research on the interactions between sexual minorities and institutions in Chinese higher education hampers a comprehensive understanding of SMSAs’ situations. This research gap impedes the development of effective interventions to foster inclusivity. Persistent discrimination and inadequate protective policies underscore the urgent need for academic, policy, and practical advancements to support sexual minorities in China [ 46 ]. Therefore, the aim of this study was to explore SMSAs’ perceptions of their sexual minority identity in Chinese sports, providing insights to guide the creation of supportive educational and organisational strategies.

Homonegativity and discrimination in sport

Homonegativity refers to any prejudicial attitude or discriminatory behaviour directed towards an individual because of their homosexual orientation [ 47 ]. Compared to the more common term “homophobia,” [ 48 ] “homonegativity” more accurately describes negative attitudes towards homosexuality [ 49 ] because the fear is not irrational but is learned from parents, peers, teachers, coaches, and the daily interaction environment [ 50 ]. Sport context is an integral part of society, and an extensive body of research has consistently demonstrated the presence of homonegativity in sport [ 12 , 21 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].

Homonegativity can manifest in forms such as verbal harassment, physical violence, or discriminatory behaviours. The “Out on the Fields” survey, conducted in 2015, represents the first large-scale international study focusing on homophobia in sports [ 60 ]. Participants were from six countries: Canada, Australia, Ireland, the United States, New Zealand, and the United Kingdom. It revealed extensive discrimination in sport, with a high percentage of gay men and lesbians experiencing verbal slander, bullying, threats, and physical assault. The OUTSPORT project, completed in 2019 and funded by the European Union, is the first comprehensive EU-wide study on homophobia and transphobia in sport. The project collected data from over 5500 sexual minority individuals across all 28 EU member countries [ 61 ]. The results revealed that a significant portion of participants faced adverse experiences in sport contexts related to their sexual orientation and gender identity, including verbal abuse, structural discrimination, physical boundary crossing, and violence. An overwhelming majority of respondents (92.9%) view homophobia and transphobia in sport as current issues. Additionally, 20% of respondents reported avoiding participation in sport due to concerns about their sexual orientation or gender identity, while 16% of active participants experienced at least one related negative incident in the past year. Notably, male student-athletes exhibited higher levels of homophobic attitudes compared to their female counterparts and non-physical education students [ 15 , 16 , 62 ]. Conversely, female athletes reported experiencing less fear of exclusion and a more inclusive team environment [ 22 , 63 , 64 ], highlighting significant gender disparities in homonegativity in sport.

Group and individual identity

The distinct team interaction inherent in sport may enhance or support expressions of homonegativity and discrimination, as Social Identity Theory posits that negative beliefs about certain groups may develop group identity [ 65 , 66 , 67 ]. This phenomenon is particularly noticeable in intercollegiate sport, where a strong emphasis on physical attributes and abilities often results in prejudices against those who deviate from established norms [ 16 ]. Such discrimination and mistreatment of SMSAs frequently stem from their teammates and coaches. Many SMSAs choose to conceal their sexual orientation due to fear of ostracism [ 60 ], with team members often identified as the primary perpetrators of discrimination [ 61 ].

Therefore, navigating sexual identity within intercollegiate sport is challenging for SMSAs, as their minority status becomes a focal point, impacting their overall experience [ 68 , 69 ]. They encounter a unique psychological and emotional burden, striving to reconcile societal norms and expectations with their true selves. This constant negotiation and management of their identity across different contexts further complicates their experiences, frequently leading to difficulties in maintaining authenticity [ 19 ]. Therefore, SMSAs in intercollegiate sport face intricate challenges in balancing their authentic identity with societal norms, significantly impacting their experience and sense of self.

Theoretical framework

Self-categorisation theory (SCT), an extension of Social Identity Theory, provides a valuable perspective for examining the perceptions of SMSAs in China, focusing on intragroup processes and individual navigation of personal and social identities [ 70 , 71 ]. Key principles of SCT, including self-categorisation, salience, depersonalisation, and individuality [ 67 ], are instrumental in understanding how SMSAs navigate their sexual identities within the confines of sport norms. Applying SCT, this study could explore the complex interplay of intragroup relations and identity processes among SMSAs in the Chinese sport context, underscoring how contextual factors distinctly shape their identity.

Purpose of the study

The purpose of this study is to explore SMSAs’ perceptions of their sexual minority identity within the Chinese sports context and understand how this identity influences their participation in sports. By illuminating the specific challenges and issues related to sexual minority identity in Chinese intercollegiate sports, this study provides a deeper understanding of the experiences of sexual minorities in this field.

Research design

This study was conducted with the interpretivist paradigm, which emphasises understanding the subjective experiences and meanings that individuals assign to their world. It posits that reality is not objective but is constructed through individual perceptions and social interactions [ 72 ]. Given the aim of exploring the perceptions of sexual minority identity in sport from SMSAs’ perspectives, a qualitative research approach is appropriate. In line with the purpose of the study, the Interpretative Phenomenological Analysis (IPA) was adopted in this study, an approach aimed at understanding people’s lived experiences and how they make sense of these experiences in the context of their personal and social worlds [ 73 ]. IPA research encompasses phenomenology, hermeneutics, and idiography and emphasises the personal significance of self-reflection among individuals with a shared experience in a specific context [ 74 ]. Additionally, IPA is particularly suitable for research focusing on identity and self-awareness [ 75 ]. The features and focus of IPA are consistent with the purpose of this study. Therefore, IPA was considered a suitable approach to explore the SMSAs’ perceptions of their sexual minority identity within the sport context in China.

Researcher characteristics and reflexivity

During the data collection phase of this study, the first researcher was a Ph.D. candidate and had obtained her Ph.D. by the time of this manuscript’s submission. Her doctoral committee continuously supervised the research. The first researcher’s doctoral committee members are proficient in qualitative research. The first researcher and the second coder have received systematic qualitative training, are skilled in qualitative analysis software (NVivo), and have published empirical studies using the IPA approach. Although none of the research team members were SMSAs, the first researcher and the second coder maintained long-term contact with SMSAs through their involvement in sport teams. The first researcher was a former student-athlete and is currently working as a coach. Given her background, she has had extensive time to interact with and understand SMSAs within student teams.

Participants and procedures

Purposive and snowball sampling methods were employed to recruit a homogeneous sample for this study, as recommended by Smith and Nizza [ 73 ]. Following approval from Universiti Putra Malaysia’s Human Research Review Committee, the researcher initially reached out to SMSAs within her network, subsequently expanding outreach through social media to reach a broader pool of potential participants. The participants were selected based on specific inclusion criteria (Table  1 ), ensuring relevance to the study’s focus. Of the 22 individuals contacted, 16 agreed to participate, while six individuals declined participation due to concerns regarding potential exposure. The sample included a diverse representation of sexual minority subgroups: one asexual man, four bisexual women, three gay men, and eight lesbians. Given the relatively low prevalence of asexual individuals [ 76 , 77 ], we only had one participant from this subgroup. Strict confidentiality measures were enforced, with participants assigned pseudonyms and their college affiliations omitted for anonymity. The demographic details of the participants are outlined in Table  2 .

In phenomenological research, the focus is on rich individual experiences rather than data saturation [ 78 ]. Similarly, IPA research aims to explore participants’ personal and social worlds through detailed, in-depth analysis [ 79 ]. Smith and Nizza [ 73 ] also highlighted that in IPA research, sample size is less crucial because of the emphasis on detailed analysis in small, homogeneous samples. Therefore, the richness of data and the depth of insight into each participant’s experience are more important than the number of participants or reaching data saturation. This study utilised IPA’s in-depth analytical approach with sixteen participants to provide detailed data. This methodological approach allows for a comprehensive exploration of individual experiences, aligning with the study’s objectives.

Data collection

Data for this study were collected through semi-structured interviews (Appendix A), allowing participants to choose the mode, time, and location, including face-to-face or online sessions on Chinese social networks. Each interview’s length is detailed in Table  2 , with an average duration of 63 min. Before each interview, participants signed informed consent forms following a detailed briefing on the study’s purpose and procedures. Given the sensitive nature of the research, the interviews were conducted solely between the researcher and the participant to ensure a safe and comfortable environment, fostering open and honest communication.

The methods of data collection exhibited some qualitative differences. In face-to-face interviews, participants were often cautious and hesitant to share personal experiences. Conversely, online interviews proved more effective, as participants felt more relaxed, leading to quicker rapport and greater openness. This difference likely stems from the reduced perceived risk of exposure in an online setting. Due to the clear objectives of the study and the structured interview guide, there were no differences between the data from current SMSAs and former SMSAs.

Notably, one participant provided data through written essays instead of a semi-structured interview due to concerns about exposure and discomfort. After discussing the matter, the participant agreed to respond to interview questions in written form. The first researcher sent the interview questions to the participant, who then provided written responses. Follow-up questions were asked based on these initial responses, resulting in four sets of essay responses. This approach, which aligns with the conventions of phenomenological research [ 80 ], allowed the participant to express their experiences comfortably. The essay data were analysed alongside the semi-structured interview data, with common themes identified across all responses.

Documents and field notes supplemented the data collection. Documents included photographs, videos, and diaries. With participant consent, these documents were analysed for relevance to the research purpose. Field notes captured contextual information during both face-to-face and online interviews, including keywords and participants’ pauses and intonations, with immediate elaboration post-interview to avoid biases [ 81 , 82 ]. These detailed notes contextualised data analysis [ 74 ] and contributed to the research’s credibility.

Data analysis

The data analysis in this study followed a seven-step process aligned with IPA research guidelines and contemporary IPA terminology. The data analysis procedure is depicted in Fig.  1 . The IPA analysis is iterative and inductive [ 83 ], involving the organisation of data into a structured format for easy tracking through various stages – from initial exploratory notes on transcripts to the development of empirical statements, theme clustering, and final group theme structure. The theoretical framework was incorporated at the final stage of empirical theme development.

To enhance the study’s validity, the first author invited another Ph.D. candidate to participate in the data analysis process. After the interview recordings were translated into transcripts using audio software, the first researcher listened to the recordings repeatedly to correct the transcripts. The second coder reviewed the recordings to ensure the transcriptions were accurate and verbatim. The first author employed NVivo software (released in March 2020) for coding, and the second coder utilised manual coding. All data were analysed in Chinese to maintain linguistic integrity and then translated into English for theme presentation.

Data Analysis Procedure. Adapted from Smith et al. ( 74 )

The procedures of this study adhered to the COREQ Checklist [ 84 ] (Appendix B) and the IPA Quality Evaluation Guide [ 85 ] to ensure rigour. The research met the good quality requirements for IPA studies as outlined by Smith [ 85 ] (Table  3 ). Throughout the research, emphasis was placed on internal validity, external validity, and reliability to maintain the study’s rigour and quality. The methods employed to address these aspects are summarised in Table  4 .

This study explored SMSAs’ perceptions of sexual minority identity within intercollegiate sport in China. From the perspective of SCT, the results uncovered four key themes from SMSA’s team-based interactive experiences. The research themes, along with their corresponding sub-themes and occurrences, are presented in Table  5 .

Hidden truths

The hidden truths refer to facts, scenarios, or knowledge that are not commonly known or readily available. In this study, the existence of SMSAs in intercollegiate sport was undeniable, yet it remained concealed due to the prevailing lack of transparency.

SMSAs are common in sport

This research uncovered the extensive existence of SMSAs in Chinese sport. Almost all participants acknowledged the ubiquity of sexual minorities in sport, with 12 out of the 16 participants specifically highlighting the presence of SMSAs in collegiate sport:

I think everyone is generally aware of sexual minorities; all people are aware of them to a greater or lesser extent. It is generally agreed that the existence of sexual minorities is a common phenomenon in modern society, and even more so in Sport, as anyone involved in sport knows that (Adam).

Participants frequently described the presence of SMSAs in intercollegiate sport, using terms like “widespread”, “common”, “normal”, and “quite many”. Several participants also provided specific details about the number of SMSAs in their respective teams. Jackie remarked, “At that time, half of my teammates were lesbians” (Jackie). Similarly, Zoe noted the significant presence of SMSAs in her team, “I think it (the number of SMSAs) was almost half of the team at that time. But I don’t know about the senior players; almost half of our junior players were SMSAs” (Zoe).

Silent identity

Participants noted the prevalence of SMSAs in sport but also emphasised the difficulty of openly discussing sexual minority identity in this context. They described the sport environment as reserved and lacking open conversations about SMSAs and their experiences.

The reticent nature of sport teams regarding sexual minority identity was evident in their attitudes. William observed, “I feel like most of my teammates just don’t take a stand. They don’t want to make a statement about SMSAs. Nor did they say they supported it or didn’t support it” (William). Similarly, Mia considered sexual minority identity as a personal issue, inappropriate for open discussion.

No one wants to ask or discuss this openly…we live in a very conservative environment all the time, and none of this content is something that teammates should be concerned about, and people would feel offended if you don’t handle it well (Mia).

Some SMSAs viewed avoiding discussions on sexual minorities in sport as respectful to teammates, aiming for a comfortable, stress-free environment. Joy said, “We came here to play, right? I don’t think any of the other players want to feel phased by who you are” (Joy). Mia echoed this sentiment:

…in team training, the game is the game, and I rarely bring other emotions into it…. In the company of most of our teammates, we don’t interact with each other in that way. It’s probably a default rule that respect is distance, I guess (Mia).

Charlotte, involved in volleyball and basketball, recounted a teammate’s public derogation due to her sexual minority identity, an incident not openly addressed by the team. She perceived sexual identity as a “taboo” topic. The narratives revealed a cautious approach among SMSAs towards expressing their sexual minority identity in sport. They felt compelled to carefully manage their sexual orientation, minimising its disclosure. This hesitancy likely stemmed from the existing reticence and limited acceptance of SMSAs in sport, fostering a sense of invisibility and concern over potential negative consequences.

Prioritisation of athlete identity

The theme of prioritisation of athlete identity suggests that for SMSAs, their identity as an athlete may play a more prominent or influential role in shaping their self-conception compared to their sexual minority identity.

Be an athlete

Several participants believed their primary role as student-athletes was to engage in sport, and they valued this aspect of their identity significantly. Joy expressed this sentiment, “I love volleyball very much … I don’t care much about relationships; I just love volleyball, and I think we are all here to do this, and nothing else matters. You don’t need to stress about it (sexual minority identity)” (Joy).

Emma echoed a similar perspective, noting, “I think my teammates are very professional; our program requires a high technical standard, and we spend most of our time training; other than that, things don’t seem that important” (Emma). When queried about the importance of sexual minority identity, she responded, “Yes, at least not concerning sport performance, or maybe it will have a bad effect” (Emma). Additionally, some participants felt that in the context of sport, sexual minority identity might be sidelined. Adam commented:

“We don’t share it (sexual minority identity) unless someone asks. We’re a team first, and then we’re individuals, and for me, I’m important personally, but in the team, we all probably need to sacrifice some of ourselves to make the team more united and stronger” (Adam).

Participants’ views as both student-athletes and sexual minorities highlighted contrasts in the intercollegiate sport environment. Their student-athlete identity was key in shaping self-perception and fostering a sense of community, while their sexual minority identity was often marginalised in aspects of interpersonal relations, team support, and self-identity development.

Sport performance first norms

In team sport, leaders are crucial in creating inclusive spaces for SMSAs and setting behavioural and attitudinal standards, including those towards SMSAs. In this study, some participants believed that coaches’ criteria for acceptance of sexual minority individuals or intra-team romantic relationships were based on athletic performance.

Some coaches firmly believe that team relationships negatively impact team performance and, therefore, strictly prohibit romantic relationships between teammates. Joy recalled,

She couldn’t accept that… she thinks being an athlete like that is ridiculous. It would make a mess; her team would be in a mess. She said you two are dating and that playing will affect your emotions, which means she meant to say there is no way I can treat another girl as a normal teammate… (Joy).

In contrast, some coaches adopt a more tolerant attitude. Jackie’s coach believes that if the team’s overall performance is not affected, issues such as sexual orientation or team relationships can be ignored. Jackie stated, “My coach is male and old, but he should know what’s going on, especially since our captain has dated several teammates and the coach pretends not to know. He would only care if we were winning games” (Jackie).

Whether it instructs prohibition or an indifferent attitude, both narratives reflect that the team’s norms for inclusivity are based on sport performance. These norms also influence how SMSAs assess their own sexual minority identity within the team, as Adam said:

As of now, I have someone in the team that I have a crush on and haven’t dated. Maybe if he and I argued over training or a game, it would affect the performance of the team and the relationship between teammates…. I don’t think I could let that happen (Adam).

The participants’ narratives emphasise how the “Sports Performance First” norms influence the attitudes and behaviours of coaches and SMSAs within the team. These norms not only shape the team culture but also profoundly affect how SMSAs navigate their identities and relationships in the team environment.

However, the excessive focus on sport performance highlights the athletic identity of student-athletes while neglecting their other identities, especially those of sexual minorities. This singular focus leads to the neglect of the personal needs and diverse identities of athletes. Although these measures may seem to ensure the overall performance of the team, they overlook the psychological health and holistic development needs of the individuals.

Self-stereotyping

Self-stereotyping denotes the tendency of SMSAs to describe themselves using stereotypical attributes in the sport context. These descriptions frequently align with stereotypical perceptions prevalent in the external environment. SMSAs tend to be perceived as having specific physical traits or behavioural tendencies.

Specific physical traits

Sophia provided an illustrative example of self-stereotyping through her personal experience. She commented:

In the beginning, I would think that if you are an SMSA, you must fit some characteristics. For example, at that time, I saw some lesbians in my team who had short hair or wore baggy t-shirts; I was a bit frustrated by my long hair and feminine appearance…and I felt that I might not quite fit those criteria. So, then I cut my hair and even wore a wrapping bra to the training ground (Sophia). Sophia’s narrative underscores how the pressure to conform to certain physical traits led her to change her appearance to fit the stereotypical image of an SMSA within the sport context.

Behavioural tendencies

In addition to physical traits, SMSAs also feel compelled to conform to certain behavioural tendencies that are stereotypically associated with SMSAs. Zoe explained, “Because of who I am (T), I felt I should have to perform stronger, so I put up with much training…. I felt I should be there to protect the other players; if I were vulnerable, I would look down on myself” (Zoe). This indicates a sense of obligation among some female SMSAs to embody strength, aligning with the stereotypical image of female SMSAs in sport. Conversely, male SMSAs in men’s teams often faced stereotypes of being fragile, weak, or exhibiting feminine traits. Royal noted that behaviours of some male SMSAs, like engaging in non-sport-related banter, led to gossip and negative perceptions within men’s sport. To avoid these stereotypes, Royal aimed to mimic the mannerisms of heterosexual athletes, as he explained:

I try to avoid being close to the team’s prominent male SMSAs and try to stay out of related conversations; I don’t want to be a standard gay; I want to have the same college life as the rest of the team (heterosexuality) (Royal).

Stereotypes in sport often forced SMSAs into roles incongruent with their authentic identities, significantly impacting their self-expression and identity. The pressure to conform to societal norms in sport settings created internal conflicts for SMSAs, challenging their ability to maintain their true sense of self.

This theme addresses situations where student-athletes engage in intra-team intimacy or mimic being SMSAs in sport. This attempt has two key elements: prolonged contact leading to intimacy and influence from sexual minority teammates.

Prolonged contact leading to intimacy

Participants noted that extensive training and competition schedules in sport fostered close bonds among team members. Lucas shared, “When we were preparing for the tournament, we trained together every morning and evening…the game spanned for almost a month, and after that, we felt as close as family to our teammates” (Lucas). Similarly, Ruby pointed out, “Back then, we were training every afternoon until late at night; it was quite hard (the training was very strenuous) … it lasted for six months” (Ruby). These prolonged interactions sometimes led to the development of more profound attractions among student-athletes.

“I think we had many moments of trust and intimacy together on the field that built up some heartfelt feelings. These feelings made me feel emotions beyond that of a teammate…. Then I realised that gender might not be so important because it’s hard to build that kind of relationship in a typical romance” (Savannah).

Influence from sexual minority teammates

Participants also described how interactions with sexual minority teammates led them to explore their own sexual identities, as illustrated by Ava’s recounting of her initial same-gender relationship experience:

That time we went out to a tournament, and I found that four of my teammates, three of them were lesbians…we didn’t have games at night, so they had been talking to their girlfriends every night on the phone, and I just felt as if that was not too bad. Probably influenced by them, I got a girlfriend at that tournament as well…. Even though we broke up when we returned, I could accept girls (Ava).

Mia described a similar experience:

There were some lesbians in my team, and then it just seemed natural that I got close to one of them…. Well, I was thinking about whether that relationship would affect the team. But then I found out that there were other couples on the team. So, I feel like I wasn’t doing anything wrong (Mia).

The phenomenon highlights the significant role of peer influence in team settings. When individuals are around many teammates in same-gender relationships, it fosters an environment that normalises such relationships. Notably, this influence is not coercive but stems from observing and interacting with teammates who are comfortable with their sexual orientations. This environment helps individuals feel accepted and more confident in exploring their identities and relationships.

This study explored the perceptions of SMSAs regarding their sexual identity within intercollegiate sport in China. Its importance lies in its contribution to understanding the complex realities of SMSAs in China, an area that has lacked depth in the literature. By reaffirming the necessity of examining these athletes’ experiences, this study reveals the intricate conflict between adhering to team norms and expressing personal characteristics within the context of the Chinese social and cultural background.

The results show that SMSAs are a recognised reality in Chinese intercollegiate sport, consistent with findings from Western countries. While precise figures of sexual minorities in sport may vary across countries, it is acknowledged that they are present at all competitive levels, from school and college sport to the professional sphere [ 22 , 86 , 87 , 88 , 89 , 90 , 91 ]. Although no national census on sexual minorities in China or in sports environments exists, related research indicates that many college and university students self-identify as sexual minorities. For instance, an online survey conducted across 26 colleges and universities in 10 Chinese provinces found that over 8% of students identify as sexual minorities [ 36 ]. Additionally, another national survey revealed that nearly a quarter of college students identify as non-heterosexual [ 92 ]. Recognising and addressing the unique challenges faced by sexual minority youth, who make up a notable percentage of the student population, is essential for sport and educational institutions.

Despite the apparent prevalence of SMSAs, the study confirms that their identities often remain hidden in the context of Chinese intercollegiate sport. This can be attributed to two main reasons: First is the concern about discrimination if exposed. Chinese sexual minorities frequently report experiencing abuse or discrimination in families, schools, and workplaces [ 93 ]. Additionally, conversion therapies and discriminatory counselling practices persist in mental health services [ 94 ], creating an environment where discrimination is a significant concern, thereby reducing the likelihood of SMSAs coming out in the sports environment. The second reason is the constraint of traditional Chinese culture. The dominant Confucian culture in China emphasises harmony, internalised homonegativity, and conformity [ 95 , 96 ], often at the expense of individual expression and identity development. This cultural backdrop influences how sexual minorities perceive their own identities [ 97 ] and creates an ideological constraint that leads to social rejection and resistance towards sexual minorities [ 98 ], thereby reducing the visibility of sexual orientation-related topics in the Chinese sport context.

Moreover, SMSAs in China often prioritise their athlete identity over their sexual minority identity, influenced by the attitudes of team leaders. This tendency is reinforced by coaches who primarily focus on the biological sex of athletes and lack training or understanding related to sexual minority issues [ 99 ]. Consequently, the Chinese coaches’ lack of knowledge about sex and sexual orientation exacerbates the silence surrounding sexual minority identities in the Chinese collegiate sport environment and intensifies the identity conflict for SMSAs. Emphasising athletic performance is central in sport but should not overshadow the holistic development of student-athletes. McCavanagh and Cadaret [ 100 ] noted that student-athletes might face challenges in reconciling various aspects of their identity in a heteronormative sport context. The suppression of sexual minority identity can lead to isolation from potential support systems that nurture positive sexual and gender identities. Prioritising athletic success over broader student development in sport departments limits growth opportunities for all students, including SMSAs. Chavez et al. [ 101 ] emphasised that student development requires recognising and valuing diversity, suggesting that a singular focus on athletic prowess can diminish the benefits of diversity among student-athletes. Embracing diversity is not only a personal journey but also one that can enhance the collective experience within sport settings.

In addition, self-stereotyping within SCT involves aligning one’s self-concept with the characteristics of valued social categories [ 102 ]. Latrofa [ 103 ] suggests that members of low-status groups, like SMSAs in sport, may self-stereotype to align more closely with their group, reflecting recognition of lower status and self-perception through peers. This study revealed SMSAs shape their self-identity based on the attitudes prevalent in their sport environment, with influences from peers and coaches being internalised as personal attitudes [ 104 ]. Such self-stereotyping supports maintaining a favourable social identity and adhering to group norms but can reinforce negative stereotypes and prejudices within sport.

Internalising stereotypes may lead SMSAs to develop prejudices against themselves and others, perpetuating discrimination. It can also hinder individual development, impacting self-esteem and confidence. For example, aligning with negative stereotypes could cause SMSAs to doubt their worth and capabilities, affecting emotional well-being and satisfaction. Liu and Song’s [ 105 ] survey of Chinese college students illustrated the direct impact of gender self-stereotypes on life satisfaction, highlighting the significant effects of self-stereotyping on individual well-being.

Furthermore, in the context of traditional and reserved Chinese culture, intercollegiate sport offers a relatively free and open space for sexual minority women. The results of this study suggest that the visibility of sexual minority women in teams and the long time spent together allow these athletes to explore and establish intimate relationships. These results are similar to findings in Spanish studies [ 103 ], which highlighted the protective and liberating role of sports teams in the sexual exploration of female sexual minority athletes. Research by Organista and Kossakowski on Polish female footballers [ 106 ] and Xiong and Guo [ 96 ] on Chinese women’s basketball teams also revealed a climate of non-heteronormativity in women’s sport. These climates provide a sanctuary from heterosexual pressures, allowing sexual minority athletes to engage in sport free from traditional constraints. Such environments help female sexual minority athletes navigate and subvert heteronormative norms by cultivating supportive subcultural networks within their teams.

This study addresses the lack of in-depth research on the experiences of SMSAs in Chinese intercollegiate sport. It fills the gap by exploring the complex realities of SMSAs, focusing on their identity conflicts and the influence of the Chinese social and cultural background. Specifically, this study provides valuable insights that align with SCT [ 71 ]. This study addresses a notable gap in the existing literature regarding sexual minority sport participation, as rarely have these perceptions been explored. Drawing from the lens of SCT, the results of this study revealed several valuable insights into how their sexual minority identity impacts their participation in sport. These findings not only enhance our understanding of how SCT applies to the sport experiences of sexual minority individuals but also contribute to the advancement of SCT in research on sexual minority sport participation. The themes uncovered in this study closely align with central SCT concepts such as identity salience, self-stereotyping, and depersonalisation, illuminating the ways SMSAs comprehend and express their sexual minority identity within the intercollegiate sport context. SCT, with its focus on both intragroup and intergroup relations within the multifaceted construct of the self, offers valuable insights into the complexities of SMSAs’ self-perceptions and the intricacies involved in developing and manifesting their identities in the realm of sport.

Based on the results, more effort needs to be put into understanding sexual minority identities in intercollegiate sport. By examining the perspectives and experiences of SMSAs, we can gain insights into the interactions and influences of sexual minority individuals in the sport context. The interplay between an individual’s self-perception and situational dynamics results in a self-identity that mirrors the collective. In addition, the prevalent pressures and normative prejudices inherent in the sport system significantly influence their self-identity. Therefore, valuing SMSAs’ understanding of their self-identity shows respect for each person’s differences and rights. We hope the findings will be incorporated into existing sport policies to promote inclusivity and ensure safe participation for sexual minority students. To encourage and support the full development of SMSAs, college athletics and related institutions should prioritise understanding and respecting their perceptions of their sexual minority identity. By doing so, institutions can create a more inclusive and supportive environment that acknowledges and addresses the unique challenges faced by SMSAs.

Nevertheless, caution should be exercised when generalizing the findings, especially for subgroups with low representation, such as asexual individuals. While the study provides valuable insights into SMSAs’ perceptions of their sexual minority identity within the Chinese sport context, the limited number of asexual participants means their unique perspectives may not be fully captured. Therefore, these findings may not fully represent all sexual minority subgroups.

Future research could focus on exploring the perceptions and experiences among various sexual minority subgroups within sport participation in China. Additionally, considering the cultural diversity across China’s vast geographic regions, it would be valuable to examine how SMSAs perceive their minority identity in different cultural contexts. Given the scarcity of related studies in China, it is also important to survey other stakeholders in the sport environment, such as coaches and heterosexual student-athletes, to gain a broader understanding of perceptions of sexual minority identities. These insights can inform the development of targeted interventions aimed at ensuring the safe and inclusive participation of SMSAs in intercollegiate sport.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to ethical considerations but are available from the corresponding author on reasonable request.

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Xiang, M., Soh, K.G., Xu, Y. et al. Navigating sexual minority identity in sport: a qualitative exploration of sexual minority student-athletes in China. BMC Public Health 24 , 2304 (2024). https://doi.org/10.1186/s12889-024-19824-9

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