what is population and sample in qualitative research

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.

Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the power to unlock the mysteries hidden within data. Understanding the dynamics between the research population and sample is crucial for researchers. It ensures the validity, reliability, and generalizability of their findings. In this article, we uncover the profound role of the research population and sample, unveiling their differences and importance that reshapes our understanding of complex phenomena. Ultimately, this empowers researchers to make informed conclusions and drive meaningful advancements in our respective fields.

Table of Contents

What Is Population?

The research population, also known as the target population, refers to the entire group or set of individuals, objects, or events that possess specific characteristics and are of interest to the researcher. It represents the larger population from which a sample is drawn. The research population is defined based on the research objectives and the specific parameters or attributes under investigation. For example, in a study on the effects of a new drug, the research population would encompass all individuals who could potentially benefit from or be affected by the medication.

When Is Data Collection From a Population Preferred?

In certain scenarios where a comprehensive understanding of the entire group is required, it becomes necessary to collect data from a population. Here are a few situations when one prefers to collect data from a population:

1. Small or Accessible Population

When the research population is small or easily accessible, it may be feasible to collect data from the entire population. This is often the case in studies conducted within specific organizations, small communities, or well-defined groups where the population size is manageable.

2. Census or Complete Enumeration

In some cases, such as government surveys or official statistics, a census or complete enumeration of the population is necessary. This approach aims to gather data from every individual or entity within the population. This is typically done to ensure accurate representation and eliminate sampling errors.

3. Unique or Critical Characteristics

If the research focuses on a specific characteristic or trait that is rare and critical to the study, collecting data from the entire population may be necessary. This could be the case in studies related to rare diseases, endangered species, or specific genetic markers.

4. Legal or Regulatory Requirements

Certain legal or regulatory frameworks may require data collection from the entire population. For instance, government agencies might need comprehensive data on income levels, demographic characteristics, or healthcare utilization for policy-making or resource allocation purposes.

5. Precision or Accuracy Requirements

In situations where a high level of precision or accuracy is necessary, researchers may opt for population-level data collection. By doing so, they mitigate the potential for sampling error and obtain more reliable estimates of population parameters.

What Is a Sample?

A sample is a subset of the research population that is carefully selected to represent its characteristics. Researchers study this smaller, manageable group to draw inferences that they can generalize to the larger population. The selection of the sample must be conducted in a manner that ensures it accurately reflects the diversity and pertinent attributes of the research population. By studying a sample, researchers can gather data more efficiently and cost-effectively compared to studying the entire population. The findings from the sample are then extrapolated to make conclusions about the larger research population.

What Is Sampling and Why Is It Important?

Sampling refers to the process of selecting a sample from a larger group or population of interest in order to gather data and make inferences. The goal of sampling is to obtain a sample that is representative of the population, meaning that the sample accurately reflects the key attributes, variations, and proportions present in the population. By studying the sample, researchers can draw conclusions or make predictions about the larger population with a certain level of confidence.

Collecting data from a sample, rather than the entire population, offers several advantages and is often necessary due to practical constraints. Here are some reasons to collect data from a sample:

1. Cost and Resource Efficiency

Collecting data from an entire population can be expensive and time-consuming. Sampling allows researchers to gather information from a smaller subset of the population, reducing costs and resource requirements. It is often more practical and feasible to collect data from a sample, especially when the population size is large or geographically dispersed.

2. Time Constraints

Conducting research with a sample allows for quicker data collection and analysis compared to studying the entire population. It saves time by focusing efforts on a smaller group, enabling researchers to obtain results more efficiently. This is particularly beneficial in time-sensitive research projects or situations that necessitate prompt decision-making.

3. Manageable Data Collection

Working with a sample makes data collection more manageable . Researchers can concentrate their efforts on a smaller group, allowing for more detailed and thorough data collection methods. Furthermore, it is more convenient and reliable to store and conduct statistical analyses on smaller datasets. This also facilitates in-depth insights and a more comprehensive understanding of the research topic.

4. Statistical Inference

Collecting data from a well-selected and representative sample enables valid statistical inference. By using appropriate statistical techniques, researchers can generalize the findings from the sample to the larger population. This allows for meaningful inferences, predictions, and estimation of population parameters, thus providing insights beyond the specific individuals or elements in the sample.

5. Ethical Considerations

In certain cases, collecting data from an entire population may pose ethical challenges, such as invasion of privacy or burdening participants. Sampling helps protect the privacy and well-being of individuals by reducing the burden of data collection. It allows researchers to obtain valuable information while ensuring ethical standards are maintained .

Key Steps Involved in the Sampling Process

Sampling is a valuable tool in research; however, it is important to carefully consider the sampling method, sample size, and potential biases to ensure that the findings accurately represent the larger population and are valid for making conclusions and generalizations. While the specific steps may vary depending on the research context, here is a general outline of the sampling process:

1. Define the Population

Clearly define the target population for your research study. The population should encompass the group of individuals, elements, or units that you want to draw conclusions about.

2. Define the Sampling Frame

Create a sampling frame, which is a list or representation of the individuals or elements in the target population. The sampling frame should be comprehensive and accurately reflect the population you want to study.

3. Determine the Sampling Method

Select an appropriate sampling method based on your research objectives, available resources, and the characteristics of the population. You can perform sampling by either utilizing probability-based or non-probability-based techniques. Common sampling methods include random sampling, stratified sampling, cluster sampling, and convenience sampling.

4. Determine Sample Size

Determine the desired sample size based on statistical considerations, such as the level of precision required, desired confidence level, and expected variability within the population. Larger sample sizes generally reduce sampling error but may be constrained by practical limitations.

5. Collect Data

Once the sample is selected using the appropriate technique, collect the necessary data according to the research design and data collection methods . Ensure that you use standardized and consistent data collection process that is also appropriate for your research objectives.

6. Analyze the Data

Perform the necessary statistical analyses on the collected data to derive meaningful insights. Use appropriate statistical techniques to make inferences, estimate population parameters, test hypotheses, or identify patterns and relationships within the data.

Population vs Sample — Differences and examples

While the population provides a comprehensive overview of the entire group under study, the sample, on the other hand, allows researchers to draw inferences and make generalizations about the population. Researchers should employ careful sampling techniques to ensure that the sample is representative and accurately reflects the characteristics and variability of the population.

Research Study: Investigating the prevalence of stress among high school students in a specific city and its impact on academic performance.

Population: All high school students in a particular city

Sampling Frame: The sampling frame would involve obtaining a comprehensive list of all high schools in the specific city. A random selection of schools would be made from this list to ensure representation from different areas and demographics of the city.

Sample: Randomly selected 500 high school students from different schools in the city

The sample represents a subset of the entire population of high school students in the city.

Research Study: Assessing the effectiveness of a new medication in managing symptoms and improving quality of life in patients with the specific medical condition.

Population: Patients diagnosed with a specific medical condition

Sampling Frame: The sampling frame for this study would involve accessing medical records or databases that include information on patients diagnosed with the specific medical condition. Researchers would select a convenient sample of patients who meet the inclusion criteria from the sampling frame.

Sample: Convenient sample of 100 patients from a local clinic who meet the inclusion criteria for the study

The sample consists of patients from the larger population of individuals diagnosed with the medical condition.

Research Study: Investigating community perceptions of safety and satisfaction with local amenities in the neighborhood.

Population: Residents of a specific neighborhood

Sampling Frame: The sampling frame for this study would involve obtaining a list of residential addresses within the specific neighborhood. Various sources such as census data, voter registration records, or community databases offer the means to obtain this information. From the sampling frame, researchers would randomly select a cluster sample of households to ensure representation from different areas within the neighborhood.

Sample: Cluster sample of 50 households randomly selected from different blocks within the neighborhood

The sample represents a subset of the entire population of residents living in the neighborhood.

To summarize, sampling allows for cost-effective data collection, easier statistical analysis, and increased practicality compared to studying the entire population. However, despite these advantages, sampling is subject to various challenges. These challenges include sampling bias, non-response bias, and the potential for sampling errors.

To minimize bias and enhance the validity of research findings , researchers should employ appropriate sampling techniques, clearly define the population, establish a comprehensive sampling frame, and monitor the sampling process for potential biases. Validating findings by comparing them to known population characteristics can also help evaluate the generalizability of the results. Properly understanding and implementing sampling techniques ensure that research findings are accurate, reliable, and representative of the larger population. By carefully considering the choice of population and sample, researchers can draw meaningful conclusions and, consequently, make valuable contributions to their respective fields of study.

Now, it’s your turn! Take a moment to think about a research question that interests you. Consider the population that would be relevant to your inquiry. Who would you include in your sample? How would you go about selecting them? Reflecting on these aspects will help you appreciate the intricacies involved in designing a research study. Let us know about it in the comment section below or reach out to us using  #AskEnago  and tag  @EnagoAcademy  on  Twitter ,  Facebook , and  Quora .

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• Population vs Sample | Definitions, Differences & Examples

Population vs Sample | Definitions, Differences & Examples

Published on 3 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organisations, countries, species, or organisms.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs sample statistic, practice questions: populations vs samples, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalised and low-income groups have been difficult to contact, locate, and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

Prevent plagiarism, run a free check.

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods . Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalise findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

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

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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7.1 Populations Versus Samples

Learning objective.

• Understand the difference between populations and samples.

When I teach research methods, my students are sometimes disheartened to discover that the research projects they complete during the course will not make it possible for them to make sweeping claims about “all” of whomever it is that they’re interested in studying. What they fail to realize, however, is that they are not alone. One of the most surprising and frustrating lessons research methods students learn is that there is a difference between one’s population of interest and one’s study sample. While there are certainly exceptions, more often than not a researcher’s population and her or his sample are not the same.

In social scientific research, a population The group (be it people, events, etc.) that you want to be able to draw conclusions about at the end of your study. is the cluster of people, events, things, or other phenomena that you are most interested in; it is often the “who” or “what” that you want to be able to say something about at the end of your study. Populations in research may be rather large, such as “the American people,” but they are more typically a little less vague than that. For example, a large study for which the population of interest really is the American people will likely specify which American people, such as adults over the age of 18 or citizens or legal residents. A sample The group (be it people, events, etc.) from which you actually collect data. , on the other hand, is the cluster of people or events, for example, from or about which you will actually gather data. Some sampling strategies allow researchers to make claims about populations that are much larger than their actually sample with a fair amount of confidence. Other sampling strategies are designed to allow researchers to make theoretical contributions rather than to make sweeping claims about large populations. We’ll discuss both types of strategies later in this chapter.

As I’ve now said a couple of times, it is quite rare for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that sociologists typically ask. For example, let’s say we wish to answer the following research question: “How do men’s and women’s college experiences differ, and how are they similar?” Would you expect to be able to collect data from all college students across all nations from all historical time periods? Unless you plan to make answering this research question your entire life’s work (and then some), I’m guessing your answer is a resounding no way. So what to do? Does not having the time or resources to gather data from every single person of interest mean having to give up your research interest? Absolutely not. It just means having to make some hard choices about sampling, and then being honest with yourself and your readers about the limitations of your study based on the sample from whom you were able to actually collect data.

Sampling The process of selecting observations that will be analyzed for research purposes. is the process of selecting observations that will be analyzed for research purposes. Both qualitative and quantitative researchers use sampling techniques to help them identify the what or whom from which they will collect their observations. Because the goals of qualitative and quantitative research differ, however, so, too, do the sampling procedures of the researchers employing these methods. First, we examine sampling types and techniques used in qualitative research. After that, we’ll look at how sampling typically works in quantitative research.

Key Takeaways

• A population is the group that is the main focus of a researcher’s interest; a sample is the group from whom the researcher actually collects data.
• Populations and samples might be one and the same, but more often they are not.
• Sampling involves selecting the observations that you will analyze.
• Read through the methods section of a couple of scholarly articles describing empirical research. How do the authors talk about their populations and samples, if at all? What do the articles’ abstracts suggest in terms of whom conclusions are being drawn about?
• Think of a research project you have envisioned conducting as you’ve read this text. Would your population and sample be one and the same, or would they differ somehow? Explain.

Sampling Techniques for Qualitative Research

• First Online: 27 October 2022

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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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The role of sampling in mixed methods-research.

Preparation of Qualitative Research

Recent developments in systematic sampling: A review

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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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• Section 2: Home
• Developing the Quantitative Research Design

Developing the Qualitative Research Design

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

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

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

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

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

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

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

Exercise #1

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

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

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

Exercise #2

Use the script below to state the sample.

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

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

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

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing 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, history, etc.

• 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 organization?
• 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, other interesting articles, 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 emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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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 analyzing 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 organize 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 categorize 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 analyzing qualitative data. Although these methods share similar processes, they emphasize 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.

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Researchers must consider practical and theoretical limitations in analyzing 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 analyzing 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 generalizability

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

• Labor-intensive

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

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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 organization 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 organizations.

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

• Prepare and organize 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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Chapter 7: Sampling Techniques

7.2 Population versus Samples

If you had all the money and resources in the world, you could potentially sample the whole population. However, money and resources usually limit sampling, and furthermore all members of a population may not actually be identifiable in a way that allows you to sample. As a result, researchers take a sample, or a subgroup of people (or objects) from the population and study that instead of the population. In social scientific research, the population is the cluster of people, events, things, or other phenomena in which you are most interested. It is often the “who” or “what” that you want to be able to say something about at the end of your study. Populations in research may be rather large, such as “the Canadian people,” but typically they are more focused than that. For example, a large study, for which the population of interest really is the Canadian people, will likely specify which Canadian people, such as adults over the age of 18 or citizens or legal residents.

One of the most surprising and often frustrating lessons students of research methods learn is that there is a difference between one’s population of interest and one’s study sample. While there are certainly exceptions, more often than not, a researcher’s population and the sample are not the same. A sample is the cluster of people or events, for example, from or about which you will actually gather data. Some sampling strategies allow researchers to make claims about populations that are much larger than their actual sample with a fair amount of confidence. Other sampling strategies are designed to allow researchers to make theoretical contributions rather than to make sweeping claims about large populations. We will discuss both types of strategies later in this chapter.

As mentioned previously, it is quite rare for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that sociologists typically ask. For example, suppose we wish to answer the following research question: “How do men’s and women’s college experiences differ, and how are they similar?” Would you expect to be able to collect data from all college students across all nations from all historical time periods? Unless you plan to make answering this research question your entire life’s work (and then some), the answer is probably “no.” So then, what is a researcher to do? Does not having the time or resources to gather data from every single person of interest mean having to give up your research interest? Absolutely not. It just means having to make some hard choices about sampling, and then being honest with yourself and your readers about the limitations of your study based on the sample from whom you were able to actually collect data. This resource can help you better understand how to get from the theoretical population (to whom you want to generalize) to your sample (who will actually be in your study) .

Now having said this, there are certainly times when it is possible to access every member of the population. This happens when the population is small, accessible, and willing to participate, or the researcher has access to relevant records. For example, suppose that a university dean wants to analyse the final graduating scores for all students enrolled in the university’s health sciences program, for 2015 to 2019. The dean wants to know if there is a trend toward an average increase in final graduating scores in health sciences, over this time period, as she suspects. Since the dean is only interested in her particular university and only those students who graduated from health sciences from 2015 to 2019, she can easily use the whole population. In this case, the population is the records of final graduating scores for all students enrolled in the university’s health sciences program from 2015 to 2019.

To summarize, we use sampling when the population is large and we simply do not have the time, financial support, and/or ability (i.e. lack of laboratory equipment) to reach the entire population.

In Table 7.1 you will find some examples of a population versus a sample, and the type of research methodology that might lead such a study. Do not worry about the methodology column now, as you have most likely not yet read the applicable chapters. Make a note to yourself and return to this table after reading Chapters 8 through 13.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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10.2 Sampling in qualitative research

Learning objectives.

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

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

Nonprobability sampling

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

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

Types of nonprobability samples

There are several types of nonprobability samples that researchers use. These include purposive samples, snowball samples, quota samples, and convenience samples. While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research, and because they are both nonprobability methods, we include them in this section of the chapter.

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

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

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

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

In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. The leading polling entity at the time, The Literary Digest, predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide, but Gallup’s polling disagreed. Gallup successfully predicted Roosevelt’s win and subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election.  [1] Among other problems, the fact that Gallup’s quota categories did not represent those who actually voted (Neuman, 2007)  [2] underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods.  [3] While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings. For that, you need probability sampling, which we will discuss in the next section.

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

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

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

Key Takeaways

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

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

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

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Part 4: Using qualitative methods

17. Qualitative data and sampling

Chapter outline.

• Ethical responsibility and cultural respectfulness (7 minute read)
• Critical considerations (8 minute read)
• Find the right qualitative data to answer my research question (17 minute read)
• How to gather a qualitative sample (21 minute read)
• What should my sample look like? (9 minute read)

Content warning: examples in this chapter contain references to substance use, ageism, injustices against the Black community in research (e.g. Henrietta Lacks and Tuskegee Syphillis Study), children and their educational experiences, mental health, research bias, job loss and business closure, mobility limitations, politics, media portrayals of LatinX families, labor protests, neighborhood crime, Batten Disease (childhood disorder), transgender youth, cancer, child welfare including kinship care and foster care, Planned Parenthood, trauma and resilience, sexual health behaviors.

Now let’s change things up! In the previous chapters, we were exploring steps to create and carry out a quantitative research study. Quantitative studies are great when we want to summarize data and examine or test relationships between ideas using numbers and the power of statistics. However, qualitative research offers us a different and equally important tool. Sometimes the aim of research is to explore meaning and experience. If these are the goals of our research proposal, we are going to turn to qualitative research. Qualitative research relies on the power of human expression through words, pictures, movies, performance and other artifacts that represent these things. All of these tell stories about the human experience and we want to learn from them and have them be represented in our research. Generally speaking, qualitative research is about the gathering up of these stories, breaking them into pieces so we can examine the ideas that make them up, and putting them back together in a way that allows us to tell a common or shared story that responds to our research question. Back in Chapter 7 we talked about different paradigms.

Before plunging further into our exploration of qualitative research, I would like to suggest that we begin by thinking about some ethical, cultural and empowerment-related considerations as you plan your proposal. This is by no means a comprehensive discussion of these topics as they relate to qualitative research, but my intention is to have you think about a few issues that are relevant at each step of the qualitative process. I will begin each of our qualitative chapters with some discussion about these topics as they relate to each of these steps in the research process. These sections are specially situated at the beginning of the chapters so that you can consider how these principles apply throughout the proceeding discussion. At the end of this chapter there will be an opportunity to reflect on these areas as they apply specifically to your proposal. Now, we have already discussed research ethics back in Chapter 8 . However, as qualitative researchers we have some unique ethical commitments to participants and to the communities that they represent. Our work as qualitative researchers often requires us to represent the experiences of others, which also means that we need to be especially attentive to how culture is reflected in our research. Cultural respectfulness suggests that we approach our work and our participants with a sense of humility. This means that we maintain an open mind, a desire to learn about the cultural context of participants’ lives, and that we preserve the integrity of this context as we share our findings.

17.1 Ethical responsibility and cultural respectfulness

Learning objectives.

Learners will be able to…

• Explain how our ethical responsibilities as researchers translate into decisions regarding qualitative sampling
• Summarize how aspects of culture and identity may influence recruitment for qualitative studies

Representation

Representation reflects two important aspects of our work as qualitative researchers, who is present and how are they presented. First, we need to consider who we are including or excluding in or sample. Recruitment and sampling is especially tied to our ethical mandate as researchers to uphold the principle of justice under the Belmont Report [1] (see Chapter 6   for additional information). Within this context we need to:

• Assure there is fair distribution of risks and benefits related to our research
• Be conscientious in our recruitment efforts to support equitable representation
• Ensure that special protections to vulnerable groups involved in research activities are in place

As you plan your qualitative research study, make sure to consider who is invited and able to participate and who is not. These choices have important implications for your findings and how well your results reflect the population you are seeking to represent. There may be explicit exclusions that don’t allow certain people to participate, but there may also be unintended reasons people are excluded (e.g. transportation, language barriers, access to technology, lack of time).

The second part of representation has to do with how we disseminate our findings and how this reflects on the population we are studying. We will speak further about this aspect of representation in Chapter 21 , which is specific to qualitative research dissemination. For now, it is enough to know that we need to be thoughtful about who we attempt to recruit and how effectively our resultant sample reflects our population.

Being mindful of history

As you plan for the recruitment of your sample, be mindful of the history of how this group (and/or the individuals you may be interacting with) has been treated – not just by the research community, but by others in positions of power. As researchers, we usually represent an outside influence and the people we are seeking to recruit may have significant reservations about trusting us and being willing to participate in our study (often grounded in good historical reasons—see Chapter 6 for additional information). Because of this, be very intentional in your efforts to be transparent about the purpose of your research and what it involves, why it is important to you, as well as how it can impact the community. Also, in helping to address this history, we need to make concerted efforts to get to know the communities that we research with well, including what is important to them.

Stories as sacred: How are we requesting them?

Finally, it is worth pointing out that as qualitative researchers, we have an extra layer of ethical and cultural responsibility. While quantitative research deals with numbers, as qualitative researchers, we are generally asking people to share their stories. Stories are intimate, full of depth and meaning, and can reveal tremendous amounts about who we are and what makes us tick. Because of this, we need to take special care to treat these stories as sacred. I will come back to this point in subsequent chapters, but as we go about asking for people to share their stories, we need to do so humbly.

Key Takeaways

• As researchers, we need to consider how our participant communities have been treated historically, how we are representing them in the present through our research, and the implications this representation could have (intended and unintended) for their lives. We need to treat research participants and their stories with respect and humility.
• When conducting qualitative research, we are asking people to share their stories with us. These “data” are personal, intimate, and often reflect the very essence of who our participants are. As researchers, we need to treat  research participants and their stories with respect and humility.

17.2 Critical considerations

• Assess dynamics of power in sampling design and recruitment for individual participants and participant communities
• Create opportunities for empowerment through early choice points in key research design elements

Related to the previous discussion regarding being mindful of history, we also need to consider the current dynamics of power between researcher and potential participant. While we may not always recognize or feel like we are in a position of power, as researchers we hold specialized knowledge, a particular skill set, and what we do can with the data we collect can have important implications and consequences for individuals, groups, and communities. All of these contribute to the formation of a role ascribed with power. It is important for us to consider how this power is perceived and whenever possible, how we can build opportunities for empowerment that can be built into our research design. Examples of some strategies include:

• Recruiting and meeting in spaces that are culturally acceptable
• Finding ways to build participant choice into the research process
• Working with a community advisory group during the research process (explained further in the example box below)
• Designing informative and educational materials that help to thoroughly explain the research process in meaningful ways
• Regularly checking with participants for understanding
• Asking participants what they would like to get out of their participation and what it has been like to participate in our research
• Determining if there are ways that we can contribute back to communities beyond our research (developing an ongoing commitment to collaboration and reciprocity)

While it may be beyond the scope of a student research project to address all of these considerations, I do think it is important that we start thinking about these more in our research practices. As social work researchers, we should be modeling empowerment practices in the field of social science research, but we often fail to meet this standard.

Example. A community advisory group can be a tremendous asset throughout our research process, but especially in early stages of planning, including recruitment. I was fortunate enough to have a community advisory group for one of the projects I worked on. They were incredibly helpful as I considered different perspectives I needed to include in my study, helping me to think through a respectful way to approach recruitment, and how we might make the research arrangement a bit more reciprocal so community members might benefit as well.

Intersectional identity

As qualitative researchers, we are often not looking to prove a hypothesis or uncover facts. Instead, we are generally seeking to expand our understanding of the breadth and depth of human experience. Breadth is reflected as we seek to uncover variation across participants and depth captures variation or detail within each participants’ story. Both are important for generating the fullest picture possible for our findings. For example, we might be interested in learning about people’s experience living in an assisted living facility by interviewing residents. We would want to capture a range of different residents’ experiences (breadth) and for each resident, we would seek as much detail as possible (depth). Do note, sometimes our research may only involve one person, such as in a case study . However, in these instances we are usually trying to understand many aspects or dimensions of that single case.

To capture this breadth and depth we need to remember that people are made of multiple stories formed by intersectional identities . This means that our participants never just represent one homogeneous social group. We need to consider the various aspects of our population that will help to give the most complete representation in our sample as we go about recruitment.

Identify a population you are interested in studying. This might be a population you are working with at your field placement (either directly or indirectly), a group you are especially interested in learning more about, or a community you want to serve in the future. As you formulate your question, you may draw your sample directly from clients that are being served, others in their support network, service providers that are providing services, or other stakeholders that might be invested in the well-being of this group or community. Below, list out two populations you are interested in studying and then for each one, think about two groups connected with this population that you might focus your study on.

Next, think about what would kind of information might help you understand this group better. If you had the chance to sit down and talk with them, what kinds of things would you want to ask? What kinds of things would help you understand their perspective or their worldview more clearly? What kinds of things do we need to learn from them and their experiences that could help us to be better social workers? For each of the groups you identified above, write out something you would like to learn from their experience.

Finally, consider how this group might perceive a request to participate. For the populations and the groups that you have identified, think about the following questions:

• How have these groups been represented in the news?
• How have these groups been represented in popular culture and popular media?
• What historical or contemporary factors might influence these group members’ opinions of research and researchers?
• In what ways have these groups been oppressed and how might research or academic institutions have contributed to this oppression?

Our impact on the qualitative process

It is important for qualitative research to thoughtfully plan for and attempt to capture our own impact on the research process. This influence that we can have on the research process represents what is known as researcher bias . This requires that we consider how we, as human beings, influence the research we conduct. This starts at the very beginning of the research process, including how we go about sampling. Our choices throughout the research process are driven by our unique values, experiences, and existing knowledge of how the world works. To help capture this contribution, qualitative researchers may plan to use tools like a reflexive journal , which is a research journal that helps the researcher to reflect on and consider their thoughts and reactions to the research process and how these may influence or shape a study (there will be more about this tool in Chapter 20 when we discuss the concept of rigor ). While this tool is not specific to the sampling process, the next few chapters will suggest reflexive journal questions to help you think through how it might be used as you develop a qualitative proposal.

Example. To help demonstrate the potential for researcher bias, consider a number of students that I work with who are placed in school systems for their field experience and choose to focus their research proposal in this area. Some are interested in understanding why parents or guardians aren’t more involved in their children’s educational experience. While this might be an interesting topic, I would encourage students to consider what kind of biases they might have around this issue.

• What expectations do they have about parenting?
• What values do they attach to education and how it should be supported in the home?
• How has their own upbringing shaped their expectations?
• What do they know about the families that the school district serves and how did they come by this information?
• How are these families’ life experiences different from their own?

The answers to these questions may unconsciously shape the early design of the study, including the research question they ask and the sources of data they seek out. For instance, their study may only focus on the behaviors and the inclinations of the families, but do little to investigate the role that the school plays in engagement and other structural barriers that might exist (e.g. language, stigma, accessibility, child-care, financial constraints, etc.).

• As researchers, we wield (sometimes subtle) power and we need to be conscientious of how we use and distribute this power.
• Qualitative study findings represent complex human experiences. As good as we may be, we are only going to capture a relatively small window into these experiences (and need to be mindful of this when discussing our findings).

In the early stages of your research process, it is a good idea to start your reflexive journal . Starting a reflexive journal is as easy as opening up a new word document, titling it and chronologically dating your entries. If you are more tactile-oriented, you can also keep your reflexive journal in paper bound journal.

To prompt your initial entry, put your thoughts down in response to the following questions:

• What led you to be interested in this topic?
• What experience(s) do you have in this area?
• What knowledge do you have about this issue and how did you come by this knowledge?
• In what ways might you be biased about this topic?

Don’t answer this last question too hastily! Our initial reaction is often—”Biased!?! Me—I don’t have a biased bone in my body! I have an open-mind about everything, toward everyone!” After all, much of our social work training directs us towards acceptance and working to understand the perspectives of others. However, WE ALL HAVE BIASES . These are conscious or subconscious preferences that lead us to favor some things over others. These preferences influence the choices we make throughout the research process. The reflexive journal helps us to reflect on these preferences, where they might stem from, and how they might be influencing our research process. For instance, I conduct research in the area of mental health. Before I became a researcher, I was a mental health clinician, and my years as a mental health practitioner created biases for me that influence my approach to research. For instance, I may be biased in perceiving mental health services as being well-intentioned and helpful. However, participants may well have very different perceptions based on their experiences or beliefs (or those of their loved ones).

17.3 Finding the right qualitative data to answer my research question

• Compare different types of qualitative data
• Begin to formulate decisions as they build their qualitative research proposal, specially in regards to selecting types of data that can effectively answer their research question

Sampling starts with deciding on the type of data you will be using. Qualitative research may use data from a variety of sources. Sources of qualitative data may come from interviews or focus groups , observations , a review of written documents, administrative data, or other forms of media, and performances. While some qualitative studies rely solely on one source of data, others incorporate a variety.

You should now be well acquainted with the term triangulation . When thinking about triangulation in qualitative research, we are often referring to our use of multiple sources of data among those listed above to help strengthen the confidence we have in our findings. Drawing on a journalism metaphor, this allows us to “fact check” our data to help ensure that we are getting the story correct. This can mean that we use one type of data (like interviews), but we intentionally plan to get a diverse range of perspectives from people we know will see things differently. In this case we are using triangulation of perspectives. In addition, we may also you a variety of different types of data, like including interviews, data from case records, and staff meeting minutes all as data sources in the same study. This reflects triangulation through types of data.

As a student conducting research, you may not always have access to vulnerable groups or communities in need, or it may be unreasonable for you to collect data from them directly due to time, resource, or knowledge constraints. Because of this, as you are reviewing the sections below, think about accessible alternative sources of data that will still allow you to answer your research question practically, and I will provide some examples along the way to get you started. In the above example, local media coverage might be a means of obtaining data that does not involve vulnerable directly collecting data from potentially vulnerable participants.

Verbal data

Perhaps the bread an d butter of t he qualitative researche r, we often rely on what people  tell us as a primary source of information for qualitative studies in the form of verbal data. The researcher who schedules interviews with recipients of public assistance to capture their experience after legislation drastically changes requirements for benefits relies on the communication between the researcher and the impacted recipients of public assistance. Focus groups are another frequently used method of gathering verbal data. Focus groups bring together a group of participants to discuss their unique perspectives and explore commonalities on a given topic. One such example is a researcher who brings together a group of child welfare workers who have been in the field for one to two years to ask them questions regarding their preparation, experiences, and perceptions regarding their work. 

A benefit of utilizing verbal data is that it offers an opportunity for researchers to hear directly from participants about their experiences, opinions, or understanding of a given topic. Of course, this requires that participants be willing to share this information with a researcher and that the information shared is genuine. If groups of participants are unwilling to participate in sharing verbal data or if participants share information that somehow misrepresents their feelings (perhaps because they feel intimidated by the research process), then our qualitative sample can become biased and lead to inaccurate or partially accurate findings.

As noted above, participant willingness and honesty can present challenges for qualitative researchers. You may face similar challenges as a student gathering verbal data directly from participants who have been personally affected by your research topic. Because of this, you might want to gather verbal data from other sources. Many of the students I work with are placed in schools. It is not feasible for them to interview the youth they work with directly, so frequently they will interview other professionals in the school, such as teachers, counselors, administration, and other staff. You might also consider interviewing other social work students about their perceptions or experiences working with a particular g roup. 

Again, because it may be problematic or unrealistic for you to obtain verbal data directly from vulnerable groups as a student researcher, you might consider gathering verbal data from the following sources:

• Interviews and focus groups with providers, social work students, faculty, the general public, administrators, local politicians, advocacy groups
• Public blogs of people invested in your topic
• Publicly available transcripts from interviews with experts in the area or people reporting experiences in popular media

Make sure to consult with your professor to ensure that what you are planning will be realistic for the purposes of your study.

Observational data

As researcher s, we sometimes rely on our own powers of observation to gather data on a particular topic. We may observe a person’s behavior, an interaction, setting, context, and maybe even our own reactions to what we are observing (i.e. what we are thinking or feeling). When observational data is used for quantitative purposes, it involves a count, such as how many times a certain behavior occurs for a child in a classroom. However, when observational data is used for qualitative purposes, it involves the researcher providing a detailed description. For instance, a qualitative researcher may conduct observations of how mothers and children interact in child and adolescent cancer units, and take notes about where exchanges take place, topics of conversation, nonverbal information, and data about the setting itself – what the unit looks like, how it is arranged, the lighting, photos on the wall, etc.

Observational data can provide important contextual information that may not be captured when we rely solely on verbal data. However, using this form of data requires us, as researchers, to correctly observe and interpret what is going on. As we don’t have direct access to what participants may be thinking or feeling to aid us (which can lead us to misinterpret or create a biased representation of what we are observing), our take on this situation may vary drastically from that of another person observing the same thing. For instance, if we observe two people talking and one begins crying, how do we know if these are tears of joy or sorrow? When you observe someone being abrupt in a conversation, I might interpret that as the person being rude while you might perceive that the person is distracted or preoccupied with something. The point is, we can’t know for sure. Perhaps one of the most challenging aspects of gathering observational data is collecting neutral, objective observations, that are not laden with our subjective value judgments placed on them. Students often find this out in class during one of our activities. For this activity, they have to go out to public space and write down observations about what they observe. When they bring them back to class and we start discussing them together, we quickly realize how often we make (unfounded) judgments. Frequent examples from our class include determining the race/ethnicity of people they observe or the relationships between people, without any confirmational knowledge. Additionally, they often describe scenarios with adverbs and adjectives that often reflect judgments and values they are placing on their data. I’m not sharing this to call them out, in fact, they do a great job with the assignment. I just want to demonstrate that as human beings, we are often less objective than we think we are! These are great examples of research bias.

Again, gaining access to observational spaces, especially private ones, might be a challenge for you as a student. As such, you might consider if observing public spaces might be an option. If you do opt for this, make sure you are not violating anyone’s right to privacy. For instance, gathering information in a narcotics anonymous meeting or a religious celebration might be perceived as offensive, invasive or in direct opposition to values (like anonymity) of participants. When making observations in public spaces be careful not to gather any information that might identify specific individuals or organizations. Also, it is important to consider the influence your presence may have on a community, particularly if your observation makes you stand out among those typically present in that setting. Always consider the needs of the individual and the communities in formulating a plan for observing public behavior. Public spaces might include commercial spaces or events open to the public as well as municipal parks. Below we will have an expanded discussion about different varieties of non-probability sampling strategies that apply to qualitative research. Recruiting in public spaces like these may work for strategies such as convenience sampling or quota sampling , but would not be a good choice for snowball sampling or purposive sampling .

As with the cautionary note for student researchers under verbal data, you may experience restricted access to spaces in which you are able to gather observational data. However, if you do determine that observational data might be a good fit for your student proposal, you might consider the following spaces:

• Shopping malls
• Public parks or beaches
• Public meetings or rallies
• Public transportation

Artifacts (documents & other media)

Existing artifacts can also be very useful for the qualitative researcher. Examples include newspapers, blogs, websites, podcasts, television shows, movies, pictures, video recordings, artwork, and live performances. While many of these sources may provide indirect information on a topic, this information can still be quite valuable in capturing the sentiment of popular culture and thereby help researchers enhance their understanding of (dominant) societal values and opinions. Conversely, researchers can intentionally choose to seek out divergent, unique or controversial perspectives by searching for artifacts that tend to take up positions that differ from the mainstream, such as independent publications and (electronic) newsletters. While we will explore this further below, it is important to understand that data and research, in all its forms, is political. Among many other purposes, it is used to create, critique, and change policy; to engage in activism; to support and refute how organizations function; and to sway public opinion.

When utilizing documents and other media as artifacts, researchers may choose to use an entire source (such as a book or movie), or they may use a segment or portion of that artifact (such as the front-page stories from newspapers, or specific scenes in a television series). Your choice of which artifacts you choose to include will be driven by your question, and remember, you want your sample of artifacts to reflect the diversity of perspectives that may exist in the population you are interested in. For instance, perhaps I am interested in studying how various forms of media portray substance use treatment. I might intentionally include a range of liberal to conservative views that are portrayed across a number of media sources.

As qualitative researchers using artifacts, we often need to do some digging to understand the context of said artifact. We do this because data is almost always affiliated or aligned with some position (again, data is political). To help us consider this, it may be helpful to reflect on the following questions:

• Who owns the artifact or where is it housed
• What values does the owner (organization or person) hold
• How might the position or identity of the owner influence what information is shared or how it is portrayed
• What is the purpose of the artifact
• Who is the audience for which the artifact is intended

Answers to questions such as these can help us to b etter under stand and give meaning to the content of the artifacts. Content is the substance of the artifact (e.g. the wor ds, picture, scene). While c ontext is the circumstances surrounding content. Both work together to help provide meaning, and further understanding of what can be derived from an artifact. As an example to illustrate this point, let’s say that you are including meeting minutes from an organizing network as a source of data for your study. The narrative description in these minutes will certainly be important, however, they may not tell the whole story. For instance, you might not know from the text that the organization has recently voted in a new president and this has created significant division within the network. Knowing this information might help you to interpret the agenda and the discussion contained in the minutes very differently. 

As student researchers, using documents and other artifacts may be a particularly appealing source of data for your study. This is because this data already exists (you aren’t creating new data) and depending on what you select, it might be relatively easy to access. Examples of utilizing existing artifacts might include studying the cultural context of movie portrayals of Latinx families or analyzing publicly available town hall meeting minutes to explore expressions of social capital. Below is a list of sources of data from documents or other media sources to consider for your student proposal:

• Movies or TV shows
• Music or music videos
• Public blogs
• Policies or other organizational documents
• Meeting minutes
• Comments in online forums
• Books, newspapers, magazines, or other print/virtual text-based materials
• Recruitment, training, or educational materials
• Musical or artistic expressions

Finally, Photovoice is a technique that merges pictures with narrative (word or voice) data that helps interpret the meaning or significance of the visual. Photovoice is often used for qualitative work that is conducted as part of Community Based Participatory Research (CBPR), wherein community members act as both participants and as co-researchers. These community members are provided with a means of capturing images that reflect their understanding of some topic, prompt or question, and then they are asked to provide a narrative description or interpretation to help give meaning to the image(s). Both the visual and n arrative information are used as qualitative data to include in the study. Dissemination of Photovoice projects often involve a public display of the works, such as through a demonstration or art exhibition to raise awareness or to produce some specific change that is desired by participants. Because this form of study is often intentionally persuasive in nature, we need to recognize that this form of data will be inherently subjective. As a student, it may be particularly challenging to implement a Photovoice project, especially due to its time-intensive nature, as well as the additional commitments of needing to engage, train, and collaborate with community partners.

How many kinds of data?

You will need to consider whether you will rely on one kind of data or multiple. While many qualitative studies solely use one type of data, such as interviews or focus groups, others may use multiple sources. The decision to use multiple sources is often made to help strengthen the confidence we have in our findings or to help us to produce a richer, more detailed description of our results. For instance, if we are conducting a case study of what the family experience is for a child with a very rare disorder like Batten Disease , we may use multiple sources of data. These can include observing family and community interactions, conducting interviews with family members and others connected to the family (such as service providers,) and examining journal entries families were asked to keep over the course of the study. By collecting data from a variety of sources such as this, we can more broadly represent a range of perspectives when answering our research question, which will hopefully provide a more holistic picture of the family experience. However, if we are trying to examine the decision-making processes of adult protective workers, it may make the most sense to rely on just one type of data, such as interviews with adult protective workers. 

• There are numerous types of qualitative data (verbal, observational, artifacts) that we may be able to access when planning a qualitative study. As we plan, we need to consider the strengths and challenges that each possess and how well each type might answer our research question.
• The use of multiple types of qualitative data does add complexity to a study, but this complication may well be worth it to help us explore multiple dimensions of our topic and thereby enrich our findings.

Reflexive Journal Entry Prompt

For your next entry, consider responding to the following:

• What types of data appeal to you?
• Why do you think you are drawn to them?
• How well does this type of data “fit” as a means of answering your question? Why?

17.4 How to gather a qualitative sample

• Compare and contrast various non-probability sampling approaches
• Select a sampling strategy that ideologically fits the research question and is practical/actionable

Before we launch into how to plan our sample, I’m going to take a brief moment to remind us of the philosophical basis surrounding the purpose of qualitative research—not to punish you, but because it has important implications for sampling.

Nomothetic vs. idiographic

As a quick reminder, as we discussed in Chapter 8   idiographic research aims to develop a rich or deep understanding of the individual or the few. The focus is on capturing the uniqueness of a smaller sample in a comprehensive manner. For example, an idiographic study might be a good approach for a case study examining the experiences of a transgender youth and her family living in a rural Midwestern state. Data for this idiographic study would be collected from a range of sources, including interviews with family members, observations of family interactions at home and in the community, a focus group with the youth and her friend group, another focus group with the mother and her social network, etc. The aim would be to gain a very holistic picture of this family’s experiences.

On the other hand, nomothetic research is invested in trying to uncover what is ‘true’ for many. It seeks to develop a general understanding of a very specific relationship between variables. The aim is to produce generalizable findings, or findings that apply to a large group of people. This is done by gathering a large sample and looking at a limited or restricted number of aspects. A nomothetic study might involve a national survey of heath care providers in which thousands of providers are surveyed regarding their current knowledge and comp etence in treating transgender individuals. It would gather data from a very large number of people, and attempt to highlight some general findings across this population on a very focused topic.

Idiographic and nomothetic research represent two different research categories existing at opposite extremes on a continuum.  Qualitative research generally exists on the idiographic end of this continuum. We are most often seeking to obtain a rich, deep, detailed understanding from a relatively small group of people.

Non-probability sampling

Non-probability sampling refers to sampling techniques for which a person’s (or event’s) likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know whether a sample represents a larger population or not. But that’s okay, because representing the population is not the goal of nonprobability samples. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind. We typically use nonprobability samples in research projects that are qualitative in nature. We will examine several types of nonprobability samples. These include purposive samples, snowball samples, quota samples, and convenience samples.

Convenience or availability

Convenience sampling, also known as availability sampling, is a nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, we would simply collect data from those people or other relevant elements to which we have the most convenient access. While convenience samples offer one major benefit—convenience—we should be cautious about generalizing from research that relies on convenience samples because we have no confidence that the sample is representative of a broader population. If you are a social work student who needs to conduct a research project at your field placement setting and you decide to conduct a focus group with the staff at your agency, you are using a convenience sampling approach – you are recruiting participants that are easily accessible to you. In addition, if you elect to analyze existing data that your social work program has collected as part of their graduation exit surveys, you are using data that you readily have access to for your project; again, you have a convenience sample. The vast majority of students I work with on their proposal design rely on convenience data due to time constraints and limited resources.

To draw a purposive sample, we begin with specific perspectives or purposive criteria in mind that we want to examine. We would then seek out research participants who cover that full range of perspectives. For example, if you are studying mental health supports on your campus, you may want to be sure to include not only students, but mental health practitioners and student affairs administrators as well. You might also select students who currently use mental health supports, those who dropped out of supports, and those who are waiting to receive supports. The “purposive” part of purposive sampling comes from selecting specific participants on purpose because you already know they have certain characteristics—being an administrator, dropping out of mental health supports, for example—that you need in your sample.

Note that these differ from inclusion criteria , which are more general requirements a person must possess to be a part of your sample; to be a potential participant that may or may not be sampled. For example, one of the inclusion criteria for a study of your campus’ mental health supports might be that participants had to have visited the mental health center in the past year. That differs from purposive sampling. In purposive sampling, you know characteristics of individuals and recruit them because of those characteristics. For example, I might recruit Jane because she stopped seeking supports this month, because she has worked at the center for many years, and so forth.

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

If you are considering using a purposive sampling approach for your research proposal, you will need to determine what your purposive criteria involves. There are a range of different purposive strategies that might be employed, including: maximum variation , typical case , extreme case , or political case , and you want to be thoughtful in thinking about which one(s) you select and why.

  It can be a bit tricky determining how to approach or formulate your purposive cases. Below are a couple additional resources to explore this strategy further.

For more information on purposive sampling consult this webpage from Laerd Statistics on purposive sampling and this webpage from the University of Connecticut on education research .

When using snowball sampling , we might know one or two people we’d like to include in our study but then we have to rely on those initial participants to help identify additional participants. Thus, our sample builds and grows as the study continues, much as a snowball builds and becomes larger as it rolls through the snow. Snowball sampling is an especially useful strategy when you wish to study a stigmatized group or behavior. These groups may have limited visibility and accessibility for a variety of reasons, including safety. 

Malebranche and colleagues (2010) [5] were interested in studying sexual health behaviors of Black, bisexual men. Anticipating that this may be a challenging group to recruit, they utilized a snowball sampling approach. They recruited initial contacts through activities such as advertising on websites and distributing fliers strategically (e.g. barbershops, nightclubs). These initial recruits were compensated with $50 and received study information sheets and five contact cards to distribute to people in their social network that fit the study criteria. Eventually the research team was able to recruit a sample of 38 men who fit the study criteria.

Snowball sampling may present some ethical quandaries for us. Since we are essentially relying on others to help advertise for us, we are giving up some of our control over the process of recruitment. We may be worried about coercion, or having people put undue pressure to have others’ they know participate in your study. To help mitigate this, we would want to make sure that any participant we recruit understands that participation is completely voluntary and if they tell others about the studies, they should also make them aware that it is voluntary, too. In addition to coercion, we also want to make sure that people’s privacy is not violated when we take this approach. For this reason, it is good practice when using a snowball approach to provide people with our contact information as the researchers and ask that they get in touch with us, rather than the other way around. This may also help to protect again potential feelings of exploitation or feeling taken advantage of. Because we often turn to snowball sampling when our population is difficult to reach or engage, we need to be especially sensitive to why this is. It is often because they have been exploited in the past and participating in research may feel like an extension of this. To address this, we need to have a very clear and transparent informed consent process and to also think about how we can use or research to benefit the people we work in the most meaningful and tangible ways.

Quota sampling is another nonprobability sampling strategy. This type of sampling is actually employed by both qualitative and quantitative researchers, but because it is a nonprobability method, we’ll discuss it in this section. When conducting quota sampling, we identify categories that are important to our study and for which there is likely to be some variation. Subgroups are created based on each category and the researcher decides how many people (or whatever element happens to be the focus of the research) to include from each subgroup and collects data from that number for each subgroup. To demonstrate, perhaps we are interested in studying support needs for children in the foster care system. We decide that we want to examine equal numbers (seven each) of children placed in a kinship placement, a non-kinship foster placement, group home, and residential placements. We expect that the experiences and needs across these settings may differ significantly, so we want to have good representation of each one, thus setting a quota of seven for each type of placement.

As you continue to plan for your proposal, below you will find some of the strengths and challenges presented by each of these types of sampling.

Wait a minute, we need a plan!

Both qualitative and quantitative research should be planful and systematic. We’ve actually covered a lot of ground already and before we get any further, we need to start thinking about what the plan for your qualitative research proposal will look like. This means that as you develop your research proposal, you need to consider what you will be doing each step of the way: how you will find data, how you will capture it, how you will organize it, and how you will store it. If you have multiple types of data, you need to have a plan in place for each type. The plan that you develop is your data collection protocol . If you have a team of researchers (or are part of a research team), the data collection protocol is an important communication tool, making sure that everyone is clear what is going on as the research proceeds. This plan is important to help keep you and others involved in your research consistent and accountable. Throughout this chapter and the next ( Chapter 18 —qualitative data gathering) we will walk through points you will want to include in your data collection protocol. While I’ve spent a fair amount of time talking about the importance of having a plan here, qualitative design often does embrace some degree of flexibility. This flexibility is related to the concept of emergent design that we find in qualitative studies. Emergent design is the idea that some decision in our design will be dynamic and fluid as our understanding of the research question evolves. The more we learn about the topic, the more we want to understand it thoroughly.

A research protocol is a document that not only defines your research project and its aims, but also comprehensively plans how you will carry it out. If this sounds like the function of a research proposal, you are right, they are similar. What differentiates a protocol from a proposal is the level of detail. A proposal is more conceptual; a protocol is more practical (right down to the dollars and cents!). A protocol offers explicit instructions for you and your research team, any funders that may be involved in your research, and any oversight bodies that might be responsible for overseeing your study. Not every study requires a research protocol, but what I’m suggesting here is that you consider constructing at least a limited one to help though the decisions you will need to make to construct your qualitative study.

Al-Jundi and Sakka (2016) [6] provide the following elements for a research protocol :

• What is the question? (Hypothesis) What is to be investigated?
• Why is the study important (Significance)
• Where and when will it take place?
• What is the methodology? (Procedures and methods to be used).
• How are you going to implement it? (Research design)
• What is the proposed time table and budget?
• What are the resources required (technical, scientific, and financial)?

While your research proposal in its entirety will focus on many of these areas, our attention for developing your qualitative research protocol will hone in on the two highlighted above. As we go through these next couple chapters, there will be a number of exercises that walk you though decision points that will form your qualitative research protocol.

To begin developing your qualitative research protocol:

• Select the question you have decided is the best to frame your research proposal.
• Write a brief paragraph about the aim of your study, ending it with the research question you have selected.

Here are a few additional resources on developing a research protocol:

Cameli et al., (2018) How to write a research protocol: Tips and tricks .

Ohio State University, Institutional Review Board (n.d.). Research protocol .

World Health Organization (n.d.). Recommended format for a research protocol .

Decision Point: What types of data will you be using?

• Why is this a good choice, given your research question?
• If so, provide support for this decision.

Decision Point: Which non-probability sampling strategy will you employ?

• Why is this is a good fit?
• What steps might your take to address these challenges?

Recruiting strategies

Much like quantitative research, recruitment for qualitative studies can take many different approaches. When considering how to draw your qualitative sample, it may be helpful to first consider which of these three general strategies will best fit your research question and general study design: public, targeted, or membership-based. While all will lead to a sample, the process for getting you there will look very different, depending on the strategy you select.

Taking a public approach to recruitment offers you access to the broadest swath of potential participants. With this approach, you are taking advantage of public spaces in an attempt to gain the attention of the general population of people that frequent that space so that they can learn about your study. These spaces can be in-person (e.g. libraries, coffee shops, grocery stores, health care settings, parks) or virtual (e.g. open chat forums, e-bulletin boards, news feeds). Furthermore, a public approach can be static (such as hanging a flier), or dynamic (such as talking to people and directly making requests to participate). While a public approach may offer broad coverage in that it attempts to appeal to an array of people, it may be perceived as impersonal or easily able to be overlooked, due to the potential presence of other announcements that may be featured in public spaces. Public recruitment is most likely to be associated with convenience or quota sampling and is unlikely to be used with purposive or snowball sampling, where we would need some advance knowledge of people and the characteristics they possess.

As an alternative, you may elect to take a targeted approach to recruitment. By targeting a select group, you are restricting your sampling frame to those individuals or groups who are potentially most well-suited to answer your research question. Additionally, you may be targeting specific people to help craft a diverse sample, particularly with respect to personal characteristics and/or opinions.

You can target your recruitment through the use of different strategies. First, you might consider the use of knowledgeable and well-connected community members. These are people who may possess a good amount of social capital in their community, which can aid in recruitment efforts. If you are considering the use of community members in this role, make sure to be thoughtful in your approach, as you are essentially asking them to share some of their social capital with you. This means learning about the community or group, approaching community members with a sense of humility, and making sure to demonstrate transparency and authenticity in your interactions. These community members may also be champions for the topic you are researching. A champion is someone who helps to draw the interest of a particular group of people. The champion often comes from within the group itself. As an example, let’s say you’re interested in studying the experiences of family members who have a loved one struggling with substance use. To aid in your recruitment for this study, you enlist the help of a local person who does a lot of work with Al-Anon, an organization facilitating mutual support groups for individuals and families affected by alcoholism.

A targeted approach can certainly help ensure that we are talking to people who are knowledgeable about the topic we are interested in, however, we still need to be aware of the potential for bias. If we target our recruitment based on connection to a particular person, event, or passion for the topic, these folks may share information that they think is viewed as favorable or that disproportionately reflects a particular perspective. This phenomenon is due to the fact that we often spend time with people who are like-minded or share many of our views. A targeted approach may be helpful for any type of non-probability sampling, but can be especially useful for purposive, quota, or snowball sampling, where we are trying to access people or groups of people with specific characteristics or expertise.

Membership-based

Finally, you might consider a membership-based approach . This approach is really a form of targeted recruitment, but may benefit from some individual attention. When using a membership-based approach, your sampling frame is the membership list of a particular organization or group. As you might have guessed, this organization or group must be well-suited for helping to answer your research question. You will need permission to access membership, and the identity of the person authorized to grant permission will depend on the organizational structure. When contacting members regarding recruitment, you may consider using directories, newsletters, listservs or membership meetings. When utilizing a membership-based approach, we often know that members possess specific inclusion criteria we need, however, because they are all associated with that particular group or organization, they may be homogenous or like-minded in other ways. This may limit the diversity in our sample and is something to be mindful of when interpreting our findings. Membership-based recruiting can be helpful when we have a membership group that fulfills our inclusion criteria. For instance, if you want to conduct research with social workers, you might attempt to recruit through the NASW membership distribution list (but this access will come with stipulations and a price tag). Membership-based recruitment may be helpful for any non-probability sampling approach, given that the membership criteria and study inclusion criteria are a close fit. Table 17.5 offers some additional considerations for each of these strategies with examples to help demonstrate sources that might correspond with them.

• Qualitative research predominately relies on non-probability sampling techniques. There are a number of these techniques to choose from (convenience/availability, purposive, snowball, quota), each with advantages and limitations to consider. As we consider these, we need to reflect on both our research question and the resources we have available to us in developing a sampling strategy.
• As we consider where and how we will recruit our sample, there are a range of general approaches, including public, targeted, and membership-based.

Decision Point: How will you recruit or gain access to your sample?

• If you are recruiting people, how will you identify them? If necessary (and it often is), how will gain permission to do this?
• If you are using documents or other artifacts for your study, how will you gain access to these? If necessary (and it often is), how will gain permission to do this?

17.5 What should my sample look like?

• Explain key factors that influence the makeup of a qualitative sample
• Develop and critique a sampling strategy to support their qualitative proposal

Once you have started your recruitment, you also need to know when to stop. Knowing when to stop recruiting for a qualitative research study generally involves a dynamic and reflective process. This means that you will actively be involved in a process of recruiting, collecting data, beginning to review your preliminary data, and conducting more recruitment to gather more data. You will continue this process until you have gathered enough data and included sufficient perspectives to answer your research question in rich and meaningful way.

The sample size of qualitative studies can vary significantly. For instance, case studies may involve only one participant or event, while some studies may involve hundreds of interviews or even thousands of documents. Generally speaking, when compared to quantitative research, qualitative studies have a considerably smaller sample. Your decision regarding sample size should be guided by a few considerations, described below.

Amount of data

When gathering quantitative data, the amount of data we are gathering is often specified at the start (e.g. a fixed number of questions on a survey or a set number of indicators on a tracking form). However, when gathering qualitative data, we are often asking people to expand on and explore their thoughts and reactions to certain things. This can produce A LOT of data. If you have ever had to transcribe an interview (type out the conversation while listening to an audio recorded interview), you quickly learn that a 15-minute discussion turns into many pages of dialogue. As such, each interview or focus group you conduct represents multi-page transcripts, all of which becomes your data. If you are conducting interviews or focus groups, y ou will know you have collected enough data from each interaction when you have covered all your questions and allowed the participant(s) to share any and all ideas they have related to the topic. If you are using observational data, you need to spend sufficient time making observations and capturing data to offer a genuine and holistic representation of the thing you are observing (at least to the best of your ability). When using documents and other sources of media, again, you want to ensure that diverse perspectives are represented through your artifact choices so that your data reflects a well-rounded representation of the issue you are studying. For any of these data sources, this involves a judgment call on the researcher’s part. Your judgment should be informed by what you have read in the existing literature and consultation with your professor. 

As part of your analysis, you will likely eventually break these larger hunks of data apart into words or small phrases, giving you potentially thousands of pieces of data. If you are relying on documents or other artifacts, the amount of data contained in each of these pieces is determined in advance, as they already exist. However, you will need to determine how many to include. With interviews, focus groups, or other forms of data generation (e.g. taking pictures for a photovoice project), we don’t necessarily know how much data will be generated with each encounter, as it will depend on the questions that are asked, the information that is shared, and how well we capture it.

Type of study

A variety of types of qualitative studies will be discussed in greater detail in Chapter 22 . While you don’t necessarily need to have an extensive understanding of them all at this point in time, it is important that you understand which of the different design types are best for answering certain research questions. For instance, if our question involves understanding some type of experience, that is often best answered by a phenomenological design. Or, if we want to better understand some process, a grounded theory study may be best suited. While there are no hard and fast rules regarding qualitative sample size, each of these different types of designs has different guidelines for what is considered an acceptable or reasonable number to include in your sample. So drawing on the previous examples, your grounded theory study might include 45 participants because you need more people to gain a clearer picture of each step of the process, while your phenomenological study includes 20 because that provides a good representation of the experience you are interested in. Both would be reasonable targets based on the respective study design type. So as you consider your research question and which specific type of qualitative design this leads you to, you will need to do some investigation to see what size samples are recommended for that particular type of qualitative design.

Diversity of perspectives

As you consider your research question, you also may want to think about the potential variation in how your study population might view this topic. If you are conducting a case study of one person, this obviously isn’t a concern, but if you are interested in exploring a range of experiences, you want to plan to intentionally recruit so this level of diversity is reflected in your sample. The level of variation you seek will have direct implications for how big your sample might be. In the example provided above in the section on quota sampling, we wanted to ensure we had equal representation across a host of placement dispositions for children in foster care. This helped us define our target sample size: (4) settings a quota of (7) participants from each type of setting = a target sample size of (28).

In Chapter 18 , we will be talking about different approaches to data gathering, which may help to dictate the range of perspectives you want to represent. For instance, if you conduct a focus group, you want all of your participants to have some experience with the thing that you are studying, but you hope that their perspectives differ from one another. Furthermore, you may want to avoid groups of participants who know each other well in the same focus group (if possible), as this may lead to groupthink or level of familiarity that doesn’t really encourage differences being expressed. Ideally, we want to encourage a discussion where a variety of ideas are shared, offering a more complete understanding of how the topic is experienced. This is true in all forms of qualitative data, in that your findings are likely to be more well-rounded and offer a broader understanding fo the issue if you recruit a sample with diverse perspectives.

Finally, the concept of saturation has important implications for both qualitative sample size and data analysis. To understand the idea of saturation, it is first important to understand that unlike most quantitative research, with qualitative research we often at least begin the process of data analysis while we are still actively collecting data. This is called an iterative approach to data analysis. So, if you are a qualitative researcher conducting interviews, you may be aiming to complete 30 interviews. After you have completed your first five interviews, you may begin reviewing and coding (a term that refers to labeling the different ideas found in your transcripts) these interviews while you are still conducting more interviews. You go on to review each new interview that you conduct and code it for the ideas that are reflected there. Eventually, you will reach a point where conducting more interviews isn’t producing any new ideas, and this is the point of saturation. Reaching saturation is an indication that we can stop data collection. This may come before or after you hit 30, but as you can see, it is driven by the presence of new ideas or concepts in your interviews, not a specific number.

This chapter represents our transition in the text to a focus on qualitative methods in research. Throughout this chapter we have explored a number of topics including various types of qualitative data, approaches to qualitative sampling, and some considerations for recruitment and sample composition. It bears repeating that your plan for sampling should be driven by a number of things: your research question, what is feasible for you, especially as a student researcher, best practices in qualitative research. Finally, in subsequent chapters, we will continue the discussion about reflexivity as it relates to the qualitative research process that we began here.

• The composition of our qualitative sample comes with some important decisions to consider, including how large should our sample be and what level and type of diversity it should reflect. These decisions are guided by the purposes or aims of our study, as well as access to resources and our population.
• The concept of saturation is important for qualitative research. It helps us to determine when we have sufficiently collected a range of perspectives on the topic we are studying.

Decision Point(s): What should your sample look like (sample composition)?

• If so, how many?
• How was this number determined?
• OR will you use the concept of saturation to determine when to stop?
• What supports your decision in regards to the previous question?

This isn’t so much a decision point, but a chance for you to reflect on the choices you’ve made thus far in your protocol with regards to your: (1) ethical responsibility, (2) commitment to cultural humility, and (3) respect for empowerment of individuals and groups as a social work researcher. Think about each of the decisions you’ve made thus far and work across this grid to identify any important considerations that you need to take into account.

You have been prompted to make a number of choices regarding how you will proceed with gathering your qualitative sample. Based on what you have learned and what you are planning, respond to the following questions below.

• What are the strengths of your sampling plan in respect to being able to answer your qualitative research question?
• How feasible is it for you, as a student researcher, to be able to carry out your sampling plan?
• What reservations or questions do you still need to have answered to adequately plan for your sample?
• What excites you about your proposal thus far?
• What worries you about your proposal thus far?

Media Attributions

• book lights © KAROLINA GRABOWSKA is licensed under a CC BY-ND (Attribution NoDerivatives) license
• women having a conversation © Andrea Piacquadio is licensed under a CC0 (Creative Commons Zero) license
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• National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. Retrieved from https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html ↵
• Patel, J., Tinker, A., & Corna, L. (2018). Younger workers’ attitudes and perceptions towards older colleagues.  Working with Older People, 22 (3), 129-138. ↵
• Veenstra, A. S., Iyer, N., Hossain, M. D., & Park, J. (2014). Time, place, technology: Twitter as an information source in the Wisconsin labor protests. Computers in Human Behavior, 31, 65-72. ↵
• Ohmer, M. L., & Owens, J. (2013). Using photovoice to empower youth and adults to prevent crime.  Journal of Community Practice, 21 (4), 410-433. ↵
• Malebranche, D. J., Arriola, K. J., Jenkins, T. R., Dauria, E., & Patel, S. N. (2010). Exploring the “bisexual bridge”: A qualitative study of risk behavior and disclosure of same-sex behavior among Black bisexual men . American Journal of Public Health, 100( 1), 159-164. ↵
• Al-Jundi, A., & SakkA, S. (2016). Protocol writing in clinical research. Journal of Clinical and Diagnostic Research: JCDR, 10 (11), ZE10. ↵

Research that involves the use of data that represents human expression through words, pictures, movies, performance and other artifacts.

One of the three ethical principles in the Belmont Report. States that benefits and burdens of research should be distributed fairly.

Case studies are a type of qualitative research design that focus on a defined case and gathers data to provide a very rich, full understanding of that case. It usually involves gathering data from multiple different sources to get a well-rounded case description.

the various aspects or dimensions that come together in forming our identity

The unintended influence that the researcher may have on the research process.

A research journal that helps the researcher to reflect on and consider their thoughts and reactions to the research process and how it may be shaping the study

Rigor is the process through which we demonstrate, to the best of our ability, that our research is empirically sound and reflects a scientific approach to knowledge building.

A form of data gathering where researchers ask individual participants to respond to a series of (mostly open-ended) questions.

A form of data gathering where researchers ask a group of participants to respond to a series of (mostly open-ended) questions.

Observation is a tool for data gathering where researchers rely on their own senses (e.g. sight, sound) to gather information on a topic.

Triangulation of data refers to the use of multiple types, measures or sources of data in a research project to increase the confidence that we have in our findings.

sampling approaches for which a person’s likelihood of being selected for membership in the sample is unknown

A convenience sample is formed by collecting data from those people or other relevant elements to which we have the most convenient access. Essentially, we take who we can get.

A quota sample involves the researcher identifying a subgroups within a population that they want to make sure to include in their sample, and then identifies a quota or target number to recruit that represent each of these subgroups.

For a snowball sample, a few initial participants are recruited and then we rely on those initial (and successive) participants to help identify additional people to recruit. We thus rely on participants connects and knowledge of the population to aid our recruitment.

In a purposive sample, participants are intentionally or hand-selected because of their specific expertise or experience.

Content is the substance of the artifact (e.g. the words, picture, scene). It is what can actually be observed.

Context is the circumstances surrounding an artifact, event, or experience.

Photovoice is a technique that merges pictures with narrative (word or voice data that helps that interpret the meaning or significance of the visual artifact. It is often used as a tool in CBPR.

A rich, deep, detailed understanding of a unique person, small group, and/or set of circumstances.

Inclusion criteria are general requirements a person must possess to be a part of your sample.

A purposive sampling strategy where you choose cases because they represent a range of very different perspectives on a topic

A purposive sampling strategy where you select cases that represent the most common/ a commonly held perspective.

A purposive sampling strategy that selects a case(s) that represent extreme or underrepresented perspectives. It is a way of intentionally focusing on or representing voices that may not often be heard or given emphasis.

A purposive sampling strategy that focuses on selecting cases that are important in representing a contemporary politicized issue.

A plan that is developed by a researcher, prior to commencing a research project, that details how data will be collected, stored and managed during the research project.

Emergent design is the idea that some decision in our research design will be dynamic and change as our understanding of the research question evolves as we go through the research process. This is (often) evident in qualitative research, but rare in quantitative research.

approach to recruitment where participants are sought in public spaces

approach to recruitment where participants are based on some personal characteristic or group association

approach to recruitment where participants are members of an organization or social group with identified membership

To type out the text of recorded interview or focus group.

A qualitative research design that aims to capture and describe the lived experience of some event or "phenomenon" for a group of people.

A type of research design that is often used to study a process or identify a theory about how something works.

The point where gathering more data doesn't offer any new ideas or perspectives on the issue you are studying.  Reaching saturation is an indication that we can stop qualitative data collection.

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• v.42(1); Jan-Feb 2020

Sample Size and its Importance in Research

Chittaranjan andrade.

Clinical Psychopharmacology Unit, Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary sample size is set for a pilot study. This article discusses sample size and how it relates to matters such as ethics, statistical power, the primary and secondary hypotheses in a study, and findings from larger vs. smaller samples.

Studies are conducted on samples because it is usually impossible to study the entire population. Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well. The sample must therefore be representative of the population. This is best ensured by the use of proper methods of sampling. The sample must also be adequate in size – in fact, no more and no less.

SAMPLE SIZE AND ETHICS

A sample that is larger than necessary will be better representative of the population and will hence provide more accurate results. However, beyond a certain point, the increase in accuracy will be small and hence not worth the effort and expense involved in recruiting the extra patients. Furthermore, an overly large sample would inconvenience more patients than might be necessary for the study objectives; this is unethical. In contrast, a sample that is smaller than necessary would have insufficient statistical power to answer the primary research question, and a statistically nonsignificant result could merely be because of inadequate sample size (Type 2 or false negative error). Thus, a small sample could result in the patients in the study being inconvenienced with no benefit to future patients or to science. This is also unethical.

In this regard, inconvenience to patients refers to the time that they spend in clinical assessments and to the psychological and physical discomfort that they experience in assessments such as interviews, blood sampling, and other procedures.

ESTIMATING SAMPLE SIZE

So how large should a sample be? In hypothesis testing studies, this is mathematically calculated, conventionally, as the sample size necessary to be 80% certain of identifying a statistically significant outcome should the hypothesis be true for the population, with P for statistical significance set at 0.05. Some investigators power their studies for 90% instead of 80%, and some set the threshold for significance at 0.01 rather than 0.05. Both choices are uncommon because the necessary sample size becomes large, and the study becomes more expensive and more difficult to conduct. Many investigators increase the sample size by 10%, or by whatever proportion they can justify, to compensate for expected dropout, incomplete records, biological specimens that do not meet laboratory requirements for testing, and other study-related problems.

Sample size calculations require assumptions about expected means and standard deviations, or event risks, in different groups; or, upon expected effect sizes. For example, a study may be powered to detect an effect size of 0.5; or a response rate of 60% with drug vs. 40% with placebo.[ 1 ] When no guesstimates or expectations are possible, pilot studies are conducted on a sample that is arbitrary in size but what might be considered reasonable for the field.

The sample size may need to be larger in multicenter studies because of statistical noise (due to variations in patient characteristics, nonspecific treatment characteristics, rating practices, environments, etc. between study centers).[ 2 ] Sample size calculations can be performed manually or using statistical software; online calculators that provide free service can easily be identified by search engines. G*Power is an example of a free, downloadable program for sample size estimation. The manual and tutorial for G*Power can also be downloaded.

PRIMARY AND SECONDARY ANALYSES

The sample size is calculated for the primary hypothesis of the study. What is the difference between the primary hypothesis, primary outcome and primary outcome measure? As an example, the primary outcome may be a reduction in the severity of depression, the primary outcome measure may be the Montgomery-Asberg Depression Rating Scale (MADRS) and the primary hypothesis may be that reduction in MADRS scores is greater with the drug than with placebo. The primary hypothesis is tested in the primary analysis.

Studies almost always have many hypotheses; for example, that the study drug will outperform placebo on measures of depression, suicidality, anxiety, disability and quality of life. The sample size necessary for adequate statistical power to test each of these hypotheses will be different. Because a study can have only one sample size, it can be powered for only one outcome, the primary outcome. Therefore, the study would be either overpowered or underpowered for the other outcomes. These outcomes are therefore called secondary outcomes, and are associated with secondary hypotheses, and are tested in secondary analyses. Secondary analyses are generally considered exploratory because when many hypotheses in a study are each tested at a P < 0.05 level for significance, some may emerge statistically significant by chance (Type 1 or false positive errors).[ 3 ]

INTERPRETING RESULTS

Here is an interesting question. A test of the primary hypothesis yielded a P value of 0.07. Might we conclude that our sample was underpowered for the study and that, had our sample been larger, we would have identified a significant result? No! The reason is that larger samples will more accurately represent the population value, whereas smaller samples could be off the mark in either direction – towards or away from the population value. In this context, readers should also note that no matter how small the P value for an estimate is, the population value of that estimate remains the same.[ 4 ]

On a parting note, it is unlikely that population values will be null. That is, for example, that the response rate to the drug will be exactly the same as that to placebo, or that the correlation between height and age at onset of schizophrenia will be zero. If the sample size is large enough, even such small differences between groups, or trivial correlations, would be detected as being statistically significant. This does not mean that the findings are clinically significant.

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• Open access
• Published: 10 August 2024

How can health systems approach reducing health inequalities? An in-depth qualitative case study in the UK

• Charlotte Parbery-Clark 1 ,
• Lorraine McSweeney 2 ,
• Joanne Lally 3 &
• Sarah Sowden 4  

BMC Public Health volume  24 , Article number:  2168 ( 2024 ) Cite this article

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Metrics details

Addressing socioeconomic inequalities in health and healthcare, and reducing avoidable hospital admissions requires integrated strategy and complex intervention across health systems. However, the understanding of how to create effective systems to reduce socio-economic inequalities in health and healthcare is limited. The aim was to explore and develop a system’s level understanding of how local areas address health inequalities with a focus on avoidable emergency admissions.

In-depth case study using qualitative investigation (documentary analysis and key informant interviews) in an urban UK local authority. Interviewees were identified using snowball sampling. Documents were retrieved via key informants and web searches of relevant organisations. Interviews and documents were analysed independently based on a thematic analysis approach.

Interviews ( n  = 14) with wide representation from local authority ( n  = 8), NHS ( n  = 5) and voluntary, community and social enterprise (VCSE) sector ( n  = 1) with 75 documents (including from NHS, local authority, VCSE) were included. Cross-referenced themes were understanding the local context, facilitators of how to tackle health inequalities: the assets, and emerging risks and concerns. Addressing health inequalities in avoidable admissions per se was not often explicitly linked by either the interviews or documents and is not yet embedded into practice. However, a strong coherent strategic integrated population health management plan with a system’s approach to reducing health inequalities was evident as was collective action and involving people, with links to a “strong third sector”. Challenges reported include structural barriers and threats, the analysis and accessibility of data as well as ongoing pressures on the health and care system.

We provide an in-depth exploration of how a local area is working to address health and care inequalities. Key elements of this system’s working include fostering strategic coherence, cross-agency working, and community-asset based approaches. Areas requiring action included data sharing challenges across organisations and analytical capacity to assist endeavours to reduce health and care inequalities. Other areas were around the resilience of the system including the recruitment and retention of the workforce. More action is required to embed reducing health inequalities in avoidable admissions explicitly in local areas with inaction risking widening the health gap.

Highlights:

• Reducing health inequalities in avoidable hospital admissions is yet to be explicitly linked in practice and is an important area to address.

• Understanding the local context helps to identify existing assets and threats including the leverage points for action.

• Requiring action includes building the resilience of our complex systems by addressing structural barriers and threats as well as supporting the workforce (training and wellbeing with improved retention and recruitment) in addition to the analysis and accessibility of data across the system.

Peer Review reports

Introduction

The health of our population is determined by the complex interaction of several factors which are either non-modifiable (such as age, genetics) or modifiable (such as the environment, social, economic conditions in which we live, our behaviours as well as our access to healthcare and its quality) [ 1 ]. Health inequalities are the avoidable and unfair systematic differences in health and healthcare across different population groups explained by the differences in distribution of power, wealth and resources which drive the conditions of daily life [ 2 , 3 ]. Essentially, health inequalities arise due to the systematic differences of the factors that influence our health. To effectively deal with most public health challenges, including reducing health inequalities and improving population health, broader integrated approaches [ 4 ] and an emphasis on systems is required [ 5 , 6 ] . A system is defined as ‘the set of actors, activities, and settings that are directly or indirectly perceived to have influence in or be affected by a given problem situation’ (p.198) [ 7 ]. In this case, the ‘given problem situation' is reducing health inequalities with a focus on avoidable admissions. Therefore, we must consider health systems, which are the organisations, resources and people aiming to improve or maintain health [ 8 , 9 ] of which health services provision is an aspect. In this study, the system considers NHS bodies, Integrated Care Systems, Local Authority departments, and the voluntary and community sector in a UK region.

A plethora of theories [ 10 ], recommended policies [ 3 , 11 , 12 , 13 ], frameworks [ 1 , 14 , 15 ], and tools [ 16 ] exist to help understand the existence of health inequalities as well as provide suggestions for improvement. However, it is reported that healthcare leaders feel under-skilled to reduce health inequalities [ 17 ]. A lack of clarity exists on how to achieve a system’s multi-agency coherence to reduce health inequalities systematically [ 17 , 18 ]. This is despite some countries having legal obligations to have a regard to the need to attend to health and healthcare inequalities. For example, the Health and Social Care Act 2012 [ 19 ], in England, mandated Clinical Commissioning Groups (CCGs), now transferred to Integrated Care Boards (ICBs) [ 20 ], to ‘have a regard to the need to reduce inequalities between patients with respect to their ability to access health services, and reduce inequalities between patients with respect to the outcomes achieved for them by the provision of health services’. The wider determinants of health must also be considered. For example, local areas have a mandatory requirement to have a joint strategic needs assessment (JSNA) and joint health and wellbeing strategy (JHWS) whose purpose is to ‘improve the health and wellbeing of the local community and reduce inequalities for all ages' [ 21 ] This includes addressing the wider determinants of health [ 21 ]. Furthermore, the hospital care costs to the NHS associated with socioeconomic inequalities has been previously reported at £4.8 billion a year due to excess hospitalisations [ 22 ]. Avoidable emergency admissions are admissions into hospital that are considered to be preventable with high-quality ambulatory care [ 23 ]. Both ambulatory care sensitive conditions (where effective personalised care based in the community can aid the prevention of needing an admission) and urgent care sensitive conditions (where a system on the whole should be able to treat and manage without an admission) are considered within this definition [ 24 ] (encompassing more than 100 International Classification of Diseases (ICD) codes). The disease burden sits disproportionately with our most disadvantaged communities, therefore highlighting the importance of addressing inequalities in hospital pressures in a concerted manner [ 25 , 26 ].

Research examining one component of an intervention, or even one part of the system, [ 27 ] or which uses specific research techniques to control for the system’s context [ 28 ] are considered as having limited use for identifying the key ingredients to achieve better population health and wellbeing [ 5 , 28 ]. Instead, systems thinking considers how the system’s components and sub-components interconnect and interrelate within and between each other (and indeed other systems) to gain an understanding of the mechanisms by which things work [ 29 , 30 ]. Complex interventions or work programmes may perform differently in varying contexts and through different mechanisms, and therefore cannot simply be replicated from one context to another to automatically achieve the same outcomes. Ensuring that research into systems and systems thinking considers real-world context, such as where individuals live, where policies are created and interventions are delivered, is vital [ 5 ]. How the context and implementation of complex or even simple interventions interact is viewed as becoming increasingly important [ 31 , 32 ]. Case study research methodology is founded on the ‘in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings’ (p.2) [ 33 ]. Case study approaches can deepen the understanding of complexity addressing the ‘how’, ‘what’ and ‘why’ questions in a real-life context [ 34 ]. Researchers have highlighted the importance of engaging more deeply with case-based study methodology [ 31 , 33 ]. Previous case study research has shown promise [ 35 ] which we build on by exploring a systems lens to consider the local area’s context [ 16 ] within which the work is implemented. By using case-study methodology, our study aimed to explore and develop an in-depth understanding of how a local area addresses health inequalities, with a focus on avoidable hospital admissions. As part of this, systems processes were included.

Study design

This in-depth case study is part of an ongoing larger multiple (collective [ 36 ]) case study approach. An instrumental approach [ 34 ] was taken allowing an in-depth investigation of an issue, event or phenomenon, in its natural real-life context; referred to as a ‘naturalistic’ design [ 34 ]. Ethics approval was obtained by Newcastle University’s Ethics Committee (ref 13633/2020).

Study selection

This case study, alongside the other three cases, was purposively [ 36 ] chosen considering overall deprivation level of the area (Indices of Multiple Deprivation (IMD) [ 37 ]), their urban/rural location, differing geographical spread across the UK (highlighted in patient and public feedback and important for considering the North/South health divide [ 38 ]), and a pragmatic judgement of likely ability to achieve the depth of insight required [ 39 ]. In this paper, we report the findings from one of the case studies, an urban local authority in the Northern region of the UK with high levels of socioeconomic disadvantage. This area was chosen for this in-depth case analysis due to high-level of need, and prior to the COVID-19 pandemic (2009-2018) had experienced a trend towards reducing socioeconomic inequalities in avoidable hospital admission rates between neighbourhoods within the local area [ 40 ]. Thereby this case study represents an ‘unusual’ case [ 41 ] to facilitate learning regarding what is reported and considered to be the key elements required to reduce health inequalities, including inequalities in avoidable admissions, in a local area.

Semi-structured interviews

The key informants were identified iteratively through the documentary analysis and in consultation with the research advisory group. Initially board level committee members (including lay, managerial, and clinical members) within relevant local organisations were purposively identified. These individuals were systems leaders charged with the remit of tackling health inequalities and therefore well placed to identify both key personnel and documents. Snowball sampling [ 42 ] was undertaken thereafter whereby interviewees helped to identify additional key informants within the local system who were working on health inequalities, including avoidable emergency admissions, at a systems level. Interview questions were based on an iteratively developed topic guide (supplementary data 1), informed from previous work’s findings [ 43 ] and the research advisory network’s input. A study information sheet was emailed to perspective interviewees, and participants were asked to complete an e-consent form using Microsoft Forms [ 42 ]. Each interviewee was interviewed by either L.M. or C.P.-C. using the online platforms Zoom or Teams, and lasted up to one hour. Participants were informed of interviewers’ role, workplace as well as purpose of the study. Interviewees were asked a range of questions including any work relating to reducing health inequalities, particularly avoidable emergency admissions, within the last 5 years. Brief notes were taken, and the interviews were recorded, transcribed verbatim and anonymised.

Documentary analysis

The documentary analysis followed the READ approach [ 44 ]. Any documents from the relevant local/regional area with sections addressing health inequalities and/or avoidable emergency admissions, either explicitly stated or implicitly inferred, were included. A list of core documents was chosen, including the local Health and Wellbeing Strategy (Table 1 ). Subsequently, other documents were identified by snowballing from these core documents and identification by the interviewees. All document types were within scope if produced/covered a period within 5 years (2017-2022), including documents in the public domain or not as well as documents pertaining to either a regional, local and neighbourhood level. This 5-year period was a pragmatic decision in line with the interviews and considered to be a balance of legacy and relevance. Attempts were made to include the final version of each document, where possible/applicable, otherwise the most up-to-date version or version available was used.

An Excel spreadsheet data extraction tool was adapted with a priori criteria [ 44 ] to extract the data. This tool included contextual information (such as authors, target area and document’s purpose). Also, information based on previous research on addressing socioeconomic inequalities in avoidable emergency admissions, such as who stands to benefit, was extracted [ 43 ]. Additionally, all documents were summarised according to a template designed according to the research’s aims. Data extraction and summaries were undertaken by L.M. and C.P.-C. A selection was doubled coded to enhance validity and any discrepancies were resolved by discussion.

Interviews and documents were coded and analysed independently based on a thematic analysis approach [ 45 ], managed by NVivo software. A combination of ‘interpretive’ and ‘positivist’ stance [ 34 , 46 ] was taken which involved understanding meanings/contexts and processes as perceived from different perspectives (interviewees and documents). This allowed for an understanding of individual and shared social meanings/reasonings [ 34 , 36 ]. For the documentary analysis, a combination of both content and thematic analysis as described by Bowen [ 47 ] informed by Braun and Clarke’s approach to thematic analysis [ 45 ] was used. This type of content analysis does not include the typical quantification but rather a review of the document for pertinent and meaningful passages of text/other data [ 47 ]. Both an inductive and deductive approach for the documentary analysis’ coding [ 46 , 47 ] was chosen. The inductive approach was developed a posteriori; the deductive codes being informed by the interviews and previous findings from research addressing socioeconomic inequalities in avoidable emergency admissions [ 43 ]. In line with qualitative epistemological approach to enquiry, the interview and documentary findings were viewed as ‘truths’ in themselves with the acceptance that multiple realities can co-exist [ 48 ]. The analysis of each set of themes (with subthemes) from the documentary analysis and interviews were cross-referenced and integrated with each other to provide a cohesive in-depth analysis [ 49 ] by generating thematic maps to explore the relationships between the themes. The codes, themes and thematic maps were peer-reviewed continually with regular meetings between L.M., C.P.-C., J.L. and S.S. Direct quotes are provided from the interviews and documentary analysis. Some quotes from the documents are paraphrased to protect anonymity of the case study after following a set process considering a range of options. This involved searching each quote from the documentary analysis in Google and if the quote was found in the first page of the result, we shortened extracts and repeated the process. Where the shortened extracts were still identifiable, we were required to paraphrase that quote. Each paraphrased quote and original was shared and agreed with all the authors reducing the likelihood of inadvertently misinterpreting or misquoting. Where multiple components over large bodies of text were present in the documents, models were used to evidence the broadness, for example, using Dahlgren’s and Whitehead’s model of health determinants [ 1 ]. Due to the nature of the study, transcripts and findings were not shared with participants for checking but will be shared in a dissemination workshop in 2024.

Patient and public involvement and engagement

Four public contributors from the National Institute for Health and Care Research (NIHR) Research Design Service (RDS) North East and North Cumbria (NENC) Public and Patient Involvement (PPI) panel have been actively engaged in this research from its inception. They have been part of the research advisory group along with professional stakeholders and were involved in the identification of the sampling frame’s key criteria. Furthermore, a diverse group of public contributors has been actively involved in other parts of the project including developing the moral argument around action by producing a public facing resource exploring what health inequalities mean to people and public views of possible solutions [ 50 ].

Semi-structured interviews: description

Sixteen participants working in health or social care, identified through the documentary analysis or snowballing, were contacted for interview; fourteen consented to participate. No further interviews were sought as data sufficiency was reached whereby no new information or themes were being identified. Participant roles were broken down by NHS ( n  = 5), local authority/council ( n  = 8), and voluntary, community and social enterprise (VSCE) ( n  = 1). To protect the participants’ anonymity, their employment titles/status are not disclosed. However, a broad spectrum of interviewees with varying roles from senior health system leadership (including strategic and commissioner roles) to roles within provider organisations and the VSCE sector were included.

Documentary analysis: description

75 documents were reviewed with documents considering regional ( n  = 20), local ( n  = 64) or neighbourhood ( n  = 2) area with some documents covering two or more areas. Table 2 summarises the respective number of each document type which included statutory documents to websites from across the system (NHS, local government and VSCE). 45 documents were named by interviewees and 42 documents were identified as either a core document or through snowballing from other documents. Of these, 12 documents were identified from both. The timescales of the documents varied and where possible to identify, was from 2014 to 2031.

Integrative analysis of the documentary analysis and interviews

The overarching themes encompass:

Understanding the local context

Facilitators to tacking health inequalities: the assets

Emerging risks and concerns

Figure 1 demonstrates the relationships between the main themes identified from the analysis for tackling health inequalities and improving health in this case study.

Diagram of the relationship between the key themes identified regarding tackling health inequalities and improving health in a local area informed by 2 previous work [ 14 , 51 ]. NCDs = non-communicable diseases; HI = health inequalities

Understanding the local context was discussed extensively in both the documents and the interviews. This was informed by local intelligence and data that was routinely collected, monitored, and analysed to help understand the local context and where inequalities lie. More bespoke, in-depth collection and analysis were also described to get a better understanding of the situation. This not only took the form of quantitative but also considered qualitative data with lived experience:

‛So, our data comes from going out to talk to people. I mean, yes, especially the voice of inequalities, those traditional mechanisms, like surveys, don't really work. And it's about going out to communities, linking in with third sector organisations, going out to communities, and just going out to listen…I think the more we can bring out those real stories. I mean, we find quotes really, really powerful in terms of helping people understand what it is that matters.’ (LP16).

However, there were limitations to the available data including the quality as well as having enough time to do the analysis justice. This resulted in difficulties in being able to fully understand the context to help identify and act on the required improvements.

‘A lack of available data means we cannot quantify the total number of vulnerable migrants in [region]’ (Document V).

‛So there’s lots of data. The issue is joining that data up and analysing it, and making sense of it. That’s where we don’t have the capacity.’ (LP15).

Despite the caveats, understanding the context and its data limitations were important to inform local priorities and approaches on tackling health inequalities. This understanding was underpinned by three subthemes which were understanding:

the population’s needs including identification of people at higher risk of worse health and health inequalities

the driving forces of those needs with acknowledgement of the impact of the wider determinants of health

the threats and barriers to physical and mental health, as well as wellbeing

Firstly, the population’s needs, including identification of people at higher risk of worse health and health inequalities, was important. This included considering risk factors, such as smoking, specific groups of people and who was presenting with which conditions. Between the interviews and documents, variation was seen between groups deemed at-risk or high-risk with the documents identifying a wider range. The groups identified across both included marginalised communities, such as ethnic minority groups, gypsy and travellers, refugees and asylum seekers as well as people/children living in disadvantaged area.

‘There are significant health inequalities in children with asthma between deprived and more affluent areas, and this is reflected in A&E admissions.' (Document J).

Secondly, the driving forces of those needs with acknowledgement of the impact of the wider determinants of health were described. These forces mapped onto Dahlgren’s and Whitehead’s model of health determinants [ 1 ] consisting of individual lifestyle factors, social and community networks, living and working conditions (which include access to health care services) as well as general socio-economic, cultural and environmental conditions across the life course.

…. at the centre of our approach considering the requirements to improve the health and wellbeing of our area are the wider determinants of health and wellbeing, acknowledging how factors, such as housing, education, the environment and economy, impact on health outcomes and wellbeing over people’s lifetime and are therefore pivotal to our ambition to ameliorate the health of the poorest the quickest. (Paraphrased Document P).

Thirdly, the threats and barriers to health included environmental risks, communicable diseases and associated challenges, non-communicable conditions and diseases, mental health as well as structural barriers. In terms of communicable diseases, COVID-19 predominated. The environmental risks included climate change and air pollution. Non-communicable diseases were considered as a substantial and increasing threat and encompassed a wide range of chronic conditions such as diabetes, and obesity.

‛Long term conditions are the leading causes of death and disability in [case study] and account for most of our health and care spending. Cases of cancer, diabetes, respiratory disease, dementia and cardiovascular disease will increase as the population of [case study] grows and ages.’ (Document A).

Structural barriers to accessing and using support and/or services for health and wellbeing were identified. These barriers included how the services are set up, such as some GP practices asking for proof of a fixed address or form of identification to register. For example:

Complicated systems (such as having to make multiple calls, the need to speak to many people/gatekeepers or to call at specific time) can be a massive barrier to accessing healthcare and appointments. This is the case particularly for people who have complex mental health needs or chaotic/destabilized circumstances. People who do not have stable housing face difficulties in registering for GP and other services that require an address or rely on post to communicate appointments. (Paraphrased Document R).

A structural threat regarding support and/or services for health and wellbeing was the sustainability of current funding with future uncertainty posing potential threats to the delivery of current services. This also affected the ability to adapt and develop the services, or indeed build new ones.

‛I would say the other thing is I have a beef [sic] [disagreement] with pilot studies or new innovations. Often soft funded, temporary funded, charity funded, partnership work run by enthusiasts. Me, I've done them, or supported people doing many of these. And they're great. They can make a huge impact on the individuals involved on that local area. You can see fantastic work. You get inspired and you want to stand up in a crowd and go, “Wahey, isn't this fantastic?” But actually the sad part of it is on these things, I've seen so many where we then see some good, positive work being done, but we can't make it permanent or we can't spread it because there's no funding behind it.’ (LP8).

Facilitators to tackling health inequalities: the assets

The facilitators for improving health and wellbeing and tackling health inequalities are considered as assets which were underpinned by values and principles.

Values driven supported by four key principles

Being values driven was an important concept and considered as the underpinning attitudes or beliefs that guide decision making [ 52 ]. Particularly, the system’s approach was underpinned by a culture and a system's commitment to tackle health inequalities across the documents and interviews. This was also demonstrated by how passionately and emotively some interviewees spoke about their work.

‛There's a really strong desire and ethos around understanding that we will only ever solve these problems as a system, not by individual organisations or even just part of the system working together. And that feels great.’ (LP3).

Other values driving the approach included accountability, justice, and equity. Reducing health inequalities and improving health were considered to be the right things to do. For example:

We feel strongly about social justice and being inclusive, wishing to reflect the diversity of [case study]. We campaign on subjects that are important to people who are older with respect and kindness. (Paraphrased Document O).

Four key principles were identified that crosscut the assets which were:

Shared vision

Strong partnership

Asset-based approaches

Willingness and ability to act on learning

The mandated strategy, identifying priorities for health and wellbeing for the local population with the required actions, provided the shared vision across each part of the system, and provided the foundations for the work. This shared vision was repeated consistently in the documents and interviews from across the system.

[Case study] will be a place where individuals who have the lowest socioeconomic status will ameliorate their health the quickest. [Case study] will be a place for good health and compassion for all people, regardless of their age. (Paraphrased Document A).

‛One thing that is obviously becoming stronger and stronger is the focus on health inequalities within all of that, and making sure that we are helping people and provide support to people with the poorest health as fast as possible, so that agenda hasn’t shifted.’ (LP7).

This drive to embed the reduction of health inequalities was supported by clear new national guidance encapsulated by the NHS Core20PLUS5 priorities. Core20PLUS5 is the UK's approach to support a system to improve their healthcare inequalities [ 53 ]. Additionally, the system's restructuring from Clinical Commissioning Groups (CCGs) to Integrated Care Boards (ICBs) and formalisation of the now statutory Integrated Care Systems (ICS) in England was also reported to facilitate the driving of further improvement in health inequalities. These changes at a regional and local level helped bring key partners across the system (NHS and local government among others) to build upon their collective responsibility for improving health and reducing health inequalities for their area [ 54 ].

‛I don’t remember the last time we’ve had that so clear, or the last time that health inequalities has had such a prominent place, both in the NHS planning guidance or in the NHS contract. ’ (LP15). ‛The Health and Care Act has now got a, kind of, pillar around health inequalities, the new establishment of ICPs and ICBs, and also the planning guidance this year had a very clear element on health inequalities.’ (LP12)

A strong partnership and collaborative team approach across the system underpinned the work from the documents and included the reoccurrence of the concept that this case study acted as one team: ‘Team [case study]'.

Supporting one another to ensure [case study] is the best it can be: Team [case study]. It involves learning, sharing ideas as well as organisations sharing assets and resources, authentic partnerships, and striving for collective impact (environmental and social) to work towards shared goals . (Paraphrased Document B).

This was corroborated in the interviews as working in partnership to tackle health inequalities was considered by the interviewees as moving in the right direction. There were reports that the relationship between local government, health care and the third sector had improved in recent years which was still an ongoing priority:

‘I think the only improvement I would cite, which is not an improvement in terms of health outcomes, but in terms of how we work across [case study] together has moved on quite a lot, in terms of teams leads and talking across us, and how we join up on things, rather than see ourselves all as separate bodies' (LP15).

‘I think the relationship between local authorities and health and the third sector, actually, has much more parity and esteem than it had before.' (LP11)

The approaches described above were supported by all health and care partners signing up to principles around partnership; it is likely this has helped foster the case study's approach. This also builds on the asset-based approaches that were another key principle building on co-production and co-creation which is described below.

We begin with people : instead of doing things to people or for them, we work with them, augmenting the skills, assets and strength of [case study]’s people, workforce and carers. We achieve : actions are focused on over words and by using intelligence, every action hones in on the actual difference that we will make to ameliorate outcomes, quality and spend [case study]’s money wisely; We are Team [case study ]: having kindness, working as one organisation, taking responsibility collectively and delivering on what we agreed. Problems are discussed with a high challenge and high support attitude. (Paraphrased Document D).

At times, the degree to which the asset-based approaches were embedded differed from the documents compared to the interviews, even when from the same part of the system. For example, the documents often referred to the asset-based approach as having occurred whilst interviewees viewed it more as a work in progress.

‘We have re-designed many of our services to focus on needs-led, asset-based early intervention and prevention, and have given citizens more control over decisions that directly affect them .’ (Document M).

‘But we’re trying to take an asset-based approach, which is looking at the good stuff in communities as well. So the buildings, the green space, the services, but then also the social capital stuff that happens under the radar.’ (LP11).

A willingness to learn and put in action plans to address the learning were present. This enables future proofing by building on what is already in place to build the capacity, capability and flexibility of the system. This was particularly important for developing the workforce as described below.

‘So we’ve got a task and finish group set up, […] So this group shows good practice and is a space for people to discuss some of the challenges or to share what interventions they are doing around the table, and also look at what other opportunities that they have within a region or that we could build upon and share and scale.’ (LP12).

These assets that are considered as facilitators are divided into four key levels which are the system, services and support, communities and individuals, and workforce which are discussed in turn below.

Firstly, the system within this case study was made up of many organisations and partnerships within the NHS, local government, VSCE sector and communities. The interviewees reported the presence of a strong VCSE sector which had been facilitated by the local council's commitment to funding this sector:

‘Within [case study], we have a brilliant third sector, the council has been longstanding funders of infrastructure in [case study], third sector infrastructure, to enable those links [of community engagement] to be made' (LP16).

In both the documents and interviews, a strong coherent strategic integrated population health management plan with a system’s approach to embed the reduction of health inequalities was evident. For example, on a system level regionally:

‘To contribute towards a reduction in health inequalities we will: take a system wide approach for improving outcomes for specific groups known to be affected by health inequalities, starting with those living in our most deprived communities….’ (Document H).

This case study’s approach within the system included using creative solutions and harnessing technology. This included making bold and inventive changes to improve how the city and the system linked up and worked together to improve health. For example, regeneration work within the city to ameliorate and transform healthcare facilities as well as certain neighbourhoods by having new green spaces, better transport links in order to improve city-wide innovation and collaboration (paraphrased Document F) were described. The changes were not only related to physical aspects of the city but also aimed at how the city digitally linked up. Being a leader in digital innovation to optimise the health benefits from technology and information was identified in several documents.

‘ Having the best connected city using digital technology to improve health and wellbeing in innovative ways.’ (Document G).

The digital approaches included ongoing development of a digitalised personalised care record facilitating access to the most up-to-date information to developing as well as having the ‘ latest, cutting edge technologies’ ( Document F) in hospital care. However, the importance of not leaving people behind by embedding digital alternatives was recognised in both the documents and interviews.

‘ We are trying to just embed the culture of doing an equity health impact assessment whenever you are bringing in a digital solution or a digital pathway, and that there is always an alternative there for people who don’t have the capability or capacity to use it. ’ (LP1).

The successful one hundred percent [redacted] programme is targeting some of our most digitally excluded citizens in [case study]. For our city to continue to thrive, we all need the appropriate skills, technology and support to get the most out of being online. (Paraphrased Document Q)

This all links in with the system that functions in a ‘place' which includes the importance of where people are born, grow, work and live. Working towards this place being welcoming and appealing was described both regionally and locally. This included aiming to make the case study the place of choice for people.

‘Making [case study] a centre for good growth becoming the place of choice in the UK to live, to study, for businesses to invest in, for people to come and work.’ (Document G).

Services and support

Secondly, a variety of available services and support were described from the local authority, NHS, and voluntary community sectors. Specific areas of work, such as local initiatives (including targeted work or campaigns for specific groups or specific health conditions) as well as parts of the system working together with communities collaboratively, were identified. This included a wide range of work being done such as avoiding delayed discharges or re-admissions, providing high quality affordable housing as well as services offering peer support.

‘We have a community health development programme called [redacted], that works with particular groups in deprived communities and ethnically diverse communities to work in a very trusted and culturally appropriate way on the things that they want to get involved with to support their health.’ (LP3 ).

It is worth noting that reducing health inequalities in avoidable admissions was not often explicitly specified in the documents or interviews. However, either specified or otherwise inferred, preventing ill health and improving access, experience, and outcomes were vital components to addressing inequalities. This was approached by working with communities to deliver services in communities that worked for all people. Having co-designed, accessible, equitable integrated services and support appeared to be key.

‘Reducing inequalities in unplanned admissions for conditions that could be cared for in the community and access to planned hospital care is key.’ (Document H)

Creating plans with people: understanding the needs of local population and designing joined-up services around these needs. (Paraphrased Document A).

‘ So I think a core element is engagement with your population, so that ownership and that co-production, if you're going to make an intervention, don't do it without because you might miss the mark. ’ (LP8).

Clear, consistent and appropriate communication that was trusted was considered important to improve health and wellbeing as well as to tackle health inequalities. For example, trusted community members being engaged to speak on the behalf of the service providers:

‘The messenger is more important than the message, sometimes.’ (LP11).

This included making sure the processes are in place so that the information is accessible for all, including people who have additional communication needs. This was considered as a work in progress in this case study.

‘I think for me, things do come down to those core things, of health, literacy, that digital exclusion and understanding the wider complexities of people.’ (LP12)

‘ But even more confusing if you've got an additional communication need. And we've done quite a lot of work around the accessible information standard which sounds quite dry, and doesn't sound very- but actually, it's fundamental in accessing health and care. And that is, that all health and care organisations should record your communication preferences. So, if I've got a learning disability, people should know. If I've got a hearing impairment, people should know. But the systems don’t record it, so blind people are getting sent letters for appointments, or if I've got hearing loss, the right provisions are not made for appointments. So, actually, we're putting up barriers before people even come in, or can even get access to services.’ (LP16).

Flexible, empowering, holistic care and support that was person-centric was more apparent in the documents than the interviews.

At the centre of our vision is having more people benefiting from the life chances currently enjoyed by the few to make [case study] a more equal place. Therefore, we accentuate the importance of good health, the requirement to boost resilience, and focus on prevention as a way of enabling higher quality service provision that is person-centred. [Paraphrased Document N).

Through this [work], we will give all children and young people in [case study], particularly if they are vulnerable and/or disadvantaged, a start in life that is empowering and enable them to flourish in a compassionate and lively city. [Paraphrased Document M].

Communities and individuals

Thirdly, having communities and individuals at the heart of the work appeared essential and viewed as crucial to nurture in this case study. The interconnectedness of the place, communities and individuals were considered a key part of the foundations for good health and wellbeing.

In [case study], our belief is that our people are our greatest strength and our most important asset. Wellbeing starts with people: our connections with our friends, family, and colleagues, our behaviour, understanding, and support for one another, as well as the environment we build to live in together . (Paraphrased Document A).

A recognition of the power of communities and individuals with the requirement to support that key principle of a strength-based approach was found. This involved close working with communities to help identify what was important, what was needed and what interventions would work. This could then lead to improved resilience and cohesion.

‛You can't make effective health and care decisions without having the voice of people at the centre of that. It just won't work. You won't make the right decisions.’ (LP16).

‘Build on the strengths in ourselves, our families, carers and our community; working with people, actively listening to what matters most to people, with a focus on what’s strong rather than what’s wrong’ (Document G).

Meaningful engagement with communities as well as strengths and asset-based approaches to ensure self-sufficiency and sustainability of communities can help communities flourish. This includes promoting friendships, building community resilience and capacity, and inspiring residents to find solutions to change the things they feel needs altering in their community . (Paraphrased Document B).

This close community engagement had been reported to foster trust and to lead to improvements in health.

‘But where a system or an area has done a lot of community engagement, worked really closely with the community, gained their trust and built a programme around them rather than just said, “Here it is. You need to come and use it now,” you can tell that has had the impact. ' (LP1).

Finally, workforce was another key asset; the documents raised the concept of one workforce across health and care. The key principles of having a shared vision, asset-based approaches and strong partnership were also present in this example:

By working together, the Health and Care sector makes [case study] the best area to not only work but also train for people of all ages. Opportunities for skills and jobs are provided with recruitment and engagement from our most disadvantaged communities, galvanizing the future’s health and care workforce. By doing this, we have a very skilled and diverse workforce we need to work with our people now as well as in the future. (Paraphrased Document E).

An action identified for the health and care system to address health inequalities in case study 1 was ‘ the importance of having an inclusive workforce trained in person-centred working practices ’ (Document R). Several ways were found to improve and support workforce skills development and embed awareness of health inequalities in practice and training. Various initiatives were available such as an interactive health inequalities toolkit, theme-related fellowships, platforms and networks to share learning and develop skills.

‛We've recently launched a [redacted] Fellowship across [case study’s region], and we've got a number of clinicians and managers on that………. We've got training modules that we've put on across [case study’s region], as well for health inequalities…we've got learning and web resources where we share good practice from across the system, so that is our [redacted] Academy.’ (LP2).

This case study also recognised the importance of considering the welfare of the workforce; being skilled was not enough. This had been recognised pre-pandemic but was seen as even more important post COVID-19 due to the impact that COVID-19 had on staff, particularly in health and social care.

‛The impacts of the pandemic cannot be underestimated; our colleagues and services are fatigued and still dealing with the pressures. This context makes it even more essential that we share the responsibility, learn from each other at least and collaborate with each other at best, and hold each other up to be the best we can.’ (Document U).

Concerns were raised such as the widening of health inequalities since the pandemic and cost of living crisis. Post-pandemic and Brexit, recruiting health, social care and third sector staff was compounding the capacity throughout this already heavily pressurised system.

In [case study], we have seen the stalling of life expectancy and worsening of the health inequality gap, which is expected to be compounded by the effects of the pandemic. (Paraphrased Document T)

‘I think key barriers, just the immense pressure on the system still really […] under a significant workload, catching up on activity, catching up on NHS Health Checks, catching up on long-term condition reviews. There is a significant strain on the system still in terms of catching up. It has been really difficult because of the impact of COVID.’ (LP7).

‘Workforce is a challenge, because the pipelines that we’ve got, we’ve got fewer people coming through many of them. And that’s not just particular to, I don't know, nursing, which is often talking talked [sic] about as a challenged area, isn't it? And of course, it is. But we’ve got similar challenges in social care, in third sector.’ (LP5).

The pandemic was reported to have increased pressures on the NHS and services not only in relation to staff capacity but also regarding increases in referrals to services, such as mental health. Access to healthcare changed during the pandemic increasing barriers for some:

‘I think people are just confused about where they're supposed to go, in terms of accessing health and care at the moment. It's really complex to understand where you're supposed to go, especially, at the moment, coming out of COVID, and the fact that GPs are not the accessible front door. You can't just walk into your GP anymore.’ (LP16).

‘Meeting this increased demand [for work related to reducing ethnic inequalities in mental health] is starting to prove a challenge and necessitates some discussion about future resourcing.’ (Document S)

Several ways were identified to aid effective adaptation and/or mitigation. This included building resilience such as developing the existing capacity, capability and flexibility of the system by learning from previous work, adapting structures and strengthening workforce development. Considerations, such as a commitment to Marmot Principles and how funding could/would contribute, were also discussed.

The funding’s [linked to Core20PLUS5] purpose is to help systems to ensure that health inequalities are not made worse when cost-savings or efficiencies are sought…The available data and insight are clear and [health inequalities are] likely to worsen in the short term, the delays generated by pandemic, the disproportionate effect of that on the most deprived and the worsening food and fuel poverty in all our places. (Paraphrased Document L).

Learning from the pandemic was thought to be useful as some working practices had altered during COVID-19 for the better, such as needing to continue to embed how the system had collaborated and resist old patterns of working:

‘So I think that emphasis between collaboration – extreme collaboration – which is what we did during COVID is great. I suppose the problem is, as we go back into trying to save money, we go back into our old ways of working, about working in silos. And I think we’ve got to be very mindful of that, and continue to work in a different way.’ (LP11).

Another area identified as requiring action, was the collection, analysis, sharing and use of data accessible by the whole system.

‘So I think there is a lot of data out there. It’s just how do we present that in such a way that it’s accessible to everyone as well, because I think sometimes, what happens is that we have one group looking at data in one format, but then how do we cascade that out?’ (LP12)

We aimed to explore a system’s level understanding of how a local area addresses health inequalities with a focus on avoidable emergency admissions using a case study approach. Therefore, the focus of our research was strategic and systematic approaches to inequalities reduction. Gaining an overview of what was occurring within a system is pertinent because local areas are required to have a regard to address health inequalities in their local areas [ 20 , 21 ]. Through this exploration, we also developed an understanding of the system's processes reported to be required. For example, an area requiring action was viewed as the accessibility and analysis of data. The case study described having health inequalities ‘at the heart of its health and wellbeing strategy ’ which was echoed across the documents from multiple sectors across the system. Evidence of a values driven partnership with whole systems working was centred on the importance of place and involving people, with links to a ‘strong third sector ’ . Working together to support and strengthen local assets (the system, services/support, communities/individuals, and the workforce) were vital components. This suggested a system’s committed and integrated approach to improve population health and reduce health inequalities as well as concerted effort to increase system resilience. However, there was juxtaposition at times with what the documents contained versus what interviewees spoke about, for example, the degree to which asset-based approaches were embedded.

Furthermore, despite having a priori codes for the documentary analysis and including specific questions around work being undertaken to reduce health inequalities in avoidable admissions in the interviews with key systems leaders, this explicit link was still very much under-developed for this case study. For example, how to reduce health inequalities in avoidable emergency admissions was not often specified in the documents but could be inferred from existing work. This included work around improving COVID-19 vaccine uptake in groups who were identified as being at high-risk (such as older people and socially excluded populations) by using local intelligence to inform where to offer local outreach targeted pop-up clinics. This limited explicit action linking reduction of health inequalities in avoidable emergency admissions was echoed in the interviews and it became clear as we progressed through the research that a focus on reduction of health inequalities in avoidable hospital admissions at a systems level was not a dominant aspect of people’s work. Health inequalities were viewed as a key part of the work but not necessarily examined together with avoidable admissions. A strengthened will to take action is reported, particularly around reducing health inequalities, but there were limited examples of action to explicitly reduce health inequalities in avoidable admissions. This gap in the systems thinking is important to highlight. When it was explicitly linked, upstream strategies and thinking were acknowledged as requirements to reduce health inequalities in avoidable emergency admissions.

Similar to our findings, other research have also found networks to be considered as the system’s backbone [ 30 ] as well as the recognition that communities need to be central to public health approaches [ 51 , 55 , 56 ]. Furthermore, this study highlighted the importance of understanding the local context by using local routine and bespoke intelligence. It demonstrated that population-based approaches to reduce health inequalities are complex, multi-dimensional and interconnected. It is not about one part of the system but how the whole system interlinks. The interconnectedness and interdependence of the system (and the relevant players/stakeholders) have been reported by other research [ 30 , 57 ], for example without effective exchange of knowledge and information, social networks and systems do not function optimally [ 30 ]. Previous research found that for systems to work effectively, management and transfer of knowledge needs to be collaborative [ 30 ], which was recognised in this case study as requiring action. By understanding the context, including the strengths and challenges, the support or action needed to overcome the barriers can be identified.

There are very limited number of case studies that explore health inequalities with a focus on hospital admissions. Of the existing research, only one part of the health system was considered with interviews looking at data trends [ 35 ]. To our knowledge, this research is the first to build on this evidence by encompassing the wider health system using wider-ranging interviews and documentary analysis. Ford et al. [ 35 ] found that geographical areas typically had plans to reduce total avoidable emergency admissions but not comprehensive plans to reduce health inequalities in avoidable emergency admissions. This approach may indeed widen health inequalities. Health inequalities have considerable health and costs impacts. Pertinently, the hospital care costs associated with socioeconomic inequalities being reported as £4.8 billion a year, mainly due to excess hospitalisations such as avoidable admissions [ 58 ] and the burden of disease lies disproportionately with our most disadvantaged communities, addressing inequalities in hospital pressures is required [ 25 , 26 ].

Implications for research and policy

Improvements to life expectancy have stalled in the UK with a widening of health inequalities [ 12 ]. Health inequalities are not inevitable; it is imperative that the health gap between the deprived and affluent areas is narrowed [ 12 ]. This research demonstrates the complexity and intertwining factors that are perceived to address health inequalities in an area. Despite the evidence of the cost (societal and individual) of avoidable admissions, explicit tackling of inequality in avoidable emergency admissions is not yet embedded into the system, therefore highlights an area for policy and action. This in-depth account and exploration of the characteristics of ‘whole systems’ working to address health inequalities, including where challenges remain, generated in this research will be instrumental for decision makers tasked with addressing health and care inequalities.

This research informs the next step of exploring each identified theme in more detail and moving beyond description to develop tools, using a suite of multidimensional and multidisciplinary methods, to investigate the effects of interventions on systems as previously highlighted by Rutter et al. [ 5 ].

Strengths and limitations

Documentary analysis is often used in health policy research but poorly described [ 44 ]. Furthermore, Yin reports that case study research is often criticised for not adhering to ‘systematic procedures’ p. 18 [ 41 ]. A clear strength of this study was the clearly defined boundary (in time and space) case as well as following a defined systematic approach, with critical thought and rationale provided at each stage [ 34 , 41 ]. A wide range and large number of documents were included as well as interviewees from across the system thereby resulting in a comprehensive case study. Integrating the analysis from two separate methodologies (interviews and documentary analysis), analysed separately before being combined, is also a strength to provide a coherent rich account [ 49 ]. We did not limit the reasons for hospital admission to enable a broad as possible perspective; this is likely to be a strength in this case study as this connection between health inequalities and avoidable hospital admissions was still infrequently made. However, for example, if a specific care pathway for a health condition had been highlighted by key informants this would have been explored.

Due to concerns about identifiability, we took several steps. These included providing a summary of the sectors that the interviewees and document were from but we were not able to specify which sectors each quote pertained. Additionally, some of the document quotes required paraphrasing. However, we followed a set process to ensure this was as rigorous as possible as described in the methods section. For example, where we were required to paraphrase, each paraphrased quote and original was shared and agreed with all the authors to reduce the likelihood to inadvertently misinterpreting or misquoting.

The themes are unlikely to represent an exhaustive list of the key elements requiring attention, but they represent the key themes that were identified using a robust methodological process. The results are from a single urban local authority with high levels of socioeconomic disadvantage in the North of England which may limit generalisability to different contexts. However, the findings are still generalisable to theoretical considerations [ 41 ]. Attempts to integrate a case study with a known framework can result in ‘force-fit’ [ 34 ] which we avoided by developing our own framework (Fig. 1 ) considering other existing models [ 14 , 59 ]. The results are unable to establish causation, strength of association, or direction of influence [ 60 ] and disentangling conclusively what works versus what is thought to work is difficult. The documents’ contents may not represent exactly what occurs in reality, the degree to which plans are implemented or why variation may occur or how variation may affect what is found [ 43 , 61 ]. Further research, such as participatory or non-participatory observation, could address this gap.

Conclusions

This case study provides an in-depth exploration of how local areas are working to address health and care inequalities, with a focus on avoidable hospital admissions. Key elements of this system’s reported approach included fostering strategic coherence, cross-agency working, and community-asset based working. An area requiring action was viewed as the accessibility and analysis of data. Therefore, local areas could consider the challenges of data sharing across organisations as well as the organisational capacity and capability required to generate useful analysis in order to create meaningful insights to assist work to reduce health and care inequalities. This would lead to improved understanding of the context including where the key barriers lie for a local area. Addressing structural barriers and threats as well as supporting the training and wellbeing of the workforce are viewed as key to building resilience within a system to reduce health inequalities. Furthermore, more action is required to embed reducing health inequalities in avoidable admissions explicitly in local areas with inaction risking widening the health gap.

Availability of data and materials

Individual participants’ data that underlie the results reported in this article and a data dictionary defining each field in the set are available to investigators whose proposed use of the data has been approved by an independent review committee for work. Proposals should be directed to [email protected] to gain access, data requestors will need to sign a data access agreement. Such requests are decided on a case by case basis.

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Acknowledgements

Thanks to our Understanding Factors that explain Avoidable hospital admission Inequalities - Research study (UNFAIR) PPI contributors, for their involvement in the project particularly in the identification of the key criteria for the sampling frame. Thanks to the research advisory team as well.

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This research was funded by the National Institute for Health and Care Research (NIHR), grant number (ref CA-CL-2018-04-ST2-010). The funding body was not involved in the study design, collection of data, inter-pretation, write-up, or submission for publication. The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care or Newcastle University.

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Charlotte Parbery-Clark

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Lorraine McSweeney

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Conceptualization - J.L. and S.S.; methodology - C.P.-C., J.L. & S.S.; formal analysis - C. P.-C. & L.M.; investigation- C. P.-C. & L.M., resources, writing of draft manuscript - C.P.-C.; review and editing manuscript L.M., J.L., & S.S.; visualization including figures and tables - C.P.-C.; supervision - J.L. & S.S.; project administration - L.M. & S.S.; funding acquisition - S.S. All authors have read and agreed to the published version of the manuscript.

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Parbery-Clark, C., McSweeney, L., Lally, J. et al. How can health systems approach reducing health inequalities? An in-depth qualitative case study in the UK. BMC Public Health 24 , 2168 (2024). https://doi.org/10.1186/s12889-024-19531-5

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ORIGINAL RESEARCH article

Development and validation of a tool to assess knowledge, attitudes, and practices toward diet sustainability.

• 1 Department of Nutrition and Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
• 2 ASPIRE Research Institute for Food Security in the Drylands (ARIFSID), United Arab Emirates University, Al Ain, United Arab Emirates
• 3 Department of Statistics and Business Analytics, College of Business and Economics, United Arab Emirates University, Al Ain, United Arab Emirates

This study aimed to develop and validate an instrument, the Sustainable Diets Questionnaire (SDQ), to assess the knowledge, attitude, and practice of sustainable diets in adult populations. A panel of four nutritionists identified 63 items through a literature review and refined them to a 54-item model for validation across four domains: Knowledge domain (K, eight items), Attitude domain (A, 18 items), Practice domain (P, 16 items) and Consumption Habits domain (D, 12 items). The validation process consisted of a pilot with 86 individuals (Phase 1) and a larger study with 389 participants (Phase 2). Confirmatory Factor Analysis (CFA) was conducted in both phases to verify model fit. In Phase 1, the initial four-factor model did not converge, indicating a need for item modification and a revised three-factor model (K domain, eight items; A domain, 18 items; new P domain, 28 items). In Phase 2, the new model showed improvement in fit indices with a Scaled Chi-Square of 2.415, Comparative Fit Index (CFI) of 0.863, Goodness of Fit Index (GFI) of 0.747, Tucker-Lewis Index (TLI) of 0.851 and the Root Mean Square Error (RMSE) was 0.066, although some indices fell below the 0.9 threshold. The Cronbach’s α for the Knowledge, Attitude, and Practice domains were 0.9, 0.96, and 0.897, respectively, with an overall α of 0.959. There was no significant difference between the first and second attempts of the SDQ model, indicating good test–retest reliability. There was also a significant positive correlation between the response scores of K, A, and P domains (K vs. A, r  = 0.575, p  < 0.001; K vs. P, r  = 0.496, p  ≤ 0.001 and A vs. P, r  = 0.665, p  ≤ 0.001). The study concludes that the three-factor model of SDQ is a valid and reliable tool for understanding the knowledge, attitudes, and practices of sustainable diets among adults.

1 Introduction

The global food system is at a critical juncture, with the dual challenges of ensuring food security for a growing population and mitigating the environmental impacts of food production and consumption ( Hallström et al., 2015 ). The food system is responsible for more than one-third of global greenhouse gas emissions, with the consumption of meat and animal products being significant contributors ( von Koerber et al., 2017 ). In contrast, plant-based foods generate much lower emissions and are associated with numerous health benefits ( von Koerber et al., 2017 ; Zurek et al., 2022 ). WHO (2003) has identified poor diet as a significant risk factor for chronic diseases. Overnutrition is the scourge of modern societies, leading to diseases like obesity, hypertension, and diabetes. Nutrition education programs to combat the global rise in obesity are launched to decrease the dietary intake of energy, the consumption of some nutrients, especially those associated with metabolic dysfunctions, like sodium, sugar and trans fats, and to increase the proportion of nutrients beneficial to health such as fiber, protein, vitamins, and minerals. The situation in the United Arab Emirates (UAE) is no different, with an estimated 44.2% of adult women and 30.9% of adult men living with obesity ( GNR, 2021 ). UAE’s obesity prevalence is higher than the regional average of 10.3% for women and 7.5% for men. Similarly, diabetes is estimated to affect 17.4% of adult women and 17.3% of adult men in the population ( GNR, 2021 ).

Empirical evidence suggests that adopting healthy and sustainable diets and transitioning to a sustainable food system is urgently needed to counteract the double burden of non-communicable diseases and climate change ( Willett et al., 2019 ). Sustainable diets has emerged as a viable solution to these challenges. The idea of a sustainable diet was introduced by Gussow and Clancy (1986) , who looked at foods from the nutritional perspective and also considered their environmental impact. More recently, the Food and Agriculture Organization has defined sustainable diets as diets protective and respectful of biodiversity and ecosystems, culturally acceptable, accessible, economically fair, and affordable; nutritionally adequate, safe, and healthy while optimizing natural and human resources ( Allès et al., 2017 ; Drewnowski et al., 2020 ). The transition toward sustainable diets warrants strategies that address the entire food chain, from sustainable land and water use to efficient handling of food waste, responsible logistics, food processing, consumption, and packaging practices ( Willett et al., 2019 ). Many vital strategies have been proposed to promote diet sustainability ( Lawrence et al., 2015 ; Hyland et al., 2017 ; de Boer and Aiking, 2019 ; Reyes et al., 2021 ; Barbour et al., 2022 ). An example of the most common initiative is reducing food waste throughout the food chain ( Conrad et al., 2018 ).

The EAT-Lancet Commission, in 2019, published its report on healthy diets from sustainable food systems, which outlined a dietary model that can help meet the scientific targets for healthy and sustainable diets ( Willett et al., 2019 ). This model, also known as the planetary health diet or the EAT-Lancet diet, was proposed to meet the health requirements of humans without exceeding planetary boundaries. The EAT-Lancet Commission proposed a global dietary shift, including a significant reduction in the consumption of animal proteins and starchy vegetables and increasing the consumption of legumes, whole grains, and nuts to achieve Sustainable Development Goals (SDGs) laid out by the United Nations (UN) and reign in climate change. Diet sustainability is, therefore, at the forefront of nutrition research, and it is vital to understand its awareness, attitude, and practices across populations. Consumer attitudes and demand for food, including sustainable diet consumption, differ widely across countries ( Pucci et al., 2022 ). Some studies were reported in this regard and they mainly focus on the willingness of consumers to pay for sustainable food products, increase their plant-based protein, vegetable or fruit consumption, and how different age groups perceive diet sustainability ( Barosh et al., 2014 ; Faber et al., 2020 ; Szczebyło et al., 2020 ; Baur et al., 2022 ; Curi-Quinto et al., 2022 ). Some studies assess food sustainability knowledge and perception among specific populations, such as healthcare professionals and those pursuing higher education ( Fresán et al., 2023 ; Irazusta-Garmendia et al., 2023 ). It is important to note that most of these studies were also conducted in European countries ( Pucci et al., 2022 ; Kenny et al., 2023 ). The broader takeaway from all of these studies is that a broader range of initiatives is required to educate consumers on diet sustainability practices and target the general unawareness of the population concerning diet sustainability behaviors.

In the UAE, the reliance on food imports and the preference for resource-intensive food items exacerbate environmental impacts. This situation uniquely challenges balancing nutritional needs, food security, and environmental sustainability. Many opportunities can be explored to reshape the food system to ensure it produces healthy and safe food with a limited environmental impact. Actionable initiatives for sustainable nutrition/diet are critical in the UAE, as the Global Nutrition Report of 2021 for UAE has underscored that the country has shown limited progress toward achieving the diet-related non-communicable disease (NCD) targets ( GNR, 2021 ). The report also indicated that the planetary impact of the food system is unsustainable on all levels. Therefore, to achieve a successful transformation toward a more sustainable food system, a remarkable shift in dietary practice is warranted. To accomplish this shift, it is crucial to comprehend the knowledge, attitudes, and practices in this population regarding sustainable diets which benefit both the health and the environment ( Andrade et al., 2020 ; Ahmed et al., 2021 ). This understanding provides essential insights that can empower researchers with the necessary information to design targeted interventions to facilitate behavioral modification that best aligns with the UN’s SDGs ( Andrade et al., 2020 ). In light of these challenges, there is a pressing need for a valid tool to assess the knowledge, attitudes, and behaviors related to sustainable diets in adult populations. This study aims to develop and validate a questionnaire to address the lack of a validated tool.

2 Methodology

2.1 study design and ethics approval.

The study was conducted at the United Arab Emirates University among the university staff and student community. The ethics approval for the study was obtained from the United Arab Emirates University’s Social Sciences Research Ethics Committee (approval number: ERSC_2024_4357). Informed consent was obtained from all participants after providing detailed information about the study’s purpose, risks, and benefits. Participants were assured that their participation was voluntary and that they could withdraw without penalty.

All participants were also provided instructions on how to access and complete the questionnaire. The tool development and validation process of items for the SDQ was undertaken in two phases, as illustrated in Figure 1 . Phase 1 involved developing items that align with the study purpose by reviewing the relevant literature and pilot testing in a subpopulation for modification. Phase 2 validated the instrument in a larger population using factor analysis, determining the construct validity and reliability of the final version of the questionnaire.

Figure 1 . Illustration of the study design.

2.2 Study participants and data collection

In Phase 1 of the study, SDQ was pilot-tested among 86 participants for questionnaire modification and Phase 2 involved data collection from 389 participants to validate the final SDQ. The sample size for Phase 2 was determined using Cochran’s formula with a 95% confidence interval, 5% significance level (0.05) and marginal error of 0.05 ( Woolson et al., 1986 ). Participants aged 18 years and above who are part of the university community were approached in person or invited via email, social media, and other online platforms to complete the survey. Participants were asked to provide basic sociodemographic information, such as their gender, age, nationality, place of residence, educational background, and income level. Moreover, self-reported weight and height were collected for body mass index calculation.

2.3 Questionnaire development and modification

To define the constructs of the questionnaire, a thorough literature review was conducted in PubMed, Google Scholar, Science Direct, and Scopus to define the domains of interest and their respective items for developing the initial questionnaire. Selective keywords such as “sustainable diets,” “sustainable nutrition,” “sustainability,” “perceptions,” “attitude,” “knowledge,” “practice,” “adherence,” and “willingness” were used to search the databases. Several studies were identified, including two most relevant studies to our research objective ( Culliford and Bradbury, 2020 ; García-González et al., 2020 ). A panel of four nutritionists initially identified 63 items in four proposed domains: knowledge (K), attitude (A), practice (P) and consumption habits (D) and later revised the questionnaire to list 54 items in the four domains to build the first version of the questionnaire for the UAE population. This first version of SDQ was pilot-tested (Phase 1), and revisions were made to several items to improve comprehension and develop the final version of SDQ for validation in a larger population in Phase 2.

2.4 Statistical analyses

Statistical analyses for the experiments were performed using SPSS ® version 29 (IBM Corporation, Armonk, NY, United States) and R statistical package. Descriptive statistics (Mean ± SD) were used to describe continuous variables, and categorical variables were expressed as counts and percentages ( N , %). Kaiser-Meyer-Olkin (KMO) and Bartlett’s Sphericity test were used in phases 1 and 2 to test the suitability of data for factor analysis. Confirmatory Factor Analysis (CFA) was carried out to verify the factorial structure of the model and confirm the construct validity in phases 1 and 2. For Phase 2 data, factor loadings and item-to-total correlations were calculated to test the validity of the SDQ. Cronbach’s- α was used to determine the internal consistency of SDQ and test–retest reliability was analyzed by conducting a T -test of the two attempts and Bland–Altman analysis. Correlation tests were also used to find the association between the domains and the domain response scores to the various sociodemographic characteristics of the participants. A p < 0.05 was considered statistically significant.

3.1 Sociodemographic characteristics of participants

The sociodemographic characteristics of participants from phases 1 and 2 of the study are listed in Table 1 . The study population mainly comprised young adults with a mean age of 28.9 in phase 1 and 25 years in phase 2. The average BMI of the respondents of Phase 1 and Phase 2 was 25.6 and 24.7 kg/m 2 , respectively. Most of our respondents were UAE nationals, 62.8% in Phase 1 and 68.9% in Phase 2, and the rest were expatriate residents from over 25 different countries. We had a significantly higher number of female respondents (80.2% in Phase 1 and 80.7% in Phase 2). The number of male participants in the study was limited to 17 (19.8%) in Phase 1 and 75 (19.3%) in Phase 2. Most of the participants were also single, 64% in Phase 1 and 76.3% in Phase 2; however, the more significant proportion of the respondents came from large households with five or more members, 66.3% in Phase 1 and 71.1% in Phase 2. The participants in the study were mostly educated since it was conducted in a university community, and all respondents had at least a high school degree or higher. Since the student body represented most of our respondents, the number of those who identified as unemployed or students was higher (62.8% in Phase 1 and 76.9% in Phase 2). Similarly, most participants indicated their income as less than 5,000 AED, 52.3% in Phase 1 and 73.8% in Phase 2. Only a very small proportion belonged to the high-income groups in our study, 12.8 and 8% in Phases 1 and 2, respectively. Regarding nutrition knowledge and training, more than half of the participants indicated that they have had some form of formal training in nutrition, 55.8% in Phase 1 and 54.8% in Phase 2. Moreover, a more significant number of respondents also indicated that they have a fair amount of knowledge regarding nutrition guidelines (45.3% in Phase 1 and 47.6% in Phase 2). In light of these numbers, it is interesting to note that the percentage of respondents who have consulted with dieticians or nutritionists is low (39.5% in Phase 1 and 35% in Phase 2).

Table 1 . Sociodemographic characteristics of study participants.

3.2 Sustainable diet questionnaire

The proposed SDQ consisted of 54 items in 4 domains: K, A, P, and D. The final version of the SDQ is provided in Supplementary File . The K domain assesses the knowledge level of the respondent regarding seven common sustainability terms (items K1–K7) such as “ ecological footprint,” “carbon footprint,” “food sustainability,” “environmental impact,” “biodiversity,” “locally produced foods,” “greenhouse gas emissions” . The responses are coded from 0 to 4 using statements indicative of knowledge such as “ No, I have not heard the term”; “I have heard the term, but I do not know what it means”; “I have a vague understanding of the term”; “I have heard the term and know what it means”; “I have a clear understanding of the term” . This domain also includes an 8th item (item K8), “Do you believe that healthy and sustainable diets mean the same?” The response is coded using a 5-point Likert scale of agreement. The A domain assesses respondents’ attitudes by asking how important a set of statements contributes to sustainable diets. The response is coded using a 5-point Likert scale of importance. The list includes 17 statements (items A1–A17). Some examples include “ Organic food production,” “Diet with plenty of fresh products,” “Diet rich in vegetables and fruits,” and “Diet with traditional foods from own culture” . Item A18 ( “How important is it for you that the products you consume are produced sustainably?” ) is a stand-alone question in the A domain that codes the response in a 5-point Likert scale of importance. Like domain A, domain P includes a list of 15 behaviors (items P1–P15) that assess the extent to which the respondents practice those behaviors. The responses are coded from 0 to 4 using the following statements: “ I’m not interested in doing this at the moment”; “I’m thinking about this, but I need more information”; “I would like to do this, but other things are stopping me”; “I have started to do this some of the time”; “I’m doing this confidently most of the time” . Item P16 asks the respondents about their willingness to pay higher prices for food products that are produced sustainably using a 5-point Likert scale of willingness. In domain D, which assesses the respondents’ weekly consumption habits, a list of 12 foods (items D1-D12) is presented with five intake frequencies (0 times, 1–2 times, 3–4 times, 5–6 times, seven or more times). The responses are codes from 0–4 for all items in the domain except D2, D4, and D6, which are reverse-coded.

3.3 Factorial structure of SDQ: phase 1

Data from the Phase 1 study of SDQ is provided in Tables 2 – 4 . The data suitability was assessed using the measure of sampling adequacy. The KMO and Bartlett’s Test of Sphericity are shown in Table 2 . The KMO measure of sampling adequacy was 0.852, 0.835, 0.835, and 0.72 for domains K, A, P, and D, respectively. The KMO values for all domains were higher than 0.5; hence, the factor analysis was appropriate for this data. Bartlett’s test was highly significant for all domains ( p < 0.001), indicating that the data was suitable for CFA. The details of the different models from factor analysis are presented in Table 3 . The factor structure of the four-factor model failed to converge with the proposed 54 items in Phase 1 of the study. This indicated that the data from the pilot study could not uniquely estimate model parameters, making it difficult to assess the model’s fit to the data. However, we were able to converge two models (Model 1 and 2) with the deletion of specific items. Model 1 converged with the deletion of items D2 and D6 in the D domain, and model 2 with the deletion of items D2 and D6 in the D domain and K8 from the K domain, but the fit indices of both models were not satisfactory. The comparative fit index (CFI) was 0.693 and 0.705, the Tucker and Lewis index (TLI) was 0.675 and 0.687, the goodness of fit index (GFI) was 0.571 and 0.577, and the Root Mean Square Error (RMSE) was 0.088 and 0.087, for model 1 and 2, respectively. The factor structure figures are provided in Supplementary File .

Table 2 . Kaiser–Meyer–Olkin’s and Bartlett’s tests.

Table 3 . Summary of fit indices of SDQ.

Table 4 . Factorial weights for items from study phase 1 ( N  = 86).

3.4 Validity of SDQ: phase 1

Table 4 shows the factor loadings for the four-factor domain of SDQ in Phase 1. The first factor, the domain K, accounted for 60.4% of the total variance and comprised eight items, of which item K4 ranked the highest (0.897) and item K8 ranked the lowest (0.067). The second factor in the model was domain A, which accounted for 39.4% of the total variance and comprised 18 items. The factor loadings in domain A ranged between 0.267 (item A18) and 0.747 (items A9 and A16). In the P domain with 39.8% variance, the factorial weights ranged between 0.407 (item P16) and 0.77 (item P3). The fourth factor in the model was domain D, which accounted for 32.9% of the total variance and comprised 12 items. There was high variability in the factorial weights in this domain, with a minimum value of 0.12 and a maximum value of 0.8. The internal consistency of the K, A and P domains was high in Phase 1, with Cronbach’s α values of 0.89, 0.903 and 0.896, respectively. The Cronbach’s α value of the domain D was low and below the acceptable range (0.462).

3.5 Factorial structure of SDQ: phase 2

Based on the data from Phase 1, the questionnaire was revised further to incorporate minor changes to the language and the modified version of SDQ was again administered to the UAE University community. The response collection was continued until the target sample size was attained. The factor structure of the revised SDQ was then tested using the data from 389 respondents of Phase 2. The data suitability was assessed again for the 389 respondents using the measures of sampling adequacy ( Table 2 ). With the increase in the sample size from Phase 1, the KMO measure of sampling adequacy improved to 0.905, 0.953, 0.948, and 0.821 for domains K, A, P, and D, respectively. Bartlett’s test was also highly significant for all domains ( p  < 0.001), indicating that the data was appropriate for CFA. The factor structure of the four-factor model (model 3) converged with all 54 items in Phase 2 of the study ( Table 3 ). There was an overall improvement in the fit indices for model 3. The CFI was 0.882, the TLI was 0.871, the GFI was 0.761, and the RMSE was 0.061 for model 3. Based on the validity data from Phase 1 of the study and the nature of the items in domain D, a new three-factor model (model 4) was proposed, which combines the 16 items in the P domain with the 12 items in domain D for a new practice domain (P + D) with 28 items. The confirmatory factor analysis was repeated for model 4 ( Table 3 ). The model converged all 54 items in the three domains, and fit indices did not vary from model 3. The CFI was 0.863, the TLI was 0.851, the GFI was 0.747, and the RMSE was 0.066 for the new model. The factor structure figures are found in the Supplementary File .

3.6 Validity of SDQ: phase 2

The factor loadings improved in model 3 of Phase 2 ( Table 5 ). Domain K recorded 62.6% of the variance, with factor loading between 0.828 and 0.899, except for K8, which again had the lowest factor loading (0.043), showing no improvement in the item value from Phase 1 to Phase 2. The variance in domain A improved to 60.3% in Phase 2 with factor loadings between 0.607 and 0.863, except for item A18 (0.315). Similarly, the variance increased to 55.5% in the P domain, and the factorial weights ranged between 0.676 and 0.835 for all items except P16 (0.288). There was no noticeable difference in variance for Domain D, which was 33.9% in Phase 2. However, overall, the factor loadings of the 12 items in the domain showed improvement with the revised questionnaire and a higher number of respondents. The factor loadings now ranged between 0.4 and 0.7 in this domain. After merging the practice and consumption habits domain, we tabulated the item-total correlations of individual items in model 4 of SDQ ( Table 6 ). In domain K, all items had high correlations between 0.7 and 0.8 except for item K8, which had the lowest correlation coefficient of 0.034. Similarly, in domain A, all items except A18 had a high correlation between 0.5 and 0.8, except A18 (0.286). In the P + D domain of SDQ with 28 items, the correlation coefficients were above 0.5 in all cases except for the following nine items: P16, D1, D2, D4, D5, D6, D8, D9, and D10.

Table 5 . Factorial weights for items from study phase 2 (model 3) ( N  = 389).

Table 6 . Item to total correlations of study phase 2 (model 4) ( N  = 389).

3.7 Internal consistency and test–retest reliability of SDQ

The overall model of SDQ had very high consistency in Phase 2 of the study with Cronbach’s α of 0.959 ( Table 7 ). The Cronbach’s α values for the K, A, P, and D domains were 0.9, 0.96, 0.944, and 0.359, respectively. The results indicate good internal consistency for K, A, and P domains. The reliability of the D domain was below the acceptable range. Upon combining the items from P and D into a new practice domain (P + D) with 28 items, the internal consistency values improved drastically with Cronbach’s α value of 0.897, which is well above the acceptable threshold of 0.7. The strategy also justified model 4 with the three-factor domain. The test–retest reliability of model 4 of the SDQ demonstrated the reproducibility of the questionnaire when nearly one-third of participants attempted the questionnaire a second time after 2–3 weeks from the first attempt. There was no significant difference in the response scores between the two attempts in the overall model or when individual domain scores were compared using the Mann–Whitney U- test ( Table 7 ). The Bland–Altman plots for the three domains and the overall model clearly highlight that both response scores were within the limits of agreement between the first and second attempts ( Figure 2 ). The results confirm the agreement between the two attempts and no proportional bias.

Table 7 . Internal consistency and test–retest reliability of SDQ.

Figure 2 . Bland–Altman plots of SDQ. Data of test–retest reliability using Bland–Altman analysis. Response 1 ( N  = 389) compared with response 2 ( N  = 131). % Difference calculated as (Response 1-Response 2)/Average of response 1&2 × 100. Practice domain (P + D).

3.8 Correlation between the domain response and sociodemographic variables

There was a positive association between the three domains of model 4 of the SDQ ( Table 8 ). The knowledge domain was highly correlated with the attitude domain ( r  = 0.575, p  < 0.001), while the coefficient was lower when associated with the practice domain ( r  = 0.496, p  < 0.001). Interestingly, the attitude domain’s association with the practice domain was higher than the knowledge domain ( r  = 0.665, p  < 0.001). The correlation of domain response scores to the participants’ sociodemographic characteristics also revealed interesting relationships ( Table 9 ). Overall, all significant correlations between the domain response scores and the sociodemographic variables were weak and ranged between 0.06 and 0.35. There was a significant positive association between the age of the respondents and the response score of all three domains of SDQ. The result indicates that with increasing age, there is an increase in the population’s knowledge, attitude, and practice concerning sustainable diets. The BMI of the participants was also significantly associated with attitudes and practice domain, but the correlations were weak, r  = 0.149 and 0.131 for the A and P domains, respectively. Gender was significantly correlated only with the knowledge domain, r  = 0.129 ( p  = 0.011). Interestingly, the respondents’ nationality also correlated significantly with all three domains of SDQ. The marital status of the respondents was positively associated with all three domains, indicating that older married respondents had higher scores for the questionnaire. Family size was negatively correlated with SDQ, indicating that smaller households have better knowledge, attitudes, and practices regarding sustainable diets. The respondents’ income and employment status positively correlate with the three domains. Similarly, the results indicate that in our population, with a higher educational status and nutrition knowledge along with formal nutrition education, the knowledge, attitude, and practice of sustainability also increases.

Table 8 . Correlation between the domains of SDQ.

Table 9 . Correlations between domain response and sociodemographic characteristics.

4 Discussion

The primary objective of our study was to develop and validate a scale to assess the KAP related to sustainable diets, and it highlights that the SDQ model with three domains and 54 items is valid. Our results demonstrate that this newly developed scale is a valid and reliable tool to evaluate KAP on sustainable diets for educated adults. The objective to generate a validated SDQ was achieved by conducting the study in two phases: Phase 1, which tested the first version of SDQ among 86 respondents and Phase 2, which tested the validity of the revised and modified SDQ among 389 respondents. The SDQ was tested in Phase 1 of the study by conducting factor analysis, which assessed the model’s fitness. The model’s failure to converge with all 54 items and fit indices indicated that the sample size could be insufficient to determine the model’s fitness. This phase of the study also highlighted several items within the questionnaire, such as K8, A18, D2, D6, D9, and D10 for modification. Moreover, we found that the internal consistency within the D domain was below the acceptable range. Hence, in Phase 2, the revised SDQ was readministered in a larger population and the factor analysis was repeated. With the increase in the sample size, the four-factor model converged with all 54 items, generating model 3 with acceptable fit indices.

We also looked at a new three-factor model (model 4) by combining the items from the P and D domains into a single domain. This strategy was adopted after considering the nature of items in both domains P and D, and also the low internal consistency and highly variable factor loadings of the Phase 1 data of domain D. The items to assess the weekly consumption habit were included in SDQ to provide a qualitative measure of the diet quality and its adherence to sustainable diets. These items can, therefore, be grouped with the items of the practice domain to get an overall qualitative measure of the respondent’s diet sustainability practices. When we compared models 3 and 4, there was no significant variation in the fit indices and Cronbach’s α value for the two practice domains (P with 16 items and P with 28 items). For evaluating the model fit of CFA, it is crucial to consider the thresholds of various model fit indices ( Goretzko et al., 2023 ). RMSE values less than 0.05 are deemed excellent, and values ranging from 0.05 to 0.08 are considered acceptable ( Fabrigar et al., 1999 ). Consequently, our model’s RMSE value of 0.066 signifies a satisfactory fit. The GFI value of 0.75, although below the preferred threshold of 0.90, is still relevant given that GFI is known to be influenced by sample size ( Mulaik et al., 1989 ). The CFI and TLI values above 0.90 suggest optimal fit ( Bentler, 1990 ). In our study, their values slightly fall short of this threshold but are closer to 0.9.

The improvement in the SDQ model in Phase 2 was also evident in the factor loadings. The factor loading represents the association between an item and its corresponding factor; typically, a factor loading exceeding 0.30 suggests a moderate relationship between the item and the factor ( Tavakol and Wetzel, 2020 ). However, the literature suggests different cut-off points for factor loadings between 0.4, 0.5, and 0.7 for accepting a model’s construct validity ( Posner and Kouzes, 1988 ; Schaufeli et al., 2006 ; Cheung et al., 2023 ). Our results indicate that 50 items in SDQ satisfied the established criteria for factor loadings. Four items, including K8, A18, P16, and D6, have factor loadings below 0.4. To validate the SDQ model, we further generated the item-to-total correlations for all items in three domains. Item-to-total correlation is another measure of construct validity ( LoBiondo-Wood and Haber, 2013 ). These values depict the correlation between a given item and the total score of all other items within a domain. A score above 0.5 is widely considered the benchmark for validity, while a score between 0.3 and 0.5 is considered acceptable ( Raharjanti et al., 2022 ). The observations were similar to factor loading data, with low correlation values for K8, A18, P16, and seven more items related to consumption habits in the new practice domain (items D1, D2, D4, D6, D8, D9, and D10).

It is important to note that the items K8 ( Do you believe that healthy and sustainable diets mean the same? ), A18 ( How important is it for you that the products you consume are produced sustainably? ) and P16 ( To what extent are you willing to pay more money for food and drinks that are produced in a sustainable way? ) are questions that are intended to assess knowledge, attitude and practice, respectively, they are also different in nature to the other items in their respective domain. Hence, the low factor loadings could be attributed to the nature of the items. However, for contextual relevance, these items are essential elements of SDQ. Item K8 assesses whether an individual knows to distinguish between healthy food and sustainable food because what is healthy may not always be sustainable ( Lindgren et al., 2018 ; Willett et al., 2019 ). Similarly, it is also essential to understand if sustainability motivates an individual’s food choice and whether price is a potential barrier to diet sustainability practice, which items A18 and P16 gauge. Items D1 to D12 in the practice domain assess the consumption frequency of various foods. Measuring accurate patterns of food consumption is significantly hindered by the tendency of individuals to under-report or over-report certain foods considered unhealthy or healthy, respectively, when using self-reported measures ( Asbeck et al., 2002 ; Hopkins et al., 2021 ). A variety of psychological, personality, and social characteristics have been proposed as potential indicators of misreporting intake measures in respondents, including dietary restraint ( Asbeck et al., 2002 ), social desirability and approval ( Hebert et al., 1997 ), as well as socioeconomic status and educational level ( Pryer et al., 1997 ). The unsatisfactory indices in our result for items related to consumption habits could be attributed to measurement errors. Despite the errors associated with self-reported intakes, a substantial body of evidence indicates that dietary intake data can effectively inform nutritional guidelines and public health policies ( Subar et al., 2015 ). The scientific rationale for including items related to consumption habit was to get a qualitative measure of sustainable consumption of the population in correlation with their knowledge, and attitudes. In this context, self-reported consumption data could be valuable in answering a number of key sustainability-related questions. These include characterizing the types of foods consumed by individuals and its frequency with higher knowledge score and positive attitude compared with those with low knowledge score and negative attitude to sustainability. Hence, all of these items are relevant to the conceptual framework of the SDQ.

Moreover, our findings underscore the reliability of all three domains of the SDQ. In many studies, Cronbach’s alpha is the most frequently reported reliability coefficient ( Tavakol and Dennick, 2011 ; Cho, 2016 ). This coefficient, a measure of internal consistency, is generally deemed reliable when values exceed 0.7 ( Tavakol and Dennick, 2011 ). At the same time, others have highlighted a reliability standard of 0.8, indicating that reliability of 0.7 implies only modest reliability ( Lance et al., 2006 ). Schmitt (1996) suggests that there is no universally accepted threshold for α coefficients and that instruments with even lower α values can still be helpful in specific contexts. Nevertheless, the Cronbach’s α value for the three domains in our study was above 0.8 and 0.959 for the whole model. Another statistical method for scale validation is test–retest reliability ( Woolson et al., 1986 ), for which the SDQ model demonstrated excellent reliability. When 131 participants completed the questionnaire a second time after an interval of 2–3 weeks, there was no significant change in the domain scores, suggesting that the responses were stable over time.

We also analyzed the association between the three domains of SDQ. Knowledge of sustainable diets was highly correlated with both attitudes and practices. However, the practice of sustainable diets had a higher association with the population’s attitude than their knowledge. It is important to note that efforts to promote sustainable food consumption compete with other contextual influences on people’s food choices ( Leng et al., 2017 ; Cairns, 2019 ). Changing food habits is difficult because they are deeply ingrained in people’s lifestyles and sociocultural surroundings ( Caso and Vecchio, 2022 ). Food choices are also influenced by the marketing strategies of food companies ( Booth et al., 2001 ), which have led to shifts in dietary norms and the cultural values that guide food behaviors ( Roudsari et al., 2017 ). The nature of food-related decisions renders them vulnerable to a wide array of social, cognitive, emotional, and environmental factors. Hence, individuals can know and express environmental concerns but do not consistently act on them. Numerous studies also report this type of gap between awareness and practice regarding healthy food choices ( Brown et al., 2000 ; Abdelhafez et al., 2020 ; Liu et al., 2022 ). It is also important to note that people may follow a sustainable diet without sustainability being the primary reason for their practice. For example, many individuals across the globe follow a vegetarian diet for religious reasons, and plant-based diets are considered the healthiest and most planet-friendly.

Our study findings indicate that in this population, older adults who are employed with higher educational status and belong to the higher income bracket have higher levels of knowledge, attitude, and practice related to sustainable diets. Although the strength of these correlations was weak, it is important to consider them because, in such population studies, the direction of the correlation is of greater interest than the strength of the association ( Mohammadi et al., 2018 ; Alhebshi et al., 2023 ). Gender and nationality were also significantly correlated to all three domains in our population, but this might be a factor of the sample itself because we had a considerably higher proportion of females and UAE nationals among our respondents. Interestingly, there was also a significant positive correlation between the domain scores with nutrition knowledge and formal nutrition education in the population. This observation indicates that with proper nutrition education, the population can practice the elements of sustainable diets, such as incorporating more plant-based proteins, fruits and vegetables. In addition, we found that household size was negatively correlated with domain response scores. This observation is not unique; a study by Hong et al. (2020) reported that the number of household members significantly affects the expenditure on unhealthy food. For each increase in the number of household members, the amount of unhealthy food expenditure increased by 0.6510 in this study ( Hong et al., 2020 ). BMI was positively correlated with higher domain scores. A critical association to consider is the positive correlation of BMI with the practice domain. It is reported that individuals with higher BMI can practice dietary restraints to control their weight ( Johnson et al., 2012 ; Alhebshi et al., 2023 ). There is also the tendency to misreport the intake of unhealthy foods in these higher BMI groups ( Hopkins et al., 2021 ), which could influence this association independently of sustainability concerns. Our study is not without limitations, including a predominance of young adults and individuals with higher educational backgrounds, reflecting the university setting of the research. Moreover, SDQ is an English tool that requires translation and validation in Arabic before being used among the wider UAE population. Additionally, the self-reported nature of consumption habits may introduce response bias, necessitating caution when extrapolating the findings to the general population.

5 Conclusion

The study demonstrated that the newly developed SDQ is a reliable and valid instrument for evaluating knowledge, attitudes, and practices concerning sustainable diets among adults, as evidenced by the scale’s construct validity. Our diverse sample, encompassing respondents from 30 countries, underscores the tool’s applicability across various demographic groups. Consequently, the SDQ is instrumental in assessing knowledge, attitudes, and practices, thereby aiding the formulation of effective strategies to promote sustainable behaviors. This knowledge can enable researchers to design targeted behavioral change interventions aligned with the United Nations’ SDGs. However, since the SDQ was validated among people with at least a high school degree, its use among the general population must be cautiously approached. An Arabic version of the tool is also warranted for broader use among the UAE population and the Arabic-speaking region. Despite these limitations, the SDQ remains valuable for advancing research and interventions in sustainable dietary practices among educated adults.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Social Sciences Research Ethics Committee at United Arab Emirates University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their informed consent to participate in this study.

Author contributions

SH: Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. SS: Data curation, Formal analysis, Investigation, Methodology, Software, Writing – review & editing, Writing – original draft. RS: Formal analysis, Methodology, Supervision, Writing – review & editing, Writing – original draft. AN: Data curation, Investigation, Writing – original draft, Writing – review & editing. SZ: Data curation, Investigation, Writing – original draft, Writing – review & editing. CP: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing, Writing – original draft.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by ASPIRE, the technology program management pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), via the ASPIRE VRI (Virtual Research Institute) Award (fund code ARIFSID 21R097).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2024.1432057/full#supplementary-material

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Keywords: knowledge, attitudes, practice, sustainable diets, sustainable nutrition, questionnaire, validity, reliability

Citation: Hilary S, Safi S, Sabir R, Numan AB, Zidan S and Platat C (2024) Development and validation of a tool to assess knowledge, attitudes, and practices toward diet sustainability. Front. Sustain. Food Syst . 8:1432057. doi: 10.3389/fsufs.2024.1432057

Received: 13 May 2024; Accepted: 23 July 2024; Published: 16 August 2024.

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Copyright © 2024 Hilary, Safi, Sabir, Numan, Zidan and Platat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carine Platat, [email protected]

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