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Navigating Sample Size Estimation for Qualitative Research

Sharma, Suresh K. 1 ; Mudgal, Shiv Kumar 2,* ; Gaur, Rakhi 2 ; Chaturvedi, Jitender 3 ; Rulaniya, Satyaveer 1 ; Sharma, Priya 1

1 College of Nursing, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India

2 College of Nursing, All India Institute of Medical Sciences, Deoghar, Jharkhand, India

3 Department of Neurosurgery, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India

Address for correspondence: Dr. Shiv Kumar Mudgal, College of Nursing, All India Institute of Medical Sciences, Deoghar, Jharkhand, India. E-mail: [email protected]

This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 4.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

There are well-established rules and methods about sample size estimation in quantitative research approaches. However, qualitative research approaches justify very little about sample size estimation principles and largely depend on subjective judgements and arbitrariness. Contrarily, an adequate sample size is essential for a study to address the core elements of validity and credibility in qualitative research too such as rigor, trustworthiness, conformability and acceptance. Therefore, this review was carried out to explain the available methods to estimate sample size for qualitative studies. After conducting a thorough literature review, we discovered related articles that explore the estimation of sample size for qualitative studies. By examining these findings and integrating the information with our personal experience for estimation of sample size in the field of qualitative studies, we have produced an all-encompassing narrative review. After an in-depth literature search, four different approaches were described in this paper to answer the question of how to estimate sample size in qualitative studies. The four approaches described in this paper are (a) rules of thumb, (b) conceptual models, (c) concept of saturation and (d) statistics-based methods for sample size estimation in qualitative research. The paper presents four methods for estimating sample size in qualitative studies and simplifies the statistical approach for saturation calculation in qualitative studies. Yet, it is vital to responsibly integrate these methods, acknowledging their limitations and maintaining the importance of sample size estimation in qualitative studies.

I NTRODUCTION

Sample size determination is one of the key components of research methodology to draw inferences precisely about the population. Therefore, every researcher needs to determine the magical number for sample size when conducting quantitative or qualitative research or mixed method research. [ 1 , 2 ] For quantitative research, experts have formulated straightforward statistics-based guidelines or formulae to determine sample size precisely before study, but sample size estimation in qualitative research is still a matter of conceptual discussion and practical uncertainty. [ 3 ]

Recently, the topic of sample size in qualitative studies has gain more interest among research community and a number of papers discussing whether the sample size in a study should be determined in advance. [ 3-8 ] Sample size estimation in qualitative research is contextual and an important factor in critique of the trustworthiness of qualitative research. Moreover, determination of sample size in advance fulfils the requirements of ethics committee or funding agencies or to plan the resources, i.e., time and money for a study. [ 7 , 9 ]

Sample size in qualitative research must be large enough to ensure that all most all the information that might be necessary to unfolded a rich and new insight of the phenomenon under study, while on the other hand it must be small enough that is not precluded the deep analysis of qualitative data. [ 10 ] Collection and analysis of large information can be wastage of resources and often simply impractical, while small sample decreases the quality of study. [ 11 ]

Qualitative research community has no clear answer to the question of ‘number of participants before study’ and qualitative sample size determination is contingent on several factors such as methodological, epistemological, theoretical, ideological and practical issues. [ 4-6 ]

Despite these issues, a number of authors attempted to address this issue and explained various methods such as rule of thumb, conceptual models, data saturation [ 6 , 9 , 12-14 ] and, more recently, a statistics-based method to sample size estimation for qualitative study, but currently, we have no standard approach. [ 14-17 ] In the present paper, the authors attempt to discuss different approaches to determine sample size in qualitative study after reviewing the existing literature on sample size in qualitative research. The present paper aims to provide a resource for novice researchers who may be struggling with a question ‘how many participants in their qualitative study.’

M ATERIALS AND M ETHODS

In this comprehensive narrative review, we systematically searched multiple databases, including ‘PubMed’ and ‘Google Scholar,’ till September 2023. Our search strategy employed specific words such as ‘sample size,’ ‘magical number,’ ‘qualitative studies,’ ‘estimation’ and ‘calculation’ to refine the selection process. After a thorough examination by the reviewers, we identified and selected 25 articles aligned with the study’s objectives. A meticulous assessment of the titles and abstracts led to the retrieval of the full texts for further analysis.

Our review specifically focused on articles related to the estimation of sample size for qualitative studies, incorporating insights from our own experiences in sample size estimation for this research design. The scope of our search was limited to articles published in English and those specifically addressing qualitative studies.

Approaches to determine sample size in qualitative research

Reviewing previous literature on this issue, the authors identify four approaches to estimating sample size in this way: rules of thumb, conceptual models, data saturation and statistical formulae for sample size estimation in qualitative research.

Rules of thumb

Based on methodological considerations and past experience, a number of researchers have recommended rules of thumb for sample size for specific qualitative research designs. Generally, these rules of thumb recommended by different authors show agreement, while at times some variations are also observed; for instance, recommendations for sample size in single case studies range from 4 to 30, whereas for grounded theory, they range from 5 to 35. In addition, rules of thumb have no detailed and clear rationale for sample size. [ 9 , 18 ] Some of proposed recommendations are summarised in Table 1 .

As discussed in Table 1 , researchers suggest different sample sizes for qualitative research based on the research design. Bernard (2013) proposed 10–20 participants for understanding major issues in lived experience studies. Morse (1994) recommended at least 6 participants for phenomenological studies and 30–50 for ethnographies and grounded theory. Creswell (1998) suggested 5–25 interviews for phenomenology and 20–30 for grounded theory. Kuzel (1992) proposed 6–8 interviews for a homogeneous sample and 12–20 for maximum variation. Guest et al . (2006) stated that 6–12 interviews are generally enough for one project, but the adequacy depends on purposive sampling, data quality and research complexity. Warren (2002) suggested 20–30 interviews for publishing qualitative studies, but narrative research may need just 1 or 2 interviews. [ 9 , 14 , 17 , 18 ]

Conceptual models

A number of researchers suggest using conceptual model for the determination of sample size according to specific characteristics of the planned study, i.e., aim of the study, theoretical framework and plan for analysis. Based on conceptual models, sample size will depend upon various factors, such as the nature of the study (the less ‘obvious,’ the larger the sample size required); the scope of the study (the narrower the scope of the research question, the smaller the sample size needed); shadowed data (if the interviewee divulge something regarding others’ viewpoint, in addition to their own perspective, a smaller sample size may be required); and the study design (a longitudinal design with a group as the unit of analysis will necessitate a smaller sample size than one interview per participant). [ 9 , 19 ]

It was recently reported that sample size can be determined based on the ‘information power’ of a given sample. This information power is influenced by the following factors: the study’s goal (the broader the goal, the larger the required sample size); the sample’s specificity (the more specific the characteristics of the participants in relation to the study’s goals, the smaller the sample size); the theoretical foundation (the poorly developed the underlying theory, the larger the sample size); the quality of conversation (the richer the discourse in the interviews, the smaller the sample size) and the analysis technique (a study with an aim of an exploratory cross-case analysis will need a more sample size than one with an aim of in-depth analysis of a few participants). [ 9 , 20 ]

In qualitative research, saturation is one of the most widely used and accepted criteria that suggests the point to discontinue data collection and/or data analysis. The concept of saturation originally lies in the grounded theory when it was proposed by Glaser and Strauss (1967), but it now attained acceptance across different approaches to qualitative research. [ 21 ] It was considered as the gold standard [ 22 ] or rule and quality criteria for qualitative research. [ 23 , 24 ]

Despite getting widespread acceptance, saturation is defined in different manners in literature. After extensive literature review, the authors found five different models of saturation: theoretical saturation, inductive thematic saturation, a priori thematic saturation, data saturation and meaning saturation. [ 21 , 25 ]

The first model of these, theoretical saturation rooted in grounded theory approach, explains that researcher reaches a point in their data analysis where additional data sampling will not yield new information relevant to research topic. It describes the process of developing theoretical or conceptual categories in accordance with grounded theory. [ 21 , 25-27 ] The second of these, inductive thematic saturation focuses on codes or themes and saturation attains at a point when no new themes or codes are emerging from sample data. [ 21 , 25 , 28 ] The third model, a priori thematic analysis, is one where data are collected to elucidate theory rather than to develop or refine theory. A priori is decided by how well the pre-determined themes or codes illustrate or exemplify the data. [ 21 , 25 , 29 ] The fourth model, the saturation, presents a wide use of the concept. In this wide range, data saturation is defined as the ‘point in data collection and analysis when new incoming data produces little or no new information to address the research question.’ It means researchers reach a point where additional data do not provide further insights into the research question. This ensures that the sample size is sufficient to capture the full range of perspectives and experiences within the study population. However, defining the exact point of data saturation can be challenging due to its subjective nature. [ 30 ] Finally, the concept of meaning saturation suggests that no ‘further insight’ is deriving from the data. It refers to the depth and richness of the data collected, focusing on the extent to which the data captures the complexity and significance of the study’s themes. Meaning saturation is achieved when the collected data not only covers all themes but also provides detailed and profound insights into them. It concerns to the quality (rich, detailed, deep and related) of data under the study. [ 31 , 32 ]

Despite the increasing use of saturation (as saturation is not required in quantitative studies, where fixed rules for calculating sample size are already established before the study begins) for estimating sample sizes in qualitative studies, novice researchers still have a limited understanding of what saturation means. The concept of saturation and its various aspects are not clear, which can make it difficult for researchers to accept suggested methods. [ 27 , 30 , 31 , 33-35 ] For instance, the comparative method for theme saturation is claimed to be easy, inclusive and comparative. This method might be time-consuming and complex for more and larger qualitative studies. It could also be challenging to implement in studies that rely on unstructured data sources and observation when collecting data. [ 36 ]

Questions remain about the right sample size for a qualitative study. How do we determine the saturation point? To answer this question, an alternative method was proposed to calculate the saturation point. For this saturation was refers ‘the point during data analysis at which incoming data points (interviews) produce little or no new useful information related to the study objectives.’ [ 30 ] In this approach, researchers may calculate the point of saturation using three specific factors: (a) the base size, (b) the run length and (c) the new information threshold [ 30 ] [ Table 2 ].

Working example by applying this approach to calculate the print of saturation

Step 1: Decide base size, run length and select a new information threshold.
Base size: We know from earlier studies that majority of novel information in qualitative research comes early and usually follows an asymptomatic curve, with a sudden drop in new themes following a small number of data collection/analysis events. That is why we may choose 4, 5 and 6 interviews as base size. [ 22 , 25 , 37 , 38 ] From the chosen base size, compute the total number of unique themes that would be used as the denominator in the saturation ratio. In this example, we chose 5 interviews as base size
Run length: It is recommended to include runs of three [ 39 , 40 ] or two [ 41 ] data collection events each time to identify the number of new themes. This process would be continuing till we reach our pre-decided new information threshold. In this example, we choose two data collection events as run length
New information threshold: Two levels of new information threshold were proposed: ≤5% new information or 0% new information. In this example, we choose ≤5% new information as threshold.
Step 2: Identify the total number of unique themes after conducting 5 interviews (we decided 5 interviews as base size for our study/example).
Suppose, we identify 17, 14, 8, 5 and 7 new themes from interview 1, 2, 3, 4 and 5, respectively
Sum up the number of new unique themes from all interviews, i.e. 17 + 14 + 8 + 5 + 7 = 51
That total number of new unique themes (base size = 51) would be denominator in the for the saturation ratio formula.
Step 3: In the phase, conduct two more interviews, i.e., interview 6 th and 7 th (as we choose a run length of two events).
Review interview 6 th and 7 th to identify new unique themes
Suppose, we identified 5 and 3 new themes from interview 6 th and 7 th , respectively
Sum up new unique themes, i.e. 5 + 3 = 8; and this total number of new unique themes in the first run would be numerator the for the saturation ratio formula.
Step 4: Calculate the saturation ratio using the following formula:
Saturation ratio = total number of new unique themes in the first run length ÷ total number of new unique themes in the base set
Put value in the formula and find new information threshold, i.e. 8 ÷ 51 = 15.6%
The answer is 15.6% new information that is greater than our pre-decided threshold (≤5%). Therefore, we continue with next run.
Step 5: Conduct next run (second run) with two interviews to identify new unique themes (one would be overlap that means first run’s last interview [7 th interview] and another new one [8 th interview]).
Sum up new unique themes, i.e. for 7 th interview, it was 3 and suppose for 8 th interview, it was 1 (i.e. 3 + 1 = 4).
Step 6: Calculate the saturation ratio again using the same formula. The total number of new unique themes in the second run length = 4 (numerator) and the total number of new unique themes in the base set = 51 (denominator).
Put value in the formula and find new information threshold, i.e. 4 ÷ 51 = 7.8%
The result (7.8%) is still greater than our pre-decided threshold (5%); hence, we continue to the next run.
Step 7: Conduct the next run (third run) with two interviews to identify new unique themes (one would be overlap that means second run’s last interview [8 th interview] and another new one [9 th interview]).
Sum up new unique themes, i.e. for 8 th interview, it was 1 and suppose for 9 th interview, it was 1 (i.e. 1 + 1 = 2)
Put value in the formula and find new information threshold, i.e. 2 ÷ 51 = 3.9%
The result (3.9%) is less than our pre-decided threshold (5%); hence, we stop data collection at this point.

At this stage, the number of new unique themes (information) we have added to the last run is below the decided new information threshold (5%). Hence, we stop data collection at this point (9 th interview). This help determine that there is a decline in the amount of new information to a level that we might say that saturation has been achieved according to our subjectively decided threshold (≤5%).

As the final two interviews did not considerably add up to the collection of information, we would argue that the interview 7 was saturated (each of the next two interviews was completed to see how much new information would be generated and whether this would fall below the set threshold). These two additional interviews (indicating the executive duration of the interview) would be included with the superscription ‘+2’ in a summary of nine interviews. Then, in making an assessment of this example of saturation, we would declare that in seven + 2 interviews, we hit the 5% new information threshold using the base size 5.

It is important to note that if any researcher wants to be more precise and confident to decide saturation point, it is good to have a run length to 3 or more events (interviews) and set a strict threshold, i.e. no new information threshold (0%).

Qualitative sample size estimation statistical formula

In a paper, the author suggested the statistical-based formula for estimating sample size in qualitative research, which is: [ 17 ]

The descriptions of these four factors of the formula are as follows:

Scope of the study

It is one of the most important factors when determining sample size. Scope of the study means: Are researcher looking to create a new understanding on a topic from study? Or are researchers trying to look at a current usability of a product? If researchers were investigating to develop a new understanding on a phenomenon, they need to a greater number of participants. While fewer participants if researchers are investigating potential problems in the function of existing system. [ 17 ]

Numerically, the scope can be ranged from 1 to infinity. It is recommended to use the value given in Table 3 to fill in score of scopes in the formula:

1 = If study looking at a current state of the product or system, i.e. identifying new characteristics, studying the existing state or usability assessment
2 = If study is intended to understand or experience, i.e. experience of your staff or patients for a new portal
3 = If researcher wants to generalised a new system or experience about a phenomenon.

After determining the scope of the study, it will be multiplied with three in the next step.

Characteristics of population under the study

Researchers need to have large sample size as the heterogeneity of study population increases. Researchers need multiple representatives of each participant with specific difference researchers are looking for. It is recommended having a minimum of three participants per user type. This provides a deeper understanding of the experience each user type might have. [ 17 ]

For example, if we want to develop a system that enables hospital to input and track the ordering and collection of samples from wards to laboratories. We would like to interview many people involved in this process: doctors, staff nurses, ward in-charge, office staff, staff from the laboratories and managers.

Numerically, we can calculate the characteristics of population: C = P × 3; where P = the number of unique types.

Experience of the researchers

As researchers in qualitative study need to adjust pattern of questioning according to real-time response, they receive from a participants and methods of data analysis. Therefore, experience matters a lot in qualitative research. An experienced researcher can develop deeper and better-quality information from each qualitative research participant and can do better with a small sample size. Experience makes them enables to know how, when, where and from whom to dig deeper according to participants’ feedback. [ 17 ]

Numerically, experience of a researcher (E) could range from one to infinity, but in practically, it ranges from 1 to 2. [ 17 ] For example, a researcher with no experience should have a 1 and add tenths (0.10) based on 5 years of experience, i.e. for a researcher with 5 years of experience E = 1.10.

It is necessary to consider the time and budget limitations when estimating the sample size. Cost and time for conducting a study affect the sample size. For example, if you increase the sample size, you have to increase either time frame or the number of researchers on a proposed study and eventually it will increase the cost of a study. [ 17 ]

Numerically, resources could range from −1 or more to +1 or more. Researchers can estimate resources based on the time and cost they will need for conducting research, i.e. recruiting participants, collection and analysis of data. Researchers might have particular numbers of participants based on previous experiences. [ 17 ] For example, researcher might be aware it will cost approximately Rs. 25,000/- to hire recruitment agency, to pay a facility for 1 week and to pay 20 participants for 45 min interviews. Researcher also knows it will take 2 weeks to select and recruit the study participants, based on the study design. Therefore, researchers will need to plan for the cost and time necessary to get the participants they need or will need to reduce or increase number of participants accordingly.

Working example using the statistical formula for sample size estimation in qualitative research

Suppose we want to estimate the number of participants to be required in a proposed qualitative study that is assessing the requirement to develop an a digital portal to be used by patient-service staff members is a hospital during their working hours. We know that there are four unique user types: patients, staff nurses, managers and administers. This proposed qualitative study will be conducted by a researcher with 10 years of experience. Researcher has adequate budget and time to carry out this study.

The formula for estimating sample size is:

Scope (S) =2 (developing a new portal)

Characteristics (C) = P × 3; ( P = 4 unique user types), therefore, (C) =4 × 3 = 12;

Experience (E)-10 years (E) =1.20;

Resources (R)-adequate for the proposed study (R) =0;

Computation for the estimation for the sample size for proposed qualitative study by pulling above values in given statistical formula.

Sample size for qualitative study:

Thus, total 20 participants will be required for conducting particular qualitative research study.

C ONCLUSION

Various perspectives exist regarding determining appropriate sample sizes for qualitative research. However, these viewpoints remain dynamic and may pose challenges for novice qualitative researchers. This review article proposes that the determination of the optimal number of participants for qualitative research studies hinges on multiple factors, including the researcher’s expertise. The paper introduces four methodologies for estimating sample size in qualitative studies: rules of thumb, conceptual models, data saturation and statistical formulas tailored for qualitative research. We endeavoured to streamline a statistical approach for straightforward saturation calculation during or post-data collection in qualitative studies. It is our aspiration that researchers will find these methodologies pragmatically beneficial when conducting qualitative research investigations.

Authors contributions

Concept: SKS and SKM. Design: SKS, SKM and RG. Supervision: SKS and SKM. Data collection and/or processing: SKM, RG, JC, PS and SR. Analysis and/or interpretation: SKM, SR and PS. Literature search: RG, JC, PS and SR. Writing manuscript: SKM, RG, JC, PS and SR. Critical review: SKM, RG, JC, PS and SR. Proofreading: SKM, RG, JC, PS and SR. All authors have critically reviewed and approved the final draft and are responsible for the content and similarity index of the manuscript as it was <10% checked by iThenticate.

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There are no conflicts of interest.

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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 in Qualitative Interview Studies: Guided by Information Power

Affiliations.

1 1 University of Copenhagen, Copenhagen, Denmark.
2 2 Uni Research Health, Bergen, Norway.
3 3 University of Bergen, Bergen, Norway.
PMID: 26613970
DOI: 10.1177/1049732315617444

Sample sizes must be ascertained in qualitative studies like in quantitative studies but not by the same means. The prevailing concept for sample size in qualitative studies is "saturation." Saturation is closely tied to a specific methodology, and the term is inconsistently applied. We propose the concept "information power" to guide adequate sample size for qualitative studies. Information power indicates that the more information the sample holds, relevant for the actual study, the lower amount of participants is needed. We suggest that the size of a sample with sufficient information power depends on (a) the aim of the study, (b) sample specificity, (c) use of established theory, (d) quality of dialogue, and (e) analysis strategy. We present a model where these elements of information and their relevant dimensions are related to information power. Application of this model in the planning and during data collection of a qualitative study is discussed.

Keywords: information power; methodology; participants; qualitative; sample size; saturation.

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Indian J Psychol Med
v.42(1); Jan-Feb 2020

Sample Size and its Importance in Research

Chittaranjan andrade.

Clinical Psychopharmacology Unit, Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary sample size is set for a pilot study. This article discusses sample size and how it relates to matters such as ethics, statistical power, the primary and secondary hypotheses in a study, and findings from larger vs. smaller samples.

Studies are conducted on samples because it is usually impossible to study the entire population. Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well. The sample must therefore be representative of the population. This is best ensured by the use of proper methods of sampling. The sample must also be adequate in size – in fact, no more and no less.

SAMPLE SIZE AND ETHICS

A sample that is larger than necessary will be better representative of the population and will hence provide more accurate results. However, beyond a certain point, the increase in accuracy will be small and hence not worth the effort and expense involved in recruiting the extra patients. Furthermore, an overly large sample would inconvenience more patients than might be necessary for the study objectives; this is unethical. In contrast, a sample that is smaller than necessary would have insufficient statistical power to answer the primary research question, and a statistically nonsignificant result could merely be because of inadequate sample size (Type 2 or false negative error). Thus, a small sample could result in the patients in the study being inconvenienced with no benefit to future patients or to science. This is also unethical.

In this regard, inconvenience to patients refers to the time that they spend in clinical assessments and to the psychological and physical discomfort that they experience in assessments such as interviews, blood sampling, and other procedures.

ESTIMATING SAMPLE SIZE

So how large should a sample be? In hypothesis testing studies, this is mathematically calculated, conventionally, as the sample size necessary to be 80% certain of identifying a statistically significant outcome should the hypothesis be true for the population, with P for statistical significance set at 0.05. Some investigators power their studies for 90% instead of 80%, and some set the threshold for significance at 0.01 rather than 0.05. Both choices are uncommon because the necessary sample size becomes large, and the study becomes more expensive and more difficult to conduct. Many investigators increase the sample size by 10%, or by whatever proportion they can justify, to compensate for expected dropout, incomplete records, biological specimens that do not meet laboratory requirements for testing, and other study-related problems.

Sample size calculations require assumptions about expected means and standard deviations, or event risks, in different groups; or, upon expected effect sizes. For example, a study may be powered to detect an effect size of 0.5; or a response rate of 60% with drug vs. 40% with placebo.[ 1 ] When no guesstimates or expectations are possible, pilot studies are conducted on a sample that is arbitrary in size but what might be considered reasonable for the field.

The sample size may need to be larger in multicenter studies because of statistical noise (due to variations in patient characteristics, nonspecific treatment characteristics, rating practices, environments, etc. between study centers).[ 2 ] Sample size calculations can be performed manually or using statistical software; online calculators that provide free service can easily be identified by search engines. G*Power is an example of a free, downloadable program for sample size estimation. The manual and tutorial for G*Power can also be downloaded.

PRIMARY AND SECONDARY ANALYSES

The sample size is calculated for the primary hypothesis of the study. What is the difference between the primary hypothesis, primary outcome and primary outcome measure? As an example, the primary outcome may be a reduction in the severity of depression, the primary outcome measure may be the Montgomery-Asberg Depression Rating Scale (MADRS) and the primary hypothesis may be that reduction in MADRS scores is greater with the drug than with placebo. The primary hypothesis is tested in the primary analysis.

Studies almost always have many hypotheses; for example, that the study drug will outperform placebo on measures of depression, suicidality, anxiety, disability and quality of life. The sample size necessary for adequate statistical power to test each of these hypotheses will be different. Because a study can have only one sample size, it can be powered for only one outcome, the primary outcome. Therefore, the study would be either overpowered or underpowered for the other outcomes. These outcomes are therefore called secondary outcomes, and are associated with secondary hypotheses, and are tested in secondary analyses. Secondary analyses are generally considered exploratory because when many hypotheses in a study are each tested at a P < 0.05 level for significance, some may emerge statistically significant by chance (Type 1 or false positive errors).[ 3 ]

INTERPRETING RESULTS

Here is an interesting question. A test of the primary hypothesis yielded a P value of 0.07. Might we conclude that our sample was underpowered for the study and that, had our sample been larger, we would have identified a significant result? No! The reason is that larger samples will more accurately represent the population value, whereas smaller samples could be off the mark in either direction – towards or away from the population value. In this context, readers should also note that no matter how small the P value for an estimate is, the population value of that estimate remains the same.[ 4 ]

On a parting note, it is unlikely that population values will be null. That is, for example, that the response rate to the drug will be exactly the same as that to placebo, or that the correlation between height and age at onset of schizophrenia will be zero. If the sample size is large enough, even such small differences between groups, or trivial correlations, would be detected as being statistically significant. This does not mean that the findings are clinically significant.

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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.

Availability of data and materials

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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Susan Thorpe

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Terry Young

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Contributions

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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Correspondence to Konstantina Vasileiou .

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Terry Young is an academic who undertakes research and occasional consultancy in the areas of health technology assessment, information systems, and service design. He is unaware of any direct conflict of interest with respect to this paper. All other authors have no competing interests to declare.

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Additional Files

Additional file 1:.

Editorial positions on qualitative research and sample considerations (where available). (DOCX 12 kb)

Additional File 2:

List of eligible articles included in the review ( N = 214). (DOCX 38 kb)

Additional File 3:

Data Extraction Form. (DOCX 15 kb)

Additional File 4:

Citations used by articles to support their position on saturation. (DOCX 14 kb)

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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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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.

Interested in running a qualitative research study? Request a proposal >

Author Bio: Joanna Jones is the founder and CEO of InterQ Research. At InterQ, she oversees study design, manages clients, and moderators studies.

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CALCULATORS

Sample Size Calculator

TGM Statistical Significance Calculator Dashboard

Enter the Population Size (if known).
Input the Confidence Level (%) (e.g., 90%, 95%, 99%).
Set the Margin of Error (%) (the precision level you want).
Click Calculate to get the recommended sample size for your survey or research.

This represents the minimum recommended sample size for your survey. If you gather responses from all individuals in this sample, the results are more likely to be accurate compared to a larger sample with a lower response rate.

What is a Sample Size Calculator?

Common uses of sample size calculators.

Ensure statistically significant results
Plan resources and timelines
Avoid over- or under-sampling

How Does a Sample Size Calculator Work?

Population Size – The total number of people in the group being studied. For large populations, sample size can remain relatively constant. However, for smaller populations, adjustments must be made to avoid over-sampling or under-sampling. The population size must be known or estimated to calculate an accurate sample size.
Confidence Level – How sure you are that the true population parameter lies within the margin of error. Common confidence levels are 90%, 95%, and 99%. A 95% confidence level is the standard in most research, meaning you are 95% certain that the results are representative of the population. Higher confidence levels require larger sample sizes.
Margin of Error – The acceptable range within which the survey results can differ from the actual population value. A typical margin of error in most surveys ranges from ±3% to ±5%. A smaller margin of error provides more precise results but requires a larger sample size. The acceptable MoE should align with your research goals and available resources.

What is the Sample Size Formula?

The standard sample size formula is:

$ n = \frac{Z^2 \cdot p \cdot (1 - p)}{e^2} $

$ n $ : required sample size
$Z = Z-value$ : (standard score corresponding to the confidence level, e.g., 1.96 for 95% confidence)
$ p $ : estimated proportion of the population that has the attribute (set to 0.5 if unknown)
$ e $ : margin of error

Breaking Down the Sample Size Formula

$ Z-value$ depends on your confidence level.
$p$ (proportion) represents the variability in the population, typically assumed as 0.5 (50%).
$e$ (margin of error) determines the precision of your results.

Interpretation of the Results:

How sample size significance varies across survey types.

The significance of sample size can vary dramatically across different types of surveys in market research.

For large-scale quantitative studies, such as national consumer behavior surveys, a larger sample size is often crucial to ensure representativeness and reduce margin of error.

However, for qualitative research methods like focus groups or in-depth interviews, smaller sample sizes can still yield valuable insights.

In B2B market research, where the total population might be smaller, even a modest sample size can provide statistically significant results. Exploratory studies may start with smaller samples and expand as needed, while confirmatory research typically requires larger samples to validate hypotheses with confidence.

The key is to balance statistical power with practical constraints like time and budget, always keeping in mind the specific objectives of your research project.

10 Expert Tips to Optimize Sample Size for Your Research

Define Your Population Clearly: Ensure you have a precise definition of your target population to avoid sampling bias.
Consider Your Confidence Level: Aim for at least a 95% confidence level for most market research studies to ensure reliable results.
Account for Response Rate: Overestimate your sample size to compensate for potential non-responses or incomplete surveys.
Use Stratified Sampling: If your population has distinct subgroups, consider stratified sampling to ensure proper representation.
Conduct Power Analysis: For studies comparing groups or testing hypotheses, perform a power analysis to determine the sample size needed to detect significant effects.
Consider Resource Constraints: Balance statistical ideals with practical limitations like budget and time.
Use Online Calculators Wisely: While online sample size calculat
Consult Historical Data: If available, use data from similar past studies to inform your sample size decisions.
Seek Expert Advice: For complex studies or when in doubt, consult with statisticians or experienced market researchers to validate your sample size calculations.

What’s next?

Once you've determined your sample size, you're ready to take the next step in your research process.

Market research involves gathering valuable insights into consumers' needs and preferences, helping you make data-driven decisions to enhance your business or better serve your clients.

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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:

Identify use cases for market research
Create and deliver effective market research campaigns
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How to calculate the sample size for a qualitative research?

Most recent answer.

Purpose and Scope : Define the depth of exploration needed.
Data Saturation : Continue sampling until no new themes or insights emerge.
Resource Constraints : Consider time and budget limitations.

Top contributors to discussions in this field

Mississippi State University (Emeritus)

Retired US Fed Govt/Home Research

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All Answers (13)

Define Scope : Determine the depth of exploration required for your study.
Saturation Point : Sample until no new themes or insights are emerging (data saturation).
Practical Constraints : Consider time, resources, and accessibility.
Guidelines : For interviews, typically 5-30 participants are used.

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COMMENTS

Big enough? Sampling in qualitative inquiry
Any senior researcher, or seasoned mentor, has a practiced response to the 'how many' question. Mine tends to start with a reminder about the different philosophical assumptions undergirding qualitative and quantitative research projects (Staller, 2013). As Abrams (2010) points out, this difference leads to "major differences in sampling ...
Determining the Sample Size in Qualitative Research
finds a variation of the sample size from 1 to 95 (averages being of 31 in the first ca se and 28 in the. second). The research region - one of t he cultural factors, plays a significant role in ...
Qualitative Sample size Calculator
What is a good sample size for a qualitative research study? ‍ Our sample size calculator will work out the answer based on your project's scope, participant characteristics, researcher expertise, and methodology. Just answer 4 quick questions to get a super actionable, data-backed recommendation for your next study.
Sample sizes for saturation in qualitative research: A systematic
These research objectives are typical of much qualitative heath research. The sample size of the datasets used varied from 14 to 132 interviews and 1 to 40 focus groups. All datasets except one (Francis et al., 2010) had a sample that was much larger than the sample ultimately needed for saturation, making them effective for assessing saturation.
Navigating Sample Size Estimation for Qualitative Research
e sample size is essential for a study to address the core elements of validity and credibility in qualitative research too such as rigor, trustworthiness, conformability and acceptance. Therefore, this review was carried out to explain the available methods to estimate sample size for qualitative studies. After conducting a thorough literature review, we discovered related articles that ...
Series: Practical guidance to qualitative research. Part 3: Sampling
In quantitative research, by contrast, the sample size is determined by a power calculation. The usually small sample size in qualitative research depends on the information richness of the data, the variety of participants (or other units), the broadness of the research question and the phenomenon, the data collection method (e.g., individual ...
Sample size: how many participants do I need in my research?
It is the ability of the test to detect a difference in the sample, when it exists in the target population. Calculated as 1-Beta. The greater the power, the larger the required sample size will be. A value between 80%-90% is usually used. Relationship between non-exposed/exposed groups in the sample.
(PDF) Qualitative Research Designs, Sample Size and Saturation: Is
Characterising and justifying sample size sufficiency in interview-based studies: systematic analysis of qualitative health research over a 15-year period. BMC Medical Research Methodology. 18(1 ...
Sample size for qualitative research
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 ...
Sample Size in Qualitative Interview Studies: Guided by Information
The prevailing concept for sample size in qualitative studies is "saturation." Saturation is closely tied to a specific methodology, and the term is inconsistently applied. We propose the concept "information power" to guide adequate sample size for qualitative studies. Information power indicates that the more information the sample holds ...
Sample Size and its Importance in Research
Sample size calculations require assumptions about expected means and standard deviations, or event risks, in different groups; or, upon expected effect sizes. For example, a study may be powered to detect an effect size of 0.5; or a response rate of 60% with drug vs. 40% with placebo. [1] When no guesstimates or expectations are possible ...
Characterising and justifying sample size sufficiency in interview
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 [] 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 ...
Sample Size in Qualitative Interview Studies:
The prevailing concept for sample size in qualitative studies is "saturation." Saturation is closely tied to a specific methodology, and the term is inconsistently applied. We propose the concept "information power" to guide adequate sample size for qualitative studies.
How to Justify 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 ...
PDF Quantitative Approaches for Estimating Sample Size for Qualitative
1) Specific approaches can be used to estimate sample size in qualitative research, e.g. to assess concept saturation. -These need to be considered alongside other issues, and may also only be able to be applied once data have been collected. 2) Sample size calculation for small samples, e.g. for exit interviews, is
PDF Determining the Sample in Qualitative Research
designing their qualitative research projects.Sampling and sample size debate in qualitative research is one of the major components that is not em. hasised enough in literature (Robinson, 2014). There is no rule of thumb or straightforward guidelines for determining the number of participants in qualitative studies (Patton, 2015), rather.
Can sample size in qualitative research be determined a priori?
There has been considerable recent interest in methods of determining sample size for qualitative research a priori, rather than through an adaptive approach such as saturation. Extending previous literature in this area, we identify four distinct approaches to determining sample size in this way: rules of thumb, conceptual models, numerical ...
Sample size determination: A practical guide for health researchers
Approaches to sample size calculation according to study design are presented with examples in health research. For sample size estimation, researchers need to (1) provide information regarding the statistical analysis to be applied, (2) determine acceptable precision levels, (3) decide on study power, (4) specify the confidence level, and (5 ...
A Guide to Sample Sizes in Qualitative UX Research
A formula for determining qualitative sample size. In 2013, Research by Design published a whitepaper by Donna Bonde which included research-backed guidelines for qualitative sampling in a market research context. Victor Yocco, writing in 2017, drew on these guidelines to create a formula determining qualitative sample sizes.
Sample size determination: A practical guide for health researchers
PS Power and Sample Size Calculation 15 or Sample Size Calculator 16 are practical tools for power and sample size calculations in studies with dichotomous, continuous, or survival outcome measures. The support offered by these tools varies in terms of the type of interface and the mathematical formula or assumptions used for calculation. 17 - 20
Sample Size Calculator
Use TGM Research's Sample Size Calculator to determine ideal sample size for your surveys. Get data-backed recommendations to improve the accuracy of your research projects. ... However, for qualitative research methods like focus groups or in-depth interviews, smaller sample sizes can still yield valuable insights. In B2B market research ...
Sample size calculator
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.
How to calculate the sample size for a qualitative research?
In qualitative research sample size depends on data saturation. Cite. Mosharop Hossian. The University of Queensland. The usual recommendation for qualitative interviews is a sample size of 30 ...

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Navigating Sample Size Estimation for Qualitative Research

I NTRODUCTION

M ATERIALS AND M ETHODS

Approaches to determine sample size in qualitative research

Rules of thumb

Conceptual models

Working example by applying this approach to calculate the print of saturation

Qualitative sample size estimation statistical formula

Scope of the study

Characteristics of population under the study

Experience of the researchers

Working example using the statistical formula for sample size estimation in qualitative research

C ONCLUSION

Authors contributions

Financial support and sponsorship

R EFERENCES

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Practical implications

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Sample Size in Qualitative Interview Studies: Guided by Information Power

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Sample Size and its Importance in Research

SAMPLE SIZE AND ETHICS

ESTIMATING SAMPLE SIZE

PRIMARY AND SECONDARY ANALYSES

INTERPRETING RESULTS

Financial support and sponsorship

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

Conclusions

Sample size in qualitative research: Conceptual developments and empirical investigations

Objectives of the present study

Study design

Search strategy to identify studies

Eligibility criteria

Data extraction and analysis

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

Pragmatic considerations

Qualities of the analysis

Meet sampling requirements

Sample size guidelines

In line with existing research

Richness and volume of data

Meet research design requirements

Researchers’ previous experience

Nature of study

Further sampling to check findings consistency

Thematic analysis of passages discussing sample size

Characterisations of sample size sufficiency

‘Small’ but why and for whom?

‘Good enough’ sample sizes

‘Large’ sample sizes - Promise or peril?

Threats from sample size insufficiency

Generalizability

Strengths and limitations of the present study

Abbreviations

Acknowledgments

Availability of data and materials

Author information

Contributions

Corresponding author

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Publisher’s Note

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How to Justify Sample Size in Qualitative Research