qualitative research survey sample size

How to Justify Sample Size in Qualitative Research

• March 21, 2023

Article Summary : Sample sizes in qualitative research can be much lower than sample sizes in quantitative research. The key is having the right participant segmentation and study design. Data saturation is also a key principle to understand.

Qualitative research is a bit of a puzzle for new practitioners: since it is done via interviewing participants, observation, or studying people’s patterns and movements (in the case of user experience design), one can’t obviously have a huge sample size that is statistically significant. Interviewing 200+ people is not only incredibly time-consuming, it’s also quite expensive.

And, moreover, the goal of qualitative research is not to understand how much or how many. The goal is to collect themes and see patterns. It’s to uncover the “why” versus the amount.

So in this post, we’re going to explore the question every qualitative researcher asks, at one point or another: How do you justify the sample size in qualitative research?

Here are some guidelines.

Qualitative sample size guideline #1: Segmentation of participants

In qualitative research, because the goal is to understand themes and patterns of a particular subset (versus a broad population), the first step is segmentation. You may also know of this as “ persona ” development, but regardless of what you call it, the idea is to first bucket your various buyer/customer types into like-categories. For example, if you’re selling sales software, your target isn’t every single company who sells products. It’s likely much more specific: like mid-market sized VP-level sales execs who have a technology product and use a cloud-based CRM. If that’s your main buyer, that’s your segment who you would focus on in qualitative research.

Generally, most companies have multiple targets, so the trick is to think about all the various buyers/consumers and identify which underlying traits they have in common, as well as which traits differentiate them from other targets. Typically, this is where quantitative data comes into play: either through internal data analysis or surveys. Whatever your process, this is step 1 to figure out the segments you will be bucketing participants into so you can move into the qualitative phase, where you’ll ask in-depth questions, via interviews, to each segment category. At this stage, it’s time to bring in your recruiting company to find your participants.

Qualitative sample size guideline #2: Figure out the appropriate study design

After you’ve tackled your segmentation exercise and know how to divide up your participants, you’ll need to think through the qualitative methodology that is most appropriate for answering your research questions. At InterQ Research, we always design studies through the lens of contextual research. This means that you want to set up your studies to be as close to real life as possible. Is your product sale done through a group discussion or individual decision? Often, when teams decide on software or technology stacks, they’ll want to test it and talk amongst themselves. If this is the case, you would need to interview the team or a team of like-minded professionals to see how they come to a decision. In this case, focus groups would be a great methodology.

Conversely, if your product is thought through on an individual-basis, like, perhaps, a person navigating a website when purchasing a plane ticket, then you’d want to interview the individual, alone. In this case, you’d want to choose a hybrid approach, of a user experience/journey mapping exercise, along with an in-depth interview.

In qualitative research, there are numerous methodologies, and frequently, mixed-methodologies work best, in order to see the context of how people behave, as well as to understand how they think.

But I digress. Let’s get back to sample sizes in qualitative research.

Qualitative sample size guideline #3: Your sample size is completed when you reach saturation

So far we’ve covered how to first segment your audiences, and then we’ve talked about the methodology to choose, based on context. The third principle in qualitative research is to understand the theory of data saturation.

Saturation in qualitative research means that, when interviewing a distinct segment of participants, you are able to explore all of the common themes the sample set has in common. In other words, after doing, let’s say, 15 interviews about a specific topic, you start to hear the participants all say similar things. Since you have a fairly homogenous sample, these themes will start to come out after 10-20 interviews, if you’ve done your recruiting well (and sometimes as soon as 6 interviews). Once you hear the same themes, with no new information, this is data saturation.

The beauty of qualitative research is that if you:

• Segment your audiences carefully, into distinct groups, and,
• Choose the right methodology

You’ll start to hit saturation, and you will get diminishing returns with more interviews. In this manner, qualitative research can have smaller sample sizes than quantitative, since it’s thematic, versus statistical.

Let’s wrap it up: So what is the ideal sample size in qualitative research?

To bring this one home, let’s answer the question we sought out to investigate: the sample size in qualitative research.

Typically, sample sizes will range from 6-20, per segment. (So if you have 5 segments, 6 is your multiplier for the total number you’ll need, so you would have a total sample size of 30.) For very specific tasks, such as in user experience research, moderators will see the same themes after as few as 5-6 interviews. In most studies, though, researchers will reach saturation after 10-20 interviews. The variable here depends on how homogenous the sample is, as well as the type of questions being asked. Some researchers aim for a bakers dozen (13), and see if they’ve reached saturation after 13. If not, the study can be expanded to find more participants so that all the themes can be explored. But 13 is a good place to start.

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Qualitative Market Research

ISSN : 1352-2752

Article publication date: 12 September 2016

Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research.

Design/methodology/approach

The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years.

In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population.

Practical implications

Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research.

Originality/value

Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

• Qualitative research
• Qualitative methodology
• Case studies
• Sample size

Boddy, C.R. (2016), "Sample size for qualitative research", Qualitative Market Research , Vol. 19 No. 4, pp. 426-432. https://doi.org/10.1108/QMR-06-2016-0053

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Sample Size Policy for Qualitative Studies Using In-Depth Interviews

• Published: 12 September 2012
• Volume 41 , pages 1319–1320, ( 2012 )

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• Shari L. Dworkin 1  

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In recent years, there has been an increase in submissions to the Journal that draw on qualitative research methods. This increase is welcome and indicates not only the interdisciplinarity embraced by the Journal (Zucker, 2002 ) but also its commitment to a wide array of methodologies.

For those who do select qualitative methods and use grounded theory and in-depth interviews in particular, there appear to be a lot of questions that authors have had recently about how to write a rigorous Method section. This topic will be addressed in a subsequent Editorial. At this time, however, the most common question we receive is: “How large does my sample size have to be?” and hence I would like to take this opportunity to answer this question by discussing relevant debates and then the policy of the Archives of Sexual Behavior . Footnote 1

The sample size used in qualitative research methods is often smaller than that used in quantitative research methods. This is because qualitative research methods are often concerned with garnering an in-depth understanding of a phenomenon or are focused on meaning (and heterogeneities in meaning )—which are often centered on the how and why of a particular issue, process, situation, subculture, scene or set of social interactions. In-depth interview work is not as concerned with making generalizations to a larger population of interest and does not tend to rely on hypothesis testing but rather is more inductive and emergent in its process. As such, the aim of grounded theory and in-depth interviews is to create “categories from the data and then to analyze relationships between categories” while attending to how the “lived experience” of research participants can be understood (Charmaz, 1990 , p. 1162).

There are several debates concerning what sample size is the right size for such endeavors. Most scholars argue that the concept of saturation is the most important factor to think about when mulling over sample size decisions in qualitative research (Mason, 2010 ). Saturation is defined by many as the point at which the data collection process no longer offers any new or relevant data. Another way to state this is that conceptual categories in a research project can be considered saturated “when gathering fresh data no longer sparks new theoretical insights, nor reveals new properties of your core theoretical categories” (Charmaz, 2006 , p. 113). Saturation depends on many factors and not all of them are under the researcher’s control. Some of these include: How homogenous or heterogeneous is the population being studied? What are the selection criteria? How much money is in the budget to carry out the study? Are there key stratifiers (e.g., conceptual, demographic) that are critical for an in-depth understanding of the topic being examined? What is the timeline that the researcher faces? How experienced is the researcher in being able to even determine when she or he has actually reached saturation (Charmaz, 2006 )? Is the author carrying out theoretical sampling and is, therefore, concerned with ensuring depth on relevant concepts and examining a range of concepts and characteristics that are deemed critical for emergent findings (Glaser & Strauss, 1967 ; Strauss & Corbin, 1994 , 2007 )?

While some experts in qualitative research avoid the topic of “how many” interviews “are enough,” there is indeed variability in what is suggested as a minimum. An extremely large number of articles, book chapters, and books recommend guidance and suggest anywhere from 5 to 50 participants as adequate. All of these pieces of work engage in nuanced debates when responding to the question of “how many” and frequently respond with a vague (and, actually, reasonable) “it depends.” Numerous factors are said to be important, including “the quality of data, the scope of the study, the nature of the topic, the amount of useful information obtained from each participant, the use of shadowed data, and the qualitative method and study designed used” (Morse, 2000 , p. 1). Others argue that the “how many” question can be the wrong question and that the rigor of the method “depends upon developing the range of relevant conceptual categories, saturating (filling, supporting, and providing repeated evidence for) those categories,” and fully explaining the data (Charmaz, 1990 ). Indeed, there have been countless conferences and conference sessions on these debates, reports written, and myriad publications are available as well (for a compilation of debates, see Baker & Edwards, 2012 ).

Taking all of these perspectives into account, the Archives of Sexual Behavior is putting forward a policy for authors in order to have more clarity on what is expected in terms of sample size for studies drawing on grounded theory and in-depth interviews. The policy of the Archives of Sexual Behavior will be that it adheres to the recommendation that 25–30 participants is the minimum sample size required to reach saturation and redundancy in grounded theory studies that use in-depth interviews. This number is considered adequate for publications in journals because it (1) may allow for thorough examination of the characteristics that address the research questions and to distinguish conceptual categories of interest, (2) maximizes the possibility that enough data have been collected to clarify relationships between conceptual categories and identify variation in processes, and (3) maximizes the chances that negative cases and hypothetical negative cases have been explored in the data (Charmaz, 2006 ; Morse, 1994 , 1995 ).

The Journal does not want to paradoxically and rigidly quantify sample size when the endeavor at hand is qualitative in nature and the debates on this matter are complex. However, we are providing this practical guidance. We want to ensure that more of our submissions have an adequate sample size so as to get closer to reaching the goal of saturation and redundancy across relevant characteristics and concepts. The current recommendation that is being put forward does not include any comment on other qualitative methodologies, such as content and textual analysis, participant observation, focus groups, case studies, clinical cases or mixed quantitative–qualitative methods. The current recommendation also does not apply to phenomenological studies or life history approaches. The current guidance is intended to offer one clear and consistent standard for research projects that use grounded theory and draw on in-depth interviews.

Editor’s note: Dr. Dworkin is an Associate Editor of the Journal and is responsible for qualitative submissions.

Baker, S. E., & Edwards, R. (2012). How many qualitative interviews is enough? National Center for Research Methods. Available at: http://eprints.ncrm.ac.uk/2273/ .

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Dworkin, S.L. Sample Size Policy for Qualitative Studies Using In-Depth Interviews. Arch Sex Behav 41 , 1319–1320 (2012). https://doi.org/10.1007/s10508-012-0016-6

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Published : 12 September 2012

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DOI : https://doi.org/10.1007/s10508-012-0016-6

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Sample size calculator

Updated December 8, 2023

Introduction to sample size

How can you calculate sample size, reduce the margin of error and produce surveys with statistically significant results? In this short guide, we explain how you can improve your surveys and showcase some of the tools and resources you can leverage in the process.

But first, when it comes to market research, how many people do you need to interview to get results representative of the target population with the level of confidence that you are willing to accept?

However, if all of this sounds new to you, let's start with what sample size is.

Free eBook: The complete guide to determining sample size

What is sample size?

Sample size is a term used in market research to define the number of subjects included in a survey, study, or experiment. In surveys with large populations, sample size is incredibly important. The reason for this is because it's unrealistic to get answers or results from everyone - instead, you can take a random sample of individuals that represent the population as a whole.

For example, we might want to compare the performance of long-distance runners that eat Weetabix for breakfast versus those who don't. Since it's impossible to track the dietary habits of every long-distance runner across the globe, we would have to focus on a segment of the survey population. This might mean selecting 1,000 runners for the study.

How can sample size influence results?

That said, no matter how diligent we are with our selection, there will always be some margin of error (also referred to as confidence interval) in the study results, that's because we can't speak to every long-distance runner or be confident of how Weetabix influences (in every possible scenario), the performance of long-distance runners. This is known as a "sampling error."

Larger sample sizes will help to mitigate the margin of error, helping to provide more statistically significant and meaningful results. In other words, a more accurate picture of how eating Weetabix can influence the performance of long-distance runners.

So what do you need to know when calculating the minimum sample size needed for a research project?

What you need to know to calculate survey sample size

Confidence interval (or margin of error).

The confidence interval is the plus-or-minus figure that represents the accuracy of the reported. Consider the following example:

A Canadian national sample showed "Who Canadians spend their money on for Mother's Day." Eighty-two percent of Canadians expect to buy gifts for their mom, compared to 20 percent for their wife and 15 percent for their mother-in-law. In terms of spending, Canadians expect to spend $93 on their wife this Mother's Day versus $58 on their mother. The national findings are accurate, plus or minus 2.75 percent, 19 times out of 20.

For example, if you use a confidence interval of 2.75 and 82% percent of your sample indicates they will "buy a gift for mom" you can be "confident (95% or 99%)" that if you had asked the question to ALL CANADIANS, somewhere between 79.25% (82%-2.75%) and 84.75% (82%+2.75%) would have picked that answer.

Confidence interval is also called the "margin of error." Are you needing to understand how the two calculations correlate?

Confidence level

The confidence level tells you how confident you are of this result. It is expressed as a percentage of times that different samples (if repeated samples were drawn) would produce this result. The 95% confidence level means that 19 times out of twenty that results would fall in this - + interval confidence interval. The 95% confidence level is the most commonly used.

When you put the confidence level and the confidence interval together, you can say that you are 95% (19 out of 20) sure that the true percentage of the population that will "buy a gift for mom" is between 79.25% and 84.75%.

Wider confidence intervals increase the certainty that the true answer is within the range specified. These wider confidence intervals come from smaller sample sizes. When the costs of an error is extremely high (a multi-million dollar decision is at stake) the confidence interval should be kept small. This can be done by increasing the sample size.

Population size

Population size is the total amount of people in the group you're trying to study. If you were taking a random sample of people across the U.K., then your population size would be just over 68 million (as of 09 August 2021).

Standard deviation

This refers to how much individual responses will vary between each other and the mean. If there's a low standard deviation, scores will be clustered near the mean with minimal variation. A higher standard deviation means that when plotted on a graph, responses will be more spread out.

Standard deviation is expressed as a decimal, and 0.5 is considered a "good" standard deviation to set to ensure a sample size that represents the population.

How can you calculate sample size?

After you've considered the four above variables, you should have everything required to calculate your sample size.

However, if you don't know your population size, you can still calculate your sample size. To do this, you need two pieces of information: a z-score and the sample size formula.

What is a z-score?

A z-score is simply the numerical representation of your desired confidence level. It tells you how many standard deviations from the mean your score is.

The most common percentages are 90%, 95%, and 99%.

z = (x – μ) / σ

As the formula shows, the z-score is simply the raw score minus the population mean and divided by the population's standard deviation.

Using a sample size calculation

Once you have your z-score, you can fill out your sample size formula, which is:

Is there an easier way to calculate sample size?

If you want an easier option, Qualtrics offers an online sample size calculator that can help you determine your ideal survey sample size in seconds. Just put in the confidence level, population size, margin of error, and the perfect sample size is calculated for you.

Best-practice tips for sample size

There are lots of variables to consider when it comes to generating a specific sample size. That said, there are a few best-practice tips (or rules) to ensure you get the best possible results:

1) Balance cost and confidence level

To increase confidence level or reduce the margin of error, you have to increase your sample size. Larger sizes almost invariably lead to higher costs. Take the time to consider what results you want from your surveys and what role it plays in your overall campaigns.

2) You don't always need statistically significant results

Depending on your target audience, you may not be able to get enough responses (or a large enough sample size) to achieve "statistically significant" results.

If it's for your own research and not a wider study, it might not be that much of a problem, but remember that any feedback you get from your surveys is important. It might not be statistically significant, but it can aid your activities going forward.

Ultimately, you should treat this on a case-by-case basis. Survey samples can still give you valuable answers without having sample sizes that represent the general population. But more on this in the section below.

3) Ask open-ended questions

Yes and no questions provide certainty, but open-ended questions provide insights you would have otherwise not received. To get the best results, it's worth having a mix of closed and open-ended questions. For a deeper dive into survey question types, check out our handbook.

Different types of surveys

From market research to customer satisfaction, there are plenty of different surveys that you can carry out to get the information you need, corresponding with your sample size.

The great thing about what we do at Qualtrics is that we offer a comprehensive collection of pre-made, customer, product, employee, and brand survey templates. This includes Net Promoter Score (NPS) surveys, manager feedback surveys, customer service surveys, and more.

The best part? You can access all of these templates for free. Each one is designed by our specialist team of subject matter experts and researchers so you can be sure that our best-practice question choices and clear designs will get more engagement and better quality data.

As well as offering free survey templates, you can check out our free survey builder. Trusted by over 11,000 brands and 99 of the top 100 business schools, our tool allows you to create, distribute and analyze surveys to find customer, employee, brand, product, and market research insights.

Drag-and-drop functionality means anyone can use it, and wherever you need to gather and analyze data, our platform can help.

Once you have determined your sample size , you’re ready for the next step in the research journey. market research.

Market research is the process of gathering information about consumers' needs and preferences, and it can provide incredible insights that help elevate your business (or your customers') to the next level.

If you want to learn more, we've got you covered. Just download our free guide and find out how you can:

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• Research article
• Open access
• Published: 21 November 2018

Characterising and justifying sample size sufficiency in interview-based studies: systematic analysis of qualitative health research over a 15-year period

• Konstantina Vasileiou   ORCID: orcid.org/0000-0001-5047-3920 1 ,
• Julie Barnett 1 ,
• Susan Thorpe 2 &
• Terry Young 3  

BMC Medical Research Methodology volume  18 , Article number:  148 ( 2018 ) Cite this article

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Choosing a suitable sample size in qualitative research is an area of conceptual debate and practical uncertainty. That sample size principles, guidelines and tools have been developed to enable researchers to set, and justify the acceptability of, their sample size is an indication that the issue constitutes an important marker of the quality of qualitative research. Nevertheless, research shows that sample size sufficiency reporting is often poor, if not absent, across a range of disciplinary fields.

A systematic analysis of single-interview-per-participant designs within three health-related journals from the disciplines of psychology, sociology and medicine, over a 15-year period, was conducted to examine whether and how sample sizes were justified and how sample size was characterised and discussed by authors. Data pertinent to sample size were extracted and analysed using qualitative and quantitative analytic techniques.

Our findings demonstrate that provision of sample size justifications in qualitative health research is limited; is not contingent on the number of interviews; and relates to the journal of publication. Defence of sample size was most frequently supported across all three journals with reference to the principle of saturation and to pragmatic considerations. Qualitative sample sizes were predominantly – and often without justification – characterised as insufficient (i.e., ‘small’) and discussed in the context of study limitations. Sample size insufficiency was seen to threaten the validity and generalizability of studies’ results, with the latter being frequently conceived in nomothetic terms.

Conclusions

We recommend, firstly, that qualitative health researchers be more transparent about evaluations of their sample size sufficiency, situating these within broader and more encompassing assessments of data adequacy . Secondly, we invite researchers critically to consider how saturation parameters found in prior methodological studies and sample size community norms might best inform, and apply to, their own project and encourage that data adequacy is best appraised with reference to features that are intrinsic to the study at hand. Finally, those reviewing papers have a vital role in supporting and encouraging transparent study-specific reporting.

Peer Review reports

Sample adequacy in qualitative inquiry pertains to the appropriateness of the sample composition and size . It is an important consideration in evaluations of the quality and trustworthiness of much qualitative research [ 1 ] and is implicated – particularly for research that is situated within a post-positivist tradition and retains a degree of commitment to realist ontological premises – in appraisals of validity and generalizability [ 2 , 3 , 4 , 5 ].

Samples in qualitative research tend to be small in order to support the depth of case-oriented analysis that is fundamental to this mode of inquiry [ 5 ]. Additionally, qualitative samples are purposive, that is, selected by virtue of their capacity to provide richly-textured information, relevant to the phenomenon under investigation. As a result, purposive sampling [ 6 , 7 ] – as opposed to probability sampling employed in quantitative research – selects ‘information-rich’ cases [ 8 ]. Indeed, recent research demonstrates the greater efficiency of purposive sampling compared to random sampling in qualitative studies [ 9 ], supporting related assertions long put forward by qualitative methodologists.

Sample size in qualitative research has been the subject of enduring discussions [ 4 , 10 , 11 ]. Whilst the quantitative research community has established relatively straightforward statistics-based rules to set sample sizes precisely, the intricacies of qualitative sample size determination and assessment arise from the methodological, theoretical, epistemological, and ideological pluralism that characterises qualitative inquiry (for a discussion focused on the discipline of psychology see [ 12 ]). This mitigates against clear-cut guidelines, invariably applied. Despite these challenges, various conceptual developments have sought to address this issue, with guidance and principles [ 4 , 10 , 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], and more recently, an evidence-based approach to sample size determination seeks to ground the discussion empirically [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].

Focusing on single-interview-per-participant qualitative designs, the present study aims to further contribute to the dialogue of sample size in qualitative research by offering empirical evidence around justification practices associated with sample size. We next review the existing conceptual and empirical literature on sample size determination.

Sample size in qualitative research: Conceptual developments and empirical investigations

Qualitative research experts argue that there is no straightforward answer to the question of ‘how many’ and that sample size is contingent on a number of factors relating to epistemological, methodological and practical issues [ 36 ]. Sandelowski [ 4 ] recommends that qualitative sample sizes are large enough to allow the unfolding of a ‘new and richly textured understanding’ of the phenomenon under study, but small enough so that the ‘deep, case-oriented analysis’ (p. 183) of qualitative data is not precluded. Morse [ 11 ] posits that the more useable data are collected from each person, the fewer participants are needed. She invites researchers to take into account parameters, such as the scope of study, the nature of topic (i.e. complexity, accessibility), the quality of data, and the study design. Indeed, the level of structure of questions in qualitative interviewing has been found to influence the richness of data generated [ 37 ], and so, requires attention; empirical research shows that open questions, which are asked later on in the interview, tend to produce richer data [ 37 ].

Beyond such guidance, specific numerical recommendations have also been proffered, often based on experts’ experience of qualitative research. For example, Green and Thorogood [ 38 ] maintain that the experience of most qualitative researchers conducting an interview-based study with a fairly specific research question is that little new information is generated after interviewing 20 people or so belonging to one analytically relevant participant ‘category’ (pp. 102–104). Ritchie et al. [ 39 ] suggest that studies employing individual interviews conduct no more than 50 interviews so that researchers are able to manage the complexity of the analytic task. Similarly, Britten [ 40 ] notes that large interview studies will often comprise of 50 to 60 people. Experts have also offered numerical guidelines tailored to different theoretical and methodological traditions and specific research approaches, e.g. grounded theory, phenomenology [ 11 , 41 ]. More recently, a quantitative tool was proposed [ 42 ] to support a priori sample size determination based on estimates of the prevalence of themes in the population. Nevertheless, this more formulaic approach raised criticisms relating to assumptions about the conceptual [ 43 ] and ontological status of ‘themes’ [ 44 ] and the linearity ascribed to the processes of sampling, data collection and data analysis [ 45 ].

In terms of principles, Lincoln and Guba [ 17 ] proposed that sample size determination be guided by the criterion of informational redundancy , that is, sampling can be terminated when no new information is elicited by sampling more units. Following the logic of informational comprehensiveness Malterud et al. [ 18 ] introduced the concept of information power as a pragmatic guiding principle, suggesting that the more information power the sample provides, the smaller the sample size needs to be, and vice versa.

Undoubtedly, the most widely used principle for determining sample size and evaluating its sufficiency is that of saturation . The notion of saturation originates in grounded theory [ 15 ] – a qualitative methodological approach explicitly concerned with empirically-derived theory development – and is inextricably linked to theoretical sampling. Theoretical sampling describes an iterative process of data collection, data analysis and theory development whereby data collection is governed by emerging theory rather than predefined characteristics of the population. Grounded theory saturation (often called theoretical saturation) concerns the theoretical categories – as opposed to data – that are being developed and becomes evident when ‘gathering fresh data no longer sparks new theoretical insights, nor reveals new properties of your core theoretical categories’ [ 46 p. 113]. Saturation in grounded theory, therefore, does not equate to the more common focus on data repetition and moves beyond a singular focus on sample size as the justification of sampling adequacy [ 46 , 47 ]. Sample size in grounded theory cannot be determined a priori as it is contingent on the evolving theoretical categories.

Saturation – often under the terms of ‘data’ or ‘thematic’ saturation – has diffused into several qualitative communities beyond its origins in grounded theory. Alongside the expansion of its meaning, being variously equated with ‘no new data’, ‘no new themes’, and ‘no new codes’, saturation has emerged as the ‘gold standard’ in qualitative inquiry [ 2 , 26 ]. Nevertheless, and as Morse [ 48 ] asserts, whilst saturation is the most frequently invoked ‘guarantee of qualitative rigor’, ‘it is the one we know least about’ (p. 587). Certainly researchers caution that saturation is less applicable to, or appropriate for, particular types of qualitative research (e.g. conversation analysis, [ 49 ]; phenomenological research, [ 50 ]) whilst others reject the concept altogether [ 19 , 51 ].

Methodological studies in this area aim to provide guidance about saturation and develop a practical application of processes that ‘operationalise’ and evidence saturation. Guest, Bunce, and Johnson [ 26 ] analysed 60 interviews and found that saturation of themes was reached by the twelfth interview. They noted that their sample was relatively homogeneous, their research aims focused, so studies of more heterogeneous samples and with a broader scope would be likely to need a larger size to achieve saturation. Extending the enquiry to multi-site, cross-cultural research, Hagaman and Wutich [ 28 ] showed that sample sizes of 20 to 40 interviews were required to achieve data saturation of meta-themes that cut across research sites. In a theory-driven content analysis, Francis et al. [ 25 ] reached data saturation at the 17th interview for all their pre-determined theoretical constructs. The authors further proposed two main principles upon which specification of saturation be based: (a) researchers should a priori specify an initial analysis sample (e.g. 10 interviews) which will be used for the first round of analysis and (b) a stopping criterion , that is, a number of interviews (e.g. 3) that needs to be further conducted, the analysis of which will not yield any new themes or ideas. For greater transparency, Francis et al. [ 25 ] recommend that researchers present cumulative frequency graphs supporting their judgment that saturation was achieved. A comparative method for themes saturation (CoMeTS) has also been suggested [ 23 ] whereby the findings of each new interview are compared with those that have already emerged and if it does not yield any new theme, the ‘saturated terrain’ is assumed to have been established. Because the order in which interviews are analysed can influence saturation thresholds depending on the richness of the data, Constantinou et al. [ 23 ] recommend reordering and re-analysing interviews to confirm saturation. Hennink, Kaiser and Marconi’s [ 29 ] methodological study sheds further light on the problem of specifying and demonstrating saturation. Their analysis of interview data showed that code saturation (i.e. the point at which no additional issues are identified) was achieved at 9 interviews, but meaning saturation (i.e. the point at which no further dimensions, nuances, or insights of issues are identified) required 16–24 interviews. Although breadth can be achieved relatively soon, especially for high-prevalence and concrete codes, depth requires additional data, especially for codes of a more conceptual nature.

Critiquing the concept of saturation, Nelson [ 19 ] proposes five conceptual depth criteria in grounded theory projects to assess the robustness of the developing theory: (a) theoretical concepts should be supported by a wide range of evidence drawn from the data; (b) be demonstrably part of a network of inter-connected concepts; (c) demonstrate subtlety; (d) resonate with existing literature; and (e) can be successfully submitted to tests of external validity.

Other work has sought to examine practices of sample size reporting and sufficiency assessment across a range of disciplinary fields and research domains, from nutrition [ 34 ] and health education [ 32 ], to education and the health sciences [ 22 , 27 ], information systems [ 30 ], organisation and workplace studies [ 33 ], human computer interaction [ 21 ], and accounting studies [ 24 ]. Others investigated PhD qualitative studies [ 31 ] and grounded theory studies [ 35 ]. Incomplete and imprecise sample size reporting is commonly pinpointed by these investigations whilst assessment and justifications of sample size sufficiency are even more sporadic.

Sobal [ 34 ] examined the sample size of qualitative studies published in the Journal of Nutrition Education over a period of 30 years. Studies that employed individual interviews ( n  = 30) had an average sample size of 45 individuals and none of these explicitly reported whether their sample size sought and/or attained saturation. A minority of articles discussed how sample-related limitations (with the latter most often concerning the type of sample, rather than the size) limited generalizability. A further systematic analysis [ 32 ] of health education research over 20 years demonstrated that interview-based studies averaged 104 participants (range 2 to 720 interviewees). However, 40% did not report the number of participants. An examination of 83 qualitative interview studies in leading information systems journals [ 30 ] indicated little defence of sample sizes on the basis of recommendations by qualitative methodologists, prior relevant work, or the criterion of saturation. Rather, sample size seemed to correlate with factors such as the journal of publication or the region of study (US vs Europe vs Asia). These results led the authors to call for more rigor in determining and reporting sample size in qualitative information systems research and to recommend optimal sample size ranges for grounded theory (i.e. 20–30 interviews) and single case (i.e. 15–30 interviews) projects.

Similarly, fewer than 10% of articles in organisation and workplace studies provided a sample size justification relating to existing recommendations by methodologists, prior relevant work, or saturation [ 33 ], whilst only 17% of focus groups studies in health-related journals provided an explanation of sample size (i.e. number of focus groups), with saturation being the most frequently invoked argument, followed by published sample size recommendations and practical reasons [ 22 ]. The notion of saturation was also invoked by 11 out of the 51 most highly cited studies that Guetterman [ 27 ] reviewed in the fields of education and health sciences, of which six were grounded theory studies, four phenomenological and one a narrative inquiry. Finally, analysing 641 interview-based articles in accounting, Dai et al. [ 24 ] called for more rigor since a significant minority of studies did not report precise sample size.

Despite increasing attention to rigor in qualitative research (e.g. [ 52 ]) and more extensive methodological and analytical disclosures that seek to validate qualitative work [ 24 ], sample size reporting and sufficiency assessment remain inconsistent and partial, if not absent, across a range of research domains.

Objectives of the present study

The present study sought to enrich existing systematic analyses of the customs and practices of sample size reporting and justification by focusing on qualitative research relating to health. Additionally, this study attempted to expand previous empirical investigations by examining how qualitative sample sizes are characterised and discussed in academic narratives. Qualitative health research is an inter-disciplinary field that due to its affiliation with medical sciences, often faces views and positions reflective of a quantitative ethos. Thus qualitative health research constitutes an emblematic case that may help to unfold underlying philosophical and methodological differences across the scientific community that are crystallised in considerations of sample size. The present research, therefore, incorporates a comparative element on the basis of three different disciplines engaging with qualitative health research: medicine, psychology, and sociology. We chose to focus our analysis on single-per-participant-interview designs as this not only presents a popular and widespread methodological choice in qualitative health research, but also as the method where consideration of sample size – defined as the number of interviewees – is particularly salient.

Study design

A structured search for articles reporting cross-sectional, interview-based qualitative studies was carried out and eligible reports were systematically reviewed and analysed employing both quantitative and qualitative analytic techniques.

We selected journals which (a) follow a peer review process, (b) are considered high quality and influential in their field as reflected in journal metrics, and (c) are receptive to, and publish, qualitative research (Additional File  1 presents the journals’ editorial positions in relation to qualitative research and sample considerations where available). Three health-related journals were chosen, each representing a different disciplinary field; the British Medical Journal (BMJ) representing medicine, the British Journal of Health Psychology (BJHP) representing psychology, and the Sociology of Health & Illness (SHI) representing sociology.

Search strategy to identify studies

Employing the search function of each individual journal, we used the terms ‘interview*’ AND ‘qualitative’ and limited the results to articles published between 1 January 2003 and 22 September 2017 (i.e. a 15-year review period).

Eligibility criteria

To be eligible for inclusion in the review, the article had to report a cross-sectional study design. Longitudinal studies were thus excluded whilst studies conducted within a broader research programme (e.g. interview studies nested in a trial, as part of a broader ethnography, as part of a longitudinal research) were included if they reported only single-time qualitative interviews. The method of data collection had to be individual, synchronous qualitative interviews (i.e. group interviews, structured interviews and e-mail interviews over a period of time were excluded), and the data had to be analysed qualitatively (i.e. studies that quantified their qualitative data were excluded). Mixed method studies and articles reporting more than one qualitative method of data collection (e.g. individual interviews and focus groups) were excluded. Figure  1 , a PRISMA flow diagram [ 53 ], shows the number of: articles obtained from the searches and screened; papers assessed for eligibility; and articles included in the review (Additional File  2 provides the full list of articles included in the review and their unique identifying code – e.g. BMJ01, BJHP02, SHI03). One review author (KV) assessed the eligibility of all papers identified from the searches. When in doubt, discussions about retaining or excluding articles were held between KV and JB in regular meetings, and decisions were jointly made.

PRISMA flow diagram

Data extraction and analysis

A data extraction form was developed (see Additional File  3 ) recording three areas of information: (a) information about the article (e.g. authors, title, journal, year of publication etc.); (b) information about the aims of the study, the sample size and any justification for this, the participant characteristics, the sampling technique and any sample-related observations or comments made by the authors; and (c) information about the method or technique(s) of data analysis, the number of researchers involved in the analysis, the potential use of software, and any discussion around epistemological considerations. The Abstract, Methods and Discussion (and/or Conclusion) sections of each article were examined by one author (KV) who extracted all the relevant information. This was directly copied from the articles and, when appropriate, comments, notes and initial thoughts were written down.

To examine the kinds of sample size justifications provided by articles, an inductive content analysis [ 54 ] was initially conducted. On the basis of this analysis, the categories that expressed qualitatively different sample size justifications were developed.

We also extracted or coded quantitative data regarding the following aspects:

Journal and year of publication

Number of interviews

Number of participants

Presence of sample size justification(s) (Yes/No)

Presence of a particular sample size justification category (Yes/No), and

Number of sample size justifications provided

Descriptive and inferential statistical analyses were used to explore these data.

A thematic analysis [ 55 ] was then performed on all scientific narratives that discussed or commented on the sample size of the study. These narratives were evident both in papers that justified their sample size and those that did not. To identify these narratives, in addition to the methods sections, the discussion sections of the reviewed articles were also examined and relevant data were extracted and analysed.

In total, 214 articles – 21 in the BMJ, 53 in the BJHP and 140 in the SHI – were eligible for inclusion in the review. Table  1 provides basic information about the sample sizes – measured in number of interviews – of the studies reviewed across the three journals. Figure  2 depicts the number of eligible articles published each year per journal.

The publication of qualitative studies in the BMJ was significantly reduced from 2012 onwards and this appears to coincide with the initiation of the BMJ Open to which qualitative studies were possibly directed.

Pairwise comparisons following a significant Kruskal-Wallis Footnote 2 test indicated that the studies published in the BJHP had significantly ( p  < .001) smaller samples sizes than those published either in the BMJ or the SHI. Sample sizes of BMJ and SHI articles did not differ significantly from each other.

Sample size justifications: Results from the quantitative and qualitative content analysis

Ten (47.6%) of the 21 BMJ studies, 26 (49.1%) of the 53 BJHP papers and 24 (17.1%) of the 140 SHI articles provided some sort of sample size justification. As shown in Table  2 , the majority of articles which justified their sample size provided one justification (70% of articles); fourteen studies (25%) provided two distinct justifications; one study (1.7%) gave three justifications and two studies (3.3%) expressed four distinct justifications.

There was no association between the number of interviews (i.e. sample size) conducted and the provision of a justification (rpb = .054, p  = .433). Within journals, Mann-Whitney tests indicated that sample sizes of ‘justifying’ and ‘non-justifying’ articles in the BMJ and SHI did not differ significantly from each other. In the BJHP, ‘justifying’ articles ( Mean rank  = 31.3) had significantly larger sample sizes than ‘non-justifying’ studies ( Mean rank  = 22.7; U = 237.000, p  < .05).

There was a significant association between the journal a paper was published in and the provision of a justification (χ 2 (2) = 23.83, p  < .001). BJHP studies provided a sample size justification significantly more often than would be expected ( z  = 2.9); SHI studies significantly less often ( z  = − 2.4). If an article was published in the BJHP, the odds of providing a justification were 4.8 times higher than if published in the SHI. Similarly if published in the BMJ, the odds of a study justifying its sample size were 4.5 times higher than in the SHI.

The qualitative content analysis of the scientific narratives identified eleven different sample size justifications. These are described below and illustrated with excerpts from relevant articles. By way of a summary, the frequency with which these were deployed across the three journals is indicated in Table  3 .

Saturation was the most commonly invoked principle (55.4% of all justifications) deployed by studies across all three journals to justify the sufficiency of their sample size. In the BMJ, two studies claimed that they achieved data saturation (BMJ17; BMJ18) and one article referred descriptively to achieving saturation without explicitly using the term (BMJ13). Interestingly, BMJ13 included data in the analysis beyond the point of saturation in search of ‘unusual/deviant observations’ and with a view to establishing findings consistency.

Thirty three women were approached to take part in the interview study. Twenty seven agreed and 21 (aged 21–64, median 40) were interviewed before data saturation was reached (one tape failure meant that 20 interviews were available for analysis). (BMJ17). No new topics were identified following analysis of approximately two thirds of the interviews; however, all interviews were coded in order to develop a better understanding of how characteristic the views and reported behaviours were, and also to collect further examples of unusual/deviant observations. (BMJ13).

Two articles reported pre-determining their sample size with a view to achieving data saturation (BMJ08 – see extract in section In line with existing research ; BMJ15 – see extract in section Pragmatic considerations ) without further specifying if this was achieved. One paper claimed theoretical saturation (BMJ06) conceived as being when “no further recurring themes emerging from the analysis” whilst another study argued that although the analytic categories were highly saturated, it was not possible to determine whether theoretical saturation had been achieved (BMJ04). One article (BMJ18) cited a reference to support its position on saturation.

In the BJHP, six articles claimed that they achieved data saturation (BJHP21; BJHP32; BJHP39; BJHP48; BJHP49; BJHP52) and one article stated that, given their sample size and the guidelines for achieving data saturation, it anticipated that saturation would be attained (BJHP50).

Recruitment continued until data saturation was reached, defined as the point at which no new themes emerged. (BJHP48). It has previously been recommended that qualitative studies require a minimum sample size of at least 12 to reach data saturation (Clarke & Braun, 2013; Fugard & Potts, 2014; Guest, Bunce, & Johnson, 2006) Therefore, a sample of 13 was deemed sufficient for the qualitative analysis and scale of this study. (BJHP50).

Two studies argued that they achieved thematic saturation (BJHP28 – see extract in section Sample size guidelines ; BJHP31) and one (BJHP30) article, explicitly concerned with theory development and deploying theoretical sampling, claimed both theoretical and data saturation.

The final sample size was determined by thematic saturation, the point at which new data appears to no longer contribute to the findings due to repetition of themes and comments by participants (Morse, 1995). At this point, data generation was terminated. (BJHP31).

Five studies argued that they achieved (BJHP05; BJHP33; BJHP40; BJHP13 – see extract in section Pragmatic considerations ) or anticipated (BJHP46) saturation without any further specification of the term. BJHP17 referred descriptively to a state of achieved saturation without specifically using the term. Saturation of coding , but not saturation of themes, was claimed to have been reached by one article (BJHP18). Two articles explicitly stated that they did not achieve saturation; instead claiming a level of theme completeness (BJHP27) or that themes being replicated (BJHP53) were arguments for sufficiency of their sample size.

Furthermore, data collection ceased on pragmatic grounds rather than at the point when saturation point was reached. Despite this, although nuances within sub-themes were still emerging towards the end of data analysis, the themes themselves were being replicated indicating a level of completeness. (BJHP27).

Finally, one article criticised and explicitly renounced the notion of data saturation claiming that, on the contrary, the criterion of theoretical sufficiency determined its sample size (BJHP16).

According to the original Grounded Theory texts, data collection should continue until there are no new discoveries ( i.e. , ‘data saturation’; Glaser & Strauss, 1967). However, recent revisions of this process have discussed how it is rare that data collection is an exhaustive process and researchers should rely on how well their data are able to create a sufficient theoretical account or ‘theoretical sufficiency’ (Dey, 1999). For this study, it was decided that theoretical sufficiency would guide recruitment, rather than looking for data saturation. (BJHP16).

Ten out of the 20 BJHP articles that employed the argument of saturation used one or more citations relating to this principle.

In the SHI, one article (SHI01) claimed that it achieved category saturation based on authors’ judgment.

This number was not fixed in advance, but was guided by the sampling strategy and the judgement, based on the analysis of the data, of the point at which ‘category saturation’ was achieved. (SHI01).

Three articles described a state of achieved saturation without using the term or specifying what sort of saturation they had achieved (i.e. data, theoretical, thematic saturation) (SHI04; SHI13; SHI30) whilst another four articles explicitly stated that they achieved saturation (SHI100; SHI125; SHI136; SHI137). Two papers stated that they achieved data saturation (SHI73 – see extract in section Sample size guidelines ; SHI113), two claimed theoretical saturation (SHI78; SHI115) and two referred to achieving thematic saturation (SHI87; SHI139) or to saturated themes (SHI29; SHI50).

Recruitment and analysis ceased once theoretical saturation was reached in the categories described below (Lincoln and Guba 1985). (SHI115). The respondents’ quotes drawn on below were chosen as representative, and illustrate saturated themes. (SHI50).

One article stated that thematic saturation was anticipated with its sample size (SHI94). Briefly referring to the difficulty in pinpointing achievement of theoretical saturation, SHI32 (see extract in section Richness and volume of data ) defended the sufficiency of its sample size on the basis of “the high degree of consensus [that] had begun to emerge among those interviewed”, suggesting that information from interviews was being replicated. Finally, SHI112 (see extract in section Further sampling to check findings consistency ) argued that it achieved saturation of discursive patterns . Seven of the 19 SHI articles cited references to support their position on saturation (see Additional File  4 for the full list of citations used by articles to support their position on saturation across the three journals).

Overall, it is clear that the concept of saturation encompassed a wide range of variants expressed in terms such as saturation, data saturation, thematic saturation, theoretical saturation, category saturation, saturation of coding, saturation of discursive themes, theme completeness. It is noteworthy, however, that although these various claims were sometimes supported with reference to the literature, they were not evidenced in relation to the study at hand.

Pragmatic considerations

The determination of sample size on the basis of pragmatic considerations was the second most frequently invoked argument (9.6% of all justifications) appearing in all three journals. In the BMJ, one article (BMJ15) appealed to pragmatic reasons, relating to time constraints and the difficulty to access certain study populations, to justify the determination of its sample size.

On the basis of the researchers’ previous experience and the literature, [30, 31] we estimated that recruitment of 15–20 patients at each site would achieve data saturation when data from each site were analysed separately. We set a target of seven to 10 caregivers per site because of time constraints and the anticipated difficulty of accessing caregivers at some home based care services. This gave a target sample of 75–100 patients and 35–50 caregivers overall. (BMJ15).

In the BJHP, four articles mentioned pragmatic considerations relating to time or financial constraints (BJHP27 – see extract in section Saturation ; BJHP53), the participant response rate (BJHP13), and the fixed (and thus limited) size of the participant pool from which interviewees were sampled (BJHP18).

We had aimed to continue interviewing until we had reached saturation, a point whereby further data collection would yield no further themes. In practice, the number of individuals volunteering to participate dictated when recruitment into the study ceased (15 young people, 15 parents). Nonetheless, by the last few interviews, significant repetition of concepts was occurring, suggesting ample sampling. (BJHP13).

Finally, three SHI articles explained their sample size with reference to practical aspects: time constraints and project manageability (SHI56), limited availability of respondents and project resources (SHI131), and time constraints (SHI113).

The size of the sample was largely determined by the availability of respondents and resources to complete the study. Its composition reflected, as far as practicable, our interest in how contextual factors (for example, gender relations and ethnicity) mediated the illness experience. (SHI131).

Qualities of the analysis

This sample size justification (8.4% of all justifications) was mainly employed by BJHP articles and referred to an intensive, idiographic and/or latently focused analysis, i.e. that moved beyond description. More specifically, six articles defended their sample size on the basis of an intensive analysis of transcripts and/or the idiographic focus of the study/analysis. Four of these papers (BJHP02; BJHP19; BJHP24; BJHP47) adopted an Interpretative Phenomenological Analysis (IPA) approach.

The current study employed a sample of 10 in keeping with the aim of exploring each participant’s account (Smith et al. , 1999). (BJHP19).

BJHP47 explicitly renounced the notion of saturation within an IPA approach. The other two BJHP articles conducted thematic analysis (BJHP34; BJHP38). The level of analysis – i.e. latent as opposed to a more superficial descriptive analysis – was also invoked as a justification by BJHP38 alongside the argument of an intensive analysis of individual transcripts

The resulting sample size was at the lower end of the range of sample sizes employed in thematic analysis (Braun & Clarke, 2013). This was in order to enable significant reflection, dialogue, and time on each transcript and was in line with the more latent level of analysis employed, to identify underlying ideas, rather than a more superficial descriptive analysis (Braun & Clarke, 2006). (BJHP38).

Finally, one BMJ paper (BMJ21) defended its sample size with reference to the complexity of the analytic task.

We stopped recruitment when we reached 30–35 interviews, owing to the depth and duration of interviews, richness of data, and complexity of the analytical task. (BMJ21).

Meet sampling requirements

Meeting sampling requirements (7.2% of all justifications) was another argument employed by two BMJ and four SHI articles to explain their sample size. Achieving maximum variation sampling in terms of specific interviewee characteristics determined and explained the sample size of two BMJ studies (BMJ02; BMJ16 – see extract in section Meet research design requirements ).

Recruitment continued until sampling frame requirements were met for diversity in age, sex, ethnicity, frequency of attendance, and health status. (BMJ02).

Regarding the SHI articles, two papers explained their numbers on the basis of their sampling strategy (SHI01- see extract in section Saturation ; SHI23) whilst sampling requirements that would help attain sample heterogeneity in terms of a particular characteristic of interest was cited by one paper (SHI127).

The combination of matching the recruitment sites for the quantitative research and the additional purposive criteria led to 104 phase 2 interviews (Internet (OLC): 21; Internet (FTF): 20); Gyms (FTF): 23; HIV testing (FTF): 20; HIV treatment (FTF): 20.) (SHI23). Of the fifty interviews conducted, thirty were translated from Spanish into English. These thirty, from which we draw our findings, were chosen for translation based on heterogeneity in depressive symptomology and educational attainment. (SHI127).

Finally, the pre-determination of sample size on the basis of sampling requirements was stated by one article though this was not used to justify the number of interviews (SHI10).

Sample size guidelines

Five BJHP articles (BJHP28; BJHP38 – see extract in section Qualities of the analysis ; BJHP46; BJHP47; BJHP50 – see extract in section Saturation ) and one SHI paper (SHI73) relied on citing existing sample size guidelines or norms within research traditions to determine and subsequently defend their sample size (7.2% of all justifications).

Sample size guidelines suggested a range between 20 and 30 interviews to be adequate (Creswell, 1998). Interviewer and note taker agreed that thematic saturation, the point at which no new concepts emerge from subsequent interviews (Patton, 2002), was achieved following completion of 20 interviews. (BJHP28). Interviewing continued until we deemed data saturation to have been reached (the point at which no new themes were emerging). Researchers have proposed 30 as an approximate or working number of interviews at which one could expect to be reaching theoretical saturation when using a semi-structured interview approach (Morse 2000), although this can vary depending on the heterogeneity of respondents interviewed and complexity of the issues explored. (SHI73).

In line with existing research

Sample sizes of published literature in the area of the subject matter under investigation (3.5% of all justifications) were used by 2 BMJ articles as guidance and a precedent for determining and defending their own sample size (BMJ08; BMJ15 – see extract in section Pragmatic considerations ).

We drew participants from a list of prisoners who were scheduled for release each week, sampling them until we reached the target of 35 cases, with a view to achieving data saturation within the scope of the study and sufficient follow-up interviews and in line with recent studies [8–10]. (BMJ08).

Similarly, BJHP38 (see extract in section Qualities of the analysis ) claimed that its sample size was within the range of sample sizes of published studies that use its analytic approach.

Richness and volume of data

BMJ21 (see extract in section Qualities of the analysis ) and SHI32 referred to the richness, detailed nature, and volume of data collected (2.3% of all justifications) to justify the sufficiency of their sample size.

Although there were more potential interviewees from those contacted by postcode selection, it was decided to stop recruitment after the 10th interview and focus on analysis of this sample. The material collected was considerable and, given the focused nature of the study, extremely detailed. Moreover, a high degree of consensus had begun to emerge among those interviewed, and while it is always difficult to judge at what point ‘theoretical saturation’ has been reached, or how many interviews would be required to uncover exception(s), it was felt the number was sufficient to satisfy the aims of this small in-depth investigation (Strauss and Corbin 1990). (SHI32).

Meet research design requirements

Determination of sample size so that it is in line with, and serves the requirements of, the research design (2.3% of all justifications) that the study adopted was another justification used by 2 BMJ papers (BMJ16; BMJ08 – see extract in section In line with existing research ).

We aimed for diverse, maximum variation samples [20] totalling 80 respondents from different social backgrounds and ethnic groups and those bereaved due to different types of suicide and traumatic death. We could have interviewed a smaller sample at different points in time (a qualitative longitudinal study) but chose instead to seek a broad range of experiences by interviewing those bereaved many years ago and others bereaved more recently; those bereaved in different circumstances and with different relations to the deceased; and people who lived in different parts of the UK; with different support systems and coroners’ procedures (see Tables 1 and 2 for more details). (BMJ16).

Researchers’ previous experience

The researchers’ previous experience (possibly referring to experience with qualitative research) was invoked by BMJ15 (see extract in section Pragmatic considerations ) as a justification for the determination of sample size.

Nature of study

One BJHP paper argued that the sample size was appropriate for the exploratory nature of the study (BJHP38).

A sample of eight participants was deemed appropriate because of the exploratory nature of this research and the focus on identifying underlying ideas about the topic. (BJHP38).

Further sampling to check findings consistency

Finally, SHI112 argued that once it had achieved saturation of discursive patterns, further sampling was decided and conducted to check for consistency of the findings.

Within each of the age-stratified groups, interviews were randomly sampled until saturation of discursive patterns was achieved. This resulted in a sample of 67 interviews. Once this sample had been analysed, one further interview from each age-stratified group was randomly chosen to check for consistency of the findings. Using this approach it was possible to more carefully explore children’s discourse about the ‘I’, agency, relationality and power in the thematic areas, revealing the subtle discursive variations described in this article. (SHI112).

Thematic analysis of passages discussing sample size

This analysis resulted in two overarching thematic areas; the first concerned the variation in the characterisation of sample size sufficiency, and the second related to the perceived threats deriving from sample size insufficiency.

Characterisations of sample size sufficiency

The analysis showed that there were three main characterisations of the sample size in the articles that provided relevant comments and discussion: (a) the vast majority of these qualitative studies ( n  = 42) considered their sample size as ‘small’ and this was seen and discussed as a limitation; only two articles viewed their small sample size as desirable and appropriate (b) a minority of articles ( n  = 4) proclaimed that their achieved sample size was ‘sufficient’; and (c) finally, a small group of studies ( n  = 5) characterised their sample size as ‘large’. Whilst achieving a ‘large’ sample size was sometimes viewed positively because it led to richer results, there were also occasions when a large sample size was problematic rather than desirable.

‘Small’ but why and for whom?

A number of articles which characterised their sample size as ‘small’ did so against an implicit or explicit quantitative framework of reference. Interestingly, three studies that claimed to have achieved data saturation or ‘theoretical sufficiency’ with their sample size, discussed or noted as a limitation in their discussion their ‘small’ sample size, raising the question of why, or for whom, the sample size was considered small given that the qualitative criterion of saturation had been satisfied.

The current study has a number of limitations. The sample size was small (n = 11) and, however, large enough for no new themes to emerge. (BJHP39). The study has two principal limitations. The first of these relates to the small number of respondents who took part in the study. (SHI73).

Other articles appeared to accept and acknowledge that their sample was flawed because of its small size (as well as other compositional ‘deficits’ e.g. non-representativeness, biases, self-selection) or anticipated that they might be criticized for their small sample size. It seemed that the imagined audience – perhaps reviewer or reader – was one inclined to hold the tenets of quantitative research, and certainly one to whom it was important to indicate the recognition that small samples were likely to be problematic. That one’s sample might be thought small was often construed as a limitation couched in a discourse of regret or apology.

Very occasionally, the articulation of the small size as a limitation was explicitly aligned against an espoused positivist framework and quantitative research.

This study has some limitations. Firstly, the 100 incidents sample represents a small number of the total number of serious incidents that occurs every year. 26 We sent out a nationwide invitation and do not know why more people did not volunteer for the study. Our lack of epidemiological knowledge about healthcare incidents, however, means that determining an appropriate sample size continues to be difficult. (BMJ20).

Indicative of an apparent oscillation of qualitative researchers between the different requirements and protocols demarcating the quantitative and qualitative worlds, there were a few instances of articles which briefly recognised their ‘small’ sample size as a limitation, but then defended their study on more qualitative grounds, such as their ability and success at capturing the complexity of experience and delving into the idiographic, and at generating particularly rich data.

This research, while limited in size, has sought to capture some of the complexity attached to men’s attitudes and experiences concerning incomes and material circumstances. (SHI35). Our numbers are small because negotiating access to social networks was slow and labour intensive, but our methods generated exceptionally rich data. (BMJ21). This study could be criticised for using a small and unrepresentative sample. Given that older adults have been ignored in the research concerning suntanning, fair-skinned older adults are the most likely to experience skin cancer, and women privilege appearance over health when it comes to sunbathing practices, our study offers depth and richness of data in a demographic group much in need of research attention. (SHI57).

‘Good enough’ sample sizes

Only four articles expressed some degree of confidence that their achieved sample size was sufficient. For example, SHI139, in line with the justification of thematic saturation that it offered, expressed trust in its sample size sufficiency despite the poor response rate. Similarly, BJHP04, which did not provide a sample size justification, argued that it targeted a larger sample size in order to eventually recruit a sufficient number of interviewees, due to anticipated low response rate.

Twenty-three people with type I diabetes from the target population of 133 ( i.e. 17.3%) consented to participate but four did not then respond to further contacts (total N = 19). The relatively low response rate was anticipated, due to the busy life-styles of young people in the age range, the geographical constraints, and the time required to participate in a semi-structured interview, so a larger target sample allowed a sufficient number of participants to be recruited. (BJHP04).

Two other articles (BJHP35; SHI32) linked the claimed sufficiency to the scope (i.e. ‘small, in-depth investigation’), aims and nature (i.e. ‘exploratory’) of their studies, thus anchoring their numbers to the particular context of their research. Nevertheless, claims of sample size sufficiency were sometimes undermined when they were juxtaposed with an acknowledgement that a larger sample size would be more scientifically productive.

Although our sample size was sufficient for this exploratory study, a more diverse sample including participants with lower socioeconomic status and more ethnic variation would be informative. A larger sample could also ensure inclusion of a more representative range of apps operating on a wider range of platforms. (BJHP35).

‘Large’ sample sizes - Promise or peril?

Three articles (BMJ13; BJHP05; BJHP48) which all provided the justification of saturation, characterised their sample size as ‘large’ and narrated this oversufficiency in positive terms as it allowed richer data and findings and enhanced the potential for generalisation. The type of generalisation aspired to (BJHP48) was not further specified however.

This study used rich data provided by a relatively large sample of expert informants on an important but under-researched topic. (BMJ13). Qualitative research provides a unique opportunity to understand a clinical problem from the patient’s perspective. This study had a large diverse sample, recruited through a range of locations and used in-depth interviews which enhance the richness and generalizability of the results. (BJHP48).

And whilst a ‘large’ sample size was endorsed and valued by some qualitative researchers, within the psychological tradition of IPA, a ‘large’ sample size was counter-normative and therefore needed to be justified. Four BJHP studies, all adopting IPA, expressed the appropriateness or desirability of ‘small’ sample sizes (BJHP41; BJHP45) or hastened to explain why they included a larger than typical sample size (BJHP32; BJHP47). For example, BJHP32 below provides a rationale for how an IPA study can accommodate a large sample size and how this was indeed suitable for the purposes of the particular research. To strengthen the explanation for choosing a non-normative sample size, previous IPA research citing a similar sample size approach is used as a precedent.

Small scale IPA studies allow in-depth analysis which would not be possible with larger samples (Smith et al. , 2009). (BJHP41). Although IPA generally involves intense scrutiny of a small number of transcripts, it was decided to recruit a larger diverse sample as this is the first qualitative study of this population in the United Kingdom (as far as we know) and we wanted to gain an overview. Indeed, Smith, Flowers, and Larkin (2009) agree that IPA is suitable for larger groups. However, the emphasis changes from an in-depth individualistic analysis to one in which common themes from shared experiences of a group of people can be elicited and used to understand the network of relationships between themes that emerge from the interviews. This large-scale format of IPA has been used by other researchers in the field of false-positive research. Baillie, Smith, Hewison, and Mason (2000) conducted an IPA study, with 24 participants, of ultrasound screening for chromosomal abnormality; they found that this larger number of participants enabled them to produce a more refined and cohesive account. (BJHP32).

The IPA articles found in the BJHP were the only instances where a ‘small’ sample size was advocated and a ‘large’ sample size problematized and defended. These IPA studies illustrate that the characterisation of sample size sufficiency can be a function of researchers’ theoretical and epistemological commitments rather than the result of an ‘objective’ sample size assessment.

Threats from sample size insufficiency

As shown above, the majority of articles that commented on their sample size, simultaneously characterized it as small and problematic. On those occasions that authors did not simply cite their ‘small’ sample size as a study limitation but rather continued and provided an account of how and why a small sample size was problematic, two important scientific qualities of the research seemed to be threatened: the generalizability and validity of results.

Generalizability

Those who characterised their sample as ‘small’ connected this to the limited potential for generalization of the results. Other features related to the sample – often some kind of compositional particularity – were also linked to limited potential for generalisation. Though not always explicitly articulated to what form of generalisation the articles referred to (see BJHP09), generalisation was mostly conceived in nomothetic terms, that is, it concerned the potential to draw inferences from the sample to the broader study population (‘representational generalisation’ – see BJHP31) and less often to other populations or cultures.

It must be noted that samples are small and whilst in both groups the majority of those women eligible participated, generalizability cannot be assumed. (BJHP09). The study’s limitations should be acknowledged: Data are presented from interviews with a relatively small group of participants, and thus, the views are not necessarily generalizable to all patients and clinicians. In particular, patients were only recruited from secondary care services where COFP diagnoses are typically confirmed. The sample therefore is unlikely to represent the full spectrum of patients, particularly those who are not referred to, or who have been discharged from dental services. (BJHP31).

Without explicitly using the term generalisation, two SHI articles noted how their ‘small’ sample size imposed limits on ‘the extent that we can extrapolate from these participants’ accounts’ (SHI114) or to the possibility ‘to draw far-reaching conclusions from the results’ (SHI124).

Interestingly, only a minority of articles alluded to, or invoked, a type of generalisation that is aligned with qualitative research, that is, idiographic generalisation (i.e. generalisation that can be made from and about cases [ 5 ]). These articles, all published in the discipline of sociology, defended their findings in terms of the possibility of drawing logical and conceptual inferences to other contexts and of generating understanding that has the potential to advance knowledge, despite their ‘small’ size. One article (SHI139) clearly contrasted nomothetic (statistical) generalisation to idiographic generalisation, arguing that the lack of statistical generalizability does not nullify the ability of qualitative research to still be relevant beyond the sample studied.

Further, these data do not need to be statistically generalisable for us to draw inferences that may advance medicalisation analyses (Charmaz 2014). These data may be seen as an opportunity to generate further hypotheses and are a unique application of the medicalisation framework. (SHI139). Although a small-scale qualitative study related to school counselling, this analysis can be usefully regarded as a case study of the successful utilisation of mental health-related resources by adolescents. As many of the issues explored are of relevance to mental health stigma more generally, it may also provide insights into adult engagement in services. It shows how a sociological analysis, which uses positioning theory to examine how people negotiate, partially accept and simultaneously resist stigmatisation in relation to mental health concerns, can contribute to an elucidation of the social processes and narrative constructions which may maintain as well as bridge the mental health service gap. (SHI103).

Only one article (SHI30) used the term transferability to argue for the potential of wider relevance of the results which was thought to be more the product of the composition of the sample (i.e. diverse sample), rather than the sample size.

The second major concern that arose from a ‘small’ sample size pertained to the internal validity of findings (i.e. here the term is used to denote the ‘truth’ or credibility of research findings). Authors expressed uncertainty about the degree of confidence in particular aspects or patterns of their results, primarily those that concerned some form of differentiation on the basis of relevant participant characteristics.

The information source preferred seemed to vary according to parents’ education; however, the sample size is too small to draw conclusions about such patterns. (SHI80). Although our numbers were too small to demonstrate gender differences with any certainty, it does seem that the biomedical and erotic scripts may be more common in the accounts of men and the relational script more common in the accounts of women. (SHI81).

In other instances, articles expressed uncertainty about whether their results accounted for the full spectrum and variation of the phenomenon under investigation. In other words, a ‘small’ sample size (alongside compositional ‘deficits’ such as a not statistically representative sample) was seen to threaten the ‘content validity’ of the results which in turn led to constructions of the study conclusions as tentative.

Data collection ceased on pragmatic grounds rather than when no new information appeared to be obtained ( i.e. , saturation point). As such, care should be taken not to overstate the findings. Whilst the themes from the initial interviews seemed to be replicated in the later interviews, further interviews may have identified additional themes or provided more nuanced explanations. (BJHP53). …it should be acknowledged that this study was based on a small sample of self-selected couples in enduring marriages who were not broadly representative of the population. Thus, participants may not be representative of couples that experience postnatal PTSD. It is therefore unlikely that all the key themes have been identified and explored. For example, couples who were excluded from the study because the male partner declined to participate may have been experiencing greater interpersonal difficulties. (BJHP03).

In other instances, articles attempted to preserve a degree of credibility of their results, despite the recognition that the sample size was ‘small’. Clarity and sharpness of emerging themes and alignment with previous relevant work were the arguments employed to warrant the validity of the results.

This study focused on British Chinese carers of patients with affective disorders, using a qualitative methodology to synthesise the sociocultural representations of illness within this community. Despite the small sample size, clear themes emerged from the narratives that were sufficient for this exploratory investigation. (SHI98).

The present study sought to examine how qualitative sample sizes in health-related research are characterised and justified. In line with previous studies [ 22 , 30 , 33 , 34 ] the findings demonstrate that reporting of sample size sufficiency is limited; just over 50% of articles in the BMJ and BJHP and 82% in the SHI did not provide any sample size justification. Providing a sample size justification was not related to the number of interviews conducted, but it was associated with the journal that the article was published in, indicating the influence of disciplinary or publishing norms, also reported in prior research [ 30 ]. This lack of transparency about sample size sufficiency is problematic given that most qualitative researchers would agree that it is an important marker of quality [ 56 , 57 ]. Moreover, and with the rise of qualitative research in social sciences, efforts to synthesise existing evidence and assess its quality are obstructed by poor reporting [ 58 , 59 ].

When authors justified their sample size, our findings indicate that sufficiency was mostly appraised with reference to features that were intrinsic to the study, in agreement with general advice on sample size determination [ 4 , 11 , 36 ]. The principle of saturation was the most commonly invoked argument [ 22 ] accounting for 55% of all justifications. A wide range of variants of saturation was evident corroborating the proliferation of the meaning of the term [ 49 ] and reflecting different underlying conceptualisations or models of saturation [ 20 ]. Nevertheless, claims of saturation were never substantiated in relation to procedures conducted in the study itself, endorsing similar observations in the literature [ 25 , 30 , 47 ]. Claims of saturation were sometimes supported with citations of other literature, suggesting a removal of the concept away from the characteristics of the study at hand. Pragmatic considerations, such as resource constraints or participant response rate and availability, was the second most frequently used argument accounting for approximately 10% of justifications and another 23% of justifications also represented intrinsic-to-the-study characteristics (i.e. qualities of the analysis, meeting sampling or research design requirements, richness and volume of the data obtained, nature of study, further sampling to check findings consistency).

Only, 12% of mentions of sample size justification pertained to arguments that were external to the study at hand, in the form of existing sample size guidelines and prior research that sets precedents. Whilst community norms and prior research can establish useful rules of thumb for estimating sample sizes [ 60 ] – and reveal what sizes are more likely to be acceptable within research communities – researchers should avoid adopting these norms uncritically, especially when such guidelines [e.g. 30 , 35 ], might be based on research that does not provide adequate evidence of sample size sufficiency. Similarly, whilst methodological research that seeks to demonstrate the achievement of saturation is invaluable since it explicates the parameters upon which saturation is contingent and indicates when a research project is likely to require a smaller or a larger sample [e.g. 29 ], specific numbers at which saturation was achieved within these projects cannot be routinely extrapolated for other projects. We concur with existing views [ 11 , 36 ] that the consideration of the characteristics of the study at hand, such as the epistemological and theoretical approach, the nature of the phenomenon under investigation, the aims and scope of the study, the quality and richness of data, or the researcher’s experience and skills of conducting qualitative research, should be the primary guide in determining sample size and assessing its sufficiency.

Moreover, although numbers in qualitative research are not unimportant [ 61 ], sample size should not be considered alone but be embedded in the more encompassing examination of data adequacy [ 56 , 57 ]. Erickson’s [ 62 ] dimensions of ‘evidentiary adequacy’ are useful here. He explains the concept in terms of adequate amounts of evidence, adequate variety in kinds of evidence, adequate interpretive status of evidence, adequate disconfirming evidence, and adequate discrepant case analysis. All dimensions might not be relevant across all qualitative research designs, but this illustrates the thickness of the concept of data adequacy, taking it beyond sample size.

The present research also demonstrated that sample sizes were commonly seen as ‘small’ and insufficient and discussed as limitation. Often unjustified (and in two cases incongruent with their own claims of saturation) these findings imply that sample size in qualitative health research is often adversely judged (or expected to be judged) against an implicit, yet omnipresent, quasi-quantitative standpoint. Indeed there were a few instances in our data where authors appeared, possibly in response to reviewers, to resist to some sort of quantification of their results. This implicit reference point became more apparent when authors discussed the threats deriving from an insufficient sample size. Whilst the concerns about internal validity might be legitimate to the extent that qualitative research projects, which are broadly related to realism, are set to examine phenomena in sufficient breadth and depth, the concerns around generalizability revealed a conceptualisation that is not compatible with purposive sampling. The limited potential for generalisation, as a result of a small sample size, was often discussed in nomothetic, statistical terms. Only occasionally was analytic or idiographic generalisation invoked to warrant the value of the study’s findings [ 5 , 17 ].

Strengths and limitations of the present study

We note, first, the limited number of health-related journals reviewed, so that only a ‘snapshot’ of qualitative health research has been captured. Examining additional disciplines (e.g. nursing sciences) as well as inter-disciplinary journals would add to the findings of this analysis. Nevertheless, our study is the first to provide some comparative insights on the basis of disciplines that are differently attached to the legacy of positivism and analysed literature published over a lengthy period of time (15 years). Guetterman [ 27 ] also examined health-related literature but this analysis was restricted to 26 most highly cited articles published over a period of five years whilst Carlsen and Glenton’s [ 22 ] study concentrated on focus groups health research. Moreover, although it was our intention to examine sample size justification in relation to the epistemological and theoretical positions of articles, this proved to be challenging largely due to absence of relevant information, or the difficulty into discerning clearly articles’ positions [ 63 ] and classifying them under specific approaches (e.g. studies often combined elements from different theoretical and epistemological traditions). We believe that such an analysis would yield useful insights as it links the methodological issue of sample size to the broader philosophical stance of the research. Despite these limitations, the analysis of the characterisation of sample size and of the threats seen to accrue from insufficient sample size, enriches our understanding of sample size (in)sufficiency argumentation by linking it to other features of the research. As the peer-review process becomes increasingly public, future research could usefully examine how reporting around sample size sufficiency and data adequacy might be influenced by the interactions between authors and reviewers.

The past decade has seen a growing appetite in qualitative research for an evidence-based approach to sample size determination and to evaluations of the sufficiency of sample size. Despite the conceptual and methodological developments in the area, the findings of the present study confirm previous studies in concluding that appraisals of sample size sufficiency are either absent or poorly substantiated. To ensure and maintain high quality research that will encourage greater appreciation of qualitative work in health-related sciences [ 64 ], we argue that qualitative researchers should be more transparent and thorough in their evaluation of sample size as part of their appraisal of data adequacy. We would encourage the practice of appraising sample size sufficiency with close reference to the study at hand and would thus caution against responding to the growing methodological research in this area with a decontextualised application of sample size numerical guidelines, norms and principles. Although researchers might find sample size community norms serve as useful rules of thumb, we recommend methodological knowledge is used to critically consider how saturation and other parameters that affect sample size sufficiency pertain to the specifics of the particular project. Those reviewing papers have a vital role in encouraging transparent study-specific reporting. The review process should support authors to exercise nuanced judgments in decisions about sample size determination in the context of the range of factors that influence sample size sufficiency and the specifics of a particular study. In light of the growing methodological evidence in the area, transparent presentation of such evidence-based judgement is crucial and in time should surely obviate the seemingly routine practice of citing the ‘small’ size of qualitative samples among the study limitations.

A non-parametric test of difference for independent samples was performed since the variable number of interviews violated assumptions of normality according to the standardized scores of skewness and kurtosis (BMJ: z skewness = 3.23, z kurtosis = 1.52; BJHP: z skewness = 4.73, z kurtosis = 4.85; SHI: z skewness = 12.04, z kurtosis = 21.72) and the Shapiro-Wilk test of normality ( p  < .001).

Abbreviations

British Journal of Health Psychology

British Medical Journal

Interpretative Phenomenological Analysis

Sociology of Health & Illness

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Acknowledgments

We would like to thank Dr. Paula Smith and Katharine Lee for their comments on a previous draft of this paper as well as Natalie Ann Mitchell and Meron Teferra for assisting us with data extraction.

This research was initially conceived of and partly conducted with financial support from the Multidisciplinary Assessment of Technology Centre for Healthcare (MATCH) programme (EP/F063822/1 and EP/G012393/1). The research continued and was completed independent of any support. The funding body did not have any role in the study design, the collection, analysis and interpretation of the data, in the writing of the paper, and in the decision to submit the manuscript for publication. The views expressed are those of the authors alone.

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Supporting data can be accessed in the original publications. Additional File 2 lists all eligible studies that were included in the present analysis.

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JB and TY conceived the study; KV, JB, and TY designed the study; KV identified the articles and extracted the data; KV and JB assessed eligibility of articles; KV, JB, ST, and TY contributed to the analysis of the data, discussed the findings and early drafts of the paper; KV developed the final manuscript; KV, JB, ST, and TY read and approved the manuscript.

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Vasileiou, K., Barnett, J., Thorpe, S. et al. Characterising and justifying sample size sufficiency in interview-based studies: systematic analysis of qualitative health research over a 15-year period. BMC Med Res Methodol 18 , 148 (2018). https://doi.org/10.1186/s12874-018-0594-7

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• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . 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.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• 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.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The 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

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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• An Bras Dermatol
• v.89(4); Jul-Aug 2014

Sample size: how many participants do I need in my research? *

Jeovany martínez-mesa.

1 Latin American Cooperative Oncology Group - Porto Alegre (RS), Brazil.

David Alejandro González-Chica

2 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (SC), Brazil.

João Luiz Bastos

Renan rangel bonamigo.

3 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

Rodrigo Pereira Duquia

The importance of estimating sample sizes is rarely understood by researchers, when planning a study. This paper aims to highlight the centrality of sample size estimations in health research. Examples that help in understanding the basic concepts involved in their calculation are presented. The scenarios covered are based more on the epidemiological reasoning and less on mathematical formulae. Proper calculation of the number of participants in a study diminishes the likelihood of errors, which are often associated with adverse consequences in terms of economic, ethical and health aspects.

INTRODUCTION

Investigations in the health field are oriented by research problems or questions, which should be clearly defined in the study project. Sample size calculation is an essential item to be included in the project to reduce the probability of error, respect ethical standards, define the logistics of the study and, last but not least, improve its success rates, when evaluated by funding agencies.

Let us imagine that a group of investigators decides to study the frequency of sunscreen use and how the use of this product is distributed in the "population". In order to carry out this task, the authors define two research questions, each of which involving a distinct sample size calculation: 1) What is the proportion of people that use sunscreen in the population?; and, 2) Are there differences in the use of sunscreen between men and women, or between individuals that are white or of another skin color group, or between the wealthiest and the poorest, or between people with more and less years of schooling? Before doing the calculations, it will be necessary to review a few fundamental concepts and identify which are the required parameters to determine them.

WHAT DO WE MEAN, WHEN WE TALK ABOUT POPULATIONS?

First of all, we must define what is a population . Population is the group of individuals restricted to a geographical region (neighborhood, city, state, country, continent etc.), or certain institutions (hospitals, schools, health centers etc.), that is, a set of individuals that have at least one characteristic in common. The target population corresponds to a portion of the previously mentioned population, about which one intends to draw conclusions, that is to say, it is a part of the population whose characteristics are an object of interest of the investigator. Finally, study population is that which will actually be part of the study, which will be evaluated and will allow conclusions to be drawn about the target population, as long as it is representative of the latter. Figure 1 demonstrates how these concepts are interrelated.

Graphic representation of the concepts of population, target population and study population

We will now separately consider the required parameters for sample size calculation in studies that aim at estimating the frequency of events (prevalence of health outcomes or behaviors, for example), to test associations between risk/protective factors and dichotomous health conditions (yes/no), as well as with health outcomes measured in numerical scales. 1 The formulas used for these calculations may be obtained from different sources - we recommend using the free online software OpenEpi ( www.openepi.com ). 2

WHICH PARAMETERS DOES SAMPLE SIZE CALCULATION DEPEND UPON FOR A STUDY THAT AIMS AT ESTIMATING THE FREQUENCY OF HEALTH OUTCOMES, BEHAVIORS OR CONDITIONS?

When approaching the first research question defined at the beginning of this article (What is the proportion of people that use sunscreen?), the investigators need to conduct a prevalence study. In order to do this, some parameters must be defined to calculate the sample size, as demonstrated in chart 1 .

Description of different parameters to be considered in the calculation of sample size for a study aiming at estimating the frequency of health ouctomes, behaviors or conditions

Chart 2 presents some sample size simulations, according to the outcome prevalence, sample error and the type of target population investigated. The same basic question was used in this table (prevalence of sunscreen use), but considering three different situations (at work, while doing sports or at the beach), as in the study by Duquia et al. conducted in the city of Pelotas, state of Rio Grande do Sul, in 2005. 3

Sample size calculation to estimate the frequency (prevalence) of sunscreen use in the population, considering different scenarios but keeping the significance level (95%) and the design effect (1.0) constant

p.p.= percentage points

The calculations show that, by holding the sample error and the significance level constant, the higher the expected prevalence, the larger will be the required sample size. However, when the expected prevalence surpasses 50%, the required sample size progressively diminishes - the sample size for an expected prevalence of 10% is the same as that for an expected prevalence of 90%.

The investigator should also define beforehand the precision level to be accepted for the investigated event (sample error) and the confidence level of this result (usually 95%). Chart 2 demonstrates that, holding the expected prevalence constant, the higher the precision (smaller sample error) and the higher the confidence level (in this case, 95% was considered for all calculations), the larger also will be the required sample size.

Chart 2 also demonstrates that there is a direct relationship between the target population size and the number of individuals to be included in the sample. Nevertheless, when the target population size is sufficiently large, that is, surpasses an arbitrary value (for example, one million individuals), the resulting sample size tends to stabilize. The smaller the target population, the larger the sample will be; in some cases, the sample may even correspond to the total number of individuals from the target population - in these cases, it may be more convenient to study the entire target population, carrying out a census survey, rather than a study based on a sample of the population.

SAMPLE CALCULATION TO TEST THE ASSOCIATION BETWEEN TWO VARIABLES: HYPOTHESES AND TYPES OF ERROR

When the study objective is to investigate whether there are differences in sunscreen use according to sociodemographic characteristics (such as, for example, between men and women), the existence of association between explanatory variables (exposure or independent variables, in this case sociodemographic variables) and a dependent or outcome variable (use of sunscreen) is what is under consideration.

In these cases, we need first to understand what the hypotheses are, as well as the types of error that may result from their acceptance or refutation. A hypothesis is a "supposition arrived at from observation or reflection, that leads to refutable predictions". 4 In other words, it is a statement that may be questioned or tested and that may be falsified in scientific studies.

In scientific studies, there are two types of hypothesis: the null hypothesis (H 0 ) or original supposition that we assume to be true for a given situation, and the alternative hypothesis (H A ) or additional explanation for the same situation, which we believe may replace the original supposition. In the health field, H 0 is frequently defined as the equality or absence of difference in the outcome of interest between the studied groups (for example, sunscreen use is equal in men and women). On the other hand, H A assumes the existence of difference between groups. H A is called two-tailed when it is expected that the difference between the groups will occur in any direction (men using more sunscreen than women or vice-versa). However, if the investigator expects to find that a specific group uses more sunscreen than the other, he will be testing a one-tailed H A .

In the sample investigated by Duquia et al., the frequency of sunscreen use at the beach was greater in men (32.7%) than in women (26.2%).3 Although this what was observed in the sample, that is, men do wear more sunscreen than women, the investigators must decide whether they refute or accept H 0 in the target population (which contends that there is no difference in sunscreen use according to sex). Given that the entire target population is hardly ever investigated to confirm or refute the difference observed in the sample, the authors have to be aware that, independently from their decision (accepting or refuting H 0 ), their conclusion may be wrong, as can be seen in figure 2 .

Types of possible results when performing a hypothesis test

In case the investigators conclude that both in the target population and in the sample sunscreen use is also different between men and women (rejecting H 0 ), they may be making a type I or Alpha error, which is the probability of rejecting H 0 based on sample results when, in the target population, H 0 is true (the difference between men and women regarding sunscreen use found in the sample is not observed in the target population). If the authors conclude that there are no differences between the groups (accepting H 0 ), the investigators may be making a type II or Beta error, which is the probability of accepting H 0 when, in the target population, H 0 is false (that is, H A is true) or, in other words, the probability of stating that the frequency of sunscreen use is equal between the sexes, when it is different in the same groups of the target population.

In order to accept or refute H 0 , the investigators need to previously define which is the maximum probability of type I and II errors that they are willing to incorporate into their results. In general, the type I error is fixed at a maximum value of 5% (0.05 or confidence level of 95%), since the consequences originated from this type of error are considered more harmful. For example, to state that an exposure/intervention affects a health condition, when this does not happen in the target population may bring about behaviors or actions (therapeutic changes, implementation of intervention programs etc.) with adverse consequences in ethical, economic and health terms. In the study conducted by Duquia et al., when the authors contend that the use of sunscreen was different according to sex, the p value presented (<0.001) indicates that the probability of not observing such difference in the target population is less that 0.1% (confidence level >99.9%). 3

Although the type II or Beta error is less harmful, it should also be avoided, since if a study contends that a given exposure/intervention does not affect the outcome, when this effect actually exists in the target population, the consequence may be that a new medication with better therapeutic effects is not administered or that some aspects related to the etiology of the damage are not considered. This is the reason why the value of the type II error is usually fixed at a maximum value of 20% (or 0.20). In publications, this value tends to be mentioned as the power of the study, which is the ability of the test to detect a difference, when in fact it exists in the target population (usually fixed at 80%, as a result of the 1-Beta calculation).

SAMPLE CALCULATION FOR STUDIES THAT AIM AT TESTING THE ASSOCIATION BETWEEN A RISK/PROTECTIVE FACTOR AND AN OUTCOME, EVALUATED DICHOTOMOUSLY

In cases where the exposure variables are dichotomous (intervention/control, man/woman, rich/poor etc.) and so is the outcome (negative/positive outcome, to use sunscreen or not), the required parameters to calculate sample size are those described in chart 3 . According to the previously mentioned example, it would be interesting to know whether sex, skin color, schooling level and income are associated with the use of sunscreen at work, while doing sports and at the beach. Thus, when the four exposure variables are crossed with the three outcomes, there would be 12 different questions to be answered and consequently an equal number of sample size calculations to be performed. Using the information in the article by Duquia et al. 3 for the prevalence of exposures and outcomes, a simulation of sample size calculations was used for each one of these situations ( Chart 4 ).

Ho - null hypothesis; Ha - alternative hypothesis

E=exposed group; NE=non-exposed group; r=NE/E relationship; PONE=prevalence of outcome in the non-exposed group (percentage of positives in non-exposed group), estimated based on formula from chart 3 , considering an PR of 1.50; PR=prevalence ratio/incidence or expected relative risk; n= minimum necessary sample size; ND=value could not be determined, as prevalence of outcome in the exposed would be above 100%, according to specified parameters.

Estimates show that studies with more power or that intend to find a difference of a lower magnitude in the frequency of the outcome (in this case, the prevalence rates) between exposed and non-exposed groups require larger sample sizes. For these reasons, in sample size calculations, an effect measure between 1.5 and 2.0 (for risk factors) or between 0.50 and 0.75 (for protective factors), and an 80% power are frequently used.

Considering the values in each column of chart 3 , we may conclude also that, when the nonexposed/exposed relationship moves away from one (similar proportions of exposed and non-exposed individuals in the sample), the sample size increases. For this reason, intervention studies usually work with the same proportion of individuals in the intervention and control groups. Upon analysis of the values on each line, it can be concluded that there is an inverse relationship between the prevalence of the outcome and the required sample size.

Based on these estimates, assuming that the authors intended to test all of these associations, it would be necessary to choose the largest estimated sample size (2,630 subjects). In case the required sample size is larger than the target population, the investigators may decide to perform a multicenter study, lengthen the period for data collection, modify the research question or face the possibility of not having sufficient power to draw valid conclusions.

Additional aspects need to be considered in the previous estimates to arrive at the final sample size, which may include the possibility of refusals and/or losses in the study (an additional 10-15%), the need for adjustments for confounding factors (an additional 10-20%, applicable to observational studies), the possibility of effect modification (which implies an analysis of subgroups and the need to duplicate or triplicate the sample size), as well as the existence of design effects (multiplication of sample size by 1.5 to 2.0) in case of cluster sampling.

SAMPLE CALCULATIONS FOR STUDIES THAT AIM AT TESTING THE ASSOCIATION BETWEEN A DICHOTOMOUS EXPOSURE AND A NUMERICAL OUTCOME

Suppose that the investigators intend to evaluate whether the daily quantity of sunscreen used (in grams), the time of daily exposure to sunlight (in minutes) or a laboratory parameter (such as vitamin D levels) differ according to the socio-demographic variables mentioned. In all of these cases, the outcomes are numerical variables (discrete or continuous) 1 , and the objective is to answer whether the mean outcome in the exposed/intervention group is different from the non-exposed/control group.

In this case, the first three parameters from chart 4 (alpha error, power of the study and relationship between non-exposed/exposed groups) are required, and the conclusions about their influences on the final sample size are also applicable. In addition to defining the expected outcome means in each group or the expected mean difference between nonexposed/exposed groups (usually at least 15% of the mean value in non-exposed group), they also need to define the standard deviation value for each group. There is a direct relationship between the standard deviation value and the sample size, the reason why in case of asymmetric variables the sample size would be overestimated. In such cases, the option may be to estimate sample sizes based on specific calculations for asymmetric variables, or the investigators may choose to use a percentage of the median value (for example, 25%) as a substitute for the standard deviation.

SAMPLE SIZE CALCULATIONS FOR OTHER TYPES OF STUDY

There are also specific calculations for some other quantitative studies, such as those aiming to assess correlations (exposure and outcome are numerical variables), time until the event (death, cure, relapse etc.) or the validity of diagnostic tests, but they are not described in this article, given that they were discussed elsewhere. 5

Sample size calculation is always an essential step during the planning of scientific studies. An insufficient or small sample size may not be able to demonstrate the desired difference, or estimate the frequency of the event of interest with acceptable precision. A very large sample may add to the complexity of the study, and its associated costs, rendering it unfeasible. Both situations are ethically unacceptable and should be avoided by the investigator.

Conflict of Interest: None

Financial Support: None

* Work carried out at the Latin American Cooperative Oncology Group (LACOG), Universidade Federal de Santa Catarina (UFSC), and Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Brazil.

Como citar este artigo: Martínez-Mesa J, González-Chica DA, Bastos JL, Bonamigo RR, Duquia RP. Sample size: how many participants do I need in my research? An Bras Dermatol. 2014;89(4):609-15.

What’s a good sample size for qualitative research?

I consistently come across folks who are unfamiliar with sample size requirements for qualitative research -- assuming it needs stat significance like a quant test like an A/B test. 

Instead of stat significance the methodological principle used is 'saturation'. 

The standard in qualitative research is that it takes 12-13 responses to reach saturation -- meaning whether you survey 13 or 130 people, the number of insights/themes you get is the same. 

There are folks who debate the exact number of participants, but most in the scientific community agree it's below 20. 

A review of 23 peer-reviewed articles suggests that 9–17 participants can be sufficient to reach saturation, especially for studies with homogenous populations and narrowly defined objectives.

Hence our recommendation is to target ~15 people as a target sample size in your qualitative research.

What is data saturation?

Data saturation is the point at which new data no longer provides new insights into the research question. 

It’s when you have learned everything you can from the data and cannot find anything new. Data saturation is not about the numbers per se, but about the depth of the data (Burmeister & Aitken, 2012).

There is no one-size-fits-all answer to how many participants you need to reach data saturation. However, researchers agree on some general principles:

• If you are not getting any new data, themes, or codes, then you have reached data saturation.
• You should also be able to repeat the study and get the same results.

Some researchers have found that you can reach data saturation with as few as six participants  (Guest et al., 2006), but it depends on the population you are studying. 

More participants just means more costs

The vast majority of your target customer research should be qualitative. The point is to collect insights to drive demand, not big numbers to impress people.

Qualitative research with 15 people is a good investment because it yields the most findings at a lower cost. Running qualitative research studies with more than 15 people provides little additional benefit (you will hit saturation at around 15 people and identify 99% of insights) but costs quite a bit.

Spend that extra budget on more studies, not more participants.

Why qualitative research doesn’t need the same numbers as quantitative research

Qualitative research doesn't need the same numbers as quantitative research because it is focused on understanding the depth and complexity of people's experiences, rather than making generalizations about the general population. 

This type of understanding cannot be achieved by simply collecting data from a large number of people.

Just because 10 people in a 15-person study claim a strong interest in X does not mean that we can say that 66% of the overall population will have a similar preference.

Another thing is that qualitative research is often exploratory in nature.

This means that people conducting the research are not sure what they are going to find before they start collecting data. 

Qualitative research is often based on small samples of participants who are carefully selected to represent the group of people that the researcher is interested in studying.

This means that the researcher can be confident that their findings are relevant to the group they are studying, even if the sample size is small.

Sample sizes in user testing

Nielsen Norman Group recommends testing with 5 to 15 users to find most usability problems, as testing more people yields diminishing returns.

The math is explained in the chart below:

• 0 users will find 0 problems. (Duh)
• 5 users will be able to identify 85% of the usability problems
• 10 users will find over 95% of issues
• 15 users will identify over 99% of issues

The same principles can be applied to message testing as the key idea is the same: you’re trying to identify sources of friction. 

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