sample size of a qualitative research

How to Justify Sample Size in Qualitative Research

• March 21, 2023

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

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

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

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

Here are some guidelines.

Qualitative sample size guideline #1: Segmentation of participants

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

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

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

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

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

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

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

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

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

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

The beauty of qualitative research is that if you:

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

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

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

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

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

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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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Qualitative Research Designs, Sample Size and Saturation: Is Enough Always Enough?

• December 2021
• Journal of Advocacy Research and Education 8(3):60-65

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• Published: 21 November 2018

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

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

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

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

Sample size in qualitative research: Conceptual developments and empirical investigations

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

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

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

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

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

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

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

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

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

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

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

Objectives of the present study

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

Study design

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

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

Search strategy to identify studies

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

Eligibility criteria

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

PRISMA flow diagram

Data extraction and analysis

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

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

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

Journal and year of publication

Number of interviews

Number of participants

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

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

Number of sample size justifications provided

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Pragmatic considerations

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

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

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

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

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

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

Qualities of the analysis

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

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

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

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

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

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

Meet sampling requirements

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

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

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

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

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

Sample size guidelines

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

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

In line with existing research

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

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

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

Richness and volume of data

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

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

Meet research design requirements

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

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

Researchers’ previous experience

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

Nature of study

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

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

Further sampling to check findings consistency

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

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

Thematic analysis of passages discussing sample size

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

Characterisations of sample size sufficiency

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

‘Small’ but why and for whom?

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

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

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

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

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

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

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

‘Good enough’ sample sizes

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

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

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

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

‘Large’ sample sizes - Promise or peril?

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

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

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

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

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

Threats from sample size insufficiency

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

Generalizability

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Strengths and limitations of the present study

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

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

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

Abbreviations

British Journal of Health Psychology

British Medical Journal

Interpretative Phenomenological Analysis

Sociology of Health & Illness

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Acknowledgments

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

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

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

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

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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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Qualitative Market Research

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

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Sample Size Policy for Qualitative Studies Using In-Depth Interviews

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

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

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

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

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

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

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

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

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

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

Charmaz, K. (1990). ‘Discovering’ chronic illness: Using grounded theory. Social Science and Medicine, 30 , 1161–1172.

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Morse, J. M. (1994). Designing funded qualitative research. In N. Denzin & Y. Lincoln (Eds.), Handbook of qualitative research (pp. 220–235). Thousand Oaks, CA: Sage Publications.

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Zucker, K. J. (2002). From the Editor’s desk: Receiving the torch in the era of sexology’s renaissance. Archives of Sexual Behavior, 31 , 1–6.

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

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Sample size in qualitative research

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• 1 Department of Women's and Children's Health, School of Nursing, University of North Carolina at Chapel Hill 27599-7460, USA.
• PMID: 7899572
• DOI: 10.1002/nur.4770180211

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

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Sampling in Qualitative Research

In gerontology the most recognized and elaborate discourse about sampling is generally thought to be in quantitative research associated with survey research and medical research. But sampling has long been a central concern in the social and humanistic inquiry, albeit in a different guise suited to the different goals. There is a need for more explicit discussion of qualitative sampling issues. This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It then describes common questions about sampling in qualitative research. In conclusion it proposes the concept of qualitative clarity as a set of principles (analogous to statistical power) to guide assessments of qualitative sampling in a particular study or proposal.

Questions of what is an appropriate research sample are common across the many disciplines of gerontology, albeit in different guises. The basic questions concern what to observe and how many observations or cases are needed to assure that the findings will contribute useful information. Throughout the history of gerontology, the most recognized and elaborate discourse about sampling has been associated with quantitative research, including survey and medical research. But concerns about sampling have long been central to social and humanistic inquiry (e.g., Mead 1953 ). The authors argue such concerns remained less recognized by quantitative researchers because of differing focus, concepts, and language. Recently, an explicit discussion about concepts and procedures for qualitative sampling issues has emerged. Despite the growing numbers of textbooks on qualitative research, most offer only a brief discussion of sampling issues, and far less is presented in a critical fashion ( Gubrium and Sankar 1994 ; Werner and Schoepfle 1987 ; Spradley 1979 , 1980 ; Strauss and Corbin 1990 ; Trotter 1991 ; but cf. Denzin and Lincoln 1994 ; DePoy and Gitlin 1993 ; Miles and Huberman 1994 ; Pelto and Pelto 1978 ).

The goal of this article is to extend and further refine the explicit discussion of sampling issues and techniques for qualitative research in gerontology. Throughout the article, the discussion draws on a variety of examples in aging, disability, ethnicity as well as more general anthropology.

The significance of the need to understand qualitative sampling and its uses is increasing for several reasons. First, emerging from the normal march of scientific developments that builds on prior research, there is a growing consensus about the necessity of complementing standardized data with insights about the contexts and insiders' perspectives on aging and the elderly. These data are best provided by qualitative approaches. In gerontology, the historical focus on aging pathology obscured our view of the role of culture and personal meanings in shaping how individuals at every level of cognitive and physical functioning personally experience and shape their lives. The individual embodying a “case” or “symptoms” continues to make sense of, manage, and represent experiences to him- or herself and to others. A second significance to enhancing our appreciation of qualitative approaches to sampling is related to the societal contexts of the scientific enterprise. Shifts in public culture now endorse the inclusion of the experiences and beliefs of diverse and minority segments of the population. A reflection of these societal changes is the new institutional climate for federally funded research, which mandates the inclusion and analysis of data on minorities. Qualitative approaches are valuable because they are suited to assessing the validity of standardized measures and analytic techniques for use with racial and ethnic subpopulations. They also permit us to explore diversities in cultural and personal beliefs, values, ideals, and experiences.

This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It describes the scientific implications of the cultural embeddedness of sampling issues as a pervasive feature in wider society. It then describes common questions about sampling in qualitative research. It concludes by proposing an analog to statistical power, qualitative clarity , as a set of principles to guide assessments of the sampling techniques in a study report or research proposal. The term clarity was chosen to express the goal of making explicit the details of how the sample was assembled, the theoretical assumptions, and the practical constraints that influenced the sampling process. Qualitative clarity should include at least two components, theoretical grounding and sensitivity to context. The concept focuses on evaluating the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation. These can be evaluated even if the factors to be measured cannot be specified.

A wide range of opinions about sampling exists in the qualitative research community. The authors take issue with qualitative researchers who dismiss these as irrelevant or even as heretical concerns. The authors also disagree with those quantitative practitioners who dismiss concerns about qualitative sampling as irrelevant in general on the grounds that qualitative research provides no useful knowledge. It is suggested that such a position is untenable and uninformed.

This article focuses only on qualitative research; issues related to combined qualitative and quantitative methods are not discussed. The focus is on criteria for designing samples; qualitative issues related to suitability of any given person for research are not addressed. The criteria for designing samples constitute what Johnson (1990) labels as “Criteria One issues,” the construction and evaluation of theory and data-driven research designs. Criteria Two issues relate to the individual subjects in terms of cooperativeness, rapport, and suitability for qualitative study methods.

Although this article may appear to overly dichotomize qualitative and quantitative approaches, this was done strictly for the purposes of highlighting key issues in a brief space. The authors write here from the perspective of researchers who work extensively with both orientations, singly and in combination, in the conduct of major in-depth and longitudinal research grants that employ both methods. It is the authors' firm belief that good research requires an openness to multiple approaches to conceptualizing and measurement phenomena.

Contributions, Logic and Issues in Qualitative Sampling

Major contributions.

Attention to sampling issues has usually been at the heart of anthropology and of qualitative research since their inception. Much work was devoted to evaluating the appropriateness of theory, design strategies, and procedures for sampling. Important contributions have been made by research devoted to identifying and describing the nature of sample universes and the relevant analytic units for sampling. For example, the “universe of kinship” ( Goodenough 1956 ) has been a mainstay of cross-cultural anthropological study. Kinship studies aim to determine the fundamental culturally defined building blocks of social relationships of affiliation and descent (e.g., Bott 1971 ; Fortes 1969 ). Ethnographic investigations document the diversity of kinship structures, categories of kith and kin, and terminologies that give each culture across the globe its distinctive worldview, social structure, family organization, and patterns to individual experiences of the world.

Concerns with sampling in qualitative research focus on discovering the scope and the nature of the universe to be sampled. Qualitative researchers ask, “What are the components of the system or universe that must be included to provide a valid representation of it?” In contrast, quantitative designs focus on determining how many of what types of cases or observations are needed to reliably represent the whole system and to minimize both falsely identifying or missing existing relationships between factors. Thus the important contributions of qualitative work derived from concerns with validity and process may be seen as addressing core concerns of sampling, albeit in terms of issues less typically discussed by quantitative studies. Two examples may clarify this; one concerns time allocation studies of Peruvian farmers and the other addresses a census on Truk Island in the South Pacific.

The Andes mountains of Peru are home to communities of peasants who farm and tend small herds to garner a subsistence living. To help guide socioeconomic modernization and to improve living conditions, refined time allocation studies (see Gross 1984 ) were conducted in the 1970s to assess the rational efficiency of traditional patterns of labor, production, and reproduction. Seemingly irrational results were obtained. A systematic survey of how villagers allocated their time to various activities identified a few healthy adults who sat in the fields much of the day. Given the marginal food supplies, such “inactivity” seemed irrational and suggested a possible avenue for the desired interventions to improve village economic production. Only after interviewing the farmers to learn why the men sat in the fields and then calculating the kilocalories of foods gained by putting these men to productive work elsewhere was an explanation uncovered. It was discovered that crop yields and available calories would decline , not increase, due to foraging birds and animals. Because the farmers sat there, the events of animal foraging never occurred in the data universe. Here, judgments about the rationality of behaviors were guided by too narrow a definition of the behavioral universe, shaped by reliance on analytic factors external to the system (e.g., biases in industrial economies that equate “busyness” with production). An important message here is that discovery and definition of the sample universe and of relevant units of activity must precede sampling and analyses.

On Truk Island in the South Pacific, two anthropologists each conducted an independent census using the same methods. They surveyed every person in the community. Statistical analyses of these total universe samples were conducted to determine the incidence of types of residence arrangements for newlywed couples. The researchers reached opposite conclusions. Goodenough (1956) argued that his colleague's conclusion that there are no norms for where new couples locate their residence clearly erred by classifying households as patrilocal (near the father), matrilocal, or neolocal (not near either parent) at one time as if isolated from other social factors. Goodenough used the same residence typology as did his colleague in his analysis, but identified a strong matralineal pattern (wife's extended family). Evidence for this pattern becomes clear when the behaviors are viewed in relation to the extended family and over time. The newlyweds settle on whatever space is available but plan to move later to the more socially preferred (e.g., matralineal) sites. This later aspect was determined by combining survey-based observations of behavior with interviews to learn “what the devil they think they are doing” ( Geertz 1973 ). Thus different analytic definitions of domestic units led to opposite conclusions, despite the use of a sample of the total universe of people! Social constructions of the lived universe, subjectively important temporal factors have to be understood to identify valid units for analyses and interpretation of the data.

The Peruvian and the Truk Island examples illustrate some of the focal contributions of qualitative approaches to sampling. Altering the quantitatively oriented sampling interval, frequency, or duration would not have produced the necessary insights. The examples also suggest some of the dilemmas challenging sampling in qualitative research. These will be addressed in a later section. Both cases reveal the influence of deeply ingrained implicit cultural biases in the scientific construction of the sampling universe and the units for sampling.

The Cultural Embeddedness of the Concept of Sampling

Sampling issues are not exclusive to science. Widespread familiarity with sampling and related issues is indicated by the pervasive popular appetite for opinion and election polls, surveys of consumer product prices and quality, and brief reports of newsworthy scientific research in the mass media. Sampling issues are at the heart of jury selection, which aims to represent a cross section of the community; frequent debates erupt over how to define the universe of larger American society (e.g., by race and gender) to use for juror selection in a specific community. We can shop for sampler boxes of chocolates to get a tasty representation of the universe of all the candies from a company. Debates about the representativeness, size, and biases in survey results because of the people selected for study or the small size of samples are a part of everyday conversation. Newspapers frequently report on medical or social science research, with accounts of experts' challenging the composition or size of the sample or the wording of the survey questions. Critical skills in sampling are instilled during schooling and on-the-job training.

Such widespread familiarity with basic sampling issues suggests a deep cultural basis for the fascination and thus the need for a more critical understanding. The concept and practices of sampling resonate with fundamental cultural ideals and taboos. It is perhaps the case that sampling is linked, in American culture, to democratic ideals and notions of inclusion and representation.

What does that mean for qualitative researchers designing sampling strategies? We need to be aware that the language of science is ladened with cultural and moral categories. Thus gerontological research may potentially be shaped by both cultural themes masked as scientific principles. Basic terms for research standards can simultaneously apply to ideals for social life ( Luborsky 1994 ). We construct and are admonished by peers to carefully protect independent and dependent variables; we design studies to provide the greatest statistical power and speak of controlling variables. At the same time, psychosocial interventions are designed to enhance these same factors of individual independence and senses of power and control. We examine constructs and data to see if they are valid or invalid; the latter word also is defined in dictionaries as referring to someone who is not upright but physically deformed or sickly. Qualitative research, likewise, needs to recognize that we share with informants in the search for themes and coherence in life, and normatively judge the performance of others in these terms ( Luborsky 1994 , 1993b ).

The ideals of representativeness and proportionality are not, in practice, unambiguous or simple to achieve as is evidenced in the complex jury selection process. Indeed, there is often more than one way to achieve representativeness. Implicit cultural values may direct scientists to define some techniques as more desirable than others. Two current examples illustrate how sampling issues are the source of vitriolic debate outside the scientific community: voting procedures, and the construction or apportionment of voting districts to represent minority, ethnic, or racial groups. Representing “the voice of the people” in government is a core tenet of American democracy, embodied in the slogan “one person one vote.” Before women's suffrage, the universe was defined as “one man one vote.” A presidential nomination for U.S. Attorney General Dr. Lani Guinier, was withdrawn, in part, because she suggested the possibility of an alternative voting system (giving citizens more than one vote to cast) to achieve proportional representation for minorities. We see in these examples that to implement generalized democratic ideals of equal rights and representation can be problematic in the context of the democratic ideal of majority rule. Another example is the continuing debate in the U.S. Supreme Court over how to reapportion voting districts so as to include sufficient numbers of minority persons to give them a voice in local elections. These examples indicate the popular knowledge of sampling issues, the intensity of feelings about representativeness, and the deep dilemmas about proportional representation and biases arising within a democratic society. The democratic ideals produce multiple conflicts at the ideological level.

It is speculated that the association of sampling issues with such core American cultural dilemmas exacerbates the rancor between qualitative and quantitative gerontology; whereas in disciplines that do not deal with social systems, there is a tradition of interdependence instead of rancor. For example, the field of chemistry includes both qualitative and quantitative methods but is not beset by the tension found in gerontology. Qualitative chemistry is the set of methods specialized in identifying the types and entire range of elements and compounds present in materials or chemical reactions. A variety of discovery-oriented methods are used, including learning which elements are reacting with one another. Quantities of elements present may be described in general ranges as being from a trace to a substantial amount. Quantitative chemistry includes measurement-oriented methods attuned to determining the exact quantity of each constituent element present. Chemists use both methods as necessary to answer research problems. The differences in social contextual factors may contribute to the lower level of tension between quantitative and qualitative traditions within the European social sciences situated as they are within alternative systems for achieving democratic representation in government (e.g., direct plebiscites or multiparty governments rather than the American electoral college approach to a two-party system).

Ideals and Techniques of Qualitative Sampling

The preceding discussion highlighted the need to first identify the ideal or goal for sampling and second to examine the techniques and dilemmas for achieving the ideal. The following section describes several ideals, sampling techniques, and inherent dilemmas. Core ideals include the determination of the scope of the universe for study and the identification of appropriate analytic units when sampling for meaning

Defining the universe

This is simultaneously one of qualitative research's greatest contributions and greatest stumbling blocks to wider acceptance in the scientific community. As the examples of the Peruvian peasants and Trukese postmarital residence norms illustrated, qualitative approaches that can identify relevant units (e.g., of farming activity or cultural ideals for matralineal residence) are needed to complement behavioral or quantitative methods if we are to provide an internally valid definition of the scope of the universe to be sampled. Probability-based approaches do not capture these dimensions adequately.

The problem is that the very nature of such discovery-oriented techniques runs counter to customary quantitative design procedures. This needs to be clearly recognized. Because the nature of the units and their character cannot be specified ahead of time, but are to be discovered, the exact number and appropriate techniques for sampling cannot be stated at the design stage but must emerge during the process of conducting the research. One consequence is that research proposals and reports may appear incomplete or inadequate when in fact they are appropriately defined for qualitative purposes. One technique in writing research proposals has been to specify the likely or probable number of subjects to be interviewed.

Evidence that a researcher devoted sufficient attention to these issues can be observed in at least two dimensions. First, one finds a wealth of theoretical development of the concepts and topics. In qualitative research, these serve as the analytic tools for discovery and aid in anticipating new issues that emerge during the analyses of the materials. Second, because standardized measurement or diagnostic tests have not yet been developed for qualitative materials, a strong emphasis is placed on analytic or interpretive perspectives to the data collection and data analyses.

Expository styles, traditional in qualitative studies, present another dilemma for qualitative discussions of sampling. An impediment to wider recognition of what constitutes an adequate design is customary, implicit notions about the “proper” or traditional formats for writing research proposals and journal articles. The traditional format for grant applications places discussions of theory in the section devoted to the general significance of the research application separate from the methods and measures. However, theoretical issues and conceptual distinctions are the research tools and methods for qualitative researchers, equivalent to the quantitative researchers' standardized scales and measures. As the authors have observed it written reviews of grant applications over many years, reviewers want such “clutter” in qualitative documents placed where it belongs elsewhere in the proposal, not in the design section ( Rubinstein 1994 ). Qualitative researchers look for the analytic refinement, rigor, and breadth in conceptualization linked to the research procedures section as signs of a strong proposal or publication. Thus basic differences in scientific emphases, complicated by expectations for standardized scientific discourse, need to be more fully acknowledged.

Appropriate analytic units: Sampling for meaning

The logic or premises for qualitative sampling for meaning is incompletely understood in gerontology. Although it appears that, in the last decade, there has been an improved interdisciplinary acceptance and communication within gerontology, gerontology is largely driven by a sense of medicalization of social aging and a bias toward survey sampling and quantitative analysis based on “adequate numbers” for model testing and other procedures. At the same time, and partly in reaction to the dominance of the quantitative ethos, qualitative researchers have demurred from legitimating or addressing these issues in their own work.

Understanding the logic behind sampling for meaning in gerontological research requires an appreciation of how it differs from other approaches. By sampling for meaning, the authors indicate the selection of subjects in research that has as its goal the understanding of individuals' naturalistic perceptions of self, society, and the environment. Stated in another way, this is research that takes the insider's perspective. Meaning is defined as the process of reference and connotation, undertaken by individuals, to evoke key symbols, values, and ideas that shape, make coherent, and inform experience ( D'Andrade 1984 ; Good & Good 1982 ; Luborsky and Rubinstein 1987 ; Mishler 1986 ; Rubinstein 1990 ; Williams 1984 ). Clearly, the qualitative approach to meaning stands in marked contrast to other approaches to assessing meaning by virtue of its focus on naturalistic data and the discovery of the informant's own evaluations and categories. For example, one approach assesses meaning by using standardized lists of predefined adjectives or phrases (e.g., semantic differential scale methods, Osgood, Succi, and Tannenbaum 1957 ); another approach uses diagnostic markers to assign individuals to predefined general types (e.g., depressed, anxious) as a way to categorize people rather than describe personal meaning (e.g., the psychiatric diagnostic manual, DSMEI-R, APA 1987 ).

The difference between the me of that night and the me of tonight is the difference between the cadaver and the surgeon doing the cutting. (Flaubert, quoted in Crapanzano 1982 , p. 181)

It is important to understand that meanings and contexts (including an individual's sense of identity), the basic building blocks of qualitative research, are not fixed, constant objects with immutable traits. Rather, meanings and identities are fluid and changeable according to the situation and the persons involved. Gustave Flaubert precisely captures the sense of active personal meaning-making and remaking across time. Cohler (1991) describes such meaning-making and remaking as the personal life history self, a self that interprets, experiences, and marshals meanings as a means to manage adversity. A classic illustration of the fluidity of meanings is the case presented by Evans-Pritchard (1940) who explains the difficulty he had determining the names of his informants at the start of his fieldwork in Africa. He was repeatedly given entirely different names by the same people. In the kinship-based society, the name or identity one provides to another person depends on factors relative to each person's respective clan membership, age, and community. Now known as the principle of segmentary opposition, the situated and contextual nature of identities was illustrated once the fieldworker discovered the informants were indexing their names to provide an identity at an equal level of social organization. For example, to explain who we are when we travel outside the United States, we identify ourselves as Americans, not as someone from 1214 Oakdale Road. When we introduce ourselves to a new neighbor at a neighborhood block party, we identify ourselves by our apartment building or house on the block, not by reference to our identity as residents at the state or national level.

Themes and personal meanings are markers of processes not fixed structures. Life stories, whose narration is organized around a strongly held personal theme(s) as opposed to a chronology of events from birth to present day, have been linked with distress and clinical depression ( Luborsky 1993b ). Williams (1984) suggests that the experience of being ill from a chronic medical disease arises when the disease disrupts the expected trajectory of one's biography. Some researchers argue that a break in the sense of continuity in personal meaning ( Becker 1993 ), rather than any particular meaning (theme), precedes illness and depression ( Atchley 1988 ; Antonovsky 1987 ).

Another example of fluid meaning is ethnicity. Ethnic identity is a set of meanings that can be fluid and vary according to the social situation, historical time period, and its personal salience over the lifetime ( Luborsky and Rubinstein 1987 , 1990 ). Ethnic identity serves as a source of fixed, basic family values during child socialization; more fluidly, as an ascribed family identity to redefine or even reject as part of psychological processes of individuation in early adulthood; sometimes a source of social stigma in communities or in times of war with foreign countries (e.g., “being Italian” during World War II); and a source of continuity of meaning and pride in later life that may serve to help adapt to bereavement and losses.

From the qualitative perspective, there are a number of contrasts that emerge between sampling for meaning and more traditional, survey-style sampling, which has different goals. Those who are not familiar with the sampling-for-meaning approach often voice concerns over such aspects as size ( Lieberson 1992 ), adequacy and, most tellingly, purpose of the sampling. Why, for example, are sample sizes often relatively small? What is elicited and why? What is the relationship between meanings and other traditional categories of analyses, such as age, sex, class, social statuses, or particular diseases?

What is perhaps the most important contrast between the sampling-for-meaning approach and more standard survey sampling is found in the model of the person that underlies elicitation strategies. The model of the person in standard research suggests that important domains of life can be tapped by a relatively small number of standardized “one size fits all” questions, organized and presented in a scientific manner, and that most responses are relatively objective, capable of being treated as a decontextualized trait, and are quantifiable ( Mishler 1986 ; Trotter 1991 ). From this perspective, individuals are viewed as sets of fixed traits and not as carriers and makers of meaning.

Sampling for meaning, in contrast, is based on four very distinct notions. The first is that responses have contexts and carry referential meaning. Thus questions about events, activities, or other categories of experience cannot be understood without some consideration of how these events implicate other similar or contrasting events in a person's life ( Scheer and Luborsky 1991 ). This is particularly important for older people.

Second, individuals often actively interpret experience. That is to say, many people—but not all—actively work to consider their experience, put it in context, and understand it. Experience is not a fixed response. Further, the concern with meanings or of remaking meaning can be more emergent during some life stages and events or attention to certain kinds of meanings than others. Examples of this include bereavement, retirement, ethnic identity, and personal life themes in later life.

Third, certain categories of data do not have a separable existence apart from their occurrences embodied within routines and habits of the day and the body. Although certain categories of elicited data may have a relatively objective status and be relatively “at hand” for a person's stock of knowledge, other topics may never have been considered in a way that enables a person to have ready access to them ( Alexander, Rubinstein, Goodman, and Luborsky 1992 ). Consequently, qualitative research provides a context and facilitates a process of collaboration between researcher and informant.

Fourth, interpretation, either as natural for the informant or facilitated in the research interview, is basically an action of interpretation of experience that makes reference to both sociocultural standards, be they general cultural standards or local community ones, as well as the ongoing template or matrix of individual experience. Thus, for example, a person knows cultural ideals about a marriage, has some knowledge of other people's marriages, and has intimate knowledge of one's own. In the process of interpretation, all these levels come into play.

These issues occur over a variety of sampling frames and processing frameworks. There are three such sampling contexts. First, sampling for meaning occurs in relation to individuals as representatives of experiential types. Here, the goal is the elucidation of particular types of meaning or experience (personal, setting-based, sociocultural), through inquiry about, discussion of, and conversation concerning experiences and the interpretation of events and social occur-rences. The goal of sampling, in this case, is to produce collections of individuals from whom the nature of experience can be elicited through verbal descriptions and narrations.

Second, sampling for meaning can occur in the context of an individual in a defined social process. An example here could include understanding the entry of a person into a medical practice as a patient, for the treatment of a disorder. Qualitatively, we might wish to follow this person as she moves through medical channels, following referrals, tests, and the like. Even beginning this research at a single primary physician, or with a sample of individuals who have a certain disorder, the structure of passage through a processing system may vary widely and complexly. However, given a fixed point of entry (a medical practice or a single disease), sampling for meaning is nested in ongoing social processes. Researchers wish to understand not only the patient's experience of this setting as she moves through it (e.g., Esteroff 1982 ) but also the perspectives of the various social actors involved.

Finally, researchers may wish to consider sampling for meaning in a fixed social setting. In a certain way, sampling for meaning in a fixed social setting is what is meant, in anthropology and other social sciences, by “participant observation.” The social setting is more or less fixed, as is the population of research informants. An example might be a nursing home unit, with a more or less fixed number of residents, some stability but some change, and regular staff of several types representing distinctive organizational strata and interests (administration, medicine, nursing, social work, aides, volunteers, family, or environmental services).

It is important to note that even though qualitative research focuses on the individual, subjectivity or individuality is not the only goal of study. Qualitative research can focus on the macrolevel. One basic goal of qualitative research in aging is to describe the contents of people's experiences of life, health, and disability. It is true that much of the research to date treats the individual as the basic unit of analysis. Yet, the development of insights into the cultural construction of life experiences is an equal priority because cultural beliefs and values instill and shape powerful experiences, ideals, and motivations and shape how individuals make sense of and respond to events.

Studying how macrolevel cultural and community ideologies pattern the microlevel of individual life is part of a tradition stretching from Margaret Mead, Max Weber, Robert Merton, Talcott Parsons, to studies of physical and mental disabilities by Edgerton (1967) , Esteroff (1982) , and Murphy (1987) . For example, Stouffer's (1949) pioneering of survey methods revealed that American soldiers in World War II responded to the shared adversity of combat differently according to personal expectations based on sociocultural value patterns and lived experiences. These findings further illustrate Merton's theories of relative deprivation and reference groups, which point to the basis of individual well-being in basic processes of social comparison.

The notion of stigma illustrates the micro- and the macrolevels of analyses. For example, stigma theory's long reign in the social and political sciences and in clinical practice illustrates the micro- and macroqualitative perspectives. Stigma theory posits that individuals are socially marked or stigmatized by negative cultural evaluations because of visible differences or deformities, as defined by the community. Patterns of avoidance and denial of the disabled mark the socially conditioned feelings of revulsion, fear, or contagion. Personal experiences of low self-esteem result when negative messages are internalized by, for example, persons with visible impairments, or the elderly in an ageist setting. Management of social stigma by individuals and family is as much a focus as is management of impairments. Stigma is related significantly to compliance with prescribed adaptive devices ( Zola 1982 ; Luborsky 1993a ). A graphic case of this phenomenon are polio survivors who were homebound due to dependence on massive bedside artificial ventilators. With the recent advent of portable ventilators, polio survivors gained the opportunity to become mobile and travel outside the home, but they did not adopt the new equipment, because the new independence was far outweighed by the public stigma they experienced ( Kaufert and Locker 1990 ).

A final point is that sampling for meaning can also be examined in terms of sampling within the data collected. For example, the entire corpus of materials and observations with informants needs to be examined in the discovery and interpretive processes aimed at describing relevant units for analyses and dimensions of meaning. This is in contrast to reading the texts to describe and confirm a finding without then systematically rereading the texts for sections that may provide alternative or contradictory interpretations.

Techniques for selecting a sample

As discussed earlier, probability sampling techniques cannot be used for qualitative research by definition, because the members of the universe to be sampled are not known a priori, so it is not possible to draw elements for study in proportion to an as yet unknown distribution in the universe sampled. A review of the few qualitative research publications that treat sampling issues at greater length (e.g., Depoy and Gitlin 1993 ; Miles and Huberman 1994 ; Morse 1994 ; Ragin and Becker 1992 ) identify five major types of nonprobability sampling techniques for qualitative research. A consensus among these authors is found in the paramount importance they assign to theory to guide the design and selection of samples ( Platt 1992 ). These are briefly reviewed as follows.

First, convenience (or opportunistic) sampling is a technique that uses an open period of recruitment that continues until a set number of subjects, events, or institutions are enrolled. Here, selection is based on a first-come, first-served basis. This approach is used in studies drawing on predefined populations such as participants in support groups or medical clinics. Second, purposive sampling is a practice where subjects are intentionally selected to represent some explicit predefined traits or conditions. This is analogous to stratified samples in probability-based approaches. The goal here is to provide for relatively equal numbers of different elements or people to enable exploration and description of the conditions and meanings occurring within each of the study conditions. The objective, however, is not to determine prevalence, incidence, or causes. Third, snowballing or word-of-mouth techniques make use of participants as referral sources. Participants recommend others they know who may be eligible. Fourth, quota sampling is a method for selecting numbers of subjects to represent the conditions to be studied rather than to represent the proportion of people in the universe. The goal of quota sampling is to assure inclusion of people who may be underrepresented by convenience or purposeful sampling techniques. Fifth, case study ( Ragin and Becker 1992 ; Patton 1990 ) samples select a single individual, institution, or event as the total universe. A variant is the key-informant approach ( Spradley 1979 ), or intensity sampling ( Patton 1990 ) where a subject who is expert in the topic of study serves to provide expert information on the specialized topic. When qualitative perspectives are sought as part of clinical or survey studies, the purposive, quota, or case study sampling techniques are generally the most useful.

How many subjects is the perennial question. There is seldom a simple answer to the question of sample or cell size in qualitative research. There is no single formula or criterion to use. A “gold standard” that will calculate the number of people to interview is lacking (cf. Morse 1994 ). The question of sample size cannot be determined by prior knowledge of effect sizes, numbers of variables, or numbers of analyses—these will be reported as findings. Sample sizes in qualitative studies can only be set by reference to the specific aims and the methods of study, not in the abstract. The answer only emerges within a framework of clearly stated aims, methods, and goals and is conditioned by the availability of staff and economic resources.

Rough “rules of thumb” exist, but these derive from three sources: traditions within social science research studies of all kinds, commonsense ideas about how many will be enough, and practical concerns about how many people can be interviewed and analyzed in light of financial and personnel resources. In practice, from 12 to 26 people in each study cell seems just about right to most authors. In general, it should be noted that Americans have a propensity to define bigger as better and smaller as inferior. Quantitative researchers, in common with the general population, question such small sample sizes because they are habituated to opinion polls or epidemiology surveys based on hundreds or thousands of subjects. However, sample sizes of less than 10 are common in many quantitative clinical and medical studies where statistical power analyses are provided based on the existence of very large effect sizes for the experimental versus control conditions.

Other considerations in evaluating sample sizes are the resources, times, and reporting requirements. In anthropological field research, a customary formula is that of the one to seven: for every 1 year of fieldwork by one researcher, 7 years are required to conduct the analysis. Thus, in studies that use more than one interviewer, the ability to collect data also increases the burden for analyses.

An outstanding volume exploring the logic, contributions, and dilemmas of case study research ( Ragin and Becker 1992 ) reports that survey researchers resort to case examples to explain ambiguities in their data, whereas qualitative researchers reach for descriptive statistics when they do not have a clear explanation for their observations. Again, the choice of sample size and group design is guided by the qualitative goal of describing the nature and contents of cultural, social, and personal values and experiences within specific conditions or circumstances, rather than of determining incidence and prevalence.

Who and who not?

In the tradition of informant-based and of participatory research, it is assumed that all members of a community can provide useful information about the values, beliefs, or practices in question. Experts provide detailed, specialized information, whereas nonexperts do so about daily life. In some cases, the choice is obvious, dictated by the topic of study, for example, childless elderly, retirees, people with chronic diseases or new disabilities. In other cases, it is less obvious, as in studies of disease, for example, that require insights from sufferers but also from people not suffering to gain an understanding for comparison with the experiences and personal meanings of similar people without the condition. Comparisons can be either on a group basis or matched more closely on a one-to-one basis for many traits (e.g., age, sex, disease, severity), sometimes referred to as yoked pairs. However, given the labor-intensive nature of qualitative work, sometimes the rationale for including control groups of people who do not have the experiences is not justifiable.

Homogeneity or diversity

Currently, when constructing samples for single study groups, qualitative research appears to be about equally split in terms of seeking homogeneity or diversity. There is little debate or attention to these contrasting approaches. For example, some argue that it is more important to represent a wide range of different types of people and experiences in order to represent the similarities and diversity in human experience, beliefs, and conditions (e.g., Kaufman 1987 , 1989 ) than it is to include sufficient numbers of people sharing an experience or condition to permit evaluation of within-group similarities. In contrast, others select informants to be relatively homogeneous on several characteristics to strengthen comparability within the sample as an aid to identifying similarities and diversity.

Summary and Reformulation for Practice

To review, the authors suggest that explicit objective criteria to use for evaluating qualitative research designs do exist, but many of these focus on different issues and aspects of the research process, in comparison to issues for quantitative studies. This article has discussed the guiding principles, features, and practices of sampling in qualitative research. The guiding rationale is that of the discovery of the insider's view of cultural and personal meanings and experience. Major features of sampling in qualitative research concern the issues of identifying the scope of the universe for sampling and the discovery of valid units for analyses. The practices of sampling, in comparison to quantitative research, are rooted in the application of multiple conceptual perspectives and interpretive stances to data collection and analyses that allow the development and evaluation of a multitude of meanings and experiences.

This article noted that sampling concerns are widespread in American culture rather than in the esoteric specialized concern of scientific endeavors ( Luborsky and Sankar 1993 ). Core scientific research principles are also basic cultural ideals ( Luborsky 1994 ). For example, “control” (statistical, personal, machinery), dependence and independence (variables and individual), a reliable person with a valid driver's license matches reliability and validity concerns about assessment scales. Knowledge about the rudimentary principles of research sampling is widespread outside of the research laboratory, particularly with the relatively new popularity of economic, political, and community polls as a staple of news reporting and political process in democratic governance. Core questions about the size, sources, and features of participants are applied to construct research populations, courtroom juries, and districts to serve as electoral universes for politicians.

The cultural contexts and popular notions about sampling and sample size have an impact on scientific judgments. It is important to acknowledge the presence and influence of generalized social sensibilities or awareness about sampling issues. Such notions may have less direct impact on research in fields with long-established and formalized criteria and procedures for determining sample size and composition. The generalized social notions may come to exert a greater influence as one moves across the spectrum of knowledge-building strategies to more qualitative and humanistic approaches. Even though such studies also have a long history of clearly articulated traditions of formal critiques (e.g., in philosophy and literary criticism), they have not been amenable to operationalization and quantification.

The authors suggested that some of the rancor between qualitative and quantitative approaches is rooted in deeper cultural tensions. Prototypic questions posed to qualitative research in interdisciplinary settings derive from both the application of frameworks derived from other disciplines' approaches to sampling as well as those of the reviewers as persons socialized into the community where the study is conceived and conducted. Such concerns may be irrelevant or even counterproductive.

Qualitative Clarity as an Analog to Statistical Power

The guiding logic of qualitative research, by design, generally prevents it from being able to fulfill the assumptions underlying statistical power analyses of research designs. The discovery-oriented goals, use of meanings as units of analyses, and interpretive methods of qualitative research dictate that the exact factors, dimensions, and distribution of phenomena identified as important for analyses may not always be specified prior to data analyses activities. These emerge from the data analyses and are one of the major contributions of qualitative study. No standardized scales or tests exist yet to identify and describe new arenas of cultural, social, or personal meanings. Meaning does not conform to normative distributions by known factors. No probability models exist that would enable prediction of distributions of meanings needed to perform statistical power analyses.

Qualitative studies however can, and should, be judged in terms of how well they meet the explicit goals and purposes relevant to such research.

The authors have suggested that the concept of qualitative clarity be developed to guide evaluations of sampling as an analog to the concept of statistical power. Qualitative clarity refers to principles that are relevant to the concerns of this type of research. That is, the adequacy of the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation can be evaluated even if the factors to be measured cannot be specified. The term clarity conveys the aim of making explicit, for open discussion, the details of how the sample was assembled, the theoretical assumptions and the pragmatic constraints that influenced the sampling process. Qualitative clarity should include at least two components, theoretical grounding and sensitivity to context. These are briefly described next.

Rich and diverse theoretical grounding

In the absence of standardized measures for assessing meaning, the analogous qualitative research tools are theory and discovery processes. Strong and well-developed theoretical preparation is necessary to provide multiple and alternative interpretations of the data. Traditionally, in qualitative study, it is the richness and sophistication of the analytic perspectives or “lenses” focused on the data that lends richness, credibility, and validity to the analyses. The relative degree of theoretical development in a research proposal or manuscript is readily apparent in the text, for example, in terms of extended descriptions of different schools of thought and possible multiple contrasting of interpretive explanations for phenomena at hand. In brief, the authors argue that given the stated goal of sampling for meaning, qualitative research can be evaluated to assess if it has adequate numbers of conceptual perspectives that will enable the study to identify a variety of meanings and to critique multiple rich interpretations of the meanings.

Sampling within the data is another important design feature. The discovery of meaning should also include sampling within the data collected. The entire set of qualitative materials should be examined rather than selectively read after identifying certain parts of the text to describe and confirm a finding without reading for sections that may provide alternative or contradictory interpretations.

Sensitivity to contexts

As a second component of qualitative clarity, sensitivity to context refers to the contextual dimensions shaping the meanings studied. It also refers to the historical settings of the scientific concepts used to frame the research questions and the methods. Researchers need to be continually attentive to examining the meanings and categories discovered for elements from the researchers' own cultural and personal backgrounds. The first of these contexts is familiar to gerontologists: patterns constructed by the individual's life history; generation; cohort; psychological, developmental, and social structure; and health. Another more implicit contextual aspect to examine as part of the qualitative clarity analysis is evidence of a critical view of the methods and theories introduced by the investigators. Because discovery of the insiders' perspective on cultural and personal meanings is a goal of qualitative study, it is important to keep an eye to biases derived from the intrusion of the researcher's own scientific categories. Qualitative research requires a critical stance as to both the kinds of information and the meanings discovered, and to the analytic categories guiding the interpretations. One example is recent work that illustrates how traditional gerontological constructs for data collection and analyses do not correspond to the ways individuals themselves interpret their own activities, conditions, or label their identities (e.g., “caregiver,” Abel 1991 ; “disabled,” Murphy 1987 ; “old and alone,” Rubinstein, 1986 ; “Alzheimer's disease,” Gubrium 1992 ; “life themes,” Luborsky 1993b ). A second example is the growing awareness of the extent to which past research tended to define problems of disability or depression narrowly in terms of the individual's ability, or failure, to adjust, without giving adequate attention to the societal level sources of the individual's distress ( Cohen and Sokolovsky 1989 ). Thus researchers need to demonstrate an awareness of how the particular questions guiding qualitative research, the methods and styles of analyses, are influenced by cultural and historical settings of the research ( Luborsky and Sankar 1993 ) in order to keep clear whose meanings are being reported.

To conclude, our outline for the concept of qualitative clarity, which is intended to serve as the qualitatively appropriate analog to statistical power, is offered to gerontologists as a summary of the main points that need to be considered when evaluating samples for qualitative research. The descriptions of qualitative sampling in this article are meant to extend the discussion and to encourage the continued development of more explicit methods for qualitative research.

Acknowledgments

Support for the first author by the National Institute of Child Health and Human Development (#RO1 HD31526) and the National Institute on Aging (#RO1 AG09065) is gratefully acknowledged. Ongoing support for the second author from the National Institute of Aging is also gratefully acknowledged.

Biographies

Mark R. Luborsky, Ph.D., is a senior research anthropologist and assistant director of research at the Philadelphia Geriatric Center. Federal and foundation grants support his studies of sociocultural values and personal meanings in early and late adulthood, and how these relate to mental and physical health, and to disability and rehabilitation processes. He also consults and teaches on these topics.

Robert L. Rubinstein, Ph.D., is a senior research anthropologist and director of research at the Philadelphia Geriatric Center. He has conducted research in the United States and Vanuatu, South Pacific Islands. His gerontological research interests include social relations of the elderly, childlessness in later life, and the home environments of old people.

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Qualitative Sample Size Calculator

Calculating qualitative sample size ensures reliable data. Our free calculator simplifies this, giving you accurate results quickly.

How would you define the scope and goals of your research?

How similar or different are your participants to each other, which qualitative method are you planning to use, how many years of experience (on average) do the researchers involved in this project have with this type of research, based on your responses, we recommend that you recruit.

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Table of Contents

Calculating the right qualitative sample size is essential for any research study, as it directly impacts the validity and richness of your data.  Too small a sample may not capture the full range of perspectives, while too large a sample can lead to data saturation wasting time and resources. Striking the right balance ensures that your study is efficient and insightful, providing comprehensive and reliable findings.  By understanding and accurately estimating your qualitative sample size , you can enhance the depth and credibility of your research, making your insights more actionable and impactful.

What is Qualitative Sample Size?

Qualitative sample size refers to the number of participants or cases selected for a qualitative research study.  Unlike quantitative research , which focuses on numerical data and large sample sizes, qualitative research emphasizes depth over breadth. The sample size in qualitative research is typically smaller. It is determined based on the need to capture diverse perspectives, reach data saturation (the point where no new information is being uncovered), and ensure the richness and complexity of the data.  The goal is to gather enough information to understand the phenomenon being studied fully, rather than to generalize findings to a larger population.

Why Calculating Qualitative Sample Size Is Important?

Calculating qualitative sample size is a critical aspect of research design. Here are five reasons why it is important:

Ensures Data Saturation

Data saturation occurs when additional data no longer brings new insights. Determining the right sample size helps researchers reach this point efficiently. If the sample is too small, crucial insights may be missed, leading to an incomplete understanding of the research topic.  Conversely, a sample that is too large may result in redundant data, increasing the time and effort required to analyze without adding value. Proper sample size calculation helps strike a balance between the depth and sufficiency of data.

Enhances Research Credibility

A well-calculated sample size enhances the credibility and reliability of the research findings. If a study includes too few participants, it may be perceived as lacking depth or biased, undermining its acceptance in the academic or professional community.  On the other hand, an overly large sample may dilute the richness of the data, leading to superficial conclusions. Accurately estimating the sample size ensures the research is respected and considered trustworthy.

Optimizes Resource Allocation

Research projects often operate under time, budget, and human resources constraints. Calculating the appropriate qualitative sample size ensures that these resources are used efficiently. A well-planned sample size minimizes unnecessary data collection and analysis, saving time and costs while producing meaningful and thorough results. This is especially important in qualitative research, where analyzing rich, detailed data can be time-consuming and resource-intensive.

Captures Diversity of Perspectives

In qualitative research, capturing a wide range of perspectives is crucial to understanding the phenomenon being studied fully. By calculating the right sample size, researchers can ensure they include enough participants to represent different viewpoints, experiences, and contexts. This diversity enriches the data and leads to more comprehensive and nuanced insights, allowing for a deeper understanding of the subject matter.

Guides Ethical Research Practices

Ethical considerations are central to research, and determining the appropriate sample size plays a role in upholding these standards. An overly large sample may involve more participants than necessary, exposing more individuals to research-related risks and obligations without adding value to the findings.  Conversely, an insufficient sample may lead to inaccurate conclusions, potentially harming those who rely on the research outcomes. Calculating the right sample size helps ensure that participants are involved meaningfully and that their contributions lead to valid, actionable insights.

How to Calculate Qualitative Sample Size?

Calculating qualitative sample size involves a more flexible and nuanced approach than quantitative research.  Here's a step-by-step guide on how to approach this:

Define the Research Objective

Clearly outline the goals of your study. What are you trying to understand, explore, or uncover? The complexity and scope of your research question will heavily influence the sample size. Broader questions may require a larger sample to capture diverse perspectives, while more focused studies might need fewer participants.

Consider the Research Methodology

The qualitative method you choose (e.g., interviews , focus groups , ethnography, case studies) will impact the sample size. For instance, in-depth interviews might require fewer participants because they generate extensive data per individual, while focus groups might need more participants to capture group dynamics.

Evaluate Data Saturation

Data saturation is reached when new data no longer provides additional insights or themes. This concept is central to determining sample size in qualitative research. Begin by collecting and analyzing data incrementally. As you proceed, assess whether new interviews or observations continue to yield new information. When this stops, you’ve likely reached data saturation, and your sample size is sufficient.

Review Similar Studies

Look at existing qualitative research in your field to see how many participants were included. While this should not dictate your sample size, it can provide a useful benchmark. Understanding how others have approached similar research questions can help you estimate an appropriate range for your study.

Factor in Practical Considerations

Consider the availability of participants, time constraints, and resource limitations. These practical factors will influence how many participants you can realistically engage. If resources are limited, focus on maximizing the depth and quality of data from a smaller sample rather than trying to include too many participants.

Consult with Experts or Advisors

If you're uncertain, consulting with experienced researchers or academic advisors can help. They can provide insights based on their experience and guide you in making informed decisions about your sample size.

Use Qualitative Sample Size Calculators

While more common than quantitative research, some tools and calculators help estimate sample sizes based on parameters like the expected number of themes, desired confidence level, and more. These can provide a starting point or validation for your estimation process. Use our free qualitative sample size calculator above to get accurate results.

What Is an Ideal Sample Size in Qualitative Research?

An ideal sample size in qualitative research is not a one-size-fits-all number but depends on various factors, such as the research question, methodology, and the concept of data saturation.  ‍ However, general guidelines can help determine a reasonable range:

In-Depth Interviews

Typically, 5 to 30 participants are considered sufficient. A smaller number (around 10-15) may be adequate for more focused studies to reach data saturation. You might need a larger sample for more complex or exploratory topics to capture a wider range of perspectives.

Focus Groups

A focus group usually consists of 6 to 10 participants per session. Depending on the population's heterogeneity and the research question's complexity, running 3 to 5 focus groups might be ideal to capture diverse perspectives.

Case Studies

For case studies, 4 to 10 cases are often sufficient. If each case is very detailed and data-rich, even fewer cases may be enough to achieve depth and provide comprehensive insights.

Ethnography

Ethnographic studies often involve prolonged engagement with a small number of participants (sometimes as few as 1 or 2 key informants) or a single community. The depth of the study usually compensates for the smaller sample size.

Grounded Theory

Grounded theory studies aim to develop a theory based on data. This often requires 20 to 50 participants, though the exact number depends on how quickly theoretical saturation is reached, where no new theoretical insights are emerging from the data.

Make the Most of Your Qualitative Studies with Decode

Decode revolutionizes qualitative research with its powerful AI capabilities.  Decode's advanced AI capabilities streamline qualitative research with automated summaries, tags, highlights, emotion, behavior, tonality, and action items, saving time and effort. With features like transcript editing and highlight creation, Decode enables efficient collaboration among research teams, facilitating the generation of actionable recommendations.

Frequently Asked Questions

What is a qualitative sample size calculator.

A Qualitative Sample Size Calculator is a tool designed to help researchers determine the optimal number of participants needed for a qualitative research study. It ensures that the sample size adequately captures diverse perspectives and achieves data saturation.

How does sample size affect research credibility?

A well-calculated sample size enhances the credibility and reliability of research findings. Too few participants may lead to biased results, while an overly large sample can dilute the richness of the data. Accurate sample size estimation helps ensure research is respected and trustworthy.

How can the Qualitative Sample Size Calculator optimize resource allocation?

Researchers can minimize unnecessary data collection and analysis by determining the appropriate sample size, thus saving time and costs. This is particularly important in qualitative research, where analyzing detailed data can be resource-intensive.

What happens if my sample size is too large?

An excessively large sample size may lead to data saturation, where additional data does not provide new insights. It can also waste time and resources on redundant data collection and analysis.

• Open access
• Published: 30 August 2024

Exploring medical and dental practitioner perspectives and developing a knowledge attitude and practice (KAP) evaluation tool for the common risk factor approach in managing non-communicable and periodontal diseases

• Lakshmi Puzhankara   ORCID: orcid.org/0000-0002-5559-5887 1 ,
• Vineetha Karuveettil   ORCID: orcid.org/0000-0002-5358-4391 2 ,
• Chandrashekar Janakiram   ORCID: orcid.org/0000-0003-1907-8708 2 ,
• Ramprasad Vasthare   ORCID: orcid.org/0000-0002-0181-7069 3 ,
• Sowmya Srinivasan   ORCID: orcid.org/0000-0001-8236-0103 4 &
• Angel Fenol   ORCID: orcid.org/0000-0001-5088-8368 5  

BMC Oral Health volume  24 , Article number:  1017 ( 2024 ) Cite this article

Metrics details

The Common Risk Factor Approach (CRFA) is one of the methods to achieve medical-dental integration. CRFA addresses shared risk factors among major Non-communicable Diseases (NCDs). This study aimed to explore the perspectives of dental and medical practitioners concerning CRFA for managing NCDs and periodontal diseases and to create and validate a tool to evaluate the Knowledge, Attitude, and Practice (KAP) of medical and dental practitioners in relation to utilization of CRFA for management of NCDs and Periodontal diseases.

This research employed a concurrent mixed-method model and was carried out from January 2021 to February 2022, focusing on medical and dental practitioners in South India. In the qualitative phase, online interviews were conducted with dental and medical practitioners, recorded, and transcribed. Thematic analysis was applied after achieving data saturation. In the quantitative phase, a KAP questionnaire was developed. The sample size was determined by using the G power statistical power analysis program. A sample size of 220 in each group (dentists and medical practitioners) was estimated. Systematic random sampling was used to recruit the potential participants. The data obtained through the online dissemination of KAP tool was analysed and scores were standardized to categorize the KAP.

Qualitative thematic analysis identified four major themes: understanding of common risk factors, risk factor reduction and disease burden, integrating CRFA into clinical practice, and barriers to CRFA. In addition, thematic analysis revealed seventeen subthemes. For the quantitative phase, standardization was applied to a 14-item KAP questionnaire for medical practitioners and a 19-item KAP questionnaire for dental practitioners. The total KAP score for medical practitioners in the study was 21.84 ± 2.87, while dental practitioners scored 22.82 ± 3.21, which indicated a high level of KAP regarding CRFA. Meta integration of qualitative and quantitative data identified eight overarching themes: four were concordant, three were discordant, and one theme provided the explanatory component.

The study’s structured, validated questionnaire showed that both medical and dental professionals had a high knowledge of CRFA. However, they were not appreciably aware of the risk factors that are shared between NCDs and periodontal disease. Both groups were interested in the idea of using CRFA in integrated medical and dental care.

Peer Review reports

Introduction

Non-communicable diseases (NCDs) account for more than 41 million deaths globally each year [ 1 ]. These diseases are influenced by both non-modifiable and modifiable risk factors [ 2 ]. Periodontal disease, another multifactorial non-communicable ailment, shares several risk factors with NCDs. Individuals with periodontal diseases, particularly periodontitis, face a heightened risk of losing multiple teeth, leading to compromised masticatory function and altered dietary habits [ 3 ]. This not only affects the quality of life and self-esteem of affected individuals but also imposes significant socio-economic burdens and healthcare costs [ 4 ]. Despite the evident connections between periodontal disease and NCDs [ 5 , 6 ], there persists a historical divide between oral and general healthcare [ 7 ], further reinforced by the establishment of medical insurance [ 8 ]. This separation has contributed to out-of-pocket expenditures (OOPE) on dental care, accounting for approximately 14% of OOPE in Organisation for Economic Co-operation and Development (OECD) countries. [ 9 ] A recent study in South India revealed that 15.4% of sanitary workers experienced Catastrophic Dental Health Expenditure (CDHE) [ 10 ]. Additionally, a global study involving 41 low- and middle-income countries found that 7% of households faced CDHE [ 11 ].

The integration of dental and medical care would bring substantial benefits to the general population. Oral health has a significant impact on general health. Simple, non-invasive periodontal therapy was found to result in a remarkable (40–70%) reduction in medical costs and hospitalizations for individuals with conditions such as diabetes, coronary artery disease, or during pregnancy [ 12 ]. This underscores the potential advantages of addressing oral health within the broader spectrum of healthcare, leading to improved overall health outcomes and reduced healthcare costs.

Several methods of integrating medical and dental care have been explored, [ 13 , 14 , 15 ] and one such strategy is risk reduction for disease prevention. Common risk factors such as smoking, obesity, poor nutrition, low socioeconomic status, stress, and inadequate oral hygiene are shared by both periodontitis and NCDs [ 5 ]. Traditional health promotion tends to focus on specific diseases, potentially contributing to the separation of oral health from general health. An alternative approach, the Common Risk Factor Approach (CRFA), addresses shared risk factors among major NCDs, including oral diseases. CRFA emphasizes managing contributing elements to enhance overall population health.

The approaches within CRFA aim to mitigate the impact of common chronic diseases [ 13 ] and include integrated action against shared risk factors and altering one risk factor that may influence others, leading to a cascade effect. For instance, changing smoking behavior could impact related behaviors like alcohol consumption and diet. Collaborative efforts across sectors, concentrating upstream on basic etiological factors, can lead to progress in oral health improvement and decreased oral health inequalities [ 16 ]. Given the clustering of both modifiable and non-modifiable risk factors in patients with NCDs and periodontal diseases, CRFA emerges as a cost-effective and rational approach [ 13 ]. Of these risk factors, modifiable risk factors can be controlled or changed. The control or modification of a few key risk factors can have a substantial impact on managing numerous chronic conditions.

The World Health Organization (WHO) advocates a global strategy for enhancing oral health alongside overall health, emphasizing shared risk factors [ 17 ]. Implementing CRFA for overall health, including oral health, presents opportunities to integrate oral health promotion into broader health policies, such as those related to food [ 15 ]. However, successful implementation requires appropriate evidence, guidelines, and policies due to perceived challenges in applying CRFA for oral health promotion [ 15 ].

To comprehensively assess the potential initiation of the CRFA for NCDs, including periodontal disease, it is crucial to understand the knowledge, attitudes, and practices of medical and dental practitioners regarding shared risk factors. While previous studies have explored knowledge about periodontitis risk factors among medical practitioners and the general population, [ 18 , 19 ] there is a notable gap in understanding the KAP of both medical and dental practitioners regarding shared risk factors between NCDs and periodontitis and the integration of CRFA into medical and dental practices.

Capacity-building measures are essential for implementing CRFA-based programs [ 15 ], and assessing the baseline KAP of the target population will bridge the evidence gap for integrations. Despite the pivotal role of CRFA in addressing health issues, there is currently no standardized instrument tailored to assess practitioners’ KAP in this context. Questionnaires are commonly used for KAP assessment [ 20 ], and a structured, validated questionnaire is essential for obtaining clear information on practitioners’ understanding and application of CRFA in managing NCDs and periodontal diseases.

The objectives of this mixed-method study are to address these gaps by understanding practitioners’ opinions on CRFA and developing a validated structured instrument to assess the Knowledge, Attitude, and Practice of medical and dental practitioners toward the use of CRFA for managing NCDs and periodontal diseases. The study will employ both quantitative and qualitative methods, utilizing a structured questionnaire to capture practitioners’ perspectives and incorporating open-ended communication to gain insights into the reasons behind their opinions, support, and potential hurdles in implementing CRFA in the Indian context.

The mixed-method study received ethical approval from the institutional ethics committee and institutional review board, and informed consent was obtained from the participants during the conduct of the study.

Research design

The study employed a concurrent mixed-methods model, incorporating both qualitative and quantitative arms, to holistically investigate the research questions. This approach combines the advantages of qualitative and quantitative data, allowing for a comprehensive exploration of the CRFA. The qualitative arm provides in-depth insights into the complex phenomena associated with CRFA, offering a contextual richness that complements the quantitative results. The lists of potential participants were obtained from the list of dentists and medical practitioners of Kerala, Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, and Goa available through the regional Indian Dental Association (IDA), Indian Medical Association (IMA), and directories of medical and dental practitioners. Based on the data obtained from the directories, a state-wise distribution of samples was done. Systematic random sampling was used to select the possible participants for the study from January 2021 to February 2022.

Qualitative arm

Study context and population.

The qualitative segment of the study sought to delve into the viewpoints of experts in medicine and general dental practice, particularly those possessing relevant expertise related to the CRFA. Participants were selected from specialties such as endocrinology, gynaecology, otorhinolaryngology, periodontology, general medicine, and general dentistry, based on their relevance to the shared risk factors between periodontal disease and various medical conditions. Purposive sampling was employed to recruit a diverse group of medical and dental practitioners, and the sampling units were identified from the directories of professional associations like the Indian Dental Association (IDA) and the Indian Medical Association (IMA). Participation in the online interviews using the ‘Zoom Meetings’ online platform was voluntary. After obtaining their consent, the link for the Zoom meeting was shared with the participants. Participants received acknowledgment certificates as an incentive. No explicit exclusion criteria were set, ensuring a broad representation of perspectives across the selected fields.

In-depth interviews

The qualitative phase of the study utilized in-depth interview guides that covered similar topics for both dental and medical practitioners. These guides included components related to the understanding of common risk factors, risk factor reduction, and disease burden, suggested methods for integrating CRFA into clinical practice, and barriers to CRFA. The semi-structured questions were developed a priori, drawing from existing literature. The interviews were conducted with consent, and a note-keeper recorded the proceedings, while in-depth interviews were recorded for transcription. The recordings were transformed into verbatim transcripts at the end of each day.

The number of participants for in-depth interviews was determined based on achieving data saturation, ensuring that the sample size was sufficient to capture a diverse range of perspectives until no new information or themes emerged. Data saturation enhances the credibility and trustworthiness of study findings, signifying theoretical sufficiency. The analysis methodology involved progressive analysis throughout the study, allowing for the incremental identification and incorporation of themes and sub-themes after each interview. This iterative process facilitated the continual refinement of emerging data patterns.

The decision to conclude interviews was guided by the observation of the ceased emergence of new themes, indicating data saturation. Close monitoring of interview data helped identify a point where further sessions yielded no novel insights or themes. After achieving data saturation, a comprehensive final thematic analysis was conducted following guidelines by Braun and Clark [ 21 ] and reiterated by Kiger et al [ 22 ]. This analysis involved data review, coding, categorization, and synthesis to derive conclusive themes and sub-themes. Each transcript underwent review by two researchers, and emerging themes were developed, involving a third author in cases of disagreement. Consensus on codes, categories, and themes was reached through regular discussions. The data was organized and managed using computer-assisted qualitative research software, QDA Miner Lite (Version 2.0.7; Provalis Research).

Quantitative arm

The quantitative segment of the mixed-method study focused on developing and validating a KAP questionnaire on the CRFA for the integration of medical and dental care. Distinct questionnaires were created for medical and dental practitioners. The development of the questionnaire occurred in two stages.

In the first stage, item and domain development took place, involving a deductive approach to form initial questions, followed by content validation and test-retest reliability. The second stage involved the validation of the questionnaire through item response theory, exploratory factor analysis, and internal consistency reliability assessment. This two-stage process ensured the robustness and appropriateness of the questionnaire for assessing the KAP of medical and dental practitioners regarding CRFA in the context of managing NCDs and Periodontal diseases.

Study population

The study included both medical practitioners and dental practitioners, encompassing those with and without a postgraduate degree or specialization. This diverse inclusion aimed to capture perspectives from practitioners with varying levels of education and expertise, providing a comprehensive understanding of the knowledge, attitudes, and practices related to the CRFA among professionals in both fields.

Sample size

The sample size was determined by using the G power statistical power analysis program. Based on the findings from a previous study [ 23 ] a sample size of 220 dentists and medical practitioners was estimated. This was done by taking into account the Chi-square test’s effect size of 0.30, the study’s power of 0.95, and the number of groups of medical and dental practitioners that could be used to compare mean knowledge, attitude, and practice scores.

Data collection

The study utilized a systematic approach for sampling dental and medical practitioners from Kerala, Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, and Goa. The directories of the regional Indian Dental Association (IDA) and Indian Medical Association (IMA) were consulted to compile a list of practitioners (both specialists and general practitioners). To ensure a representative sample, the distribution of participants was organized by state (Table  1 ).

Systematic random sampling was employed to select potential participants, minimizing bias in participant selection. Contact details were then used to send a web-based questionnaire via Google Forms, accompanied by an invitation to participate. Anticipating a 50% non-response rate, the questionnaires were distributed to twice the required number of participants. The final analysis included responses from 225 medical practitioners and 307 dental practitioners across South India.

Questionnaire development

The development and validation of the KAP questionnaire occurred in two distinct stages. In the first stage, item and domain development were undertaken through a three-step process: (i) Deductive approach, (ii) Content validation, (iii) Test-retest reliability. The second stage involved the validation of the questionnaire using: (i) Item response theory, (ii) Exploratory factor analysis, (iii) Internal consistency reliability assessment. Subsequently, scores were standardized to categorize the KAP of the population into low, medium, and high categories. This multi-stage process ensured the reliability and validity of the questionnaire for assessing participants’ knowledge, attitude, and practice regarding the CRFA.

Stage one: item and domain development

The deductive approach was employed to develop items for the questionnaire based on existing literature related to the CRFA in the management of periodontal disease and NCDs. Eight referenced articles contributed to the conceptual definition of knowledge, attitude, and practice regarding CRFA [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. The definition of CRFA emphasized its role in creating cross-disciplinary health promotion programs that address common risk factors for diseases. Knowledge, attitude, and practice were defined in terms of awareness, thoughts, behaviors, and understanding of shared risk factors and etiology related to periodontal disease and NCDs, as well as CRFA.

The initial questionnaire, developed in English, consisted of 28 items for the dental questionnaire and 24 items for the medical questionnaire, distributed across four domains: (1) Demography of participants; (2) Knowledge towards CRFA for NCDs and oral health; (3) Attitude towards CRFA for NCDs and oral health; and (4) Practice towards implementing CRFA for NCDs and oral health. To ensure content validity, the initial questionnaire underwent review by an expert panel comprising dental and medical practitioners. The test-retest reliability of the questionnaire was assessed by administering it twice to 30 participants within a one-month timeframe.

Stage two: questionnaire validation

The study included responses from 225 medical practitioners and 307 dental practitioners across six states in South India to evaluate the additional psychometric properties of the questionnaire. Data analysis was conducted using JMETRIK software.

Item response theory (IRT)

In the knowledge domain, a two-parameter logistic item response theory (2-PL IRT) analysis was conducted using responses categorized as either correct or incorrect. The analysis was performed in JMETRIK (version 4.0.0, Charlottesville, Virginia, USA) using the RASCH (log odds ratio) limited package. The analysis considered the range of difficulty (-4 to + 4) and discrimination (0.20 to infinity) as the cut-off values for evaluating psychometric properties. Item fit was assessed using chi-square goodness-of-fit per item, and p values were reported. The modified parallel analysis was employed to evaluate one-dimensionality.

Exploratory factor analysis

The adequacy of sampling was assessed using the Kaiser–Meyer–Olkin measure (KMO) and Bartlett’s test of sphericity [ 20 ]. A KMO value above 0.5 and a significant result in Bartlett’s test ( p  < 0.001) were considered indicative of a sufficient sample.

Internal consistency reliability

The internal consistency (IC) of the items was calculated using the coefficient of Cronbach’s alpha [ 31 ] and correlation between items.

Standardization of scores

The responses to the questions in the Knowledge, Attitude, and Practice groups were coded, and scores were calculated for each group. The scores were then split into percentiles for standardization. The total KAP score was also calculated and interpreted as low KAP (0 to 24th percentile), medium KAP (25th to 75th percentile), and high KAP (76th to 100th percentile) based on the percentile scores [ 32 ].

In-depth interviews involved five medical practitioners specializing in endocrinology, gynaecology, otorhinolaryngology, and general medicine, along with five general dental practitioners and five periodontists. The qualitative thematic analysis identified four major themes: understanding of common risk factors, risk factor reduction and disease burden, integrating CRFA into clinical practice, and barriers to CRFA. Subsequently, seventeen subthemes emerged, encompassing topics such as enumerating risk factors, transitioning from disease-specific to risk factor approaches, diagnosing systemic NCDs through identifying risk factors and oral signs, controlling risk factors and NCD burden, the impact of periodontal therapy on NCD burden, the influence of medical practitioners over periodontists, measures for integrating CRFA, barriers to integration, and more.

Theme 1: understanding shared common risk factors

The study revealed that medical and dental practitioners, including periodontists, demonstrated awareness of the association between diabetes and periodontal disease, as well as the shared risk factor of smoking. However, their knowledge regarding risk factors common to other major NCDs and periodontal disease was limited. Many practitioners were unable to identify shared risk factors such as obesity, the presence of oral pathogens, and nutritional deficiency [ 5 ]. This knowledge gap may be attributed to the prevailing practice of treating patients based on specific diseases rather than targeting shared risk factors. Although there is a gradual shift toward a risk factor-based approach in certain specialties, there remains a general scepticism about patient compliance with long-term risk factor reduction strategies. The subthemes that emerged under this major theme are: (i) Enumeration of the risk factors (ii) Transition from disease specific to risk factor approach (iii) Diagnosis of a systemic NCD through identification of presence of risk factors and oral signs .

‘There are many risk factors, ranging from smoking to genetics. Very common ones are smoking, alcohol, lifestyle. Each and every factor has a specific role. Genetics has a significant role. If a parent is diabetic by his or her 50s then the next generation will become diabetic by 30s’. (MP1)

There was a consensus regarding the need for a change from a disease specific approach to a risk factor approach.

MP1 had supported CRFA. ‘This is a very good approach. Common risk factors are present for many diseases. So, if we can create an awareness regarding smoking, alcohol, and sedentary lifestyle, it can significantly reduce the development of many diseases.’

The identification of clustering of risk factors for periodontal disease and NCDs in patients, in addition to the occurrence of oral signs, can sometimes lead to the diagnosis of systemic diseases.

PR5 ‘In diabetes we have noticed. They come with multiple abscesses, then we advise them to check the blood glucose level and they are diagnosed with diabetes. They are not aware of the condition before. So, once we treat the patient and with the consultation with the diabetologist, we have noticed an improvement in the status.’

Theme 2: risk factor reduction and disease burden

All practitioners concurred on the potential positive impact of early identification of risk factors, counselling, and reducing risk factors to mitigate disease burden. Nevertheless, medical practitioners acknowledged that a significant portion of them tend to overlook oral health, possibly due to a lack of awareness regarding its association with systemic conditions. The thematic analysis revealed subthemes such as (i) Control of Risk Factors and Impact on NCD Burden (ii) The Role of Periodontal Therapy in Alleviating the NCD Burden (iii) Reciprocal Impact of Other NCD Therapies on Periodontal Disease Burden (iv) Influence of Medical Practitioners in Shaping Patient Decisions Over Periodontists. These subthemes underscored the interconnectedness of risk factors, diverse therapies, and the collaborative role of medical and dental practitioners in addressing both oral and systemic health.

PR1 stated that ‘Lifestyle modification…I have been following the periodontal patients in my clinic. There are patients whom I have been following for last 6 to 7 years. Patients who have been motivated to maintain the oral hygiene, their rate of progression (of periodontal disease) and diabetic control is much better than patients who are not maintaining their oral hygiene properly.’

The dental practitioners have observed that periodontal therapy can result in improving the NCD status and that a better compliance is observed when the advice is given by a medical practitioner.

‘Yes, after periodontal treatment, sugar level often reduces as noticed in diabetes. Diabetics with uncontrolled sugar levels, fluctuating sugar levels, after periodontal therapy usually have better controlled sugar levels’, GP1 said.

PR3 said, ‘Yes definitely, when a physician refers the patient to us, they are more willing to listen to us and adapt to whatever changes we say.’

Theme 3: methods suggested for integrating CRFA into clinical practice

Various approaches have been proposed to integrate the CRFA into clinical practice. These strategies encompass capacity building initiatives to promote medical-dental integration, such as establishing NCD clinics; raising awareness among the medical community regarding the interconnectedness of medical and dental health; advocating for policies that underscore the significance of CRFA integration in clinical settings; developing effective healthcare referral systems and cross-disciplinary health promotion strategies, including oral health care; and encouraging patient education and motivation. The subthemes within this overarching theme are: (i) Capacity Building (ii) Advocacy and Policy Implications (iii) Healthcare Partnerships Involving Referrals and Cross-Disciplinary Health Promotion Strategies (iv) Patient Education and Motivation.

The interviews highlighted diverse strategies for capacity building, including the implementation of regular check-ups and screening camps as integral components of healthcare services. Furthermore, suggestions encompassed the use of awareness posters and videos, adoption of evidence-based practices, and the establishment of NCD clinicsx [ 33 ]. NCD clinics, as proposed, would serve as essential hubs for screening, diagnosing, and managing NCDs. These clinics would offer comprehensive examinations, including diet counselling, lifestyle management, and home-based care. Patients could be referred to these clinics by other healthcare centres, health workers, or they could directly report to the clinic, enabling the identification and management of complications or advanced stages of NCDs. MP1 stated, ‘In government clinics, there are NCD clinic. Along with the NCD clinic, if a dental clinic can be set up, a lot of cases with oral manifestations will be diagnosed. So integrated clinics with NCD and dental will be very useful.’

Advocacy enables stakeholders and government decision-makers to have discussions and bring out suggestions and recommendations to a prevailing policy that is of interest to them.

MP1 Suggested that ‘Even for a job opportunity, basic examination is physical examination and evaluation for systemic diseases. Oral examination may be included in the basic fitness requirement for the job.’

Interdisciplinary collaboration is also essential for medical dental integration as stated by MP3, ‘There should be a rapport between the medical and dental practitioner so that there is communication regarding the cases and there is a follow-up of the cases.’

Communication through mass media and other visual aids, generating social and cultural awareness for patient education, and motivation for holistic health care have also been suggested to facilitate the implementation of integrated care delivery.

PR2 has mentioned, ‘When this gets published, apart from journals, this should reach the common population also. The common population rarely see the journal articles. So, it should be brought forth in mass media so that it reaches the population.’

Theme 4: barriers for implementation of CRFA

CRFA is considered a relatively novel approach, as the comprehensive exploration of shared risk factors and risk reduction strategies for common NCDs and periodontal disease is a recent development. The lack of awareness regarding this concept has been identified as a significant barrier to its implementation, coupled with challenges such as time constraints, concerns about the sustainability of long-term risk reduction strategies, and the need for extended resources. Moreover, the existing strict specialization within healthcare disciplines and the lack of interdisciplinary coordination pose additional obstacles to the effective execution of CRFA. The subthemes encompass: (i) Lack of awareness (ii) Time constraints (iii) Sustainability (iv) Long-term outcomes or no outcomes (v) Lack of resources (vi) Lack of interdisciplinary coordination and strict specialization.MP2 said ‘One is that among us practitioners, we do not give due significance to the link between oral health and systemic health. There are no awareness programs as far as I know. The emphasis is less’.

GP5 said, ‘They (medical practitioners) don’t have time to peep into the oral cavity to say you have caries, go to a dentist or say you have diabetes and there is a chance to develop periodontal disease. Such opportunities are less.’

The results of following the risk reduction strategies may take a long time to manifest, and sometimes the outcomes are not as significant as what the patient would have expected. This results in a spiralling of the patient’s attitude and a failure of further follow-up.

‘In long term, the patients may become uncooperative, and patients will not be willing for a follow-up, they will go for things that have cost-benefit’MP1.

The lack of resources, manpower and facilities to deliver the care act as significant barriers to implementation of CRFA.

MP3 has stated, ‘Cost is a problem, social acceptance is a problem, policy makers and political involvement are a problem, lack of communication between communities…In the western countries, like UK, they have NHS care, we don’t have that in India and patients hence don’t go for any care if they feel it is unnecessary’.

i) Content validation

The total number of questions included in the dental and medical questionnaires using the deductive approach was 28 and 24 respectively. After discussion, one question was eliminated from both the medical and dental questionnaires as it had a similar connotation to a previous question. Content validation of each scale was performed by five experts to ensure content relevance, representativeness, and technical quality. The KAP questionnaire was reduced to 26 questions for dental practitioners after content validation. Item reduction was performed to 22 for the questionnaire for medical practitioners after eliminating 1 question. A few questions were rephrased based on the suggestions given by the expert panel prior to administering the questionnaire for test-retest reliability assessment. The details of content validation are given in Table  2 .

ii) Test-re test reliability

The scoring of items was done, and the data was utilized to assess the reliability of the questionnaire. 21 questions in dental and medical questionnaires were subjected to test-retest reliability assessment. Five questions in the dental questionnaire were option questions, leading questions, or open-ended questions (Eg: Are you a periodontist) and one question in the medical questionnaire was open ended, hence they were not subjected to test-retest reliability. The unweighted Kappa coefficient was used to assess the reliability of the items with binary responses (Table  3 ). The intraclass correlation coefficient (ICC) was used for assessing the questions in the attitude category with categorical variables (Table  3 ). Based on the test-retest reliability assessment, three questions from the dental questionnaire and two questions from the medical questionnaire were eliminated.

iii)Psychometric evaluation of questionnaire

The 20-item medical and 23-item dental KAP questionnaires (including the open-ended and leading questions) were administered to 450 medical and dental practitioners, and responses were obtained from 225 samples in the medical stream and 307 in the dental stream.

In the medical KAP questionnaire, four items from the knowledge domain and one item each from the attitude and practice domain were eliminated owing to the high difficulty statistic. One item each from the knowledge and practice domain was retained considering the importance of the items, even though they had a higher difficulty range. After item reduction using item response theory, 14 items (including the open-ended question) remained in the final questionnaire for medical practitioners. The KMO sampling adequacy and test of sphericity for the domains of knowledge, attitude, and practice were found to be in an acceptable range. Internal consistency measured using Cronbach’s alpha improved from 0.471 to 0.658 for the attitude domain after item deletion. For knowledge and practice, the Cronbach’s alpha after item deletion was reported to be 0.553 and 0.727, respectively.

The 23-item questionnaire was reduced to 19 items with the elimination of 3 items from THE knowledge domain and single item from attitude domain. Two items with poor scores of difficulty were deemed to be important in the questionnaire and were not eliminated. After item reduction, a total of 14 items remained in the final questionnaire in addition to the five leading/option questions. The KMO sampling adequacy and test of sphericity for the domains of knowledge, attitude, and practice were found to be in acceptable range. Internal consistency measured using Cronbach’s alpha was found to be slightly reliable in case of the attitude domain (0.459). While for knowledge and practice domain internal consistency was within the acceptable range (Knowledge 0.634, Practice 0.513) after item deletion.

Multivariate logistic regression was attempted between the parameters such as age, gender, qualification, experience, type of service, location, and number of patients seen per day and the knowledge, attitude, and practice regarding CRFA for both medical and dental practitioners, and no significant results were obtained for both medical and dental practitioners. (The details of the psychometric evaluation of the questionnaire and the characteristics of the study population are given in supplementary file 1)

iv) standardization of scores

For the south Indian population, the 14 item questionnaire scores were standardized (Table  4 ).

The validated questionnaires for medical and dental practitioners are given in supplementary file 2. For the medical KAP questionnaire, scores below 14 indicated low KAP, scores between 15 and 18 indicated medium, and scores greater than 18 indicated good knowledge, attitude, and practice of CRFA. For dental practitioners, scores less than 16 were reported to be low KAP; scores 16 to 19 indicated medium level; and scores greater than 20 indicated a good level of knowledge, attitude, and practice regarding CRFA.

Total KAP amongst the medical practitioners who participated in the present study was 21.84 ± 2.87 and that of dental practitioners was 22.82 ± 3.21. Both values indicated a high level of KAP amongst the participants regarding CRFA.

Meta-integration

Eight overarching themes emerged in the meta integration of the qualitative and quantitative data (Fig.  1 ). The themes that had a confirmatory fit as assessed from both the quantitative and qualitative aspects of the study include (i) awareness of common risk factors for NCDs including periodontal diseases, (ii) neglect of dental status while assessing general health, (iii) awareness of effect of systemic diseases on oral health, (iv) awareness of risk factor reduction and improvement of NCD status. Contradictory observations from the quantitative and qualitative arms of the study resulted in a discordant fit in the following themes: (i) regular follow-up of periodontal health of patients with NCDs (ii) awareness regarding need for referral for periodontal examination and management in patients with NCDs (iii) awareness of perio-systemic interlink. The qualitative arm of the study explained the theme ‘Reasons for lack of referral to dental practitioners by medical practitioners’ and provided reasons such a reduced emphasis on oral health with a lack of awareness regarding the same amongst the practitioners, resource and time constraints that prevent the medical practitioners from looking into the overall health of the patient apart from the presenting complaint, overspecialization of the medical field with focus only on the specific field of specialization, to state a few.

Awareness of common risk factors for NCDs including periodontal diseases

NCDs and periodontal disease pose substantial societal burdens in terms of economic costs and years lost to ill-health, disability, or premature death [ 34 ]. Various factors, including social, demographic, environmental, behavioural, and personal elements, predispose individuals to major NCDs and oral diseases [ 5 ]. The CRFA addresses these shared risk factors, allowing the regulation of a few risk factors to exert a significant impact on controlling multiple chronic conditions [ 5 ]. This study has successfully developed and validated a questionnaire with satisfactory content validity and reliability to assess the knowledge, attitude, and behavior regarding CRFA for managing NCDs and periodontal disease.

To the best of our knowledge, this is the first study to create a suitable questionnaire for this purpose, incorporating a qualitative component to comprehend potential pathways and barriers to CRFA implementation. All retained questionnaire items demonstrated discrimination and difficulty parameters within acceptable ranges [ 20 ]. The KAP questionnaire exhibited acceptable internal consistency, validating its effectiveness for assessing CRFA-related KAP.

A crucial finding is the lack of understanding among medical and dental practitioners regarding common risk factors for NCDs and periodontal disease, hindering the implementation of CRFA. Literature that demonstrates the presence of shared risk factors between periodontal disease and other non-communicable diseases has, perchance, not been extensively explored by the health-care community. Almeida et al., in their systematic review, showed that the inflammatory mediators CRP and IL-6 had a significant association with both periodontitis and atherosclerosis [ 35 ]. A study by Arregoces et al. [ 36 ] showed an increase in ultrasensitive CRP (usCRP) in acute myocardial infarction (AMI), diabetes and periodontal disease. abdominal obesity [ 37 ] and insulin resistance [ 38 ] are proven to be contributing risk factors for metabolic syndrome and periodontal disease. The risk for CVD and periodontal disease is related to poor glycemic control, dyslipidemia, and chronic inflammatory state [ 39 , 40 , 41 ]. Smoking has been proven as a risk factor for periodontal disease, hypertension, diabetes, and metabolic syndrome through several studies [ 42 , 43 , 44 , 45 ]. Holmlund et al. have demonstrated the association between immunoglobulin G levels against P gingivalis and the risk for AMI and periodontal disease [ 46 ]. The presence of Aggregatibacter actinomycetemcomitans (Aa) is shown to be a risk factor for Coronary Artery Disease (CAD) and periodontal diseasecx [ 47 ]. The role of stress and depression as risk factors for CVD and periodontal disease has been investigated and recognized [ 48 ].

Apart from the lack of sufficient knowledge regarding the shared risk factors between periodontal disease and NCDs, there are additional barriers to the implementation of CRFA for the management of periodontal disease and NCDs. Barriers include time and resource constraints, oral health neglect in general health assessments, insufficient recognition of the need for oral health care referral for NCD patients, and limited acknowledgment of the perio-systemic interlink. However, the integration of medical and dental care is not impossible, and efforts such as creating awareness, education programs, mass media campaigns, and efficient referral systems are advocated by healthcare professionals.

The Health Resources and Services Administration (HRSA) has explained initiatives for incorporating oral health into primary medical care practice and training primary health care professionals in oral health assessment and clinical competencies [ 49 ]. The combination of preventive dental care with general health care practice can help reduce duplication of care modalities and expenses incurred. Six levels of integration, with the evolution of the key elements involved in the integration, from communication to physical proximity to practice change, have been described [ 50 ]. Communication is the key element in the first and second levels of integration in which there is minimal collaboration and basic collaboration at a distance respectively. Basic collaboration onsite and close collaboration onsite with some system integration form the third and fourth levels of integration, in which physical proximity is the key element. The fifth and sixth levels of integration include practice change, in which there is close collaboration with an integrated practice and full collaboration with a merged, integrated practice [ 50 ].

This research indicates that while the presence of shared risk factors among NCDs is acknowledged, medical practitioners often overlook the link between oral health and systemic health. Addressing this gap in healthcare practice involves providing basic oral health care training as an integral part of general health education.

In India, the checklist for early detection of NCDs, which is used in community based NCD surveillance, takes into consideration risk factors such as age of patient, smoking, alcohol consumption, measurement of waist, physical activities, and family history of NCDs [ 51 ]. These risk factors are similar to the risk factors for periodontal disease [ 13 ]. Thus, the risk factor surveillance may be extended to include periodontal disease as well. The primary healthcare teams can be trained in strategies to reduce or modify the risk factors associated with systemic diseases and oral diseases. The methods to assess the efficiency of the integrated practice in the primary health care setting include the calculation of the percentage of patients assessed using the surveillance tool, to the percentage of staff satisfied with the referral process [ 52 ]. Research conducted in Saudi Arabia showed that the availability of an appropriate source of oral health knowledge was significantly associated with increased odds of inter-disciplinary practice [ 53 ]. Regular patient reviews and examinations, along with the reinforcement of risk reduction strategies, can be achieved through the application of knowledge regarding shared risk factors, facilitating the efficient integration of medical and dental care.

This combined mixed-methods study has the limitation that the quantitative aspect was primarily conducted through online Google Forms, which were sent only to the medical and dental practitioners who are registered in the databases that were utilized in the study, and hence the representativeness of the sample may be compromised. However, given the study’s design, which provides insights into the perspectives of healthcare professionals in various fields, the results offer a valuable reflection of the KAP regarding CRFA among medical and dental practitioners.

The questionnaire derived from the quantitative segment of this study stands as a straightforward and effective tool for evaluating KAP related to the CRFA concerning both oral and general health. In alignment with the ongoing global efforts to enhance oral health strategies, CRFA emerges as a promising approach for seamlessly integrating medical and dental care. The qualitative aspect of this study showed that to foster this integration, key recommendations include raising awareness about the interconnectedness of oral and systemic conditions, addressing constraints related to time and resources, and establishing robust referral systems between medical and dental practitioners. These measures collectively aim to establish a unified and integrated medical-dental care system.

Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.The mixed-method study received ethical approvals from the Institutional Review Board of Amrita Institute of Medical Sciences, Kochi, with the reference IRB-AIMS-2020-165, and the Kasturba Medical College and Kasturba Hospital Institutional Ethics Committee, under the reference IEC-664/2020 and informed consent was obtained from the participants.

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LP: conception and design, acquisition of data and interpretation of data, drafting the article, final approval of the version to be published; VK: conception and design, acquisition of data, analysis and interpretation of data, drafting the article, final approval of the version to be published; LP and VK contributed equally for the preparation of the manuscript; CJ: conception and design, analysis and interpretation of data, revising article critically, final approval of the version to be published; RV: Design, interpretation of data, revising article critically, final approval of the version to be published; SS: Design, interpretation of data, revising article critically, final approval of the version to be published; AF: Design, interpretation of data, revising article critically, final approval of the version to be published.

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Puzhankara, L., Karuveettil, V., Janakiram, C. et al. Exploring medical and dental practitioner perspectives and developing a knowledge attitude and practice (KAP) evaluation tool for the common risk factor approach in managing non-communicable and periodontal diseases. BMC Oral Health 24 , 1017 (2024). https://doi.org/10.1186/s12903-024-04772-y

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Received : 02 May 2024

Accepted : 20 August 2024

Published : 30 August 2024

DOI : https://doi.org/10.1186/s12903-024-04772-y

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