case study vs mixed methods research

Mixed methods research: what it is and what it could be

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• Published: 29 March 2019
• Volume 48 , pages 193–216, ( 2019 )

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• Rob Timans 1 ,
• Paul Wouters 2 &
• Johan Heilbron 3  

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Combining methods in social scientific research has recently gained momentum through a research strand called Mixed Methods Research (MMR). This approach, which explicitly aims to offer a framework for combining methods, has rapidly spread through the social and behavioural sciences, and this article offers an analysis of the approach from a field theoretical perspective. After a brief outline of the MMR program, we ask how its recent rise can be understood. We then delve deeper into some of the specific elements that constitute the MMR approach, and we engage critically with the assumptions that underlay this particular conception of using multiple methods. We conclude by offering an alternative view regarding methods and method use.

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The interest in combining methods in social scientific research has a long history. Terms such as “triangulation,” “combining methods,” and “multiple methods” have been around for quite a while to designate using different methods of data analysis in empirical studies. However, this practice has gained new momentum through a research strand that has recently emerged and that explicitly aims to offer a framework for combining methods. This approach, which goes by the name of Mixed Methods Research (MMR), has rapidly become popular in the social and behavioural sciences. This can be seen, for instance, in Fig.  1 , where the number of publications mentioning “mixed methods” in the title or abstract in the Thomson Reuters Web of Science is depicted. The number increased rapidly over the past ten years, especially after 2006. Footnote 1

Fraction of the total of articles mentioning Mixed Method Research appearing in a given year, 1990–2017 (yearly values sum to 1). See footnote 1

The subject of mixed methods thus seems to have gained recognition among social scientists. The rapid rise of the number of articles mentioning the term raises various sociological questions. In this article, we address three of these questions. The first question concerns the degree to which the approach of MMR has become institutionalized within the field of the social sciences. Has MMR become a recognizable realm of knowledge production? Has its ascendance been accompanied by the production of textbooks, the founding of journals, and other indicators of institutionalization? The answer to this question provides an assessment of the current state of MMR. Once that is determined, the second question is how MMR’s rise can be understood. Where does the approach come from and how can its emergence and spread be understood? To answer this question, we use Pierre Bourdieu’s field analytical approach to science and academic institutions (Bourdieu 1975 , 1988 , 2004 , 2007 ; Bourdieu et al. 1991 ). We flesh out this approach in the next section. The third question concerns the substance of the MMR corpus seen in the light of the answers to the previous questions: how can we interpret the specific content of this approach in the context of its socio-historical genesis and institutionalization, and how can we understand its proposal for “mixing methods” in practice?

We proceed as follows. In the next section, we give an account of our theoretical approach. Then, in the third, we assess the degree of institutionalization of MMR, drawing on the indicators of academic institutionalization developed by Fleck et al. ( 2016 ). In the fourth section, we address the second question by examining the position of the academic entrepreneurs behind the rise of MMR. The aim is to understand these agents’ engagement in MMR, as well as its distinctive content as being informed by their position in this field. Viewing MMR as a position-taking of academic entrepreneurs, linked to their objective position in this field, allows us to reflect sociologically on the substance of the approach. We offer this reflection in the fifth section, where we indicate some problems with MMR. To get ahead of the discussion, these problems have to do with the framing of MMR as a distinct methodology and its specific conceptualization of data and methods of data analysis. We argue that these problems hinder fruitfully combining methods in a practical understanding of social scientific research. Finally, we conclude with some tentative proposals for an alternative view on combining methods.

A field approach

Our investigation of the rise and institutionalization of MMR relies on Bourdieu’s field approach. In general, field theory provides a model for the structural dimensions of practices. In fields, agents occupy a position relative to each other based on the differences in the volume and structure of their capital holdings. Capital can be seen as a resource that agents employ to exert power in the field. The distribution of the form of capital that is specific to the field serves as a principle of hierarchization in the field, differentiating those that hold more capital from those that hold less. This principle allows us to make a distinction between, respectively, the dominant and dominated factions in a field. However, in mature fields all agents—dominant and dominated—share an understanding of what is at stake in the field and tend to accept its principle of hierarchization. They are invested in the game, have an interest in it, and share the field’s illusio .

In the present case, we can interpret the various disciplines in the social sciences as more or less autonomous spaces that revolve around the shared stake in producing legitimate scientific knowledge by the standards of the field. What constitutes legitimate knowledge in these disciplinary fields, the production of which bestows scholars with prestige and an aura of competence, is in large part determined by the dominant agents in the field, who occupy positions in which most of the consecration of scientific work takes place. Scholars operating in a field are endowed with initial and accumulated field-specific capital, and are engaged in the struggle to gain additional capital (mainly scientific and intellectual prestige) in order to advance their position in the field. The main focus of these agents will generally be the disciplinary field in which they built their careers and invested their capital. These various disciplinary spaces are in turn part of a broader field of the social sciences in which the social status and prestige of the various disciplines is at stake. The ensuing disciplinary hierarchy is an important factor to take into account when analysing the circulation of new scientific products such as MMR. Furthermore, a distinction needs to be made between the academic and the scientific field. While the academic field revolves around universities and other degree-granting institutions, the stakes in the scientific field entail the production and valuation of knowledge. Of course, in modern science these fields are closely related, but they do not coincide (Gingras and Gemme 2006 ). For instance, part of the production of legitimate knowledge takes place outside of universities.

This framework makes it possible to contextualize the emergence of MMR in a socio-historical way. It also enables an assessment of some of the characteristics of MMR as a scientific product, since Bourdieu insists on the homology between the objective positions in a field and the position-takings of the agents who occupy these positions. As a new methodological approach, MMR is the result of the position-takings of its producers. The position-takings of the entrepreneurs at the core of MMR can therefore be seen as expressions in the struggles over the authority to define the proper methodology that underlies good scientific work regarding combining methods, and the potential rewards that come with being seen, by other agents, as authoritative on these matters. Possible rewards include a strengthened autonomy of the subfield of MMR and an improved position in the social-scientific field.

The role of these entrepreneurs or ‘intellectual leaders’ who can channel intellectual energy and can take the lead in institution building has been emphasised by sociologists of science as an important aspect of the production of knowledge that is visible and recognized as distinct in the larger scientific field (e.g., Mullins 1973 ; Collins 1998 ). According to Bourdieu, their position can, to a certain degree, explain the strategy they pursue and the options they perceive to be viable in the trade-off regarding the risks and potential rewards for their work.

We do not provide a full-fledged field analysis of MMR here. Rather, we use the concept as a heuristic device to account for the phenomenon of MMR in the social context in which it emerged and diffused. But first, we take stock of the current situation of MMR by focusing on the degree of institutionalization of MMR in the scientific field.

The institutionalization of mixed methods research

When discussing institutionalization, we have to be careful about what we mean by this term. More precisely, we need to be specific about the context and distinguish between institutionalization in the academic field and institutionalization within the scientific field (see Gingras and Gemme 2006 ; Sapiro et al. 2018 ). The first process refers to the establishment of degrees, curricula, faculties, etc., or to institutions tied to the academic bureaucracy and academic politics. The latter refers to the emergence of institutions that support the autonomization of scholarship such as scholarly associations and scientific journals. Since MMR is still a relatively young phenomenon and academic institutionalization tends to lag scientific institutionalization (e.g., for the case of sociology and psychology, see Sapiro et al. 2018 , p. 26), we mainly focus here on the latter dimension.

Drawing on criteria proposed by Fleck et al. ( 2016 ) for the institutionalization of academic disciplines, MMR seems to have achieved a significant degree of institutionalization within the scientific field. MMR quickly gained popularity in the first decade of the twenty-first century (e.g., Tashakkori and Teddlie 2010c , pp. 803–804). A distinct corpus of publications has been produced that aims to educate those interested in MMR and to function as a source of reference for researchers: there are a number of textbooks (e.g., Plowright 2010 ; Creswell and Plano Clark 2011 ; Teddlie and Tashakkori 2008 ); a handbook that is now in its second edition (Tashakkori and Teddlie 2003 , 2010a ); as well as a reader (Plano Clark and Creswell 2007 ). Furthermore, a journal (the Journal of Mixed Methods Research [ JMMR] ) was established in 2007. The JMMR was founded by the editors John Creswell and Abbas Tashakkori with the primary aim of “building an international and multidisciplinary community of mixed methods researchers.” Footnote 2 Contributions to the journal must “fit the definition of mixed methods research” Footnote 3 and explicitly integrate qualitative and quantitative aspects of research, either in an empirical study or in a more theoretical-methodologically oriented piece.

In addition, general textbooks on social research methods and methodology now increasingly devote sections to the issue of combining methods (e.g., Creswell 2008 ; Nagy Hesse-Biber and Leavy 2008 ; Bryman 2012 ), and MMR has been described as a “third paradigm” (Denscombe 2008 ), a “movement” (Bryman 2009 ), a “third methodology” (Tashakkori and Teddlie 2010b ), a “distinct approach” (Greene 2008 ) and an “emerging field” (Tashakkori and Teddlie 2011 ), defined by a common name (that sets it apart from other approaches to combining methods) and shared terminology (Tashakkori and Teddlie 2010b , p. 19). As a further indication of institutionalization, a research association (the Mixed Methods International Research Association—MMIRA) was founded in 2013 and its inaugural conference was held in 2014. Prior to this, there have been a number of conferences on MMR or occasions on which MMR was presented and discussed in other contexts. An example of the first is the conference on mixed method research design held in Basel in 2005. Starting also in 2005, the British Homerton School of Health Studies has organised a series of international conferences on mixed methods. Moreover, MMR was on the list of sessions in a number of conferences on qualitative research (see, e.g., Creswell 2012 ).

Another sign of institutionalization can be found in efforts to forge a common disciplinary identity by providing a narrative about its history. This involves the identification of precursors and pioneers as well as an interpretation of the process that gave rise to a distinctive set of ideas and practices. An explicit attempt to chart the early history of MMR is provided by Johnson and Gray ( 2010 ). They frame MMR as rooted in the philosophy of science, particularly as a way of thinking about science that has transcended some of the most salient historical oppositions in philosophy. Philosophers like Aristotle and Kant are portrayed as thinkers who sought to integrate opposing stances, forwarding “proto-mixed methods ideas” that exhibited the spirit of MMR (Johnson and Gray 2010 , p. 72, p. 86). In this capacity, they (as well as other philosophers like Vico and Montesquieu) are presented as part of MMR providing a philosophical validation of the project by presenting it as a continuation of ideas that have already been voiced by great thinkers in the past.

In the second edition of their textbook, Creswell and Plano Clark ( 2011 ) provide an overview of the history of MMR by identifying five historical stages: the first one being a precursor to the MMR approach, consisting of rather atomised attempts by different authors to combine methods in their research. For Creswell and Plano Clark, one of the earliest examples is Campbell and Fiske’s ( 1959 ) combination of quantitative methods to improve the validity of psychological scales that gave rise to the triangulation approach to research. However, they regard this and other studies that combined methods around that time, as “antecedents to (…) more systematic attempts to forge mixed methods into a complete research design” (Creswell and Plano Clark 2011 , p. 21), and hence label this stage as the “formative period” (ibid., p. 25). Their second stage consists of the emergence of MMR as an identifiable research strand, accompanied by a “paradigm debate” about the possibility of combining qualitative and quantitative data. They locate its beginnings in the late 1980s when researchers in various fields began to combine qualitative and quantitative methods (ibid., pp. 20–21). This provoked a discussion about the feasibility of combining data that were viewed as coming from very different philosophical points of view. The third stage, the “procedural development period,” saw an emphasis on developing more hands-on procedures for designing a mixed methods study, while stage four is identified as consisting of “advocacy and expansion” of MMR as a separate methodology, involving conferences, the establishment of a journal and the first edition of the aforementioned handbook (Tashakkori and Teddlie 2003 ). Finally, the fifth stage is seen as a “reflective period,” in which discussions about the unique philosophical underpinnings and the scientific position of MMR emerge.

Creswell and Plano Clark thus locate the emergence of “MMR proper” at the second stage, when researchers started to use both qualitative and quantitative methods within a single research effort. As reasons for the emergence of MMR at this stage they identify the growing complexity of research problems, the perception of qualitative research as a legitimate form of inquiry (also by quantitative researchers) and the increasing need qualitative researchers felt for generalising their findings. They therefore perceive the emergence of the practice of combining methods as a bottom up process that grew out of research practices, and at some point in time converged towards a more structural approach. Footnote 4 Historical accounts such as these add a cognitive dimension to the efforts to institutionalize MMR. They lay the groundwork for MMR as a separate subfield with its own identity, topics, problems and intellectual history. The use of terms such as “third paradigm” and “third methodology” also suggests that there is a tendency to perceive and promote MMR as a distinct and coherent way to do research.

In view of the brief exploration of the indicators of institutionalisation of MMR, it seems reasonable to conclude that MMR has become a recognizable and fairly institutionalized strand of research with its own identity and profile within the social scientific field. This can be seen both from the establishment of formal institutions (like associations and journals) and more informal ones that rely more on the tacit agreement between agents about “what MMR is” (an example of this, which we address later in the article, is the search for a common definition of MMR in order to fix the meaning of the term). The establishment of these institutions supports the autonomization of MMR and its emancipation from the field in which it originated, but in which it continues to be embedded. This way, it can be viewed as a semi-autonomous subfield within the larger field of the social sciences and as the result of a differentiation internal to this field (Steinmetz 2016 , p. 109). It is a space that is clearly embedded within this higher level field; for example, members of the subfield of MMR also qualify as members of the overarching field, and the allocation of the most valuable and current form of capital is determined there as well. Nevertheless, as a distinct subfield, it also has specific principles that govern the production of knowledge and the rewards of domination.

We return to the content and form of this specific knowledge later in the article. The next section addresses the question of the socio-genesis of MMR.

Where does mixed methods research come from?

The origins of the subfield of MMR lay in the broader field of social scientific disciplines. We interpret the positions of the scholars most involved in MMR (the “pioneers” or “scientific entrepreneurs”) as occupying particular positions within the larger academic and scientific field. Who, then, are the researchers at the heart of MMR? Leech ( 2010 ) interviewed 4 scholars (out of 6) that she identified as early developers of the field: Alan Bryman (UK; sociology), John Creswell (USA; educational psychology), Jennifer Greene (USA; educational psychology) and Janice Morse (USA; nursing and anthropology). Educated in the 1970s and early 1980s, all four of them indicated that they were initially trained in “quantitative methods” and later acquired skills in “qualitative methods.” For two of them (Bryman and Creswell) the impetus to learn qualitative methods was their involvement in writing on, and teaching of, research methods; for Greene and Morse the initial motivation was more instrumental and related to their concrete research activity at the time. Creswell describes himself as “a postpositivist in the 1970s, self-education as a constructivist through teaching qualitative courses in the 1980s, and advocacy for mixed methods (…) from the 1990s to the present” (Creswell 2011 , p. 269). Of this group, only Morse had the benefit of learning about qualitative methods as part of her educational training (in nursing and anthropology; Leech 2010 , p. 267). Independently, Creswell ( 2012 ) identified (in addition to Bryman, Greene and Morse) John Hunter, Allen Brewer (USA; Northwestern and Boston College) and Nigel Fielding (University of Surrey, UK) as important early movers in MMR.

The selections that Leech and Creswell make regarding the key actors are based on their close involvement with the “MMR movement.” It is corroborated by a simple analysis of the articles that appeared in the Journal of Mixed Methods Research ( JMMR ), founded in 2007 as an outlet for MMR.

Table 1 lists all the authors that have published in the issues of the journal since its first publication in 2007 and that have either received more than 14 (4%) of the citations allocated between the group of 343 authors (the TLCS score in Table 1 ), or have written more than 2 articles for the Journal (1.2% of all the articles that have appeared from 2007 until October 2013) together with their educational background (i.e., the discipline in which they completed their PhD).

All the members of Leech’s selection, except for Morse, and the members of Creswell’s selection (except Hunter, Brewer, and Fielding) are represented in the selection based on the entries in the JMMR . Footnote 5 The same holds for two of the three additional authors identified by Creswell. Hunter and Brewer have developed a somewhat different approach to combining methods that explicitly targets data gathering techniques and largely avoids epistemological discussions. In Brewer and Hunter ( 2006 ) they discuss the MMR approach very briefly and only include two references in their bibliography to the handbook of Tashakkori and Teddlie ( 2003 ), and at the end of 2013 they had not published in the JMMR . Fielding, meanwhile, has written two articles for the JMMR (Fielding and Cisneros-Puebla 2009 ; Fielding 2012 ). In general, it seems reasonable to assume that a publication in a journal that positions itself as part of a systematic attempt to build a research tradition, and can be viewed as part of a strategic effort to advance MMR as a distinct alternative to more “traditional” academic research—particularly in methods—at least signals a degree of adherence to the effort and acceptance of the rules of the game it lays out. This would locate Fielding closer to the MMR movement than the others.

The majority of the researchers listed in Table 1 have a background in psychology or social psychology (35%), and sociology (25%). Most of them work in the United States or are UK citizens, and the positions they occupied at the beginning of 2013 indicates that most of these are in applied research: educational research and educational psychology account for 50% of all the disciplinary occupations of the group that were still employed in academia. This is consistent with the view that MMR originated in applied disciplines and thematic studies like education and nursing, rather than “pure disciplines” like psychology and sociology (Tashakkori and Teddlie ( 2010b ), p. 32). Although most of the 20 individuals mentioned in Table 1 have taught methods courses in academic curricula (for 15 of them, we could determine that they were involved in the teaching of qualitative, quantitative, or mixed methods), there are few individuals with a background in statistics or a neighbouring discipline: only Amy Dellinger did her PhD in “research methodology.” In addition, as far as we could determine, only three individuals held a position in a methodological department at some time: Dellinger, Tony Onwuegbuzie, and Nancy Leech.

The pre-eminence of applied fields in MMR is supported when we turn our attention to the circulation of MMR. To assess this we proceeded as follows. We selected 10 categories in the Web of Science that form a rough representation of the space of social science disciplines, taking care to include the most important so-called “studies.” These thematically orientated, interdisciplinary research areas have progressively expanded since they emerged at the end of the 1960s as a critique of the traditional disciplines (Heilbron et al. 2017 ). For each category, we selected the 10 journals with the highest 5-year impact factor in their category in the period 2007–2015. The lists were compiled bi-annually over this period, resulting in 5 top ten lists for the following Web of Science categories: Economics, Psychology, Sociology, Anthropology, Political Science, Nursing, Education & Educational Research, Business, Cultural Studies, and Family Studies. After removing multiple occurring journals, we obtained a list of 164 journals.

We searched the titles and abstracts of the articles appearing in these journals over the period 1992–2016 for occurrences of the terms “mixed method” or “multiple methods” and variants thereof. We chose this particular period and combination of search terms to see if a shift from a more general use of the term “multiple methods” to “mixed methods” occurred following the institutionalization of MMR. In total, we found 797 articles (out of a total of 241,521 articles that appeared in these journals during that time), published in 95 different journals. Table 2 lists the 20 journals that contain at least 1% (8 articles) of the total amount of articles.

As is clear from Table 2 , the largest number of articles in the sample were published in journals in the field of nursing: 332 articles (42%) appeared in journals that can be assigned to this category. The next largest category is Education & Educational Research, to which 224 (28 percentage) of the articles can be allocated. By contrast, classical social science disciples are barely represented. In Table 2 only the journal Field Methods (Anthropology) and the Journal of Child Psychology and Psychiatry (Psychology) are related to classical disciplines. In Table 3 , the articles in the sample are categorized according to the disciplinary category of the journal in which they appeared. Overall, the traditional disciplines are clearly underrepresented: for the Economics category, for example, only the Journal of Economic Geography contains three articles that make a reference to mixed methods.

Focusing on the core MMR group, the top ten authors of the group together collect 458 citations from the 797 articles in the sample, locating them at the center of the citation network. Creswell is the most cited author (210 citations) and his work too receives most citations from journals in nursing and education studies.

The question whether a terminological shift has occurred from “multiple methods” to “mixed methods” must be answered affirmative for this sample. Prior to 2001 most articles (23 out of 31) refer to “multiple methods” or “multi-method” in their title or abstract, while the term “mixed methods” gains traction after 2001. This shift occurs first in journals in nursing studies, with journals in education studies following somewhat later. The same fields are also the first to cite the first textbooks and handbooks of MMR.

Taken together, these results corroborate the notion that MMR circulates mainly in nursing and education studies. How can this be understood from a field theoretical perspective? MMR can be seen as an innovation in the social scientific field, introducing a new methodology for combining existing methods in research. In general, innovation is a relatively risky strategy. Coming up with a truly rule-breaking innovation often involves a small probability of great success and a large probability of failure. However, it is important to add some nuance to this general observation. First, the risk an innovator faces depends on her position in the field. Agents occupying positions at the top of their field’s hierarchy are rich in specific capital and can more easily afford to undertake risky projects. In the scientific field, these are the agents richest in scientific capital. They have the knowledge, authority, and reputation (derived from recognition by their peers; Bourdieu 2004 , p. 34) that tends to decrease the risk they face and increase the chances of success. Moreover, the positions richest in scientific capital will, by definition, be the most consecrated ones. This consecration involves scientific rather than academic capital (cf. Wacquant 2013 , p. 20) and within disciplines these consecrated positions often are related to orthodox position-takings. This presents a paradox: although they have the capital to take more risks, they have also invested heavily in the orthodoxy of the field and will thus be reluctant to upset the status quo and risk destroying the value of their investment. This results in a tendency to take a more conservative stance, aimed at preserving the status quo in the field and defending their position. Footnote 6

For agents in dominated positions this logic is reversed. Possessing less scientific capital, they hold less consecrated positions and their chances of introducing successful innovations are much lower. This leaves them too with two possible strategies. One is to revert to a strategy of adaptation, accepting the established hierarchy in the field and embarking on a slow advancement to gain the necessary capital to make their mark from within the established order. However, Bourdieu notes that sometimes agents with a relatively marginal position in the field will engage in a “flight forward” and pursue higher risk strategies. Strategies promoting a heterodox approach challenge the orthodoxy and the principles of hierarchization of the field, and, if successful (which will be the case only with a small probability), can rake in significant profits by laying claim to a new orthodoxy (Bourdieu 1975 , p. 104; Bourdieu 1993 , pp. 116–117).

Thus, the coupling of innovative strategies to specific field positions based on the amount of scientific capital alone is not straightforward. It is therefore helpful to introduce a second differentiation in the field that, following Bourdieu ( 1975 , p. 103), is based on the differences between the expected profits from these strategies. Here a distinction can be made between an autonomous and a heteronomous pole of the field, i.e., between the purest, most “disinterested” positions and the most “temporal” positions that are more pervious to the heteronomous logic of social hierarchies outside the scientific field. Of course, this difference is a matter of degree, as even the works produced at the most heteronomous positions still have to adhere to the standards of the scientific field to be seen as legitimate. But within each discipline this dimension captures the difference between agents predominantly engaged in fundamental, scholarly work—“production solely for the producers”—and agents more involved in applied lines of research. The main component of the expected profit from innovation in the first case is scientific, whereas in the second case the balance tends to shift towards more temporal profits. This two-fold structuring of the field allows for a more nuanced conception of innovation than the dichotomy “conservative” versus “radical.” Holders of large amounts of scientific capital at the autonomous pole of the field are the producers and conservators of orthodoxy, producing and diffusing what can be called “orthodox innovations” through their control of relatively powerful networks of consecration and circulation. Innovations can be radical or revolutionary in a rational sense, but they tend to originate from questions raised by the orthodoxy of the field. Likewise, the strategy to innovate in this sense can be very risky in that success is in no way guaranteed, but the risk is mitigated by the assurance of peers that these are legitimate questions, tackled in a way that is consistent with orthodoxy and that does not threaten control of the consecration and circulation networks.

These producers are seen as intellectual leaders by most agents in the field, especially by those aspiring to become part of the specific networks of production and circulation they maintain. The exception are the agents located at the autonomous end of the field who possess less scientific capital and outright reject this orthodoxy produced by the field’s elite. Being strictly focused on the most autonomous principles of legitimacy, they are unable to accommodate and have no choice but to reject the orthodoxy. Their only hope is to engage in heterodox innovations that may one day become the new orthodoxy.

The issue is less antagonistic at the heteronomous side of the field, at least as far as the irreconcilable position-takings at the autonomous pole are concerned. The main battle here is also for scientific capital, but is complemented by the legitimacy it brings to gain access to those who are in power outside of the scientific field. At the dominant side, those with more scientific capital tend to have access to the field of power, agents who hold the most economic and cultural capital, for example by holding positions in policy advisory committees or company boards. The dominated groups at this side of the field will cater more to practitioners or professionals outside of the field of science.

Overall, there will be fewer innovations on this side. Moreover, innovative strategies will be less concerned with the intricacies of the pure discussions that prevail at the autonomous pole and be of a more practical nature, but pursued from different degrees of legitimacy according to the differences in scientific capital. This affects the form these more practical, process-orientated innovations take. At the dominant side of this pole, agents tend to accept the outcome of the struggles at the autonomous pole: they will accept the orthodoxy because mastery of this provides them with scientific capital and the legitimacy they need to gain access to those in power. In contrast, agents at the dominated side will be more interested in doing “what works,” neutralizing the points of conflict at the autonomous pole and deriving less value from strictly following the orthodoxy. This way, a four-fold classification of innovative strategies in the scientific field emerges (see Fig.  2 ) that helps to understand the context in which MMR was developed.

Scientific field and scientific innovation

In summary, the small group of researchers who have been identified as the core of MMR consist predominantly of users of methods, who were educated and have worked exclusively at US and British universities. The specific approach to combining methods that is proposed by MMR has been successful from an institutional point of view, achieving visibility through the foundation of a journal and association and a considerable output of core MMR scholars in terms of books, conference proceedings, and journal articles. Its origins and circulation in vocational studies rather than classical academic disciplines can be understood from the position these studies occupy in the scientific field and the kinds of position-taking and innovations these positions give rise to. This context allows a reflexive understanding of the content of MMR and the issues that are dominant in the approach. We turn to this in the next section.

Mixed methods research: Position-taking

The position of the subfield of MMR in the scientific field is related to the position-takings of agents that form the core of this subfield (Bourdieu 1993 , p. 35). The space of position takings, in turn, provides the framework to study the most salient issues that are debated within the subfield. Since we can consider MMR to be an emerging subfield, where positions and position takings are not as clearly defined as in more mature and settled fields, it comes as no surprise that there is a lively discussion of fundamental matters. Out of the various topics that are actively discussed, we have distilled three themes that are important for the way the subfield of MMR conveys its autonomy as a field and as a distinct approach to research. Footnote 7 In our view, these also represent the main problems with the way MMR approaches the issue of combining methods.

Methodology making and standardization

The first topic is that the approach is moving towards defining a unified MMR methodology. There are differences in opinion as to how this is best achieved, but there is widespread agreement that some kind of common methodological and conceptual foundation of MMR is needed. To this end, some propose a broad methodology that can serve as distinct marker of MMR research. For instance, in their introduction to the handbook, Tashakkori and Teddlie ( 2010b ) propose a definition of the methodology of mixed methods research as “the broad inquiry logic that guides the selection of specific methods and that is informed by conceptual positions common to mixed methods practitioners” (Tashakkori and Teddlie 2010b , p. 5). When they (later on in the text) provide two methodological principles that differentiate MMR from other communities of scholars, they state that they regard it as a “crucial mission” for the MMR community to generate distinct methodological principles (Tashakkori and Teddlie 2010b , pp. 16–17). They envision an MMR methodology that can function as a “guide” for selecting specific methods. Others are more in favour of finding a philosophical foundation that underlies MMR. For instance, Morgan ( 2007 ) and Hesse-Biber ( 2010 ) consider pragmatism as a philosophy that distinguishes MMR from qualitative (constructivism) and quantitative (positivist) research and that can provide a rationale for the paradigmatic pluralism typical of MMR.

Furthermore, there is wide agreement that some unified definition of MMR would be beneficial, but it is precisely here that there is a large variation in interpretations regarding the essentials of MMR. This can be seen in the plethora of definitions that have been proposed. Johnson et al. ( 2007 ) identified 19 alternative definitions of MMR at the time, out of which they condensed their own:

[MMR] is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purpose of breath and depth of understanding and corroboration. Footnote 8

Four years later, the issue is not settled yet. Creswell and Plano Clark ( 2011 ) list a number of authors who have proposed a different definition of MMR, and conclude that there is a common trend in the content of these definitions over time. They take the view that earlier texts on mixing methods stressed a “disentanglement of methods and philosophy,” while later texts locate the practice of mixing methods in “all phases of the research process” (Creswell and Plano Clark 2011 , p. 2). It would seem, then, that according to these authors the definitions of MMR have become more abstract, further away from the practicality of “merely” combining methods. Specifically, researchers now seem to speak of mixing higher order concepts: some speak of mixing methodologies, others refer to mixing “research approaches,” or combining “types of research,” or engage in “multiple ways of seeing the social world” (Creswell and Plano Clark 2011 ).

This shift is in line with the direction in which MMR has developed and that emphasises practical ‘manuals’ and schemas for conducting research. A relatively large portion of the MMR literature is devoted to classifications of mixed methods designs. These classifications provide the basis for typologies that, in turn, provide guidelines to conduct MMR in a concrete research project. Tashakkori and Teddlie ( 2003 ) view these typologies as important elements of the organizational structure and legitimacy of the field. In addition, Leech and Onwuegbuzie ( 2009 ) see typologies as helpful guides for researchers and of pedagogical value (Leech and Onwuegbuzie 2009 , p. 272). Proposals for typologies can be found in textbooks, articles, and contributions to the handbook(s). For example, Creswell et al. ( 2003 , pp. 169-170) reviewed a number of studies and identified 8 different ways to classify MMR studies. This list was updated and extended by Creswell and Plano Clark ( 2011 , pp. 56-59) to 15 typologies. Leech and Onwuegbuzie ( 2009 ) identified 35 different research designs in the contributions to Teddlie and Tashakkori (2003) alone, and proposed their own three-dimensional typology that resulted in 8 different types of mixed methods studies. As another example of the ubiquity of these typologies, Nastasi et al. ( 2010 ) classified a large number of existing typologies in MMR into 7”meta-typologies” that each emphasize different aspects of the research process as important markers for MMR. According to the authors, these typologies have the same function in MMR as the more familiar names of “qualitative” or “quantitative” methods (e.g., “content analysis” or “structural equation modelling”) have: to signal readers of research what is going on, what procedures have been followed, how to interpret results, etc. (see also Creswell et al. 2003 , pp. 162–163). The criteria underlying these typologies mainly have to do with the degree of mixing (e.g., are methods mixed throughout the research project or not?), the timing (e.g., sequential or concurrent mixing of methods) and the emphasis (e.g., is one approach dominant, or do they have equal status?).

We find this strong drive to develop methodologies, definitions, and typologies of MMR as guides to valid mixed methods research problematic. What it amounts to in practice is a methodology that lays out the basic guidelines for doing MMR in a “proper way.” This entails the danger of straight-jacketing reflection about the use of methods, decoupling it from theoretical and empirical considerations, thus favouring the unreflexive use of a standard methodology. Researchers are asked to make a choice for a particular MMR design and adhere to the guidelines for a “proper” MMR study. Such methodological prescription diametrically opposes the initial critique of the mechanical and unreflexive use of methods. The insight offered by Bourdieu’s notion of reflexivity is, on the contrary, that the actual research practice is fundamentally open in terms of being guided by a logic of practice that cannot be captured by a preconceived and all-encompassing logic independent of that practice. Reflexivity in this view cannot be achieved by hiding behind the construct of a standardized methodology—of whatever signature—it can only be achieved by objectifying the process of objectification that goes on within the context of the field in which the researcher is embedded. This reflexivity, then, requires an analysis of the position of the researcher as a critical component of the research process, both as the embodiment of past choices that have consequences for the strategic position in the scientific field, and as predispositions regarding the choice for the subject and content of a research project. By adding the insight of STS researchers that the point of deconstructing science and technology is not so much to offer a new best way of doing science or technology, but to provide insights into the critical moments in research (for a take on such a debate, see, for example, Edge 1995 , pp. 16–20), this calls for a sociology of science that takes methods much more seriously as objects of study. Such a programme should be based on studying the process of codification and standardization of methods in their historical context of production, circulation, and use. It would provide a basis for a sociological understanding of methods that can illuminate the critical moments in research alluded to above, enabling a systematic reflection on the process of objectification. This, in turn, allows a more sophisticated validation of using—and combining—methods than relying on prescribed methodologies.

The role of epistemology

The second theme discussed in a large number of contributions is the role epistemology plays in MMR. In a sense, epistemology provides the lifeblood for MMR in that methods in MMR are mainly seen in epistemological terms. This interpretation of methods is at the core of the knowledge claim of MMR practitioners, i.e., that the mixing of methods means mixing broad, different ways of knowing, which leads to better knowledge of the research object. It is also part of the identity that MMR consciously assumes, and that serves to set it apart from previous, more practical attempts to combine methods. This can be seen in the historical overview that Creswell and Plano Clark ( 2011 ) presented and that was discussed above. This reading, in which combining methods has evolved from the rather unproblematic level (one could alternatively say “naïve” or “unaware”) of instrumental use of various tools and techniques into an act that requires deeper thinking on a methodological and epistemological level, provides the legitimacy of MMR.

At the core of the MMR approach we thus find that methods are seen as unproblematic representations of different epistemologies. But this leads to a paradox, since the epistemological frameworks need to be held flexible enough to allow researchers to integrate elements of each of them (in the shape of methods) into one MMR design. As a consequence, the issue becomes the following: methods need to be disengaged from too strict an interpretation of the epistemological context in which they were developed in order for them to be “mixable,”’, but, at the same time, they must keep the epistemology attributed to them firmly intact.

In the MMR discourse two epistemological positions are identified that matter most: a positivist approach that gives rise to quantitative methods and a constructivist approach that is home to qualitative methods. For MMR to be a feasible endeavour, the differences between both forms of research must be defined as reconcilable. This position necessitates an engagement with those who hold that the quantitative/qualitative dichotomy is unbridgeable. Within MMR an interesting way of doing so has emerged. In the first issue of the Journal of Mixed Methods Research, Morgan ( 2007 ) frames the debate about research methodology in the social sciences in terms of Kuhnian paradigms, and he argues that the pioneers of the emancipation of qualitative research methods used a particular interpretation of the paradigm-concept to state their case against the then dominant paradigm in the social sciences. According to Morgan, they interpreted a paradigm mainly in metaphysical terms, stressing the connections among the trinity of ontology, epistemology, and methodology as used in the philosophy of knowledge (Morgan 2007 , p. 57). This allowed these scholars to depict the line between research traditions in stark, contrasting terms, using Kuhn’s idea of “incommensurability” in the sense of its “early Kuhn” interpretation. This strategy fixed the contrast between the proposed alternative approach (a “constructivist paradigm”), and the traditional approach (constructed as “the positivist paradigm”) to research as a whole, and offered the alternative approach as a valid option rooted in the philosophy of knowledge. Morgan focuses especially on the work of Egon Guba and Yvonne Lincoln who developed what they initially termed a “naturalistic paradigm” as an alternative to their perception of positivism in the social sciences (e.g., Guba and Lincoln 1985 ). Footnote 9 MMR requires a more flexible or “a-paradigmatic stance” towards research, which would entail that “in real-world practice, methods can be separated from the epistemology out of which they emerged” (Patton 2002 , quoted in Tashakkori and Teddlie 2010b , p. 14).

This proposal of an ‘interpretative flexibility’ (Bijker 1987 , 1997 ) regarding paradigms is an interesting proposition. But it immediately raises the question: why stop there? Why not take a deeper look into the epistemological technology of methods themselves, to let the muted components speak up in order to look for alternative “mixing interfaces” that could potentially provide equally valid benefits in terms of the understanding of a research object? The answer, of course, was already seen above. It is that the MMR approach requires situating methods epistemologically in order to keep them intact as unproblematic mediators of specific epistemologies and, thus, make the methodological prescriptions work. There are several problems with this. First, seeing methods solely through an epistemological lens is problematic, but it would be less consequential if it were applied to multiple elements of methods separately. This would at least allow a look under the hood of a method, and new ways of mixing methods could be opened up that go beyond the crude “qualitative” versus “quantitative” dichotomy. Second, there is also the issue of the ontological dimension of methods that is disregarded in an exclusively epistemological framing of methods (e.g., Law 2004 ). Taking this ontological dimension seriously has at least two important facets. First, it draws attention to the ontological assumptions that are woven into methods in their respective fields of production and that are imported into fields of users. Second, it entails the ontological consequences of practising methods: using, applying, and referring to methods and the realities this produces. This latter facet brings the world-making and boundary-drawing capacities of methods to the fore. Both facets are ignored in MMR. We say more about the first facet in the next section. With regard to the second facet, a crucial element concerns the data that are generated, collected, and analysed in a research project. But rather than problematizing the link between the performativity of methods and the data that are enacted within the frame of a method, here too MMR relies on a dichotomy: that between quantitative and qualitative data. Methods are primarily viewed as ways of gathering data or as analytic techniques dealing with a specific kind of data. Methods and data are conceptualised intertwiningly: methods too are seen as either quantitative or qualitative (often written as QUANT and QUAL in the literature), and perform the role of linking epistemology and data. In the final analysis, the MMR approach is based on the epistemological legitimization of the dichotomy between qualitative and quantitative data in order to define and combine methods: data obtain epistemological currency through the supposed in-severable link to certain methods, and methods are reduced to the role of acting as neutral mediators between them.

In this way, methods are effectively reduced to, on the one hand, placeholders for epistemological paradigms and, on the other hand, mediators between one kind of data and the appropriate epistemology. To put it bluntly, the name “mixed methods research” is actually a misnomer, because what is mixed are paradigms or “approaches,” not methods. Thus, the act of mixing methods à la MMR has the paradoxical effect of encouraging a crude black box approach to methods. This is a third problematic characteristic of MMR, because it hinders a detailed study of methods that can lead to a much richer perspective on mixing methods.

Black boxed methods and how to open them

The third problem that we identified with the MMR approach, then, is that with the impetus to standardize the MMR methodology by fixing methods epistemologically, complemented by a dichotomous view of data, they are, in the words of philosopher Bruno Latour, “blackboxed.” This is a peculiar result of the prescription for mixing methods as proposed by MMR that thus not only denies practice and the ontological dimensions of methods and data, but also casts methods in the role of unyielding black boxes. Footnote 10 With this in mind, it will come as no surprise that most foundational contributions to the MMR literature do not explicitly define what a method is, nor that they do not provide an elaborative historical account of individual methods. The particular framing of methods in MMR results in a blind spot for the historical and social context of the production and circulation of methods as intellectual products. Instead it chooses to reify the boundaries that are drawn between “qualitative” and “quantitative” methods and reproduce them in the methodology it proposes. Footnote 11 This is an example of “circulation without context” (Bourdieu 2002 , p. 4): classifications that are constructed in the field of use or reception without taking the constellation within the field of production seriously.

Of course, this does not mean that the reality of the differences between quantitative and qualitative research must be denied. These labels are sticky and symbolically laden. They have come, in many ways, to represent “two cultures” (Goertz and Mahony 2012 ) of research, institutionalised in academia, and the effects of nominally “belonging” to (or being assigned to) one particular category have very real consequences in terms of, for instance, access to research grants and specific journals. However, if the goal of an approach such as MMR is to open up new pathways in social science research, (and why should that not be the case?) it is hard to see how that is accomplished by defining the act of combining methods solely in terms of reified differences between research using qualitative and quantitative data. In our view, methods are far richer and more interesting constructs than that, and a practice of combining methods in research should reflect that. Footnote 12

Addressing these problems entices a reflection on methods and using (multiple) methods that is missing in the MMR perspective. A fruitful way to open up the black boxes and take into account the epistemological and ontological facets of methods is to make them, and their use, the object of sociological-historical investigation. Methods are constituted through particular practices. In Bourdieusian terms, they are objectifications of the subjectively understood practices of scientists “in other fields.” Rather than basing a practice of combining methods on an uncritical acceptance of the historically grown classification of types of social research (and using these as the building stones of a methodology of mixing methods), we propose the development of a multifaceted approach that is based on a study of the different socio-historical contexts and practices in which methods developed and circulated.

A sociological understanding of methods based on these premises provides the tools to break with the dichotomously designed interface for combining methods in MMR. Instead, focusing on the historical and social contexts of production and use can reveal the traces that these contexts leave, both in the internal structure of methods, how they are perceived, how they are put into practice, and how this practice informs the ontological effects of methods. Seeing methods as complex technologies, with a history that entails the struggles among the different agents involved in their production, and use opens the way to identify multiple interfaces for combining them: the one-sided boxes become polyhedra. The critical study of methods as “objects of objectification” also entices analyses of the way in which methods intervene between subject (researcher) and object and the way in which different methods are employed in practice to draw this boundary differently. The reflexive position generated by such a systematic juxtaposition of methods is a fruitful basis to come to a richer perspective on combining methods.

We critically reviewed the emerging practice of combining methods under the label of MMR. MMR challenges the mono-method approaches that are still dominant in the social sciences, and this is both refreshing and important. Combining methods should indeed be taken much more seriously in the social sciences.

However, the direction that the practice of combining methods is taking under the MMR approach seems problematic to us. We identified three main concerns. First, MMR scholars seem to be committed to designing a standardized methodological framework for combining methods. This is unfortunate, since it amounts to enforcing an unnecessary codification of aspects of research practices that should not be formally standardized. Second, MMR constructs methods as unproblematic representations of an epistemology. Although methods must be separable from their native epistemology for MMR to work, at the same time they have to be nested within a qualitative or a quantitative research approach, which are characterized by the data they use. By this logic, combining quantitative methods with other quantitative methods, or qualitative methods with other qualitative methods, cannot offer the same benefits: they originate from the same way of viewing and knowing the world, so it would have the same effect as blending two gradations of the same colour paint. The importance attached to the epistemological grounding of methods and data in MMR also disregards the ontological aspects of methods. In this article, we are arguing that this one-sided perspective is problematic. Seeing combining methods as equivalent to combining epistemologies that are somehow pure and internally homogeneous because they can be placed in a qualitative or quantitative framework essentially amounts to reifying these categories.

It also leads to the third problem: the black boxing of methods as neutral mediators between these epistemologies and data. This not only constitutes a problem for trying to understand methods as intellectual products, but also for regarding the practice of combining methods, because it ignores the social-historical context of the development of individual methods and hinders a sociologically grounded notion of combining methods.

We proceed from a different perspective on methods. In our view, methods are complex constructions. They are world-making technologies that encapsulate different assumptions on causality, rely on different conceptual relations and categorizations, allow for different degrees of emergence, and employ different theories of the data that they internalise as objects of analysis. Even more importantly, their current form as intellectual products cannot be separated from the historical context of their production, circulation, and use.

A fully developed exposition of such an approach will have to await further work. Footnote 13 So far, the sociological study of methods has not (yet) developed into a consistent research programme, but important elements can be derived from existing contributions such as MacKenzie ( 1981 ), Chapoulie ( 1984 ), Platt ( 1996 ), Freeman ( 2004 ), and Desrosières ( 2008a , b ). The work on the “social life of methods” (e.g., Savage 2013 ) also contains important leads for the development of a systematic sociological approach to method production and circulation. Based on the discussion in this article and the contributions listed above, some tantalizing questions can be formulated. How are methods and their elements objectified? How are epistemology and ontology defined in different fields and how do those definitions feed into methods? How do they circulate and how are they translated and used in different contexts? What are the main controversies in fields of users and how are these related to the field of production? What are the homologies between these fields?

Setting out to answer these questions opens up the possibility of exploring other interesting combinations of methods that emerge from the combination of different practices, situated in different historical and epistemological contexts, and with their unique set of interpretations regarding their constituent elements. One of these must surely be the data-theoretical elements that different methods incorporate. The problematization of data has become all the more pressing now that the debate about the consequences of “big data” for social scientific practices has become prominent (Savage and Burrows 2007 ; Levallois et al. 2013 ; Burrows and Savage 2014 ). Whereas MMR emphasizes the dichotomy between qualitative and quantitative data, a historical analysis of the production and use of methods can explore the more subtle, different interpretations and enactments of the “same” data. These differences inform method construction, controversies surrounding methods and, hence, opportunities for combining methods. These could then be constructed based on alternative conceptualisations of data. Again, while in some contexts it might be enlightening to rely on the distinction between data as qualitative or quantitative, and to combine methods based on this categorization, it is an exciting possibility that in other research contexts other conceptualisations of data might be of more value to enhance a specific (contextual) form of knowledge.

Change history

06 may 2019.

Unfortunately, figure 2 was incorrectly published.

The search term used was “mixed method*” in the “topic” search field of SSCI, A&HCI, and CPCI-SSH as contained in the Web of Science. A Google NGram search (not shown) confirmed this pattern. The results of a search for “mixed methods” and “mixed methods research” showed a very steep increase after 1994: in the first case, the normalized share in the total corpus increased by 855% from 1994 till 2008. Also, Creswell ( 2012 ) reports an almost hundred-fold increase in the number of theses and dissertations with mixed methods’ in the citation and abstract (from 26 in 1990–1994 to 2524 in 2005–2009).

Retrieved from https://uk.sagepub.com/en-gb/eur/journal-of-mixed-methods-research/journal201775#aims-and-scope on 1/17/2019.

In terms of antecedents of mixed methods research, it is interesting to note that Bourdieu, whose sociology of science we draw on, was, from his earliest studies in Algeria onwards, a strong advocate of combining research methods. He made it into a central characteristic of his approach to social science in Bourdieu et al. ( 1991 [1968]). His approach, as we see below, was very different from the one now proposed under the banner of MMR. Significantly, there is no mention of Bourdieu’s take on combining methods in any of the sources we studied.

Morse’s example in particular warns us that restricting the analysis to the authors that have published in the JMMR runs the risk of missing some important contributors to the spread of MMR through the social sciences. On her website, Morse lists 11 publications (journal articles, book chapters, and books) that explicitly make reference to mixed methods (and a substantial number of other publications are about methodological aspects of research), so the fact that she has not (yet) published in the JMMR cannot, by itself, be taken as an indication of a lesser involvement with the practice of combining methods. See the website of Janice Morse at https://faculty.utah.edu/u0556920-Janice_Morse_RN,_PhD,_FAAN/hm/index.hml accessed 1/17/2019.

Bourdieu ( 1999 , p. 26) mentions that one has to be a scientific capitalist to be able to start a scientific revolution. But here he refers explicitly to the autonomy of the scientific field, making it virtually impossible for amateurs to stand up against the historically accumulated capital in the field and incite a revolution.

The themes summarize the key issues through which MMR as a group comes “into difference” (Bourdieu 1993 , p. 32). Of course, as in any (sub)field, the agents identified above often differ in their opinions on some of these key issues or disagree on the answer to the question if there should be a high degree of convergence of opinions at all. For instance, Bryman ( 2009 ) worried that MMR could become “a ghetto.” For him, the institutional landmarks of having a journal, conferences, and a handbook increase the risk of “not considering the whole range of possibilities.” He added: “I don’t regard it as a field, I kind of think of it as a way of thinking about how you go about research.” (Bryman, cited in Leech 2010 , p. 261). It is interesting to note that Bryman, like fellow sociologists Morgan and Denscombe, had published only one paper in the JMMR by the end of 2016 (Bryman passed away in June of 2017). Although these papers are among the most cited papers in the journal (see Table 1 ), this low number is consistent with the more eclectic approach that Bryman proposed.

Johnson, Onwuegbuzie, and Turner ( 2007 , p. 123).

Guba and Lincoln ( 1985 ) discuss the features of their version of a positivistic approach mainly in ontological and epistemological terms, but they are also careful to distinguish the opposition between naturalistic and positivist approaches from the difference between what they call the quantitative and the qualitative paradigms. Since they go on to state that, in principle, quantitative methods can be used within a naturalistic approach (although in practice, qualitative methods would be preferred by researchers embracing this paradigm), they seem to locate methods on a somewhat “lower,” i.e., less incommensurable level. However, in their later work (both together as well as with others or individually) and that of others in their wake, there seems to have been a shift towards a stricter interpretation of the qualitative/quantitative divide in metaphysical terms, enabling Teddlie and Tashakkori (2010b) to label this group “purists” (Tashakkori and Teddlie 2010b , p. 13).

See, for instance, Onwuegbuzie et al.’s ( 2011 ) classification of 58 qualitative data analysis techniques and 18 quantitative data analysis techniques.

This can also be seen in Morgan’s ( 2018 ) response to Sandelowski’s ( 2014 ) critique of the binary distinctions in MMR between qualitative and quantitative research approaches and methods. Morgan denounces the essentialist approach to categorizing qualitative and quantitative research in favor of a categorization based on “family resemblances,” in which he draws on Wittgenstein. However, this denies the fact that the essentialist way of categorizing is very common in the MMR corpus, particularly in textbooks and manuals (e.g., Plano Clark and Ivankova 2016 ). Moreover, and more importantly, he still does not extend this non-essentialist model of categorization to the level of methods, referring, for instance, to the different strengths of qualitative and quantitative methods in mixed methods studies (Morgan 2018 , p. 276).

While it goes beyond the scope of this article to delve into the history of the qualitative-quantitative divide in the social sciences, some broad observations can be made here. The history of method use in the social sciences can briefly be summarized as first, a rather fluid use of what can retrospectively be called different methods in large scale research projects—such as the Yankee City study of Lloyd Warner and his associates (see Platt 1996 , p. 102), the study on union democracy of Lipset et al. ( 1956 ), and the Marienthal study by Lazarsfeld and his associates (Jahoda et al. 1933 ); see Brewer and Hunter ( 2006 , p. xvi)—followed by an increasing emphasis on quantitative data and the objectification and standardization of methods. The rise of research using qualitative data can be understood as a reaction against this use and interpretation of method in the social sciences. However, out of the ensuing clash a new, still dominant classification of methods emerged, one that relies on the framing of methods as either “qualitative” or “quantitative.” Moreover, these labels have become synonymous with epistemological positions that are reproduced in MMR.

A proposal to come to such an approach can be found in Timans ( 2015 ).

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Acknowledgments

This research is part of the Interco-SSH project, funded by the European Union under the 7th Research Framework Programme (grant agreement no. 319974). Johan Heilbron would like to thank Louise and John Steffens, members of the Friends Founders’ Circle, who assisted his stay at the Princeton Institute for Advanced Study in 2017-18 during which he completed his part of the present article.

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Timans, R., Wouters, P. & Heilbron, J. Mixed methods research: what it is and what it could be. Theor Soc 48 , 193–216 (2019). https://doi.org/10.1007/s11186-019-09345-5

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By blending both quantitative and qualitative data, mixed methods research allows for a more thorough exploration of a research question. It can answer complex research queries that cannot be solved with either qualitative or quantitative research .

Analyze your mixed methods research

Dovetail streamlines analysis to help you uncover and share actionable insights

Mixed methods research combines the elements of two types of research: quantitative and qualitative.

Quantitative data is collected through the use of surveys and experiments, for example, containing numerical measures such as ages, scores, and percentages. 

Qualitative data involves non-numerical measures like beliefs, motivations, attitudes, and experiences, often derived through interviews and focus group research to gain a deeper understanding of a research question or phenomenon.

Mixed methods research is often used in the behavioral, health, and social sciences, as it allows for the collection of numerical and non-numerical data.

• When to use mixed methods research

Mixed methods research is a great choice when quantitative or qualitative data alone will not sufficiently answer a research question. By collecting and analyzing both quantitative and qualitative data in the same study, you can draw more meaningful conclusions. 

There are several reasons why mixed methods research can be beneficial, including generalizability, contextualization, and credibility. 

For example, let's say you are conducting a survey about consumer preferences for a certain product. You could collect only quantitative data, such as how many people prefer each product and their demographics. Or you could supplement your quantitative data with qualitative data, such as interviews and focus groups , to get a better sense of why people prefer one product over another.

It is important to note that mixed methods research does not only mean collecting both types of data. Rather, it also requires carefully considering the relationship between the two and method flexibility.

You may find differing or even conflicting results by combining quantitative and qualitative data . It is up to the researcher to then carefully analyze the results and consider them in the context of the research question to draw meaningful conclusions.

When designing a mixed methods study, it is important to consider your research approach, research questions, and available data. Think about how you can use different techniques to integrate the data to provide an answer to your research question.

• Mixed methods research design

A mixed methods research design  is   an approach to collecting and analyzing both qualitative and quantitative data in a single study.

Mixed methods designs allow for method flexibility and can provide differing and even conflicting results. Examples of mixed methods research designs include convergent parallel, explanatory sequential, and exploratory sequential.

By integrating data from both quantitative and qualitative sources, researchers can gain valuable insights into their research topic . For example, a study looking into the impact of technology on learning could use surveys to measure quantitative data on students' use of technology in the classroom. At the same time, interviews or focus groups can provide qualitative data on students' experiences and opinions.

• Types of mixed method research designs

Researchers often struggle to put mixed methods research into practice, as it is challenging and can lead to research bias. Although mixed methods research can reveal differences or conflicting results between studies, it can also offer method flexibility.

Designing a mixed methods study can be broken down into four types: convergent parallel, embedded, explanatory sequential, and exploratory sequential.

Convergent parallel

The convergent parallel design is when data collection and analysis of both quantitative and qualitative data occur simultaneously and are analyzed separately. This design aims to create mutually exclusive sets of data that inform each other. 

For example, you might interview people who live in a certain neighborhood while also conducting a survey of the same people to determine their satisfaction with the area.

Embedded design

The embedded design is when the quantitative and qualitative data are collected simultaneously, but the qualitative data is embedded within the quantitative data. This design is best used when you want to focus on the quantitative data but still need to understand how the qualitative data further explains it.

For instance, you may survey students about their opinions of an online learning platform and conduct individual interviews to gain further insight into their responses.

Explanatory sequential design

In an explanatory sequential design, quantitative data is collected first, followed by qualitative data. This design is used when you want to further explain a set of quantitative data with additional qualitative information.

An example of this would be if you surveyed employees at a company about their satisfaction with their job and then conducted interviews to gain more information about why they responded the way they did.

Exploratory sequential design

The exploratory sequential design collects qualitative data first, followed by quantitative data. This type of mixed methods research is used when the goal is to explore a topic before collecting any quantitative data.

An example of this could be studying how parents interact with their children by conducting interviews and then using a survey to further explore and measure these interactions.

Integrating data in mixed methods studies can be challenging, but it can be done successfully with careful planning.

No matter which type of design you choose, understanding and applying these principles can help you draw meaningful conclusions from your research.

• Strengths of mixed methods research

Mixed methods research designs combine the strengths of qualitative and quantitative data, deepening and enriching qualitative results with quantitative data and validating quantitative findings with qualitative data. This method offers more flexibility in designing research, combining theory generation and hypothesis testing, and being less tied to disciplines and established research paradigms.

Take the example of a study examining the impact of exercise on mental health. Mixed methods research would allow for a comprehensive look at the issue from different angles. 

Researchers could begin by collecting quantitative data through surveys to get an overall view of the participants' levels of physical activity and mental health. Qualitative interviews would follow this to explore the underlying dynamics of participants' experiences of exercise, physical activity, and mental health in greater detail.

Through a mixed methods approach, researchers could more easily compare and contrast their results to better understand the phenomenon as a whole.  

Additionally, mixed methods research is useful when there are conflicting or differing results in different studies. By combining both quantitative and qualitative data, mixed methods research can offer insights into why those differences exist.

For example, if a quantitative survey yields one result while a qualitative interview yields another, mixed methods research can help identify what factors influence these differences by integrating data from both sources.

Overall, mixed methods research designs offer a range of advantages for studying complex phenomena. They can provide insight into different elements of a phenomenon in ways that are not possible with either qualitative or quantitative data alone. Additionally, they allow researchers to integrate data from multiple sources to gain a deeper understanding of the phenomenon in question.  

• Challenges of mixed methods research

Mixed methods research is labor-intensive and often requires interdisciplinary teams of researchers to collaborate. It also has the potential to cost more than conducting a stand alone qualitative or quantitative study . 

Interpreting the results of mixed methods research can be tricky, as it can involve conflicting or differing results. Researchers must find ways to systematically compare the results from different sources and methods to avoid bias.

For example, imagine a situation where a team of researchers has employed an explanatory sequential design for their mixed methods study. After collecting data from both the quantitative and qualitative stages, the team finds that the two sets of data provide differing results. This could be challenging for the team, as they must now decide how to effectively integrate the two types of data in order to reach meaningful conclusions. The team would need to identify method flexibility and be strategic when integrating data in order to draw meaningful conclusions from the conflicting results.

• Advanced frameworks in mixed methods research

Mixed methods research offers powerful tools for investigating complex processes and systems, such as in health and healthcare.

Besides the three basic mixed method designs—exploratory sequential, explanatory sequential, and convergent parallel—you can use one of the four advanced frameworks to extend mixed methods research designs. These include multistage, intervention, case study , and participatory. 

This framework mixes qualitative and quantitative data collection methods in stages to gather a more nuanced view of the research question. An example of this is a study that first has an online survey to collect initial data and is followed by in-depth interviews to gain further insights.

Intervention

This design involves collecting quantitative data and then taking action, usually in the form of an intervention or intervention program. An example of this could be a research team who collects data from a group of participants, evaluates it, and then implements an intervention program based on their findings .

This utilizes both qualitative and quantitative research methods to analyze a single case. The researcher will examine the specific case in detail to understand the factors influencing it. An example of this could be a study of a specific business organization to understand the organizational dynamics and culture within the organization.

Participatory

This type of research focuses on the involvement of participants in the research process. It involves the active participation of participants in formulating and developing research questions, data collection, and analysis.

An example of this could be a study that involves forming focus groups with participants who actively develop the research questions and then provide feedback during the data collection and analysis stages.

The flexibility of mixed methods research designs means that researchers can choose any combination of the four frameworks outlined above and other methodologies , such as convergent parallel, explanatory sequential, and exploratory sequential, to suit their particular needs.

Through this method's flexibility, researchers can gain multiple perspectives and uncover differing or even conflicting results when integrating data.

When it comes to integration at the methods level, there are four approaches.

Connecting involves collecting both qualitative and quantitative data during different phases of the research.

Building involves the collection of both quantitative and qualitative data within a single phase.

Merging involves the concurrent collection of both qualitative and quantitative data.

Embedding involves including qualitative data within a quantitative study or vice versa.

• Techniques for integrating data in mixed method studies

Integrating data is an important step in mixed methods research designs. It allows researchers to gain further understanding from their research and gives credibility to the integration process. There are three main techniques for integrating data in mixed methods studies: triangulation protocol, following a thread, and the mixed methods matrix.

Triangulation protocol

This integration method combines different methods with differing or conflicting results to generate one unified answer.

For example, if a researcher wanted to know what type of music teenagers enjoy listening to, they might employ a survey of 1,000 teenagers as well as five focus group interviews to investigate this. The results might differ; the survey may find that rap is the most popular genre, whereas the focus groups may suggest rock music is more widely listened to. 

The researcher can then use the triangulation protocol to come up with a unified answer—such as that both rap and rock music are popular genres for teenage listeners. 

Following a thread

This is another method of integration where the researcher follows the same theme or idea from one method of data collection to the next. 

A research design that follows a thread starts by collecting quantitative data on a specific issue, followed by collecting qualitative data to explain the results. This allows whoever is conducting the research to detect any conflicting information and further look into the conflicting information to understand what is really going on.

For example, a researcher who used this research method might collect quantitative data about how satisfied employees are with their jobs at a certain company, followed by qualitative interviews to investigate why job satisfaction levels are low. They could then use the results to explore any conflicting or differing results, allowing them to gain a deeper understanding of job satisfaction at the company. 

By following a thread, the researcher can explore various research topics related to the original issue and gain a more comprehensive view of the issue.

Mixed methods matrix

This technique is a visual representation of the different types of mixed methods research designs and the order in which they should be implemented. It enables researchers to quickly assess their research design and adjust it as needed. 

The matrix consists of four boxes with four different types of mixed methods research designs: convergent parallel, explanatory sequential, exploratory sequential, and method flexibility. 

For example, imagine a researcher who wanted to understand why people don't exercise regularly. To answer this question, they could use a convergent parallel design, collecting both quantitative (e.g., survey responses) and qualitative (e.g., interviews) data simultaneously.

If the researcher found conflicting results, they could switch to an explanatory sequential design and collect quantitative data first, then follow up with qualitative data if needed. This way, the researcher can make adjustments based on their findings and integrate their data more effectively.

Mixed methods research is a powerful tool for understanding complex research topics. Using qualitative and quantitative data in one study allows researchers to understand their subject more deeply. 

Mixed methods research designs such as convergent parallel, explanatory sequential, and exploratory sequential provide method flexibility, enabling researchers to collect both types of data while avoiding the limitations of either approach alone.

However, it's important to remember that mixed methods research can produce differing or even conflicting results, so it's important to be aware of the potential pitfalls and take steps to ensure that data is being correctly integrated. If used effectively, mixed methods research can offer valuable insight into topics that would otherwise remain largely unexplored.

What is an example of mixed methods research?

An example of mixed methods research is a study that combines quantitative and qualitative data. This type of research uses surveys, interviews, and observations to collect data from multiple sources.

Which sampling method is best for mixed methods?

It depends on the research objectives, but a few methods are often used in mixed methods research designs. These include snowball sampling, convenience sampling, and purposive sampling. Each method has its own advantages and disadvantages.

What is the difference between mixed methods and multiple methods?

Mixed methods research combines quantitative and qualitative data in a single study. Multiple methods involve collecting data from different sources, such as surveys and interviews, but not necessarily combining them into one analysis. Mixed methods offer greater flexibility but can lead to differing or conflicting results when integrating data.

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

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Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• Allison Shorten 1 ,
• Joanna Smith 2
• 1 School of Nursing , University of Alabama at Birmingham , USA
• 2 Children's Nursing, School of Healthcare , University of Leeds , UK
• Correspondence to Dr Allison Shorten, School of Nursing, University of Alabama at Birmingham, 1720 2nd Ave South, Birmingham, AL, 35294, USA; [email protected]; ashorten{at}uab.edu

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Introduction

‘Mixed methods’ is a research approach whereby researchers collect and analyse both quantitative and qualitative data within the same study. 1 2 Growth of mixed methods research in nursing and healthcare has occurred at a time of internationally increasing complexity in healthcare delivery. Mixed methods research draws on potential strengths of both qualitative and quantitative methods, 3 allowing researchers to explore diverse perspectives and uncover relationships that exist between the intricate layers of our multifaceted research questions. As providers and policy makers strive to ensure quality and safety for patients and families, researchers can use mixed methods to explore contemporary healthcare trends and practices across increasingly diverse practice settings.

What is mixed methods research?

Mixed methods research requires a purposeful mixing of methods in data collection, data analysis and interpretation of the evidence. The key word is ‘mixed’, as an essential step in the mixed methods approach is data linkage, or integration at an appropriate stage in the research process. 4 Purposeful data integration enables researchers to seek a more panoramic view of their research landscape, viewing phenomena from different viewpoints and through diverse research lenses. For example, in a randomised controlled trial (RCT) evaluating a decision aid for women making choices about birth after caesarean, quantitative data were collected to assess knowledge change, levels of decisional conflict, birth choices and outcomes. 5 Qualitative narrative data were collected to gain insight into women’s decision-making experiences and factors that influenced their choices for mode of birth. 5

In contrast, multimethod research uses a single research paradigm, either quantitative or qualitative. Data are collected and analysed using different methods within the same paradigm. 6 7 For example, in a multimethods qualitative study investigating parent–professional shared decision-making regarding diagnosis of suspected shunt malfunction in children, data collection included audio recordings of admission consultations and interviews 1 week post consultation, with interactions analysed using conversational analysis and the framework approach for the interview data. 8

What are the strengths and challenges in using mixed methods?

Selecting the right research method starts with identifying the research question and study aims. A mixed methods design is appropriate for answering research questions that neither quantitative nor qualitative methods could answer alone. 4 9–11 Mixed methods can be used to gain a better understanding of connections or contradictions between qualitative and quantitative data; they can provide opportunities for participants to have a strong voice and share their experiences across the research process, and they can facilitate different avenues of exploration that enrich the evidence and enable questions to be answered more deeply. 11 Mixed methods can facilitate greater scholarly interaction and enrich the experiences of researchers as different perspectives illuminate the issues being studied. 11

The process of mixing methods within one study, however, can add to the complexity of conducting research. It often requires more resources (time and personnel) and additional research training, as multidisciplinary research teams need to become conversant with alternative research paradigms and different approaches to sample selection, data collection, data analysis and data synthesis or integration. 11

What are the different types of mixed methods designs?

Mixed methods research comprises different types of design categories, including explanatory, exploratory, parallel and nested (embedded) designs. 2   Table 1 summarises the characteristics of each design, the process used and models of connecting or integrating data. For each type of research, an example was created to illustrate how each study design might be applied to address similar but different nursing research aims within the same general nursing research area.

• View inline

Types of mixed methods designs*

What should be considered when evaluating mixed methods research?

When reading mixed methods research or writing a proposal using mixed methods to answer a research question, the six questions below are a useful guide 12 :

Does the research question justify the use of mixed methods?

Is the method sequence clearly described, logical in flow and well aligned with study aims?

Is data collection and analysis clearly described and well aligned with study aims?

Does one method dominate the other or are they equally important?

Did the use of one method limit or confound the other method?

When, how and by whom is data integration (mixing) achieved?

For more detail of the evaluation guide, refer to the McMaster University Mixed Methods Appraisal Tool. 12 The quality checklist for appraising published mixed methods research could also be used as a design checklist when planning mixed methods studies.

• Elliot AE , et al
• Creswell JW ,
• Plano ClarkV L
• Greene JC ,
• Caracelli VJ ,
• Ivankova NV
• Shorten A ,
• Shorten B ,
• Halcomb E ,
• Cheater F ,
• Bekker H , et al
• Tashakkori A ,
• Creswell JW
• 12. ↵ National Collaborating Centre for Methods and Tools . Appraising qualitative, quantitative, and mixed methods studies included in mixed studies reviews: the MMAT . Hamilton, ON : BMJ Publishing Group , 2015 . http://www.nccmt.ca/resources/search/232 (accessed May 2017) .

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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Mixed methods research.

According to the National Institutes of Health , mixed methods strategically integrates or combines rigorous quantitative and qualitative research methods to draw on the strengths of each. Mixed method approaches allow researchers to use a diversity of methods, combining inductive and deductive thinking, and offsetting limitations of exclusively quantitative and qualitative research through a complementary approach that maximizes strengths of each data type and facilitates a more comprehensive understanding of health issues and potential resolutions.¹ Mixed methods may be employed to produce a robust description and interpretation of the data, make quantitative results more understandable, or understand broader applicability of small-sample qualitative findings.

Integration

This refers to the ways in which qualitative and quantitative research activities are brought together to achieve greater insight. Mixed methods is not simply having quantitative and qualitative data available or analyzing and presenting data findings separately. The integration process can occur during data collection, analysis, or in the presentation of results.

¹ NIH Office of Behavioral and Social Sciences Research: Best Practices for Mixed Methods Research in the Health Sciences

Basic Mixed Methods Research Designs 

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Five Key Questions for Getting Started

• What do you want to know?
• What will be the detailed quantitative, qualitative, and mixed methods research questions that you hope to address?
• What quantitative and qualitative data will you collect and analyze?
• Which rigorous methods will you use to collect data and/or engage stakeholders?
• How will you integrate the data in a way that allows you to address the first question?

Rationale for Using Mixed Methods

• Obtain different, multiple perspectives: validation
• Build comprehensive understanding
• Explain statistical results in more depth
• Have better contextualized measures
• Track the process of program or intervention
• Study patient-centered outcomes and stakeholder engagement

Sample Mixed Methods Research Study

The EQUALITY study used an exploratory sequential design to identify the optimal patient-centered approach to collect sexual orientation data in the emergency department.

Qualitative Data Collection and Analysis : Semi-structured interviews with patients of different sexual orientation, age, race/ethnicity, as well as healthcare professionals of different roles, age, and race/ethnicity.

Builds Into : Themes identified in the interviews were used to develop questions for the national survey.

Quantitative Data Collection and Analysis : Representative national survey of patients and healthcare professionals on the topic of reporting gender identity and sexual orientation in healthcare.

Other Resources:

  Introduction to Mixed Methods Research : Harvard Catalyst’s eight-week online course offers an opportunity for investigators who want to understand and apply a mixed methods approach to their research.

Best Practices for Mixed Methods Research in the Health Sciences [PDF] : This guide provides a detailed overview of mixed methods designs, best practices, and application to various types of grants and projects.

Mixed Methods Research Training Program for the Health Sciences (MMRTP ): Selected scholars for this summer training program, hosted by Johns Hopkins’ Bloomberg School of Public Health, have access to webinars, resources, a retreat to discuss their research project with expert faculty, and are matched with mixed methods consultants for ongoing support.

Michigan Mixed Methods : University of Michigan Mixed Methods program offers a variety of resources, including short web videos and recommended reading.

To use a mixed methods approach, you may want to first brush up on your qualitative skills. Below are a few helpful resources specific to qualitative research:

• Qualitative Research Guidelines Project : A comprehensive guide for designing, writing, reviewing and reporting qualitative research.
• Fundamentals of Qualitative Research Methods – What is Qualitative Research : A six-module web video series covering essential topics in qualitative research, including what is qualitative research and how to use the most common methods, in-depth interviews, and focus groups.

View PDF of the above information.

• Open access
• Published: 28 June 2024

Perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education: a mixed methods study

• Basma Mohammed Al Yazeedi   ORCID: orcid.org/0000-0003-2327-6918 1 ,
• Lina Mohamed Wali Shakman 1 ,
• Sheeba Elizabeth John Sunderraj   ORCID: orcid.org/0000-0002-9171-7239 1 ,
• Harshita Prabhakaran   ORCID: orcid.org/0000-0002-5470-7066 1 ,
• Judie Arulappan 1 ,
• Erna Judith Roach   ORCID: orcid.org/0000-0002-5817-8886 1 ,
• Aysha Al Hashmi 1 , 2 &
• Zeinab Al Azri   ORCID: orcid.org/0000-0002-3376-9380 1  

BMC Nursing volume  23 , Article number:  441 ( 2024 ) Cite this article

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Metrics details

Case analysis is a dynamic and interactive teaching and learning strategy that improves critical thinking and problem-solving skills. However, there is limited evidence about its efficacy as an assessment strategy in nursing education.

This study aimed to explore nursing students’ perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education.

This study used a mixed methods design. Students filled out a 13-item study-advised questionnaire, and qualitative data from the four focus groups was collected. The setting of the study was the College of Nursing at Sultan Qaboos University, Oman. Descriptive and independent t-test analysis was used for the quantitative data, and the framework analysis method was used for the qualitative data.

The descriptive analysis of 67 participants showed that the mean value of the perceived efficacy of case analysis as an assessment method was 3.20 (SD = 0.53), demonstrating an 80% agreement rate. Further analysis indicated that 78.5% of the students concurred with the acceptability of case analysis as an assessment method (mean = 3.14, SD = 0.58), and 80.3% assented its association with clinical competencies as reflected by knowledge and cognitive skills (m = 3.21, SD = 0.60). No significant difference in the perceived efficacy between students with lower and higher GPAs (t [61] = 0.05, p  > 0.05) was identified Three qualitative findings were discerned: case analysis is a preferred assessment method for students when compared to MCQs, case analysis assesses students’ knowledge, and case analysis assesses students’ cognitive skills.

Conclusions

This study adds a potential for the case analysis to be acceptable and relevant to the clinical competencies when used as an assessment method. Future research is needed to validate the effectiveness of case analysis exams in other nursing clinical courses and examine their effects on academic and clinical performance.

Peer Review reports

Introduction

Nurses play a critical role in preserving human health by upholding core competencies [ 1 ]. Clinical competence in nursing involves a constant process of acquiring knowledge, values, attitudes, and abilities to deliver safe and high-quality care [ 2 , 3 ]. Nurses possessing such competencies can analyze and judge complicated problems, including those involving crucial patient care, ethical decision-making, and nurse-patient disputes, meeting the constantly altering health needs [ 4 , 5 ]. To optimize the readiness of the new graduates for the challenging clinical work environment needs, nurse leaders call for integrating clinical competencies into the nursing curriculum [ 6 , 7 ] In 2021, the American Association of Colleges of Nursing (AACN) released updated core competencies for professional nursing education [ 8 ]. These competencies were classified into ten fundamental essentials, including knowledge of nursing practice and person-centered care (e.g. integrate assessment skills in practice, diagnose actual or potential health problems and needs, develop a plan of care), representing clinical core competencies.

Nursing programs emphasize clinical competencies through innovative and effective teaching strategies, including case-based teaching (CBT) [ 9 ]. CBT is a dynamic teaching method that enhances the focus on learning goals and increases the chances of the instructor and students actively participating in teaching and learning [ 10 , 11 ]. Additionally, it improves the students’ critical thinking and problem-solving skills and enriches their capacity for independent study, cooperation capacity, and communication skills [ 12 , 13 , 14 , 15 ]. It also broadens students’ perspectives and helps develop greater creativity in fusing theory and practice [ 16 , 17 , 18 , 19 , 20 ]. As the learning environment significantly impacts the students’ satisfaction, case analysis fosters a supportive learning atmosphere and encourages active participation in learning, ultimately improving their satisfaction [ 21 , 22 ].

In addition to proper teaching strategies for clinical competencies, programs are anticipated to evaluate the students’ attainment of such competencies through effective evaluation strategies [ 23 ]. However, deploying objective assessment methods for the competencies remains challenging for most educators [ 24 ]. The standard assessment methods used in clinical nursing courses, for instance, include clinical evaluations (direct observation), skills checklists, Objective Structured Clinical Examination (OSCE), and multiple-choice questions (MCQs) written exams [ 25 ]. MCQs tend to test the recall of factual information rather than the application of knowledge and cognitive skills, potentially leading to assessment inaccuracies [ 26 ].

Given the aforementioned outcomes of CBT, the deployment of case analysis as a clinical written exam is more closely aligned with the course’s expected competencies. A mixed methods study was conducted among forty nursing students at the University of Southern Taiwan study concluded that the unfolding case studies create a safe setting where nursing students can learn and apply their knowledge to safe patient care [ 6 ]. In a case analysis, the patient’s sickness emerges in stages including the signs and symptoms of the disease, urgent care to stabilize the patient, and bedside care to enhance recovery. Thus, unfolding the case with several scenarios helps educators track students’ attained competencies [ 27 ]. However, case analysis as an assessment method is sparsely researched [ 28 ]. A literature review over the past five years yielded no studies investigating case analysis as an assessment method, necessitating new evidence. There remains uncertainty regarding its efficacy as an assessment method, particularly from the students’ perspectives [ 29 ]. In this study, we explored the undergraduate nursing students’ perceived efficacy of case analysis as an assessment method for clinical competencies. Results from this study will elucidate the position of case analysis as an assessment method in nursing education. The potential benefits are improved standardization of clinical assessment and the ability to efficiently evaluate a broad range of competencies.

Research design

Mixed-method research with a convergent parallel design was adopted in the study. This approach intends to converge two data types (quantitative and qualitative) at the interpretation stage to ensure an inclusive research problem analysis [ 30 ]. The quantitative aspect of the study was implemented through a cross-sectional survey. The survey captured the perceived efficacy of using case analysis as an assessment method in clinical nursing education. The qualitative part of the study was carried out through a descriptive qualitative method using focus groups to provide an in-depth understanding of the perceived strengths experienced by the students.

Study setting

Data were collected in the College of Nursing at Sultan Qaboos University (SQU), Oman, during the Spring and Fall semesters of 2023. At the end of each clinical course, the students have a clinical written exam and a clinical practical exam, which constitute their final exam. Most clinical courses use multiple-choice questions (MCQs) in their written exam. However, the child health clinical course team initiated the case analysis as an assessment method in the clinical written exam, replacing the MCQs format.

Participants

For this study, the investigators invited undergraduate students enrolled in the child health nursing clinical course in the Spring and Fall semesters of 2023. Currently, the only course that uses case analysis is child health. Other courses use MCQs. A total enumeration sampling technique was adopted. All the students enrolled in child health nursing clinical courses in the Spring and Fall 2023 semesters were invited to participate in the study. In the Spring, 36 students registered for the course, while 55 students were enrolled in the Fall. We included students who completed the case analysis as a final clinical written exam on the scheduled exam time. Students who did not show up for the exam during the scheduled time and students not enrolled in the course during the Spring and Fall of 2023 were excluded. Although different cases were used each semester, both had the same structure and level of complexity. Further, both cases were peer-reviewed.

Case analysis format

The format presents open-ended questions related to a clinical case scenario. It comprises three main sections: Knowledge, Emergency Room, and Ward. The questions in the sections varied in difficulty based on Bloom’s cognitive taxonomy levels, as presented in Table  1 . An answer key was generated to ensure consistency among course team members when correcting the exam. Three experts in child health nursing peer-reviewed both the case analysis exam paper and the answer key paper. The students were allocated two hours to complete the exam.

Study instruments

Quantitative stage.

The researchers developed a study questionnaire to meet the study objectives. It included two parts. The first was about the demographic data, including age, gender, type of residence, year in the program, and cumulative grade point average (GPA). The second part comprised a 13-item questionnaire assessing the perceived efficacy of case analysis as an assessment method. The perceived efficacy was represented by the acceptability of case analysis as an assessment method (Items 1–5 and 13) and the association with clinical competencies (Items 6 to 12). Acceptability involved format organization and clarity, time adequacy, alignment with course objectives, appropriateness to students’ level, and recommendation for implementation in other clinical nursing courses. Clinical competencies-related items were relevant to knowledge (motivation to prepare well for the exam, active learning, interest in topics, collaboration while studying) and cognitive skills (critical thinking, decision-making, and problem-solving skills) (The questionnaire is attached as a supplementary document).

The questionnaire is answered on a 4-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree. Higher scores indicated better perceived efficacy and vice versa. The tool underwent content validity testing with five experts in nursing clinical education, resulting in an item-content validity index ranging from 0.7 to 1. The Cronbach alpha was 0.83 for acceptability and 0.90 for clinical competencies.

Qualitative stage

For the focus group interviews, the investigators created a semi-structured interview guide to obtain an in-depth understanding of the students’ perceived strengths of case analysis as an assessment method. See Table  2 .

Data collection

Data was collected from the students after they gave their written informed consent. Students were invited to fill out the study questionnaire after they completed the case analysis as a clinical written exam.

All students in the child health course were invited to participate in focus group discussions. Students who approached the PI to participate in the focus group discussion were offered to participate in four different time slots. So, the students chose their time preferences. Four focus groups were conducted in private rooms at the College of Nursing. Two trained and bilingual interviewers attended the focus groups, one as a moderator while the other took notes on the group dynamics and non-verbal communication. The discussion duration ranged between 30 and 60 min. After each discussion, the moderator transcribed the audio recording. The transcriptions were rechecked against the audio recording for accuracy. Later, the transcriptions were translated into English by bilingual researchers fluent in Arabic and English for the analysis.

Rigor and trustworthiness

The rigor and trustworthiness of the qualitative method were enhanced using multiple techniques. Firstly, quantitative data, literature reviews, and focus groups were triangulated. Participants validated the summary after each discussion using member checking to ensure the moderator’s understanding was accurate. Third, the principal investigator (PI) reflected on her assumptions, experiences, expectations, and feelings weekly. In addition, the PI maintained a detailed audit trail of study details and progress. The nursing faculty conducted the study with experience in qualitative research and nursing education. This report was prepared following the Standard for Reporting Qualitative Research (SRQR) protocol [ 31 ].

Data analysis

Quantitative data were entered in SPSS version 24 and analyzed using simple descriptive analysis using means, standard deviations, and percentages. After computing the means of each questionnaire item, an average of the means was calculated to identify the perceived efficacy rate. A similar technique was used to calculate the rate of acceptability and clinical competencies. The percentage was calculated based on the mean: gained score/total score* 100. In addition, the investigators carried out an independent t-test to determine the relationship between the perceived efficacy and students’ GPA.

The qualitative data were analyzed using the framework analysis method. In our analysis, we followed the seven interconnected stages of framework analysis: (1) transcription, (2) familiarization with the interview, (3) coding, (4) developing a working analytical framework, (5) applying the analytical framework, (6) charting data into framework matrix and (7) interpreting the data [ 32 ]. Two members of the team separately analyzed the transcriptions. Then, they discussed the coding, and discrepancies were solved with discussion.

Mixed method integration

In our study, the quantitative and qualitative data were analyzed separately, and integration occurred at the interpretation level by merging the data [ 33 ]. As a measure of integration between qualitative and quantitative data, findings were assessed through confirmation, expansion, and discordance. If both data sets confirmed each other’s findings, it was considered confirmation, and if they expanded each other’s insight, it was considered expansion. Discordance was determined if the findings were contradictory.

Ethical considerations

Ethical approval was obtained from the Research and Ethics Committee of the College of Nursing, SQU (CON/NF/2023/18). Informed consent was collected, and no identifiable information was reported. For the focus group interviews, students were reassured that their grades were finalized, and their participation would not affect their grades. Also, the interviewers were instructed to maintain a non-judgmental and non-biased position during the interview. Data were saved in a locked cabinet inside a locked office room. The electronic data were saved in a password-protected computer.

The results section will present findings from the study’s quantitative and qualitative components. The integration of the two data types is described after each qualitative finding.

Quantitative findings

We analyzed the data of 67 participants, representing a 73.6% response rate. The mean age was 21.0 years old (SD 0.73) and 36.4% were male students. See Table  3 for more details.

The descriptive analysis showed that the mean value of the perceived efficacy of case analysis as an assessment method was 3.20 (SD = 0.53), demonstrating an 80% agreement rate. Further analysis indicated that 78.5% of the students concurred the acceptability of case analysis as an assessment method (mean = 3.14, SD = 0.58) and 80.3% (m = 3.21, SD = 0.60) assented the clinical competencies associated with it.

For the items representing acceptability, 81.8% of the students agreed that the case analysis was written clearly, and 80.3% reported that it was well organized. As per the questions, 81% described they were appropriate to their level, and 79.8% agreed upon their alignment with the course objectives. Moreover, the time allocated was adequate for 74.5% of the students, and 73.5% recommend using case analysis as an evaluation strategy for other clinical written examinations.

Regarding the clinical competencies, 77.3% of students agreed that the case analysis motivated them to prepare well for the exam, 81.3% reported that it encouraged them to be active in learning, and 81.0% indicated that it stimulated their interest in the topics discussed in the course. Additionally, 76.5% of the students agreed that the case analysis encouraged them to collaborate with other students when studying for the exam. Among the students, 82.5% reported that the case analysis as an assessment method enhanced their critical thinking skills, 81.0% agreed that it helped them practice decision-making skills, and 81.8% indicated that it improved their problem-solving abilities. See Table  4 .

The independent t-test analysis revealed no significant difference in the perceived efficacy between students with lower and higher GPAs (t [61] = 0.05, p  > 0.05). Further analysis showed that the means of acceptability and clinical competencies were not significantly different between the lower GPA group and higher GPA group, t [62] = 0.72, p  > 0.05 and t [63] = -0.83, p  > 0.05, respectively (Table  5 ).

Qualitative findings

A total of 22 had participated in four focus groups, each group had 5–6 students. The qualitative framework analysis revealed three main findings; case analysis is a preferred assessment method to students when compared to MCQs, case analysis assesses students’ knowledge, and case analysis assesses students’ cognitive skills.

Qualitative Finding 1: case analysis is a preferred assessment method to students when compared to MCQs

Most of the students’ statements about the case analysis as an assessment method were positive. One student stated, “Previously, we have MCQs in clinical exams, but they look as if they are theory exams. This exam makes me deal with cases like a patient, which is good for clinical courses.” . At the same time, many students conveyed optimism about obtaining better grades with this exam format. A student stated, “Our grades, with case analysis format, will be better, … may be because we can write more in open-ended questions, so we can get some marks, in contrast to MCQs where we may get it right or wrong” . On the other hand, a few students suggested adding multiple-choice questions, deleting the emergency department section, and lessening the number of care plans in the ward section to secure better grades.

Although the case analysis was generally acceptable to students, they have repeatedly expressed a need to allocate more time for this type of exam. A student stated, “The limited time with the type of questions was a problem, …” . When further discussion was prompted to understand this challenge, we figured that students are not used to handwriting, which has caused them to be exhausted during the exam. An example is “writing is time-consuming and energy consuming in contrast to MCQs …” . These statements elucidate that the students don’t necessarily mind writing but recommend more practice as one student stated, “More experience of this type of examination is required, more examples during clinical practice are needed.” Some even recommended adopting this format with other clinical course exams by saying “It’s better to start this method from the first year for the new cohort and to apply it in all other courses.”

Mixed Methods Inference 1: Confirmation and Expansion

The abovementioned qualitative impression supports the high acceptability rate in quantitative analysis. In fact, there is a general agreement that the case analysis format surpasses the MCQs when it comes to the proper evaluation strategies for clinical courses. Expressions in the qualitative data revealed more details, such as the limited opportunities to practice handwriting, which negatively impacted the perceived adequacy of exam time.

Qualitative Finding 2: case analysis assesses students’ knowledge

Students conferred that they were reading more about the disease pathophysiology, lab values, and nursing care plans, which they did not usually do with traditional means of examination. Examples of statements include “… before we were not paying attention to the normal lab results but …in this exam, we went back and studied them which was good for our knowledge” and “we cared about the care plan. In previous exams, we were not bothered by these care plans”. Regarding the burden that could be perceived with this type of preparation, the students expressed that this has helped them prepare for the theory course exam; as one student said, “We also focus on theory lectures to prepare for this exam …. this was very helpful to prepare us for the theory final exam as well.” However, others have highlighted the risks of limiting the exam’s content to one case analysis. The argument was that some students may have not studied the case completely or been adequately exposed to the case in the clinical setting. To solve this risk, the students themselves advocated for frequent case group discussions in the clinical setting as stated by one student: “There could be some differences in the cases that we see during our clinical posting, for that I recommend that instructors allocate some time to gather all the students and discuss different cases.” Also, the participants advocated for more paper-based case analysis exercises as it is helpful to prepare them for the exams and enhance their knowledge and skills.

Mixed Methods Inferences 2: Confirmation and Expansion

The qualitative finding supports the quantitative data relevant to items 6, 7, and 8. Students’ expressions revealed more insights, including the acquisition of deeper knowledge, practicing concept mapping, and readiness for other course-related exams. At the same time, students recommended that faculty ensure all students’ exposure to common cases in the clinical setting for fair exam preparation.

Qualitative Finding 3. case analysis assesses students’ cognitive skills

Several statements conveyed how the case analysis format helped the students use their critical thinking and analysis skills. One student stated, “It, the case analysis format, enhanced our critical thinking skills as there is a case with given data and we analyze the case….” . Therefore, the case analysis format as an exam is potentially a valid means to assess the student’s critical thinking skills. Students also conveyed that the case analysis format helped them link theory to practice and provided them with the platform to think like real nurses and be professional. Examples of statements are: “…we connect our knowledge gained from theory with the clinical experience to get the answers…” and “The questions were about managing a case, which is what actual nurses are doing daily.” Another interesting cognitive benefit to case analysis described by the students was holistic thinking. For example, one student said, “Case analysis format helped us to see the case as a whole and not only from one perspective.”

Mixed Methods Inferences 3: Confirmation

The quantitative data indicated mutual agreement among the students that the case analysis enhanced their critical thinking, decision-making, and problem-solving skills. The students’ statements from the interviews, including critical thinking, linking theory to practice, and holistic thinking, further supported these presumptions.

This research presents the findings from a mixed methods study that explored undergraduate nursing students’ perceived efficacy of using case analysis as an assessment method. The perceived efficacy was reflected through acceptability and association with two core competencies: knowledge and cognitive skills. The study findings showed a high rate of perceived efficacy of case analysis as an assessment method among nursing students. Additionally, three findings were extracted from the qualitative data that further confirmed the perceived efficacy: (1) case analysis is a preferred assessment method to students compared to MCQs, (2) case analysis assesses students’ knowledge, and (3) case analysis assesses students’ cognitive skills. Moreover, the qualitative findings revealed details that expanded the understanding of the perceived efficacy among nursing students.

Previous literature reported students’ preference for case analysis as a teaching method. A randomized controlled study investigated student’s satisfaction levels with case-based teaching, in addition to comparing certain outcomes between a traditional teaching group and a case-based teaching group. They reported that most students favored the use of case-based teaching, whom at the same time had significantly better OSCE scores compared to the other group [ 34 ]. As noted, this favorable teaching method ultimately resulted in better learning outcomes and academic performance. Although it may be challenging since no answer options are provided, students appreciate the use of case analysis format in their exams because it aligns better with the course objectives and expected clinical competencies. The reason behind students’ preference for case analysis is that it allows them to interact with the teaching content and visualize the problem, leading to a better understanding. When case analysis is used as an assessment method, students can connect the case scenario presented in the exam to their clinical training, making it more relevant.

In this study, students recognized the incorporation of nursing knowledge in the case analysis exam. They also acknowledged improved knowledge and learning abilities similar to those observed in case-based teaching. Boney et al. (2015) reported that students perceived increased learning gains and a better ability to identify links between different concepts and other aspects of life through case-based teaching [ 35 ]. Additionally, case analysis as an exam promotes students’ in-depth acquirement of knowledge through the type of preparation it entails. Literature suggested that case-based teaching promotes self-directed learning with high autonomous learning ability [ 34 , 36 ]. Thus, better achievement in the case analysis exam could be linked with a higher level of knowledge, making it a suitable assessment method for knowledge integration in nursing care.

The findings of this study suggest that case analysis can be a useful tool for evaluating students’ cognitive skills, such as critical thinking, decision-making, and problem-solving. A randomized controlled study implied better problem-solving abilities among the students in the case-based learning group compared to those in the traditional teaching methods group [ 12 ]. Moreover, students in our study conveyed that case analysis as an exam was an opportunity for them to think like real nurses. Similar to our findings, a qualitative study on undergraduate nutrition students found that case-based learning helped students develop professional competencies for their future practice, in addition to higher-level cognitive skills [ 37 ]. Therefore, testing students through case analysis allows educators to assess the student’s readiness for entry-level professional competencies, including the thinking process. Also, to evaluate students’ high-level cognitive skills according to Bloom’s taxonomy (analysis, synthesis, and evaluation), which educators often find challenging.

Case analysis as an assessment method for clinical courses is partially integrated in case presentation or OSCE evaluation methods. However, the written format is considered to be more beneficial for both assessment and learning processes. A qualitative study was conducted to examine the impact of paper-based case learning versus video-based case learning on clinical decision-making skills among midwifery students. The study revealed that students paid more attention and were able to focus better on the details when the case was presented in a paper format [ 38 ]. Concurrently, the students in our study recommended more paper-based exercises, which they believed would improve their academic performance.

This study has possible limitations. The sample size was small due to the limited experience of case analysis as a clinical written exam in the program. Future studies with larger sample sizes and diverse nursing courses are needed for better generalizability.

Implications

Little evidence relates to the efficacy of case analysis as an evaluation method, suggesting the novelty of this study. Despite the scarcity of case-based assessment studies, a reader can speculate from this study’s findings that there is a potential efficacy of case analysis as an assessment method in nursing education. Future research is warranted to validate the effectiveness of case-analysis assessment methods and investigate the effects of case-analysis exams on academic and clinical performance.

Overall, our findings are in accordance with the evidence suggesting students’ perceived efficacy of case analysis as a teaching method. This study adds a potential for the case analysis to be acceptable and relevant to the clinical competencies when used as an assessment method. Future research is needed to validate the effectiveness of case analysis exams in other nursing clinical courses and examine their effects on academic and clinical performance.

Data availability

The datasets used and/or analyzed during the current study are available fromthe Principal Investigator (BAY) upon reasonable request.

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Acknowledgements

The authors wish to thank the nursing students at SQU who voluntarily participated in this study.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Basma Mohammed Al Yazeedi, Lina Mohamed Wali Shakman, Sheeba Elizabeth John Sunderraj, Harshita Prabhakaran, Judie Arulappan, Erna Judith Roach, Aysha Al Hashmi & Zeinab Al Azri

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Dr. Basma Mohammed Al Yazeedi contributed to conceptualization, methods, data collection, data analysis, writing the draft, and reviewing the final draft. Ms. Lina Mohamed Wali Shakman contributed to conceptualization, data collection, data analysis, writing the draft, and reviewing the final draft. Ms. Sheeba Elizabeth John Sunderraj contributed to conceptualization, methods, data collection, writing the draft, and reviewing the final draft.Ms. Harshita Prabhakaran contributed to conceptualization, data collection, writing the draft, and reviewing the final draft.Dr. Judie Arulappan contributed to conceptualization and reviewing the final draft.Dr. Erna Roach contributed to conceptualization writing the draft and reviewing the final draft.Ms. Aysha Al Hashmi contributed to the conceptualization and reviewing the final draft. Dr. Zeinab Al Azri contributed to data collection, data analysis, writing the draft, and reviewing the final draft.All auhors reviewed and approved the final version of the manuscirpt.

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Yazeedi, B.M.A., Shakman, L.M.W., Sunderraj, S.E.J. et al. Perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education: a mixed methods study. BMC Nurs 23 , 441 (2024). https://doi.org/10.1186/s12912-024-02102-9

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The Growing Importance of Mixed-Methods Research in Health

Sharada prasad wasti.

1,2 School of Human and Health Sciences, University of Huddersfield, United Kingdom

Padam Simkhada

3 Centre for Midwifery, Maternal and Perinatal Health, Bournemouth University, Bournemouth, United Kingdom

Edwin R. van Teijlingen

Brijesh sathian.

4 Geriatrics and long term care Department, Rumailah Hospital, Hamad Medical Corporation, Doha, Qatar

Indrajit Banerjee

5 Sir Seewoosagur Ramgoolam Medical College, Belle Rive, Mauritius

All authors have made substantial contributions to all of the following: (1) the conception and design of the study (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the version to be submitted

There is no conflict of interest for any author of this manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

This paper illustrates the growing importance of mixed-methods research to many health disciplines ranging from nursing to epidemiology. Mixed-methods approaches requires not only the skills of the individual quantitative and qualitative methods but also a skill set to bring two methods/datasets/findings together in the most appropriate way. Health researchers need to pay careful attention to the ‘best’ approach to designing, implementing, analysing, integrating both quantitative (number) and qualitative (word) information and writing this up in a way offers greater insights and enhances its applicability. This paper highlights the strengths and weaknesses of mixed-methods approaches as well as some of the common mistakes made by researchers applying mixed-methods for the first time.

Quantitative and qualitative research methods each address different types of questions, collect different kinds of data and deliver different kinds of answers. Each set of methods has its own inherent strengths and weaknesses, and each offers a particular approach to address specific types of research questions (and agendas). Health disciplines such as dentistry, nursing, speech and language therapy, and physiotherapy often use either quantitative or qualitative research methods on their own. However, there is a steadily growing literature showing the advantages of mixed-methods research is used in the health care and health service field [ 1-2 ]. Although we have advocated the use of mixed-methods in this journal eight years ago [ 3 ], there is still not enough mixed-methods research training in the health research field, particularly for health care practitioners, such as nurses, physiotherapists, midwives, and doctors, wanting to do research. Mixed-methods research has been popular in the social sciences since the twentieth century [ 4 ], and it has been growing in popularity among healthcare professionals [ 5 ], although it is still underdeveloped in disciplines such nursing and midwifery [ 6 , 7 ].

Underpinning philosophies

To help understand that mixed-methods research is not simply employing two different methods in the same study, one needs to consider their underpinning research philosophies (also called paradigms). First, quantitative research is usually underpinned by positivism. This includes most epidemiological studies; such research is typically based on the assumption that there is one single real world out there that can be measured. For example, quantitative research would address the question “What proportion of the population of India drinks coffee?” Secondly, qualitative research is more likely to be based on interpretivism. This includes research based on interviews and focus groups, research which us is typically based on the assumption that we all experience the world differently. Since we all live in a slightly different world in our heads the task of qualitative research is to analyse the interpretations of the people in the sample. For example, qualitative research would address the question “How do people experience drinking coffee in India?”, and “What does drinking coffee mean to them?”

Mixed-methods research brings together questions from two different philosophies in what is being referred to as the third path [ 8 ], third research paradigm [ 9 , 10 ], the third methodology movement [ 11 , 12 ] and pragmatism [ 5 ]. The two paradigms differ in key underlying assumptions that ultimately lead to choices in research methodology and methods and often give a breadth by answering more complicated research questions [ 4 ]. The roles of mixed-methods are clear in an understanding of the situation (the what), meaning, norms, values (the why or how) within a single research question which combine the strength of two different method and offer multiple ways of looking at the research question [ 13 ]. Epidemiology sits strongly in the quantitative research corner, with a strong emphasis on large data sets and sophisticated statistical analysis. Although the use of mixed methods in health research has been discussed widely researchers raised concerns about the explanation of why and how mixed methods are used in a single research question [ 5 ].

The relevance of mixed-methods in health research

The overall goal of the mixed-methods research design is to provide a better and deeper understanding, by providing a fuller picture that can enhance description and understanding of the phenomena [ 4 ]. Mixed-methods research has become popular because it uses quantitative and qualitative data in one single study which provides stronger inference than using either approach on its own [ 4 ]. In other words, a mixed-methods paper helps to understand the holistic picture from meanings obtained from interviews or observation to the prevalence of traits in a population obtained from surveys, which add depth and breadth to the study. For example, a survey questionnaire will include a limited number of structured questions, adding qualitative methods can capture other unanticipated facets of the topic that may be relevant to the research problem and help in the interpretation of the quantitative data. A good example of a mixed-methods study, it one conducted in Australia to understand the nursing care in public hospitals and also explore what factors influence adherence to nursing care [ 14 ]. Another example is a mixed-methods study that explores the relationship between nursing care practices and patient satisfaction. This study started with a quantitative survey to understand the general nursing services followed by qualitative interviews. A logistic regression analysis was performed to quantify the associations between general nursing practice variables supplemented with a thematic analysis of the interviews [ 15 ]. These research questions could not be answered if the researchers had used either qualitative or quantitative alone. Overall, this fits well with the development of evidence-based practice.

Despite the strengths of mixed-methods research but there is not much of it in nursing and other fields [ 7 ]. A recent review paper shows that the prevalence of mixed-methods studies in nursing was only 1.9% [ 7 ]. Similarly, a systematic review synthesised a total of 20 papers [ 16 ], and 16 papers [ 17 ] on nursing-related research paper among these only one mixed-methods paper was identified. Worse, a further two mixed-methods review recently revealed that out of 48 [ 18 , 19 ] synthesised nursing research papers, not one single mixed-methods paper was identified. This clearly depicts that mixed-methods research is still in its infancy stage in nursing but we can say there is huge scope to implement it to understand research questions on both sides of coin [ 4 ]. Therefore, there is a great need for mixed-methods training to enhance the evidence-based decision making in health and nursing practices.

Strengths and weaknesses of mixed-methods

There are several challenges in identifying expertise of both methods and in working with a multidisciplinary, interdisciplinary, or transdisciplinary team [ 20 ]. It increases costs and resources, takes longer to complete as mixed-methods design often involves multiple stages of data collection and separate data analysis [ 4 , 5 ]. Moreover, conducting mixed-methods research does not necessarily guarantee an improvement in the quality of health research. Therefore, mixed-methods research is only appropriate when there are appropriate research questions [ 4 , 6 ].

Identifying an appropriate mixed-methods journal can also be challenging when writing mixed-methods papers [ 21 ]. Mixed-methods papers need considerably more words than single-methods papers as well as sympathetic editors who understand the underlying philosophy of a mixed-methods approach. Such papers, simply require more words. The mixed-methods researcher must be reporting two separate methods with their own characteristics, different samples, and ways of analysing, therefore needs more words to describe both methods as well as both sets of findings. Researcher needs to find a journal that accepts longer articles to help broaden existing evidence-based practice and promote its applicability in the nursing field [ 22 ].

Common mistakes in applying mixed-methods

Not all applied researchers have insight into the underlying philosophy and/or the skills to apply each set of methods appropriately. Younas and colleagues’ review identified that around one-third (29%) of mixed-methods studies did not provide an explicit label of the study design and 95% of studies did not identify the research paradigm [ 7 ]. Whilst several mixed-methods publications did not provide clear research questions covering both quantitative and qualitative approaches. Another common issue is how to collect data either concurrent or sequential and the priority is given to each approach within the study where equal or dominant which are not clearly stated in writing which is important to mention while writing in the methods section. Similarly, a commonly overlooked aspect is how to integrate both findings in a paper. The responsibility lies with the researcher to ensure that findings are sufficiently plausible and credible [ 4 ]. Therefore, intensive mixed-methods research training is required for nursing and other health practitioners to ensure its appropriate.

The way forward

Despite the recognised strengths and benefits of doing mixed-methods research, there is still only a limited number of nursing and related-health research publications using such this approach. Researchers need training in how to design, conduct, analyse, synthesise and disseminate mixed-methods research. Most importantly, they need to consider appropriate research questions that can be addressed using a mixed methods approach to add to our knowledge in evidence-based practice. In short, we need more training on mixed-methods research for a range of health researchers and health professionals.

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From Digital Practices to Bond Formation: A Mixed-Method Case Study of BTS Online Fandom Communities

• Suchismita Naik
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Harnessing peer mentorship as a tool to turn human resource for health brain drain into brain gain: a case study of a Nigerian peer-mentored research group

• Patience Toyin-Thomas 1 , 2 ,
• Oghenebrume Wariri   ORCID: orcid.org/0000-0002-7432-8995 3 , 4 &
• Paul Ikhurionan 5  

Human Resources for Health volume  22 , Article number:  46 ( 2024 ) Cite this article

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Peer mentorship can be a potential tool to reduce the disparities in health research capacity between high- and low- and middle-income countries. This case study describes the potential of peer mentorship to tackle two critical issues: bridging health research capacity of doctors from low- and middle-income countries (LMICs) and the transformation of human resource for health brain drain into "brain gain".

Case presentation

In 2021, a virtual peer mentorship group was established by 16 alumni of the University of Benin College of Medical Sciences' 2008 graduating class, residing across three continents. This program aimed to facilitate research collaboration and skill development among colleagues with diverse research experience levels, fostering a supportive environment for career development in research. The group relied heavily on digital technology to carry out its activities due to the different geographical locations of the group members. Led by experienced peer leaders, the group fostered a collaborative learning environment where members leveraged each other's expertise. Within 18 months, we published two research papers in high-impact peer-reviewed global health journals, launched a mixed-methods research study, and conducted training sessions on research design and implementation. Findings from our work were presented at conferences and workshops. However, logistical hurdles, competing priorities, structural constraints, and uneven participation presented challenges.

The peer mentorship collaboration has achieved some successes so far, and this model can be emulated by other cohorts of medical professionals across LMICs. Despite the group's success at a micro- or individual level, there remain significant structural barriers to research capacity building in LMICs that can only be addressed at the meso- and macro-levels by institutions and government, respectively. A systems-level approach is required to develop and support research capacity building and foster global research collaboration and effectively turn brain drain into brain gain.

Peer Review reports

Africa, which contributes about 18% of the world's population [ 1 ], accounts for only 1.3% of the world's annual research output, with just three countries—Kenya, Nigeria, and South Africa—contributing 52% of Africa's total output [ 2 ]. Building and retaining human resources for health (HRH) research capacity is essential for improving the quality and effectiveness of healthcare systems in Africa's low and middle-income countries (LMICs) [ 2 , 3 , 4 ]. Unfortunately, research capacity development in these settings is limited and fraught with numerous challenges, including inadequate training in research methodologies, lack of mentorship and institutional support in conducting research, and lack of research funding opportunities , [ 4 , 5 , 6 , 7 , 8 ] Indeed, this lack of availability of resources and support for a career in research in their local institutions is one of the key drivers for the migration of health workers to high-income countries (HICs), where such opportunities abound [ 9 ]. The migration of these highly trained individuals, aptly coined "brain drain" causes a loss of intellectual and technical resources for LMICs [ 10 ], and has reached alarming levels in recent years. As of 2021, less than half of the registered doctors in Nigeria were practicing in the country, as many of whom had migrated to HICs in search of better career prospects. [ 11 ]

Mentoring fosters professional development and facilitates collaborations, particularly in academic medicine [ 12 ]. Frazen et al. (2016) found that peer mentorship was a popular strategy proposed to address the barriers to research in LMICs in their systematic review of major approaches to health research capacity development [ 6 ]. While traditional mentoring approaches are widely recognized, alternative models like peer mentoring have recently gained attention and investigation. In peer-mentoring programs, individuals of similar academic rank and interests collaborate within a facilitated framework, sometimes guided by a faculty member of higher academic standing, to collectively pursue their scholarly goals [ 13 ]. Peer mentoring can be a legitimate means through which academics from LMICs who have emigrated to HICs to collaborate with their colleagues back in their source countries on important public health research that affects the global population, given their understanding of both contexts [ 14 , 15 ]. Peer mentoring can aid the transfer of knowledge from the mentors to mentees, of various skillsets such as developing research skills, writing academic papers and grants, navigating the academic publishing process, networking with other researchers, and securing fellowship funding opportunities [ 14 ]. Peer mentoring, thus, offers an opportunity to convert "brain-drain" to resource profit or "brain gain" for LMICs [ 10 ], by strengthening research capacity in LMICs through these collaborations.

In recent years, Nigeria has witnessed a significant exodus of highly skilled health workers, including doctors and dentists from the 2008 graduating class (colloquially called the Diamante graduating class) of the University of Benin, to pursue better career opportunities and improved living standards abroad. This alarming trend not only jeopardizes the quality of healthcare services back home, but also highlights the urgent need to strengthen health research capacity within the country. The inadequate research capacity among clinicians, not just in Nigeria but globally, is a pressing issue that demands immediate attention. This concern is particularly acute given the increasing prominence of research in shaping effective healthcare delivery and leadership [ 3 ]. As members of the Diamante graduating class ascend to key leadership positions within the Nigerian healthcare system, a noticeable divide might emerge, separating those trained solely in core clinical skills from those who have become additionally equipped with strong global health research capabilities. This unique opportunity necessitates a strategic intervention to harness the skillset of the latter group, enabling them to contribute their expertise to the development of the former group. We present a case study of a peer-mentoring program for doctors who graduated from the University of Benin College of Medical Sciences in 2008. This case study explores the potential of peer mentorship to tackle two critical issues: bridging health research capacity of doctors from LMICs and the transformation of human resource for health brain drain into "brain gain".

The "Diamante Research Group" was created in 2021 as a subset of the University of Benin College of Medical Sciences Medical and Dental graduating class of 2008. The group was formed to foster research collaboration among graduates who were passionate about pursuing a research career, but had different levels of experience and skills in conducting research. The overarching aim of the group was to leverage the research expertise of colleagues with extensive training and experience to enhance the research capabilities of interested clinicians who lacked the necessary support or experience.

The conceptual framework guiding the formation and activities of the Diamante Research Group is comparable theoretically to the peer mentorship model developed by the Internal Medicine Research Group at Emory (IMeRGE) group to build the research and academic skills of the group members and ensure their career advancement in academic medicine (see Fig.  1 ) [ 16 ]. Unlike the IMeRGE group, however, the Diamante Research Group members were location in different institutions in different countries, spanning three continents, including Africa, Europe and North America. Hence, the group relied on digital technology to carry out its activities.

Diamante Research Group peer mentoring model adapted from the iMeRGE peer mentoring model by Bussey-Jones et al. [ 16 ]

Group structure

The group started with 16 doctors in diverse clinical and non-clinical areas of specialization, including pediatrics, oral medicine, obstetrics and gynecology, orthopedics, and research. The group members, consisting of six women and ten men, resided in four countries: Canada, Nigeria, the United Kingdom, and the United States of America. Three members were selected to co-lead the group and facilitate its activities to achieve the outlined goals and objectives. These group leaders were chosen based on their research experience and training. Two of them were well-published and had previously and currently led research projects. They had pursued additional research training in the United Kingdom and the United States of America, ultimately earning PhDs after completing rigorous research programs. The third group leader was in the process of obtaining a master’s degree in health policy, was still resident in Nigeria, thus provided contextual and local support. Additionally, several other group members had either acquired or were enrolled in master’s degrees in public health in Nigeria or elsewhere. Table 1 shows the details of the initial group members.

Group activities

The inaugural meeting of the Diamante Research Group was held in December 2021. At this meeting, the objectives and expectations were established through a priority-setting approach involving all group members, to guide activities from the outset. The group objectives fell into three main categories (see Fig.  1 ):

Skills acquisition. Increasing research skills and knowledge via didactic sessions on topics, such as conducting systematic reviews, grant-writing, research design and methodologies, and publication ethics, etc.

Access to resources. Increasing access to research tools, resources, and opportunities such as access to grant information, access to journals and databases through academic network and membership subscriptions, access to databases, access to academic librarian expertise for research projects.

Skills application. Conducting research projects together to provide hands-on experience in all stages of research, from developing a research question to submission of a completed project for publication, guided by international best practices in research and publication ethics.

Leveraging digital technology, the group addressed the challenges posed by members being in different locations and time zones. WhatsApp, Zoom, and email were essential for coordinating meetings and events, with all gatherings held virtually via Zoom. These Zoom meetings occurred every two to four weeks, depending on the demands of ongoing research projects or key milestones. Additional modes of correspondence included regular email exchanges, updates on the Diamante Research Group WhatsApp platform, and phone calls between members. Minutes of the Zoom meetings were shared on the WhatsApp platform. The group leaders maintained a dedicated WhatsApp platform for ongoing engagement. Additionally, the group created a secure folder on Google Drive to store documents related to group projects, accessible to all members.

For each research project, the group created task-sharing documents detailing all tasks involved in the various project stages. Members volunteered for tasks based on their interests and perceived competencies. Each task was led by a mini-team head, who was supervised by the main group leaders. For some tasks, the main group leaders provided direct supervision due to their technical nature. The group also used Gantt charts to create timelines and track the progress of each project. To ensure fairness and adhere to ethical publication standards regarding authorship, we tracked participation in group projects using an authorship tool guide.

The main goal in the formation of the Diamante Research Group, like similar peer-mentorship research groups, was to collaboratively work on research projects that would lead to tangible outcomes, such as publications, conference abstracts, and grant funding [ 5 , 14 , 16 ].

Skills acquisition and access to resources. The group conducted real-time virtual lecture series on systematic reviews, including guided practice sessions on tasks such as formulating review questions, conducting database searches, exporting search outputs, and screening articles using collaborative platforms like Rayyan®. Additionally, the group developed search strategies, conducted searches, and exported search results for the main systematic review project via Zoom. This approach provided members with hands-on experience, reinforcing the knowledge gained from the lecture series. The group leveraged the institutional access of its leaders to obtain access to databases and journal articles that would have otherwise required significant monetary commitment, especially for those still residing in Nigeria [ 17 ].

Skills application. Within the first 18 months, the group had published two research papers in high-impact, peer-reviewed global health journals and commenced a primary mixed-methods study. The inaugural project undertaken by the group was a systematic review on the drivers of migration of health workers from LMICs. The group collectively prioritized conducting this systematic review, which subsequently laid the foundation for the group's ongoing primary mixed-methods cohort study. Before starting the review, the group registered the protocol in an open-access online database of systematic review protocols. The review protocol and the subsequent research article were both published in British Medical Journals (BMJ Open and BMJ Global Health) [ 9 , 18 ]. The group successfully obtained waivers for the Article Processing Charges (APC) for both journal publications. High APCs are a significant barrier, often excluding researchers from LMICs, especially those without research fundings and perpetuating the under-representation of their voices in high-impact journals. [ 17 ]

Dissemination of research. The group members presented their research at the Inaugural University of Benin College of Medicine Scientific Conference, themed “Medical Education and Brain Drain: Implications for Health Workforce Development in Nigeria”, held in August 2023 at their alma mater.

Throughout these processes, the group leaders mentored their peers, leveraging their wealth of experience to guide them on how to navigate the process, fostering a shared learning approach. The transfer of knowledge on research skills from more experienced colleagues in the diaspora to those with less experience and access to research tools, and resources, that would have otherwise been unavailable, represent some of the ways the group transformed brain drain to brain gain.

At present, we have completed the data collection for our cohort study and are in the process of writing up the findings for submission for the third publication by the group. In addition, we are conducting training sessions on designing and implementing qualitative and mixed-methods studies in preparation for our next research project. The goal for the next year is to focus on training on grant-writing with practical applications in the form of applying for and hopefully, securing grant funding to carry out future research projects on a larger scale. Lastly, an additional eight colleagues from the 2008 graduating class have requested to join the research group following the success of the initial projects, bringing the number of group members from the initial 16 to a total of 24 members.

Challenges and reflections

We did encounter several challenges as a peer mentoring group (Table  2 ), similar to other peer mentorship programs reported in the literature [ 14 , 16 ]. Firstly, the significant differences in time zones due to the location of the members made it difficult to schedule meetings at a time that was convenient for all group members. In addition, other professional and personal responsibilities of the members such as on-call duties and caring for their young children also deterred attendance of or full participation during group meetings [ 16 ]. Unstable Wi-Fi signal, especially for our peers in Nigeria also interfered with the quality of the Zoom meetings, further highlighting critical infrastructure issues that hamper research in LMICs [ 14 ]. To address these challenges, we scheduled meetings on weekends at a consistent day and time that was convenient for most of the group. Meeting dates were set in advance, and multiple reminders were sent beforehand. The group leaders shared the agenda ahead of time, and one of them facilitated the meetings to ensure efficient use of time. After each meeting, the minutes and action items were shared on the WhatsApp group to keep absent members informed.

We also encountered challenges related to inconsistencies in the quality of work performed by different group members. These variations stemmed from factors such as insufficient skills to complete assignments, inadequate commitment to tasks, or a combination of both factors. Unlike in traditional mentor–mentee relationships, where power dynamics often drive performance, our peer-group structure lacked such pressures, resulting in less stringent adherence to expectations [ 16 ]. Upon identifying these issues early in the project, the group leaders decided it was crucial to establish clear, predefined criteria for what constituted acceptable completion of each task and to communicate these expectations upfront going forward using an authorship guide tool.

Points on the authorship guide tool were awarded only upon satisfactory completion of a task. It also became necessary to limit the number of volunteers for specific tasks and, in certain cases, select group members based on their demonstrated proficiency and commitment in previous tasks. The authorship guide tool was regularly updated and shared as each project progressed to ensure transparency and accountability. Members were encouraged to discuss any concerns about the points allocated to them with group leaders, and these issues were addressed individually. Ultimately, establishing clear expectations and maintaining a transparent, albeit imperfect, system for tracking member contributions has proven effective in promoting fairness and accountability within the group.

We have established a successful peer mentoring group which could be emulated by other cohorts of medical professionals across LMICs, and we provide several actionable recommendations in setting up similar programs for interested parties (see panel). However, we note that there remain significant structural barriers to research capacity building that can only be addressed at the meso- and macro-levels such as protected research time and funding for research projects. A systems-level approach led by institutions and government is required to develop and support research capacity building and foster global research collaboration to effectively turn brain drain into brain gain.

The peer mentorship group has proven beneficial to all participants involved, offering members the opportunity to collaborate on research topics of mutual interest. During the group audit following the successful completion of our initial project, participants expressed satisfaction with the group's achievements and noted an increase in their research knowledge and skills essential for academic success. Through teaching and mentoring their peers, group leaders enhanced their own research leadership abilities and gained valuable experience in guiding teams to project completion.

Moreover, personal connections have flourished among members, leading to planned future research collaborations beyond the primary group among those sharing similar interests. Peer mentorship plays a critical role in professional development and career advancement, potentially bridging gaps in access to expertise, funding, research tools, and opportunities between academic institutions in high-, middle-, and low-income countries. Ultimately, this approach aims to transform brain drain into brain gain.

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Abbreviations

Human resources for health.

• Low- and middle-income countries

High-income countries

Internal Medicine Research Group at Emory

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Acknowledgements

We would like to acknowledge all members of the Diamante Research Group for their immense contribution to the success of the research group. We also thank Dr. John Osakwe, Dr. Ifeoma Ujomu and Dr. Olayiwola Oyerinde who did the initial work of leading the Diamante Educational Series which laid the groundwork for the formation of the Diamante Research Group.

The Diamante Research Group has so far received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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PTT, OW and IP currently lead the Diamante Research Group; PTT conceived the idea for this manuscript, with input from OW and PI; PTT wrote the initial draft of the manuscript; OW and PI critically reviewed the manuscript; PTT, OW and IP approved the final draft of the manuscript.

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Toyin-Thomas, P., Wariri, O. & Ikhurionan, P. Harnessing peer mentorship as a tool to turn human resource for health brain drain into brain gain: a case study of a Nigerian peer-mentored research group. Hum Resour Health 22 , 46 (2024). https://doi.org/10.1186/s12960-024-00932-x

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Received : 25 December 2023

Accepted : 20 June 2024

Published : 27 June 2024

DOI : https://doi.org/10.1186/s12960-024-00932-x

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