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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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Sampling in Qualitative Research

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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

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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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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Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

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Sampling Techniques for Qualitative Research

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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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Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research, the role of sampling in mixed methods-research.

Preparation of Qualitative Research

Douglas, H. (2010). Divergent orientations in social entrepreneurship organisations. In K. Hockerts, J. Robinson, & J. Mair (Eds.), Values and opportunities in social entrepreneurship (pp. 71–95). Palgrave Macmillan.

Chapter   Google Scholar  

Douglas, H., Eti-Tofinga, B., & Singh, G. (2018a). Contextualising social enterprise in Fiji. Social Enterprise Journal, 14 (2), 208–224. https://doi.org/10.1108/SEJ-05-2017-0032

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Douglas, H., Eti-Tofinga, B., & Singh, G. (2018b). Hybrid organisations contributing to wellbeing in small Pacific island countries. Sustainability Accounting, Management and Policy Journal, 9 (4), 490–514. https://doi.org/10.1108/SAMPJ-08-2017-0081

Douglas, H., & Borbasi, S. (2009). Parental perspectives on disability: The story of Sam, Anna, and Marcus. Disabilities: Insights from across fields and around the world, 2 , 201–217.

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Douglas, H. (1999). Community transport in rural Queensland: Using community resources effectively in small communities. Paper presented at the 5th National Rural Health Conference, Adelaide, South Australia, pp. 14–17th March.

Douglas, H. (2006). Action, blastoff, chaos: ABC of successful youth participation. Child, Youth and Environments, 16 (1). Retrieved from http://www.colorado.edu/journals/cye

Douglas, H. (2007). Methodological sampling issues for researching new nonprofit organisations. Paper presented at the 52nd International Council for Small Business (ICSB) 13–15 June, Turku, Finland.

Draper, H., Wilson, S., Flanagan, S., & Ives, J. (2009). Offering payments, reimbursement and incentives to patients and family doctors to encourage participation in research. Family Practice, 26 (3), 231–238. https://doi.org/10.1093/fampra/cmp011

Puamua, P. Q. (1999). Understanding Fijian under-achievement: An integrated perspective. Directions, 21 (2), 100–112.

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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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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

Availability of data and materials

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The authors declare no competing interests.

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Qualitative research: methods and examples

Last updated

13 April 2023

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Qualitative research involves gathering and evaluating non-numerical information to comprehend concepts, perspectives, and experiences. It’s also helpful for obtaining in-depth insights into a certain subject or generating new research ideas. 

As a result, qualitative research is practical if you want to try anything new or produce new ideas.

There are various ways you can conduct qualitative research. In this article, you'll learn more about qualitative research methodologies, including when you should use them.

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• What is qualitative research?

Qualitative research is a broad term describing various research types that rely on asking open-ended questions. Qualitative research investigates “how” or “why” certain phenomena occur. It is about discovering the inherent nature of something.

The primary objective of qualitative research is to understand an individual's ideas, points of view, and feelings. In this way, collecting in-depth knowledge of a specific topic is possible. Knowing your audience's feelings about a particular subject is important for making reasonable research conclusions.

Unlike quantitative research , this approach does not involve collecting numerical, objective data for statistical analysis. Qualitative research is used extensively in education, sociology, health science, history, and anthropology.

• Types of qualitative research methodology

Typically, qualitative research aims at uncovering the attitudes and behavior of the target audience concerning a specific topic. For example,  “How would you describe your experience as a new Dovetail user?”

Some of the methods for conducting qualitative analysis include:

Focus groups

Hosting a focus group is a popular qualitative research method. It involves obtaining qualitative data from a limited sample of participants. In a moderated version of a focus group, the moderator asks participants a series of predefined questions. They aim to interact and build a group discussion that reveals their preferences, candid thoughts, and experiences.

Unmoderated, online focus groups are increasingly popular because they eliminate the need to interact with people face to face.

Focus groups can be more cost-effective than 1:1 interviews or studying a group in a natural setting and reporting one’s observations.

Focus groups make it possible to gather multiple points of view quickly and efficiently, making them an excellent choice for testing new concepts or conducting market research on a new product.

However, there are some potential drawbacks to this method. It may be unsuitable for sensitive or controversial topics. Participants might be reluctant to disclose their true feelings or respond falsely to conform to what they believe is the socially acceptable answer (known as response bias).

Case study research

A case study is an in-depth evaluation of a specific person, incident, organization, or society. This type of qualitative research has evolved into a broadly applied research method in education, law, business, and the social sciences.

Even though case study research may appear challenging to implement, it is one of the most direct research methods. It requires detailed analysis, broad-ranging data collection methodologies, and a degree of existing knowledge about the subject area under investigation.

Historical model

The historical approach is a distinct research method that deeply examines previous events to better understand the present and forecast future occurrences of the same phenomena. Its primary goal is to evaluate the impacts of history on the present and hence discover comparable patterns in the present to predict future outcomes.

Oral history

This qualitative data collection method involves gathering verbal testimonials from individuals about their personal experiences. It is widely used in historical disciplines to offer counterpoints to established historical facts and narratives. The most common methods of gathering oral history are audio recordings, analysis of auto-biographical text, videos, and interviews.

Qualitative observation

One of the most fundamental, oldest research methods, qualitative observation , is the process through which a researcher collects data using their senses of sight, smell, hearing, etc. It is used to observe the properties of the subject being studied. For example, “What does it look like?” As research methods go, it is subjective and depends on researchers’ first-hand experiences to obtain information, so it is prone to bias. However, it is an excellent way to start a broad line of inquiry like, “What is going on here?”

Record keeping and review

Record keeping uses existing documents and relevant data sources that can be employed for future studies. It is equivalent to visiting the library and going through publications or any other reference material to gather important facts that will likely be used in the research.

Grounded theory approach

The grounded theory approach is a commonly used research method employed across a variety of different studies. It offers a unique way to gather, interpret, and analyze. With this approach, data is gathered and analyzed simultaneously.  Existing analysis frames and codes are disregarded, and data is analyzed inductively, with new codes and frames generated from the research.

Ethnographic research

Ethnography  is a descriptive form of a qualitative study of people and their cultures. Its primary goal is to study people's behavior in their natural environment. This method necessitates that the researcher adapts to their target audience's setting. 

Thereby, you will be able to understand their motivation, lifestyle, ambitions, traditions, and culture in situ. But, the researcher must be prepared to deal with geographical constraints while collecting data i.e., audiences can’t be studied in a laboratory or research facility.

This study can last from a couple of days to several years. Thus, it is time-consuming and complicated, requiring you to have both the time to gather the relevant data as well as the expertise in analyzing, observing, and interpreting data to draw meaningful conclusions.

Narrative framework

A narrative framework is a qualitative research approach that relies on people's written text or visual images. It entails people analyzing these events or narratives to determine certain topics or issues. With this approach, you can understand how people represent themselves and their experiences to a larger audience.

Phenomenological approach

The phenomenological study seeks to investigate the experiences of a particular phenomenon within a group of individuals or communities. It analyzes a certain event through interviews with persons who have witnessed it to determine the connections between their views. Even though this method relies heavily on interviews, other data sources (recorded notes), and observations could be employed to enhance the findings.

• Qualitative research methods (tools)

Some of the instruments involved in qualitative research include:

Document research: Also known as document analysis because it involves evaluating written documents. These can include personal and non-personal materials like archives, policy publications, yearly reports, diaries, or letters.

Focus groups:  This is where a researcher poses questions and generates conversation among a group of people. The major goal of focus groups is to examine participants' experiences and knowledge, including research into how and why individuals act in various ways.

Secondary study: Involves acquiring existing information from texts, images, audio, or video recordings.

Observations:   This requires thorough field notes on everything you see, hear, or experience. Compared to reported conduct or opinion, this study method can assist you in getting insights into a specific situation and observable behaviors.

Structured interviews :  In this approach, you will directly engage people one-on-one. Interviews are ideal for learning about a person's subjective beliefs, motivations, and encounters.

Surveys:  This is when you distribute questionnaires containing open-ended questions

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• What are common examples of qualitative research?

Everyday examples of qualitative research include:

Conducting a demographic analysis of a business

For instance, suppose you own a business such as a grocery store (or any store) and believe it caters to a broad customer base, but after conducting a demographic analysis, you discover that most of your customers are men.

You could do 1:1 interviews with female customers to learn why they don't shop at your store.

In this case, interviewing potential female customers should clarify why they don't find your shop appealing. It could be because of the products you sell or a need for greater brand awareness, among other possible reasons.

Launching or testing a new product

Suppose you are the product manager at a SaaS company looking to introduce a new product. Focus groups can be an excellent way to determine whether your product is marketable.

In this instance, you could hold a focus group with a sample group drawn from your intended audience. The group will explore the product based on its new features while you ensure adequate data on how users react to the new features. The data you collect will be key to making sales and marketing decisions.

Conducting studies to explain buyers' behaviors

You can also use qualitative research to understand existing buyer behavior better. Marketers analyze historical information linked to their businesses and industries to see when purchasers buy more.

Qualitative research can help you determine when to target new clients and peak seasons to boost sales by investigating the reason behind these behaviors.

• Qualitative research: data collection

Data collection is gathering information on predetermined variables to gain appropriate answers, test hypotheses, and analyze results. Researchers will collect non-numerical data for qualitative data collection to obtain detailed explanations and draw conclusions.

To get valid findings and achieve a conclusion in qualitative research, researchers must collect comprehensive and multifaceted data.

Qualitative data is usually gathered through interviews or focus groups with videotapes or handwritten notes. If there are recordings, they are transcribed before the data analysis process. Researchers keep separate folders for the recordings acquired from each focus group when collecting qualitative research data to categorize the data.

• Qualitative research: data analysis

Qualitative data analysis is organizing, examining, and interpreting qualitative data. Its main objective is identifying trends and patterns, responding to research questions, and recommending actions based on the findings. Textual analysis is a popular method for analyzing qualitative data.

Textual analysis differs from other qualitative research approaches in that researchers consider the social circumstances of study participants to decode their words, behaviors, and broader meaning. 

Learn more about qualitative research data analysis software

• When to use qualitative research

Qualitative research is helpful in various situations, particularly when a researcher wants to capture accurate, in-depth insights. 

Here are some instances when qualitative research can be valuable:

Examining your product or service to improve your marketing approach

When researching market segments, demographics, and customer service teams

Identifying client language when you want to design a quantitative survey

When attempting to comprehend your or someone else's strengths and weaknesses

Assessing feelings and beliefs about societal and public policy matters

Collecting information about a business or product's perception

Analyzing your target audience's reactions to marketing efforts

When launching a new product or coming up with a new idea

When seeking to evaluate buyers' purchasing patterns

• Qualitative research methods vs. quantitative research methods

Qualitative research examines people's ideas and what influences their perception, whereas quantitative research draws conclusions based on numbers and measurements.

Qualitative research is descriptive, and its primary goal is to comprehensively understand people's attitudes, behaviors, and ideas.

In contrast, quantitative research is more restrictive because it relies on numerical data and analyzes statistical data to make decisions. This research method assists researchers in gaining an initial grasp of the subject, which deals with numbers. For instance, the number of customers likely to purchase your products or use your services.

What is the most important feature of qualitative research?

A distinguishing feature of qualitative research is that it’s conducted in a real-world setting instead of a simulated environment. The researcher is examining actual phenomena instead of experimenting with different variables to see what outcomes (data) might result.

Can I use qualitative and quantitative approaches together in a study?

Yes, combining qualitative and quantitative research approaches happens all the time and is known as mixed methods research. For example, you could study individuals’ perceived risk in a certain scenario, such as how people rate the safety or riskiness of a given neighborhood. Simultaneously, you could analyze historical data objectively, indicating how safe or dangerous that area has been in the last year. To get the most out of mixed-method research, it’s important to understand the pros and cons of each methodology, so you can create a thoughtfully designed study that will yield compelling results.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organisation?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

• Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Pritha Bhandari

Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

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MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

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On This Page:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

• Gain deep contextual understandings of the subjective social reality of individuals
• To answer questions about experience and meaning from the participant’s perspective
• To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

Examples of qualitative research questions include: 

• How does stress influence young adults’ behavior?
• What factors influence students’ school attendance rates in developed countries?
• How do adults interpret binge drinking in the UK?
• What are the psychological impacts of cervical cancer screening in women?
• How can mental health lessons be integrated into the school curriculum? 

Characteristics 

Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it. 

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

• Define a Research Question : To start content analysis, a clear research question should be developed.
• Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
• Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
• Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
• Code the Data : Follow the coding rules to categorize data into themes.
• Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

• Define the Research Question : Develop a relevant research question to frame the analysis.
• Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
• Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
• Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

• Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
• Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
• Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
• Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
• Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
• Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

• Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
• Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
• Indexing : Apply the framework systematically to the entire study data.
• Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
• Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 

Co-coding 

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

1. Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

2. Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

3. Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

4. Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 

Subjectivity 

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

While individual qualitative studies are often limited in their generalizability due to factors such as sample size and context, metasynthesis enables researchers to synthesize findings from multiple studies, potentially leading to more generalizable conclusions.

By integrating findings from studies conducted in diverse settings and with different populations, metasynthesis can provide broader insights into the phenomenon of interest.

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

• Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
• Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
• Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

What is narrative analysis?

Narrative analysis is a qualitative research method used to understand how individuals create stories from their personal experiences.

There is an emphasis on understanding the context in which a narrative is constructed, recognizing the influence of historical, cultural, and social factors on storytelling.

Researchers can use different methods together to explore a research question.

Some narrative researchers focus on the content of what is said, using thematic narrative analysis, while others focus on the structure, such as holistic-form or categorical-form structural narrative analysis. Others focus on how the narrative is produced and performed.

Boeije, H. (2014). Analysis in qualitative research. Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology , 3 (2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Brooks, J., McCluskey, S., Turley, E., & King, N. (2014). The utility of template analysis in qualitative psychology research. Qualitative Research in Psychology , 12 (2), 202–222. https://doi.org/10.1080/14780887.2014.955224

Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological research and practice , 2 (1), 14-14. https://doi.org/10.1186/s42466-020-00059-z 

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology nursing forum , 41 (5), 545–547. https://doi.org/10.1188/14.ONF.545-547

Critical Appraisal Skills Programme. (2018). CASP Checklist: 10 questions to help you make sense of a Qualitative research. https://casp-uk.net/images/checklist/documents/CASP-Qualitative-Studies-Checklist/CASP-Qualitative-Checklist-2018_fillable_form.pdf Accessed: March 15 2023

Clarke, V., & Braun, V. (2013). Successful qualitative research: A practical guide for beginners. Successful Qualitative Research , 1-400.

Denny, E., & Weckesser, A. (2022). How to do qualitative research?: Qualitative research methods. BJOG : an international journal of obstetrics and gynaecology , 129 (7), 1166-1167. https://doi.org/10.1111/1471-0528.17150 

Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory. The Discovery of Grounded Theory , 1–18. https://doi.org/10.4324/9780203793206-1

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. doi:10.1177/1525822X05279903

Halpren, E. S. (1983). Auditing naturalistic inquiries: The development and application of a model (Unpublished doctoral dissertation). Indiana University, Bloomington.

Hammarberg, K., Kirkman, M., & de Lacey, S. (2016). Qualitative research methods: When to use them and how to judge them. Human Reproduction , 31 (3), 498–501. https://doi.org/10.1093/humrep/dev334

Koch, T. (1994). Establishing rigour in qualitative research: The decision trail. Journal of Advanced Nursing, 19, 976–986. doi:10.1111/ j.1365-2648.1994.tb01177.x

Lincoln, Y., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Mays, N., & Pope, C. (2000). Assessing quality in qualitative research. BMJ, 320(7226), 50–52.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16 (1). https://doi.org/10.1177/1609406917733847

Petty, N. J., Thomson, O. P., & Stew, G. (2012). Ready for a paradigm shift? part 2: Introducing qualitative research methodologies and methods. Manual Therapy , 17 (5), 378–384. https://doi.org/10.1016/j.math.2012.03.004

Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. London: Sage

Reeves, S., Kuper, A., & Hodges, B. D. (2008). Qualitative research methodologies: Ethnography. BMJ , 337 (aug07 3). https://doi.org/10.1136/bmj.a1020

Russell, C. K., & Gregory, D. M. (2003). Evaluation of qualitative research studies. Evidence Based Nursing, 6 (2), 36–40.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity , 52 (4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8

Scarduzio, J. A. (2017). Emic approach to qualitative research. The International Encyclopedia of Communication Research Methods, 1–2 . https://doi.org/10.1002/9781118901731.iecrm0082

Schreier, M. (2012). Qualitative content analysis in practice / Margrit Schreier.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 , 63–75.

Starks, H., & Trinidad, S. B. (2007). Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qualitative health research , 17 (10), 1372–1380. https://doi.org/10.1177/1049732307307031

Tenny, S., Brannan, J. M., & Brannan, G. D. (2022). Qualitative Study. In StatPearls. StatPearls Publishing.

Tobin, G. A., & Begley, C. M. (2004). Methodological rigour within a qualitative framework. Journal of Advanced Nursing, 48, 388–396. doi:10.1111/j.1365-2648.2004.03207.x

Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences , 15 (3), 398-405. https://doi.org/10.1111/nhs.12048

Wood L. A., Kroger R. O. (2000). Doing discourse analysis: Methods for studying action in talk and text. Sage.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences. European journal of education , 48 (2), 311-325. https://doi.org/10.1111/ejed.12014

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Qualitative Research Methods: Types, Analysis + Examples

Qualitative research is based on the disciplines of social sciences like psychology, sociology, and anthropology. Therefore, the qualitative research methods allow for in-depth and further probing and questioning of respondents based on their responses. The interviewer/researcher also tries to understand their motivation and feelings. Understanding how your audience makes decisions can help derive conclusions in market research.

What is qualitative research?

Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication .

This method is about “what” people think and “why” they think so. For example, consider a convenience store looking to improve its patronage. A systematic observation concludes that more men are visiting this store. One good method to determine why women were not visiting the store is conducting an in-depth interview method with potential customers.

For example, after successfully interviewing female customers and visiting nearby stores and malls, the researchers selected participants through random sampling . As a result, it was discovered that the store didn’t have enough items for women.

So fewer women were visiting the store, which was understood only by personally interacting with them and understanding why they didn’t visit the store because there were more male products than female ones.

Gather research insights

Types of qualitative research methods with examples

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience with reference to a particular topic. There are different types of qualitative research methods, such as in-depth interviews, focus groups, ethnographic research, content analysis, and case study research that are usually used.

The results of qualitative methods are more descriptive, and the inferences can be drawn quite easily from the obtained data .

Qualitative research methods originated in the social and behavioral research sciences. Today, our world is more complicated, and it is difficult to understand what people think and perceive. Online research methods make it easier to understand that as it is a more communicative and descriptive analysis .

The following are the qualitative research methods that are frequently used. Also, read about qualitative research examples :

1. One-on-one interview

Conducting in-depth interviews is one of the most common qualitative research methods. It is a personal interview that is carried out with one respondent at a time. This is purely a conversational method and invites opportunities to get details in depth from the respondent.

One of the advantages of this method is that it provides a great opportunity to gather precise data about what people believe and their motivations . If the researcher is well experienced, asking the right questions can help him/her collect meaningful data. If they should need more information, the researchers should ask such follow-up questions that will help them collect more information.

These interviews can be performed face-to-face or on the phone and usually can last between half an hour to two hours or even more. When the in-depth interview is conducted face to face, it gives a better opportunity to read the respondents’ body language and match the responses.

2. Focus groups

A focus group is also a commonly used qualitative research method used in data collection. A focus group usually includes a limited number of respondents (6-10) from within your target market.

The main aim of the focus group is to find answers to the “why, ” “what,” and “how” questions. One advantage of focus groups is you don’t necessarily need to interact with the group in person. Nowadays, focus groups can be sent an online survey on various devices, and responses can be collected at the click of a button.

Focus groups are an expensive method as compared to other online qualitative research methods. Typically, they are used to explain complex processes. This method is very useful for market research on new products and testing new concepts.

3. Ethnographic research

Ethnographic research is the most in-depth observational research method that studies people in their naturally occurring environment.

This method requires the researchers to adapt to the target audiences’ environments, which could be anywhere from an organization to a city or any remote location. Here, geographical constraints can be an issue while collecting data.

This research design aims to understand the cultures, challenges, motivations, and settings that occur. Instead of relying on interviews and discussions, you experience the natural settings firsthand.

This type of research method can last from a few days to a few years, as it involves in-depth observation and collecting data on those grounds. It’s a challenging and time-consuming method and solely depends on the researcher’s expertise to analyze, observe, and infer the data.

4. Case study research

T he case study method has evolved over the past few years and developed into a valuable quality research method. As the name suggests, it is used for explaining an organization or an entity.

This type of research method is used within a number of areas like education, social sciences, and similar. This method may look difficult to operate; however , it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in new research. This is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can likely be used in the research.

6. Process of observation

Qualitative Observation is a process of research that uses subjective methodologies to gather systematic information or data. Since the focus on qualitative observation is the research process of using subjective methodologies to gather information or data. Qualitative observation is primarily used to equate quality differences.

Qualitative observation deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing. This doesn’t involve measurements or numbers but instead characteristics.

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative research: data collection and analysis

A. qualitative data collection.

Qualitative data collection allows collecting data that is non-numeric and helps us to explore how decisions are made and provide us with detailed insight. For reaching such conclusions the data that is collected should be holistic, rich, and nuanced and findings to emerge through careful analysis.

• Whatever method a researcher chooses for collecting qualitative data, one aspect is very clear the process will generate a large amount of data. In addition to the variety of methods available, there are also different methods of collecting and recording the data.

For example, if the qualitative data is collected through a focus group or one-to-one discussion, there will be handwritten notes or video recorded tapes. If there are recording they should be transcribed and before the process of data analysis can begin.

• As a rough guide, it can take a seasoned researcher 8-10 hours to transcribe the recordings of an interview, which can generate roughly 20-30 pages of dialogues. Many researchers also like to maintain separate folders to maintain the recording collected from the different focus group. This helps them compartmentalize the data collected.
• In case there are running notes taken, which are also known as field notes, they are helpful in maintaining comments, environmental contexts, environmental analysis , nonverbal cues etc. These filed notes are helpful and can be compared while transcribing audio recorded data. Such notes are usually informal but should be secured in a similar manner as the video recordings or the audio tapes.

B. Qualitative data analysis

Qualitative data analysis such as notes, videos, audio recordings images, and text documents. One of the most used methods for qualitative data analysis is text analysis.

Text analysis is a  data analysis method that is distinctly different from all other qualitative research methods, where researchers analyze the social life of the participants in the research study and decode the words, actions, etc. 

There are images also that are used in this research study and the researchers analyze the context in which the images are used and draw inferences from them. In the last decade, text analysis through what is shared on social media platforms has gained supreme popularity.

Characteristics of qualitative research methods

• Qualitative research methods usually collect data at the sight, where the participants are experiencing issues or research problems . These are real-time data and rarely bring the participants out of the geographic locations to collect information.
• Qualitative researchers typically gather multiple forms of data, such as interviews, observations, and documents, rather than rely on a single data source .
• This type of research method works towards solving complex issues by breaking down into meaningful inferences, that is easily readable and understood by all.
• Since it’s a more communicative method, people can build their trust on the researcher and the information thus obtained is raw and unadulterated.

Qualitative research method case study

Let’s take the example of a bookstore owner who is looking for ways to improve their sales and customer outreach. An online community of members who were loyal patrons of the bookstore were interviewed and related questions were asked and the questions were answered by them.

At the end of the interview, it was realized that most of the books in the stores were suitable for adults and there were not enough options for children or teenagers.

By conducting this qualitative research the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research now the bookstore owner can now keep books for different age categories and can improve his sales and customer outreach.

Such qualitative research method examples can serve as the basis to indulge in further quantitative research , which provides remedies.

When to use qualitative research

Researchers make use of qualitative research techniques when they need to capture accurate, in-depth insights. It is very useful to capture “factual data”. Here are some examples of when to use qualitative research.

• Developing a new product or generating an idea.
• Studying your product/brand or service to strengthen your marketing strategy.
• To understand your strengths and weaknesses.
• Understanding purchase behavior.
• To study the reactions of your audience to marketing campaigns and other communications.
• Exploring market demographics, segments, and customer care groups.
• Gathering perception data of a brand, company, or product.

LEARN ABOUT: Steps in Qualitative Research

Qualitative research methods vs quantitative research methods

The basic differences between qualitative research methods and quantitative research methods are simple and straightforward. They differ in:

• Their analytical objectives
• Types of questions asked
• Types of data collection instruments
• Forms of data they produce
• Degree of flexibility

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You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).

• Look around on the home page. Does anything seem interesting to you?
• How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
• What do the reviews say about the restaurant you've chosen?
• What was the most important factor for you in choosing this spot?
• You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
• Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
• Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

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What is Qualitative Research? Definition, Types, Methods, Examples and Best Practices

What is Qualitative Research?

Qualitative research is defined as a research method used to understand qualitative aspects of consumer or human behaviour and expectations, through open ended questions and answers. 

Unlike quantitative research that focuses on quantifiable numerical data derived from typically closed-ended questionnaires, qualitative research emphasizes on open-ended and conversational exploration and interpretation of subjective insights. This enables in-depth exploration as per the flow of the conversation, rather than being limited to specific questions and limited options to select responses. 

Qualitative research methodology involves collecting non-numeric data, such as those derived from interviews, observations, or open-ended questionnaire surveys, to gain a holistic understanding of a particular subject.

The emphasis is on capturing the richness and context of the participants’ perspectives, enabling a more nuanced comprehension of social phenomena. Researchers actively engage with participants, employing methods like interviews or focus groups to gather detailed information and delve into the underlying meanings and motivations behind behaviors.

Qualitative research analysis relies on qualitative data analysis techniques, which involve interpreting textual or visual data through coding, thematic analysis, or narrative exploration. This interpretive process aims to uncover underlying meanings and generate insights that contribute to a deeper understanding of the studied phenomena. Qualitative research is particularly valuable when exploring complex social issues, understanding holistic consumer perceptions on brands and products, cultural contexts, or individuals’ subjective experiences where quantitative methods may fall short in capturing the intricacies of expectations and behavior.

Characteristics of Qualitative Research

Here are the key characteristics of qualitative research:

• In-Depth Understanding: Qualitative research aims to provide a comprehensive and in-depth understanding of a particular phenomenon. Researchers delve into the context, meanings, and perspectives of the participants, allowing for a nuanced exploration of the subject.
• Flexible Study Designs: Unlike rigid experimental designs in quantitative research, qualitative studies often have flexible and evolving methodologies. Researchers may adapt their approach based on emerging insights, allowing for a more dynamic and responsive investigation.
• Subjectivity and Interpretation: Qualitative research recognizes the subjective nature of human experiences. Researchers actively engage with participants, and the interpretation of data involves the researcher’s subjective insights. This acknowledgment of subjectivity contributes to the richness of the findings.
• Non-Numeric Data Collection: Qualitative research primarily relies on non-numeric data, such as interviews, observations, or open-ended surveys. This approach enables the collection of detailed and context-rich information, emphasizing the quality and depth of data over numerical precision.
• Participant Perspectives: Qualitative researchers often seek to understand the world from the participants’ perspectives. This involves exploring individuals’ lived experiences, beliefs, and emotions, providing a more holistic view of the studied phenomenon.
• Emergent Design: Qualitative studies often have an emergent design, meaning that the research design and data collection methods may evolve during the course of the study. This adaptability allows researchers to explore unforeseen aspects and adjust their focus based on emerging patterns.
• Qualitative Data Analysis Techniques: Qualitative research involves unique data analysis techniques such as coding, thematic analysis, and narrative exploration. These methods help researchers identify patterns, themes, and meanings within the qualitative data collected.
• Contextualized Findings: Qualitative research emphasizes the importance of context in understanding behaviors and phenomena. Findings are often presented with detailed contextual information, providing a more holistic view of the studied subject within its natural setting.

These characteristics collectively contribute to the strength of qualitative research in exploring the complexity and depth of human experiences and social phenomena.

Key Components of Qualitative Research

The key components of qualitative research include:

• Research Design: This outlines the overall plan for the study, including the research questions or objectives, the chosen qualitative approach (e.g., phenomenology, grounded theory, ethnography), and the rationale for the selected methodology.
• Participants: Describes the individuals or groups involved in the study, including the criteria for selection. Qualitative research often involves purposeful sampling to ensure participants can provide rich and relevant information.
• Data Collection Methods: Specifies the techniques used to gather qualitative data. Common methods include interviews, focus groups, participant observations, and document analysis. Researchers choose methods based on the research questions and the nature of the phenomenon under investigation.
• Data Analysis Techniques: Details the approach to analyzing qualitative data. Techniques such as coding, thematic analysis, and constant comparison are employed to identify patterns, themes, and meanings within the collected data.
• Ethical Considerations: Addresses ethical issues and safeguards for participants. This includes obtaining informed consent, ensuring confidentiality, and minimizing any potential harm to participants throughout the research process.
• Researcher’s Role: Acknowledges the influence of the researcher in the study. This includes reflexivity—being aware of and transparent about the researcher’s biases, perspectives, and potential impact on the research.
• Validity and Reliability: While qualitative research doesn’t adhere to traditional notions of validity and reliability as in quantitative research, it emphasizes concepts like trustworthiness and credibility. Researchers employ various strategies, such as triangulation and member checking, to enhance the rigor of their findings.
• Results/Findings: Presents the outcomes of the data analysis, often organized around themes or patterns. Findings are typically illustrated with quotations or examples from participants to support the interpretation.
• Discussion and Interpretation: Involves a thorough examination and interpretation of the results in relation to existing literature and theoretical frameworks. Researchers discuss the implications of their findings and consider broader contexts.
• Conclusion: Summarizes the main insights, contributions, and potential avenues for future research. It provides a concise overview of the study’s significance and relevance in the broader academic or practical context.

These components work together to ensure a comprehensive and rigorous qualitative research study, allowing for a deep exploration of the complexities inherent in human experiences and social phenomena.

Types of Qualitative Research Methods with Examples

There are several types of qualitative research methods, each suited to different research questions and objectives. Here are some common types with examples:

• Definition: In-depth, one-on-one conversations between the researcher and the participant(s) to gather detailed information about their experiences, opinions, or perspectives.
• Example: Conducting interviews with survivors of a natural disaster to understand the psychological impact and coping strategies they employed.
• Definition: A group discussion led by a researcher to explore a specific topic, allowing participants to share their thoughts and engage in conversation with each other.
• Example: Using a focus group to gather insights from parents about their preferences and concerns regarding a new school curriculum.
• Definition: Systematic and careful observation of behavior, events, or phenomena in their natural setting, without intervention or manipulation by the researcher.
• Example: Observing and recording communication patterns in a workplace to understand team dynamics.
• Definition: An in-depth examination of a specific instance, situation, or individual, providing a detailed and holistic understanding of the subject.
• Example: Conducting a case study on a successful community health intervention program to identify key factors contributing to its effectiveness.
• Definition: Immersive research involving prolonged engagement and participation in the daily lives of a specific group or community to understand their culture and practices.
• Example: Living among and studying a nomadic tribe to document their traditions, social structures, and rituals.
• Definition: A method of developing theories by systematically gathering and analyzing data, allowing themes and concepts to emerge directly from the data.
• Example: Using grounded theory to explore the process of decision-making in a business organization without preconceived notions.
• Definition: Systematic analysis of textual, visual, or audio content to identify patterns, themes, and meanings.
• Example: Analyzing online forum discussions to understand public sentiment and concerns about a controversial policy.
• Definition: Exploration of individual or collective stories to understand the meaning and significance of experiences.
• Example: Collecting and analyzing personal narratives of cancer survivors to uncover common themes and coping strategies.
• Definition: A philosophical approach and research method focused on exploring and describing lived experiences from the perspective of the individuals who have had them.
• Example: Studying the phenomenon of “flow” by exploring the subjective experiences of individuals deeply engaged in challenging activities like sports or creative endeavors.

Benefits of Qualitative Research

Qualitative research offers several benefits, including:

• In-Depth Understanding: Qualitative research allows researchers to explore complex phenomena in-depth, providing a rich and nuanced understanding of the subject matter. It goes beyond surface-level insights, capturing the depth and context of human experiences.
• Flexibility: Qualitative methods are flexible and adaptable, allowing researchers to adjust their approach based on emerging insights. This flexibility is particularly valuable when exploring dynamic or unexpected aspects of a phenomenon.
• Contextual Insight: By emphasizing the context in which behaviors or phenomena occur, qualitative research provides a holistic view. This contextual insight is crucial for understanding the cultural, social, or environmental factors influencing the subject of study.
• Participant Perspectives: Qualitative research actively involves participants, allowing them to share their perspectives, experiences, and voices. This participant-centered approach contributes to a more authentic and representative portrayal of the studied phenomenon.
• Exploratory Nature: Qualitative research is well-suited for exploratory studies where the goal is to generate hypotheses, theories, or a deeper understanding of a topic. It helps researchers uncover new insights and explore uncharted territory.
• Applicability to Complex Social Issues: Qualitative research is particularly effective in studying complex social issues, diverse cultures, and subjective experiences. It enables researchers to navigate and make sense of intricate social dynamics.
• Cultural Sensitivity: Qualitative methods allow for cultural sensitivity and the exploration of cultural nuances. Researchers can adapt their approach to different cultural contexts, ensuring that the study is respectful and relevant to the participants.
• Naturalistic Settings: Qualitative research often takes place in naturalistic settings, providing a realistic and ecologically valid environment for studying behaviors. This setting enhances the ecological validity of the findings.
• Theory Development: Qualitative research contributes to the development and refinement of theories. Through inductive reasoning, researchers can generate new concepts and theoretical frameworks based on the patterns and themes identified in the data.
• Humanizing Data: Qualitative research humanizes data by bringing personal stories and experiences to the forefront. This approach fosters empathy and a deeper connection with the subjects under investigation.
• Validity and Trustworthiness: While qualitative research doesn’t strictly adhere to traditional notions of validity, it emphasizes trustworthiness through strategies like triangulation, member checking, and prolonged engagement, enhancing the credibility of the findings.

These benefits make qualitative research a valuable approach for exploring the complexities of human behavior, attitudes, and social phenomena.

Potential Challenges of Qualitative Research

Qualitative research comes with its own set of challenges, including:

• Subjectivity and Bias: The researcher’s subjectivity and biases can influence the study, from data collection to analysis and interpretation. Maintaining objectivity can be challenging, and researchers must be aware of their own perspectives.
• Limited Generalizability: Findings from qualitative research are often context-specific and may not be easily generalizable to broader populations. The emphasis on depth can sometimes limit the applicability of the results beyond the studied group.
• Data Interpretation Complexity: Analyzing qualitative data can be complex and subjective. Different researchers may interpret the same data differently, leading to potential variations in findings.
• Resource Intensiveness: Qualitative research can be time-consuming and resource-intensive. Conducting interviews, transcribing data, and analyzing rich textual information can require a significant investment of time and effort.
• Small Sample Sizes: While qualitative research allows for in-depth exploration, it may raise questions about the representativeness of findings.
• Ethical Challenges: Dealing with ethical considerations, such as ensuring informed consent, maintaining confidentiality, and minimizing harm, can be intricate, especially when studying sensitive topics or vulnerable populations.
• Validity and Reliability Concerns: Traditional notions of validity and reliability may not apply directly to qualitative research. Establishing the trustworthiness of findings involves alternative strategies like triangulation, member checking, and peer review.
• Difficulty in Replication: Due to the unique nature of qualitative studies and the importance of context, replication of findings can be challenging. Other researchers may find it difficult to recreate the exact conditions or interpret data in the same way.
• Risk of Misinterpretation: Misinterpreting participant responses or cultural nuances is a risk in qualitative research. Careful attention to language, context, and cultural sensitivity is essential to minimize misinterpretation.
• Overemphasis on Verbal Data: Qualitative research often relies on verbal data, potentially neglecting non-verbal cues. This limitation might hinder a comprehensive understanding of participants’ experiences.
• Limited Quantification: Qualitative data is predominantly non-numeric, making it challenging to quantify and measure the extent or frequency of specific phenomena. This can limit the ability to make statistical comparisons.

Understanding these challenges helps researchers navigate the complexities of qualitative research and enhances the rigor and credibility of their studies.

Best Practices for Qualitative Research in 2024

To ensure the rigor and credibility of qualitative research, consider the following best practices:

• Clearly Define Research Questions: Clearly articulate your research questions or objectives to guide the study. This clarity helps maintain focus and ensures that data collection and analysis align with the research goals.
• Choose Appropriate Methods: Select qualitative research methods that align with your research questions. Consider the strengths and limitations of each method, such as interviews, focus groups, or observations, and choose the most suitable approach for your study.
• Pilot Test Data Collection Instruments: Before full-scale data collection, conduct a pilot test of your interview guides, surveys, or observation protocols. This helps identify potential issues, refine questions, and ensure the instruments are effective.
• Establish Trust with Participants: Build rapport and trust with participants to encourage open and honest responses. Clearly communicate the purpose of the study, assure confidentiality, and obtain informed consent.
• Use Purposive Sampling: Select participants purposefully based on criteria relevant to your research questions. This approach ensures that participants have valuable insights related to the study’s objectives.
• Record and Transcribe Interviews: Record interviews (with participant consent) to capture nuances and details accurately. Transcribe the recordings verbatim to facilitate thorough data analysis.
• Maintain Reflexivity: Acknowledge and reflect on your own biases, values, and perspectives throughout the research process. Reflexivity enhances transparency and helps mitigate the impact of the researcher’s subjectivity.
• Ensure Data Saturation: Continue data collection until data saturation is achieved—meaning that new information ceases to emerge. Saturation ensures that the study comprehensively explores the research questions.
• Thorough Data Analysis: Use rigorous and systematic data analysis techniques, such as coding, thematic analysis, or grounded theory, to derive meaningful insights from the collected data. Maintain transparency in the analytical process.
• Member Checking: Validate findings with participants through member checking. Share preliminary results or interpretations with participants to ensure accuracy and gain their perspectives on the findings.
• Triangulation: Use multiple data sources, methods, or researchers to enhance the validity and reliability of findings. Triangulation helps corroborate results and provides a more robust understanding of the phenomenon.
• Maintain Ethical Standards: Adhere to ethical guidelines throughout the research process. Prioritize informed consent, protect participant confidentiality, and consider the potential impact of the research on participants.
• Document Decision-Making Processes: Keep detailed records of decisions made during the research, such as changes in the research design or data analysis approach. This documentation enhances transparency and replicability.
• Peer Review: Seek feedback from colleagues or experts in qualitative research to validate your study’s rigor. Peer review provides an external perspective and helps identify potential biases or oversights.

By following these best practices, qualitative researchers can enhance the quality, reliability, and validity of their studies, ultimately contributing to a more robust understanding of the researched phenomena.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

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What is Qualitative Research? Methods, Types, Approaches and Examples

Qualitative research is a type of method that researchers use depending on their study requirements. Research can be conducted using several methods, but before starting the process, researchers should understand the different methods available to decide the best one for their study type. The type of research method needed depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. The two main types of methods are qualitative research and quantitative research. Sometimes, researchers may find it difficult to decide which type of method is most suitable for their study. Keeping in mind a simple rule of thumb could help you make the correct decision. Quantitative research should be used to validate or test a theory or hypothesis and qualitative research should be used to understand a subject or event or identify reasons for observed patterns.  

Qualitative research methods are based on principles of social sciences from several disciplines like psychology, sociology, and anthropology. In this method, researchers try to understand the feelings and motivation of their respondents, which would have prompted them to select or give a particular response to a question. Here are two qualitative research examples :  

• Two brands (A & B) of the same medicine are available at a pharmacy. However, Brand A is more popular and has higher sales. In qualitative research , the interviewers would ideally visit a few stores in different areas and ask customers their reason for selecting either brand. Respondents may have different reasons that motivate them to select one brand over the other, such as brand loyalty, cost, feedback from friends, doctor’s suggestion, etc. Once the reasons are known, companies could then address challenges in that specific area to increase their product’s sales.  
• A company organizes a focus group meeting with a random sample of its product’s consumers to understand their opinion on a new product being launched.  

Table of Contents

What is qualitative research? 1

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data. The findings of qualitative research are expressed in words and help in understanding individuals’ subjective perceptions about an event, condition, or subject. This type of research is exploratory and is used to generate hypotheses or theories from data. Qualitative data are usually in the form of text, videos, photographs, and audio recordings. There are multiple qualitative research types , which will be discussed later.  

Qualitative research methods 2

Researchers can choose from several qualitative research methods depending on the study type, research question, the researcher’s role, data to be collected, etc.  

The following table lists the common qualitative research approaches with their purpose and examples, although there may be an overlap between some.  

Types of qualitative research 3,4

The data collection methods in qualitative research are designed to assess and understand the perceptions, motivations, and feelings of the respondents about the subject being studied. The different qualitative research types include the following:  

• In-depth or one-on-one interviews : This is one of the most common qualitative research methods and helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event. These interviews are usually conversational and encourage the respondents to express their opinions freely. Semi-structured interviews, which have open-ended questions (where the respondents can answer more than just “yes” or “no”), are commonly used. Such interviews can be either face-to-face or telephonic, and the duration can vary depending on the subject or the interviewer. Asking the right questions is essential in this method so that the interview can be led in the suitable direction. Face-to-face interviews also help interviewers observe the respondents’ body language, which could help in confirming whether the responses match.  
• Document study/Literature review/Record keeping : Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.  
• Focus groups : Usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic. Focus groups ensure constructive discussions to understand the why, what, and, how about the topic. These group meetings need not always be in-person. In recent times, online meetings are also encouraged, and online surveys could also be administered with the option to “write” subjective answers as well. However, this method is expensive and is mostly used for new products and ideas.  
• Qualitative observation : In this method, researchers collect data using their five senses—sight, smell, touch, taste, and hearing. This method doesn’t include any measurements but only the subjective observation. For example, “The dessert served at the bakery was creamy with sweet buttercream frosting”; this observation is based on the taste perception.  

Qualitative research : Data collection and analysis

• Qualitative data collection is the process by which observations or measurements are gathered in research.  
• The data collected are usually non-numeric and subjective and could be recorded in various methods, for instance, in case of one-to-one interviews, the responses may be recorded using handwritten notes, and audio and video recordings, depending on the interviewer and the setting or duration.  
• Once the data are collected, they should be transcribed into meaningful or useful interpretations. An experienced researcher could take about 8-10 hours to transcribe an interview’s recordings. All such notes and recordings should be maintained properly for later reference.  
• Some interviewers make use of “field notes.” These are not exactly the respondents’ answers but rather some observations the interviewer may have made while asking questions and may include non-verbal cues or any information about the setting or the environment. These notes are usually informal and help verify respondents’ answers.  

2. Qualitative data analysis 

• This process involves analyzing all the data obtained from the qualitative research methods in the form of text (notes), audio-video recordings, and pictures.  
• Text analysis is a common form of qualitative data analysis in which researchers examine the social lives of the participants and analyze their words, actions, etc. in specific contexts. Social media platforms are now playing an important role in this method with researchers analyzing all information shared online.   

There are usually five steps in the qualitative data analysis process: 5

• Prepare and organize the data  
• Transcribe interviews  
• Collect and document field notes and other material  
• Review and explore the data  
• Examine the data for patterns or important observations  
• Develop a data coding system  
• Create codes to categorize and connect the data  
• Assign these codes to the data or responses  
• Review the codes  
• Identify recurring themes, opinions, patterns, etc.  
• Present the findings  
• Use the best possible method to present your observations  

The following table 6 lists some common qualitative data analysis methods used by companies to make important decisions, with examples and when to use each. The methods may be similar and can overlap.  

Characteristics of qualitative research methods 4

• Unstructured raw data : Qualitative research methods use unstructured, non-numerical data , which are analyzed to generate subjective conclusions about specific subjects, usually presented descriptively, instead of using statistical data.  
• Site-specific data collection : In qualitative research methods , data are collected at specific areas where the respondents or researchers are either facing a challenge or have a need to explore. The process is conducted in a real-world setting and participants do not need to leave their original geographical setting to be able to participate.  
• Researchers’ importance : Researchers play an instrumental role because, in qualitative research , communication with respondents is an essential part of data collection and analysis. In addition, researchers need to rely on their own observation and listening skills during an interaction and use and interpret that data appropriately.  
• Multiple methods : Researchers collect data through various methods, as listed earlier, instead of relying on a single source. Although there may be some overlap between the qualitative research methods , each method has its own significance.  
• Solving complex issues : These methods help in breaking down complex problems into more useful and interpretable inferences, which can be easily understood by everyone.  
• Unbiased responses : Qualitative research methods rely on open communication where the participants are allowed to freely express their views. In such cases, the participants trust the interviewer, resulting in unbiased and truthful responses.  
• Flexible : The qualitative research method can be changed at any stage of the research. The data analysis is not confined to being done at the end of the research but can be done in tandem with data collection. Consequently, based on preliminary analysis and new ideas, researchers have the liberty to change the method to suit their objective.  

When to use qualitative research   4

The following points will give you an idea about when to use qualitative research .  

• When the objective of a research study is to understand behaviors and patterns of respondents, then qualitative research is the most suitable method because it gives a clear insight into the reasons for the occurrence of an event.  
• A few use cases for qualitative research methods include:  
• New product development or idea generation  
• Strengthening a product’s marketing strategy  
• Conducting a SWOT analysis of product or services portfolios to help take important strategic decisions  
• Understanding purchasing behavior of consumers  
• Understanding reactions of target market to ad campaigns  
• Understanding market demographics and conducting competitor analysis  
• Understanding the effectiveness of a new treatment method in a particular section of society  

A qualitative research method case study to understand when to use qualitative research 7

Context : A high school in the US underwent a turnaround or conservatorship process and consequently experienced a below average teacher retention rate. Researchers conducted qualitative research to understand teachers’ experiences and perceptions of how the turnaround may have influenced the teachers’ morale and how this, in turn, would have affected teachers’ retention.  

Method : Purposive sampling was used to select eight teachers who were employed with the school before the conservatorship process and who were subsequently retained. One-on-one semi-structured interviews were conducted with these teachers. The questions addressed teachers’ perspectives of morale and their views on the conservatorship process.  

Results : The study generated six factors that may have been influencing teachers’ perspectives: powerlessness, excessive visitations, loss of confidence, ineffective instructional practices, stress and burnout, and ineffective professional development opportunities. Based on these factors, four recommendations were made to increase teacher retention by boosting their morale.  

Advantages of qualitative research 1

• Reflects real-world settings , and therefore allows for ambiguities in data, as well as the flexibility to change the method based on new developments.  
• Helps in understanding the feelings or beliefs of the respondents rather than relying only on quantitative data.  
• Uses a descriptive and narrative style of presentation, which may be easier to understand for people from all backgrounds.  
• Some topics involving sensitive or controversial content could be difficult to quantify and so qualitative research helps in analyzing such content.  
• The availability of multiple data sources and research methods helps give a holistic picture.  
• There’s more involvement of participants, which gives them an assurance that their opinion matters, possibly leading to unbiased responses.   

Disadvantages of qualitative research 1

• Large-scale data sets cannot be included because of time and cost constraints.  
• Ensuring validity and reliability may be a challenge because of the subjective nature of the data, so drawing definite conclusions could be difficult.  
• Replication by other researchers may be difficult for the same contexts or situations.  
• Generalization to a wider context or to other populations or settings is not possible.  
• Data collection and analysis may be time consuming.  
• Researcher’s interpretation may alter the results causing an unintended bias.  

Differences between qualitative research and quantitative research 1

Frequently asked questions on qualitative research  

Q: how do i know if qualitative research is appropriate for my study  .

A: Here’s a simple checklist you could use:  

• Not much is known about the subject being studied.  
• There is a need to understand or simplify a complex problem or situation.  
• Participants’ experiences/beliefs/feelings are required for analysis.  
• There’s no existing hypothesis to begin with, rather a theory would need to be created after analysis.  
• You need to gather in-depth understanding of an event or subject, which may not need to be supported by numeric data.  

Q: How do I ensure the reliability and validity of my qualitative research findings?  

A: To ensure the validity of your qualitative research findings you should explicitly state your objective and describe clearly why you have interpreted the data in a particular way. Another method could be to connect your data in different ways or from different perspectives to see if you reach a similar, unbiased conclusion.   

To ensure reliability, always create an audit trail of your qualitative research by describing your steps and reasons for every interpretation, so that if required, another researcher could trace your steps to corroborate your (or their own) findings. In addition, always look for patterns or consistencies in the data collected through different methods.  

Q: Are there any sampling strategies or techniques for qualitative research ?   

A: Yes, the following are few common sampling strategies used in qualitative research :  

1. Convenience sampling  

Selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.  

2. Purposive sampling  

Participants are grouped according to predefined criteria based on a specific research question. Sample sizes are often determined based on theoretical saturation (when new data no longer provide additional insights).  

3. Snowball sampling  

Already selected participants use their social networks to refer the researcher to other potential participants.  

4. Quota sampling  

While designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.  

Q: What ethical standards need to be followed with qualitative research ?  

A: The following ethical standards should be considered in qualitative research:  

• Anonymity : The participants should never be identified in the study and researchers should ensure that no identifying information is mentioned even indirectly.  
• Confidentiality : To protect participants’ confidentiality, ensure that all related documents, transcripts, notes are stored safely.  
• Informed consent : Researchers should clearly communicate the objective of the study and how the participants’ responses will be used prior to engaging with the participants.  

Q: How do I address bias in my qualitative research ?  

  A: You could use the following points to ensure an unbiased approach to your qualitative research :  

• Check your interpretations of the findings with others’ interpretations to identify consistencies.  
• If possible, you could ask your participants if your interpretations convey their beliefs to a significant extent.  
• Data triangulation is a way of using multiple data sources to see if all methods consistently support your interpretations.  
• Contemplate other possible explanations for your findings or interpretations and try ruling them out if possible.  
• Conduct a peer review of your findings to identify any gaps that may not have been visible to you.  
• Frame context-appropriate questions to ensure there is no researcher or participant bias.

We hope this article has given you answers to the question “ what is qualitative research ” and given you an in-depth understanding of the various aspects of qualitative research , including the definition, types, and approaches, when to use this method, and advantages and disadvantages, so that the next time you undertake a study you would know which type of research design to adopt.  

References:  

• McLeod, S. A. Qualitative vs. quantitative research. Simply Psychology [Accessed January 17, 2023]. www.simplypsychology.org/qualitative-quantitative.html    
• Omniconvert website [Accessed January 18, 2023]. https://www.omniconvert.com/blog/qualitative-research-definition-methodology-limitation-examples/  
• Busetto L., Wick W., Gumbinger C. How to use and assess qualitative research methods. Neurological Research and Practice [Accessed January 19, 2023] https://neurolrespract.biomedcentral.com/articles/10.1186/s42466-020-00059  
• QuestionPro website. Qualitative research methods: Types & examples [Accessed January 16, 2023]. https://www.questionpro.com/blog/qualitative-research-methods/  
• Campuslabs website. How to analyze qualitative data [Accessed January 18, 2023]. https://baselinesupport.campuslabs.com/hc/en-us/articles/204305675-How-to-analyze-qualitative-data  
• Thematic website. Qualitative data analysis: Step-by-guide [Accessed January 20, 2023]. https://getthematic.com/insights/qualitative-data-analysis/  
• Lane L. J., Jones D., Penny G. R. Qualitative case study of teachers’ morale in a turnaround school. Research in Higher Education Journal . https://files.eric.ed.gov/fulltext/EJ1233111.pdf  
• Meetingsnet website. 7 FAQs about qualitative research and CME [Accessed January 21, 2023]. https://www.meetingsnet.com/cme-design/7-faqs-about-qualitative-research-and-cme     
• Qualitative research methods: A data collector’s field guide. Khoury College of Computer Sciences. Northeastern University. https://course.ccs.neu.edu/is4800sp12/resources/qualmethods.pdf  

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Qualitative research is a powerful tool that helps you unlock insights into the user experience—quintessential to building effective products and services. It provides a deeper understanding of complex behaviors, needs, and motivations. But what is qualitative research, and when is it ideal to use it? Let’s explore its methodologies and implementation with a few qualitative research examples.

What is qualitative research?

Qualitative research is a behavioral research method that seeks to understand the undertones, motivations, and subjective interpretations inherent in human behavior. It involves gathering nonnumerical data, such as text, audio, and video, allowing you to explore nuances and patterns that quantitative data can’t capture.

Instead of focusing on how many or how much, qualitative research questions delve into the why and how. This approach is instrumental in gaining a comprehensive understanding of a particular context, issue, or phenomenon from the perspective of those experiencing it. Examples of qualitative research questions include “How did you feel when you first used our product?” and “Could you describe your experience when you purchased a product from our website?”

Qualitative research methodology

Qualitative research design employs a variety of methodologies to collect and analyze data. The primary objective is to gather detailed and nuanced insights rather than generalizable findings. Steps include the following:

• Formulating research questions:  Qualitative research begins by identifying specific research questions to guide the study. These questions should align with the research objectives and provide a clear focus for data collection and analysis.  
• Selection of participants:  Participant selection is a critical step in qualitative research. You must recruit participants who provide relevant and diverse perspectives on the research topic. It involves purposive sampling, where participants are chosen based on their knowledge or experiences related to the research questions. ​​​​​​
• Data collection:  Qualitative research uses various methods to collect data, such as interviews, focus groups, observation, and document analysis. You often employ multiple methods to comprehensively understand the research topic.
• Data analysis:  Once the data is collected, it’s analyzed to identify recurring themes, patterns, and meanings. This analysis uses coding, thematic analysis, and constant comparison. The goal is to uncover the underlying perspectives of the participant.
• Interpretation and reporting:  This is the final step in which findings are synthesized and interpreted, revealing their significance to the research questions. You can present your findings through descriptive narratives, quotes, and illustrative examples to provide a rich understanding of the research topic. 

Types of qualitative research methods

The best qualitative research method primarily depends on your research questions and objectives. Different methods uncover different discernments.

One-on-one interviews

You often use one-on-one interviews to delve deep into a topic or understand individual experiences or perspectives. An interviewer asks a participant open-ended questions to understand their perspective, thoughts, feelings, and experiences regarding a specific topic, product, or service. Read about open ended vs closed ended questions to learn which questions will be most effective in an interview.

Say you’re developing a new electric vehicle mode. You can conduct one-on-one interviews to understand user experiences, probing into aspects such as comfort, design, driving experience, and more.

Focus groups

In-person or remote focus groups involve a small group of people (usually 6–10) discussing a given topic or question under the guidance of a moderator. This method is beneficial when you want to understand group dynamics or collective views. The interaction among group members can disclose awarenesses that may not arise in one-on-one interviews.

In the gaming industry, for example, you can use focus groups to explore player reactions to a new game design. You can encourage group interaction to spark discussions about usability, game mechanics, graphics, storyline, and other aspects.

Case study research

Case study research provides an in-depth analysis of a particular case (an individual, group, organization, event, etc.) within its real-life context. It’s a valuable method for exploring something in-depth and in its natural setting.

For instance, a healthcare case study could explore implementing a new electronic health record system in a hospital, focusing on challenges, successes, and lessons learned.

Ethnographic research

Ethnographic research (or an ethnographic stud y) involves an immersive investigation into a group’s behaviors, culture, and practices. It requires you to engage directly with the participants over a prolonged period in their natural environment. It can help uncover how people interact with products or services in natural settings.

A gaming organization may choose to study players in their natural gaming environments (such as home, game cafes, or e-sport tournaments) to understand their gaming habits, social interactions, and responses to specific features. These insights can inform the development of more engaging and user-friendly games.

Process of observation

The process of observation typically doesn’t involve the same level of immersion as ethnographic research. You observe and record behavior related to a specific context or activity. It can be in natural settings (naturalistic observation) or a controlled environment. It’s more about observing and recording specific behaviors or situations rather than cultural norms or dynamics.

For example, a consumer technology organization could observe how users interact with a new software interface, noting challenges, efficiencies, and overall user experience.

Record keeping

Record keeping refers to collecting and analyzing documents, records, and artifacts that provide an understanding of the study area. Record keeping allows you to access historical and contextual data that can be examined and reexamined. It’s a nonobtrusive method, meaning it doesn’t involve direct contact with the participants, nor does it affect or alter the situation you’re studying.

An online retailer might examine shopping cart abandonment records to identify at what point in the buying process customers tend to drop off. This information can help streamline the checkout process and improve conversion rates.

Qualitative research: Data collection and analysis

Data collection and analysis in qualitative research are closely linked processes that help generate meaningful and useful results.

Data collection

Data collection involves gathering rich, detailed materials to explain and understand the subject. These include interview transcripts, meeting notes, personal diaries, and photographs. 

There are various qualitative data collection methods to consider depending on your research questions and the context of your study. For example, you could use one-on-one interviews to understand personal user experiences with a financial services app. A moderated focus group may be more appropriate to discuss user preferences in a new media and entertainment platform.

Data analysis

Once data are collected, the analysis process begins. It’s where you extract patterns, themes, and insights from the collected data. It’s one of the most critical aspects of qualitative research, turning raw, unstructured data into valuable insights.

Qualitative data analysis usually takes place with several steps, such as:

• Organizing and preparing the data for analysis
• Reading through the data
• Coding the data
• Generating themes or categories
• Interpreting the findings and 
• Representing the data

Your choice of qualitative data analysis method depends on your research questions and the data type you collected. Common analysis methods include thematic, content, discourse, and narrative analysis. Some research platforms provide AI features that can do much of this analysis for researchers to speed up insight gathering.

When to use qualitative research

Qualitative techniques are ideal for understanding human experiences and perspectives. Here are common situations where qualitative research is invaluable:

• Exploring customer motivations, needs, behaviors, and pain points
• Gathering in-depth user feedback on products and services
• Understanding decision-making and buyer journeys
• Discovering barriers to adoption and satisfaction
• Developing hypotheses for future quantitative research
• Testing concepts , interfaces, or designs
• Identifying problems and improvement opportunities
• Learning about group norms, cultures, and social interactions
• Collecting evidence to develop theories and models
• Capturing complex, nuanced insights beyond numbers

Qualitative research methods vs. quantitative research methods

Qualitative and quantitative research  differ in their approach to data collection, analysis, and the nature of the findings. Here are some key differences:

• Data collection:  Qualitative research uses in-depth interviews , focus groups, observations, and analysis of documents to gather data. In contrast, quantitative research relies on structured surveys, experiments, and standard measurements.
• Analysis:  Qualitative research involves analyzing textual or visual data through coding, categorization, and theme identification techniques. Quantitative research uses statistical analysis to examine numerical data for patterns, correlations, and trends.
• Sample size:  Qualitative research typically involves smaller sample sizes, often selected through purposive sampling to ensure diversity and relevance. Quantitative research uses larger sample sizes to ensure statistical power and generalizability.
• Generalizability:  Qualitative research seeks in-depth insight into specific contexts or groups and does not prioritize generalizability. On the other hand, quantitative research seeks to draw conclusions that apply to a broader context.
• Findings:  Qualitative research generates descriptive and explanatory results that provide a deeper understanding of phenomena. Quantitative research produces numerical data that allows for statistical inferences and comparisons.
• Theory development:  Qualitative research often contributes to theory development by generating new concepts, theories, or frameworks based on the rich and context-specific data collected. However, quantitative research tests preexisting theories and hypotheses using statistical models.

Advantages and strengths of qualitative research

Qualitative research enriches your research process and outcomes, making it an invaluable tool in many fields, including UX research, marketing, and digital product development. 

In-depth understanding

Qualitative research provides a rich, detailed, in-depth understanding of the research subject.  Proactive qualitative research  takes this further with ongoing data collection, allowing organizations to continuously capture insights and adapt strategies based on evolving user needs.

Contextual data

Qualitative research collects contextually relevant data. It captures nuances that might be missed in numerically-based quantitative data, allowing you to understand the contexts in which behaviors and interactions occur.

Flexibility

The methods used in qualitative research, like interviews and focus groups, enable you to explore different topics in depth and adapt your approach based on the participants’ responses.

Human perspective

Qualitative research lets you capture human experiences and thoughts. It’s advantageous in fields such as UX research, where the human perspective is critical. 

Hypothesis generation

The exploratory nature of qualitative research helps you identify new areas for exploration or generate hypotheses you can test using quantitative methods.

Trendspotting

Qualitative research reveals trends in thought and opinions, diving deeper into the problem. This is helpful when trying to understand behaviors, culture, and user interactions.

Disadvantages and limitations of qualitative research

While qualitative research offers many advantages, it’s essential to acknowledge its limitations. 

Time-consuming

Collecting and analyzing qualitative data, particularly from in-depth interviews or focus groups, requires significant time investment.

Qualitative research relies on the skills and judgment of the researcher, introducing potential bias into the research process. The researcher may actively shape the research by posing questions, interpreting data, and influencing the findings.

Requires skilled researchers

The quality of qualitative research heavily depends on the researcher’s skills, experience, and perspective. A less experienced researcher may overlook important nuances, potentially affecting the depth and accuracy of the findings.

Lacks generalizability

Qualitative research often involves a smaller, nonrepresentative sample size than quantitative research. Therefore, the findings may not be generalizable to a larger context.

Limited numeric representation

Qualitative research usually focuses on words, observations, or experiences, so it doesn’t provide the numeric estimates often desired in research studies.

Challenging to replicate and standardize

Qualitative research’s inherent flexibility and context dependence make it challenging to repeat the study under the same conditions. This flexibility can often make it hard to standardize. Researchers approach and conduct the study in various ways, leading to inconsistent results and interpretations.

Difficult to measure reliability and validity

Assessing reliability and validity is more difficult with qualitative research since it relies on subjective human interpretation and has few established metrics and statistical tools compared to quantitative research. Triangulation and member checking add credibility but lack the discreteness of quantitative measures. However, there have been advancement s in the measurement of qualitative research that help to quantify its impact. 

Qualitative research gives you the opportunity to dive deep into human behavior, experiences, and perceptions. It offers a prolific, intricate perspective that quantifiable data alone can’t provide. Combine qualitative research methodologies with techniques like  A/B testing  to gain a more holistic understanding of user experiences and preferences. 

Despite its limitations, the depth and richness of data procured through qualitative research are undeniable assets. By understanding and utilizing its diverse methods, you will uncover detailed insights from your target audience and enhance your products or services to meet their needs. 

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Why is qualitative research important?

Qualitative research delves into subjective experiences and social contexts, providing in-depth insights and understanding. It provides a deep understanding of individuals’ needs, motivations, and preferences, allowing organizations to develop products and services that meet customer expectations.

What’s the difference between quantitative and qualitative methods?

Quantitative methods focus on numerical data and statistical analysis, aiming for generalizability and objectivity. Qualitative methods explore meanings, experiences, and behaviors, seeking in-depth understanding and detailed descriptions.

What are the main qualitative research approaches?

The main qualitative research approaches include one-on-one interviews, focus groups, case study research, ethnographic research, observation, and record-keeping. Each approach offers unique benefits and applications.

What is data collection?

Data collection in qualitative research involves gathering information through various methods such as interviews, focus groups, observations, and document analysis. It’s a critical step in generating meaningful insights and understanding human experiences.

How do you analyze qualitative data?

What are the ethical considerations in qualitative research.

Ethical considerations refer to the protection of participants’ rights, privacy, and confidentiality. You must obtain informed consent, maintain anonymity, and handle sensitive information responsibly. Additionally, maintaining transparency, addressing power imbalances, and conducting research unbiased and respectfully are vital ethical considerations in qualitative research.

How can I incorporate qualitative research into my study or project?

To incorporate qualitative research into your study, you must first define your research objectives to guide the choice of methodology. Next, choose a suitable qualitative method, such as interviews or focus groups. Then, collect and analyze the data using appropriate techniques and, finally, interpret and present the findings clearly and meaningfully. Remember to be mindful of the ethical considerations throughout the process.

How do you effectively communicate and present qualitative research findings to stakeholders?

For a quality presentation, create engaging visual representations, such as infographics or data visualizations, and use storytelling techniques to highlight key insights. Also, prepare concise and informative reports and organize interactive presentations or workshops to facilitate discussion and understanding.

How do you translate qualitative research findings into actionable insights?

Identify key themes linked to research goals and propose strategic solutions to address core needs and barriers. These solutions should be tailored to specific needs.

How can I ensure the validity and reliability of qualitative research findings?

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Qualitative Research: Methods

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

Table of Contents: Methods Section

Duke resources.

Duke's Office of Assessment:

Assessment Resources (qualitative and quantitative)

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

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

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

Choosing a Method for Collecting Qualitative Data

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

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Unit 6: Qual vs Quant.

28 Qualitative Methods in Communication Research

Qualitative methods in communication research.

In communication research, both quantitative and qualitative methods are essential for understanding different aspects of communication processes and effects. Here’s how qual methods can be applied:

• Conducting in-depth interviews to explore individuals’ experiences and perceptions of their interpersonal relationships
• Conducting in-depth interviews with individuals to explore their experiences, opinions, and feelings about communication topics.
• Facilitating group discussions to gather diverse perspectives on communication issues within relationships.
• Facilitating group discussions to gather diverse perspectives on communication issues or media content.
• Observing and documenting communication practices within specific social or cultural groups to understand their norms and behaviors.
• Observing and documenting communication practices within specific cultural or social groups to understand their communication norms and behaviors.
• Thematic Analysis : Analyzing qualitative data from interviews, focus groups, or media content to identify recurring themes and patterns, for example, patterns in interpersonal communication and relationships.

Communication Research in Real Life Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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Qualitative Research Definition

Qualitative research methods and examples.

• Advantages and Disadvantages of Qualitative Approaches
• Qualitative vs. Quantitative Research
• Showing Qualitative Research Skills on Resumes

What is Qualitative Research? Methods and Examples

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Table of Contents

Qualitative research seeks to understand people’s experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people’s beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or service, such as in user experience (UX) design or marketing . 

Researchers use qualitative approaches to “determine answers to research questions on human behavior and the cultural values that drive our thinking and behavior,” says Margaret J. King, director at The Center for Cultural Studies & Analysis in Philadelphia.

Data in qualitative research typically can’t be assessed mathematically — the data is not sets of numbers or quantifiable information. Rather, it’s collections of images, words, notes on behaviors, descriptions of emotions, and historical context. Data is collected through observations, interviews, surveys, focus groups, and secondary research. 

However, a qualitative study needs a “clear research question at its base,” notes King, and the research needs to be “observed, categorized, compared, and evaluated (along a scale or by a typology chart) by reference to a baseline in order to determine an outcome with value as new and reliable information.”

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Who Uses Qualitative Research?

Researchers in social sciences and humanities often use qualitative research methods, especially in specific areas of study like anthropology, history, education, and sociology. 

Qualitative methods are also applicable in business, technology , and marketing spaces. For example, product managers use qualitative research to understand how target audiences respond to their products. They may use focus groups to gain insights from potential customers on product prototypes and improvements or surveys from existing customers to understand what changes users want to see. 

Other careers that may involve qualitative research include: 

• Marketing analyst
• UX and UI analyst
• Market researcher
• Statistician
• Business analyst
• Data analyst
• Research assistant
• Claims investigator

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Good research begins with a question, and this question informs the approach used by qualitative researchers. 

Grounded Theory

Grounded theory is an inductive approach to theory development. In many forms of research, you begin with a hypothesis and then test it to see if you’re correct. In grounded theory, though, you go in without any assumptions and rely on the data you collect to form theories. You start with an open question about a phenomenon you are studying and collect and analyze data until you can form a fully-fledged theory from the information. 

Example: A company wants to improve its brand and marketing strategies. The company performs a grounded theory approach to solving this problem by conducting interviews and surveys with past, current, and prospective customers. The information gathered from these methods helps the company understand what type of branding and marketing their customer-base likes and dislikes, allowing the team to inductively craft a new brand and marketing strategy from the data. 

Action Research

Action research is one part study and one part problem-solving . Through action research, analysts investigate a problem or weakness and develop practical solutions. The process of action research is cyclical —- researchers assess solutions for efficiency and effectiveness, and create further solutions to correct any issues found. 

Example: A manager notices her employees struggle to cooperate on group projects. She carefully reviews how team members interact with each other and asks them all to respond to a survey about communication. Through the survey and study, she finds that guidelines for group projects are unclear. After changing the guidelines, she reviews her team again to see if there are any changes to their behavior.  

>>MORE: Explore how action research helps consultants serve clients with Accenture’s Client Research and Problem Identification job simulation .

Phenomenological Research

Phenomenological research investigates a phenomenon in depth, looking at people’s experiences and understanding of the situation. This sort of study is primarily descriptive and seeks to broaden understanding around a specific incident and the people involved. Researchers in phenomenological studies must be careful to set aside any biases or assumptions because the information used should be entirely from the subjects themselves. 

Example : A researcher wants to better understand the lived experience of college students with jobs. The purpose of this research is to gain insights into the pressures of college students who balance studying and working at the same time. The researcher conducts a series of interviews with several college students, learning about their past and current situations. Through the first few interviews, the researcher builds a relationship with the students. Later discussions are more targeted, with questions prompting the students to discuss their emotions surrounding both work and school and the difficulties and benefits arising from their situation. The researcher then analyzes these interviews, and identifies shared themes to contextualize the experiences of the students.

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• Volume 13, Issue 3
• Patient safety and the COVID-19 pandemic: a qualitative study of perspectives of front-line clinicians
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• http://orcid.org/0000-0002-9774-6372 Lucy Schulson 1 , 2 ,
• http://orcid.org/0000-0002-5485-6272 Julia Bandini 2 ,
• Armenda Bialas 3 ,
• Shreya Huigol 2 ,
• George Timmins 4 ,
• Sangeeta Ahluwalia 4 ,
• Courtney Gidengil 2 , 5
• 1 Section of General Internal Medicine , Boston University Chobanian & Avedisian School of Medicine , Boston , Massachusetts , USA
• 2 RAND , Boston , Massachusetts , USA
• 3 RAND , Pittsburgh , Pennsylvania , USA
• 4 RAND , Santa Monica , California , USA
• 5 Division of Infectious Disease , Boston Children's Hospital , Boston , Massachusetts , USA
• Correspondence to Dr Lucy Schulson; lucy.schulson{at}bmc.org

Introduction Studies on the impacts of COVID-19 on patient safety are emerging. However, few studies have elicited the perspectives of front-line clinicians.

Methods We interviewed clinicians from 16 US hospitals who worked in the emergency department, intensive care unit or inpatient unit during the COVID-19 pandemic. We asked about their experiences with both clinician well-being and patient care throughout the pandemic. We used a rigorous thematic analysis to code the interview transcripts. This study was part of a larger randomised control trial of an intervention to improve healthcare worker well-being during the COVID-19 pandemic; the findings described here draw from clinicians who spontaneously raised issues related to patient safety.

Results 11 physicians and 16 nurses in our sample raised issues related to patient safety. We identified two primary themes: (1) compromised access to healthcare and (2) impaired care delivery. First, clinicians discussed how changes in access to healthcare early in the pandemic–including a shift to telehealth and deferred care–led to delays in accurate diagnosis and patients presenting later in their disease course. Second, clinicians discussed the effects of COVID-19 on care delivery related to staffing, equipment shortages and space constraints and how they deviated from the standard of care to manage these constraints. Clinicians noted how these issues led to patient safety events such as central line infections, patient falls and serious medication administration errors.

Conclusions Several well-intentioned interventions implemented in the early weeks of the pandemic created a unique context that affected patient safety throughout the pandemic. Future pandemic preparedness should consider planning that incorporates a patient safety lens to mitigate further harm from occurring during a public health crisis.

• Patient safety
• Attitude of Health Personnel
• Standards of care

Data availability statement

No data are available.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjoq-2023-002692

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Patient safety events affected as many as one in four hospitalised patients in the USA prior to the COVID-19 pandemic. The pandemic created unprecedented circumstances for clinical care that further affected patient safety. Understanding the perspectives of front-line clinicians on patient safety during the COVID-19 pandemic is crucial.

WHAT THIS STUDY ADDS

A national, multidisciplinary sample of clinicians identified multiple, potentially preventable patient safety events and near misses due to compromised access to care and impaired care delivery due to the pandemic.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Future pandemic preparedness should consider planning that incorporates a patient safety lens to prevent further harm during a public health crisis.

Introduction

Prior to the COVID-19 pandemic, one in four hospitalised patients in the USA experienced a patient safety event. 1 The emergence of COVID-19 coupled with a lack of preparedness for a global pandemic placed strain on an already fragile public health infrastructure and health systems across the USA. 2 This backdrop created a unique context for compromised patient safety.

A patient safety event is defined as a healthcare event that either did or could have resulted in patient harm. 3 4 Health systems were overwhelmed by critically ill patients with a novel virus, which was a clear distraction from routine patient safety practices. 5 While research on patient safety during the COVID-19 pandemic is emerging, examining patient safety during the pandemic has been difficult. 6 Many patient safety events are identified by incident reporting systems that rely on providers. Yet incident reporting, which is often time intensive and underutilised in non-pandemic times, 7 decreased even further due to the challenges brought on by the pandemic. 5 8 9 Additionally, many of the typical approaches used to identify and address patient safety events (eg, morbidity and mortality conferences and root causes analysis reviews) were suspended during the first wave of the pandemic. 5 Even when patient safety events were identified using other methods (eg, billing and electronic medical record data) they were unlikely to capture the breadth of patient safety events that were occurring. A study conducted by the Agency for Healthcare Research and Quality analysed 301 patient safety events submitted to a Patient Safety Organization from March 2020 to October 2020. The most commonly reported events were not actually patient safety events but rather policy and procedure concerns related to COVID-19. 10

Nonetheless, through the literature on care processes more broadly, the impact of care disruptions due to COVID-19 on patient safety is starting to emerge. 11–14 Many of these studies elicited clinicians’ perspectives to understand patient safety challenges during the pandemic. Clinicians’ impressions are particularly important as a source of information to understand the landscape of patient safety when formal reporting processes decrease. 12–16 The majority of these studies, however, focused exclusively on the initial wave of the pandemic 12–14 or only on the perspective of critical care physicians. 16

To better understand the impact of the multiple waves of the COVID-19 pandemic on patient safety, we conducted a qualitative study of front-line clinicians from multiple disciplines across the USA to understand their perspectives.

As part of a larger randomised control trial of implementing Stress First Aid, a framework used to improve recovery from stress to front-line healthcare workers (HCWs) during the COVID-19 pandemic, 17 57 clinicians from 16 hospitals across the USA (8 intervention and 8 usual care sites) working in the emergency department (ED), intensive care unit (ICU) or an inpatient unit 17 were recruited to participate in an interview. Semistructured interviews were conducted by members of the study team (LS, JB, GT, SA and CG) between September 2021 and September 2022.

The interview guide was rooted in the Consolidated Framework for Implementation Research 18 and asked respondents about workplace factors that played an important role in their mental health and risk for burn-out. In addition to asking about the perceived effects of the intervention (for intervention sites), clinicians were asked general questions about their experiences as a HCW during the pandemic and how decisions were made about patient care. We conducted an additional set of interviews with 23 registered nurses and physicians from two of the health systems about their experiences as clinicians during the pandemic. For these subsequent interviews, a separate protocol guided respondents throughout different parts of the pandemic, eliciting their general experiences and emotions around providing care at different times in the pandemic. Clinicians from both sets of interviews who spontaneously raised issues around patient safety were included in he analysis for this study.

Interviews were conducted by Microsoft Teams or Zoom and were generally 30–60 min in length. Participants were given a US$50 e-gift card for their participation. The interviews were transcribed, deidentified and uploaded to Dedoose, a qualitative software program for rigorous qualitative coding using a thematic analysis. 19 We developed a codebook based on emerging themes identified in initial interviews and updated it as new themes arose. Researchers with experience in qualitative methods (including physician researchers, a health services researcher, a medical sociologist and doctorate-level students in policy analysis) coded (LS, JB, AB, SH and GT) and analysed (LS, JB, SA and CG) the interviews (see Consolidated criteria for reporting qualitative research (COREQ) checklist). Patients and the public were not involved in any stage of the research process given the focus on a HCW intervention.

Of the 80 clinicians interviewed across both sets of interviews, 27 clinicians spontaneously raised issues around patient safety. We identified two primary themes related to patient safety: (1) compromised access to healthcare often resulting in diagnostic delays that may have or did result in patient harm and (2) impaired care delivery that may have or do cause a patient safety events. Table 1 presents additional clinician quotes by theme.

• View inline

Clinician quotes by theme

Compromised access to healthcare and diagnostic delays

Clinicians discussed how the COVID-19 pandemic affected access to care, particularly preventative care, and how each stage of the pandemic presented new barriers to access. For example, early in the pandemic, when many outpatient clinics transitioned to telehealth and other remote care, clinicians noted that lack of access to in-person care impacted the timeliness of diagnosis and control of clinical conditions (eg, cancers and chronic diseases). Later in the pandemic, patients experienced additional barriers to care due to increased demand for care. Many who deferred care early on or had had care delayed due to early protocols (eg, social distancing and cancelled procedures) to reduce staff and patient exposure to COVID-19, re-entered the healthcare system. Clinicians noted an influx of sick patients with multiple illnesses, many of which were chronic diseases that had acutely worsened during the pandemic’s early months. Clinicians also described serious medical conditions for which delay in diagnosis or treatment may have meant a worse prognosis. One resident physician noted that they had diagnosed more cancers in the ED than ever before, even 2 years after the start of the pandemic, suggesting that without appropriate access to primary care, these cancers were diagnosed at a later stage than they would have been during non-pandemic times.

But you still do see the repercussions of primary care doctors not seeing patients in person for the last two years … I’ve diagnosed more cancer in the ER than I ever thought I would because people had symptoms for a year and then haven’t been able to see a doctor and last resort come to the ER. (Resident Physician)

Impaired care delivery

Staffing concerns.

One of the most commonly cited themes was patient safety events related to staffing shortages. Specifically, HCWs discussed the impact of shortages on roles and responsibilities, patient to clinician ratios and the need to hire per diem staff who were less familiar with a given health system. Clinicians reflected on how, early in the pandemic, they felt spread thin as they cared for high volumes of critically ill patients with COVID-19 (eg, more intubated patients than was typical) in addition to caring for other critically ill patients. They deemed this high volume ‘unsafe’. One trainee physician shared how higher than normal patient to clinician ratios led to a patient’s demise. Rather than make more nuanced care decisions and reflect on the patient’s particular clinical situation, the clinician followed a protocol and administered too much intravenous fluid which may have contributed to the patient going into respiratory failure (need for a breathing tube). They felt in non-pandemic times this demise might not have happened. In addition, a nurse shared how the high volume of critically ill patients and patient to nurse ratios meant that one of her patients experienced significant harm that could have resulted in death.

I remember I took care of two Nimbex [a paralytic agent] patients, both paralyzed. Those are supposed to be one to one for safety and I remember I got paired with two Nimbex patients on ventilators and there were tons of drips in each room, IV medications and that was really hard. At [one] point, I remember the propofol [pain and sedative medication] ran out and my patient’s blood pressure was so high, but I was stuck in my other room. … I had to call for help. There was really nobody around because their workload was so heavy that day. So it’s scary. But I just eventually ran over there and hung a new bottle. (Nurse)

Clinicians who were deployed from other units to work in new departments also posed a patient safety risk, as they were less likely to be familiar with clinical conditions and equipment. One nurse noted that delivery of care was ‘compromised’ in part due to staff, who were less experienced with patient-facing or critical care, being deployed from other departments:

It was very stressful during that peak because I felt that we were [delivering] compromis[ed] care because we were just so short staffed …The very little times that they were even able to send staff from upstairs, the other departments, sometimes they would send staff that had not worked at the bedside for years, so their skills weren't up to par, so they weren't as helpful as we would had hoped for. (Nurse)

As the pandemic progressed, staffing shortages seemed to become more acute in multiple departments. Simultaneously, many patients returned to in-person care just as delta and omicron variants were surging. To manage staffing shortages, many health systems started to depend on per diem staff to fill these gaps, which presented its own patient safety issues. These staff were not as familiar with the health system, equipment and other clinical team members, leading to potential or actual patient safety events. One nurse shared how a patient safety event occurred due to a per diem nurse being less familiar with the protocols and equipment in the ICU, noting that while per diem staff had been hired to offload demands on the existing staff, they ultimately created more work.

We had a lot of travel nurses in the past two years and some of them were great, and some of them were horrible. I just remember taking on their load too because they don't know our protocols and they don't know like how to manage our specific ICU patients ….Some of them are not even scanning medications, they're hanging wrong bags like they're hanging Levophed, one that’s a blood pressure [support] medication instead of a fentanyl [a sedation medication]. (Nurse)

This nurse also emphasised the mental load of working with per diem staff—the need to be extra alert to ensure patients remained safe under their care.

Equipment and space

In addition to staffing shortages, clinicians discussed how the pandemic affected resources, including physical space and equipment, and the impact of these deficiencies on care delivery and patient safety. Early in the pandemic, they struggled with inadequate personal protective equipment (PPE) and critical care beds. One nurse, reflecting on the early stage of the pandemic, shared the impact of an inadequate supply of ICU beds,

We had so many ICU patients that they converted the step-down unit… to ICU rooms, which is very unsafe. It’s not like the same monitoring that we use in the ICU… There’s not as much suction, like wall suction, which you need suction for these patients, usually ICU, at least two minimum. There’s just not enough equipment in the room. You can't properly monitor the patients, because their monitors aren't set up for ICU patients…they were forcing us to go out there and take care of patients in conditions that were not safe. (Nurse)

Later in the pandemic, concerns around space and equipment became more widespread. All parts of the health system saw an influx of patients, many of whom had deferred care. Referring to later waves of the pandemic, a resident physician reflected on how overwhelmed their hospital was with both COVID-19 and non-COVID-19 patients, and how they had to practice medicine in ways that were atypical, such as providing critical care in the hallways:

We were coding people in the hallway. We were seeing gunshot wounds in chairs like I was saying. There were so many intensive care unit patients in the emergency department that were crashing all the time …It’s like you are working on minimal resources, minimal room, nursing is always short. So it was just, it just felt unsafe at some times. (Resident Physician)

Deviation from standard of care

Clinicians also described how the challenges they faced affected their ability to provide the standard of care. One clinician shared that, typically, central lines are only reserved for the sickest patients given their risk of causing bloodstream infection. However, due to concerns about maintaining fast and reliable venous access, many more patients received these lines than was typical. Sometimes, due to the sheer volume of ill patients and inadequate staff, these lines remained in place longer than was standard of care resulting in central line-associated bloodstream infections. 20 21 Another clinician shared how there were not enough negative pressure rooms (needed to prevent airborne COVID-19 transmission) in their hospital for all their patients with COVID-19, so they attempted to create more negative pressure rooms using an air vent and long tubing. Some patients had falls–a Joint Commission sentinel safety event 22 –due to the tubing.

Many clinicians shared their feelings of desperation; given the circumstances, they had no other choice but to provide sub-standard care. One nurse stated: ‘ It was probably the hardest ethical, mental dilemma that I faced… I can't properly care for my patients because I don't want to bring anybody into the room with me .’ Many clinicians discussed how their health systems changed protocols to reduce staff and clinician exposure to patients with COVID-19 early in the pandemic. These changes occurred when less was known about the virus’s transmission and there were supply chain issues with PPE. In retrospect, they wondered whether this had been the right decision and worried about the impacts of these changes on patient outcomes.

Clinicians also expressed sentiments of moral injury related to these deviations in standard of care and the ethical dilemmas they faced having to choose between quality of care, following new protocols, and their own and their colleagues’ safety. One clinician reflected on the unintended consequences of reduced patient contact during the early months of the pandemic. While clinicians noted that these changes were made for infection control, numerous patients suffered because of the social isolation within the hospital,

We ended up converting a lot of our ER rooms into ICU rooms. So I had one gentleman who was very awake and alert, but his room was like a dungeon…There was no tv and no phone in that room and the internet doesn't work very well down there, so he couldn't even use his own electronic devices. And he literally went crazy. One day I walked in the room he said can you just intubate me, and I said but you don't need it. He goes ‘it will move me out of this place.’ (Attending Physician)

As the pandemic progressed, the usual standard of care continued to be compromised to compensate for the high number of critically ill patients. Although more was known about the virus and there were better tools to both prevent and treat COVID-19, clinicians continued to have to sacrifice patient safety given these new challenges.

Diagnostic anchoring

Clinical care was affected by diagnostic anchoring—the tendency to retain an initial impression even as more information became available. 23 Less experienced clinicians had become accustomed to managing ‘only’ COVID-19. Particularly for those early in their training when the pandemic began, COVID-19 may have been one of the few diagnoses with which they had experienced. Even clinicians who were not in training discussed how there were delays in diagnosis of other illnesses because clinicians were focused on ruling out and treating COVID-19.

Everyone was being screened for COVID-19. [but]there’s other things that can cause coughs or shortness of breath besides covid…people get kind of pushed into a bucket of COVID-like symptoms or not. And sometimes those people had heart failure, pneumonia and maybe didn't get the traditional care that they would have. (Attending Physician)

Clinicians also reported not tailoring treatment to the particular clinical condition and treated ‘everyone like they had COVID-19’ even when another diagnoses had been made, which also led to potential patient harm.

Our findings add to the literature on clinicians’ perspectives on the effect of the multiple waves of the COVID-19 pandemic on patient safety. Similar to prior studies, the clinicians we interviewed noted that several of the interventions put in place to keep clinicians and patients safe by minimising in-person contact during the early weeks of the pandemic created a unique context that resulted in compromised patient safety. 12 As the pandemic progressed, new challenges arose that further affected patient care and exacerbated unsafe conditions, including staffing, equipment and space shortages. 13 16 Clinicians also described how the pandemic affected their clinical decision-making—particularly diagnostic anchoring—which may have led to diagnostic errors. They described the emotional toll of the difficult decisions they had to make about patient care during the pandemic. While most clinicians described clinical situations that may have compromised patient care, only a few clinicians shared instances of true patient harm due to these unsafe conditions.

Early in the pandemic, some patients delayed routine care as well as urgent and emergent care out of fear of contracting the virus. 24–26 Much routine care was also either cancelled due to surges that threatened health systems’ capacity or managed remotely to avoid in-person contact. Many of the unsafe conditions clinicians shared in this study were the result of processes and protocols that were developed with good intentions to keep both staff and patients safe from a novel, highly transmissible and deadly virus. However, these very efforts delayed needed care, sometimes resulting in late diagnosis of serious medical conditions. Clinicians also reflected on the lasting impact of these decisions even months later as health systems saw high numbers of patients presenting with multiple illnesses, due in part to delays in care. This sentiment is reflected in real-world data; there was a decrease in diagnosis of ‘screening detectable cancers’ (eg, colon cancer) in 2020 compared with 2019. While data are still emerging, reduced cancer screening may mean more cancers diagnosed at an advanced stage. 27

Clinicians discussed how the delivery of patient care was reallocated in ways that were unsafe. Critical care was provided in areas that were not conducive to supporting severely ill patients. Staff with limited experience in caring for critically ill patients were also redeployed and asked to provide care beyond the level for which they had been trained. While these decisions were necessary to address surges of patients who would not otherwise have received care, the trade-offs have not been studied. Better understanding and even quantifying the full impact of such decisions could, for example, help health system decision-makers decide when to step down relocation and redeployment efforts because the benefits no longer outweigh the risks.

Isolation precautions to prevent nosocomial spread of COVID-19 may also have had unintended consequences. Drawing on experience from parallel infection control efforts in hospitals–such as contact precautions for methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci–one might expect that putting patients on precautions decreases the frequency of clinician visits into the room. Indeed, prior work on contact precautions suggests an inadvertent increase in patient safety events among those on precautions. 28 There is emerging evidence that COVID-19 isolation precautions may have increased the risk of falls and unplanned extubations. 6 While we are not suggesting forgoing airborne and contact precautions for COVID-19 patients, we cannot actively guard against the associated harms without better understanding the extent of such harms.

As highlighted in our study, the COVID-19 pandemic forced health systems to make difficult decisions about patient care around COVID-19. However, many of the circumstances described are in part, a result of failures of public health and political systems. Early in the pandemic, the shortage of PPE, testing and ventilators was due to a lack of preparedness months to years before the pandemic started. 29 30 It is possible that with optimised public health planning, many of these unsafe conditions and patient safety events could have been prevented. Similarly, the threats to patient safety later in the pandemic (eg, staffing shortages) might also have been averted or at least mitigated. Even prior to the COVID-19 pandemic, the USA had been projected to experience a nursing shortage. 31 32 With earlier interventions—such as more funding for nurse education, streamlining credentialing and making it easier for those who trained abroad to enter the workforce 31 33 —some of the patient safety events described by clinicians in our study possibly could have been prevented.

In the immediate future, healthcare systems continue to be challenged by the pandemic. HCWs are grappling with high levels of burn-out and secondary trauma, 34 which only worsens the ongoing staffing and supply shortages. Burn-out has also been associated with medical errors and patient safety events, which suggests a continued threat of patient safety events. 35 Given these continued circumstances, health systems, policy-makers and oversight organisations such as the Joint Commission should work together to thoughtfully implement patient-centred policies and practices aimed at reducing harms.

Future pandemic preparedness should also consider planning that incorporates a patient safety lens to identify preventable harms before they occur and to explicitly recognise the trade-offs of pandemic-related decisions. One important deficit highlighted by the current pandemic is the shortcomings of our current systems for identifying patient safety events. Healthcare staff reports of patient safety events, which is the primary source of data on patient safety events, dramatically decreased during the pandemic 5 8 9 highlighting the need for more robust systems to identify patient safety events that are less dependent on an already overstretched healthcare workforce. Future research also needs to fully assess the impact of the decisions made during this pandemic such as reduced in-person care, no visitor policies and delayed non-urgent care, to determine if a different approach would have resulted in more or less harm. This will allow the development of crisis standards of care that are mindful of the impact of such decisions on patient safety.

Limitations

Our study had several limitations. First, we did not explicitly ask clinicians about the impact of COVID-19 on patient safety. Rather, these were comments that clinicians spontaneously brought up as part of a larger study on clinician burn-out. Therefore, this study likely under-reports the types and number of patient safety events. However, using these spontaneously raised comments about patient safety also serves to strengthen the findings of this study, as clinicians highlighted patient safety as an important issue in discussing their experiences of providing care during the pandemic. Second, while some clinicians discussed unsafe conditions, we do not know if these events resulted in patient harm; we have no quantitative data on the number of patient safety events at these health systems during the pandemic. Third, we only interviewed nurses and physicians. Other HCWs, for example, respiratory therapists, were also on the front lines and likely have important insights to share about patient safety. Finally, many clinicians shared stories of unsafe care that occurred months prior to the interview so there is the potential for recall bias.

This study provides insight into clinician perspectives on how the COVID-19 pandemic affected patients’ safety. These clinician narratives provide a deeper understanding of the multiple layers of patient safety that were impacted and how each wave of the pandemic presented different challenges to patient safety. Future research is needed to quantify the impact of COVID-19 on patient safety and to identify best practices to reduce risk of such events during future health crises.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by RAND Corporation’s Human Subjects Protection Committee, IRB approval number is 2020-N0697. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors are grateful to our partners at Vizient and Clinical Directors Network for their collaboration, including efforts with recruitment during the pandemic. This work was previously presented at the Society of General Internal Medicine conference on 11 May 2023.

• Nguyen D , et al
• World Health Organization & WHO Patient Safety
• World Health Organization
• Taylor MA ,
• Reynolds CM ,
• Classen D ,
• Miller S , et al
• Pauletti G ,
• Girotto C ,
• De Luca G , et al
• ↵ NRLS national patient safety incident reports: commentary (September 2021) . National Health System ; 2021 . Available : https://www.england.nhs.uk/publication/nrls-national-patient-safety-incident-reports-commentary-september-2021/
• ↵ NPSD data spotlight, patient safety and COVID-19: a qualitative analysis of concerns during the public health emergency, 2021 . Rockville, MD Agency for Healthcare Research and Quality ; 2021 . Available : https://www.ahrq.gov/sites/default/files/wysiwyg/npsd/data/spotlights/spotlight-ptsafety-and-covid-19.pdf
• Gardner LA , et al
• Vranas KC ,
• Golden SE ,
• Mathews KS , et al
• Butler CR ,
• Wightman AG , et al
• Mancini D ,
• Tong A , et al
• Fournier J-P ,
• Amélineau J-B ,
• Hild S , et al
• Applebaum JR , et al
• Meredith LS ,
• Farmer CM , et al
• Damschroder LJ ,
• Keith RE , et al
• ↵ Dedoose version 9.0.17, web application for managing, analyzing, and presenting qualitative and mixed method research data . 2021 . Available : www.dedoose.com
• Kluger DM ,
• The Joint Commission
• ↵ Anchoring Bias With Critical Implications . AORN J 2016 ; 103 : 658 . doi:10.1016/j.aorn.2016.03.012 OpenUrl
• Findling MG ,
• Blendon RJ ,
• Blecker S ,
• Petrilli CM , et al
• Bhambhvani HP ,
• Rodrigues AJ ,
• Yu JS , et al
• Villazana R ,
• Hofer B , et al
• Schrank GM ,
• Snyder GM ,
• Davis RB , et al
• Sanger DE ,
• Sullivan E , et al
• ↵ Fact sheet: nursing shortage . 2022 . Available : https://www.aacnnursing.org/Portals/42/News/Factsheets/Nursing-Shortage-Factsheet.pdf
• Pforsich H , et al
• Washington Post
• American Association of Critical-Care Nurses
• Johnson J ,
• Watt I , et al

Presented at Poster presetation at Society of General Internal Medicine annual meeting, April 2022, Aurora, CO

Contributors LS accepts full responsibility for the work and conduct of the study, had access to the data, and controlled the decision to publish. LS, JB and CG conceived of the study. LS, JB, AB, SH and GT conducted interviews and coded the results. LS, JB, SA and CG analysed the results. LS took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Funding Research reported in this report was funded through a Patient-Centered Outcomes Research Institute (PCORI) Award (PCORI ID: COVID‐2020C2‐10721). Further information available at: https://www.pcori.org/research-results/2020/does-stress-first-aid-program-improve-well-being-among-healthcare-workers-during-covid-19-pandemic-cover-hcw-project .

Disclaimer The statements presented in this article are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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Interpretative Phenomenological Analysis: Theory, Method and Research

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This book presents a comprehensive guide to interpretative phenomenological analysis (IPA) which is an increasingly popular approach to qualitative inquiry taught to undergraduate and postgraduate students today. The first chapter outlines the theoretical foundations for IPA. It discusses phenomenology, hermeneutics, and idiography and how they have been taken up by IPA. The next four chapters provide detailed, step by step guidelines to conducting IPA research: study design, data collection and interviewing, data analysis, and writing up. In the next section, the authors give extended worked examples from their own studies in health, sexuality, psychological distress, and identity to illustrate the breadth and depth of IPA research. The final section of the book considers how IPA connects with other contemporary qualitative approaches like discourse and narrative analysis and how it addresses issues to do with validity.

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• Presents a comprehensive guide to interpretative phenomenological analysis.
• Outlines the theoretical foundations for IPA.
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• Title : Interpretative Phenomenological Analysis: Theory, Method and Research
• Authors : Jonathan A. Smith , Paul Flowers , Michael Larkin
• Edition: 2nd Edition
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• Language : English
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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

Metrics details

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.

Gallup. A STUDY OF NCAA, STUDENT-ATHLETES. : Undergraduate Experiences and Post-College Outcomes. 2020.

Graupensperger S, Panza MJ, Budziszewski R, Evans MB. Growing into us: trajectories of Social Identification with College Sport teams Predict Subjective Well-Being. Appl Psychol Health Well Being. 2020;12(3):787–807.

Article   PubMed   PubMed Central   Google Scholar  

Gohn LA, Albin GR. Understanding college student subpopulations: a guide for student affairs professionals. NASPA Student Affairs Administrators In HIgher Education; 2006.

American Psychological Association. APA Task Force on Psychological Practice with Sexual Minority Persons. Guidelines for Psychological Practice with Sexual Minority Persons. American Psychologist. 2021.

Team DC. 2023 Scholarship Recipients-Team DC awarded the following seven student-athletes with scholarships in 2023: Team DC; 2023 [cited 2023 July, 22]. https://teamdc.org/scholarships/

Porter M. Check out these LGBTQ student-athlete scholarship opportunities: Various organizations give out scholarships to LGBTQ students and student-athletes 2021 [updated Mar 31, 2021. https://www.outsports.com/lgbtq-high-school-athlete-resources/2021/3/31/22310564/lgbtq-student-athlete-scholarship-list

lacrosse U. USA LACROSSE LGBTQ + INCLUSION SCHOLARSHIP 2023 [ https://www.usalacrosse.com/lgbtq-plus-scholarship

National Collegiate Athletic Association. Ethnic Minority and Women’s Enhancement Graduate Scholarship 2023 [ https://www.ncaa.org/sports/2013/11/21/ethnic-minority-and-women-s-enhancement-graduate-scholarship.aspx

Pigozzi F, Bigard X, Steinacker J, Wolfarth B, Badtieva V, Schneider C, et al. Joint position statement of the International Federation of Sports Medicine (FIMS) and European Federation of Sports Medicine Associations (EFSMA) on the IOC framework on fairness, inclusion and non-discrimination based on gender identity and sex variations. BMJ Open Sport Exerc Med. 2022;8(1):e001273.

International Olympic Committee. IOC Framework on fairness, inclusion and non-discrimination on the basis of gender identity and sex variations. 2021.

Denison E, Bevan N, Jeanes R. Reviewing evidence of LGBTQ + discrimination and exclusion in sport. Sport Manage Rev. 2020;24(3):389–409.

Article   Google Scholar  

Symons CM, O’Sullivan GA, Polman R. The impacts of discriminatory experiences on lesbian, gay and bisexual people in sport. Annals Leisure Res. 2017;20(4):467–89.

Anderson AR, Smith CM, Stokowski SE. The impact of Religion and Ally Identity on individual sexual and gender prejudice at an NCAA Division II institution. J Issues Intercoll Athletics. 2019(12):154–77.

Toomey RB, McGeorge CR, Carlson TS. Athletes’ perceptions of the climate for sexual and gender minority athletes and their intervention in bias. J Study Sports Athletes Educ. 2018;12(2):133–54.

Roper EA, Halloran E. Attitudes toward Gay men and lesbians among Heterosexual male and female student-athletes. Sex Roles. 2007;57(11):919–28.

O’Brien KS, Shovelton H, Latner JD. Homophobia in physical education and sport: the role of physical/sporting identity and attributes, authoritarian aggression, and social dominance orientation. Int J Psychol. 2013;48(5):891–9.

Article   PubMed   Google Scholar  

Gill DL, Morrow RG, Collins KE, Lucey AB, Schultz AM. Perceived climate in physical activity settings. J Homosex. 2010;57(7):895–913.

Petty L, Trussell DE. Experiences of identity development and sexual stigma for lesbian, gay, and bisexual young people in sport: ‘Just survive until you can be who you are’. Qualitative Res Sport Exerc Health. 2018;10(2):176–89.

Anderson AR, Stokowski S, Smith CML, Turk MR. You have to validate it: experiences of female sexual minority student-athletes. J Homosex. 2021:1–22.

Storr R, Nicholas L, Robinson K, Davies C. Game to play?’: barriers and facilitators to sexuality and gender diverse young people’s participation in sport and physical activity. Sport Educ Soc. 2022;27(5):604–17.

Denison E, Jeanes R, Faulkner N, O’Brien KS. The relationship between ‘Coming out’ as Lesbian, Gay, or bisexual and experiences of homophobic Behaviour in Youth Team sports. Sexuality Res Social Policy. 2021;18(3):765–73.

Pariera K, Brody E, Scott DT. Now that they’re out: experiences of College Athletics Teams with openly LGBTQ players. J Homosex. 2021;68(5):733–51.

Kulick A, Wernick LJ, Espinoza MAV, Newman TJ, Dessel AB. Three strikes and you’re out: culture, facilities, and participation among LGBTQ youth in sports. Sport Educ Soc. 2019;24(9):939–53.

Jeffreys E. Public policy and LGBT people and activism in mainland China. Routledge handbook of the Chinese communist party. Routledge; 2017. pp. 283–96.

Chinese Classification of Mental Disorders. Classification and diagnostic criteria of Mental disorders in China. Shandong: Shandong Publishing House of Science and Technology; 2001.

Google Scholar  

Wei W. The normalization project: the Progress and limitations of promoting LGBTQ Research and Teaching in Mainland China. J Homosex. 2020;67(3):335–45.

Shaw G, Zhang X. Cyberspace and gay rights in a digital China: Queer documentary filmmaking under state censorship. China Inform. 2018;32(2):270–92.

Zhao P, Cao B, Bien-Gund CH, Tang W, Ong JJ, Ding Y, et al. Identifying MSM-competent physicians in China: a national online cross-sectional survey among physicians who see male HIV/STI patients. BMC Health Serv Res. 2018;18(1):1–9.

Article   CAS   Google Scholar  

Xu G, Wang X, Budge SL, Sun S. We don’t have a template to follow: sexual identity development and its facilitative factors among sexual minority men in the context of China. J Couns Psychol. 2022.

Suen YT, Chan RCH, Badgett MVL. The experiences of sexual and gender minorities in employment: evidence from a large-scale survey of Lesbian, Gay, Bisexual, Transgender and Intersex People in China. China Q. 2021;245:142–64.

Liu X, Peng C, Huang Y, Yang M, Wen L, Qiu X, et al. Association between sexual orientation and school bullying behaviors among senior high school students. Chin J Public Health. 2020;36(6):880–3.

Wang Y, Yu H, Yang Y, Li R, Wilson A, Wang S, et al. The victim-bully cycle of sexual minority school adolescents in China: prevalence and the association of mood problems and coping strategies. Epidemiol Psychiatr Sci. 2020;29:e179.

Li X, Zheng H, Tucker W, Xu W, Wen X, Lin Y et al. Research on relationships between sexual identity, adverse childhood experiences and Non-suicidal Self-Injury among Rural High School Students in Less developed areas of China. Int J Environ Res Public Health. 2019;16(17).

Zhao M, Xiao D, Wang W, Wu R, Zhang W, Guo L, et al. Association among Maltreatment, Bullying and Mental Health, Risk Behavior and sexual attraction in Chinese students. Acad Pediatr. 2021;21(5):849–57.

Zhao J, Teng S, Zhang X, Yang X, Chen J. Change in sexual orientation identity and its influencing factors among college students. Chin J Public Health. 2018;34(4):563–6.

Zhang Y, Fu F. Investigation on Mental Health Status of Sexual Minority College Students based on National Samples of China. Chin J Clin Psychol. 2019;27(5):997–1002.

Wen G, Zheng L. The influence of internalized Homophobia on Health-related quality of life and life satisfaction among Gay and Bisexual men in China. Am J Mens Health. 2019;13(4):1557988319864775.

Xu W, Zheng L, Xu Y, Zheng Y. Internalized homophobia, mental health, sexual behaviors, and outness of gay/bisexual men from Southwest China. Int J Equity Health. 2017;16(1):36.

Li P, Huang Y, Guo L, Wang W, Xi C, Lei Y, et al. Sexual attraction and the nonmedical use of opioids and sedative drugs among Chinese adolescents. Drug Alcohol Depend. 2018;183:169–75.

Shi J, Dewaele A, Lai W, Lin Z, Chen X, Li Q, et al. Gender differences in the association of sexual orientation with depressive symptoms: a national cross-sectional study among Chinese college students. J Affect Disord. 2022;302:1–6.

Guo M, Wang Y, Duan Z, Wei L, Li S, Yan H. Depressive symptoms, perceived social support and their correlation among adolescent lesbians. Chin J Public Health. 2021;37(12):1805–8.

Huang Y, Li P, Guo L, Gao X, Xu Y, Huang G, et al. Sexual minority status and suicidal behaviour among Chinese adolescents: a nationally representative cross-sectional study. BMJ Open. 2018;8(8):e020969.

Denison E, Bevan N, Jeanes R. Reviewing evidence of LGBTQ + discrimination and exclusion in sport. Sport Manage Rev. 2021;24(3):389–409.

Abreu RL, Audette L, Mitchell YL, Simpson I, Ward J, Ackerman L, et al. LGBTQ student experiences in schools from 2009–2019: a systematic review of study characteristics and recommendations for prevention and intervention in school psychology journals. Psychol Sch. 2021;59(1):115–51.

Mann M, Krane V. Inclusion or illusion? Lesbians’ experiences in sport. Sex, gender, and sexuality in Sport. Routledge; 2019. pp. 69–86.

Tang R, Feng Y. Bibliometric analysis of studies on homosexuality in the Chinese psychology. J Jiangxi Normal University(Philosophy Social Sci Edition). 2018;56(6):108–14.

MORRISON TG, McLEOD LD, MORRISON MA, ANDERSON D, O’CONNOR WE. Gender stereotyping, Homonegativity, and misconceptions about sexually coercive behavior among adolescents. Youth Soc. 1997;28(3):351–82.

Weinberg GH. Society and the healthy homosexual. New York: St. Martin; 1972.

HEREK GM. On heterosexual masculinity:some Psychical consequences of the Social Construction of Gender and sexuality. Am Behav Sci. 1986;29(5):563–77.

Barber H, Krane V. Creating a positive climate for Lesbian, Gay, Bisexual, and Transgender youths. J Phys Educ Recreation Dance. 2007;78(7):6–52.

Anderson E. Openly Gay athletes:contesting hegemonic masculinity in a homophobic environment. Gend Soc. 2002;16(6):860–77.

Lenskyj H. Power and play: gender and sexuality issues in Sport and Physical Activity. Int Rev Sociol Sport. 1990;25(3):235–45.

Smith M, Cuthbertson S, Gale N. Out for sports -tackling Homophobia and Transphobia in Sport Content. Edinburgh, Edinburgh: Equality Network; 2012.

Anderson E, Bullingham R. Openly lesbian team sport athletes in an era of decreasing homohysteria. Int Rev Sociol Sport. 2015;50(6):647–60.

Saraç L, McCullick B. The life of a gay student in a university physical education and sports department: a case study in Turkey. Sport Educ Soc. 2017;22(3):338–54.

Kavasoğlu İ, Anderson E. Gay men and masculinity in a homohysteric Turkish body building culture. J Gend Stud. 2021;31(7):812–24.

Morales L, White AJ. Perception versus reality: Gay male American athletes and coming-out stories from outsports. Com. LGBT athletes in the sports media. 2019:27–50.

Scandurra C, Braucci O, Bochicchio V, Valerio P, Amodeo AL. Soccer is a matter of real men? Sexist and homophobic attitudes in three Italian soccer teams differentiated by sexual orientation and gender identity. Int J Sport Exerc Psychol. 2019;17(3):285–301.

Rollè L, Cazzini E, Santoniccolo F, Trombetta T. Homonegativity and sport: a systematic review of the literature. J Gay Lesbian Social Serv. 2022;34(1):86–111.

Denison E, Kitchen A. Out on the fields: The first international study on homophobia in sport. Nielsen Sport, Bingham Cup 2014, Sydney Convicts, Sport Australia; 2015.

Menzel T, Braumüller B, Hartmann-Tews I. The relevance of sexual orientation and gender identity in sport in Europe: findings from the outsport survey. Cologne: German Sport University Colognen, Istitute of Sociology and Gender Studies; 2019.

Tseng Y-H, Sum RK-W. Attitudes of Taiwan and Hong Kong collegiate student athletes towards homosexuality in sport participation. Asia Pac J Sport Social Sci. 2017;6(3):267–80.

Velez L, Piedra J. Does sexuality play in the stadium? Climate of tolerance/rejection towards sexual diversity among soccer players in Spain. Soccer Soc. 2018;21(1):29–38.

Halbrook MK, Watson JC, Voelker DK. High School coaches’ experiences with openly lesbian, Gay, and bisexual athletes. J Homosex. 2019;66(6):838–56.

Hunter JA, Kypri K, Stokell NM, Boyes M, O’Brien KS, McMenamin KE. Social identity, self-evaluation and in-group bias: the relative importance of particular domains of self-esteem to the in-group. Br J Soc Psychol. 2004;43(1):59–81.

Tajfel H, Turner JC. The social identity theory of intergroup behavior. Political psychology: Psychology; 2004. pp. 276–93.

Trepte S, Loy LS. Social Identity Theory and Self-Categorization Theory. In: Rössler P, Hoffner CA, Zoonen L, editors. The International Encyclopedia of Media Effects2017. pp. 1–13.

Rankin SS, Merson D. 2012 LGBTQ National College Athlete Report. Charlotte, NC: Campus Pride; 2012.

Fynes JM, Fisher LA. Is authenticity and Integrity possible for sexual minority athletes? Lesbian Student-Athlete experiences of U.S. NCAA Division I Sport. Women Sport Phys Activity J. 2016;24(1):60–9.

Ellemers N, Haslam SA. Social identity theory. Handb Theor Social Psychol. 2012;2:379–98.

Turner JC, Hogg MA, Oakes PJ, Reicher SD, Wetherell MS. Rediscovering the social group: A self-categorization theory. Cambridge, MA, US: Basil Blackwell; 1987. x, 239-x, p.

Creswell JW, Poth CN. Qualitative inquiry and research design: choosing among five approaches. Sage; 2016.

Smith JA, Nizza IE. Essentials of interpretative phenomenological analysis. Washington, DC, US: American Psychological Association; 2022. viii, 94-viii, p.

Smith JA, Flowers P, Larkin M. Interpretative Phenomenological Analysis: Theory, Method and Research. London: SAGE Publications Ltd; 2021. http://digital.casalini.it/9781529780796

Eatough V, Smith JA. Interpretative phenomenological analysis. The Sage handbook of qualitative research in psychology. 2017:193–209.

Rothblum ED, Krueger EA, Kittle KR, Meyer IH. Asexual and Non-asexual respondents from a U.S. Population-based study of sexual minorities. Arch Sex Behav. 2020;49(2):757–67.

Aicken CR, Mercer CH, Cassell JA. Who reports absence of sexual attraction in Britain? Evidence from national probability surveys. Asexuality and Sexual Normativity: Routledge; 2015. pp. 10–24.

Van Manen M, Higgins I, van der Riet P. A conversation with Max Van Manen on phenomenology in its original sense. Nurs Health Sci. 2016;18(1):4–7.

Smith JA, Flower P, Larkin M. Interpretative phenomenological analysis: theory, Method and Research. Qualitative Res Psychol. 2009;6(4):346–7.

Groenewald T. A Phenomenological Research Design Illustrated. Int J Qualitative Methods. 2004;3(1):42–55.

Asplund M, Welle CG. Advancing Science: how Bias holds us back. Neuron. 2018;99(4):635–9.

Article   PubMed   CAS   Google Scholar  

Lofland J, Lofland LH. Data logging in observation: Fieldnotes. London: Sage; 1999.

Smith JA. Hermeneutics, human sciences and health: linking theory and practice. Int J Qualitative Stud Health Well-being. 2007;2(1):3–11.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Smith JA. Evaluating the contribution of interpretative phenomenological analysis. Health Psychol Rev. 2011;5(1):9–27.

Georgiou YS, Patsantaras N, Kamberidou I. Homophobia predictors–A case study in Greece: heterosexual physical education student attitudes towards male and female homosexuality. J Phys Educ Sport. 2018;18:1209–16.

Halbrook M, Watson JC. High school coaches’ perceptions of their efficacy to work with lesbian, gay, and bisexual athletes. Int J Sports Sci Coaching. 2018;13(6):841–8.

Atteberry-Ash B, Woodford MR. Support for policy protecting LGBT Student athletes among heterosexual students participating in Club and Intercollegiate sports. Sexuality Res Social Policy. 2017;15(2):151–62.

Mullin EM, Cook S. Collegiate coach attitudes towards lesbians and gay men. Int J Sports Sci Coaching. 2020;16(3):519–27.

Cassidy WP. Sports journalism and women athletes: Coverage of coming out stories. Springer Nature; 2019.

Martos-Garcia D, Garcia-Puchades W, Soler S, Vilanova A. From the via Crucis to Paradise. The experiences of women football players in Spain surrounding gender and homosexuality. Int Rev Sociol Sport. 2023;0(0):10126902231153349.

China Family Planning Association. 2019–2020 National College Student sexual and Reproductive Health Survey Report. China Family Planning Association; 2020.

United Nations Development Programme. Being LGBTI in China - A National Survey on Social Attitudes towards Sexual Orientation, Gender Identity and Gender Expression. 2016.

Suen YT, Chan RCH. A nationwide cross-sectional study of 15,611 lesbian, gay and bisexual people in China: disclosure of sexual orientation and experiences of negative treatment in health care. Int J Equity Health. 2020;19(1):46.

Nguyen T, Angelique H. Internalized Homonegativity, Confucianism, and self-esteem at the emergence of an LGBTQ Identity in Modern Vietnam. J Homosex. 2017;64(12):1617–31.

United States Census Bureau. US and World Population Clock 2020 [ https://www.census.gov/popclock/world

Hu X, Wang Y. LGB identity among young Chinese: the influence of traditional culture. J Homosex. 2013;60(5):667–84.

Xu L, Bao Y. Study on the influence of traditional confucianism on the Social attitude of homosexuality in Contemporary China. Chin Med Ethics. 2019;32(6):733–6.

Tseng Y-h, Sum RK-W. The attitudes of collegiate coaches toward gay and lesbian athletes in Taiwan, Hong Kong, and China. Int Rev Sociol Sport. 2020;56(3):416–35.

McCavanagh TM, Cadaret MC. Creating safe spaces for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) student–athletes. Affirming LGBTQ + students in higher education. Perspectives on sexual orientation and gender diversity. Washington, DC, US: American Psychological Association; 2022. pp. 141–59.

Chavez AF, Guido-DiBrito F, Mallory SL. Learning to value the other: a framework of individual diversity development. J Coll Student Dev. 2003;44(4):453–69.

Turner JC, Reynolds KJ, Self-Categorization T. 2012 2023/06/09. In: Handbook of Theories of Social Psychology [Internet]. London: SAGE Publications Ltd. https://sk.sagepub.com/reference/hdbk_socialpsychtheories2

Latrofa M, Vaes J, Cadinu M. Self-stereotyping: the central role of an ingroup threatening identity. J Soc Psychol. 2012;152(1):92–111.

Seddig D. Individual attitudes toward deviant behavior and perceived attitudes of friends: self-stereotyping and Social Projection in Adolescence and Emerging Adulthood. J Youth Adolesc. 2020;49(3):664–77.

Liu Y, Song J. The relationship between gender self-stereotyping and life satisfaction: the Mediation Role of Relational Self-Esteem and Personal Self-Esteem. Front Psychol. 2022;12.

Organista N, Kossakowski R. Enclaved non-heteronormativity and pragmatic acceptance. The experiences of Polish female football players. Int Rev Sociol Sport. 2023;0(0):10126902231180402.

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Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis

• Hsin-Hsiung Huang 1 ,
• Ming-Hao Chang 1 ,
• Peng-Ting Chen 1 , 2 ,
• Chih-Lung Lin 3 ,
• Pi-Shan Sung 4 ,
• Chien-Hsu Chen 5 &
• Sheng-Yu Fan 6  

BMC Geriatrics volume  24 , Article number:  694 ( 2024 ) Cite this article

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This study conducted in-depth interviews to explore the factors that influence the adoption of fall detection technology among older adults and their families, providing a valuable evaluation framework for healthcare providers in the field of fall detection, with the ultimate goal of assisting older adults immediately and effectively when falls occur.

The method employed a qualitative approach, utilizing semi-structured interviews with 30 older adults and 29 families, focusing on their perspectives and expectations of fall detection technology. Purposive sampling ensured representation from older adults with conditions such as Parkinson's, dementia, and stroke.

The results reveal key considerations influencing the adoption of fall-detection devices, including health factors, reliance on human care, personal comfort, awareness of market alternatives, attitude towards technology, financial concerns, and expectations for fall detection technology.

Conclusions

This study identifies seven key factors influencing the adoption of fall detection technology among older adults and their families. The conclusion highlights the need to address these factors to encourage adoption, advocating for user-centered, safe, and affordable technology. This research provides valuable insights for the development of fall detection technology, aiming to enhance the safety of older adults and reduce the caregiving burden.

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Introduction

As the population of older adults grows, an emerging concern revolves around the prevalence of falls. Age-related gait and balance issues are prevalent and significant in the older adults, increasing the risk of falls and injuries [ 1 ]. Falls can result in a range of injuries, such as fracture or head injury [ 2 , 3 ]. Undoubtedly, the aging population faces a substantial risk related to falls, leading to both mortality and morbidity [ 4 ]. In the United States, statistics indicate that in 2018, 27.5% of adults aged 65 and older reported experiencing at least one fall in the previous year [ 5 ]. One out of five falls results in severe injuries, such as fractures or head trauma. These falls incurred a staggering $50 billion in total medical expenses in the US in 2015 [ 6 ]. There has been a concerning rise in the number of falls resulting in injuries over the years. One study revealed that only 39% of older individuals reported experiencing a fall [ 7 ]. Furthermore, research suggests that the impact of falls continues to affect both admitted and non-admitted older adults, leading to a reduced quality of life for up to nine months following the injury [ 8 ]. On the one hand, a study revealed significant concern and fear among individuals regarding the possibility of the older adults experiencing another fall [ 9 ]. On the other hand, time on the ground (TOG) has been identified as a crucial factor affecting prognosis after a fall. TOG refers to the duration an individual remains on the ground after falling. This factor has been specifically examined in dementia patients, as falls frequently occur in memory care facilities [ 10 ]. However, falls occurring within the home environment during old age often signal the presence of severe underlying health conditions, especially without intime assistance like memory care facilities [ 11 ]. Obviously, falls among older adults is an imperative issue that needs to be addressed.

Given the fact that falls pose a significant concern in healthcare and for family caregivers, there is a growing interest in the development of methods to detect falls. Previous studies on fall detection technology explore the use of sensors in detecting fall-related events among older individuals [ 12 , 13 , 14 ]. One study states that fall detection technology covers three dimensions, including wearable devices, camera-based devices, and ambiance devices. It's worth mentioning that many fall detection methods are already mature and commercially available. These include video-based systems using cameras to monitor movements, microwave-based methods with radar technology to detect falls, and acoustic monitoring that analyzes sounds to identify fall events. These technologies provide valuable alternatives and enhancements to sensor-based fall detection systems [ 15 ]. Wearable devices gather data on body posture and movement, utilizing algorithms to determine if a fall has occurred. Cameras strategically positioned enable ongoing monitoring of older adults, with captured data stored for subsequent analysis and reference. Ambience devices are placed in the surroundings, like walls, floors, and beds. Data from sensors are collected, and an algorithm analyzes the input to determine if a fall has occurred [ 14 ]. Another study found that many solutions also use mobile device sensors, particularly accelerometers, for fall detection in older adults [ 13 ]. The above literature review provides examples of fall detection technology application areas that already exist in the market. Therefore, fall detection technology among older adults has the potential to alleviate the societal burden. However, technology-based solutions, despite their potential benefits, often face resistance from older adults, creating barriers to the adoption of health-related information and communication technology. To address these barriers, we conducted a comprehensive literature review, examining the challenges that older adults may encounter when using fall detection technology.

In 1987, Ram introduced an innovation resistance model [ 16 ], aiming to address the reluctance of consumers to adopt new innovations, particularly when these innovations have the potential to disrupt their existing satisfaction levels or clash with their established beliefs. Building upon this framework, Ram and Sheth [ 17 ] (1989) identified a range of obstacles that hinder consumers' willingness to embrace innovations, classifying them into two main categories: functional barriers and psychological barriers. Functional barriers encompass aspects such as usage limitations, value considerations, and risk perceptions. We conducted a literature review on the barriers that older adults may face when using the technology. Among usage barriers, age-related factors, including hearing impairments, reduced dexterity, declining vision, and mild cognitive challenges, can significantly impact the ease with which users adopt new technologies [ 18 , 19 , 20 , 21 , 22 ]. Previous research [ 18 , 23 , 24 , 25 , 26 ] has emphasized that technical unfamiliarity, which includes inadequate technical skills, a lack of understanding about how to use technology, and limited computer literacy, poses significant challenges for older individuals in adopting new technologies. Additionally, a lack of clear and comprehensive instructions has been identified as a common obstacle for older adults in the literature [ 24 , 27 , 28 ]. Given that the value barrier concept suggests innovative products must offer greater value than existing ones to motivate consumers to switch, there is a scarcity of references related to this description. On the other hand, risk barriers encompass concerns about product reliability, including issues like false alarms and inaccurate data, which can be functional risks that older individuals may encounter [ 19 , 27 , 29 , 30 , 31 ]. High costs also contribute to risk barriers. Many older adults are concerned about the price of the product itself [ 22 , 30 , 32 ]. Furthermore, privacy concerns have been raised by many older individuals, adding to the array of issues related to risk barriers [ 18 , 21 , 22 , 33 , 34 ].

Psychological barriers encompass traditional belief barriers and image-related barriers. Older adults also encounter psychological barriers when using information and communication technology. Among older adults, attitude toward technology represents a common traditional belief barrier, reflecting issues related to trust in their ability to manage devices and their reluctance to adopt it [ 18 , 21 , 35 ]. Image barriers involve concerns about a product's appearance [ 27 ], with some older individuals perceiving certain products as designed for younger generations, which may deter their adoption [ 24 ].

While numerous articles have explored the barriers older individuals face in adopting information and communication technology (ICT) [ 18 , 22 , 36 ], it's essential to acknowledge that ICT encompasses a wide range of applications, making it a diverse and multifaceted topic. Within healthcare, various applications exist, which can make it challenging for healthcare providers to develop products that cater specifically to their target users. While the previous studies encompass fall prevalence, economic burden of falls, and the challenges older adults may face when using ICT, this study focuses more on barriers of these technological products used by older adults and their families, providing a valuable evaluation framework that can aid healthcare providers, particularly in the field of fall detection. Through this research, we aim to offer a valuable assessment framework for making the best use of ICT to help older adults immediately and effectively when falls happen.

Study design

In order to address our research inquiry on the perceived challenges associated with the adoption of fall detection technology and expectations of fall detection technology among older adults and their families, we employed a qualitative approach. Our primary sources of data analysis were semi-structured interviews from in-depth interviews. In-depth interviews are widely acknowledged and commonly used in qualitative research [ 37 ]. The semi-structured interview outline utilized in our study provided a well-defined yet flexible and open-ended framework for exploring the topic [ 38 ]. To align with the research objectives, we developed a semi-structured interview outline, including the background of participants, expectations of fall detection technology, and innovation resistance (see Tables 1 and 2 ). Face-to-face interviews were then conducted with older adults along with their families.

Study subject and recruitment

The aim of this study was to understand the perspectives of older adults with chronic disease, who are prone to falls [ 1 ], and their family caregivers, who are the older adults’ spouses or children. Purposive sampling was employed, and specific inclusion criteria were set for the study participants. These criteria consisted of: (1) healthy individuals over the age of 20 who agreed to participate; (2) participants aged 45 or above, including those affected by stroke, frailty, dementia, Parkinson's disease, and other diseases; (3) participants whose condition was stable, able to mobilize, and willing to take part in the study. We included participants younger than 60 years old in our study because they have chronic diseases such as stroke, dementia, and Parkinson's disease. Individuals with these conditions are more prone to falls compared to others. Although these diseases are typically associated with older adults, we believe that younger participants with these conditions are potential future users of fall detection technology. Therefore, our sample includes individuals under 60 years old and their respective family caregivers.

To ensure clear comprehension of the study's purpose, procedures, and potential risks, an individualized approach was adopted in explaining the study to each participant. Additionally, oral explanations were provided to ensure their understanding of the research instructions and terms outlined in the consent form. In total, interviews were conducted with 30 older adults and 29 families (with one family unable to attend).

Data collection

The study received ethical approval from the Human Research Ethics Review Committee, and the case number assigned was A-ER-110–211. From September 2022 to April 2023, in-depth interviews were conducted in NCKU outpatient hospital using a semi-structured interview outline. The interview process began with the researchers introducing themselves to the participants and providing a detailed explanation of the study's purpose, the interview procedure, and the rights of the participants. Privacy regulations were emphasized, assuring the interviewees that their personal data would be treated confidentially. Following comprehension of the study's objectives and their rights, the participants were informed about the recording of the interview. It was made clear that if they preferred not to be recorded, the investigators would respect their decision and take handwritten notes instead. Each interview lasted approximately 40–60 min. After each interview, research assistants were responsible for transcribing the recorded interview files to create a written transcript of the data. Prior to analysis, the researchers reviewed the verbatim transcripts of the interviews to ensure accuracy and identify any potential errors. If any inconsistencies or missing information were found, another researcher would review the audio recording and the transcript to ensure accuracy and correct any deviations from the original intended meaning.

Data analysis

The qualitative interview data in this study was subjected to content analysis. To streamline the content analysis process and identify themes within the qualitative responses, a panel consisting of four members was established. In addition, the whole process of data analysis was supervised by the professor. The panels include one doctoral researcher, one research assistant, and two graduate students. In employing the inductive approach, 4 researchers employed a systematic process that involved dividing the data into distinct units of meaning, condensing these units, assigning codes, categorizing the codes, and identifying overarching themes [ 39 , 40 ]. The analysis began with the researchers thoroughly reading and rereading the interview data, treating each segment as a unit of analysis. Similar statements within the text were identified and extracted to form meaning units. These meaning units were then condensed through a careful reduction process while ensuring the preservation of their core essence. Subsequently, the meaning units were systematically coded based on their content, with researchers assigning specific codes to each unit. Once the coding process was complete, all the codes were further organized into meaningful categories. Finally, the researchers identified and grouped together different categories that shared related underlying meanings, thereby forming overarching themes [ 41 ]. This rigorous approach to content analysis enabled a comprehensive exploration and interpretation of the qualitative interview data in the study.

Respondent characteristics

From September 2022 to April 2023, the study included 30 older adults and 29 family members, all recruited from NCKU Medical Center in Taiwan. These participants are referred to as N_ Interviewee (older adults /family). The older adults, primarily diagnosed with Parkinson's disease, dementia, or stroke, were selected based on their scores on the Morse Scale [ 42 ], Clinical Frailty Scales [ 43 ], and Barthel Index [ 44 ]. Additionally, the study documented the history of fall events and the relationship between the older adults and their family. Among the older adults, 19 older adults had experience using smartphones, while the remaining older adults did not have the experience (Table 3 ).

Based on the interviews conducted with older adults and their families, we have identified the primary considerations influencing the decision to use wearable fall-detection devices (as detailed in Fig. 1 ; Appendix). These considerations span various aspects, including (1) health considerations, (2) reliance on human care, (3) personal comfort issues, (4) market alternatives, (5) attitude towards technology, (6) financial concerns, and (7) expectations for fall detection technology. The main factors are described below.

Factors influencing adoption of fall detection technology in older adults and families

Health considerations

Concerns about potential health risks associated with wearable fall-detection devices emerged as a significant barrier to their adoption. older adults and their families expressed apprehensions about adverse effects such as dizziness, skin irritation, electrical leakage, and electromagnetic radiation. These concerns are particularly pronounced among older individuals, who tend to be more cautious about new technologies that interact directly with their bodies.

“Yeah, older adults won’t wear it if it's uncomfortable; it's just about avoiding dizziness.” (8_family)

For instance, some family members voiced worries about the possible radiation-related functions of these devices. Others were concerned about the risk of skin allergies and electrical leakage due to the close contact of these devices with the skin. These apprehensions highlight a broader fear of unknown health impacts, which can deter older adults from embracing new technological solutions for fall detection.

“Well, just now, it's just that I've heard that there might be some concerns about it. Because it's worn on the skin, so there's a fear of it having some impact on their skin. Also, there's the question of whether it might have electrical leakage.” (6_family)

“Perhaps, he has some kind of fear, like he might think that this thing could cause harm to the body? Or maybe he's worried about things like skin allergies or getting an electric shock, and so on.” (20_family)

Reliance on human care

Despite the potential benefits of fall-detection technology, many participants in the study emphasized a strong preference for human care and assistance. The majority believe that hiring caregivers or relying on family members is a more reliable and comforting approach. This trust in human assistance is deeply rooted and may significantly hinder the adoption of technological solutions.

Several older adults indicated that they felt no need for fall-detection devices because they were constantly accompanied by attentive family members or professional caregivers. For instance, some older adults mentioned that their spouses or foreign domestic workers were always available to assist them with daily activities, rendering the technology unnecessary. Others noted that their children, who are medical professionals, provided adequate care, further diminishing the perceived need for such devices.

Additionally, the cultural context plays a significant role in this reliance on human care. The close-knit family structure and the high value placed on personal interaction and caregiving contribute to the resistance against technological interventions. Many participants expressed a preference for investing in human care over spending money on devices, indicating that they view personal care as more effective and compassionate.

“Most people now hire foreign domestic workers to provide care. If he needs to get up to go to the bathroom, he'll definitely inform the foreign caregiver, saying, "I need this, I need that, please help me up.” (22_older adults)

“So instead of this, we might end up hiring someone to take care of him or considering long-term care services. Because rather than spending that money, it's the same as having someone look after you 24 h a day.” (2_family)

In summary, both health considerations and a strong reliance on human care are critical factors influencing the adoption of wearable fall-detection devices among older adults. Addressing these concerns through better education about the safety and benefits of these technologies, as well as integrating them into existing caregiving practices, may help in overcoming these barriers.

Personal comfort issues

The comfort and practicality of wearable devices are critical concerns for potential users, significantly impacting their adoption. Key issues identified include the weight and physical discomfort of these devices. Users are generally inclined to avoid technologies that cause inconvenience or discomfort in their daily lives, highlighting the necessity for user-friendly and ergonomic designs.

Participants indicated that the weight of the devices is a primary concern; many stated a preference for lightweight options. Physical discomfort, such as restrictions in movement, emerged as a significant factor. For example, older adults expressed concerns about devices causing discomfort when attached to the knee or foot, which could interfere with their mobility and overall comfort. There is a clear preference for devices that are unobtrusive and do not hinder daily activities.

“Fastened around the knee, I can't do it now. I'm afraid I'll get stuck when I'm walking.” (1_older adults)

“I care about the weight. It shouldn't be too heavy; it should be relatively lightweight.” (20_older adults)

Market alternatives

The preference for traditional fall prevention tools, such as canes and emergency buttons, was evident among many participants. These established solutions are familiar and trusted, making them more appealing than newer technological alternatives. Additionally, some participants believed that canes provide proactive assistance to prevent falls, whereas fall detection technology only alerts family members after a fall has occurred, which does not prevent the incident itself.

Participants noted that they already possess reliable fall prevention tools at home, such as emergency buttons, which they trust for their effectiveness in emergencies. The familiarity and simplicity of these tools make them a preferred choice over fall detection technology. Additionally, canes with stable bases are viewed as effective in ensuring personal safety and preventing falls, further reducing the perceived need for fall detection technology. To compete with traditional methods, fall-detection technology must not only match but surpass the reliability and convenience of existing tools.

“I currently have an emergency button installed in my home. If I have an accident, I can just press that button, and the security company will come to assist me.” (19_older adults)

“Because he just took the crutch and walked with it. Yes, if he wears this, he will still fall.” (8_family)

Attitude towards technology

A prevailing theme in the interviews is resistance to change, with some older individuals expressing a reluctance to adapt to new technologies. This resistance is often rooted in perceptions of inconvenience, unfamiliarity, and a general aversion to having devices attached to their bodies. Overcoming this resistance will require addressing user concerns and providing user-friendly solutions.

Elderly individuals frequently describe new devices as uncomfortable and cumbersome. For example, one older adult noted feeling "strange" and "not used to it" when considering wearing fall-detection devices. Others expressed outright resistance, emphasizing a strong preference for maintaining their current routines without the addition of new technological elements. This sentiment is further compounded by a dislike for the perceived hassle of wearing or carrying additional items, such as glasses or wearable devices.

“It's a strange feeling, doesn't feel like it, not used to it, feels weird.” (16_older adults)

“I'm just too lazy to wear glasses. We usually don't like having things hanging here and there.” (24_older adults)

“And to be honest, older people might have a greater psychological burden. If you ask them to carry something every day, they might not like it or feel that it restricts their mobility, and they might not want it.” (20_family)

Financial concerns

The cost of fall-detection devices is a significant consideration for many older adults and their families. Affordability is a key factor in their decision-making process, with financial capability greatly impacting the willingness to adopt new technology.

Many participants highlighted the financial burden that expensive fall-detection devices could impose. For families already managing substantial living expenses, the additional cost of advanced technology may be prohibitive. This financial strain is particularly acute for those on fixed incomes or with limited financial resources.

“I don’t want this if it’s too much money.” (9_older adults)

“I think financial capability comes first. If there are no issues with economic conditions, you have to make sure they have the financial ability to afford it. That's the main issue.” (5_family)

Expectations for fall detection technology

Participants highlighted several key expectations for fall detection technology, which, if met, could facilitate its adoption. These expectations include features such as remote notifications, physical support, real-time older adults status updates, and immediate assistance functions. Meeting these expectations can enhance the perceived value of fall detection technology and increase user willingness to adopt it.

A major expectation is the ability of the technology to provide real-time notifications to caregivers or family members when a fall occurs. Participants expressed a desire for systems that could alert them regardless of their location, ensuring timely intervention. For example, one family member emphasized the need for notifications even if older adults are far away, illustrating the importance of reliable and far-reaching communication capabilities.

Another expectation is for the technology to offer some form of physical support to prevent falls before they happen. Participants envisioned devices that could sense an impending fall and provide immediate physical assistance to prevent the incident. This proactive approach would not only enhance safety but also provide peace of mind for both users and their caregivers.

Real-time older adults’ status updates and the ability to monitor the condition of older adults remotely were also highly valued. For instance, having access to visual data or images of the older adults’ home environment was seen as a way to increase the sense of security and ensure timely responses to any issues. Comprehensive data on the older adults' health and activity levels could help in managing and understanding their overall condition.

“If we can assist her just before she falls, that would be the ideal scenario. Being able to support her right before the fall occurs.” (1_family)

“So, if we talk about it in terms of shoes, if it can sense that a person might slip or fall, can it prevent them from falling?” (2_family)

“It might be like this. If he wears it and triggers the alarm when he's far away, like what I just mentioned, if he's in Xitou and triggers the alarm, we're in Tainan.” (6_family)

“Data, as I just mentioned, is about being able to have a more immediate and clear understanding of the progression of the condition. And assuming that there is also the capability to capture images or, in a way, for me to see their condition at home, this might make me feel more at ease.” (10_family)

The adoption of fall-detection wearable devices among older individuals and their families is influenced by a complex interplay of factors, as revealed by the findings of this study. Understanding these factors is essential for the successful integration of such technologies into the lives of older adults. The participants' concerns about safety issues, such as skin irritation, dizziness, electrical leakage and radiation, may stem from a heightened awareness of the potential risks associated with electrical products, especially for wearable devices. These concerns can deter older adults from embracing wearable information and communications technology, implying that safety issue could be the potential barrier. Similarly, another study has identified safety factors, including concerns relate to radiation and the use of electricity [ 45 ]. Thus, to address this barrier, device designers should prioritize safety issues, reducing any safety-related risks. These considerations can help alleviate concerns and enhance user’s confidence. Another theme is the preference for human care over technology, with many participants believing that caregivers or family members provided more reliable support. One review study [ 30 ] emphasizes that companionship plays a crucial role in the context of having a source of support and presence in one's life. The preference for human care in taking care of older adults suggests that fall-detection devices should be viewed as complementary tools rather than replacements for caregivers. This aligns with concerns about the fear of losing social connections and experiencing loneliness [ 46 ]. In other words, while technology can aid in ensuring safety, the emotional and social aspects provided by human caregivers are irreplaceable. This is an important finding that emphasizing this perspective may decrease the barriers of using fall detection technology among older adults.

Issues related to device comfort and practicality were highlighted as significant factors influencing adoption as well. Concerns from stakeholders include device weight and physical discomfort. Obviously, user-friendly design is essential to mitigate these concerns [ 47 ]. Designers should aim to create lightweight, comfortable devices that seamlessly integrate into daily life, or design a fall detection technology that does not require older adults to wear. In addition, participants expressed a preference for traditional fall prevention tools, such as canes or emergency buttons, citing familiarity and trust in these established solutions. Several participants voiced the opinion that a cane is more beneficial than a fall detection device since a cane can provide support to older adults and reduce the risk of falls, whereas they believe that fall detection devices may not effectively prevent older adults from falling. This concept that the product is able to prevent falls is similar to fall prediction systems [ 48 ]. On the one hand, this factor may require fall detection technology to demonstrate its superiority over existing options or complement the characteristics of existing products. On the other hand, perception of inconvenience, unfamiliarity, and embarrassment were common attitudes among older adults [ 19 , 32 , 47 ]. In our study, some participants also stated that fall detection devices are troublesome. We suggest making fall detection devices easy to use by designing them to be simple and not bothersome.

The cost of fall detection devices emerged as a significant consideration for both older adults and their families. Affordability is a key factor in their decision-making process [ 22 , 27 , 30 , 32 , 47 ], highlighting the importance of exploring options for making these devices more accessible, such as through insurance coverage or subsidies. On the other hand, one study investigated the preferred specifications, perceived ease of use, and perceived usefulness of an automated fall detection device among older adults who rely on wheelchairs or scooters. It was noted that participants expressed a belief in the utility and user-friendliness of an automated fall detection device. The features include wireless charging, a wristwatch-like design, the option to change the emergency contact person in case of a fall, and the ability to deactivate notifications in case of false alarms [ 49 ]. In our study, participants emphasized the importance of comprehensive fall detection solutions, including remote notifications, real-time older adults’ status updates, and immediate assistance functions. It seems that the function of fall detection technology is oriented toward notifying the families, enabling them to assist immediately. Therefore, prioritizing the creation of devices that detect falls and provide added value through additional features is beneficial for enhancing overall safety and well-being.

Limitations

Although this study contributes to the field of fall detection technology, the study has several limitations. First, the sample of older adults comes from neurology outpatient. This limits the findings to this specific group and decreases their generalizability. Second, the findings of this study are based on the opinions and experiences of the respondents and may not be fully representative of all potential users of fall detection technology. The experiences and preferences of non-respondents remain unknown and might differ from those who participated in the study. In addition, the study involved respondents with varying levels of fall risk, as they suffered from different health conditions such as acute stroke, mild to moderate dementia, impaired cognitive function, and poor balance and gait. Third, as fall risk factors can significantly influence the perception and acceptance of fall detection technology, the results may not fully capture the nuances of specific subgroups within older population. The in-depth, face-to-face interviews were conducted in the outpatient area of the hospital. Although none of the interviewees discontinued the interviews due to privacy concerns, it is important to consider the potential influence of the interview setting. In addition, the outpatient waiting area in a hospital is an open and public space, which might have affected the responses of the interviewees. They may have been conscious of their surroundings and the presence of other individuals, possibly influencing the openness of their responses. Finally, the study focused on a specific population in Taiwan, and the findings may be influenced by cultural and regional factors unique to this context. Cultural differences and healthcare practices may lead to varying perspectives on fall detection technology in other regions or countries.

Conclusion and suggestions

In this study, we examined the factors influencing the adoption of wearable fall-detection devices among older adults and their caregivers. We identified several key considerations: concerns about potential health risks associated with these devices, the preference for human care over technology, the importance of device comfort and practicality, market alternatives, cost considerations, the attitude towards technology, and expectations of technology. Based on our evaluation framework, it is essential to consider safety, usability, affordability, and complementary to human care when developing fall detection products. In addition, meeting user expectations for comprehensive features like remote notifications and immediate assistance functions can further enhance adoption. Addressing these factors and challenges is expected to enhance the safety and quality of life for older adults, thereby relieving the burden of care.

Availability of data and materials

Data is provided within the manuscript.

Abbreviations

Information and communications technology

Mild Cognitive Impairment

Hypertension

Viswanathan A, Sudarsky L. Balance and gait problems in the elderly. Handb Clin Neurol. 2012;103:623–34.

Article   PubMed   Google Scholar  

Institute of Medicine (US) Division of Health Promotion and Disease Prevention. Berg RL, Cassells JS, eds. The second fifty years: promoting health and preventing disability. Washington (DC): National Academies Press (US); 1992.

Centers for Disease Control and Prevention. Older adult fall prevention. https://www.cdc.gov/falls/data-research/facts-stats/?CDC_AAref_Val=https://www.cdc.gov/falls/facts.html . Accessed 22 Nov 2023.

Ambrose AF, Paul G, Hausdorff JM. Risk factors for falls among older adults: a review of the literature. Maturitas. 2013;75(1):51–61.

Moreland B, Kakara R, Henry A. Trends in nonfatal falls and fall-related injuries among adults aged ≥65 years - United States, 2012–2018. MMWR Morb Mortal Wkly Rep. 2020;69(27):875–81.

Article   PubMed   PubMed Central   Google Scholar  

Florence CS, Bergen G, Atherly A, Burns E, Stevens J, Drake C. Medical Costs of fatal and nonfatal falls in older adults. J Am Geriatr Soc. 2018;66(4):693–8.

Boongird C, Ross R. Views and expectations of community-dwelling thai elderly in reporting falls to their primary care physicians. J Appl Gerontol. 2017;36(4):480–98.

Hartholt KA, van Beeck EF, Polinder S, van der Velde N, van Lieshout EM, Panneman MJ, van der Cammen TJ, Patka P. Societal consequences of falls in the older population: injuries, healthcare costs, and long-term reduced quality of life. J Trauma. 2011;71(3):748–53.

PubMed   Google Scholar  

Dhar M, Kaeley N, Mahala P, Saxena V, Pathania M. The prevalence and associated risk factors of fear of fall in the elderly: a hospital-based, cross-sectional study. Cureus. 2022;14(3):e23479.

PubMed   PubMed Central   Google Scholar  

Bayen E, Nickels S, Xiong G, Jacquemot J, Subramaniam R, Agrawal P, Hemraj R, Bayen A, Miller BL, Netscher G. Reduction of Time on the ground related to real-time video detection of falls in memory care facilities: observational study. J Med Internet Res. 2021;23(6):e17551.

Wild D, Nayak US, Isaacs B. How dangerous are falls in old people at home?. Br Med J (Clin Res Ed). 1981;282(6260):266–8.

Article   PubMed   CAS   Google Scholar  

Lapierre N, Neubauer N, Miguel-Cruz A, Rios Rincon A, Liu L, Rousseau J. The state of knowledge on technologies and their use for fall detection: a scoping review [published correction appears in int j med inform. 2018 aug; 116:9]. Int J Med Inform. 2018;111:58–71.

Mrozek D, Koczur A, Małysiak-Mrozek B. Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Inf Sci. 2020;537:132–47.

Article   Google Scholar  

Tanwar R, Nandal N, Zamani M, Manaf AA. Pathway of trends and technologies in fall detection: a systematic review. Healthcare (Basel). 2022;10(1):172.

Newaz NT, Hanada E. The methods of fall detection: a literature review. Sensors (Basel, Switzerland). 2023;23(11): 5212. https://doi.org/10.3390/s23115212 .

Ram S. A model of innovation resistance. Adv Consum Res. 1987;14(1):208–12.

Google Scholar  

Ram S, Sheth JN. Consumer resistance to innovations: the marketing problem and its solutions. J Consum Mark. 1989;6(2):5–14.

Fischer SH, David D, Crotty BH, Dierks M, Safran C. Acceptance and use of health information technology by community-dwelling elders. Int J Med Inform. 2014;83(9):624–35.

Demiris G, Chaudhuri S, Thompson HJ. Older adults’ experience with a novel fall detection device. Telemed J E Health. 2016;22(9):726–32.

Guzman-Parra J, Barnestein-Fonseca P, Guerrero-Pertiñez G, Anderberg P, Jimenez-Fernandez L, Valero-Moreno E, Goodman-Casanova JM, Cuesta-Vargas A, Garolera M, Quintana M, García-Betances RI, Lemmens E, Sanmartin Berglund J, Mayoral-Cleries F. Attitudes and use of information and communication technologies in older adults with mild cognitive impairment or early stages of dementia and their caregivers: cross-sectional study. J Med Internet Res. 2020;22(6):e17253.

Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health. 2021;21(1):1556–1556.

Zaman SB, Khan RK, Evans RG, Thrift AG, Maddison R, Islam SMS. Exploring barriers to and enablers of the adoption of information and communication technology for the care of older adults with chronic diseases: scoping review. JMIR Aging. 2022;5(1):e25251.

Saracchini R, Catalina C, Bordoni L. A mobile augmented reality assistive technology for the elderly. Comunicar. 2015;23:23.

Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR Mhealth Uhealth. 2016;4(1):e7.

Jain SR, Sui Y, Ng CH, Chen ZX, Goh LH, Shorey S. Patients’ and healthcare professionals’ perspectives towards technology-assisted diabetes self-management education. A qualitative systematic review. PLoS One. 2020;15(8):e0237647.

Article   PubMed   PubMed Central   CAS   Google Scholar  

O’Brien J, Mason A, Cassarino M, Chan J, Setti A. Older women’s experiences of a community-led walking programme using activity trackers. Int J Environ Res Public Health. 2021;18(18):9818.

Kononova A, Li L, Kamp K, Bowen M, Rikard RV, Cotten S, Peng W. The use of wearable activity trackers among older adults: focus group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change. JMIR Mhealth Uhealth. 2019;7(4):e9832.

Jiwani R, Dennis B, Bess C, Monk S, Meyer K, Wang J, Espinoza S. Assessing acceptability and patient experience of a behavioral lifestyle intervention using fitbit technology in older adults to manage type 2 diabetes amid COVID-19 pandemic: a focus group study. Geriatr Nurs. 2021;42(1):57–64.

Ehn M, Eriksson LC, Åkerberg N, Johansson AC. Activity monitors as support for older persons’ physical activity in daily life: qualitative study of the users’ experiences. JMIR Mhealth Uhealth. 2018;6(2):e34.

Tsertsidis A, Kolkowska E, Hedström K. Factors influencing seniors’ acceptance of technology for ageing in place in the post-implementation stage: a literature review. Int J Med Inform. 2019;129:324–33.

Moore K, O’Shea E, Kenny L, Barton J, Tedesco S, Sica M, Crowe C, Alamäki A, Condell J, Nordström A, Timmons S. Older adults’ experiences with using wearable devices: qualitative systematic review and meta-synthesis. JMIR Mhealth Uhealth. 2021;9(6):e23832.

Chiu CJ, Liu CW. Understanding older adult’s technology adoption and withdrawal for elderly care and education: mixed method analysis from national survey. J Med Internet Res. 2017;19(11): e374.

Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, van der Voort CS, Aarts S, van Hoof J, Vrijhoef HJ, Wouters EJ. Older adults’ reasons for using technology while aging in place. Gerontology. 2016;62(2):226–37.

Perotti L, Stamm O, Mesletzky L, Vorwerg S, Fournelle M, Müller-Werdan U. Needs and attitudes of older chronic back pain patients towards a wearable for ultrasound biofeedback during stabilization exercises: a qualitative analysis. Int J Environ Res Public Health. 2023;20(6):4927.

Abouzahra M, Ghasemaghaei M. The antecedents and results of seniors’ use of activity tracking wearable devices. Health Policy Technol. 2020;9(2):213–7.

Finkelstein R, Wu Y, Brennan-Ing M. Older adults’ experiences with using information and communication technology and tech support services in New York City: findings and recommendations for post-pandemic digital pedagogy for older adults. Front Psychol. 2023;14:1129512.

Dickinson A, Horton K, Machen I, Bunn F, Cove J, Jain D, Maddex T. The role of health professionals in promoting the uptake of fall prevention interventions: a qualitative study of older people’s views. Age Ageing. 2011;40(6):724–30.

McIntosh MJ, Morse JM. Situating and constructing diversity in semi-structured interviews. Glob Qual Nurs Res. 2015;2:2333393615597674.

Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today. 2004;24(2):105–12.

Hsu YH, Lee TH, Chung KP, Tung YC. Determining the factors influencing the selection of post-acute care models by patients and their families: a qualitative content analysis. BMC Geriatr. 2023;23(1):179.

Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107–15.

Morse JM, Black C, Oberle K, Donahue P. A prospective study to identify the fall-prone patient. Soc Sci Med. 1989;28(1):81–6.

Rockwood K, Theou O. Using the clinical frailty scale in allocating scarce health care resources. Can Geriatr J. 2020;23(3):210.

Mahoney FI, Barthel DW. Functional evaluation: the barthel index. Md State Med J. 1965;14:61–5.

PubMed   CAS   Google Scholar  

Felber NA, Lipworth W, Tian YJA, Roulet Schwab D, Wangmo T. Informing existing technology acceptance models: a qualitative study with older persons and caregivers. Eur J Ageing. 2024;21(1):12.

Tian YJA, Felber NA, Pageau F, Schwab DR, Wangmo T. Benefits and barriers associated with the use of smart home health technologies in the care of older persons: a systematic review. BMC geriatr. 2024;24(1):152.

Puri A, Kim B, Nguyen O, Stolee P, Tung J, Lee J. User acceptance of wrist-worn activity trackers among community-dwelling older adults: mixed method study. JMIR Mhealth Uhealth. 2017;5(11):e173.

El-Bendary N, Tan Q, Pivot F, Lam A. Fall detection and prevention for the elderly: a review of trends and challenges. Int J Smart Sensing Intellig Syst. 2013;6:1230–66.

Rice LA, Fliflet A, Frechette M, Brokenshire R, Abou L, Presti P, Mahajan H, Sosnoff J, Rogers WA. Insights on an automated fall detection device designed for older adult wheelchair and scooter users: a qualitative study. Disabil Health J. 2022;15(1S):101207.

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Acknowledgements

This research was made possible by the support and assistance of a number of people whom we would like to thank. We are very grateful to the anonymous referees for their valuable comments and constructive suggestions on interview and coding. We would like to thank all the respondents for their valuable opinions. This research was supported by the Ministry of Technology and Science under grant number NSTC 112-2628-E-006-008-MY3, NSTC 112-2627-M-006 -005, and the Medical Device Innovation Center (MDIC), National Cheng Kung University(NCKU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MoE) in Taiwan. This research was approved by the local Institutional Review Board of NCKUH (IRB Approval No. A-ER-110-211).

This research was supported by the National Science Council under grant number NSTC 112–2628-E-006–008-MY3 and NSTC 112–2627-M-006-005.

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Hsin-Hsiung Huang, Ming-Hao Chang & Peng-Ting Chen

Medical Device Innovation Center, National Cheng Kung University, No.138, Shengli Rd., North District, Tainan City, 704, Taiwan, ROC

Peng-Ting Chen

Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC

Chih-Lung Lin

Department of Neurology, National Cheng Kung University Hospital, Tainan, Taiwan, ROC

Pi-Shan Sung

Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan, ROC

Chien-Hsu Chen

Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan, ROC

Sheng-Yu Fan

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Contributions

Hsin-Hsiung Huang contributed significantly as the main interviewer, played a key role in coding, and contributed to the conception of the article.  Ming-Hao Chang participated in designing interview questions, coding, and ensuring the quality of language in the article.  Peng-Ting Chen assisted in conceptualizing research directions, overseeing the interview, coding, and the writing process, and shaped the article's concept.  Chih-Lung Lin, Pi-Shan Sung, Chien-Hsu Chen, and Sheng-Yu Fan assisted in conceptualizing research directions.

Authors' information

Hsin-Hsiung Huang is pursuing his Ph.D. degree in the Department of Biomedical Engineering from National Cheng Kung University, Taiwan. His major research interests fall in medical device commercialization in the elderly market.

Ming-Hao Chang is pursuing his Master’s degree in the Department of Biomedical Engineering from National Cheng Kung University, Taiwan. His major research interests fall in medical device commercialization, especially in startups.

Professor Peng-Ting Chen received her Ph.D. in Technology Management from the University of National Chiao-Tung University, Taiwan. She is a professor in the Department of Biomedical Engineering, at National Cheng Kung University, Taiwan. Her current research interests include biomedical device-related business planning, strategies, and policies.

Corresponding author

Correspondence to Peng-Ting Chen .

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The study was approved by the Institutional Review Board of NCKUH (IRB number: A-ER-110–211) before commencement. Informed consent was obtained from all subjects.

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Huang, HH., Chang, MH., Chen, PT. et al. Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis. BMC Geriatr 24 , 694 (2024). https://doi.org/10.1186/s12877-024-05262-0

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Received : 06 March 2024

Accepted : 30 July 2024

Published : 20 August 2024

DOI : https://doi.org/10.1186/s12877-024-05262-0

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