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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.

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.

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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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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

Samples and populations #

An important principle in rigorous data analysis is that we should not exclusively be interested in understanding the data that we have collected. Instead, our goal should be to use the data that we have collected to learn about the population that our data represent. The act of using results from the sample (the data in-hand) to make statements about the population is called generalization , or inference . In order to do this in a meaningful way, it is important to be precise about what population we aim to generalize to, and about how our data were collected.

Populations #

In some situations, the population of interest is clear. If we are studying characteristics of the US adult population, such as income, or health, then the population of interest could be taken to be the set of all adults currently in the United States. This population is quite tangible, but even so, there are some ambiguities. In the time that it takes to read this paragraph, someone may have been born, and someone else may have died, so the population will have changed. Also, in some cases we may wish to treat the US population as people residing in the US, or as people currently physically present in the US, or as people with US citizenship or permanent residency, regardless of where they are located at the moment. Other meaningful definitions of US population are also possible.

In some other settings where we wish to conduct a statistical analysis, the appropriate notion of the population is somewhat less clear. Suppose our goal is to understand how the levels of different soil nutrients relate to the yield of a crop such as corn. We may conduct an experiment in which we create plots of land with controlled concentrations of the nutrients of interest, then grow corn in each plot and measure the yield at the end of the growing season. Suppose we have 10 such plots of land. It is clear that our sample consists of these 10 plots. The population is a bit more ambiguous. We might think of the population in this case as being all the plots that we could have created for our experiment, but did not. These plots will be slightly different from the actual experimental plots with respect to the key variables of interest (soil nutrients), and also with respect to other variables that we cannot control, such as sunlight and rainfall. Therefore, they would have different corn yields than the 10 plots that we actually observed.

As noted above, understanding how the sample was obtained is very important. The most basic type of sample is a simple random sample , or SRS . A simple random sample of the US adult population is one in which each possible sample of the population that has the desired sample size, say n=1000, is equally likely to be chosen. We have not covered probability yet so cannot discuss this notion here in a precise way. But intuitively, you should imagine a SRS as resulting from a completely random process where you select people from the population like drawing beans from a jar. Once a person is selected, they cannot be selected again. Thus, this type of sampling is sometimes called sampling without replacement .

Although an SRS is ideal, in practice such a sample can be difficult or impossible to obtain. For example, we do not have access to a list of all 328 million Americans, and even if we did, we would not have a way to contact many of the people on the list, and those who we contact cannot be compelled to participate in our research project. For a population such as the entire United States, obtaining a SRS is an ideal that cannot realistically be achieved.

For populations that are much smaller than a whole country, say the population of all physicians working for the University of Michigan Health System, it is more realistic that we could get an actual SRS. At least we could imagine having a list of all such people, and some means to contact those whom we select. However it would generally still be the case that some people may be difficult to reach, or may refuse to participate in our study.

Now return to the research study described above, aiming to understand the relationship between soil nutrients and crop yield. It is not clear what it would mean to obtain an SRS in this setting. For such situations, it is common to consider a different type of random sample called an independent and identically distributed sample , or IID sample. An IID sample is defined in a slightly more abstract way than an SRS. An IID sample, like a SRS, is formally defined using probability, so we will visit this topic again later when we have developed some probability tools. But intuitively, in an IID sample, each element of the (possibly infinite) population is equally likely to be selected, and selection of one element into the sample does not change the likelihood of selecting any other element. An IID sample can be thought of as sampling with replacement from a possibly infinite target population.

It might be helpful to describe some ways of collecting data that generally will not produce an IID sample. We describe two such examples below.

Suppose we place ads on a social media platform like Facebook, inviting subjects to participate in a study. Suppose further that there is a 50 USD payment for participating, and that the ad is shareable with your Facebook friends. This may be a good way to quickly recruit a lot of participants, but it is quite unlikely to produce an IID sample of the US population. Some people in the US do not have a Facebook account, or do not actively use the platform. These people will not be reached. If Facebook users and non-users differ in ways that are relevant for the research aims, this will render the sample non-IID for the overall US population. Also, the 50 USD incentive may motivate some types of people to respond more than others, and in general the type of person who will respond to such an offer is different from the type of person who will not. Finally, allowing the offer to be shareable means that the first few people who respond to the offer may share the ad within their network of friends, so the remaining subjects will tend to resemble thee initial responders. This violates the requirement in an iid sample that the inclusion of one individual in the sample must not impact the likelihood that anyone else is included in the sample.

As a second example of a non-iid sample, suppose we observe daily rainfall amounts at 50 locations in Texas, for every day during May of 2019. For several reasons, it is not clear what the population should be here. First, note that there will only ever be one May of 2019, and that is the sample. To construct a population that this sample may generalize to, imagine that we are actually interested in rainfall throughout Texas, but we only have the ability to collect data at 50 locations. Also, suppose that we are actually interested in the rainfall in Texas in May of any year, not only in May of 2019. Specifically, suppose that our idealized population involves choosing a random location in Texas, and a random day within May from the past 100 years, and obtaining the rainfall on the given day and at the given location. Does our sample, consisting of 50 arbitrarily chosen locations that are then observed for every day in May of 2019 represent this target population in an iid manner? Almost certainly it does not. First, May of 2019 may have been an unusually wet or dry year compared to other years in the population. Second, some of the locations where we collected data may have been so close together that if it is raining in one location it is almost certain to be raining in the other (this is sometimes called “twinning”). If we have strong or perfect twinning, only one meaningful unit of information is provided by the two twinned locations.

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1.12: Chapter 12 Interpretive Research

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William Pelz
Herkimer College via Lumen Learning

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The last chapter introduced interpretive research, or more specifically, interpretive case research. This chapter will explore other kinds of interpretive research. Recall that positivist or deductive methods, such as laboratory experiments and survey research, are those that are specifically intended for theory (or hypotheses) testing, while interpretive or inductive methods, such as action research and ethnography, are intended for theory building. Unlike a positivist method, where the researcher starts with a theory and tests theoretical postulates using empirical data, in interpretive methods, the researcher starts with data and tries to derive a theory about the phenomenon of interest from the observed data.

The term “interpretive research” is often used loosely and synonymously with “qualitative research”, although the two concepts are quite different. Interpretive research is a research paradigm (see Chapter 3) that is based on the assumption that social reality is not singular or objective, but is rather shaped by human experiences and social contexts (ontology), and is therefore best studied within its socio-historic context by reconciling the subjective interpretations of its various participants (epistemology). Because interpretive researchers view social reality as being embedded within and impossible to abstract from their social settings, they “interpret” the reality though a “sense-making” process rather than a hypothesis testing process. This is in contrast to the positivist or functionalist paradigm that assumes that the reality is relatively independent of the context, can be abstracted from their contexts, and studied in a decomposable functional manner using objective techniques such as standardized measures. Whether a researcher should pursue interpretive or positivist research depends on paradigmatic considerations about the nature of the phenomenon under consideration and the best way to study it.

However, qualitative versus quantitative research refers to empirical or data -oriented considerations about the type of data to collect and how to analyze them. Qualitative research relies mostly on non-numeric data, such as interviews and observations, in contrast to quantitative research which employs numeric data such as scores and metrics. Hence, qualitative research is not amenable to statistical procedures such as regression analysis, but is coded using techniques like content analysis. Sometimes, coded qualitative data is tabulated quantitatively as frequencies of codes, but this data is not statistically analyzed. Many puritan interpretive researchers reject this coding approach as a futile effort to seek consensus or objectivity in a social phenomenon which is essentially subjective.

Although interpretive research tends to rely heavily on qualitative data, quantitative data may add more precision and clearer understanding of the phenomenon of interest than qualitative data. For example, Eisenhardt (1989), in her interpretive study of decision making n high-velocity firms (discussed in the previous chapter on case research), collected numeric data on how long it took each firm to make certain strategic decisions (which ranged from 1.5 months to 18 months), how many decision alternatives were considered for each decision, and surveyed her respondents to capture their perceptions of organizational conflict. Such numeric data helped her clearly distinguish the high-speed decision making firms from the low-speed decision makers, without relying on respondents’ subjective perceptions, which then allowed her to examine the number of decision alternatives considered by and the extent of conflict in high-speed versus low-speed firms. Interpretive research should attempt to collect both qualitative and quantitative data pertaining to their phenomenon of interest, and so should positivist research as well. Joint use of qualitative and quantitative data, often called “mixed-mode designs”, may lead to unique insights and are highly prized in the scientific community.

Interpretive research has its roots in anthropology, sociology, psychology, linguistics, and semiotics, and has been available since the early 19 th century, long before positivist techniques were developed. Many positivist researchers view interpretive research as erroneous and biased, given the subjective nature of the qualitative data collection and interpretation process employed in such research. However, the failure of many positivist techniques to generate interesting insights or new knowledge have resulted in a resurgence of interest in interpretive research since the 1970’s, albeit with exacting methods and stringent criteria to ensure the reliability and validity of interpretive inferences.

Distinctions from Positivist Research

In addition to fundamental paradigmatic differences in ontological and epistemological assumptions discussed above, interpretive and positivist research differ in several other ways. First, interpretive research employs a theoretical sampling strategy, where study sites, respondents, or cases are selected based on theoretical considerations such as whether they fit the phenomenon being studied (e.g., sustainable practices can only be studied in organizations that have implemented sustainable practices), whether they possess certain characteristics that make them uniquely suited for the study (e.g., a study of the drivers of firm innovations should include some firms that are high innovators and some that are low innovators, in order to draw contrast between these firms), and so forth. In contrast, positivist research employs random sampling (or a variation of this technique), where cases are chosen randomly from a population, for purposes of generalizability. Hence, convenience samples and small samples are considered acceptable in interpretive research as long as they fit the nature and purpose of the study, but not in positivist research.

Second, the role of the researcher receives critical attention in interpretive research. In some methods such as ethnography, action research, and participant observation, the researcher is considered part of the social phenomenon, and her specific role and involvement in the research process must be made clear during data analysis. In other methods, such as case research, the researcher must take a “neutral” or unbiased stance during the data collection and analysis processes, and ensure that her personal biases or preconceptions does not taint the nature of subjective inferences derived from interpretive research. In positivist research, however, the researcher is considered to be external to and independent of the research context and is not presumed to bias the data collection and analytic procedures.

Third, interpretive analysis is holistic and contextual, rather than being reductionist and isolationist. Interpretive interpretations tend to focus on language, signs, and meanings from the perspective of the participants involved in the social phenomenon, in contrast to statistical techniques that are employed heavily in positivist research. Rigor in interpretive research is viewed in terms of systematic and transparent approaches for data collection and analysis rather than statistical benchmarks for construct validity or significance testing.

Lastly, data collection and analysis can proceed simultaneously and iteratively in interpretive research. For instance, the researcher may conduct an interview and code it before proceeding to the next interview. Simultaneous analysis helps the researcher correct potential flaws in the interview protocol or adjust it to capture the phenomenon of interest better. The researcher may even change her original research question if she realizes that her original research questions are unlikely to generate new or useful insights. This is a valuable but often understated benefit of interpretive research, and is not available in positivist research, where the research project cannot be modified or changed once the data collection has started without redoing the entire project from the start.

Benefits and Challenges of Interpretive Research

Interpretive research has several unique advantages. First, they are well-suited for exploring hidden reasons behind complex, interrelated, or multifaceted social processes, such as inter-firm relationships or inter-office politics, where quantitative evidence may be biased, inaccurate, or otherwise difficult to obtain. Second, they are often helpful for theory construction in areas with no or insufficient a priori theory. Third, they are also appropriate for studying context-specific, unique, or idiosyncratic events or processes. Fourth, interpretive research can also help uncover interesting and relevant research questions and issues for follow-up research.

At the same time, interpretive research also has its own set of challenges. First, this type of research tends to be more time and resource intensive than positivist research in data collection and analytic efforts. Too little data can lead to false or premature assumptions, while too much data may not be effectively processed by the researcher. Second, interpretive research requires well-trained researchers who are capable of seeing and interpreting complex social phenomenon from the perspectives of the embedded participants and reconciling the diverse perspectives of these participants, without injecting their personal biases or preconceptions into their inferences. Third, all participants or data sources may not be equally credible, unbiased, or knowledgeable about the phenomenon of interest, or may have undisclosed political agendas, which may lead to misleading or false impressions. Inadequate trust between participants and researcher may hinder full and honest self-representation by participants, and such trust building takes time. It is the job of the interpretive researcher to

“see through the smoke” (hidden or biased agendas) and understand the true nature of the problem. Fourth, given the heavily contextualized nature of inferences drawn from interpretive research, such inferences do not lend themselves well to replicability or generalizability. Finally, interpretive research may sometimes fail to answer the research questions of interest or predict future behaviors.

Characteristics of Interpretive Research

All interpretive research must adhere to a common set of principles, as described below.

Naturalistic inquiry : Social phenomena must be studied within their natural setting. Because interpretive research assumes that social phenomena are situated within and cannot be isolated from their social context, interpretations of such phenomena must be grounded within their socio-historical context. This implies that contextual variables should be observed and considered in seeking explanations of a phenomenon of interest, even though context sensitivity may limit the generalizability of inferences.

Researcher as instrument : Researchers are often embedded within the social context that they are studying, and are considered part of the data collection instrument in that they must use their observational skills, their trust with the participants, and their ability to extract the correct information. Further, their personal insights, knowledge, and experiences of the social context is critical to accurately interpreting the phenomenon of interest. At the same time, researchers must be fully aware of their personal biases and preconceptions, and not let such biases interfere with their ability to present a fair and accurate portrayal of the phenomenon.

Interpretive analysis : Observations must be interpreted through the eyes of the participants embedded in the social context. Interpretation must occur at two levels. The first level involves viewing or experiencing the phenomenon from the subjective perspectives of the social participants. The second level is to understand the meaning of the participants’ experiences in order to provide a “thick description” or a rich narrative story of the phenomenon of interest that can communicate why participants acted the way they did.

Use of expressive language : Documenting the verbal and non-verbal language of participants and the analysis of such language are integral components of interpretive analysis. The study must ensure that the story is viewed through the eyes of a person, and not a machine, and must depict the emotions and experiences of that person, so that readers can understand and relate to that person. Use of imageries, metaphors, sarcasm, and other figures of speech is very common in interpretive analysis.

Temporal nature : Interpretive research is often not concerned with searching for specific answers, but with understanding or “making sense of” a dynamic social process as it unfolds over time. Hence, such research requires an immersive involvement of the researcher at the study site for an extended period of time in order to capture the entire evolution of the phenomenon of interest.

Hermeneutic circle : Interpretive interpretation is an iterative process of moving back and forth from pieces of observations (text) to the entirety of the social phenomenon (context) to reconcile their apparent discord and to construct a theory that is consistent with the diverse subjective viewpoints and experiences of the embedded participants. Such iterations between the understanding/meaning of a phenomenon and observations must continue until “theoretical saturation” is reached, whereby any additional iteration does not yield any more insight into the phenomenon of interest.

Interpretive Data Collection

Data is collected in interpretive research using a variety of techniques. The most frequently used technique is interviews (face-to-face, telephone, or focus groups). Interview types and strategies are discussed in detail in a previous chapter on survey research. A second technique is observation. Observational techniques include direct observation, where the researcher is a neutral and passive external observer and is not involved in the phenomenon of interest (as in case research), and participant observation, where the researcher is an active participant in the phenomenon and her inputs or mere presence influence the phenomenon being studied (as in action research). A third technique is documentation, where external and internal documents, such as memos, electronic mails, annual reports, financial statements, newspaper articles, websites, may be used to cast further insight into the phenomenon of interest or to corroborate other forms of evidence.

Interpretive Research Designs

Case research . As discussed in the previous chapter, case research is an intensive longitudinal study of a phenomenon at one or more research sites for the purpose of deriving detailed, contextualized inferences and understanding the dynamic process underlying a phenomenon of interest. Case research is a unique research design in that it can be used in an interpretive manner to build theories or in a positivist manner to test theories. The previous chapter on case research discusses both techniques in depth and provides illustrative exemplars. Furthermore, the case researcher is a neutral observer (direct observation) in the social setting rather than an active participant (participant observation). As with any other interpretive approach, drawing meaningful inferences from case research depends heavily on the observational skills and integrative abilities of the researcher.

Action research . Action research is a qualitative but positivist research design aimed at theory testing rather than theory building (discussed in this chapter due to lack of a proper space). This is an interactive design that assumes that complex social phenomena are best understood by introducing changes, interventions, or “actions” into those phenomena and observing the outcomes of such actions on the phenomena of interest. In this method, the researcher is usually a consultant or an organizational member embedded into a social context (such as an organization), who initiates an action in response to a social problem, and examines how her action influences the phenomenon while also learning and generating insights about the relationship between the action and the phenomenon. Examples of actions may include organizational change programs, such as the introduction of new organizational processes, procedures, people, or technology or replacement of old ones, initiated with the goal of improving an organization’s performance or profitability in its business environment. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may bring forth the desired social change. The theory is validated by the extent to which the chosen action is successful in remedying the targeted problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from other research methods (which may not involve problem solving) and from consulting (which may not involve insight generation). Hence, action research is an excellent method for bridging research and practice.

There are several variations of the action research method. The most popular of these method is the participatory action research, designed by Susman and Evered (1978) [13] . This method follows an action research cycle consisting of five phases: (1) diagnosing, (2) action planning, (3) action taking, (4) evaluating, and (5) learning (see Figure 10.1). Diagnosing involves identifying and defining a problem in its social context. Action planning involves identifying and evaluating alternative solutions to the problem, and deciding on a future course of action (based on theoretical rationale). Action taking is the implementation of the planned course of action. The evaluation stage examines the extent to which the initiated action is successful in resolving the original problem, i.e., whether theorized effects are indeed realized in practice. In the learning phase, the experiences and feedback from action evaluation are used to generate insights about the problem and suggest future modifications or improvements to the action. Based on action evaluation and learning, the action may be modified or adjusted to address the problem better, and the action research cycle is repeated with the modified action sequence. It is suggested that the entire action research cycle be traversed at least twice so that learning from the first cycle can be implemented in the second cycle. The primary mode of data collection is participant observation, although other techniques such as interviews and documentary evidence may be used to corroborate the researcher’s observations.

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v.67(6); Nov-Dec 2014

Qualitative Research: Getting Started

Introduction.

As scientifically trained clinicians, pharmacists may be more familiar and comfortable with the concept of quantitative rather than qualitative research. Quantitative research can be defined as “the means for testing objective theories by examining the relationship among variables which in turn can be measured so that numbered data can be analyzed using statistical procedures”. 1 Pharmacists may have used such methods to carry out audits or surveys within their own practice settings; if so, they may have had a sense of “something missing” from their data. What is missing from quantitative research methods is the voice of the participant. In a quantitative study, large amounts of data can be collected about the number of people who hold certain attitudes toward their health and health care, but what qualitative study tells us is why people have thoughts and feelings that might affect the way they respond to that care and how it is given (in this way, qualitative and quantitative data are frequently complementary). Possibly the most important point about qualitative research is that its practitioners do not seek to generalize their findings to a wider population. Rather, they attempt to find examples of behaviour, to clarify the thoughts and feelings of study participants, and to interpret participants’ experiences of the phenomena of interest, in order to find explanations for human behaviour in a given context.

WHAT IS QUALITATIVE RESEARCH?

Much of the work of clinicians (including pharmacists) takes place within a social, clinical, or interpersonal context where statistical procedures and numeric data may be insufficient to capture how patients and health care professionals feel about patients’ care. Qualitative research involves asking participants about their experiences of things that happen in their lives. It enables researchers to obtain insights into what it feels like to be another person and to understand the world as another experiences it.

Qualitative research was historically employed in fields such as sociology, history, and anthropology. 2 Miles and Huberman 2 said that qualitative data “are a source of well-grounded, rich descriptions and explanations of processes in identifiable local contexts. With qualitative data one can preserve chronological flow, see precisely which events lead to which consequences, and derive fruitful explanations.” Qualitative methods are concerned with how human behaviour can be explained, within the framework of the social structures in which that behaviour takes place. 3 So, in the context of health care, and hospital pharmacy in particular, researchers can, for example, explore how patients feel about their care, about their medicines, or indeed about “being a patient”.

THE IMPORTANCE OF METHODOLOGY

Smith 4 has described methodology as the “explanation of the approach, methods and procedures with some justification for their selection.” It is essential that researchers have robust theories that underpin the way they conduct their research—this is called “methodology”. It is also important for researchers to have a thorough understanding of various methodologies, to ensure alignment between their own positionality (i.e., bias or stance), research questions, and objectives. Clinicians may express reservations about the value or impact of qualitative research, given their perceptions that it is inherently subjective or biased, that it does not seek to be reproducible across different contexts, and that it does not produce generalizable findings. Other clinicians may express nervousness or hesitation about using qualitative methods, claiming that their previous “scientific” training and experience have not prepared them for the ambiguity and interpretative nature of qualitative data analysis. In both cases, these clinicians are depriving themselves of opportunities to understand complex or ambiguous situations, phenomena, or processes in a different way.

Qualitative researchers generally begin their work by recognizing that the position (or world view) of the researcher exerts an enormous influence on the entire research enterprise. Whether explicitly understood and acknowledged or not, this world view shapes the way in which research questions are raised and framed, methods selected, data collected and analyzed, and results reported. 5 A broad range of different methods and methodologies are available within the qualitative tradition, and no single review paper can adequately capture the depth and nuance of these diverse options. Here, given space constraints, we highlight certain options for illustrative purposes only, emphasizing that they are only a sample of what may be available to you as a prospective qualitative researcher. We encourage you to continue your own study of this area to identify methods and methodologies suitable to your questions and needs, beyond those highlighted here.

The following are some of the methodologies commonly used in qualitative research:

Ethnography generally involves researchers directly observing participants in their natural environments over time. A key feature of ethnography is the fact that natural settings, unadapted for the researchers’ interests, are used. In ethnography, the natural setting or environment is as important as the participants, and such methods have the advantage of explicitly acknowledging that, in the real world, environmental constraints and context influence behaviours and outcomes. 6 An example of ethnographic research in pharmacy might involve observations to determine how pharmacists integrate into family health teams. Such a study would also include collection of documents about participants’ lives from the participants themselves and field notes from the researcher. 7
Grounded theory, first described by Glaser and Strauss in 1967, 8 is a framework for qualitative research that suggests that theory must derive from data, unlike other forms of research, which suggest that data should be used to test theory. Grounded theory may be particularly valuable when little or nothing is known or understood about a problem, situation, or context, and any attempt to start with a hypothesis or theory would be conjecture at best. 9 An example of the use of grounded theory in hospital pharmacy might be to determine potential roles for pharmacists in a new or underserviced clinical area. As with other qualitative methodologies, grounded theory provides researchers with a process that can be followed to facilitate the conduct of such research. As an example, Thurston and others 10 used constructivist grounded theory to explore the availability of arthritis care among indigenous people of Canada and were able to identify a number of influences on health care for this population.
Phenomenology attempts to understand problems, ideas, and situations from the perspective of common understanding and experience rather than differences. 10 Phenomenology is about understanding how human beings experience their world. It gives researchers a powerful tool with which to understand subjective experience. In other words, 2 people may have the same diagnosis, with the same treatment prescribed, but the ways in which they experience that diagnosis and treatment will be different, even though they may have some experiences in common. Phenomenology helps researchers to explore those experiences, thoughts, and feelings and helps to elicit the meaning underlying how people behave. As an example, Hancock and others 11 used a phenomenological approach to explore health care professionals’ views of the diagnosis and management of heart failure since publication of an earlier study in 2003. Their findings revealed that barriers to effective treatment for heart failure had not changed in 10 years and provided a new understanding of why this was the case.

ROLE OF THE RESEARCHER

For any researcher, the starting point for research must be articulation of his or her research world view. This core feature of qualitative work is increasingly seen in quantitative research too: the explicit acknowledgement of one’s position, biases, and assumptions, so that readers can better understand the particular researcher. Reflexivity describes the processes whereby the act of engaging in research actually affects the process being studied, calling into question the notion of “detached objectivity”. Here, the researcher’s own subjectivity is as critical to the research process and output as any other variable. Applications of reflexivity may include participant-observer research, where the researcher is actually one of the participants in the process or situation being researched and must then examine it from these divergent perspectives. 12 Some researchers believe that objectivity is a myth and that attempts at impartiality will fail because human beings who happen to be researchers cannot isolate their own backgrounds and interests from the conduct of a study. 5 Rather than aspire to an unachievable goal of “objectivity”, it is better to simply be honest and transparent about one’s own subjectivities, allowing readers to draw their own conclusions about the interpretations that are presented through the research itself. For new (and experienced) qualitative researchers, an important first step is to step back and articulate your own underlying biases and assumptions. The following questions can help to begin this reflection process:

Why am I interested in this topic? To answer this question, try to identify what is driving your enthusiasm, energy, and interest in researching this subject.
What do I really think the answer is? Asking this question helps to identify any biases you may have through honest reflection on what you expect to find. You can then “bracket” those assumptions to enable the participants’ voices to be heard.
What am I getting out of this? In many cases, pressures to publish or “do” research make research nothing more than an employment requirement. How does this affect your interest in the question or its outcomes, or the depth to which you are willing to go to find information?
What do others in my professional community think of this work—and of me? As a researcher, you will not be operating in a vacuum; you will be part of a complex social and interpersonal world. These external influences will shape your views and expectations of yourself and your work. Acknowledging this influence and its potential effects on personal behaviour will facilitate greater self-scrutiny throughout the research process.

FROM FRAMEWORKS TO METHODS

Qualitative research methodology is not a single method, but instead offers a variety of different choices to researchers, according to specific parameters of topic, research question, participants, and settings. The method is the way you carry out your research within the paradigm of quantitative or qualitative research.

Qualitative research is concerned with participants’ own experiences of a life event, and the aim is to interpret what participants have said in order to explain why they have said it. Thus, methods should be chosen that enable participants to express themselves openly and without constraint. The framework selected by the researcher to conduct the research may direct the project toward specific methods. From among the numerous methods used by qualitative researchers, we outline below the three most frequently encountered.

DATA COLLECTION

Patton 12 has described an interview as “open-ended questions and probes yielding in-depth responses about people’s experiences, perceptions, opinions, feelings, and knowledge. Data consists of verbatim quotations and sufficient content/context to be interpretable”. Researchers may use a structured or unstructured interview approach. Structured interviews rely upon a predetermined list of questions framed algorithmically to guide the interviewer. This approach resists improvisation and following up on hunches, but has the advantage of facilitating consistency between participants. In contrast, unstructured or semistructured interviews may begin with some defined questions, but the interviewer has considerable latitude to adapt questions to the specific direction of responses, in an effort to allow for more intuitive and natural conversations between researchers and participants. Generally, you should continue to interview additional participants until you have saturated your field of interest, i.e., until you are not hearing anything new. The number of participants is therefore dependent on the richness of the data, though Miles and Huberman 2 suggested that more than 15 cases can make analysis complicated and “unwieldy”.

Focus Groups

Patton 12 has described the focus group as a primary means of collecting qualitative data. In essence, focus groups are unstructured interviews with multiple participants, which allow participants and a facilitator to interact freely with one another and to build on ideas and conversation. This method allows for the collection of group-generated data, which can be a challenging experience.

Observations

Patton 12 described observation as a useful tool in both quantitative and qualitative research: “[it involves] descriptions of activities, behaviours, actions, conversations, interpersonal interactions, organization or community processes or any other aspect of observable human experience”. Observation is critical in both interviews and focus groups, as nonalignment between verbal and nonverbal data frequently can be the result of sarcasm, irony, or other conversational techniques that may be confusing or open to interpretation. Observation can also be used as a stand-alone tool for exploring participants’ experiences, whether or not the researcher is a participant in the process.

Selecting the most appropriate and practical method is an important decision and must be taken carefully. Those unfamiliar with qualitative research may assume that “anyone” can interview, observe, or facilitate a focus group; however, it is important to recognize that the quality of data collected through qualitative methods is a direct reflection of the skills and competencies of the researcher. 13 The hardest thing to do during an interview is to sit back and listen to participants. They should be doing most of the talking—it is their perception of their own life-world that the researcher is trying to understand. Sophisticated interpersonal skills are required, in particular the ability to accurately interpret and respond to the nuanced behaviour of participants in various settings. More information about the collection of qualitative data may be found in the “Further Reading” section of this paper.

It is essential that data gathered during interviews, focus groups, and observation sessions are stored in a retrievable format. The most accurate way to do this is by audio-recording (with the participants’ permission). Video-recording may be a useful tool for focus groups, because the body language of group members and how they interact can be missed with audio-recording alone. Recordings should be transcribed verbatim and checked for accuracy against the audio- or video-recording, and all personally identifiable information should be removed from the transcript. You are then ready to start your analysis.

DATA ANALYSIS

Regardless of the research method used, the researcher must try to analyze or make sense of the participants’ narratives. This analysis can be done by coding sections of text, by writing down your thoughts in the margins of transcripts, or by making separate notes about the data collection. Coding is the process by which raw data (e.g., transcripts from interviews and focus groups or field notes from observations) are gradually converted into usable data through the identification of themes, concepts, or ideas that have some connection with each other. It may be that certain words or phrases are used by different participants, and these can be drawn together to allow the researcher an opportunity to focus findings in a more meaningful manner. The researcher will then give the words, phrases, or pieces of text meaningful names that exemplify what the participants are saying. This process is referred to as “theming”. Generating themes in an orderly fashion out of the chaos of transcripts or field notes can be a daunting task, particularly since it may involve many pages of raw data. Fortunately, sophisticated software programs such as NVivo (QSR International Pty Ltd) now exist to support researchers in converting data into themes; familiarization with such software supports is of considerable benefit to researchers and is strongly recommended. Manual coding is possible with small and straightforward data sets, but the management of qualitative data is a complexity unto itself, one that is best addressed through technological and software support.

There is both an art and a science to coding, and the second checking of themes from data is well advised (where feasible) to enhance the face validity of the work and to demonstrate reliability. Further reliability-enhancing mechanisms include “member checking”, where participants are given an opportunity to actually learn about and respond to the researchers’ preliminary analysis and coding of data. Careful documentation of various iterations of “coding trees” is important. These structures allow readers to understand how and why raw data were converted into a theme and what rules the researcher is using to govern inclusion or exclusion of specific data within or from a theme. Coding trees may be produced iteratively: after each interview, the researcher may immediately code and categorize data into themes to facilitate subsequent interviews and allow for probing with subsequent participants as necessary. At the end of the theming process, you will be in a position to tell the participants’ stories illustrated by quotations from your transcripts. For more information on different ways to manage qualitative data, see the “Further Reading” section at the end of this paper.

ETHICAL ISSUES

In most circumstances, qualitative research involves human beings or the things that human beings produce (documents, notes, etc.). As a result, it is essential that such research be undertaken in a manner that places the safety, security, and needs of participants at the forefront. Although interviews, focus groups, and questionnaires may seem innocuous and “less dangerous” than taking blood samples, it is important to recognize that the way participants are represented in research can be significantly damaging. Try to put yourself in the shoes of the potential participants when designing your research and ask yourself these questions:

Are the requests you are making of potential participants reasonable?
Are you putting them at unnecessary risk or inconvenience?
Have you identified and addressed the specific needs of particular groups?

Where possible, attempting anonymization of data is strongly recommended, bearing in mind that true anonymization may be difficult, as participants can sometimes be recognized from their stories. Balancing the responsibility to report findings accurately and honestly with the potential harm to the participants involved can be challenging. Advice on the ethical considerations of research is generally available from research ethics boards and should be actively sought in these challenging situations.

GETTING STARTED

Pharmacists may be hesitant to embark on research involving qualitative methods because of a perceived lack of skills or confidence. Overcoming this barrier is the most important first step, as pharmacists can benefit from inclusion of qualitative methods in their research repertoire. Partnering with others who are more experienced and who can provide mentorship can be a valuable strategy. Reading reports of research studies that have utilized qualitative methods can provide insights and ideas for personal use; such papers are routinely included in traditional databases accessed by pharmacists. Engaging in dialogue with members of a research ethics board who have qualitative expertise can also provide useful assistance, as well as saving time during the ethics review process itself. The references at the end of this paper may provide some additional support to allow you to begin incorporating qualitative methods into your research.

CONCLUSIONS

Qualitative research offers unique opportunities for understanding complex, nuanced situations where interpersonal ambiguity and multiple interpretations exist. Qualitative research may not provide definitive answers to such complex questions, but it can yield a better understanding and a springboard for further focused work. There are multiple frameworks, methods, and considerations involved in shaping effective qualitative research. In most cases, these begin with self-reflection and articulation of positionality by the researcher. For some, qualitative research may appear commonsensical and easy; for others, it may appear daunting, given its high reliance on direct participant– researcher interactions. For yet others, qualitative research may appear subjective, unscientific, and consequently unreliable. All these perspectives reflect a lack of understanding of how effective qualitative research actually occurs. When undertaken in a rigorous manner, qualitative research provides unique opportunities for expanding our understanding of the social and clinical world that we inhabit.

Female labor force participation

Across the globe, women face inferior income opportunities compared with men. Women are less likely to work for income or actively seek work. The global labor force participation rate for women is just over 50% compared to 80% for men. Women are less likely to work in formal employment and have fewer opportunities for business expansion or career progression. When women do work, they earn less. Emerging evidence from recent household survey data suggests that these gender gaps are heightened due to the COVID-19 pandemic.

Women’s work and GDP

Women’s work is posited to be related to development through the process of economic transformation.

Levels of female labor force participation are high for the poorest economies generally, where agriculture is the dominant sector and women often participate in small-holder agricultural work. Women’s participation in the workforce is lower in middle-income economies which have much smaller shares of agricultural activities. Finally, among high-income economies, female labor force participation is again higher, accompanied by a shift towards a service sector-based economy and higher education levels among women.

This describes the posited U-shaped relationship between development (proxied by GDP per capita) and female labor force participation where women’s work participation is high for the poorest economies, lower for middle income economies, and then rises again among high income economies.

This theory of the U-shape is observed globally across economies of different income levels. But this global picture may be misleading. As more recent studies have found, this pattern does not hold within regions or when looking within a specific economy over time as their income levels rise.

In no region do we observe a U-shape pattern in female participation and GDP per capita over the past three decades.

Structural transformation, declining fertility, and increasing female education in many parts of the world have not resulted in significant increases in women’s participation as was theorized. Rather, rigid historic, economic, and social structures and norms factor into stagnant female labor force participation.

Historical view of women’s participation and GDP

Taking a historical view of female participation and GDP, we ask another question: Do lower income economies today have levels of participation that mirror levels that high-income economies had decades earlier?

The answer is no.

This suggests that the relationship of female labor force participation to GDP for lower-income economies today is different than was the case decades past. This could be driven by numerous factors -- changing social norms, demographics, technology, urbanization, to name a few possible drivers.

Gendered patterns in type of employment

Gender equality is not just about equal access to jobs but also equal access for men and women to good jobs. The type of work that women do can be very different from the type of work that men do. Here we divide work into two broad categories: vulnerable work and wage work.

The Gender gap in vulnerable and wage work by GDP per capita

Vulnerable employment is closely related to GDP per capita. Economies with high rates of vulnerable employment are low-income contexts with a large agricultural sector. In these economies, women tend to make up the higher share of the vulnerably employed. As economy income levels rise, the gender gap also flips, with men being more likely to be in vulnerable work when they have a job than women.

From COVID-19 crisis to recovery

The COVID-19 crisis has exacerbated these gender gaps in employment. Although comprehensive official statistics from labor force surveys are not yet available for all economies, emerging studies have consistently documented that working women are taking a harder hit from the crisis. Different patterns by sector and vulnerable work do not explain this. That is, this result is not driven by the sectors in which women work or their higher rates of vulnerable work—within specific work categories, women fared worse than men in terms of COVID-19 impacts on jobs.

Among other explanations is that women have borne the brunt of the increase in the demand for care work (especially for children). A strong and inclusive recovery will require efforts which address this and other underlying drivers of gender gaps in employment opportunities.

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Week 5 Research: Populations and Samples Flashcards
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COMMENTS

NUR 307 Final Flashcards
Study with Quizlet and memorize flashcards containing terms like In the research context, what is a population? a) a patient group to whom the study findings are intended to apply b) a patient group who agrees to participate in the study c) a patient group who receives the experimental treatment d) a patient group from whom complete data is collected, All of the following are categorized as ...
Week 5 (chapter 10) Flashcards
difference between population values and sample values. non-probability sampling in quantitative research types (4) convenience, Quota, consecutive, purposive. convenience sampling. non-probability; uses the most readily available or convenient people. Week 5 (chapter 10) Get a hint.
Population vs. Sample
A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. In research, a population doesn't always refer to people. It can mean a group containing elements of anything you want to study ...
What Is the Big Deal About Populations in Research?
In research, there are 2 kinds of populations: the target population and the accessible population. The accessible population is exactly what it sounds like, the subset of the target population that we can easily get our hands on to conduct our research. While our target population may be Caucasian females with a GFR of 20 or less who are ...
Understanding Population in Scientific Research: A Comprehensive
The first step in addressing the population in research is to clearly define the target population. This involves specifying the characteristics of the larger group to which the study's findings will be generalized. The target population should be explicitly defined in terms of relevant factors such as demographic characteristics, geographic ...
Population vs Sample
A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. In research, a population doesn't always refer to people. It can mean a group containing elements of anything you want to study ...
Statistics without tears: Populations and samples
A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. The usual criteria we use in defining population are geographic, for example, "the population of Uttar Pradesh". In medical research, the criteria for population may be clinical, demographic and time related.
Taking account of context in population health intervention research
Although we are primarily concerned in this guidance with the context in which an intervention takes place, and the ways in which the relationship between intervention and context contributes to explaining outcomes, it is important to recognise that public health problems are framed within historical, cultural, political and economic contexts.29,33 In this section, we present examples to show ...
Chapter 5. Sampling
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 ...
Samples Populations
Samples and populations. An important principle in rigorous data analysis is that we should not exclusively be interested in understanding the data that we have collected. Instead, our goal should be to use the data that we have collected to learn about the population that our data represent. The act of using results from the sample (the data ...
Defining and Identifying Members of a Research Study Population: CTSA
The defined population then will become the basis for applying the research results to other relevant populations. Clearly defining a study population early in the research process also helps assure the overall validity of the study results. Many research reports fail to define or describe a study population adequately.
When are populations used in research?
Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research. Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group.As a result, the characteristics of the participants who drop out differ from the characteristics of those who ...
Measuring public opinion: lesson overview
Polls are a common way to measure public opinion. A group of people that a researcher wants to study. This might be a large group, such as all voting-age citizens in the United States, or a smaller group like members of a club or church. The group of people a researcher surveys to gauge the whole population's opinion.
1.12: Chapter 12 Interpretive Research
This method follows an action research cycle consisting of five phases: (1) diagnosing, (2) action planning, (3) action taking, (4) evaluating, and (5) learning (see Figure 10.1). Diagnosing involves identifying and defining a problem in its social context. Action planning involves identifying and evaluating alternative solutions to the problem ...
What is meant by population health—and why it matters
The population health approach helps by: Focusing on wellness instead of sick care. Using data more effectively to improve care. Engaging patients in their care. Coordinating care that was previously siloed and fragmented, something that is easier to do as accountable care organizations and patient-centered medical homes have evolved.
Context of the Study
The context of a study refers to the set of circumstances or background factors that provide a framework for understanding the research question, the methods used, and the findings. It includes the social, cultural, economic, political, and historical factors that shape the study's purpose and significance, as well as the specific setting in ...
Series: Practical guidance to qualitative research. Part 2: Context
This second article addresses FAQs about context, research questions and designs. Qualitative research takes into account the natural contexts in which individuals or groups function to provide an in-depth understanding of real-world problems. The research questions are generally broad and open to unexpected findings.
Qualitative Research: Getting Started
Qualitative research is concerned with participants' own experiences of a life event, and the aim is to interpret what participants have said in order to explain why they have said it. Thus, methods should be chosen that enable participants to express themselves openly and without constraint.
Female labor force participation
The global labor force participation rate for women is just over 50% compared to 80% for men. Women are less likely to work in formal employment and have fewer opportunities for business expansion or career progression. When women do work, they earn less. Emerging evidence from recent household survey data suggests that these gender gaps are ...