47 10.2 Sampling in qualitative research
- Define nonprobability sampling and describe instances when a researcher might choose this sampling technique
- Describe the different types of nonprobability samples
Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of the phenomenon 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 refers to sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. Since we don’t know the likelihood of selection, we don’t know whether a nonprobability sample is truly representative of a larger population. That’s okay because generalizing to a larger population is not the goal with nonprobability samples or qualitative research. That said, this does not mean that nonprobability samples 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). Later, we’ll look more closely at the process of selecting research elements when drawing a nonprobability sample. First, let’s consider why a researcher might choose to use a nonprobability sample.
When are nonprobability samples ideal? One instance might be when we’re starting a big research project. For example, if we are conducting survey research, we may want to administer a draft of our survey to a few people who resemble the folks we’re interested in studying so they can help work out potential kinks. We might also use a nonprobability sample if we’re conducting a pilot study or exploratory research, as it would be a quick way to gather some initial data and help us get a feel of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples are useful for setting up, framing, or beginning any type of 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. They are both nonprobability methods, so 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 they wish to examine and then they seek out research participants who cover that full range of characteristics. For example, if you are studying mental health supports on your campus, you want to be sure to include not only students, but also 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 intentionally selecting specific participants because you know they have characteristics that you need in your sample, like being an administrator or dropping out of mental health supports.
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. Differently, purposive sampling assumes that you know individuals’ characteristics and recruit them based on these criteria. For example, I might recruit Jane for my study because they stopped seeking supports this month, or I might recruit JD because they have worked at the center for many years.
Also, it is important to recognize that purposive sampling requires the researcher to have information about the participants prior to recruitment. In other words, you need to know their perspectives or experiences before you know whether you want them in your sample. While many of my students claim they are using purposive sampling by “recruiting people from the health center,” or something along those lines, purposive sampling involves recruiting specific people based on the characteristics and perspectives they bring to your sample. To solidify this concept, let’s imagine we are recruiting a focus group. In this case, a purposive sample might gather clinicians, current patients, administrators, staff, and former patients so they can talk as a group. Purposive sampling would seek out people that have each of those attributes.
Quota sampling takes purposive sampling one step further by identifying categories that are important to the study and for which there is likely to be some variation. In this nonprobability sampling method, subgroups are created based on each category, the researcher decides how many people to include from each subgroup, and then 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. In addition, it is possible 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 Literary Digest, the leading polling entity at the time, 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.  Among other problems, Gallup’s quota categories did not represent those who actually voted (Neuman, 2007).  This underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods.  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 they would like to include in their 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 interested in studying how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting an ad in the newspaper or by announcing the study at a social gathering. Instead, the researcher might know someone with the condition, interview that person, and ask the person to refer others they may know with the genital herpes to contact you to participate in the study. Having a previous participant vouch for the researcher may help new potential participants feel more comfortable about being included in the study.
Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. Aside from being a useful strategy for stigmatized groups, snowball sampling is also useful when the interest group may be difficult to find or the group may be relatively rare. This was the case for Steven Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011)  who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers initially relied on their own networks to identify study participants, but 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 in their situation. 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 people or other relevant elements that they can access conveniently. Also known as availability sampling, convenience sampling is the most useful in exploratory research or student projects where 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. In the next section on probability sampling, we will discuss this concept in greater detail.
|Purposive||Researcher seeks out participants with specific characteristics.|
|Snowball||Researcher relies on participant referrals to recruit new participants.|
|Quota||Researcher selects cases from within several different subgroups.|
|Convenience||Researcher gathers data from whatever cases happen to be convenient.|
- 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
- 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. ↵