See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/284904065 Sample Size in Qualitative Interview Studies: Guided by Information Power Article in Qualitative Health Research · November 2015 DOI: 10.1177/1049732315617444 CITATIONS READS 5,052 57,640 3 authors, including: Ann Dorrit Guassora University of Copenhagen 59 PUBLICATIONS 5,908 CITATIONS SEE PROFILE All content following this page was uploaded by Ann Dorrit Guassora on 10 December 2015. The user has requested enhancement of the downloaded file. 617444 research-article2015 QHRXXX10.1177/1049732315617444Qualitative Health ResearchMalterud et al. Article Sample Size in Qualitative Interview Studies: Guided by Information Power Qualitative Health Research 1­–8 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1049732315617444 qhr.sagepub.com Kirsti Malterud1,2,3, Volkert Dirk Siersma1, and Ann Dorrit Guassora1 Abstract Sample sizes must be ascertained in qualitative studies like in quantitative studies but not by the same means. The prevailing concept for sample size in qualitative studies is “saturation.” Saturation is closely tied to a specific methodology, and the term is inconsistently applied. We propose the concept “information power” to guide adequate sample size for qualitative studies. Information power indicates that the more information the sample holds, relevant for the actual study, the lower amount of participants is needed. We suggest that the size of a sample with sufficient information power depends on (a) the aim of the study, (b) sample specificity, (c) use of established theory, (d) quality of dialogue, and (e) analysis strategy. We present a model where these elements of information and their relevant dimensions are related to information power. Application of this model in the planning and during data collection of a qualitative study is discussed. Keywords sample size; participants; methodology; saturation; information power; qualitative Background Qualitative researchers need tools to evaluate sample size first while planning a study, then during the research process to appraise sample size continuously, and finally to ascertain whether the sample size is adequate for analysis and final publication (Guest, Bunce, & Johnson, 2006; Morse, 1995; Sandelowski, 1995). In quantitative studies, power calculations determines which sample size (N) is necessary to demonstrate effects of a certain magnitude from an intervention. For qualitative interview studies, no similar standards for assessment of sample size exist. Reviews indicate that qualitative researchers demonstrate a low level of transparency regarding sample sizes and the underlying arguments for these (Carlsen & Glenton, 2011; Mason, 2010). Often, the authors just claim that saturation was achieved, inferring that addition of more participants did not add anything to the analysis, without specifying their understanding of how saturation has been assessed. The saturation concept was originally coined by Glaser and Strauss (1999) as a specific element of constant comparison in Grounded Theory (GT) analysis. Within the GT framework, sample size is appraised as an element of the ongoing analysis where every new observation is compared with previous analysis to identify similarities and differences. The saturation concept is, however, again and again claimed in studies based on other analytic approaches, without any explanation of how the concept should be understood in this non-GT context and how it serves to justify the number of participants. A commonly stated principle for determining sample size in a qualitative study is that N should be sufficiently large and varied to elucidate the aims of the study (Kuzel, 1999; Marshall, 1996; Patton, 2015). However, this principle provides no guidance for planning, although experienced researchers seem to follow their own rules of thumb about approximate numbers of units that were needed in previous comparable studies to arrive at a responsible analysis (Mason, 2010). The authors of the present article have extensive experience from planning, conducting, publishing, and supervising qualitative as well as quantitative studies, and we share a concern for methodology across research methods. We agree with Mason (2010) that qualitative researchers should try hard to make our methods as robust and defensible as possible, aiming for intersubjectivity on 1 University of Copenhagen, Copenhagen, Denmark Uni Research Health, Bergen, Norway 3 University of Bergen, Bergen, Norway 2 Corresponding Author: Kirsti Malterud, Research Unit for General Practice, Uni Research Health, Kalfarveien 31, N-5018 Bergen, Norway. Email: Kirsti.malterud@gmail.com Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 2 Qualitative Health Research why and how decisions regarding design, sampling, and analysis are taken (Malterud, 2001). We shared the preconception that an approximation of sample size is necessary for planning, while the adequacy of the final sample size must be continuously evaluated during the research process. Reviewing principles of sample size in qualitative studies, we shall below argue that sample size cannot be predicted by formulae or by perceived redundancy. Tools to guide sample size should not rely on procedures from a specific analysis method, but rest on shared methodological principles for estimating an adequate number of units, events, or participants. For this purpose, we propose the concept information power. The larger information power the sample holds, the lower N is needed, and vice versa. In this article, we shall concentrate on information power applied in the context of qualitative interview studies. The aim of this article is to present and discuss a pragmatic model for assessment of sample size in qualitative studies, reflecting on how the information power needed for a specific study can be achieved. Method We have developed and elaborated the model inductively. First, we sketched a case presented as a fictional study. This study has neither been planned nor conducted but served as a specific reference for our discussions and elaborations. Then, we took this case as our point of departure for a review of conditions we considered to have an impact on information power and sample size in this specific study. Finally, we conceptualized the items and their dimensions as a model, intended to be transferable to interview studies beyond the particular context of the fictional study. We conducted the process as a pragmatic focus group conversation between the authors, taking our shared experiences as a point of departure for constructing the case. Our ongoing discussions functioned as analysis, identifying and bit by bit prioritizing the most important items having an impact on sample size from a logical point of view. A parallel discussion concerned the concept “information power.” The process was supported by available literature about the current state of the art regarding sample size in qualitative studies as well as literature discussing weaknesses in these standards. Below, we shall present and discuss the model. The Case: Planning an Interview Study on Diabetic Foot Ulcer Experiences We situated the case as the first of three subprojects of a PhD study where the overall objective was to contribute to theories of self-care and to describe patients’ practices for health professionals. The aim of the present subproject was to explore self-care among patients with diabetic foot ulcers by describing activities performed by patients to treat the ulcers and their motivation for doing so. The PhD student was a young MD who already had some experience with qualitative research from a previous project where descriptive cross-case analysis had been conducted. Participants would be recruited among patients in a diabetes out-patient clinic who had recently been diagnosed with their first ulcer. Further sampling strategies and criteria would be informed by stepwise analysis along with data collection by means of semistructured individual interviews. When to stop recruitment during this process would not be a simple decision for the novice researcher. The grant proposal requested an advance estimate of the number of interviews to plan how many participants were needed to elucidate the aim of the study and to get an idea of how much time the data collection would require. From previous research, her supervisor had some ideas about the number of participants needed for this project. The student would, however, prefer to plan her study and make her decisions on the basis of some standards about how many participants she would need to conduct a responsible analysis. Below, we present the model we developed as a tool to appraise sample size by appraisal of five major items that in different ways determine the information power of the sample. Items Having an Impact on Information Power Reviewing alternative choices of design and method for the fictional interview study, we identified five items that along different dimensions have an impact on the information power of the sample: (a) study aim, (b) sample specificity, (c) use of established theory, (d) quality of dialogue, and (e) analysis strategy. Below, we present these items and their dimensions separately and systematically. In real life, however, the items are related and have a mutual impact on each other. Study Aim—Narrow or Broad? Information power, guiding adequate sample size is related to the study aim. A broad study aim requires a larger sample than a narrow aim to offer sufficient information power, because the phenomenon under study is more comprehensive. A study aiming to explore how patients with their first diabetic foot ulcer manage shift of bandages would need notably fewer participants than a study about how patients with foot ulcer generally manage self-care in everyday life. Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 3 Malterud et al. In our case, the researcher would have to choose between extending the number of participants by recruiting a larger, purposive sample, or narrowing the aim of the study to maintain sufficient information power. If, however, the aim of the study concerns a very specific or rare experience, such as self-care among blind patients with diabetic foot ulcers, this would in itself limit the number of eligible participants. An alternative emphasis of the study could be to explore how individual resources interfere with self-care of diabetic foot ulcers. If so, a study based on interviews with one single or just a few participants might provide access to exciting hypotheses from a high level of information power. Defining the aim of the interview study, the researcher also offers some promises regarding transferability of the findings. The information power of the sample will be critical to achieve the aim. Sample Specificity—Dense or Sparse? Information power is also related to the specificity of experiences, knowledge, or properties among the participants included in the sample. To offer sufficient information power, a less extensive sample is needed with participants holding characteristics that are highly specific for the study aim compared with a sample containing participants of sparse specificity. Specificity concerns here participants who belong to the specified target group while also exhibiting some variation within the experiences to be explored. A sample including individuals from the target group holding experiences not previously described could also enhance information power. Knowing that self-care is limited by patient resources, we could, for example, aim for an especially specific sample identified by discussions with the nurses at the diabetic clinic including variations of both success in handling ulcers and some variation in age, gender, and type of diabetes. If we do not constrain recruitment procedures to include only patients with foot ulcers, a much larger number of participants would be needed to cover those whose experiences we study. Still, a purposive sample, established with specific aspects of variation in mind, is not always feasible. The strategy of convenience sampling, accepting participants who are available, without trying to influence the configuration of the sample, implies the risk of more limited specificity, thereby requiring more participants. Following such a recruitment strategy, we would probably need more interviews and participants to obtain a sufficiently broad scope of activities performed by patients to treat the ulcers and their underlying motivations. However, we might be fortunate and drop into a group of participants with a diversity of experiences. Hence, sample specificity cannot always be predicted but can be supported by suitable recruitment. Established Theory—Applied or Not? Furthermore, information power, guiding adequate sample size is related to the level of theoretical background of the study. A study supported by limited theoretical perspectives would usually require a larger sample to offer sufficient information power than a study that applies specific theories for planning and analysis. Theories from social science about the authority that professionals exercise might, for example, enhance the information power of our study about self-care experiences with diabetic foot ulcers. New knowledge, even from a rather small sample, might be obtained by looking for strategies used by patients to counter professional authority intended to make them perform specific self-care. Theory serves to synthesize existing knowledge as well as extending the sources of knowledge beyond the empirical interview data. On the contrary, another study starting from scratch with no theoretical background must establish its own foundation for grounding the conclusions. If so, a larger sample size would probably be needed to grant sufficient information power. Theoretical frameworks offer models and concepts that may explain relations between different aspects of the empirical data in a coherent way. Empirical studies with very small numbers can make a difference if they address and elucidate something crucial to theory. Quality of Dialogue—Strong or Weak? Information power is also related to the quality of the interview dialogue. A study with strong and clear communication between researcher and participants requires fewer participants to offer sufficient information power than a study with ambiguous or unfocused dialogues. In a qualitative study, empirical data are co-constructed by complex interaction between researcher and participant, and a number of issues determine the quality of the communication from which the information power is established. Analytic value of the empirical data depends on the skills of the interviewer, the articulateness of the participant, and the chemistry between them, and it is difficult to predict the quality of the dialogue in advance. In our study, the PhD student holds more than average background knowledge about diabetic foot ulcers, because she has been a consultant for the home nursing service in her area within this field the last 2 years. For her, the interviews would not be her first encounter with the subject area, and she would easily approach the participants’ self-care practices. However, by nature, she is rather shy, and it takes her some time to establish trust and report. It might therefore be necessary for her to obtain some extra interview training in advance, or to increase sample size. Her more experienced supervisor, well read in diabetes complications, and an experienced Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 4 Qualitative Health Research interviewer, used six interviews to establish a sample with adequate information power for an analysis that could contribute to existing knowledge in her previous project. An interview interaction with tensions and conflicting views may reduce the confidence needed to talk about intimate details. However, a researcher who never challenges his or her participant runs the risk of developing empirical data holding low information power, which, during analysis, only reproduces what is known from before. Analysis Strategy—Case or Cross-Case? Finally, information power is related to the strategy chosen for analysis in the specific project. An exploratory cross-case analysis requires more participants to offer sufficient information power compared with a project heading for in-depth analysis of narratives or discourse details from a few, selected participants. In this project, a thematic cross-case analysis will be conducted, because we want to uncover realistic and pragmatic descriptions of customary self-care practices and their foundations as a contribution applicable in clinical practice (Malterud, 2012). Referring to the supervisor’s experience, a purposive sample of six to 10 participants with diverse experiences might therefore provide sufficient information power for descriptions of different self-care practices teaching health professionals some useful lessons. Within an exploratory analysis, the ambition is not to cover the whole range of phenomena, but to present selected patterns relevant for the study aim. A single, deliberately chosen and well-articulated participant might illustrate a typical case but not demonstrate variations in self-care. Two participants with diametrically opposite habits might illustrate different aspects of a continuum but would not be sufficient to embrace discrepancies deviating from the main line. Fifty participants might provide all the sufficient variations as well as deviances regarding the actual practices. However, the overview of empirical data, needed as the point of departure for an accountable, thematic analysis of potentially relevant patterns, would become difficult to grasp, to present appropriate intersubjectivity, and to organize for further analysis. Information Power in Qualitative Interview Studies—The Model From our reflections above, we have conceptualized the items and their dimensions as a model intended as a tool to appraise sample size in qualitative interview studies in general (Figure 1). The model can be used to reflect systematically on items with an impact on the information power in the actual study. Figure 1. Information power—Items and dimensions. According to the model, considerations about study aim, sample specificity, theoretical background, quality of dialogue, and strategy for analysis should determine whether sufficient information power will be obtained with less or more participants included in the sample. A study will need the least amount of participants when the study aim is narrow, if the combination of participants is highly specific for the study aim, if it is supported by established theory, if the interview dialogue is strong, and if the analysis includes longitudinal in-depth exploration of narratives or discourse details. A study will need a larger number of participants when the study aim is broad, if the combination of participants is less specific for the research question, if it is not theoretically informed, if the interview dialogue is weak, and if cross-case analysis is conducted, especially if the aim is to cover the broadest possible range of variations of the phenomena studied. The dynamic interaction between the different items included in the model involves a trade-off between conditions that require more versus fewer participants in a sample. For example, an experienced researcher who expresses a narrow aim and achieves an excellent interview dialogue may be able to conduct a cross-case analysis with sufficient variation of results even with a small sample. However, a novice researcher with limited theoretical knowledge may need a larger group of participants to reveal something new although the aim is well-focused and the interview dialogues are good. Our model is not intended as a checklist to calculate N but is meant as a recommendation of what to consider systematically about recruitment at different steps of the research process. An initial appraisal of the number of Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 5 Malterud et al. informants needed in our case should consider the fact that the researcher is a novice researcher. Her personal shyness affects her ability to establish a good dialogue (more participants). Her study is, however, theoretically founded, and she has thorough experience with the empirical matters in question (less participants). She is heading for cross-case analysis requiring more participants, and the aim of her study is neither especially broad nor narrow. Because nurses will help her select participants with characteristics specific to her study, the need for participants will be smaller. Finally, her experienced research supervisor conducted a similar study last year, with thick data from six successful interviews. Based on these considerations, a provisional number of 10 participants could be an example of a cautious initial appraisal for our case. Appraisal of information power should be repeated along the process, supported by preliminary analysis. After the first three interviews, a first review of the data can be done and first suggestions of relevant theory can be made. In our case, it appears that some patients do not want to participate and that it might not be possible to achieve as much variation of self-care as expected. Due to some extra interview training and extensive reading, the researcher manages to make good report and steer the dialogue well. The interviews conducted so far have a high relevance for the research question. Initial analytic ideas have emerged at this point and are helpful in making the aim of the study more accurate, and some information seems promising in terms of adding new knowledge to the field. At this point, the attained and projected information power appears to be unexpectedly strong, and the number of participants needed may be adjusted downward. This assessment will have to be considered again before closing data collection. Besides the use of conducting own research projects, our model may be used to evaluate empirical data from other researchers, if the five items included in the model can be derived from study reports. We therefore encourage fellow researchers to present some reflections on information power in their publications. Discussion The Logic of Particularities Formal power calculations have been proposed as an alternative to informal, heuristic rules of thumb in qualitative studies for appraisal of sample size (DePaulo, 2000; Guest et al., 2006). The basic principle behind such attempts assumes a population where a set of information (such as self-care methods for management of diabetic foot ulcers) of some sort is available, each with different prevalence, and the aim is to identify as much of this information as possible with the least number of participants, selected at random. We do not repudiate the existence of settings where such assumptions might be adequate. Most often, however, they will be violated in a qualitative study. Participants are selected purposively as to provide the most information, and information will simply not exist, but is elaborated by the researcher, supported by the theory applied (Kvale, 1996; Patton, 2015; Sandelowski, 1995). A straightjacket of untenable assumptions may harm the research process (Bacchetti, 2010). McWhinney urged medical researchers to focus more on particularities, not only universals (McWhinney, 1989), and Sandelowski argued that the case study (N = 1) is the basic unit of analysis in any qualitative study, independent of the amount of empirical data (Sandelowski, 1996). In qualitative research, belonging to the interpretative paradigm, the logic of exploration is more emphasized than the logic of justification, and other assumptions for sampling are usually more adequate than what can possibly be predicted or calculated (Kuhn, 1962; Malterud, 2001; Marshall, 1996; Sandelowski, 1996). We have presented a pragmatic model for appraisal of sample size in qualitative interview studies. Our model offers a manageable strategy where the principal assumptions have been explicated for implementation and can be contested for methodological elaboration. Below, we shall discuss the strengths and limitations of this model and compare it with current leading standards regarding sample size in qualitative studies. The Model—Strengths and Limitations Information power is the core concept of our model. We have argued that information power of an interview sample is determined by items such as study aim, sample specificity, use of established theory, quality of dialogue, and analysis strategy. For each of these items, we have proposed dimensions along a continuum where researchers are invited to position themselves and their study to assess an approximate number of participants needed for responsible analysis. We argue that such an assessment should be stepwise revisited along the research process and not definitely decided in advance. In this way, recruitment can be brought to an end when the sample holds sufficient information power. Still, the model may offer support also in the initial planning of a qualitative interview study. The five items we have included in our model are neither mutually exclusive nor the only conceivable determinants of information power. A common denominator is that exploration of a comprehensive phenomenon requires data with appropriate variation regarding some selected qualities. However, a pragmatic model intended for implementation Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 6 Qualitative Health Research calls for prioritization. Following the inductive development path we have described, we therefore decided to include a limited and feasible amount of vital compatible items whose dimensions with an impact for information power could be easily identified, appraised, and presented. On a list of potential items to be included in the model, we have omitted the recruitment issue, which actually raises a paradox. When recruitment is easy, the researcher is at liberty to select a relevant and purposive sample and thereby reduce the number of participants. However, if only a few among many potential participants volunteer, the specificity of the sample may be jeopardized and thereby increase the number of participants necessary. If so, information power may be enhanced by considering the reasons for the declines. Simple changes in procedure, such as interviewing at home instead of in the clinic, may remove these obstacles and contribute to a sample where fewer participants are needed. The five items do not have universal importance, and their relative importance may therefore change from project to project and over the course of a research process. To make the model simple and readily understood, we chose to develop it for the context of individual interview studies, where the question of sample size usually refers to the number of participants. The sample size concept is more ambiguous when it comes to other qualitative research designs, such as focus group studies (number of groups, number of participant, or number of interviews), observational studies (number of events to be recorded, number of people to be included, number of sites to visit), or studies with data from written sources (pages of text, number of documents, number of organizations). Something Old, Something New, Something Borrowed, Something Blue . . . The information power concept and the items it comprises share some features with existing concepts and ideas within qualitative methodology. In our model, specificity covers issues usually discussed as matters of sampling (Patton, 2015). The role of aim in our model with regard to sample size has also been discussed by Morse (2000), and it is likewise related to Patton’s discussion of trade-offs between breadth and depth in a study (Patton, 2015). The dialogue item in our model shares some features with Spradley’s notion of “good informants” (Spradley, 1979), which is discussed as an aspect of adequacy by sampling (Morse, 1991, 2000, 2015b). Our model differs, however, in that we emphasize the quality of the dialogue rather than the nature of the topic, although these dimensions both cover the accessibility of the data. Adequacy, as discussed by Morse, concerns the sufficiency and quality of data. Unlike the concept of adequacy, our model is not tied to development of theory or theoretical sampling, which are specific procedures of GT. The most notable advantages of our model are therefore perhaps the addition of the relevance of established theory applied in a study, furthermore that the model considers types of analysis beyond cross-case analysis. The best qualitative analysis is conducted from empirical data containing abundant and various accounts of new aspects of the phenomenon we intend to explore (Morse, 1991, 2015a; Patton, 2015). The sample should be neither too small nor too large (Kvale, 1996; Sandelowski, 1995). In our experience, reviewers often seem to be more concerned with samples being too small than being too large, instead of appraising the outcome of analysis from these particular interviews. We would warn against methodological ideologies or strategies unreflectedly leading to too large samples (Chamberlain, 2000). By initial and consecutive assessment of information power, the researcher may avoid waste of time and resources for collection of unnecessary data, elaboration of information that is not relevant for the aim of the study, and lack of overview needed for a thorough analysis. Our model indicates that this can be obtained even with a sample of rather few participants, provided that the information power is sufficient. Should “Saturation” Be Replaced by “Information Power?” Saturation is often mentioned as a criterion for sample size in qualitative studies (Morse, 1995). The concept has been presented as an element of the constant comparative method, which is a central element of GT, intended to generate theories from empirical data (Glaser & Strauss, 1999). During data collection, the researcher compares sequentially added events until exhaustive saturation of properties of categories and of relations among them is obtained (Charmaz, 2006). Furthermore, theoretical sampling based on preliminary theory developed in the study is required for saturation in a GT analysis to finally arrive at saturation. Saturation occurs when the researcher no longer receives information that adds to the theory that has been developed. These procedures are, however, not part of all qualitative studies, and O’Reilly and Parker (2013) argue that adopting saturation as a generic quality marker is inappropriate. Although GT has clear guidance about what constitutes theoretical saturation, the meaning of saturation within other qualitative approaches is not clear. Authors claiming saturation are not always transparent about how it has been achieved (Morse, 2015a), and several studies are actually not compatible with the saturation concept of GT. Reviews reveal that the concept is often poorly specified and definitely not corresponding Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 7 Malterud et al. with the original meaning of saturation from GT (Carlsen & Glenton, 2011). For an exploratory study, we do not head for a complete description of all aspects of the phenomenon we study. We are usually satisfied when a study offers new insights that contribute substantially to or challenge current understandings. Furthermore, the epistemological anticipation of GT that exhaustive sampling of a definite set of variations can be obtained and covered by saturation is not the theory of science at the heart of most qualitative research (Malterud, 2012). To be sure, Morse rejects such an understanding of saturation, spelling out characteristics within categories as the domain to be saturated (Morse, 2015a). We consider Morse’s accuracy on this point as rather unusual among qualitative researchers, who more often refers to “heard it all” (Morse, 2015a). Research with social constructivist roots, where knowledge is considered partial, intermediate, and dependent of the situated view of the researcher, does not support an idea that qualitative studies ideally should comprise a “total” amount of facts (Alvesson & Sköldberg, 2009; Haraway, 1991). There are differences in how various approaches frame research questions, sample participants, and collect data to achieve richness and depth of analysis. DePaulo warns against the risk of missing something important when the sample of a qualitative study is inappropriate or too small (DePaulo, 2000). We agree to his point, but not to his ambitions of covering the full range of the phenomenon in question. Finally, saturation is not as objective and indisputable as it might appear, at least from a peer reviewer’s perspective. One researcher may regard the case as closed and get bored by further interviewing, while another colleague, perhaps with a less thorough knowledge of the field or with empirical data containing less variation, may assess further data as new information (Malterud, 2012; Morse, 1995). Information power is a concept that differs from saturation in several respects. Our model is, however, not based on a very original methodological idea. We look on information power as an aspect of internal validity, influencing the potential of the available empirical data to provide access to new knowledge by means of analysis and theoretical interpretations (Cohen & Crabtree, 2008; Kvale, 1996). In this regard, sample adequacy, data quality, and variability of relevant events are often more important than the number of participants. Hence, information power of a sample is not very different from being sufficiently large and varied to elucidate the aims of the study but can be considered a specification of how to accomplish it (Kuzel, 1999; Marshall, 1996; Morse, 1995; Patton, 2015; Sandelowski, 1995). Implications for Research Practice Qualitative interview studies may benefit from sampling strategies by shifting attention from numerical input of participants to the contribution of new knowledge from the analysis. Information power indicates that the more information the sample holds, relevant for the actual study, the lower number of participants is needed. An initial approximation of sample size is necessary for planning, while the adequacy of the final sample size must be evaluated continuously during the research process. The results presented in the final publication will demonstrate whether actual sample held adequate information power to develop new knowledge, referring to the aim of the study at hand. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article. References Alvesson, M., & Sköldberg, K. (2009). Reflexive methodology: New vistas for qualitative research (2nd ed.). Los Angeles: SAGE. Bacchetti, P. (2010). 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Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd= Retrieve&;db=PubMed&dopt=Citation&list_uids=7899572 Sandelowski, M. (1996). One is the liveliest number: The case orientation of qualitative research. Research in Nursing and Health, 19, 525–529. doi:10.1002/(SICI)1098240X(199612)19:6<525::AID-NUR8>3.0.CO;2-Q Spradley, J. (1979). The ethnographic interview. New York: Holt, Rinehart, & Winston. Author Biographies Kirsti Malterud, MD, PhD, is a senior researcher and professor of general practice at the Research Unit for General Practice (Copenhagen/Denmark), the Research Unit for General Practice, Uni Research Health (Bergen/Norway) and Department of Global Public Health and Primary Care, University of Bergen/Norway. Volkert Dirk Siersma, PhD, is statistician at The Research Unit for General Practice and The Section of General Practice, Department of Public Health, University of Copenhagen Ann Dorrit Guassora, MD, PhD, is an associate research professor at The Research Unit for General Practice and assistant professor at The Section of General Practice, Department of Public Health, University of Copenhagen. Downloaded from qhr.sagepub.com at Copenhagen University Library on November 30, 2015 View publication stats
