Your choice of sampling strategy can deeply impact your research findings, especially in qualitative studies, where every person counts. Show
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 strategySo, 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 determinationComprehensive samplingComprehensive (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 itIt’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
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. Pros
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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 samplingExtreme/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). When to use itIt’s ideal for studying special/unusual cases in a particular context. Pros
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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 samplingIntensity 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). When to use itPatton (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. Pros
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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 samplingThe 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. Pros
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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 samplingThe 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). When to use itUse it if your research aims to specifically focus on a group with shared characteristics. Pros
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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 samplingTheory-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. When to use itUse this one when you want to study a pre-existing theory-derived concept that is of interest to your research. Pros
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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 samplingIn 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:
When to use itUse 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. Pros
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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 determinationTheoretical samplingTheoretical 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). When to use itYou can use this when you’re aiming to generate a new theory about a certain phenomenon. Pros
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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 samplingSnowball 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. 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). When to use itAs hard-to-reach groups are, well, hard to reach, snowball sampling is effective when you need an inroad and cannot easily recruit and sample. Pros
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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 samplingConvenience 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 to use itWhen 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. Pros
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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 casesConfirming 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). When to use itAs the name indicates, generally, it’s ideal for testing emergent findings from your data. Pros
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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. ReferencesAugusto, F. R., & Simões, M. J. (2017). To see and be seen, to know and be known : Perceptions and prevention strategies on Facebook surveillance. Social Science Information, 56(4), 596–618. https://doi.org/10.1177/0539018417734974 Benoot, C., Hannes, K., & Bilsen, J. (2016). The use of purposeful sampling in a qualitative evidence synthesis : A worked example on sexual adjustment to a cancer trajectory. BMC Medical Research Methodology, 16(21), 1–12. https://doi.org/10.1186/s12874-016-0114-6 Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10(2), 141–163. Cloud, W., & Granfield, R. (1994). Terminating Addiction Naturally : Post-Addict Identity and the Avoidance of Treatment Terminating Addiction Naturally : Post-Addict Identity and the Avoidance of Treatment. Clinical Sociology Review, 12(1), 159–174. Flick, U. (2009). An Introduction To Qualitative Research. SAGE Publications (4th ed.). London: Sage Publications, Inc. https://doi.org/978-1-84787-323-1 Gray, D. E. (2004). Doing Research in the Real World. London: Sage Publications, Inc. Heckathorn, D. D. (2011). Comment: snowball versus respondent-driven sampling, 355–366. https://doi.org/10.1111/j.1467-9531.2011.01244.x Nesbitt, S. A., Campbell, K. A., Jack, S. M., Robinson, H., Piehl, K., & Bogdan, J. C. (2012). Canadian adolescent mothers’ perceptions of influences on breastfeeding decisions: a qualitative descriptive study, 1–14. Palacic, R. (2017). The phenomenon of entrepreneurial leadership in gazelles and mice : a qualitative study from Bosnia and Herzegovina. World Review of Entrepreneurship, Management and Sustainable Development, 13(2/3). Patton, M. Q. (2002). Qualitative Research & Evaluation Methods (3rd ed.). California: Sage Publications, Inc. Peters, T. J., & Waterman, R. (2004). In Search of Excellence: Lessons from America’s Best-Run Companies. New York: First Harper Business Essentials. Schreier, M. (2018). Sampling and Generalization In U. Flick (Ed.), The SAGE Handbook of Qualitative Data Collection (pp. 84–98). London, Sage Publications, Inc. Ziebland, S., Chapple, A., Dumelow, C., Evans, J., Prinjha, S., & Rozmovits, L. (2004). Information in practice study: How the internet affects patients’ experience of cancer: A qualitative study. The BMJ, 328(7434). What type of data collection method is most common in qualitative research?Interviews are one of the most common qualitative data-collection methods, and they're a great approach when you need to gather highly personalized information. Informal, conversational interviews are ideal for open-ended questions that allow you to gain rich, detailed context.
What type of data collection method is most common in qualitative research quizlet?B - Because the data in most qualitative studies are the participants thoughts, ideas, and perceptions, data collection is most often done by interviewing and observing participants.
Is random sampling used in qualitative research?For some cases, the use of random sampling in qualitative research comes closer to what is technically known as “random assignment.” In particular, after a purposive sampling process locates a set of eligible data sources, the next step might be to use random selection in deciding which cases to study.
Which type of sampling do qualitative researchers typically use quizlet?Qualitative researchers may start with convenience or snowball sampling, but usually rely on purposive sampling to guide them in selecting data sources that maximize information richness. One purposive strategy is maximum variation sampling, which entails purposely selecting cases with a wide range of variation.
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