Can sample size in qualitative research be determined a priori?

Purpose 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. Findings 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.

Determining appropriate sample sizes in qualitative research has traditionally relied on data saturation, lacking predetermined mathematical formulas. Initial efforts by Tullis and Wood introduced basic calculation methods for user experience research, while Yocco proposed preliminary equations for interview-based studies. Balboni further developed these concepts, introducing complexity factors in sample size determination. Building on these foundations, the Benneh-Mensah (BM) Formula integrates study complexity parameters, researcher expertise levels, and statistical confidence intervals into a comprehensive calculation method. Statistical analysis demonstrates strong correlation (r=0.998) between BM Formula and traditional approaches, with no significant differences in moderate to complex studies (t=1.632, p=0.178). The formula provides systematic sample size reductions based on researcher expertise, ranging from 74.8% for advanced researchers to 80.3% for experts. Key findings validate its effectiveness across diverse research contexts, from healthcare to cross-cultural studies. The BM Formula offers a mathematically robust alternative to traditional saturation approaches, enabling precise sample size determination before data collection. Researchers are recommended to implement this formula with appropriate parameter documentation and continuous monitoring of saturation indicators.

ABSTRACTIn his detailed response to our paper on sample size in qualitative research, Norman Blaikie raises important issues concerning conceptual definitions and taxonomy. In particular, he points out the problems associated with a loose, generic application of adjectives such as ‘qualitative’ or ‘inductive’. We endorse this concern, though we suggest that in some specific contexts a broad categorization may be more appropriate than a more nuanced distinction – provided that it is clear in which sense the terms are employed. However, other concepts, such as saturation, do not lend themselves to generic use, and require a more detailed conceptualization. Blaikie’s analysis also makes it clear that meaningful discussion of sample size in qualitative research cannot occur with reference to an undifferentiated conception of the nature of qualitative research; clear distinctions need to be made within this approach in terms of methodology, ontological and epistemological assumptions and broader research paradigms.

Quantitative approaches offer important insights into human behaviour, yet they may not adequately reflect the contextual and subjective nuances inherent in such behaviour. While quantitative research methods are useful for explaining relationships between variables, they may have limitations when it comes to gaining in-depth understanding of a specific context. Quantitative research has generally accepted criteria regarding validity, reliability, sampling methods, and sample size; however, these aspects remain a topic of debate for qualitative research. Quantitative research has established standards for validity, reliability, sampling methods, and sample size. In contrast, these criteria are often debated in qualitative research, especially when qualitative approaches are evaluated using frameworks derived from quantitative traditions. Qualitative research relies on distinct methodologies that are tailored to explore complex phenomena in context. This study addresses the debates on the validity and reliability of qualitative research and provides information about the validity, reliability, sampling methods, and sample size in qualitative studies. Within this framework, the study aims to clarify ongoing debates regarding the validity, reliability, sampling methods, and sample size in qualitative research, thereby offering a structured overview that may guide researchers in designing and evaluating qualitative studies.

Qualitative inquiry plays an essential role in optimising cardiovascular care and facilitating person-centred approaches through understanding in-depth experiences that quantitative data are unable to capture. In quantitative research, rigorous power calculations are used to determine minimum sample size required to support a finding. In qualitative research, power calculations do not apply, but rather, determination of adequate sample size relies on the concept of data saturation. Operational definitions of data saturation simplify its definition to 'no new information emerging', which can be problematic, particularly if researchers do not provide adequate methodological support to ensure transparency. Information power is a useful concept that provides guidance on estimating sample size prior to the research being conducted (i.e. helpful for grant applications), and during data collection and analysis to determine final sample size. Five considerations to determine the information power of the sample include: 1) narrow study aim/objectives, 2) specific/homogenous sample, 3) use of theory, 4) quality of dialogue, and 5) analysis strategy. This article explores the concept of data saturation, information redundancy, and the alternative concept of information power to determine when there are 'enough' data to support findings. Considerations for each are described. This approach is helpful for researchers conducting qualitative research in determining which approach may be best suited to determine adequate sample size.

The determination of sample size in qualitative research introduces a unique and multifaceted challenge, setting it apart from the more structured methodology of quantitative research. Contrary to sampling methods in quantitative research, which primarily aim to secure random and statistically representative samples that facilitate the generalisation of findings to broader populations, sampling in qualitative research requires a distinct set of considerations in its pursuit of a deeper understanding of specific phenomena. The objective of this editorial is to provide qualitative researchers with clear and foundational guidance for effectively communicating the methodological aspects of their research papers, particularly pertaining to sample size justification. Building on this, we present S.C.A.D.E, an acronym comprising five key actionable elements—Selecting, Clarifying, Aligning, Deploying and Evaluating—to guide researchers in determining the appropriate sample size and ensuring that data saturation is achieved as they plan their qualitative exploration.

BackgroundChoosing 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.MethodsA 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.ResultsOur 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.ConclusionsWe 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.

ABSTRACTThe debate on determining sample size in qualitative research is confounded by four fundamental methodological issues: the exclusive focus on theme analysis; the diverse and imprecise use of ‘qualitative’; a reliance on only two logics of inquiry, induction and deduction, and the occasional confusion of abduction with induction; and a general lack of recognition of the importance of differences in ontological assumptions. Embedded in these issues is an unwarranted acceptance of limited associations between certain assumptions, logics, forms of data, and methods of data collection/generation and analysis. What is required is a reformulation of the problem and its discussion with reference to ontological assumptions and logics of inquiry.

This review aimed to answer the question of how many interviews are enough for one qualitative research? The question ‘how many interviews are enough for one qualitative research is persistently controversial among qualitative researchers in social science. For this frequently occurring question especially among novice practitioners, the majority of scholars are opted to say no universally guiding rule to decide on a required number of sample for qualitative research rather ‘it depends’. But, this also raises another insight among researchers urging them to look for different things, i.e., on what circumstance would be making a decision about the required number of respondents depend? Though we lack one guideline dictating researchers how to decide on the number of sample size, the majority of researchers agree on one reasonable answer this is ‘it depends’. Indeed, in our work, we endeavor to identify, on what it depends? We also attempted to figure out or indicate the commonly referred range of sample size in qualitative research. Generally when we sum up our review work, the decision on “How many” is depended on several factors among which the following are some; the focus of the research, the type of research question, available resource and time, institutional committee requirements, the judgments of epistemic community in which a researcher is located, the nature of the selected group, the domain of inquiry, the experience of the researcher with qualitative research, and so on. Specific to number 20-60 is the most frequently observed range of sample size in qualitative research which of course is determined by the aforementioned factors.

Determining appropriate sample sizes in qualitative research has traditionally relied on data saturation, lacking predetermined mathematical formulas. Initial efforts by Tullis and Wood introduced basic calculation methods for user experience research, while Yocco proposed preliminary equations for interview-based studies. Balboni further developed these concepts, introducing complexity factors in sample size determination. Building on these foundations, the Benneh-Mensah (BM) Formula integrates study complexity parameters, researcher expertise levels, and statistical confidence intervals into a comprehensive calculation method. Statistical analysis demonstrates strong correlation (r=0.998) between BM Formula and traditional approaches, with no significant differences in moderate to complex studies (t=1.632, p=0.178). The formula provides systematic sample size reductions based on researcher expertise, ranging from 74.8% for advanced researchers to 80.3% for experts. Key findings validate its effectiveness across diverse research contexts, from healthcare to cross-cultural studies. The BM Formula offers a mathematically robust alternative to traditional saturation approaches, enabling precise sample size determination before data collection. Researchers are recommended to implement this formula with appropriate parameter documentation and continuous monitoring of saturation indicators.

I explore the sample size in qualitative research that is required to reach theoretical saturation. I conceptualize a population as consisting of sub-populations that contain different types of information sources that hold a number of codes. Theoretical saturation is reached after all the codes in the population have been observed once in the sample. I delineate three different scenarios to sample information sources: “random chance,” which is based on probability sampling, “minimal information,” which yields at least one new code per sampling step, and “maximum information,” which yields the largest number of new codes per sampling step. Next, I use simulations to assess the minimum sample size for each scenario for systematically varying hypothetical populations. I show that theoretical saturation is more dependent on the mean probability of observing codes than on the number of codes in a population. Moreover, the minimal and maximal information scenarios are significantly more efficient than random chance, but yield fewer repetitions per code to validate the findings. I formulate guidelines for purposive sampling and recommend that researchers follow a minimum information scenario.

This study explored the reasons and lived experiences of 20 Filipino teacher respondents in the school year 2023-2024. The qualitative method through phenomenological research was adopted. It is the method of discovering and structuring the meaning of human incidents through interviews with people involved in the real-life experience (Creswell, 2007; Polit & Beck, 2012). Patton (2002) describes that determining sample size in qualitative research does not follow one rule. There is no one rule related to sample size when using a qualitative research design. The respondents expressed heart-warming and overwhelming insights into their acculturation in the USA and Southeast Asian countries. Homesickness was the only negative thought, but it was easily overcome due to the availability of social media, which established the communication link with family members. Teaching abroad served as the turning point as their personal and professional “inputs” were outweighed by the “outputs” in terms of financial benefits, perks, and other work-life balance initiatives by schools abroad. Hence, the Department of Education should improve the economic conditions of teachers to avoid the exodus of a greater number of trained teachers in the service years ahead.

A common misconception about sampling in qualitative research is that numbers are unimportant in ensuring the adequacy of a sampling strategy. Yet, simple sizes may be too small to support claims of having achieved either informational redundancy or theoretical saturation, or too large to permit the deep, case-oriented analysis that is the raison-d'être of qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be put, the particular research method and purposeful sampling strategy employed, and the research product intended.

In this paper I explore the sample size in qualitative research that is required to reach theoretical saturation. I conceptualize a population as consisting of sub-populations that contain differen...

PurposeThe lack of a definite standard for determining the sample size in qualitative research leaves the research process to the initiative of the researcher, and this situation overshadows the scientificity of the research. The primary purpose of this research is to propose a model by questioning the problem of determining the sample size, which is one of the essential issues in qualitative research. The fuzzy logic model is proposed to determine the sample size in qualitative research.Design/methodology/approachConsidering the structure of the problem in the present study, the proposed fuzzy logic model will benefit and contribute to the literature and practical applications. In this context, ten variables, namely scope of research, data quality, participant genuineness, duration of the interview, number of interviews, homogeneity, information strength, drilling ability, triangulation and research design, are used as inputs. A total of 20 different scenarios were created to demonstrate the applicability of the model proposed in the research and how the model works.FindingsThe authors reflected the results of each scenario in the table and showed the values for the sample size in qualitative studies in Table 4. The research results show that the proposed model's results are of a quality that will support the literature. The research findings show that it is possible to develop a model using the laws of fuzzy logic to determine the sample size in qualitative research.Originality/valueThe model developed in this research can contribute to the literature, and in any case, it can be argued that determining the sample volume is a much more effective and functional model than leaving it to the initiative of the researcher.