qualitative research methods tend to use probability sampling methods

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

Qualitative Research Sampling Methods: Pros and Cons to Help You Choose

Your choice of sampling strategy can deeply impact your research findings, especially in qualitative studies, where every person counts.

There’s so much written on methods that it can sometimes feel overwhelming when you’re first discovering what’s out there. Even if you’re well into your research career, you may find yourself sticking with the same methodology again and again.

Many researchers focus on quantitative methodology. But they can greatly benefit from knowing qualitative methodology for use in mixed-methods studies and to better understand other studies.

This article aims to help you dive into the most widely recognized qualitative sampling strategies shortly and objectively.

What you ’ ll learn in this post

• All the most common types of qualitative research sampling methods.

• When to use each method.

• Pros and cons of each method.

• Specific examples of these qualitative sampling methods in use.

• Where to get your research both critiqued and edited, be it qualitative, quantitative, or mixed methods.

Your first step in choosing a qualitative sampling strategy

So, where do you start when you know you need to do more than grab students walking by your office? One of the first and most important decisions you must make about your sampling strategy is defining a clear sampling frame .

The cases you choose for your sample need to cover the various issues and variables you want to explore in your research. A fundamental aspect of your sample is that it should always contain the cases most likely to provide you with the richest data (Gray, 2004).

Owing to time and expense, qualitative research often works with small samples of people, cases, or phenomena in particular contexts. Therefore, unlike in quantitative research, samples tend to be more purposive (using your judgment) than they are random (Flick, 2009). This post will cover those main purposive sampling strategies.

It’s also important to keep in mind that qualitative samples are sometimes predetermined – what’s known as a priori determination, and other times follow more flexible determination (Flick, 2009).

So this article is organized based on those two parameters: a priori and more flexible determination.

And take note that in certain strategies it’s possible to start with a predetermined sample and end up extending it, or even varying it, for a valid reason.

Qualitative research is much more flexible than quantitative research. You iterate, you run another round, you seek saturation.

OK? Let’s see what’s on the qualitative menu. Hope you find something tasty.

A priori determination

Comprehensive sampling.

Comprehensive (or total population) sampling is a strategy that examines every case or instance of a given population that has specific characteristics (e.g., attributes, traits, experience, knowledge) you’re interested in for your study (Gray, 2004).

This sampling strategy is somewhat unusual because it’s often hard to sample the entire population of interest.

When to use it

It’s ideal for studies that focus on a specific organization or people with such specific characteristics that it’s possible to contact the whole population that has them (Gray, 2004).

Basically, two aspects are key to using this method

population size being somewhat small
having uncommon characteristics

One example would be studying perceptions about leadership within a small company (e.g., 10–30 people), where your sample could easily be every employee within the company.

Ideal for further analyzing, differentiating, and perhaps testing (Flick, 2009).
It might facilitate confidence in the validity of the results of research that use this method because it covers every case in a given population.
Reduced risk of missing valuable insights.
Only applicable to very specific studies because it requires the targeted population to be small and have uncommon characteristics.
Very limited potential for generalizability.

Practical example: Gerhard (as cited in Flick, 2009, p. 117) used this strategy to study the careers of patients with chronic renal failure. The sample was a complete collection of all patients with predetermined characteristics (male, married, age 30–50 years, at the start of treatment at five hospitals in the UK).

Note that for this particular study, sampling was limited to several criteria: a specific sex, disease, marital status, age, region, and a limited period.

These predetermined characteristics were what allowed the researchers to achieve a comprehensive (total population) sample.

Extreme/deviant sampling

Extreme/deviant sampling is intentionally selecting extremes and trying to identify the factors that affect them (Gray, 2004).

It’s usually used to focus on special or uncommon cases such as noteworthy successes or failures. For instance, if you’re conducting a study about a reform program, you can include particularly successful examples and/or cases of big failures – these are two extremes, which is where the “extreme/deviant” name comes from (Flick, 2009).

It’s ideal for studying special/unusual cases in a particular context.

Allows you to collect focused information on a very particular phenomenon.
It’s sometimes regarded as producing the “purest” form of insight into a particular phenomenon.
Lets you collect insights from two very distinct perspectives, which will help you get an understanding of the phenomena as a whole.
The danger of mistakenly generalizing from extreme cases.
Selection bias

Practical example: Perhaps one of the most widely recognized studies that used this sampling method was Waterman and Peters’ In Search of Excellence: Lessons from America’s Best-Run Companies , published in 1982.

The researchers chose 62 companies based on their outstanding (extreme) success in terms of innovation and excellence (see Peters & Waterman [2004]).

Intensity sampling

Intensity sampling fundamentally involves the same logic as extreme/deviant case sampling, but it has less emphasis on the extremes.

Cases chosen for an intensity sample should be information-rich, manifesting the phenomenon intensely but not extremely; therefore capturing more typical cases compared with those at the extremes (Patton, 2002; Gray, 2004; Benoot, Hannes & Bilsen, 2016).

Patton (2002) argues that ideally, you should use this when you already have prior information about the variation of the subject you want to study. Some exploratory research might be needed depending on what you are researching.

Great for heuristic research/inquiry (Patton, 2002).
By choosing intensive cases that aren’t extreme/deviant, you can avoid the distortion that extreme cases sometimes bring (Patton, 2002).
Involves some prior information and considerable judgment. The researcher must do some exploratory work to grasp the nature of the variation of the specific situation he is researching about (Patton, 2002)
It requires an extended knowledge of the phenomena being studied to not mix cases that have sufficient intensity with the ones at the extremes (Patton, 2002).

Practical example: Researching above average/below average students would be a time to use this sampling method. This is because they experience the educational system intensely but aren’t extreme cases.

Maximum variation sampling

The maximum variation sampling strategy aims at capturing and describing a wide range of variations and that cut across what you want to research (Patton, 2002; Gray, 2004). How can you proceed to guarantee that you capture a high level of variation?

You can start by setting specific characteristics where you’ll look for variation that the literature (or you) identify as relevant for the phenomenon you’re researching. These may be education level, ethnicity, age, or socioeconomic status.

For small samples, having too much heterogeneity can be a problem because each case may be very different from the other.

But according to Patton (2002), this method might turn that weakness into a strength.

It does so by applying this logic: any common pattern that emerges from this kind of sample is of particular interest and value in capturing the core experiences and central, shared dimensions of a setting or phenomenon.

When to use it: Whenever you want to explore the variation of perceptions/practices concerning a broad phenomenon.

Allows the researcher to capture all variations of a phenomenon (Patton, 2002; Schreier, 2018).
Finds detailed insights about each variation (Patton, 2002; Schreier, 2018).
In small samples, sometimes cases are so different from one another that no common patterns emerge (Patton, 2002).

Practical example: Ziebland et al. (2004) was about how the internet affects patients’ experiences with cancer. It used a maximum variation sample to maximize the variety of insights.

The researchers purposively looked for people that differed in: type of cancer they had, stage of cancer, age, and sex.

Homogenous sampling

The homogenous sampling strategy can be seen as the exact opposite of maximum variation sampling because it seeks homogenous groups of people, settings, or contexts to be studied in-depth.

With this kind of sample, using focus group interviewing might prove extremely productive (Gray, 2004).

Use it if your research aims to specifically focus on a group with shared characteristics.

Produces highly detailed insights regarding a specific group (Patton, 2002).
Highly compatible with focus group interviews (Patton, 2002).
Can simplify the analysis (Patton, 2002).
Doesn’t let the researcher capture much variation (Patton, 2002).

Practical example: Nestbitt et al. (2012) was a study about Canadian adolescent mothers’ perceptions of influences on breastfeeding decisions. The researchers purposefully collected 16 homogenous cases of adolescent mothers (15–19 years) that lived in the Durham region and had children up to 12 months old.

Other criteria included speaking English fluently and breastfeeding their infant at least once.

The aim of the researchers by using this method was to produce an in-depth look at this very specific group.

Theory-based sampling

Theory-based sampling is basically a more formal type of criterion sampling, it’s more conceptually oriented, and the cases are chosen on the basis that they represent a theoretical construct (Patton, 2002; Gray, 2004).

The researcher samples incidents, periods of someone’s life, time periods, or people based on the potential manifestation or representation of important theoretical constructs.

Use this one when you want to study a pre-existing theory-derived concept that is of interest to your research.

Elaborating on previous theoretical and established concepts can facilitate the analysis.
Working on established theoretical concepts allows you to contribute new insights for an established theory.
The odds of finding out something entirely “new” are somewhat limited.
It might be harder to determine the population of interest because it’s hard to find people, programs, organizations, or communities of interest to a specific theoretical construct. This is unlike what happens when sampling based on determined people’s characteristics (Patton, 2002).

Practical example: Buckhold (as cited in Patton [2002, p. 238]) researched people who met specific theory-derived criteria for being “resilient.” She aimed to analyze the resilience of women who were victims of abuse and were able to survive.

Stratified purposive sampling

In stratified purposive sampling, decisions about the sample’s composition are made before data collection .

Schreier (2018) notes that it can be done in four steps:

Deciding which factors are known or likely to cause variation in the phenomenon of interest.
Selecting from two to a maximum of four factors for constructing a sampling guide.
Combining the factors of choice in a cross-table, though when picking more than two factors, it might be impossible to conduct sampling for all factor combinations.
Deciding on how many units for each cell/or factor combination.

Use this method when you want to explore known factors that influence the phenomenon of your interest.

These might be hypothesized in theory while having no empirical data supporting them. You can also purpose a factor and by including it on your sampling you might grasp its importance regarding the phenomena you’re researching.

Allows you to focus on several known factors that of interest for your research (Schreier, 2018).
Predetermining the composition of your sample might facilitate finding the cases/people/groups to research.
Sticking to the predetermined composition might have trouble with new factors discovered from your first cases that are left unresearched.
Finding the cases with the factors that are of most interest for your research might be challenging.

Practical example: Palacic (2017) examined entrepreneurial leadership and business performance in “gazelles” and “MICE” (business/market terms to describe a type of company). The sample was purposively constituted to contain cases from both types of companies that were involved in three major industrial sectors – manufacturing, sales, and services.

More flexible determination

Theoretical sampling.

Theoretical sampling was developed in the context of grounded theory methodology.

Fundamentally, it’s a process of data collection that aims to generate theory. It takes place in a constant interrelation between data collection and data analysis, and it’s guided by the concepts and/or theory emerging from the research process (Gray, 2004; Flick, 2009).

The sample is usually composed of heterogeneous cases that allow comparison of different instantiations (Schreier, 2018).

You can use this when you’re aiming to generate a new theory about a certain phenomenon.

May bring more innovation to your research (Schreier, 2018).
Your sample is more flexible compared with many other methods because there are no “static” criteria for your sample’s population.
Not ideal for inexperienced researchers because generating a new theory is very challenging.
Very time-consuming and complex.

Practical example: Glaser and Strauss (as cited in Flick, 2009, pp. 118–119) famously used this method to research awareness of dying in hospitals.

The researchers chose to conduct participant observation in different hospitals to develop a new theory about the way dying in a hospital is organized as a social process.

They built their sample through a step-by-step process while in direct contact with the field. First they studied awareness of dying in conditions that minimized patient awareness (e.g., comatose). Then they moved to situations where staff’s and patients’ awareness was high and death often was quick (e.g., intensive care). Then to situations where staff expectations of terminality were high, but dying tended to be slow (e.g., cancer). And ultimately to situations where death was unforeseen and rapid (e.g., emergency services).

Snowball sampling

Snowball sampling (or, chain referral sampling) is a method widely used in qualitative sociological research (Biernacki & Waldorf, 1981; Gray, 2004; Flick, 2009; Heckathorn, 2011). It’s used a lot because it’s effective at getting numbers. It’s premised on the idea that people know people similar to themselves.

Snowballing especially useful for studying hard-to-reach populations. Snowball sampling has been most applicable in studies where the focus relies on a sensitive issue, something that might be a private matter that requires knowing insiders so you can locate, contact, and receive consent from the true target population (Biernacki & Waldorf, 1981; Heckathorn, 2011).

The researcher forms a study sample through referrals made among people who are acquainted with others who have the characteristics of interest for the research. It begins through a convenience sample of someone of a hard-to-reach population.

qualitative sampling - snowball sampling

After successfully interviewing/communicating with this person, the researcher will ask them to introduce other people with the same characteristics. After acquiring contacts, the research proceeds in the same way (Heckathorn, 2011).

As hard-to-reach groups are, well, hard to reach, snowball sampling is effective when you need an inroad and cannot easily recruit and sample.

Ideal for studying hard-to-reach groups (Biernacki & Waldorf, 1981; Gray, 2004; Flick, 2009; Heckathorn, 2011).
Able to produce highly detailed insights regarding a specific group through the sampling of, in principle, information-rich cases (Patton, 2002).
If the researcher is studying a topic that involves moral, legal, or socially sensitive issues (e.g., prostitution, drug addiction) and does not know anyone from this group, it might be hard to start the first “chain” that bring in more recruits.
Very limited generalization potential.

Practical example: Cloud and Granfield (1994) used snowball sampling to study drug and alcohol addicts who beat their addictions without resorting to a treatment.

Using the snowballing method was fundamental to the authors because they were researching a widely distributed population (unlike those who participate in self-help groups or in treatment), and because the participants did not wish to expose their past as former drug addicts (i.e., sensitive issue).

Convenience sampling

Convenience sampling is a strategy that involves simply choosing cases in a way that is fast and convenient.

It’s probably the most common sampling strategy and, according to Patton (2002), the least desirable because it can’t be regarded as purposeful or strategic.

Many researchers choose this method thinking that their sample size is too small to generalize anyway, so they might as well pick cases that are easy to access and inexpensive to study (Patton, 2002).

This is a very common strategy among master’s students – asking fellow students to be part of the sample of their dissertation. That’s convenience sampling (Schreier, 2018). Also notable is that online surveying makes convenience sampling even simpler, beyond geographic limitations.

When you have few resources (mainly time and money) for your qualitative research, this is the go-to method. This is why so many studies are conducted on university students – they’re literally all over the place, whether you’re a student or researcher. As students, they’re also easier to incentivize with small compensation and they often are in the same boat.

Saves time, money, and effort (Patton, 2002).
Might be optimal for unfinanced and strictly timed qualitative research (often in master’s theses and in many doctoral dissertations).
Something of a “bad reputation” (Schreier, 2018).
Lowest credibility (Patton, 2002).
Might yield information-poor cases (Patton, 2002).

Practical example: Augusto and Simões (2017) used a convenience sampling strategy to capture perceptions and prevention strategies on Facebook surveillance.

As the original fieldwork was part of a master’s dissertation, convenience sampling was chosen because of the main author’s limited time and resources. This is in no way to discredit the study and findings – it was simply the most feasible way to get the research done.

Confirming and disconfirming cases

Confirming and disconfirming cases is frequently a second-stage sampling strategy.

Cases are chosen on the premise that they can confirm or disconfirm emerging patterns from the first stage of sampling (Gray, 2004).

After an exploratory process, one might consider testing ideas, confirming the importance and/or meaning of eventual patterns, and ultimately the viability of the findings through collecting new data and/or sampling additional cases (Patton, 2002).

As the name indicates, generally, it’s ideal for testing emergent findings from your data.

Strengthens emergent findings.
Allows you to identify possible “exceptions that prove the rule” or exceptions that might disconfirm a finding (Patton, 2002).
Usually requires a “first stage” of sampling.
While definitely useful, one can certainly make an argument about quantitative research being better able to test certain findings.

Practical example: If you were researching students’ motives for applying for college, and on the first interviews you found out the interviewees’ main reason for pursuing their education was to avoid having a routine day-job, this might be a good sampling method to use. The findings, however, would have to carefully look at trends and check for outliers.

So, how’s your research going?

Here’s hoping you find the right qualitative sampling method(s) that work for you. Putting this together was a lesson for me as well.

And when you’re ready for a professional edit or scientific review, check out Edanz’s author-guidance services , which have been leading the way since 1995. Good luck with your research!

This is a guest post from Adam Goulston, PsyD, MBA, MS, MISD, ELS. Adam runs science marketing firm Scize and has worked an in-house Senior Language Editor, as well as a manuscript editor, with Edanz.

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Principles of Social Research Methodology pp 415–426 Cite as

Sampling Techniques for Qualitative Research

Heather Douglas 4
First Online: 27 October 2022

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

Phenomenon. Methodology. Research Question. Methods. Tools and Techniques. Purposive Sampling. Sampling Frame. Trustworthiness

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

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

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Unit 12: Sampling. When enough is enough.

45 probability sampling.

Ok, I’m just going to say it: The way we (researcher types) use probability and random in this context is downright confusing. Colloquially, we (regular humans) tend to use random to mean that something is really out there, or without thought, or, frankly, we’re kind of confused as to WHY…how….what…HUH?!? Like perhaps this TikTok complication that came up when I searched for random funny videos [1] . On the flipside, random, RESEARCH RANDOM is like the opposite of all this! It is absolutely mindful, systematic, purposeful, etc. It has a reason, an important reason, and a process, etc. This is definitely one of those things that students screw up on exams – I think mostly when they don’t read or come to class because I harp on this whole random thing pretty hard – so when you’re reading your exam questions related to randomness think RESEARCH RANDOM, not…well…the other (more popular) kind.

And now…you know what’s coming, right?

Learning Objectives

Understand what probability sampling is, what methodology (that we discuss) generally uses it, and a handful of different mainstream strategies.

Probability Sampling

Probability sampling is a type of quantitative , or measurable, method of finding a sample that represents a population. Probability or random sampling uses methods to systematically select candidates. Ideally, each member of the population has an equal chance of being chosen for a study, making it as representative of the population as possible. A researcher interested in generalizable results may lean towards these types of sampling methods.

Methods of Sampling:

Random Sampling : Having a system to ensure an equally possible outcome/most representative of the population

Types of Random or Probability Sampling (a selection. Yes, there are more…):

Simple Random Sampling

Example: Assigning numbers 1 to 10 to participants and then drawing a number from 1 to 10 out of a hat to select candidates for your study.

Systematic Random Sampling

Another Example: Giving a survey to every fourth customer that comes into the movie theater.

Stratified Sampling

Some examples of strata might me sex, generational group, or economic background.

Proportional Stratified Sampling

Example: In the U.S., in the 85 and older age group, women outnumber men by a ratio of 2-to-1 (4.0 million to 2.0 million). So, your sample should represent that with a 2-to-1 ratio of women to men.

Cluster Sampling

Example: A researcher wants to survey the academic performance of high school students in Spain. She can divide the entire population (population of Spain) into different clusters (cities). Then the researcher selects a number of clusters depending on her research through simple or systematic random sampling.

ok, last one, REALLY – but it’s by the Pew Research Center so how can I resist?

Got ideas for questions to include on the exam?

Click this link to add them! [this course element is paused because ya’ll aren’t submitting many questions…]

… Unit 1 … Unit 2 …. Unit 3 … Unit 4 … Unit 5 … Unit 6 … Unit 7 … Unit 8 … Unit 9 … Unit 10 … Unit 11 … Unit 12 … Unit 13 … Unit 14 … Unit 15 … Unit 16 …

Non-Probability Sampling
Considerations When Sampling
On being skeptical [cuz they didn’t rep-re-sent]
Listen, I spent way too much time looking for an acceptable video. I don't like the ones where people get hurt, which is a mainstay of "funny" (I guess it's the mom in me), and heaven knows that I probably have no idea what ya'll young whippersnappers find funny these days. I ended up choosing this video because TikTok (took a chance) and because it's titled "hilarious clean tiktoks that made me giggle in class." IN CLASS. I'm being ironic, obviously. ↵

a type of quantitative, or measurable, method of finding a sample that represents a population

having a system to ensure an equally possible outcome/most representative of the population

Simple random sampling is where we select a group of subjects (a sample) for study from a larger group (a population)

Every nth person is chosen

when you break people into groups, called strata

It is the same as stratified except the groups (strata) are proportional to the population the researcher is trying to emulate

when people within the sampling frame are broken into random groups (usually geographic) and candidates are chosen randomly

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Home » Probability Sampling – Methods, Types and Examples

Probability Sampling – Methods, Types and Examples

Table of Contents

Probability Sampling

Definition:

Probability sampling is a method of sampling where each member of a population has a known, non-zero probability of being selected to be part of the sample. This means that each member of the population has an equal chance of being selected for the sample, and the selection of one member does not influence the selection of any other member.

This method of sampling is used in research studies and surveys where the goal is to generalize the results to the larger population. By using probability sampling, researchers can ensure that the sample is representative of the population and that the results can be generalized with a known level of confidence.

Probability Sampling Methods

Probability Sampling Methods are as follows:

Simple Random Sampling

This method involves selecting a sample of individuals from the population randomly and without any bias. Each member of the population has an equal chance of being selected.

Systematic Sampling

This method involves selecting every kth member of the population, where k is a fixed interval calculated by dividing the population size by the desired sample size.

Stratified Sampling

This method involves dividing the population into homogeneous subgroups or strata, based on some relevant characteristic, and then selecting a random sample from each stratum. This ensures that each subgroup is represented in the sample.

Cluster Sampling

This method involves dividing the population into clusters or groups, such as geographical areas or schools, and then selecting a random sample of clusters. Data is then collected from all individuals in the selected clusters.

Multi-stage Sampling

This method combines two or more sampling methods, such as cluster sampling and stratified sampling, to create a more complex sample design that is appropriate for the research question and the characteristics of the population being studied.

How to conduct Probability Sampling

To conduct probability sampling, follow these general steps:

Define the Population: Identify the population you want to study and define its characteristics.
Determine the Sample Size: Decide on the size of the sample you want to select from the population. This should be based on the research question and the desired level of precision.
Choose a Sampling Method: Choose the most appropriate probability sampling method based on the research question and the characteristics of the population.
Identify the Sampling Frame: Create a list of all the individuals or units that make up the population. This is known as the sampling frame.
Select the Sample: Use the selected probability sampling method to randomly select individuals from the sampling frame until the desired sample size is reached.
Conduct Data Collection: Collect data from the selected individuals using appropriate data collection methods such as surveys, interviews, or observations.
Analyze the Data: Analyze the data collected to draw conclusions and make inferences about the population.

Examples of Probability Sampling

Here are some examples of probability sampling:

Simple Random Sampling: Suppose you want to study the attitudes of students towards their school’s policies. You could randomly select a sample of students from the school’s enrollment list, ensuring that each student has an equal chance of being selected.
Stratified Sampling: Suppose you want to study the average income of households in a city. You could divide the population into strata based on income levels, and then randomly select a sample from each stratum in proportion to the size of the stratum in the population.
Systematic Sampling: Suppose you want to study the customer satisfaction of a particular store. You could select every 10th customer entering the store during a specific time period to participate in the study.
Cluster Sampling: Suppose you want to study the prevalence of a particular disease in a region. You could randomly select several neighborhoods from the region, and then randomly select a sample of individuals from each neighborhood.
Multi-Stage Sampling: Suppose you want to study the educational attainment of a population in a country. You could first divide the country into regions, then randomly select several regions, and finally randomly select a sample of individuals from each region.

Applications of Probability Sampling

Probability sampling has various applications in research and statistical analysis. Here are some of the main applications:

Scientific Research : Probability sampling is commonly used in scientific research to study the characteristics of a population, such as attitudes, behaviors, and health outcomes. Researchers use probability sampling to ensure that their samples are representative of the population and the results can be generalized to the population.
Market Research: Probability sampling is used in market research to study consumer behavior, preferences, and attitudes. Companies use probability sampling to ensure that their samples are representative of their target market, and the results can be used to inform their marketing strategies.
Public Health: Probability sampling is used in public health research to study the prevalence of diseases, risk factors, and health outcomes in a population. Public health researchers use probability sampling to ensure that their samples are representative of the population, and the results can be used to inform public health policies and interventions.
Political Polling: Probability sampling is used in political polling to estimate the opinions and voting behavior of a population. Pollsters use probability sampling to ensure that their samples are representative of the population, and the results can be used to predict election outcomes.
Quality Control: Probability sampling is used in quality control to monitor and improve the quality of products and services. Quality control professionals use probability sampling to select a sample of products or services for inspection, and the results can be used to identify and correct quality issues.

When to use Probability Sampling

Here are some situations where probability sampling is particularly appropriate:

When the research question involves estimating population parameters: If the research question involves estimating population parameters, such as the mean or proportion, then probability sampling should be used to ensure that the sample is representative of the population.
When the population is homogeneous: If the population is homogeneous, meaning that all members have similar characteristics, then probability sampling can be used to ensure that the sample is representative of the population.
When the population is large : If the population is large, probability sampling can be used to select a smaller, manageable sample that is still representative of the population.
When the research is exploratory : If the research is exploratory, meaning that the research question is open-ended and the goal is to generate new ideas or hypotheses, then probability sampling can be used to ensure that the sample is diverse and representative of the population.

Purpose of Probability Sampling

The purpose of probability sampling is to obtain a sample of participants that is representative of a larger population, with a known level of accuracy or confidence. The goal is to select participants for the sample in such a way that every member of the population has an equal chance of being included in the sample.

By using probability sampling, researchers can increase the likelihood that the sample accurately represents the population, which can allow them to make inferences about the population with greater confidence. Probability sampling also reduces the likelihood of bias in the sample, which can result in more accurate and reliable research findings.

Characteristics of Probability Sampling

The main characteristics of probability sampling are as follows:

Random selection: Probability sampling involves randomly selecting participants from the population of interest. This means that every member of the population has an equal chance of being selected for the sample.
Known probability of selection : In probability sampling, the probability of any member of the population being selected for the sample is known and can be calculated.
Representative sample : Probability sampling aims to obtain a sample that is representative of the larger population. This means that the sample should reflect the characteristics of the population in terms of demographics, behaviors, attitudes, and other relevant variables.
Sampling error: Probability sampling allows researchers to estimate the amount of sampling error, which is the degree of uncertainty in the sample estimates due to chance.
Generalizability: Probability sampling is designed to increase the generalizability of the findings from the sample to the larger population. This means that researchers can make accurate inferences about the population based on the sample data.
Elimination of bias: Probability sampling reduces the likelihood of bias in the sample, as every member of the population has an equal chance of being selected for the sample. This helps to ensure that the sample accurately reflects the population.

Advantages of Probability Sampling

There are several advantages to using probability sampling in research:

Reduced bias: Probability sampling reduces the likelihood of bias in the sample, as every member of the population has an equal chance of being selected for the sample. This helps to ensure that the sample accurately reflects the population.
Known sampling error: Probability sampling allows researchers to estimate the amount of sampling error, which is the degree of uncertainty in the sample estimates due to chance.
Statistical inferences: Probability sampling provides a solid foundation for statistical inferences about the population because the sample is selected randomly and representative of the population.
Comparability of samples: Probability sampling also allows for the comparability of samples over time, which can be useful for tracking changes in the population over time.

Disadvantages of Probability Sampling

Some Disadvantages of Probability Sampling are as follows:

Time-consuming and expensive : Probability sampling requires a list of the population and often involves more resources and time than other sampling methods.
Difficult to access certain populations: In some cases, it may be difficult or impossible to access certain populations, such as those who are homeless, institutionalized, or living in remote areas. This can make it challenging to obtain a representative sample.
Limited sample size : Probability sampling may not be practical or feasible when the population is very large or when the sample size needs to be very small.
Potential non-response bias: Despite using a probability sample, some individuals may choose not to participate in the study, which could introduce non-response bias.
Sampling error : While probability sampling aims to minimize sampling error, there is always the potential for chance variations in the sample that can impact the accuracy of the findings.
Limited flexibility: Probability sampling is generally more rigid and less flexible than other types of sampling methods, which can limit the ability to make changes or adapt to unexpected circumstances.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Stratified Random Sampling – Definition, Method...

Purposive Sampling – Methods, Types and Examples

Non-probability Sampling – Types, Methods and...

Cluster Sampling – Types, Method and Examples

Systematic Sampling – Types, Method and Examples

Snowball Sampling – Method, Types and Examples

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

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

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

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

Factors to Consider When Choosing a Sampling Technique

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4 Types of Sampling Techniques and Their Applications

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

1. Purposive Sampling

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

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

Purposive Sampling: Strengths and Weaknesses

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

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

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

2. Convenience Sampling

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

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

Convenience Sampling: Strengths and Weaknesses

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

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

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

3. Snowball Sampling

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

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

Snowball Sampling: Strengths and Weaknesses

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

4. Theoretical Sampling

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

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

Theoretical Sampling: Strengths and Weaknesses

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

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

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

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

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

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

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

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

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

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

Tips for Selecting Participants

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

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

Ensuring Diversity in Samples

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

Maintaining Ethical Considerations

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

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

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

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

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Qualitative study design: Sampling

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As part of your research, you will need to identify "who" you need to recruit or work with to answer your research question/s. Often this population will be quite large (such as nurses or doctors across Victoria), or they may be difficult to access (such as people with mental health conditions). Sampling is a way that you can choose a smaller group of your population to research and then generalize the results of this across the larger population.

There are several ways that you can sample. Time, money, and difficulty or ease in reaching your target population will shape your sampling decisions. While there are no hard and fast rules around how many people you should involve in your research, some researchers estimate between 10 and 50 participants as being sufficient depending on your type of research and research question (Creswell & Creswell, 2018). Other study designs may require you to continue gathering data until you are no longer discovering new information ("theoretical saturation") or your data is sufficient to answer your question ("data saturation").

Why is it important to think about sampling?

It is important to match your sample as far as possible to the broader population that you wish to generalise to. The extent to which your findings can be applied to settings or people outside of who you have researched ("generalisability") can be influenced by your sample and sampling approach. For example, if you have interviewed homeless people in hospital with mental health conditions, you may not be able to generalise the results of this to every person in Australia with a mental health condition, or every person who is homeless, or every person who is in hospital. Your sampling approach will vary depending on what you are researching, but you might use a non-probability or probability (or randomised) approach.

Non-Probability sampling approaches

Non-Probability sampling is not randomised, meaning that some members of your population will have a higher chance of being included in your study than others. If you wanted to interview homeless people with mental health conditions in hospital and chose only homeless people with mental health conditions at your local hospital, this would be an example of convenience sampling; you have recruited participants who are close to hand. Other times, you may ask your participants if they can recommend other people who may be interested in the study: this is an example of snowball sampling. Lastly, you might want to ask Chief Executive Officers at rural hospitals how they support their staff mental health; this is an example of purposive sampling.

Examples of non-probability sampling include:

Purposive (judgemental)
Convenience

Probability (Randomised) sampling

Probability sampling methods are also called randomised sampling. They are generally preferred in research as this approach means that every person in a population has a chance of being selected for research. Truly randomised sampling is very complex; even a simple random sample requires the use of a random number generator to be used to select participants from a list of sampling frame of the accessible population. For example, if you were to do a probability sample of homeless people in hospital with a mental health condition, you would need to develop a table of all people matching this criteria; allocate each person a number; and then use a random number generator to find your sample pool. For this reason, while probability sampling is preferred, it may not be feasible to draw out a probability sample.

Things to remember:

Sampling involves selecting a small subsection of your population to generalise back to a larger population
Your sampling approach (probability or non-probability) will reflect how you will recruit your participants, and how generalisable your results are to the wider population
How many participants you include in your study will vary based on your research design, research question, and sampling approach

10.2 Sampling in qualitative research

Learning objectives.

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

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

Nonprobability sampling

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

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

Types of nonprobability samples

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

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

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

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

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

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

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

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

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

Key Takeaways

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

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

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

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Methodology

What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Flexibility

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

Natural settings

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

Meaningful insights

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

Generation of new ideas

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

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

Unreliability

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

Subjectivity

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

Limited generalizability

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

Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Methodology Series Module 5: Sampling Strategies

Maninder singh setia.

Epidemiologist, MGM Institute of Health Sciences, Navi Mumbai, Maharashtra, India

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ‘ Sampling Method’. There are essentially two types of sampling methods: 1) probability sampling – based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ‘ random sample’ when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ‘ generalizability’ of these results. In such a scenario, the researcher may want to use ‘ purposive sampling’ for the study.

Introduction

The purpose of this section is to discuss various sampling methods used in research. After finalizing the research question and the research design, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the “Sampling Method” [ Figure 1 ].

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Flowchart from “Universe” to “Sampling Method”

Why do we need to sample?

Let us answer this research question: What is the prevalence of HIV in the adult Indian population?

The best response to this question will be obtained when we test every adult Indian for HIV. However, this is logistically difficult, time consuming, expensive, and difficult for a single researcher – do not forget about ethics of conducting such a study. The government usually conducts an exercise regularly to measure certain outcomes in the whole population – ”the census.” However, as researchers, we often have limited time and resources. Hence, we will have to select a few adult Indians who will consent to be a part of the study. We will test them for HIV and present out results (as our estimates of HIV prevalence). These selected individuals are called our “sample.” We hope that we have selected the appropriate sample that is required to answer our research question.

The researcher should clearly and explicitly mention the sampling method in the manuscript. The description of these helps the reviewers and readers assess the validity and generalizability of the results. Furthermore, the authors should also acknowledge the limitations of their sampling method and its effects on estimated obtained in the study.

Types of Methods

We will try to understand some of these sampling methods that are commonly used in clinical research. There are essentially two types of sampling methods: (1) Probability sampling – based on chance events (such as random numbers, flipping a coin, etc.) and (2) nonprobability sampling – based on researcher's choice, population that accessible and available.

What is a “convenience sample?”

Research question: How many patients with psoriasis also have high cholesterol levels (according to our definition)?

We plan to conduct the study in the outpatient department of our hospital.

This is a common scenario for clinical studies. The researcher recruits the participants who are easily accessible in a clinical setting – this type of sample is called a “convenience sample.” Furthermore, in such a clinic-based setting, the researcher will approach all the psoriasis patients that he/she comes across. They are informed about the study, and all those who consent to be the study are evaluated for eligibility. If they meet the inclusion criteria (and need not be excluded as per the criteria), they are recruited for the study. Thus, this will be “consecutive consenting sample.”

This method is relatively easy and is one of the common types of sampling methods used (particularly in postgraduate dissertations).

Since this is clinic-based sample, the estimates from such a study may not necessarily be generalizable to the larger population. To begin with, the patients who access healthcare potentially have a different “health-seeking behavior” compared with those who do not access health in these settings. Furthermore, many of the clinical cases in tertiary care centers may be severe, complicated, or recalcitrant. Thus, the estimates of biological parameters or outcomes may be different in these compared with the general population. The researcher should clearly discuss in the manuscript/report as to how the convenience sample may have biased the estimates (for example: Overestimated or underestimated the outcome in the population studied).

What is a “random sample?”

A “random sample” is a probability sample where every individual has an equal and independent probability of being selected in the sample.

Please note that “random sample” does not mean arbitrary sample. For example, if the researcher selects 10–12 individuals from the waiting area (without any structure), it is not a random sample. Randomization is a specific process, and only samples that are recruited using this process is a “random sample.”

What is a “simple random sample?”

Let us recruit a “simple random sample” in the above example. The center only allows a fixed number of patients every day. All the patients have to confirm the appointment a day in advance and should present in the clinic between 9 and 9:30 a.m. for the appointment. Thus, by 9:30 a.m., you will all have all the individuals who will be examined day.

We wish to select 50% of these patients for posttreatment survey.

Make a list of all the patients present at 9:30 a.m.
Give a number to each individual
Use a “randomization method” to select five of these numbers. Although “random tables” have been used as a method of randomization, currently, many researchers use “computer-generated lists for random selection” of participants. Most of the statistical packages have programs for random selection of population. Please state the method that you have used for random selection in the manuscript
Recruit the individuals whose numbers have been selected by the randomization method.

The process is described in Figure 2 .

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Representation of Simple Random Sample

What is a major issue with this recruitment process?

As you may notice, “only males” have been recruited for the study. This scenario is possible in a simple random sample selection.

This is a limitation of this type of sampling method – population units which are smaller in number in the sampling frame may be underrepresented in this sample.

What is “stratified sample?”

In a stratified sample, the population is divided into two or more similar groups (based on demographic or clinical characteristics). The sample is recruited from each stratum. The researcher may use a simple random sample procedure within each stratum.

Let us address the limitation in the above example (selection of 50% of the participants for postprocedure survey).

Divide the list into two strata: Males and females
Use a “randomization method” to select three numbers among males and two numbers among females. As discussed earlier, the researcher may use random tables or computer generated random selection. Please state the method that you have used for random selection in the manuscript

The process is described in Figure 3 .

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Representation of Stratified Random Sample

Thus, with this sampling method, we ensure that people from both sexes are included in the sample. This type of sampling method is used for sampling when we want to ensure that minority populations (in number) are adequately represented in the sample.

Kindly note that in this example, we sampled 50% of the population in each stratum. However, the researcher may oversample in one particular stratum and under-sample in the other. For instance, in this example, we may have taken three females and three males (if want to ensure equal representation of both). All this should be discussed explicitly in methods.

What is a “systematic sample?”

Sometimes, the researcher may decide to include study participants using a fixed pattern. For example, the researcher may recruit every second patient, or every patient whose registration ends with an even number or those who are admitted in certain days of the week (Tuesday/Thursday/Saturday). This type of sample is generally easy to implement. However, a lot of the recruitments are based on the researcher and may lead to selection bias. Furthermore, patients who come to the hospital may differ on different days of the week. For example, a higher proportion of working individuals may access the hospital on Saturdays.

This is not a “random sample.” Please do not write that “we selected the participants using a random sample method” if you have selected the sample systematically.

Another type of sampling discussed by some authors is “systematic random sample.” The steps for this method are:

Make a list of all the potential recruits
Using a random method (described earlier) to select a starting point (example number 4)
Select this number and every fifth number from this starting point. Thus, the researcher will select number 9, 14, and so on.

Please note that the “skip” depends on the total number of potential participants and the total sample size. For instance, you have a total of fifty potential participants and you wish to recruit ten participants, do not skip to every 10 th patient.

Aday (1996) states that the skip depends on the total number of participants and the total sample size required.

Fraction = total number of participants/total sample size
In the above example, it will be 50/10 = 5
Thus, using a random table or computer-generated random number selection, the researcher will select a random number from 1 to 5
The number selected in two
The researcher selects the second patient
The next patient will be the fifth patient after patient number two – patient number 7
The next patient will be patient number 12 and so on.

What is a “cluster sample?”

For some studies, the sample is selected from larger units or “clusters.” This type of method is generally used for “community-based studies.”

Research question: What is the prevalence of dermatological conditions in school children in city XXXXX?

In this study, we will select students from multiple schools. Thus, each school becomes one cluster. Each individual child in the school has much in common with other children in the same school compared with children from other schools (for example, they are more likely to have the same socioeconomic background). Thus, these children are recruited from the same cluster.

If the researcher uses “cluster sample,” he/she also performs “cluster analysis.” The statistical methods for these are different compared with nonclustered analysis (the methods we use commonly).

What is a “multistage sample?”

In many studies, we have to combine multiple methods for the appropriate and required sample.

Let us use a multistage sample to answer this research question.

Research question: What is the prevalence of dermatological conditions in school children in city XXXXX? (Assumption: The city is divided into four zones).

We have a list of all the schools in the city. How do we sample them?

Method 1: Select 10% of the schools using “simple random sample” method.

Question: What is the problem with this type of method?

Answer: As discussed earlier, it is possible that we may miss most of the schools from one particular zone.

However, we are interested to ensure that all zones are adequately represented in the sample.

Stage 1: List all the schools in all zones
Stage 2: Select 10% of schools from each zone using “random selection method” (first stratum)
Stage 3: List all the students in Grade VIII, IX, and X(population of interest) in each school (second stratum)
Stage 4: Create a separate list for males and females in each grade in each school (third stratum)
Stage 5: Select 10% of males and females in each grade in each school.

Please note that this is just an example. You may have to change the proportion selected from each stratum based on the sample size and the total number of individuals in each stratum.

What are other types of sampling methods?

Although these are the common types of sampling methods that we use in clinical studies, we have also listed some other sampling methods in Table 1 .

Some other types of sampling methods

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It is important to understand the different sampling methods used in clinical studies. As stated earlier, please mention this method clearly in the manuscript
Do not misrepresent the sampling method. For example, if you have not used “random method” for selection, do not state it in the manuscript
Sometimes, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the “generalizability” of these results. In such a scenario, the researcher may want to use ‘purposive sampling’.

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Conflicts of interest.

There are no conflicts of interest.

Bibliography

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COMMENTS

What Is Probability Sampling?
Revised on June 22, 2023. Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling. To qualify as being random, each research unit (e.g., person, business, or organization in your population) must have an equal chance ...
(PDF) Sampling in Qualitative Research
Abstract. The chapter discusses different types of sampling methods used in qualitative research to select information-rich cases. Two types of sampling techniques are discussed in the past ...
Chapter 5. Sampling
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.
Qualitative Research Sampling Methods: Pros and Cons to Help You Choose
Owing to time and expense, qualitative research often works with small samples of people, cases, or phenomena in particular contexts. Therefore, unlike in quantitative research, samples tend to be more purposive (using your judgment) than they are random (Flick, 2009). This post will cover those main purposive sampling strategies.
Big enough? Sampling in qualitative inquiry
Mine tends to start with a reminder about the different philosophical assumptions undergirding qualitative and quantitative research projects ( Staller, 2013 ). As Abrams (2010) points out, this difference leads to "major differences in sampling goals and strategies." (p.537). Patton (2002) argues, "perhaps nothing better captures the ...
Series: Practical guidance to qualitative research. Part 3: Sampling
A sampling plan is a formal plan specifying a sampling method, a sample size, and procedure for recruiting participants (Box 1) . A qualitative sampling plan describes how many observations, interviews, focus-group discussions or cases are needed to ensure that the findings will contribute rich data.
PDF Sampling Strategies in Qualitative Research
Sampling Strategies in Qualitative Research In: The SAGE Handbook of Qualitative Data Analysis By: Tim Rapley Edited by: Uwe Flick Pub. Date: 2013 ... SAGE Research Methods. Page 2 of 21. Sampling Strategies in Qualitative Research. 1. 1. Sampling can be divided in a number of different ways. At a basic level, with the exception
Sampling Techniques for Qualitative Research
Purposive Sampling. Purposive (or purposeful) sampling is a non-probability technique used to deliberately select the best sources of data to meet the purpose of the study. Purposive sampling is sometimes referred to as theoretical or selective or specific sampling. Theoretical sampling is used in qualitative research when a study is designed ...
Sampling in Qualitative Research
Abstract. In gerontology the most recognized and elaborate discourse about sampling is generally thought to be in quantitative research associated with survey research and medical research. But sampling has long been a central concern in the social and humanistic inquiry, albeit in a different guise suited to the different goals.
Sampling Methods
Abstract. Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration.
Probability Sampling
Probability or random sampling uses methods to systematically select candidates. Ideally, each member of the population has an equal chance of being chosen for a study, making it as representative of the population as possible. A researcher interested in generalizable results may lean towards these types of sampling methods. Methods of Sampling:
Qualitative Sampling Methods
Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of ...
Sampling Methods
There are two primary types of sampling methods that you can use in your research: Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
Purposeful sampling for qualitative data collection and analysis in
Principles of Purposeful Sampling. Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources (Patton, 2002).This involves identifying and selecting individuals or groups of individuals that are especially knowledgeable about or experienced with a phenomenon of interest ...
Probability Sampling
To conduct probability sampling, follow these general steps: Define the Population: Identify the population you want to study and define its characteristics. Determine the Sample Size: Decide on the size of the sample you want to select from the population. This should be based on the research question and the desired level of precision.
Different Types of Sampling Techniques in Qualitative Research
In this section, let's explore four standard sampling techniques used in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We'll break down the definition of each technique, when to use it, and its advantages and disadvantages. 1. Purposive Sampling.
Sampling
Your sampling approach (probability or non-probability) will reflect how you will recruit your participants, and how generalisable your results are to the wider population; How many participants you include in your study will vary based on your research design, research question, and sampling approach . Further reading: Babbie, E. (2008).
10.2 Sampling in qualitative research
Qualitative researchers can also use snowball sampling techniques to identify study participants. In snowball sampling, a researcher identifies one or two people she'd like to include in her study but then relies on those initial participants to help identify additional study participants. Thus, the researcher's sample builds and becomes ...
How to use and assess qualitative research methods
Abstract. This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions ...
What Is Qualitative Research?
Qualitative research methods. Each of the research approaches involve using one or more data collection methods.These are some of the most common qualitative methods: Observations: recording what you have seen, heard, or encountered in detailed field notes. Interviews: personally asking people questions in one-on-one conversations. Focus groups: asking questions and generating discussion among ...
Can we use probability sampling in qualitative research?
Probability sampling cannot be utilized in qualitative inquiry since members of the population of interest are most likely unknown a priori. Besides, probability sampling is of good use only if ...
Methodology Series Module 5: Sampling Strategies
The method by which the researcher selects the sample is the ' Sampling Method'. There are essentially two types of sampling methods: 1) probability sampling - based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling - based on researcher's choice, population that accessible & available.

Chapter 5. Sampling

The “Who” of Your Research Study

Sampling People

When Your Population is Not Composed of People

Case Studies

Content: Documents, Narrative Accounts, And So On

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

What is the Correct “Number” to Sample?

Examples of “Sample” Sections in Journal Articles

Final Words

Further Readings

Qualitative Research Sampling Methods: Pros and Cons to Help You Choose

What you ’ ll learn in this post

Your first step in choosing a qualitative sampling strategy

A priori determination

When to use it

Extreme/deviant sampling

Intensity sampling

Maximum variation sampling

Homogenous sampling

Theory-based sampling

Stratified purposive sampling

More flexible determination

Snowball sampling

Convenience sampling

Confirming and disconfirming cases

So, how’s your research going?

Sampling Techniques for Qualitative Research

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Unit 12: Sampling. When enough is enough.

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Probability Sampling – Methods, Types and Examples

Probability Sampling

Probability Sampling Methods

Simple Random Sampling

Systematic Sampling

Stratified Sampling

Cluster Sampling

Multi-stage Sampling

How to conduct Probability Sampling

Examples of Probability Sampling

Applications of Probability Sampling

When to use Probability Sampling

Purpose of Probability Sampling

Characteristics of Probability Sampling

Advantages of Probability Sampling

Disadvantages of Probability Sampling

About the author

Muhammad Hassan

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Key Takeaways:

In this Article:

Factors to Consider When Choosing a Sampling Technique

4 Types of Sampling Techniques and Their Applications

1. Purposive Sampling

Purposive Sampling: Strengths and Weaknesses

2. Convenience Sampling

Convenience Sampling: Strengths and Weaknesses