limitations of qualitative research theory

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Qualitative data analysis and the use of theory.

• Carol Grbich Carol Grbich Flinders University
• https://doi.org/10.1093/acrefore/9780190264093.013.554
• Published online: 23 May 2019

The role of theory in qualitative data analysis is continually shifting and offers researchers many choices. The dynamic and inclusive nature of qualitative research has encouraged the entry of a number of interested disciplines into the field. These discipline groups have introduced new theoretical practices that have influenced and diversified methodological approaches. To add to these, broader shifts in chronological theoretical orientations in qualitative research can be seen in the four waves of paradigmatic change; the first wave showed a developing concern with the limitations of researcher objectivity, and empirical observation of evidence based data, leading to the second wave with its focus on realities - mutually constructed by researcher and researched, participant subjectivity, and the remedying of societal inequalities and mal-distributed power. The third wave was prompted by the advent of Postmodernism and Post- structuralism with their emphasis on chaos, complexity, intertextuality and multiple realities; and most recently the fourth wave brought a focus on visual images, performance, both an active researcher and an interactive audience, and the crossing of the theoretical divide between social science and classical physics. The methods and methodological changes, which have evolved from these paradigm shifts, can be seen to have followed a similar pattern of change. The researcher now has multiple paradigms, co-methodologies, diverse methods and a variety of theoretical choices, to consider. This continuum of change has shifted the field of qualitative research dramatically from limited choices to multiple options, requiring clarification of researcher decisions and transparency of process. However, there still remains the difficult question of the role that theory will now play in such a high level of complex design and critical researcher reflexivity.

• qualitative research
• data analysis
• methodologies

Theory and Qualitative Data Analysis

Researchers new to qualitative research, and particularly those coming from the quantitative tradition, have often expressed frustration at the need for what appears to be an additional and perhaps unnecessary process—that of the theoretical interpretation of their carefully designed, collected, and analyzed data. The justifications for this process have tended to fall into one of two areas: the need to lift data to a broader interpretation beyond the Monty Pythonesque “this is my theory and it’s my very own,” to illumination of findings from another perspective—by placing the data in its relevant discipline field for comparison with previous theoretical data interpretations, while possibly adding something original to the field.

“Theory” is broadly seen as a set of assumptions or propositions, developed from observation or investigation of perceived realties, that attempt to provide an explanation of relationships or phenomena. The framing of data via theoretical imposition can occur at different levels. At the lowest level, various concepts such as “role,” “power,” “socialization,” “evaluation,” or “learning styles” refer to limited aspects of social organization and are usually applied to a specific group of people.

At a more complex level, theories of the Middle Range, identified by Robert Merton to link theory and practice, are used to build theory from empirical data. These tend to be discipline specific and incorporate concepts plus variables such as “gender,” “race,” or “class.” Concepts and variables are then combined into meaningful statements, which can be applied to more diverse social groups. For example, in education an investigation of student performance could emphasize such concepts as “safety,” “zero bullying,” “communication,” and “tolerance,” with variables such as “race” and “gender” to lead to a statement that good microsystems and a focus on individual needs are necessary for optimal student performance.

The third and most complex level uses the established or grand theories such as those of Sigmund Freud’s stages of children’s development, Jean Piaget’s theory of cognitive development, or Urie Bronfenbrenner’s ecological systems, which have been widely accepted as meaningful across a number of disciplines and provide abstract explanations of the uniformity of aspects of social organization, social behavior, and social change.

The trend in qualitative research regarding the application of chosen levels of theory has been generally either toward theory direction/verification or theory generation, although the two are often intertwined. In the first, a relevant existing theory is chosen early and acts as a point of critical comparison for the data to be collected. This approach requires the researcher to think theoretically as s/he designs the study, collects data, and collates it into analytical groupings. The danger of theory direction is that an over focus on a chosen theoretical orientation may limit what the researcher can access or “see” in the data, but on the upside, this approach can also enable the generation of new theoretical aspects, as it is rare that findings will fall precisely within the implications of existing statements. Theory generation is a much looser approach and involves either one or a range of relevant levels of theory being identified at any point in the research process, and from which, in conjunction with data findings, some new combination or distillation can enhance interpretation.

The question of whether a well-designed study should negate the need for theoretical interpretation has been minimally debated. Mehdi and Mansor ( 2010 ) identified three trends in the literature on this topic: that theory in qualitative research relates to integrated methodology and epistemology; that theory is a separate and additional element to any methodological underpinnings; and that theory has no solid relationship with qualitative research. No clear agreement on any of these is evident. Overall, there appears to be general acceptance that the process of using theory, albeit etically (imposed) or emically (integrated), enhances outcomes, and moves research away from being a-theoretical or unilluminated by other ideas. However, regarding praxis, a closer look at the issue of the use of theory and data may be in order. Theoretical interpretation, as currently practiced, has limits. To begin with, the playing field is not level. In the grounded theory tradition, Glaser and Strauss ( 1967 ) were initially clear that in order to prevent undue influence on design and interpretation, the researcher should avoid reviewing the literature on a topic until after some data collection and analysis had been undertaken. The presumption that most researchers would already be well versed in theory/ies and would have a broad spectrum to draw on in order to facilitate the constant comparative process from which data-based concepts could be generated was found to be incorrect. Glaser ( 1978 ) suggested this lack could be improved at the conceptual level via personal and professional reflexivity.

This issue became even more of a problem with the advent of practice-led disciplines such as education and health into the field of qualitative research. These groups had not been widely exposed to the theories of the traditional social sciences such as sociology, psychology, and philosophy, although in education they would have been familiar with John Dewey’s concept of “pragmatism” linking learning with hands-on activity, and were more used to developing and using models of practice for comparison with current realities. By the mid- 20th century , Education was more established in research and had moved toward the use of middle range theories and the late 20th-century grand theorists: Michel Foucault, with his emphasis on power and knowledge control, and Jurgen Habermas, with his focus on pragmatism, communication, and knowledge management.

In addition to addictive identification with particular levels of theory and discipline-preferred theories and methods, activity across qualitative research seems to fall between two extremes. At one end it involves separate processes of data collection and analysis before searching for a theoretical framework within which to discuss the findings—often choosing a framework that has gained traction in a specific discipline. This “best/most acceptable fit” approach often adds little to the relevant field beyond repetition and appears somewhat forced. At the other extreme there are those who weave methods, methodologies, data, and theory throughout the whole research process, actively critiquing and modifying it as they go, usually with the outcome of creating some new direction for both theory and practice. The majority of qualitative research practice, however, tends to fall somewhere between these two.

The final aspect of framing data lies in the impact of researchers themselves, and the early- 21st-century emphasis is on exposing relevant personal frames, particularly those of culture, gender, socioeconomic class, life experiences such as education, work, and socialization, and the researcher’s own values and beliefs. The twin purposes of this exposure are to create researcher awareness and encourage accountability for their impact on the data, as well as allowing the reader to assess the value of research outcomes in terms of potential researcher bias or prejudice. This critical reflexivity is supposed to be undertaken at all stages of the research but it is not always clear that it has occurred.

Paradigms: From Interactionism to Performativity

It appears that there are potentially five sources of theory: that which is generally available and can be sourced from different disciplines; that which is imbedded in the chosen paradigm/s; that which underpins particular methodologies; that which the researcher brings, and that which the researched incorporate within their stories. Of these, the paradigm/s chosen are probably the most influential in terms of researcher position and design. The variety of the sets of assumptions, beliefs, and researcher practices that comprise the theoretical paradigms, perspectives, or broad world views available to researchers, and within which they are expected to locate their individual position and their research approach, has shifted dramatically since the 1930s. The changes have been distinct and identifiable, with their roots located in the societal shifts prompted by political, social, and economic change.

The First Wave

The Positivist paradigm dominated research, largely unquestioned, prior to the early 20th century . It emphasized the distancing of the researcher from his/her subjects; researcher objectivity; a focus on objective, cause–effect, evidence-based data derived from empirical observation of external realities; experimental quantitative methods involving testing hypotheses; and the provision of finite answers and unassailable future predictions. From the 1930s, concerns about the limitations of findings and the veracity of research outcomes, together with improved communication and exposure to the worldviews of other cultures, led to the advent of the realist/post-positivist paradigm. Post-positivism, or critical realism, recognized that certainty in proving the truth of a hypothesis was unachievable and that outcomes were probably limited to falsification (Popper, 1963 ), that true objectivity was unattainable and that the researcher was most likely to impact on or to contaminate data, that both qualitative and quantitative approaches were valuable, and that methodological pluralism was desirable.

The Second Wave

Alongside the worldwide political shifts toward “people power” in the 1960s and 1970s, two other paradigms emerged. The first, the Interpretivist/Constructivist, focused on the social situations in which we as humans develop and how our construction of knowledge occurs through interactions with others in these contexts. This paradigm also emphasized the gaining of an understanding of the subjective views or experiences of the participants being researched, and recognized the impact of the researcher on researcher–researched mutually constructed realities. Here, theory generation is the preferred outcome to explain the what, how, and why of the findings. This usually involves the development of a conceptual model, forged from both the data gained and from the application/integration of relevant theory, to provide explanations for and interpretations of findings, together with a new perspective for the field/discipline.

The second paradigm, termed the Critical/Emancipatory, focused on locating, critiquing, and changing inequalities in society. The identification of the location of systemic power discrepancies or systematic power misuse in situations involving gender, sexuality, class, and race is expected to be followed by moves to right any oppression discovered. Here, the use of theory has been focused more on predetermined concept application for “fit.” This is because the very strong notion of problematic societal structures and power inappropriately wielded have been the dominant underpinnings.

In both the Interpretive and Critical paradigms, researcher position shifted from the elevated and distant position of positivism, to one of becoming equal with those being researched, and the notion of researcher framing emerged to cover this shift and help us—the readers—to “see” (and judge) the researcher and her/his processes of data management more clearly.

The Third Wave

In the 1980s, the next wave of paradigmatic options—postmodernism and poststructuralism—emerged. Postmodernism, with its overarching cultural implications, and poststructuralism, with its focus on language, severely challenged the construction, limitations, and claims to veracity of all knowledge and in particular the use of theory derived from siloed disciplines and confined research methods. Regardless of whether the postmodern/poststructural label is attached to grounded theory, ethnography, phenomenology, action, or evaluative designs, one general aspect that prevails is a focus on language. Language has become viewed as dubious, with notions of “slippage”—the multiple meanings of individual words, and “difference”—the difference and deferral of textual meaning (Derrida, 1970 , 1972 ), adding complexity. Double coding, irony, and juxtaposition are encouraged to further identify meaning, and to uncover aspects of social organization and behavior that have been previously marginalized or made invisible by existing discourses and discursive practices. Texts are seen as complex constructions, and intertextuality is favored, resulting in multiply constructed texts. The world is viewed as chaotic and unknowable; individuals are no longer seen as two dimensional—they are viewed as multifaceted with multiple realities. Complex “truths” are perceived as limited by time and context, requiring multiple data sets and many voices to illuminate them, and small-scale focused local research is seen as desirable. The role of researcher also changed: the politics of position and self-reflexivity dominate and the researcher needs to clearly expose past influences and formerly hidden aspects of his/her life. S/he inhabits the position of an offstage or decentered facilitator, presenting data for the reader to judge.

Theory is used mainly at the conceptual level with no particular approach being privileged. The researcher has become a “bricoleur” (Levi-Strauss, 1962 ) or handyman, using whatever methods or theories that are within reach, to adapt, craft, and meld technological skills with mythical intellectual reflection in order to create unique perspectives on the topic. Transitional interpretations dominate, awaiting further challenges and deconstruction by the next researcher in the field.

The need for multifaceted data sets in the 1990s led inevitably to a search for other research structures, and mixed and multiple methods have become topical. In crossing the divide between qualitative and quantitative approaches, the former initially developed its own sub-paradigms: pragmatist (complimentary communication and shared meanings) and transformative/emancipatory (inequalities in race, class, gender, and disability, to be righted). An increasing focus on multiple methods led to the advent of dialectics (multiple paradigm use) and critical realism (the acceptance of divergent results) (Shannon-Baker, 2016 ). The dilemmas of theory use raised by these changes include whether to segregate data sets and try to explain disparate outcomes in terms of diversity using different theories; whether to integrate them through a homogeneous “smoothing” process—one theory fits all, in order to promote a singular interpretation; or whether to let the strongest paradigm—in terms of data—dominate the theoretical findings.

The Fourth Wave

During the early 21st century , as the third wave was becoming firmly established, the Performative paradigm emerged. The incorporation of fine art–based courses into universities has challenged the prescribed rules of the doctoral thesis, initially resulting in a debate—with echoes of Glaser and Strauss—as to whether theory, if used initially, is too directive, thereby potentially contaminating the performance, or whether theory application should be an outcome to enhance performances, or even whether academic guidelines regarding theory use need to be changed to accommodate these disciplines (Bolt, 2004 ; Freeman, 2010 ; Riley & Hunter, 2009 ). Performativity is seen in terms of “effect,” a notion derived from John Austin’s ( 1962 ) assertion that words and speech utterances do not just act as descriptors of content, they have social force and impact on reality. Following this, a productive work is seen as capable of transforming reality (Bolt, 2016 ). The issue most heard here is the problem of how to judge this form of research when traditional guidelines of dependability, transformability, and trustworthiness appear to be irrelevant. Barbara Bolt suggests that drawing on Austin’s ( 1962 ) terms “locutionary” (semantic meaning), “illocutionary” (force), and “perlocutionary” (effect achieved on receivers), together with the mapping of these effects in material, effective, and discursive domains, may be useful, despite the fact that mapping transformation may be difficult to track in the short term.

During the second decade of the 21st century , however, discussions relating to the use of theory have increased dramatically in academic performative research and a variety of theoreticians are now cited apart from John Austin. These include Maurice Merleu-Ponty ( 1945 and the spatiality of lived events; Jacques Derrida ( 1982 ) on iterability, simultaneous sameness, and difference; Giles Deleuze and Felix Guatarri ( 1987 ) on rituals of material objects and transformative potential; Jean-Francois Lyotard ( 1988 ) on plurality of micro narratives, “affect,” and its silent disruption of discourse; and Bruno Latour ( 2005 ) with regard to actor network theory—where theory is used to engage with rather than to explain the world in a reflective political manner.

In performative doctoral theses, qualitative theory and methods are being creatively challenged. For example, from the discipline of theater and performance Lee Miller and Joanne/Bob Whalley ( 2010 ) disrupt the notion of usual spaces for sincere events by taking their six-hour-long performance Partly Cloudy, Chance of Rain , involving a public reaffirmation of their marriage vows, out of the usual habitats to a service station on a highway. The performance involves a choir, a band, a pianist, 20 performers dressed as brides and grooms, photographers, a TV crew, an Anglican priest, plus 50 guests. The theories applied to this event include an exploration of Marc Auge’s ( 1992 ) conception of the “non-place”; Mikhail Bakhtin’s ( 1992 ) concepts of “dialogism” (many voices) together with “heteroglossia” (juxtaposition of many voices in a dialogue); and Ludwig Wittgenstein’s ( 1953 ) discussion of the “duck rabbit”—once the rabbit is seen (participatory experience) the duck (audience) is always infected by its presence. This couple further challenged the guidelines of traditional doctoral theses by successfully negotiating two doctoral awards for a joint piece of research

A more formal example of a doctoral thesis (Reik, 2014 ) using traditional qualitative approaches has examined at school level the clash of paradigms of performative creative styles of teaching with the neoliberalist focus on testing, curriculum standardization, and student outcomes.

Leah Mercer ( 2012 ), an academic in performative studies, used the performative paradigm in her doctoral thesis to challenge and breach not only the methodological but also the theoretical silos of the quantitative–qualitative divide. The physics project is an original work using live performances of personal storytelling with video and web streaming to depict the memories, preoccupations, and the formative relationship of two women, an Australian and an American, living in contemporary mediatized society. Using scientific theory, Mercer explores personal identity by reframing the principles of contemporary physics (quantum mechanics and uncertainty principle) as aesthetic principles (uncertainty and light) with the physics of space (self), time (memory), light (inspiration), and complementarity (the reconciliation of opposites) to illuminate these experiences.

The performative paradigm has also shifted the focus on the reader, developed in postmodernism, to a broader group—an active audience. Multi-methods have been increased to include symbolic imagery, in particular visual images, as well as sound and live action. The researcher’s role here is often that of performer within a cultural frame, creating and investigating multiple realities and providing the link between the text/script and the audience/public. Theory is either minimized to the level of concepts or used to break through the silos of different disciplines to integrate and reconcile aspects from long-lasting theoretical divides.

In these chronological lines of paradigm shifts, changes in researcher position and changes in the application of theory can clearly be seen. The researcher has moved out of the shadows and into the mainstream; her/his role has shifted from an authoritarian collector and presenter of finite “truths” to a creator and often performer of multiple and disparate data images for the audience to respond to. Theory options have shifted from direction and generation within existing perspectives to creative amalgamations of concepts from disciplines previously rarely combined.

Methodologies: From Anthropology to Fine Arts

It would be a simple matter if all the researcher had to contend with was siting oneself in a particular paradigm/s. Unfortunately, not only have paradigms shifted in terms of researcher position and theoretical usage but so also have methodological choices and research design. One of the most popular methodologies, ethnography, with its roots in classical anthropology and its fieldwork-based observations of action and interaction in cultural contexts, can illustrate the process of methodological change following paradigm shift. If a researcher indicates that he/she has undertaken an ethnographic study, the reader will be most likely to query “which form?”: classical?, critical?, auto?, visual?, ethno drama?, cyber/net?, or performative? The following examples from this methodology should indicate how paradigm shifts have resulted in increasing complexity of design, methods, and interpretive options.

In c lassical ethnography the greatest borrowing is from traditional anthropology in terms of process and tools, and this can be seen with the inclusion of initial time spent in the setting to learn the language of the culture and to generally “bathe” oneself in the environment, often with minimal data collection. This process is supposed to help increase researcher understanding of the culture and minimize the problem of “othering” (treating as a different species/alien). Then a fairly lengthy amount of time is usually spent in the cultural setting either as an observer or as a participant observer to collect as much data as is relevant to answer the research question. This is followed by a return to post-check whether the findings previously gathered have stood the test of time. The analytical toolkit can involve domain analysis, freelists, pilesorts, triads and taxonomies, frame and social network, and event analysis. Truncated mini-ethnographies became more common as time became an issue, but these can still involve years of managing descriptive data, often collected by several participating researchers as seen in Douglas, Rasmussen, and Flanagan’s ( 1977 ) study of the culture of a nudist beach. Shorter versions undertaken by one researcher, for example Sohn ( 2015 ), have explored strategies of teacher and student learning in a science classroom. Theoretical interpretation can be by conceptual application for testing, such as Margaret Mead’s ( 1931 ) testing of the concept of “adolescence”—derived from American culture—in Samoan culture, or, more generally, by concept generation. The latter can be seen in David Rozenhan’s ( 1973 ) investigation of the experience of a group of researcher pseudo-patients admitted to hospitals for the mentally ill in the United States. The main concepts generated were labeling, powerlessness, and depersonalization.

De-colonial ethnography recognizes the “othering” frames of colonial and postcolonial research and takes a position that past colonial supremacy over Third World countries persists in political, economic, educational, and social constructions. Decolonizing requires a critical examination of language, attitudes, and research methods. Kakal Battacharya ( 2016 ) has exposed the micro-discourses of the continuing manifestation of colonial power in a parallel narrative written by a South Asian woman and a white American male. Concepts of colonialism and patriarchy, displayed through the discourses exposed, provide a theoretical critique.

Within critical ethnography , with its focus on power location and alleviation of oppression, Dale Spender ( 1980 ) used structured and timed observations of the styles, quality, and quantity of interaction between staff and students in a range of English classrooms. The theory-directive methodological frames of feminism and gender inequality were applied to identify and expose the lesser time and lesser quality of interaction that teachers had with female students in comparison with that assigned to male students. Widespread distribution of these results alerted education authorities and led to change, in some environments, toward introducing single-sex classrooms for certain topics. This was seen as progress toward alleviating oppressive behaviors. This approach has produced many excellent educational studies, including Peter Willis ( 1977 ) on the preparation of working-class kids for working-class jobs; Michele Fine ( 1991 ) on African American and Latino students who dropped out of a New York high school; Angela Valenzuela ( 1999 ) on emigrant and other under-achievers in American schools; Lisa Patel ( 2013 ) on inclusion and exclusion of immigrants into education; and Jean Anyon ( 1981 ) on social stratification of identical curriculum knowledge in different classrooms

A less concept-driven and more descriptive approach to critical ethnography was emphasized by Phil Carspecken’s hermeneutic approach ( 1996 ), which triggered a move toward data-generated theoretical concepts that could then be used to challenge mainstream theoretical positions.

Post-critical ethnography emphasizes power and ideology and the social practices that contribute to oppression, in particular objectivity, positionality, representation and reflexivity, and critical insufficiency or “antipower.”

Responsibility is shifted to the researcher for the world they create and critique when they interpret their research contexts (Noblit, Flores, & Murillo, 2004 ).

Autoethnography emerged from the postmodern paradigm, with its search for different “truths” and different relationships with readers, and prompted an emphasis on personal experience and documentation of the self in a particular cultural context (Ellis, 2004 ). In order to achieve this, the researcher has to inhabit the dual positions of being the focus of activities, feelings, and emotions experienced in the setting while at the same time being positioned distantly—observing and recording the behaviors of the self in that culture. Well-developed skills of critical reflexivity are required. The rejection of the power-laden discourses/grand theories of the past and the emphasis on transitional explanations has resulted in minimal theorizing and an emphasis on data display, the reader, and the reader’s response. Open presentations of data can be seen in the form of narrative storytelling, or re-presentations in the form of fiction, dramatic performances, and poetry. Carolyn Ellis ( 2004 ) has argued that “story is theory and theory is story” and our “making sense of stories” involves contributing to a broader understanding of human existence. Application/generation of concepts may also occur, and the term “Critical Autoethnography” has been used (Hughes & Pennington, 2017 ), particularly where experiences of race, class, or gender inequality are being experienced. Jennifer Potter ( 2015 ) used the concept “whiteness of silence” to introduce a critical race element into her autoethnographic experience of black–white racial hatred experiences within a university class on African American communication in which she was a student.

Visual ethnography uses a variety of tools, including photography, sketches, movies, social media, the Web and virtual reality, body art, clothing, painting, and sculpture, to demonstrate and track culture. This approach has been available for some time both as a methodology in its own right and as a method of data collection. An example of this approach, which mixes classical and visual ethnography, is Philippe Bourgois and Jeff Schonberg’s 12-year study of two dozen homeless heroin injectors and crack smokers living under a freeway overpass in San Francisco ( 2009 ). Their data comprised extensive black and white photos, dialogue, taped conversations, and fieldwork observation notes. The themes of violence, race relations, family trauma, power relations, and suffering were theoretically interpreted through reworked notions of “power” that incorporated Pierre Bourdieu’s ( 1977 , 1999 ) concepts of “symbolic violence”—linking observed practices to social domination, and “habitus”—an individual’s personal disposition comprising unique feelings and actions grounded in biography and history; Karl Marx’s “lumpen” from “lumpenproletariat” ( 1848 ), the residual class—the vagrants and beggars together with criminal elements that lie beneath the labor force; and Michel Foucault’s “biopower” ( 1978 , 2008 )—the techniques of subjugation used by the state on the population, and “governmentality” ( 1991 )—where individuals are disciplined through institutions and the “knowledge–power” nexus. The ideas of these three theorists were used to create and weave a theory of “lumpen abuse” to interpret the lives of the participants.

Ethno Drama involves transforming the results from an ethnographic study into a performance to be shared, for example the educational experiences of children and youth (Gabriel & Lester, 2013 ). The performance medium can vary from a film (Woo, 2008 ), an article presented in dramatic form (Carter, 2014 ), or more usually a play script to be staged for an audience in a theater (Ethno Theater). One of the main purposes is to provide a hearing space for voices that have been marginalized or previously silenced. These voices and their contexts can be presented by research participants, actors, or the research team, and are often directed at professionals from the field. Audience-based meetings to devise recommendations for further action may follow a performance. Because of the focus on inequality, critical theory has been the major theoretical orientation for this approach. The structure of the presentation invites audiences to identify situations of oppression, in the hope that this will inform them sufficiently to enable modification of their own practices or to be part of the development of recommendations for future change.

Lesnick and Humphrie ( 2018 ) explored the views of identity of LGBTQ+ youth between 14 and 24 years of age via interviews and online questionnaires, the transcriptions of which were woven into a script that was performed by actors presenting stories not congruent with their own racial/gender scripts in order to challenge audience expectations and labels. The research group encouraged the schools where they performed to structure discussion groups to follow the school-located performances. The scripts and discussions revealed and were lightly interpreted through concepts of homelessness, racism, and “oppression Olympics”—the way oppressed people sometimes view one another in competition rather than in solidarity. These issues were found to be relevant to both school and online communities. Support for these young people was discovered to be mostly from virtual sources, being provided by dialogues within Facebook groups.

Cyber/net or/virtual ethnographies involve the study of online communities within particular cultures. Problems which have emerged from the practice of this approach include; discovery of the researcher lurking without permission on sites, gaining prior permission which often disturbs the threads of interaction, gaining permission post–data collection but having many furious people decline participation, the “facelessness” of individuals who may have uncheckable multiple personas, and trying to make sense of very disparate data in incomplete and non-chronological order.. There has been acceptance that online and offline situations can influence each other. Dibbell ( 1993 ) demonstrated that online sexual violence toward another user’s avatar in a text-based “living room” reduced the violated person to tears as she posted pleas for the violator to be removed from the site. Theoretical interpretation at the conceptual level is common; Michel Foucault’s concept of heterotopia ( 1967 , 1984 ) was used to explain such spatio-temporal prisons as online rooms. Heterotropic spaces are seen as having the capacity to reflect and distort real and imagined experiences.

Poststructural ethnography tracks the instability of concepts both culturally and linguistically. This can be demonstrated in the deconstruction of language in education (Lather, 2001 ), particularly the contradictions and paradoxes of sexism, gender, and racism both in texts and in the classroom. These discourses are implicated in relations of power that are dynamic and within which resistance can be observed. Poststructuralism accepts that texts are multiple, as are the personas of those who created them, and that talk such as that which occurs in a classroom can be linked with knowledge control. Walter Humes ( 2000 ) discovered that the educational management discourses of “community,” “leadership,” and “participation” could be disguised by such terms as “learning communities” and “transformational leadership.” He analyzed the results with a conceptual framework derived from management theory and policy studies and linked the findings with political power.

Performative ethnography , from the post-postmodern paradigm, integrates the performances of art and theater with the focus on culture of ethnography (Denzin, 2003 ). A collaborative performance ethnography (van Katwyk & Seko, 2017 ) used a poem re-presenting themes from a previous research study on youth self-harming to form the basis of the creation of a performative dance piece. This process enabled the researcher participants to explore less dominant ways of knowing through co-learning and through the discovery of self-vulnerability. The research was driven by a social justice-derived concern that Foucault’s notion of “sovereignty” was being implemented through a web of relations that commodified and limited knowledge, and sanctioned the exploitation of individuals and communities.

This exploration of the diversity in ethnographic methods, methodologies, and interpretive strategies would be repeated in a similar trek through the interpretive, critical, postmodern, and post-postmodern approaches currently available for undertaking the various versions of grounded theory, phenomenology, feminist research, evaluation, action, or performative research.

Implications of Changes for the Researcher

The onus is now less on finding the “right” (or most familiar in a field) research approaches and following them meticulously, and much more on researchers making their own individual decisions as to which aspects of which methodologies, methods and theoretical explanations will best answer their research question. Ideally this should not be constrained by the state of the discipline they are part of; it should be equally as easy for a fine arts researcher to carry out a classical ethnography with a detailed theoretical interpretation derived from a grand theorist/s as it would be for a researcher in law to undertake a performative study with the minimum of conceptual insights and the maximum of visual and theoretical performances. Unfortunately, the reality is that trends within disciplines dictate publication access, thereby reinforcing the prevailing boundaries of knowledge.

However, the current diversity of choice has indeed shifted the field of qualitative research dramatically away from the position it was in several decades ago. The moves toward visual and performative displays may challenge certain disciplines but these approaches have now become well entrenched in others, and in qualitative research publishing. The creativity of the performative paradigm in daring to scale the siloed and well-protected boundaries of science in order to combine theoretical physics with the theories of social science, and to re-present data in a variety of newer ways from fiction to poetry to researcher performances, is exciting.

Given that theoretical as well as methodological and methods’ domains are now wide open to researchers to pick and choose from, two important aspects—justification and transparency of process—have become essential elements in the process of convincing the reader.

Justification incorporates the why of decision-making. Why was the research question chosen? Why was the particular paradigm, or paradigms, chosen best for the question? Why were the methodology and methods chosen most appropriate for both the paradigm/s and research question/s? And why were the concepts used the most appropriate and illuminating for the study?

Transparency of process not only requires that the researcher clarifies who they are in the field with relation to the research question and the participants chosen, but demands an assessment of what impact their background and personal and professional frames have had on research decisions at all stages from topic choice to theoretical analysis. Problems faced in the research process and how they were managed or overcome also requires exposition as does the chronology of decisions made and changed at all points of the research process.

Now to the issue of theory and the question of “where to?” This brief walk through the paradigmatic, methodological, and theoretical changes has demonstrated a significant move from the use of confined paradigms with limited methodological options to the availability of multiple paradigms, co-methodologies, and methods of many shades, for the researcher to select among Regarding theory use, there has been a clear move away from grand and middle range theories toward the application of individual concepts drawn from a variety of established and minor theoreticians and disciplines, which can be amalgamated into transitory explanations. The examples of theoretical interpretation presented in this article, in my view, very considerably extend, frame, and often shed new light on the themes that have been drawn out via analytical processes. Well-argued theory at any level is a great enhancer, lifting data to heights of illumination and comparison, but it could equally be argued that in the presence of critical researcher reflexivity, complex, layered, longitudinal, and well-justified design, meticulous analysis, and monitored audience response, it may no longer be essential.

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The use of theory in qualitative research: Challenges, development of a framework and exemplar

Affiliations.

• 1 School of Nursing and Midwifery, Edith Cowan University, Joondalup, Western Australia, Australia.
• 2 School of Nursing, Eastern International University, Binh Duong, Vietnam.
• 3 School of Nursing Midwifery and Paramedicine, Sunshine Coast University, Maroochydore DC, Queensland, Australia.
• PMID: 34585419
• DOI: 10.1111/jan.15053

Background: Whilst theoretical grounding is considered important for sound research methodology, consensus on the application of theory in qualitative research remains elusive. Novice researchers may experience challenges in applying theory in qualitative research and these may contribute to the under-use, over-reliance or inappropriate application of theory. Practical guidance on how theory can inform and guide the conduct of qualitative research is needed.

Purpose: The purpose of this paper was to propose a framework for the application of theory in qualitative research and provide an exemplar.

Methods: The Theoretical Application Framework for Qualitative Studies (TAF-QS) was developed from the synthesis of existing literature and the authors' own experience of the application of theory.

Results: The TAF-QS encourages researchers to articulate which theoretical framework or conceptional framework they are drawing on and how this will be applied by reflecting on the purpose and the context of the study.

Conclusion: The TAF-QS provides support for researchers to explore how theory can be applied and how to achieve this in qualitative research.

Tweetable abstract: The use of theory in qualitative research.

Keywords: conceptual framework; deductive; framework development; inductive; qualitative research; theoretical framework; theory application.

© 2021 John Wiley & Sons Ltd.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE :   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
• Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods. Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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5 Strengths and 5 Limitations of Qualitative Research

Lauren Christiansen

Insight into qualitative research.

Anyone who reviews a bunch of numbers knows how impersonal that feels. What do numbers really reveal about a person's beliefs, motives, and thoughts? While it's critical to collect statistical information to identify business trends and inefficiencies, stats don't always tell the full story. Why does the customer like this product more than the other one? What motivates them to post this particular hashtag on social media? How do employees actually feel about the new supply chain process? To answer more personal questions that delve into the human experience, businesses often employ a qualitative research process.

10 Key Strengths and Limitations of Qualitative Research

Qualitative research helps entrepreneurs and established companies understand the many factors that drive consumer behavior. Because most organizations collect and analyze quantitative data, they don't always know exactly how a target market feels and what it wants. It helps researchers when they can observe a small sample size of consumers in a comfortable environment, ask questions, and let them speak. Research methodology varies depending on the industry and type of business needs. Many companies employ mixed methods to extract the insights they require to improve decision-making. While both quantitative research and qualitative methods are effective, there are limitations to both. Quantitative research is expensive, time-consuming, and presents a limited understanding of consumer needs. However, qualitative research methods generate less verifiable information as all qualitative data is based on experience. Businesses should use a combination of both methods to overcome any associated limitations.

Strengths of Qualitative Research

• Captures New Beliefs - Qualitative research methods extrapolate any evolving beliefs within a market. This may include who buys a product/service, or how employees feel about their employers.
• Fewer Limitations - Qualitative studies are less stringent than quantitative ones. Outside the box answers to questions, opinions, and beliefs are included in data collection and data analysis.
• More Versatile - Qualitative research is much easier at times for researchers. They can adjust questions, adapt to circumstances that change or change the environment to optimize results.
• Greater Speculation - Researchers can speculate more on what answers to drill down into and how to approach them. They can use instinct and subjective experience to identify and extract good data.
• More Targeted - This research process can target any area of the business or concern it may have. Researchers can concentrate on specific target markets to collect valuable information. This takes less time and requires fewer resources than quantitative studies.

Limitations of Qualitative Research

• Sample Sizes - Businesses need to find a big enough group of participants to ensure results are accurate. A sample size of 15 people is not enough to show a reliable picture of how consumers view a product. If it is not possible to find a large enough sample size, the data collected may be insufficient.
• Bias - For internal qualitative studies, employees may be biased. For example, workers may give a popular answer that colleagues agree with rather than a true opinion. This can negatively influence the outcome of the study.
• Self-Selection Bias - Businesses that call on volunteers to answer questions worry that the people who respond are not reflective of the greater group. It is better if the company selects individuals at random for research studies, particularly if they are employees. However, this changes the process from qualitative to quantitative methods.
• Artificial - It isn't typical to observe consumers in stores, gather a focus group together, or ask employees about their experiences at work. This artificiality may impact the findings, as it is outside the norm of regular behavior and interactions.
• Quality - Questions It's hard to know whether researcher questions are quality or not because they are all subjective. Researchers need to ask how and why individuals feel the way they do to receive the most accurate answers.

Key Takeaways on Strengths and Limitations of Qualitative Research

• Qualitative research helps entrepreneurs and small businesses understand what drives human behavior. It is also used to see how employees feel about workflows and tasks.
• Companies can extract insights from qualitative research to optimize decision-making and improve products or services.
• Qualitative research captures new beliefs, has fewer limitations, is more versatile, and is more targeted. It also allows researchers to speculate and insert themselves more into the research study.
• Qualitative research has many limitations which include possible small sample sizes, potential bias in answers, self-selection bias, and potentially poor questions from researchers. It also can be artificial because it isn't typical to observe participants in focus groups, ask them questions at work, or invite them to partake in this type of research method.

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How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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POST-DOCTORAL RESEARCH SCIENTIST IN QUALITATIVE IMPACT EVALUATION (IE)

The African Population and Health Research Center (APHRC) is an African-led and Africa-based international research institution committed to conducting high-quality and policy-relevant multidisciplinary research. Our goal is to generate evidence for meaningful action, engage with policymakers in the region to disseminate our research findings, influence policy decisions, and improve the quality of life in Africa. APHRC’s Data Synergy Unit supports all Program Units across the Center in the application and utility of monitoring and IE approaches to inform the implementation and assess the performance of the projects aligned to the different research priorities at subnational and national levels across Africa.

To this end, APHRC seeks to recruit one (1) Post-Doctoral Research Scientist in Qualitative IE to support the qualitative IE activities at the center in the following research thematic areas:

• Human development with focusing on inclusive policies and practices for early childhood development, education, and youth empowerment systems in Africa;
• Health and Wellbeing with focusing on effective interventions/ or strategies and policies to promote equitable health and well-being of all people in Africa; and,
• Population Dynamics and Urbanization focusing on the generation of evidence in the areas of urbanization, fertility, and aging and their implications for developing sustainable and resilient cities in Africa.

As a Postdoctoral Research Scientist in Qualitative IE, the successful candidate will support all four thematic areas, working collaboratively with APHRC researchers.

The position will be based at the Center’s Head Office in Nairobi, Kenya; and may include some travels to conduct qualitative IE (IE) activities in other Anglophone and Francophone African countries. As a Qualitative IE Specialist , the Postdoctoral Research Scientist will be primarily responsible for the management of IE projects and supporting researchers at the Center on appropriate qualitative IE designs for different undertakings in the designated research thematic areas. The expected specific duties and responsibilities include the following:

Technical leadership and support

• Leading the design and implementation of development-related qualitative IEs and research studies;
• Support researchers at the Center in the adoption of qualitative IE designs in research studies including complex IEs based on modern practices such as appreciative inquiry, participatory approaches, most significant change, outcome harvesting, contribution analysis, comparative case studies, and process tracing among others .;
• Develop and deepen innovative approaches to IE for example, through training of researchers at the center in IE methodology, developing and writing papers that utilize IE methodologies in peer-reviewed journals, engaging with IE communities of practice to strengthen the Center’s visibility in IE practice, etc.

Fundraising for IE sustainability

• Contribute to the resource mobilization for IE projects to ensure that the Center sustains its IE mandate to the foreseeable future;
• Support IE proposal writing and other fundraising activities – including designing qualitative IE approaches geared towards leveraging funds to broaden the current mandate of the Data Synergy and Evaluations (DSE) Unit;
• Actively collaborate with and contribute to the work of other Program Areas at APHRC to strengthen the IE mandate;

Management of IE Projects

• Establish, monitor, and update work plans and budgets for IE projects;
• Oversee mobilization of IE teams, including organizing and managing team planning meetings and overseeing all necessary logistical preparations for field data collection;
• Identify, recruit, and train research assistants; support teams technically; assist in orienting consultant team members to procedures for working with APHRC;
• Assure high-quality deliverables and evaluation reports;
• Support development of IE including background and supporting research for ongoing/upcoming IE and oversight of survey development: interview and focus group discussion guides, and observation checklists among others;
• Manage qualitative IE datasets (audio recordings and transcripts) throughout the IE life-cycle from coordination of local data collection partners to data management including transcriptions cleaning and coding to preliminary and final analyses;
• Serve as a responsive point of contact for clients, ensure contract compliance, and support contract reporting requirements;
• Support the IE team to develop, review, and finalize IE analysis plans, and evaluation frameworks, and contribute to the drafting of work plans, data collection tools, and field monitoring tools for each IE activity;
• Work with the Project Evaluation Team to provide timely updates and reports on evaluation activities as needed;
• Participate and/or support the dissemination of IE findings at local, national, and international fora through abstract presentations, roundtable discussions, publications, blogs, policy briefs, and working papers among others;
• Carry out any other functions that may be assigned to you from time to time by the Senior Management Team at the Center;

Key Competencies, qualifications, and experience

• Ph.D. in Social Sciences, Sociology, Medical Anthropology, Public Health, International Health, Social Work and Social Administration, Public Policy, and other related disciplines, with specialization and significant demonstrated experience in Qualitative IE of development projects, preferably public health;
• Advanced skills in qualitative IE designs, data collection approaches, and rigorous data analysis;
• Strong qualitative research skills, and ability to conduct and work with large qualitative datasets using computer assisted qualitative data analysis (CAQDAs) software programs (RQDA, Dedoose, Nvivo, MAXQDA, and ATLAS.ti);
• At least 3 years of work experience in project evaluation and impact analysis is essential;
• Demonstrated experience in in-depth qualitative impact analysis methods, including mixed-methods IE;
• Experience designing and implementing one or more of the following: implementation research, formative evaluation, process evaluations, process monitoring, summative evaluation, and IE;
• At least five (5) publications or technical reports (or a doctoral research thesis) that utilized qualitative IE methodologies/approaches, with 3 or more first-author publications .
• Excellent qualitative scientific writing, presentation, and communication skills including the ability to present arguments and analysis in a structured and succinct manner;
• Sound causal reasoning, and good knowledge of the theory of change, evaluation criteria, causal attribution, and limitations of qualitative IEs;
• Ability to work independently; self-starter and highly motivated;
• Strong theoretical and applied qualitative knowledge; fluency in French is highly desirable
• Knowledge of economic evaluation and quantitative IE methods will be an added advantage;
• Proficiency in the English language is a must;
• Working knowledge of East, Southern, Northern, and West African regions is desirable.

Interested candidates are invited to submit their applications as one PDF document in English through this link by Monday, April 15, 2024 and include:

• A letter of application not exceeding 1-page, highlighting your qualifications and experience relevant to the terms of reference, and the thematic areas ;
• A statement of research interests and goals (1 page) about the position;
• A detailed CV (5 pages max.) with contact information for three professional references.

Non-adherence to these requirements will lead to your application not being reviewed. Applications will be reviewed on a rolling basis and incomplete applications will not be considered . Only shortlisted candidates will be contacted.

Special Notice

APHRC is an equal opportunity employer that is committed to creating a diverse and inclusive workplace. All employment decisions are made on the basis of qualifications and organizational needs. Reasonable accommodation may be provided to applicants with disabilities upon request, to support their participation in the recruitment process.

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Limited by our limitations

Paula t. ross.

Medical School, University of Michigan, Ann Arbor, MI USA

Nikki L. Bibler Zaidi

Study limitations represent weaknesses within a research design that may influence outcomes and conclusions of the research. Researchers have an obligation to the academic community to present complete and honest limitations of a presented study. Too often, authors use generic descriptions to describe study limitations. Including redundant or irrelevant limitations is an ineffective use of the already limited word count. A meaningful presentation of study limitations should describe the potential limitation, explain the implication of the limitation, provide possible alternative approaches, and describe steps taken to mitigate the limitation. This includes placing research findings within their proper context to ensure readers do not overemphasize or minimize findings. A more complete presentation will enrich the readers’ understanding of the study’s limitations and support future investigation.

Introduction

Regardless of the format scholarship assumes, from qualitative research to clinical trials, all studies have limitations. Limitations represent weaknesses within the study that may influence outcomes and conclusions of the research. The goal of presenting limitations is to provide meaningful information to the reader; however, too often, limitations in medical education articles are overlooked or reduced to simplistic and minimally relevant themes (e.g., single institution study, use of self-reported data, or small sample size) [ 1 ]. This issue is prominent in other fields of inquiry in medicine as well. For example, despite the clinical implications, medical studies often fail to discuss how limitations could have affected the study findings and interpretations [ 2 ]. Further, observational research often fails to remind readers of the fundamental limitation inherent in the study design, which is the inability to attribute causation [ 3 ]. By reporting generic limitations or omitting them altogether, researchers miss opportunities to fully communicate the relevance of their work, illustrate how their work advances a larger field under study, and suggest potential areas for further investigation.

Goals of presenting limitations

Medical education scholarship should provide empirical evidence that deepens our knowledge and understanding of education [ 4 , 5 ], informs educational practice and process, [ 6 , 7 ] and serves as a forum for educating other researchers [ 8 ]. Providing study limitations is indeed an important part of this scholarly process. Without them, research consumers are pressed to fully grasp the potential exclusion areas or other biases that may affect the results and conclusions provided [ 9 ]. Study limitations should leave the reader thinking about opportunities to engage in prospective improvements [ 9 – 11 ] by presenting gaps in the current research and extant literature, thereby cultivating other researchers’ curiosity and interest in expanding the line of scholarly inquiry [ 9 ].

Presenting study limitations is also an ethical element of scientific inquiry [ 12 ]. It ensures transparency of both the research and the researchers [ 10 , 13 , 14 ], as well as provides transferability [ 15 ] and reproducibility of methods. Presenting limitations also supports proper interpretation and validity of the findings [ 16 ]. A study’s limitations should place research findings within their proper context to ensure readers are fully able to discern the credibility of a study’s conclusion, and can generalize findings appropriately [ 16 ].

Why some authors may fail to present limitations

As Price and Murnan [ 8 ] note, there may be overriding reasons why researchers do not sufficiently report the limitations of their study. For example, authors may not fully understand the importance and implications of their study’s limitations or assume that not discussing them may increase the likelihood of publication. Word limits imposed by journals may also prevent authors from providing thorough descriptions of their study’s limitations [ 17 ]. Still another possible reason for excluding limitations is a diffusion of responsibility in which some authors may incorrectly assume that the journal editor is responsible for identifying limitations. Regardless of reason or intent, researchers have an obligation to the academic community to present complete and honest study limitations.

A guide to presenting limitations

The presentation of limitations should describe the potential limitations, explain the implication of the limitations, provide possible alternative approaches, and describe steps taken to mitigate the limitations. Too often, authors only list the potential limitations, without including these other important elements.

Describe the limitations

When describing limitations authors should identify the limitation type to clearly introduce the limitation and specify the origin of the limitation. This helps to ensure readers are able to interpret and generalize findings appropriately. Here we outline various limitation types that can occur at different stages of the research process.

Study design

Some study limitations originate from conscious choices made by the researcher (also known as delimitations) to narrow the scope of the study [ 1 , 8 , 18 ]. For example, the researcher may have designed the study for a particular age group, sex, race, ethnicity, geographically defined region, or some other attribute that would limit to whom the findings can be generalized. Such delimitations involve conscious exclusionary and inclusionary decisions made during the development of the study plan, which may represent a systematic bias intentionally introduced into the study design or instrument by the researcher [ 8 ]. The clear description and delineation of delimitations and limitations will assist editors and reviewers in understanding any methodological issues.

Data collection

Study limitations can also be introduced during data collection. An unintentional consequence of human subjects research is the potential of the researcher to influence how participants respond to their questions. Even when appropriate methods for sampling have been employed, some studies remain limited by the use of data collected only from participants who decided to enrol in the study (self-selection bias) [ 11 , 19 ]. In some cases, participants may provide biased input by responding to questions they believe are favourable to the researcher rather than their authentic response (social desirability bias) [ 20 – 22 ]. Participants may influence the data collected by changing their behaviour when they are knowingly being observed (Hawthorne effect) [ 23 ]. Researchers—in their role as an observer—may also bias the data they collect by allowing a first impression of the participant to be influenced by a single characteristic or impression of another characteristic either unfavourably (horns effect) or favourably (halo effort) [ 24 ].

Data analysis

Study limitations may arise as a consequence of the type of statistical analysis performed. Some studies may not follow the basic tenets of inferential statistical analyses when they use convenience sampling (i.e. non-probability sampling) rather than employing probability sampling from a target population [ 19 ]. Another limitation that can arise during statistical analyses occurs when studies employ unplanned post-hoc data analyses that were not specified before the initial analysis [ 25 ]. Unplanned post-hoc analysis may lead to statistical relationships that suggest associations but are no more than coincidental findings [ 23 ]. Therefore, when unplanned post-hoc analyses are conducted, this should be clearly stated to allow the reader to make proper interpretation and conclusions—especially when only a subset of the original sample is investigated [ 23 ].

Study results

The limitations of any research study will be rooted in the validity of its results—specifically threats to internal or external validity [ 8 ]. Internal validity refers to reliability or accuracy of the study results [ 26 ], while external validity pertains to the generalizability of results from the study’s sample to the larger, target population [ 8 ].

Examples of threats to internal validity include: effects of events external to the study (history), changes in participants due to time instead of the studied effect (maturation), systematic reduction in participants related to a feature of the study (attrition), changes in participant responses due to repeatedly measuring participants (testing effect), modifications to the instrument (instrumentality) and selecting participants based on extreme scores that will regress towards the mean in repeat tests (regression to the mean) [ 27 ].

Threats to external validity include factors that might inhibit generalizability of results from the study’s sample to the larger, target population [ 8 , 27 ]. External validity is challenged when results from a study cannot be generalized to its larger population or to similar populations in terms of the context, setting, participants and time [ 18 ]. Therefore, limitations should be made transparent in the results to inform research consumers of any known or potentially hidden biases that may have affected the study and prevent generalization beyond the study parameters.

Explain the implication(s) of each limitation

Authors should include the potential impact of the limitations (e.g., likelihood, magnitude) [ 13 ] as well as address specific validity implications of the results and subsequent conclusions [ 16 , 28 ]. For example, self-reported data may lead to inaccuracies (e.g. due to social desirability bias) which threatens internal validity [ 19 ]. Even a researcher’s inappropriate attribution to a characteristic or outcome (e.g., stereotyping) can overemphasize (either positively or negatively) unrelated characteristics or outcomes (halo or horns effect) and impact the internal validity [ 24 ]. Participants’ awareness that they are part of a research study can also influence outcomes (Hawthorne effect) and limit external validity of findings [ 23 ]. External validity may also be threatened should the respondents’ propensity for participation be correlated with the substantive topic of study, as data will be biased and not represent the population of interest (self-selection bias) [ 29 ]. Having this explanation helps readers interpret the results and generalize the applicability of the results for their own setting.

Provide potential alternative approaches and explanations

Often, researchers use other studies’ limitations as the first step in formulating new research questions and shaping the next phase of research. Therefore, it is important for readers to understand why potential alternative approaches (e.g. approaches taken by others exploring similar topics) were not taken. In addition to alternative approaches, authors can also present alternative explanations for their own study’s findings [ 13 ]. This information is valuable coming from the researcher because of the direct, relevant experience and insight gained as they conducted the study. The presentation of alternative approaches represents a major contribution to the scholarly community.

Describe steps taken to minimize each limitation

No research design is perfect and free from explicit and implicit biases; however various methods can be employed to minimize the impact of study limitations. Some suggested steps to mitigate or minimize the limitations mentioned above include using neutral questions, randomized response technique, force choice items, or self-administered questionnaires to reduce respondents’ discomfort when answering sensitive questions (social desirability bias) [ 21 ]; using unobtrusive data collection measures (e.g., use of secondary data) that do not require the researcher to be present (Hawthorne effect) [ 11 , 30 ]; using standardized rubrics and objective assessment forms with clearly defined scoring instructions to minimize researcher bias, or making rater adjustments to assessment scores to account for rater tendencies (halo or horns effect) [ 24 ]; or using existing data or control groups (self-selection bias) [ 11 , 30 ]. When appropriate, researchers should provide sufficient evidence that demonstrates the steps taken to mitigate limitations as part of their study design [ 13 ].

In conclusion, authors may be limiting the impact of their research by neglecting or providing abbreviated and generic limitations. We present several examples of limitations to consider; however, this should not be considered an exhaustive list nor should these examples be added to the growing list of generic and overused limitations. Instead, careful thought should go into presenting limitations after research has concluded and the major findings have been described. Limitations help focus the reader on key findings, therefore it is important to only address the most salient limitations of the study [ 17 , 28 ] related to the specific research problem, not general limitations of most studies [ 1 ]. It is important not to minimize the limitations of study design or results. Rather, results, including their limitations, must help readers draw connections between current research and the extant literature.

The quality and rigor of our research is largely defined by our limitations [ 31 ]. In fact, one of the top reasons reviewers report recommending acceptance of medical education research manuscripts involves limitations—specifically how the study’s interpretation accounts for its limitations [ 32 ]. Therefore, it is not only best for authors to acknowledge their study’s limitations rather than to have them identified by an editor or reviewer, but proper framing and presentation of limitations can actually increase the likelihood of acceptance. Perhaps, these issues could be ameliorated if academic and research organizations adopted policies and/or expectations to guide authors in proper description of limitations.