qualitative research 19

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• 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.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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.

Prevent plagiarism. Run a free check.

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 .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

What is Qualitative in Qualitative Research

• Open access
• Published: 27 February 2019
• Volume 42 , pages 139–160, ( 2019 )

Cite this article

You have full access to this open access article

• Patrik Aspers 1 , 2 &
• Ugo Corte 3  

623k Accesses

332 Citations

24 Altmetric

Explore all metrics

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

Similar content being viewed by others

What is Qualitative in Research

Unsettling definitions of qualitative research, what is “qualitative” in qualitative research why the answer does not matter but the question is important, explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Åkerström, Malin. 2013. Curiosity and serendipity in qualitative research. Qualitative Sociology Review 9 (2): 10–18.

Google Scholar  

Alford, Robert R. 1998. The craft of inquiry. Theories, methods, evidence . Oxford: Oxford University Press.

Alvesson, Mats, and Dan Kärreman. 2011. Qualitative research and theory development. Mystery as method . London: SAGE Publications.

Book   Google Scholar  

Aspers, Patrik. 2006. Markets in Fashion, A Phenomenological Approach. London Routledge.

Atkinson, Paul. 2005. Qualitative research. Unity and diversity. Forum: Qualitative Social Research 6 (3): 1–15.

Becker, Howard S. 1963. Outsiders. Studies in the sociology of deviance . New York: The Free Press.

Becker, Howard S. 1966. Whose side are we on? Social Problems 14 (3): 239–247.

Article   Google Scholar  

Becker, Howard S. 1970. Sociological work. Method and substance . New Brunswick: Transaction Books.

Becker, Howard S. 1996. The epistemology of qualitative research. In Ethnography and human development. Context and meaning in social inquiry , ed. Jessor Richard, Colby Anne, and Richard A. Shweder, 53–71. Chicago: University of Chicago Press.

Becker, Howard S. 1998. Tricks of the trade. How to think about your research while you're doing it . Chicago: University of Chicago Press.

Becker, Howard S. 2017. Evidence . Chigaco: University of Chicago Press.

Becker, Howard, Blanche Geer, Everett Hughes, and Anselm Strauss. 1961. Boys in White, student culture in medical school . New Brunswick: Transaction Publishers.

Berezin, Mabel. 2014. How do we know what we mean? Epistemological dilemmas in cultural sociology. Qualitative Sociology 37 (2): 141–151.

Best, Joel. 2004. Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , eds . Charles, Ragin, Joanne, Nagel, and Patricia White, 53-54. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf .

Biernacki, Richard. 2014. Humanist interpretation versus coding text samples. Qualitative Sociology 37 (2): 173–188.

Blumer, Herbert. 1969. Symbolic interactionism: Perspective and method . Berkeley: University of California Press.

Brady, Henry, David Collier, and Jason Seawright. 2004. Refocusing the discussion of methodology. In Rethinking social inquiry. Diverse tools, shared standards , ed. Brady Henry and Collier David, 3–22. Lanham: Rowman and Littlefield.

Brown, Allison P. 2010. Qualitative method and compromise in applied social research. Qualitative Research 10 (2): 229–248.

Charmaz, Kathy. 2006. Constructing grounded theory . London: Sage.

Corte, Ugo, and Katherine Irwin. 2017. “The Form and Flow of Teaching Ethnographic Knowledge: Hands-on Approaches for Learning Epistemology” Teaching Sociology 45(3): 209-219.

Creswell, John W. 2009. Research design. Qualitative, quantitative, and mixed method approaches . 3rd ed. Thousand Oaks: SAGE Publications.

Davidsson, David. 1988. 2001. The myth of the subjective. In Subjective, intersubjective, objective , ed. David Davidsson, 39–52. Oxford: Oxford University Press.

Denzin, Norman K. 1970. The research act: A theoretical introduction to Ssociological methods . Chicago: Aldine Publishing Company Publishers.

Denzin, Norman K., and Yvonna S. Lincoln. 2003. Introduction. The discipline and practice of qualitative research. In Collecting and interpreting qualitative materials , ed. Norman K. Denzin and Yvonna S. Lincoln, 1–45. Thousand Oaks: SAGE Publications.

Denzin, Norman K., and Yvonna S. Lincoln. 2005. Introduction. The discipline and practice of qualitative research. In The Sage handbook of qualitative research , ed. Norman K. Denzin and Yvonna S. Lincoln, 1–32. Thousand Oaks: SAGE Publications.

Emerson, Robert M., ed. 1988. Contemporary field research. A collection of readings . Prospect Heights: Waveland Press.

Emerson, Robert M., Rachel I. Fretz, and Linda L. Shaw. 1995. Writing ethnographic fieldnotes . Chicago: University of Chicago Press.

Esterberg, Kristin G. 2002. Qualitative methods in social research . Boston: McGraw-Hill.

Fine, Gary Alan. 1995. Review of “handbook of qualitative research.” Contemporary Sociology 24 (3): 416–418.

Fine, Gary Alan. 2003. “ Toward a Peopled Ethnography: Developing Theory from Group Life.” Ethnography . 4(1):41-60.

Fine, Gary Alan, and Black Hawk Hancock. 2017. The new ethnographer at work. Qualitative Research 17 (2): 260–268.

Fine, Gary Alan, and Timothy Hallett. 2014. Stranger and stranger: Creating theory through ethnographic distance and authority. Journal of Organizational Ethnography 3 (2): 188–203.

Flick, Uwe. 2002. Qualitative research. State of the art. Social Science Information 41 (1): 5–24.

Flick, Uwe. 2007. Designing qualitative research . London: SAGE Publications.

Frankfort-Nachmias, Chava, and David Nachmias. 1996. Research methods in the social sciences . 5th ed. London: Edward Arnold.

Franzosi, Roberto. 2010. Sociology, narrative, and the quality versus quantity debate (Goethe versus Newton): Can computer-assisted story grammars help us understand the rise of Italian fascism (1919- 1922)? Theory and Society 39 (6): 593–629.

Franzosi, Roberto. 2016. From method and measurement to narrative and number. International journal of social research methodology 19 (1): 137–141.

Gadamer, Hans-Georg. 1990. Wahrheit und Methode, Grundzüge einer philosophischen Hermeneutik . Band 1, Hermeneutik. Tübingen: J.C.B. Mohr.

Gans, Herbert. 1999. Participant Observation in an Age of “Ethnography”. Journal of Contemporary Ethnography 28 (5): 540–548.

Geertz, Clifford. 1973. The interpretation of cultures . New York: Basic Books.

Gilbert, Nigel. 2009. Researching social life . 3rd ed. London: SAGE Publications.

Glaeser, Andreas. 2014. Hermeneutic institutionalism: Towards a new synthesis. Qualitative Sociology 37: 207–241.

Glaser, Barney G., and Anselm L. Strauss. [1967] 2010. The discovery of grounded theory. Strategies for qualitative research. Hawthorne: Aldine.

Goertz, Gary, and James Mahoney. 2012. A tale of two cultures: Qualitative and quantitative research in the social sciences . Princeton: Princeton University Press.

Goffman, Erving. 1989. On fieldwork. Journal of Contemporary Ethnography 18 (2): 123–132.

Goodwin, Jeff, and Ruth Horowitz. 2002. Introduction. The methodological strengths and dilemmas of qualitative sociology. Qualitative Sociology 25 (1): 33–47.

Habermas, Jürgen. [1981] 1987. The theory of communicative action . Oxford: Polity Press.

Hammersley, Martyn. 2007. The issue of quality in qualitative research. International Journal of Research & Method in Education 30 (3): 287–305.

Hammersley, Martyn. 2013. What is qualitative research? Bloomsbury Publishing.

Hammersley, Martyn. 2018. What is ethnography? Can it survive should it? Ethnography and Education 13 (1): 1–17.

Hammersley, Martyn, and Paul Atkinson. 2007. Ethnography. Principles in practice . London: Tavistock Publications.

Heidegger, Martin. [1927] 2001. Sein und Zeit . Tübingen: Max Niemeyer Verlag.

Heidegger, Martin. 1988. 1923. Ontologie. Hermeneutik der Faktizität, Gesamtausgabe II. Abteilung: Vorlesungen 1919-1944, Band 63, Frankfurt am Main: Vittorio Klostermann.

Hempel, Carl G. 1966. Philosophy of the natural sciences . Upper Saddle River: Prentice Hall.

Hood, Jane C. 2006. Teaching against the text. The case of qualitative methods. Teaching Sociology 34 (3): 207–223.

James, William. 1907. 1955. Pragmatism . New York: Meredian Books.

Jovanović, Gordana. 2011. Toward a social history of qualitative research. History of the Human Sciences 24 (2): 1–27.

Kalof, Linda, Amy Dan, and Thomas Dietz. 2008. Essentials of social research . London: Open University Press.

Katz, Jack. 2015. Situational evidence: Strategies for causal reasoning from observational field notes. Sociological Methods & Research 44 (1): 108–144.

King, Gary, Robert O. Keohane, S. Sidney, and S. Verba. 1994. Designing social inquiry. In Scientific inference in qualitative research . Princeton: Princeton University Press.

Chapter   Google Scholar  

Lamont, Michelle. 2004. Evaluating qualitative research: Some empirical findings and an agenda. In Report from workshop on interdisciplinary standards for systematic qualitative research , ed. M. Lamont and P. White, 91–95. Washington, DC: National Science Foundation.

Lamont, Michèle, and Ann Swidler. 2014. Methodological pluralism and the possibilities and limits of interviewing. Qualitative Sociology 37 (2): 153–171.

Lazarsfeld, Paul, and Alan Barton. 1982. Some functions of qualitative analysis in social research. In The varied sociology of Paul Lazarsfeld , ed. Patricia Kendall, 239–285. New York: Columbia University Press.

Lichterman, Paul, and Isaac Reed I (2014), Theory and Contrastive Explanation in Ethnography. Sociological methods and research. Prepublished 27 October 2014; https://doi.org/10.1177/0049124114554458 .

Lofland, John, and Lyn Lofland. 1995. Analyzing social settings. A guide to qualitative observation and analysis . 3rd ed. Belmont: Wadsworth.

Lofland, John, David A. Snow, Leon Anderson, and Lyn H. Lofland. 2006. Analyzing social settings. A guide to qualitative observation and analysis . 4th ed. Belmont: Wadsworth/Thomson Learning.

Long, Adrew F., and Mary Godfrey. 2004. An evaluation tool to assess the quality of qualitative research studies. International Journal of Social Research Methodology 7 (2): 181–196.

Lundberg, George. 1951. Social research: A study in methods of gathering data . New York: Longmans, Green and Co..

Malinowski, Bronislaw. 1922. Argonauts of the Western Pacific: An account of native Enterprise and adventure in the archipelagoes of Melanesian New Guinea . London: Routledge.

Manicas, Peter. 2006. A realist philosophy of science: Explanation and understanding . Cambridge: Cambridge University Press.

Marchel, Carol, and Stephanie Owens. 2007. Qualitative research in psychology. Could William James get a job? History of Psychology 10 (4): 301–324.

McIntyre, Lisa J. 2005. Need to know. Social science research methods . Boston: McGraw-Hill.

Merton, Robert K., and Elinor Barber. 2004. The travels and adventures of serendipity. A Study in Sociological Semantics and the Sociology of Science . Princeton: Princeton University Press.

Mannay, Dawn, and Melanie Morgan. 2015. Doing ethnography or applying a qualitative technique? Reflections from the ‘waiting field‘. Qualitative Research 15 (2): 166–182.

Neuman, Lawrence W. 2007. Basics of social research. Qualitative and quantitative approaches . 2nd ed. Boston: Pearson Education.

Ragin, Charles C. 1994. Constructing social research. The unity and diversity of method . Thousand Oaks: Pine Forge Press.

Ragin, Charles C. 2004. Introduction to session 1: Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , 22, ed. Charles C. Ragin, Joane Nagel, Patricia White. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf

Rawls, Anne. 2018. The Wartime narrative in US sociology, 1940–7: Stigmatizing qualitative sociology in the name of ‘science,’ European Journal of Social Theory (Online first).

Schütz, Alfred. 1962. Collected papers I: The problem of social reality . The Hague: Nijhoff.

Seiffert, Helmut. 1992. Einführung in die Hermeneutik . Tübingen: Franke.

Silverman, David. 2005. Doing qualitative research. A practical handbook . 2nd ed. London: SAGE Publications.

Silverman, David. 2009. A very short, fairly interesting and reasonably cheap book about qualitative research . London: SAGE Publications.

Silverman, David. 2013. What counts as qualitative research? Some cautionary comments. Qualitative Sociology Review 9 (2): 48–55.

Small, Mario L. 2009. “How many cases do I need?” on science and the logic of case selection in field-based research. Ethnography 10 (1): 5–38.

Small, Mario L 2008. Lost in translation: How not to make qualitative research more scientific. In Workshop on interdisciplinary standards for systematic qualitative research, ed in Michelle Lamont, and Patricia White, 165–171. Washington, DC: National Science Foundation.

Snow, David A., and Leon Anderson. 1993. Down on their luck: A study of homeless street people . Berkeley: University of California Press.

Snow, David A., and Calvin Morrill. 1995. New ethnographies: Review symposium: A revolutionary handbook or a handbook for revolution? Journal of Contemporary Ethnography 24 (3): 341–349.

Strauss, Anselm L. 2003. Qualitative analysis for social scientists . 14th ed. Chicago: Cambridge University Press.

Strauss, Anselm L., and Juliette M. Corbin. 1998. Basics of qualitative research. Techniques and procedures for developing grounded theory . 2nd ed. Thousand Oaks: Sage Publications.

Swedberg, Richard. 2017. Theorizing in sociological research: A new perspective, a new departure? Annual Review of Sociology 43: 189–206.

Swedberg, Richard. 1990. The new 'Battle of Methods'. Challenge January–February 3 (1): 33–38.

Timmermans, Stefan, and Iddo Tavory. 2012. Theory construction in qualitative research: From grounded theory to abductive analysis. Sociological Theory 30 (3): 167–186.

Trier-Bieniek, Adrienne. 2012. Framing the telephone interview as a participant-centred tool for qualitative research. A methodological discussion. Qualitative Research 12 (6): 630–644.

Valsiner, Jaan. 2000. Data as representations. Contextualizing qualitative and quantitative research strategies. Social Science Information 39 (1): 99–113.

Weber, Max. 1904. 1949. Objectivity’ in social Science and social policy. Ed. Edward A. Shils and Henry A. Finch, 49–112. New York: The Free Press.

Download references

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Author information

Authors and affiliations.

Department of Sociology, Uppsala University, Uppsala, Sweden

Patrik Aspers

Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Patrik Aspers .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Aspers, P., Corte, U. What is Qualitative in Qualitative Research. Qual Sociol 42 , 139–160 (2019). https://doi.org/10.1007/s11133-019-9413-7

Download citation

Published : 27 February 2019

Issue Date : 01 June 2019

DOI : https://doi.org/10.1007/s11133-019-9413-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative research
• Epistemology
• Philosophy of science
• Phenomenology
• Find a journal
• Publish with us
• Track your research

• Technical Support
• Find My Rep

You are here

The SAGE Handbook of Qualitative Research

• Norman K. Denzin - University of Illinois, Urbana - Champaign, USA
• Yvonna S. Lincoln - Texas A&M University, USA
• Description

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

• New contributors offer 19 completely new chapter topics , including indigenous methodologies, methodologies in an age of new technologies, queer/quare theory, ethnodrama, data and its problematics, triangulation, collaborative inquiry, digital ethnography, the global audit culture, and much more.
• Substantial revisions from returning authors provide reinvigorated content resulting in very different chapters.
• Content on a wide range of key topics, diverse perspectives, and current controversies derived from members of an international and interdisciplinary editorial board ensure the timeliest revision.
• Six classic chapters cover topics from paradigmatic controversies, contradictions, and emerging confluences, to performance ethnography, writing as a method of inquiry, strategies for composition, and creating narratives and research reports.
• Contributions from well-known international scholars allow readers to study the differences in approach among European, Australian, and American practitioners and theoreticians, as well as to hear the voices of non-Western authors.
• Coverage of state-of-the-art t opics include critical social science, critical pedagogy, mixed methods, narrative inquiry, qualitative research and technology, online ethnography, oral history, human rights, disability communities, queer theory, and performance ethnography.
• Discussions on a wide range of methods expand the reader's repertoire of methodologies, enlarging the range of data that can be brought to bear on social and educational issues. 

Sample Materials & Chapters

Select a purchasing option.

Shipped Options:

BUNDLE: Denzin: The SAGE Handbook of Qualitative Research 5E + Plano Clark: Mixed Methods Research

Related Products

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing 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 analysing 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, and history.

• 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 organisation?
• 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, 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 emphasise different aims and perspectives.

Prevent plagiarism, run a free check.

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 analysing 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 organise 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 categorise 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 analysing qualitative data. Although these methods share similar processes, they emphasise 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 analysing 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 analysing 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 generalisability

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

• Labour-intensive

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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 organisation 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 organisations.

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

• Prepare and organise 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 .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2023, January 30). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-qualitative-research/

Is this article helpful?

Pritha Bhandari

Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

Events and Workshops

• Introduction to NVivo Have you just collected your data and wondered what to do next? Come join us for an introductory session on utilizing NVivo to support your analytical process. This session will only cover features of the software and how to import your records. Please feel free to attend any of the following sessions below: April 25th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125 May 9th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125
• Next: Choose an approach >>
• Choose an approach
• Find studies
• Learn methods
• Getting Started
• Get software
• Get data for secondary analysis
• Network with researchers

• Last Updated: Aug 9, 2024 2:09 PM
• URL: https://guides.library.stanford.edu/qualitative_research

Quantitative and Qualitative Research

• I NEED TO . . .
• What is Quantitative Research?
• What is Qualitative Research?
• Quantitative vs Qualitative
• Step 1: Accessing CINAHL
• Step 2: Create a Keyword Search
• Step 3: Create a Subject Heading Search
• Step 4: Repeat Steps 1-3 for Second Concept
• Step 5: Repeat Steps 1-3 for Quantitative Terms
• Step 6: Combining All Searches
• Step 7: Adding Limiters
• Step 8: Save Your Search!
• What Kind of Article is This?
• More Research Help This link opens in a new window

What is qualitative research?

Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within their natural setting. It focuses on the "why" rather than the "what" of social phenomena and relies on the direct experiences of human beings as meaning-making agents in their every day lives. Rather than by logical and statistical procedures, qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

University of Utah College of Nursing, (n.d.). What is qualitative research? [Guide] Retrieved from  https://nursing.utah.edu/research/qualitative-research/what-is-qualitative-research.php#what 

The following video will explain the fundamentals of qualitative research.

• << Previous: What is Quantitative Research?
• Next: Quantitative vs Qualitative >>
• Last Updated: Aug 14, 2024 3:26 PM
• URL: https://libguides.uta.edu/quantitative_and_qualitative_research

University of Texas Arlington Libraries 702 Planetarium Place · Arlington, TX 76019 · 817-272-3000

• Internet Privacy
• Accessibility
• Problems with a guide? Contact Us.

• Privacy Policy

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Basic Research – Types, Methods and Examples

Survey Research – Types, Methods, Examples

Triangulation in Research – Types, Methods and...

Descriptive Research Design – Types, Methods and...

Research Methods – Types, Examples and Guide

Explanatory Research – Types, Methods, Guide

• Open access
• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

768k Accesses

348 Citations

90 Altmetric

Metrics details

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

Philipsen, H., & Vernooij-Dassen, M. (2007). Kwalitatief onderzoek: nuttig, onmisbaar en uitdagend. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Qualitative research: useful, indispensable and challenging. In: Qualitative research: Practical methods for medical practice (pp. 5–12). Houten: Bohn Stafleu van Loghum.

Chapter   Google Scholar  

Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches . London: Sage.

Kelly, J., Dwyer, J., Willis, E., & Pekarsky, B. (2014). Travelling to the city for hospital care: Access factors in country aboriginal patient journeys. Australian Journal of Rural Health, 22 (3), 109–113.

Article   Google Scholar  

Nilsen, P., Ståhl, C., Roback, K., & Cairney, P. (2013). Never the twain shall meet? - a comparison of implementation science and policy implementation research. Implementation Science, 8 (1), 1–12.

Howick J, Chalmers I, Glasziou, P., Greenhalgh, T., Heneghan, C., Liberati, A., Moschetti, I., Phillips, B., & Thornton, H. (2011). The 2011 Oxford CEBM evidence levels of evidence (introductory document) . Oxford Center for Evidence Based Medicine. https://www.cebm.net/2011/06/2011-oxford-cebm-levels-evidence-introductory-document/ .

Eakin, J. M. (2016). Educating critical qualitative health researchers in the land of the randomized controlled trial. Qualitative Inquiry, 22 (2), 107–118.

May, A., & Mathijssen, J. (2015). Alternatieven voor RCT bij de evaluatie van effectiviteit van interventies!? Eindrapportage. In Alternatives for RCTs in the evaluation of effectiveness of interventions!? Final report .

Google Scholar  

Berwick, D. M. (2008). The science of improvement. Journal of the American Medical Association, 299 (10), 1182–1184.

Article   CAS   Google Scholar  

Christ, T. W. (2014). Scientific-based research and randomized controlled trials, the “gold” standard? Alternative paradigms and mixed methodologies. Qualitative Inquiry, 20 (1), 72–80.

Lamont, T., Barber, N., Jd, P., Fulop, N., Garfield-Birkbeck, S., Lilford, R., Mear, L., Raine, R., & Fitzpatrick, R. (2016). New approaches to evaluating complex health and care systems. BMJ, 352:i154.

Drabble, S. J., & O’Cathain, A. (2015). Moving from Randomized Controlled Trials to Mixed Methods Intervention Evaluation. In S. Hesse-Biber & R. B. Johnson (Eds.), The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (pp. 406–425). London: Oxford University Press.

Chambers, D. A., Glasgow, R. E., & Stange, K. C. (2013). The dynamic sustainability framework: Addressing the paradox of sustainment amid ongoing change. Implementation Science : IS, 8 , 117.

Hak, T. (2007). Waarnemingsmethoden in kwalitatief onderzoek. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Observation methods in qualitative research] (pp. 13–25). Houten: Bohn Stafleu van Loghum.

Russell, C. K., & Gregory, D. M. (2003). Evaluation of qualitative research studies. Evidence Based Nursing, 6 (2), 36–40.

Fossey, E., Harvey, C., McDermott, F., & Davidson, L. (2002). Understanding and evaluating qualitative research. Australian and New Zealand Journal of Psychiatry, 36 , 717–732.

Yanow, D. (2000). Conducting interpretive policy analysis (Vol. 47). Thousand Oaks: Sage University Papers Series on Qualitative Research Methods.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 , 63–75.

van der Geest, S. (2006). Participeren in ziekte en zorg: meer over kwalitatief onderzoek. Huisarts en Wetenschap, 49 (4), 283–287.

Hijmans, E., & Kuyper, M. (2007). Het halfopen interview als onderzoeksmethode. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [The half-open interview as research method (pp. 43–51). Houten: Bohn Stafleu van Loghum.

Jansen, H. (2007). Systematiek en toepassing van de kwalitatieve survey. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Systematics and implementation of the qualitative survey (pp. 27–41). Houten: Bohn Stafleu van Loghum.

Pv, R., & Peremans, L. (2007). Exploreren met focusgroepgesprekken: de ‘stem’ van de groep onder de loep. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Exploring with focus group conversations: the “voice” of the group under the magnifying glass (pp. 53–64). Houten: Bohn Stafleu van Loghum.

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology Nursing Forum, 41 (5), 545–547.

Boeije H: Analyseren in kwalitatief onderzoek: Denken en doen, [Analysis in qualitative research: Thinking and doing] vol. Den Haag Boom Lemma uitgevers; 2012.

Hunter, A., & Brewer, J. (2015). Designing Multimethod Research. In S. Hesse-Biber & R. B. Johnson (Eds.), The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (pp. 185–205). London: Oxford University Press.

Archibald, M. M., Radil, A. I., Zhang, X., & Hanson, W. E. (2015). Current mixed methods practices in qualitative research: A content analysis of leading journals. International Journal of Qualitative Methods, 14 (2), 5–33.

Creswell, J. W., & Plano Clark, V. L. (2011). Choosing a Mixed Methods Design. In Designing and Conducting Mixed Methods Research . Thousand Oaks: SAGE Publications.

Mays, N., & Pope, C. (2000). Assessing quality in qualitative research. BMJ, 320 (7226), 50–52.

O'Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine : Journal of the Association of American Medical Colleges, 89 (9), 1245–1251.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality and Quantity, 52 (4), 1893–1907.

Moser, A., & Korstjens, I. (2018). Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. European Journal of General Practice, 24 (1), 9–18.

Marlett, N., Shklarov, S., Marshall, D., Santana, M. J., & Wasylak, T. (2015). Building new roles and relationships in research: A model of patient engagement research. Quality of Life Research : an international journal of quality of life aspects of treatment, care and rehabilitation, 24 (5), 1057–1067.

Demian, M. N., Lam, N. N., Mac-Way, F., Sapir-Pichhadze, R., & Fernandez, N. (2017). Opportunities for engaging patients in kidney research. Canadian Journal of Kidney Health and Disease, 4 , 2054358117703070–2054358117703070.

Noyes, J., McLaughlin, L., Morgan, K., Roberts, A., Stephens, M., Bourne, J., Houlston, M., Houlston, J., Thomas, S., Rhys, R. G., et al. (2019). Designing a co-productive study to overcome known methodological challenges in organ donation research with bereaved family members. Health Expectations . 22(4):824–35.

Piil, K., Jarden, M., & Pii, K. H. (2019). Research agenda for life-threatening cancer. European Journal Cancer Care (Engl), 28 (1), e12935.

Hofmann, D., Ibrahim, F., Rose, D., Scott, D. L., Cope, A., Wykes, T., & Lempp, H. (2015). Expectations of new treatment in rheumatoid arthritis: Developing a patient-generated questionnaire. Health Expectations : an international journal of public participation in health care and health policy, 18 (5), 995–1008.

Jun, M., Manns, B., Laupacis, A., Manns, L., Rehal, B., Crowe, S., & Hemmelgarn, B. R. (2015). Assessing the extent to which current clinical research is consistent with patient priorities: A scoping review using a case study in patients on or nearing dialysis. Canadian Journal of Kidney Health and Disease, 2 , 35.

Elsie Baker, S., & Edwards, R. (2012). How many qualitative interviews is enough? In National Centre for Research Methods Review Paper . National Centre for Research Methods. http://eprints.ncrm.ac.uk/2273/4/how_many_interviews.pdf .

Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18 (2), 179–183.

Sim, J., Saunders, B., Waterfield, J., & Kingstone, T. (2018). Can sample size in qualitative research be determined a priori? International Journal of Social Research Methodology, 21 (5), 619–634.

Download references

Acknowledgements

no external funding.

Author information

Authors and affiliations.

Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

Loraine Busetto, Wolfgang Wick & Christoph Gumbinger

Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Wolfgang Wick

You can also search for this author in PubMed   Google Scholar

Contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

Corresponding author

Correspondence to Loraine Busetto .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

Download citation

Received : 30 January 2020

Accepted : 22 April 2020

Published : 27 May 2020

DOI : https://doi.org/10.1186/s42466-020-00059-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative research
• Mixed methods
• Quality assessment

Neurological Research and Practice

ISSN: 2524-3489

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Qualitative Methods in Health Care Research

Affiliations.

• 1 School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain.
• 2 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
• 3 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
• 4 School of Nursing, MGH Institute of Health Professions, Boston, USA.
• 5 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
• PMID: 34084317
• PMCID: PMC8106287
• DOI: 10.4103/ijpvm.IJPVM_321_19

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Keywords: Ethnography; grounded theory; qualitative research; research design.

Copyright: © 2021 International Journal of Preventive Medicine.

PubMed Disclaimer

Conflict of interest statement

There are no conflicts of interest.

Similar articles

• A qualitative systematic review of internal and external influences on shared decision-making in all health care settings. Truglio-Londrigan M, Slyer JT, Singleton JK, Worral P. Truglio-Londrigan M, et al. JBI Libr Syst Rev. 2012;10(58):4633-4646. doi: 10.11124/jbisrir-2012-432. JBI Libr Syst Rev. 2012. PMID: 27820528
• The experience of adults who choose watchful waiting or active surveillance as an approach to medical treatment: a qualitative systematic review. Rittenmeyer L, Huffman D, Alagna M, Moore E. Rittenmeyer L, et al. JBI Database System Rev Implement Rep. 2016 Feb;14(2):174-255. doi: 10.11124/jbisrir-2016-2270. JBI Database System Rev Implement Rep. 2016. PMID: 27536798 Review.
• Experiences and perceptions of spousal/partner caregivers providing care for community-dwelling adults with dementia: a qualitative systematic review. Macdonald M, Martin-Misener R, Weeks L, Helwig M, Moody E, MacLean H. Macdonald M, et al. JBI Evid Synth. 2020 Apr;18(4):647-703. doi: 10.11124/JBISRIR-2017-003774. JBI Evid Synth. 2020. PMID: 32813338
• Health professionals' experience of teamwork education in acute hospital settings: a systematic review of qualitative literature. Eddy K, Jordan Z, Stephenson M. Eddy K, et al. JBI Database System Rev Implement Rep. 2016 Apr;14(4):96-137. doi: 10.11124/JBISRIR-2016-1843. JBI Database System Rev Implement Rep. 2016. PMID: 27532314 Review.
• Patient experiences of partnering with healthcare professionals for hand hygiene compliance: a systematic review. Butenko S, Lockwood C, McArthur A. Butenko S, et al. JBI Database System Rev Implement Rep. 2017 Jun;15(6):1645-1670. doi: 10.11124/JBISRIR-2016-003001. JBI Database System Rev Implement Rep. 2017. PMID: 28628522 Review.
• Health Workers' Mindfulness-Based Stress Reduction and Resilience During COVID-19 Pandemic. Ma HY, Chiang NT, Kao RH, Lee CY. Ma HY, et al. J Multidiscip Healthc. 2024 Aug 2;17:3691-3713. doi: 10.2147/JMDH.S464285. eCollection 2024. J Multidiscip Healthc. 2024. PMID: 39114858 Free PMC article.
• A Mixed-Method Study of the Utilization and Determinants of Private Health Insurance Schemes in the Residents of Rural Communities in Central India: A Study Protocol. Jambholkar PC, Choudhari SG, Dhonde A. Jambholkar PC, et al. Cureus. 2024 Jul 2;16(7):e63648. doi: 10.7759/cureus.63648. eCollection 2024 Jul. Cureus. 2024. PMID: 39092375 Free PMC article.
• Developing a module on the care of LGBTQIA+ individuals for health professionals: Research protocol. Pai MS, Yesodharan R, Palimar V, Thimmappa L, Bhat BB, Krishnan M N, Shetty D, Babu BV. Pai MS, et al. F1000Res. 2024 Jul 23;12:1437. doi: 10.12688/f1000research.140518.1. eCollection 2023. F1000Res. 2024. PMID: 39070118 Free PMC article.
• The "what, why, and how?" of story completion in health services research: a scoping review. Pong CY, Roberts NJ, Lum E. Pong CY, et al. BMC Med Res Methodol. 2024 Jul 23;24(1):159. doi: 10.1186/s12874-024-02274-7. BMC Med Res Methodol. 2024. PMID: 39044143 Free PMC article. Review.
• Early Impressions and Adoption of the AtriAmp for Managing Arrhythmias Following Congenital Heart Surgery. Leopold SM, Brown DH, Zhang X, Nguyen XT, Al-Subu AM, Olson KR. Leopold SM, et al. Pediatr Cardiol. 2024 Jul 6. doi: 10.1007/s00246-024-03573-y. Online ahead of print. Pediatr Cardiol. 2024. PMID: 38970655
• Polit DF, Beck CT. Philadelphia: Lippincott Williams & Wilkins; 2008. Nursing Research: Generating and Assessing Evidence for Nursing Practice.
• Sorrell JM. Qualitative research in clinical nurse specialist practice. Clin Nurse Spec. 2013;27:175–8. - PubMed
• Draper AK. The principles and application of qualitative research. Proc Nutr Soc. 2004;63:641–6. - PubMed
• Alasuutari P. The rise and relevance of qualitative research. Int J Soc Res Methodol. 2010;13:139–55.
• Morse JM. Qualitative health research: One quarter of a century. Qual Health Res. 2014;25:3–4. - PubMed

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources.

• Europe PubMed Central
• Ovid Technologies, Inc.
• PubMed Central
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Part 4: Using qualitative methods

19. A survey of approaches to qualitative data analysis

Chapter outline.

• Ethical responsibility and cultural respect (6 minute read)
• Critical considerations (6 minute read)
• Preparations: Creating a plan for qualitative data analysis (11 minute read)
• Thematic analysis (15 minute read)
• Content analysis (13 minute read)
• Grounded theory analysis (7 minute read)
• Photovoice (5 minute read)

Content warning: Examples in this chapter contain references to LGBTQ+ ageing, damaged-centered research, long-term older adult care, family violence and violence against women, vocational training, financial hardship, educational practices towards rights and justice, Schizophrenia, mental health stigma, and water rights and water access.

Just a brief disclaimer, this chapter is not intended to be a comprehensive discussion on qualitative data analysis. It does offer an overview of some of the diverse approaches that can be used for qualitative data analysis, but as you will read, even within each one of these there are variations in how they might be implemented in a given project. If you are passionate (or at least curious 😊) about conducting qualitative research, use this as a starting point to help you dive deeper into some of these strategies. Please note that there are approaches to analysis that are not addressed in this chapter, but still may be very valuable qualitative research tools. Examples include heuristic analysis, [1] narrative analysis, [2] discourse analysis, [3] and visual analysis, [4] among a host of others. These aren’t mentioned to confuse or overwhelm you, but instead to suggest that qualitative research is a broad field with many options. Before we begin reviewing some of these strategies, here a few considerations regarding ethics, cultural responsibility, power and control that should influence your thinking and planning as you map out your data analysis plan.

19.1 Ethical responsibility and cultural respectfulness

Learning objectives.

Learners will be able to…

• Identify how researchers can conduct ethically responsible qualitative data analysis.
• Explain the role of culture and cultural context in qualitative data analysis (for both researcher and participant)

The ethics of deconstructing stories

Throughout this chapter, I will consistently suggest that you will be deconstructing data . That is to say, you will be taking the information that participants share with you through their words, performances, videos, documents, photos, and artwork, then breaking it up into smaller points of data, which you will then reassemble into your findings. We have an ethical responsibility to treat what is shared with a sense of respect during this process of deconstruction and reconstruction . This means that we make conscientious efforts not to twist, change, or subvert the meaning of data as we break them down or string them back together.

The act of bringing together people’s stories through qualitative research is not an easy one and shouldn’t be taken lightly. Through the informed consent process, participants should learn about the ways in which their information will be used in your research, including giving them a general idea what will happen in your analysis and what format the end results of that process will likely be.

A deep understanding of cultural context as we make sense of meaning

Similar to the ethical considerations we need to keep in mind as we deconstruct stories, we also need to work diligently to understand the cultural context in which these stories are shared. This requires that we approach the task of analysis with a sense of cultural humility, meaning that we don’t assume that our perspective or worldview as the researcher is the same as our participants. Their life experiences may be quite different from our own, and because of this, the meaning in their stories may be very different than what we might initially expect.

As such, we need to ask questions to better understand words, phrases, ideas, gestures, etc. that seem to have particular significance to participants. We also can use activities like member checking , another tool to support qualitative rigor , to ensure that our findings are accurately interpreted by vetting them with participants prior to the study conclusion. We can spend a good amount of time getting to know the groups and communities that we work with, paying attention to their values, priorities, practices, norms, strengths, and challenges. Finally, we can actively work to challenge more traditional methods research and support more participatory models that advance community co-researchers or consistent oversight of research by community advisory groups to inform, challenge, and advance this process; thus elevating the wisdom of community members and their influence (and power) in the research process.

Accounting for our influence in the analysis process

Along with our ethical responsibility to our research participants, we also have an accountability to research consumers, the scientific community at large, and other stakeholders in our qualitative research. As qualitative researchers (or quantitative researchers, for that matter), people should expect that we have attempted, to the best of our abilities, to account for our role in the research process. This is especially true in analysis. Our finding should not emerge from some ‘black box’, where raw data goes in and findings pop out the other side, with no indication of how we arrive at them. Thus, an important part of rigor is transparency and the use of tools such as writing in reflexive journals , memoing , and creating an audit trail to assist us in documenting both our thought process and activities in reaching our findings. There will be more about this in Chapter 20 dedicated to qualitative rigor.

Key Takeaways

• Ethics, as it relates specifically to the analysis phase of qualitative research, requires that we are especially thoughtful in how we treat the data that participants share with us. This data often represents very intimate parts of people’s lives and/or how they view the world. Therefore, we need to actively conduct our analysis in a way that does not misrepresent, compromise the privacy of, and/or disenfranchise or oppress our participants and the groups they belong to.
• Part of demonstrating this ethical commitment to analysis involves capturing and documenting our influence as researchers to the qualitative research process.

After you have had a chance to read through this chapter, come back to this exercise. Think about your qualitative proposal. Based on the strategies that you might consider for analysis of your qualitative data:

• What ethical concerns do you have specific to this approach to analyzing your data?
• What steps might you take to anticipate and address these concerns?

19.2 Critical considerations

• Explain how data analysis may be used as tool for power and control
• Develop steps that reflect increased opportunities for empowerment of your study population, especially during the data analysis phase

How are participants present in the analysis process; What power or influence do they have

Remember, research is political. We need to consider that our findings represent ideas that are shared with us by living and breathing human beings and often the groups and communities that they represent. They have been gracious enough to share their time and their stories with us, yet they often have a limited role once we gather data from them. They are essentially putting their trust in us that we won’t be misrepresenting or culturally appropriating their stories in ways that will be harmful, damaging, or demeaning. Elliot (2016) [5] discusses the problems of "damaged-centered" research , which is research that portrays groups of people or communities as flawed, surrounded by problems, or incapable of producing change. Her work specifically references the way research and media have often portrayed people from the Appalachian region, and how these influences have perpetuated, reinforced, and even created stereotypes that these communities face. We need to thoughtfully consider how the research we are involved in will reflect on our participants and their communities.

Now, some research approaches, particularly participatory approaches, suggest that participants should be trained and actively engaged throughout the research process, helping to shape how our findings are presented and how the target population is portrayed. Implementing a participatory approach requires academic researchers to give up some of their power and control to community co-researchers. Ideally these co-researchers provide their input and are active members in determining what the findings are and interpreting why/how they are important. I believe this is a standard we need to strive for. However, this is the exception, not the rule. As such, if you are participating in a more traditional research role where community participants are not actively engaged, whenever possible, it is good practice to find ways to allow participants or other representatives to help lend their validation to our findings. While to a smaller extent, these opportunities suggest ways that community members can be empowered during the research process (and researchers can turn over some of our control). You may do this through activities like consulting with community representatives early and often during the analysis process and using member checking (referenced above and in our chapter on qualitative rigor) to help review and refine results. These are distinct and important roles for the community and do not mean that community members become researchers; but that they lend their perspectives in helping the researcher to interpret their findings.

The bringing together of voices: What does this represent and to whom

As social work researchers, we need to be mindful that research is a tool for advancing social justice. However, that doesn’t mean that all research fulfills that capacity or that all parties perceive it in this way. Qualitative research generally involves a relatively small number of participants (or even a single person) sharing their stories. As researchers, we then bring together this data in the analysis phase in an attempt to tell a broader story about the issue we are studying. Our findings often reflect commonalities and patterns, but also should highlight contradictions, tensions, and dissension about the topic.

Reflexive Journal Entry Prompt

Pause for a minute. Think about what the findings for your research proposal might represent.

• What do they represent to you as a researcher?
• What do they represent to participants directly involved in your study?
• What do they represent to the families of these participants?
• What do they represent to the groups and communities that represent or are connected to your population?

For each of the perspectives outlined in the reflexive journal entry prompt above, there is no single answer. As a student researcher, your study might represent a grade, an opportunity to learn more about a topic you are interested in, and a chance to hone your skills as a researcher. For participants, the findings might represent a chance to share their input or frustration that they are being misrepresented. Community members might view the research findings with skepticism that research produces any kind of change or anger that findings bring unwanted attention to the community. Obviously we can’t foretell all the answers to these questions, but thinking about them can help us to thoughtfully and carefully consider how we go about collecting, analyzing and presenting our data. We certainly need to be honest and transparent in our data analysis, but additionally, we need to consider how our analysis impacts others. It is especially important that we anticipate this and integrate it early into our efforts to educate our participants on what the research will involve, including potential risks.

It is important to note here that there are a number of perspectives that are rising to challenge traditional research methods. These challenges are often grounded in issues of power and control that we have been discussing, recognizing that research has and continues to be used as a tool for oppression and division. These perspectives include but are not limited to: Afrocentric methodologies, Decolonizing methodologies, Feminist methodologies, and Queer methodologies. While it’s a poor substitute for not diving deeper into these valuable contributions, I do want to offer a few resources if you are interested in learning more about these perspectives and how they can help to more inclusively define the research process.

• Research findings can represent many different things to many different stakeholders. Rather than as an afterthought, as qualitative researchers, we need to thoughtfully consider a range of these perspectives prior to and throughout the analysis to reduce the risk of oppression and misrepresentation through our research.
• There are a variety of strategies and whole alternative research paradigms that can aid qualitative researchers in conducting research in more empowering ways when compared to traditional research methods where the researcher largely maintain control and ownership of the research process and agenda.

This type of research means that African indigenous culture must be understood and kept at the forefront of any research and recommendations affecting indigenous communities and their culture.

Afrocentric methodologies : These methods represent research that is designed, conducted, and disseminated in ways that center and affirm African cultures, knowledge, beliefs, and values.

• Pellerin, M. (2012). Benefits of Afrocentricity in exploring social phenomena: Understanding Afrocentricity as a social science methodology .
• University of Illinois Library. (n.d.). The Afrocentric Research Center .

Decolonizing methodologies : These methods represent research that is designed, conducted, and disseminated in ways to reclaim control over indigenous ways of knowing and being. [6]

• Paris, D., & Winn, M. T. (Eds.). (2013).  Humanizing research: Decolonizing qualitative inquiry with youth and communities . Sage Publications.
• Smith, L. T. (2012). Decolonizing methodologies: Research and indigenous peoples (2nd ed.). Zed Books Ltd.

Feminist methodologies : Research methods in this tradition seek to, “remove the power imbalance between research and subject; (are) politically motivated in that (they) seeks to change social inequality; and (they) begin with the standpoints and experiences of women”. [7]

• Gill, J. (n.d.) Feminist research methodologies. Feminist Perspectives on Media and Technology .
• U.C.Davis., Feminist Research Institute. (n.d.). What is feminist research?

Queer(ing) methodologies : Research methods using this approach aim to question, challenge and often reject knowledge that is commonly accepted and privileged in society and elevate and empower knowledge and perspectives that are often perceived as non-normative.

• de Jong, D. H. (2014). A new paradigm in social work research: It’s here, it’s queer, get used to it! .
• Ghaziani, A., & Brim, M. (Eds.). (2019).  Imagining queer methods . NYU Press.

19.3 Preparations: Creating a plan for qualitative data analysis

• Identify how your research question, research aim, sample selection, and type of data may influence your choice of analytic methods
• Outline the steps you will take in preparation for conducting qualitative data analysis in your proposal

Now we can turn our attention to planning your analysis. The analysis should be anchored in the purpose of your study. Qualitative research can serve a range of purposes. Below is a brief list of general purposes we might consider when using a qualitative approach.

• Are you trying to understand how a particular group is affected by an issue?
• Are you trying to uncover how people arrive at a decision in a given situation?
• Are you trying to examine different points of view on the impact of a recent event?
• Are you trying to summarize how people understand or make sense of a condition?
• Are you trying to describe the needs of your target population?

If you don’t see the general aim of your research question reflected in one of these areas, don’t fret! This is only a small sampling of what you might be trying to accomplish with your qualitative study. Whatever your aim, you need to have a plan for what you will do once you have collected your data.

Decision Point: What are you trying to accomplish with your data?

• Consider your research question. What do you need to do with the qualitative data you are gathering to help answer that question?

To help answer this question, consider:

• What action verb(s) can be associated with your project and the qualitative data you are collecting? Does your research aim to summarize, compare, describe, examine, outline, identify, review, compose, develop, illustrate, etc.?
• Then, consider noun(s) you need to pair with your verb(s)—perceptions, experiences, thoughts, reactions, descriptions, understanding, processes, feelings, actions responses, etc.

Iterative or linear

We touched on this briefly in Chapter 17 about qualitative sampling, but this is a n important distinction to consider. Some qualitative research is linear , meaning it follows more of a tra ditionally quantitative process: create a plan, gather data, and analyze data; each step is completed before we proceed to the next. You can think of this like how information is presented in this book. We discuss each topic, one after another. 

However, many times qualitative research is iterative , or evolving in cycles. An iterative approach means that once we begin collecting data, we also begin analyzing data as it is coming in. This early and ongoing analysis of our (incomplete) data then impacts our continued planning, data gathering and future analysis. Again, coming back to this book, while it may be written linear, we hope that you engage with it iteratively as you are building your proposal. By this we mean that you will revisit previous sections so you can understand how they fit together and you are in continuous process of building and revising how you think about the concepts you are learning about. 

As you may have guessed, there are benefits and challenges to both linear and iterative approaches. A linear approach is much more straightforward, each step being fairly defined. However, linear research being more defined and rigid also presents certain challenges. A linear approach assumes that we know what we need to ask or look for at the very beginning of data collection, which often is not the case.

With iterative research, we have more flexibility to adapt our approach as we learn new things. We still need to keep our approach systematic and organized, however, so that our work doesn’t become a free-for-all. As we adapt, we do not want to stray too far from the original premise of our study. It’s also important to remember with an iterative approach that we may risk ethical concerns if our work extends beyond the original boundaries of our informed consent and IRB agreement. If you feel that you do need to modify your original research plan in a significant way as you learn more about the topic, you can submit an addendum to modify your original application that was submitted. Make sure to keep detailed notes of the decisions that you are making and what is informing these choices. This helps to support transparency and your credibility throughout the research process.

Decision Point: Will your analysis reflect more of a linear or an iterative approach?

• What justifies or supports this decision?

Think about:

• Fit with your research question
• Available time and resources
• Your knowledge and understanding of the research process
• What evidence are you basing this on?
• How might this help or hinder your qualitative research process?
• How might this help or hinder you in a practice setting as you work with clients?

Acquainting yourself with your data

As y ou begin your analysis, y ou need to get to know your data. This usually means reading through your data prior to any attempt at breaking it apart and labeling it. You mig ht read through a couple of times, in fact. This helps give you a more comprehensive feel for each piece of data and the data as a whole, again, before you start to break it down into smaller units or deconstruct it. This is especially important if others assisted us in the data collection process. We often gather data as part of team and everyone involved in the analysis needs to be very familiar with all of the data. 

Capturing your reaction to the data

During the review process, our understanding of the data often evolves as we observe patterns and trends. It is a good practice to document your reaction and evolving understanding. Your reaction can include noting phrases or ideas that surprise you, similarities or distinct differences in responses, additional questions that the data brings to mind, among other things. We often record these reactions directly in the text or artifact if we have the ability to do so, such as making a comment in a word document associated with a highlighted phrase. If this isn’t possible, you will want to have a way to track what specific spot(s) in your data your reactions are referring to. In qualitative research we refer to this process as memoing . Memoing is a strategy that helps us to link our findings to our raw data, demonstrating transparency. If you are using a Computre-Assisted Qualitative Data Analysis Software (CAQDAS) software package, memoing functions are generally built into the technology.

Capturing your emerging understanding of the data

During your reviewing and memoing you will start to develop and evolve your understanding of what the data means. This understanding should be dynamic and flexible, but you want to have a way to capture this understanding as it evolves. You may include this as part of your memoing or as part of your codebook where you are tracking the main ideas that are emerging and what they mean. Figure 19.3 is an example of how your thinking might change about a code and how you can go about capturing it. Coding is a part of the qualitative data analysis process where we begin to interpret and assign meaning to the data. It represents one of the first steps as we begin to filter the data through our own subjective lens as the researcher. We will discuss coding in much more detail in the sections below covering various different approaches to analysis.

Decision Point: How to capture your thoughts?

• What will this look like?
• How often will you do it?
• How will you keep it organized and consistent over time?

In addition, you will want to be actively using your reflexive journal during this time. Document your thoughts and feelings throughout the research process. This will promote transparency and help account for your role in the analysis.

For entries during your analysis, respond to questions such as these in your journal:

• What surprises you about what participants are sharing?
• How has this information challenged you to look at this topic differently?
• Where might these have come from?
• How might these be influencing your study?
• How will you proceed differently based on what you are learning?

By including community members as active co-researchers, they can be invaluable in reviewing, reacting to and leading the interpretation of data during your analysis. While it can certainly be challenging to converge on an agreed-upon version of the results; their insider knowledge and lived experience can provide very important insights into the data analysis process.

Determining when you are finished

When conducting quantitative research, it is perhaps easier to decide when we are finished with our analysis. We determine the tests we need to run, we perform them, we interpret them, and for the most part, we call it a day. It’s a bit more nebulous for qualitative research. There is no hard and fast rule for when we have completed our qualitative analysis. Rather, our decision to end the analysis should be guided by reflection and consideration of a number of important questions. These questions are presented below to help ensure that your analysis results in a finished product that is comprehensive, systematic, and coherent.

Have I answered my research question?

Your analysis should be clearly connected to and in service of answering your research question. Your examination of the data should help you arrive at findings that sufficiently address the question that you set out to answer. You might find that it is surprisingly easy to get distracted while reviewing all your data. Make sure as you conducted the analysis you keep coming back to your research question.

Have I utilized all my data?

Unless you have intentionally made the decision that certain portions of your data are not relevant for your study, make sure that you don’t have sources or segments of data that aren’t incorporated into your analysis. Just because some data doesn’t “fit” the general trends you are uncovering, find a way to acknowledge this in your findings as well so that these voices don’t get lost in your data.

Have I fulfilled my obligation to my participants?

As a qualitative researcher, you are a craftsperson. You are taking raw materials (e.g. people’s words, observations, photos) and bringing them together to form a new creation, your findings. These findings need to both honor the original integrity of the data that is shared with you, but also help tell a broader story that answers your research question(s).

Have I fulfilled my obligation to my audience?

Not only do your findings need to help answer your research question, but they need to do so in a way that is consumable for your audience. From an analysis standpoint, this means that we need to make sufficient efforts to condense our data. For example, if you are conducting a thematic analysis, you don’t want to wind up with 20 themes. Having this many themes suggests that you aren’t finished looking at how these ideas relate to each other and might be combined into broader themes. Having these sufficiently reduced to a handful of themes will help tell a more complete story, one that is also much more approachable and meaningful for your reader.

In the following subsections, there is information regarding a variety of different approaches to qualitative analysis. In designing your qualitative study, you would identify an analytical approach as you plan out your project. The one you select would depend on the type of data you have and what you want to accomplish with it.

• Qualitative research analysis requires preparation and careful planning. You will need to take time to familiarize yourself with the data in general sense before you begin analyzing.
• Once you begin your analysis, make sure that you have strategies for capture and recording both your reaction to the data and your corresponding developing understanding of what the collective meaning of the data is (your results). Qualitative research is not only invested in the end results but also the process at which you arrive at them.

Decision Point: When will you stop?

• How will you know when you are finished? What will determine your endpoint?
• How will you monitor your work so you know when it’s over?

19.4 Thematic analysis

• Explain defining features of thematic analysis as a strategy for qualitative data analysis and identify when it is most effectively used
• Formulate an initial thematic analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with thematic analysis?

As its name suggests, with thematic analysis we are attempting to identify themes or common ideas across our data. Themes can help us to:

• Determine shared meaning or significance of an event
• Povide a more complete understanding of concept or idea by exposing different dimensions of the topic
• Explore a range of values, beliefs or perceptions on a given topic

Themes help us to identify common ways that people are making sense of their world. Let’s say that you are studying empowerment of older adults in assisted living facilities by interviewing residents in a number of these facilities. As you review your transcripts, you note that a number of participants are talking about the importance of maintaining connection to previous aspects of their life (e.g. their mosque, their Veterans of Foreign Wars (VFW) Post, their Queer book club) and having input into how the facility is run (e.g. representative on the board, community town hall meetings). You might note that these are two emerging themes in your data. After you have deconstructed your data, you will likely end up with a handful (likely three or four) central ideas or take-aways that become the themes or major findings of your research.

Variations in approaches to thematic analysis

There are a variety of ways to approach qualitative data analysis, but even within the broad approach of thematic analysis, there is variation. Some thematic analysis takes on an inductive analysis approach. In this case, we would first deconstruct our data into small segments representing distinct ideas (this is explained further in the section below on coding data). We then go on to see which of these pieces seem to group together around common ideas.

In direct contrast, you might take a deductive analysis approach (like we discussed in Chapter 8 ), in which you start with some idea about what grouping might look like and we see how well our data fits into those pre-identified groupings. These initial deductive groupings (we call these a priori categories) often come from an existing theory related to the topic we are studying. You may also elect to use a combination of deductive and inductive strategies, especially if you find that much of your data is not fitting into deductive categories and you decide to let new categories inductively emerge.

A couple things to note here. If you are using a deductive approach, be clear in specifying where your a priori categories came from. For instance, perhaps you are interested in studying the conceptualization of social work in other cultures. You begin your analysis with prior research conducted by Tracie Mafile’o (2004) that identified the concepts of fekau’aki (connecting) and fakatokilalo (humility) as being central to Tongan social work practice. [8] You decide to use these two concepts as part of your initial deductive framework, because you are interested in studying a population that shares much in common with the Tongan people. When using an inductive approach, you need to plan to use memoing and reflexive journaling to document where the new categories or themes are coming from.

Coding data

Coding is the process of breaking down your data into smaller meaningful units. Just like any story is made up by the bringing together of many smaller ideas, you need to uncover and label these smaller ideas within each piece of your data. After you have reviewed each piece of data you will go back and assign labels to words, phrases, or pieces of data that represent separate ideas that can stand on their own. Identifying and labeling codes can be tricky. When attempting to locate units of data to code, look for pieces of data that seem to represent an idea in-and-of-itself; a unique thought that stands alone. For additional information about coding, check out this brief video from Duke’s Social Science Research Institute on this topic. It offers a nice concise overview of coding and also ties into our previous discussion of memoing to help encourage rigor in your analysis process.

As suggested in the video [9] , when you identify segments of data and are considering what to label them ask yourself:

• How does this relate to/help to answer my research question?
• How does this connect with what we know from the existing literature?
• How does this fit (or contrast) with the rest of my data?

You might do the work of coding in the margins if you are working with hard copies, or you might do this through the use of comments or through copying and pasting if you are working with digital materials (like pasting them into an excel sheet, as in the example below). If you are using a CAQDAS, there will be a function(s) built into the software to accomplish this.

Regardless of which strategy you use, the central task of thematic analysis is to have a way to label discrete segments of your data with a short phrase that reflects what it stands for. As you come across segments that seem to mean the same thing, you will want to use the same code. Make sure to select the words to represent your codes wisely, so that they are clear and memorable. When you are finished, you will likely have hundreds (if not thousands!) of different codes – again, a story is made up of many different ideas and you are bringing together many different stories! A cautionary note, if you are physically manipulating your data in some way, for example copying and pasting, which I frequently do, you need to have a way to trace each code or little segment back to its original home (the artifact that it came from).

When I’m working with interview data, I will assign each interview transcript a code and use continuous line numbering. That way I can label each segment of data or code with a corresponding transcript code and line number so I can find where it came from in case I need to refer back to the original.

The following is an excerpt from a portion of an autobiographical memoir (Wolf, 2010) [10] . Continuous numbers have been added to the transcript to identify line numbers (Figure 19.4). A few preliminary codes have been identified from this data and entered into a data matrix (below) with information to trace back to the raw data (transcript) (Figure 19.5).

Below is another excerpt from the same memoir [11]

What segments of this interview can you pull out and what initial code would you place on them?

Create a data matrix as you reflect on this.

It was painful to think, even at an early age, that a part of the world I was beginning to love—Europe—was being substantially destroyed by the war; that cities with their treasures, to say nothing of innocent people, were being bombed and consumed in flames. I was a patriotic young American and wanted “us” to win the war, but I also wanted Europe to be saved.

Some displaced people began to arrive in our apartment house, and even as I knew that they had suffered in Europe, their names and language pointed back to a civilized Europe that I wanted to experience. One person, who had studied at Heidelberg, told me stories about student life in the early part of the 20 th  century that inspired me to want to become an accomplished student, if not a “student prince.” He even had a dueling scar. A baby-sitter showed me a photo of herself in a feathered hat, standing on a train platform in Bratislava. I knew that she belonged in a world that was disappearing.

For those of us growing up in New York City in the 1940s, Japan, following Pearl Harbor and the “death march” in Corregidor, seemed to be our most hated enemy. The Japanese were portrayed as grotesque and blood-thirsty on posters. My friends and I were fighting back against the “Japs” in movie after movie: Gung Ho, Back to Bataan, The Purple Heart, Thirty Seconds Over Tokyo, They Were Expendable, and Flying Tigers, to name a few.

We wanted to be like John Wayne when we grew up. It was only a few decades after the war, when we realized the horrors of Hiroshima and Nagasaki, that some of us began to understand that the Japanese, whatever else was true, had been dehumanized as a people; that we had annihilated, guiltlessly at the time, hundreds of thousands of non-combatants in a horrific flash. It was only after the publication of John Hersey’s Hiroshima(1946), that we began to think about other sides of the war that patriotic propaganda had concealed.

When my friends and I went to summer camp in the foothills of the Berkshires during the late years of the war and sang patriotic songs around blazing bonfires, we weren’t thinking about the firestorms of Europe (Dresden) and Japan. We were worried that our counselors would be drafted and suddenly disappear, leaving us unprotected.

Identifying, reviewing, and refining themes

Now we have our codes, we need to find a sensible way of putting them together. Remember, we want to narrow this vast field of hundreds of codes down to a small handful of themes. If we don’t review and refine all these codes, the story we are trying to tell with our data becomes distracting and diffuse. An example is provided below to demonstrate this process. 

As we refine our thematic analysis, our first step will be to identify groups of codes that hang together or seem to be related. Let’s say y ou are studying the experience of people who are in a vocational preparation program and you have codes labeled “worrying about paying the bills” and “loss of benefits”. You might group these codes into a category you label “income & expenses” (Figrue 19.6). 

You may review and refine the groups of your codes many times during the course of your analysis, including shifting codes around from one grouping to another as you get a clearer picture of what each of the groups represent. This reflects the iterative process we were describing earlier. While you are shifting codes and relabeling categories, track this! A research journal is a good place to do this. So, as in the example above, you would have a journal entry that explains that you changed the label on the category from “income & expenses” to “financial insecurities” and you would briefly explain why. Your research journal can take many different forms. It can be hard copy, an evolving word document, or a spreadsheet with multiple tabs (Figure 19.8). 

Now, eventually you may decide that some of these categories can also be grouped together, but still stand alone as separate ideas. Continuing with our example above, you have another category labeled “financial potential” that contains codes like “money to do things” and “saving for my future”. You determine that “financial insecurities” and “financial potential” are related, but distinctly different aspects of a broader grouping, which you go on to label “financial considerations”. This broader grouping reflects both the more worrisome or stressful aspects of people’s experiences that you have interviewed, but also the optimism and hope that was reflected related to finances and future work (Figure 19.9).

This broadest grouping then becomes your theme and utilizing the categories and the codes contained therein, you create a description of what each of your themes means based on the data you have collected, and again, can record this in your research journal entry (Figure 19.10).

Building a thematic representation

However, providing a list of themes may not really tell the whole story of your study. It may fail to explain to your audience how these individual themes relate to each other. A thematic map or thematic array can do just that: provides a visual representation of how each individual category fits with the others. As you build your thematic representation, be thoughtful of how you position each of your themes, as this spatially tells part of the story. [12] You should also make sure that the relationships between the themes represented in your thematic map or array are narratively explained in your text as well.

Figure 19.11 offers an illustration of the beginning of thematic map for the theme we had been developing in the examples above. I emphasize that this is the beginning because we would likely have a few other themes (not just “financial considerations”). These other themes might have codes or categories in common with this theme, and these connections would be visual evident in our map. As you can see in the example, the thematic map allows the reader, reviewer, or researcher can quickly see how these ideas relate to each other. Each of these themes would be explained in greater detail in our write up of the results. Additionally, sample quotes from the data that reflected those themes are often included.

• Thematic analysis offers qualitative researchers a method of data analysis through which we can identify common themes or broader ideas that are represented in our qualitative data.
• Themes are identified through an iterative process of coding and categorizing (or grouping) to identify trends during your analysis.
• Tracking and documenting this process of theme identification is an important part of utilizing this approach.

References for learning more about Thematic Analysis

Clarke, V. (2017, December 9). What is thematic analysis?

Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars .

Nowell et al. (2017). Thematic analysis: Striving to meet the trustworthiness criteria .

The University of Auckland. (n.d.). Thematic analysis: A reflexive approach .

A few exemplars of studies employing Thematic Analysis

Bastiaensens et al. (2019). “Were you cyberbullied? Let me help you.” Studying adolescents’ online peer support of cyberbullying victims using thematic analysis of online support group Fora .

Borgström, Å., Daneback, K., & Molin, M. (2019). Young people with intellectual disabilities and social media: A literature review and thematic analysis .

Kapoulitsas, M., & Corcoran, T. (2015). Compassion fatigue and resilience: A qualitative analysis of social work practice .

19.5 Content analysis

• Explain defining features of content analysis as a strategy for analyzing qualitative data
• Determine when content analysis can be most effectively used
• Formulate an initial content analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with content analysis

Much like with thematic analysis, if you elect to use content analysis to analyze your qualitative data, you will be deconstructing the artifacts that you have sampled and looking for similarities across these deconstructed parts. Also consistent with thematic analysis, you will be seeking to bring together these similarities in the discussion of your findings to tell a collective story of what you learned across your data. While the distinction between thematic analysis and content analysis is somewhat murky, if you are looking to distinguish between the two, content analysis:

• Places greater emphasis on determining the unit of analysis. Just to quickly distinguish, when we discussed sampling in Chapter 10 we also used the term “unit of analysis. As a reminder, when we are talking about sampling, unit of analysis refers to the entity that a researcher wants to say something about at the end of her study (individual, group, or organization). However, for our purposes when we are conducting a content analysis, this term has to do with the ‘chunk’ or segment of data you will be looking at to reflect a particular idea. This may be a line, a paragraph, a section, an image or section of an image, a scene, etc., depending on the type of artifact you are dealing with and the level at which you want to subdivide this artifact.
• Content analysis is also more adept at bringing together a variety of forms of artifacts in the same study. While other approaches can certainly accomplish this, content analysis more readily allows the researcher to deconstruct, label and compare different kinds of ‘content’. For example, perhaps you have developed a new advocacy training for community members. To evaluate your training you want to analyze a variety of products they create after the workshop, including written products (e.g. letters to their representatives, community newsletters), audio/visual products (e.g. interviews with leaders, photos hosted in a local art exhibit on the topic) and performance products (e.g. hosting town hall meetings, facilitating rallies). Content analysis can allow you the capacity to examine evidence across these different formats.

For some more in-depth discussion comparing these two approaches, including more philosophical differences between the two, check out this article by Vaismoradi, Turunen, and Bondas (2013) . [13]

Variations in the approach

There are also significant variations among different content analysis approaches. Some of these approaches are more concerned with quantifying (counting) how many times a code representing a specific concept or idea appears. These are more quantitative and deductive in nature. Other approaches look for codes to emerge from the data to help describe some idea or event. These are more qualitative and inductive . Hsieh and Shannon (2005) [14] describe three approaches to help understand some of these differences:

• Conventional Content Analysis. Starting with a general idea or phenomenon you want to explore (for which there is limited data), coding categories then emerge from the raw data. These coding categories help us understand the different dimensions, patterns, and trends that may exist within the raw data collected in our research.
• Directed Content Analysis. Starts with a theory or existing research for which you develop your initial codes (there is some existing research, but incomplete in some aspects) and uses these to guide your initial analysis of the raw data to flesh out a more detailed understanding of the codes and ultimately, the focus of your study.
• Summative Content Analysis. Starts by examining how many times and where codes are showing up in your data, but then looks to develop an understanding or an “interpretation of the underlying context” (p.1277) for how they are being used. As you might have guessed, this approach is more likely to be used if you’re studying a topic that already has some existing research that forms a basic place to begin the analysis.

This is only one system of categorization for different approaches to content analysis. If you are interested in utilizing a content analysis for your proposal, you will want to design an approach that fits well with the aim of your research and will help you generate findings that will help to answer your research question(s). Make sure to keep this as your north star, guiding all aspects of your design.

Determining your codes

We are back to coding! As in thematic analysis, you will be coding your data (labeling smaller chunks of information within each data artifact of your sample). In content analysis, you may be using pre-determined codes, such as those suggested by an existing theory (deductive) or you may seek out emergent codes that you uncover as you begin reviewing your data (inductive). Regardless of which approach you take, you will want to develop a well-documented codebook.

A codebook is a document that outlines the list of codes you are using as you analyze your data, a descriptive definition of each of these codes, and any decision-rules that apply to your codes. A decision-rule provides information on how the researcher determines what code should be placed on an item, especially when codes may be similar in nature. If you are using a deductive approach, your codebook will largely be formed prior to analysis, whereas if you use an inductive approach, your codebook will be built over time. To help illustrate what this might look like, Figure 19.12 offers a brief excerpt of a codebook from one of the projects I’m currently working on.

Coding, comparing, counting

Once you have (or are developing) your codes, your next step will be to actually code your data. In most cases, you are looking for your coding structure (your list of codes) to have good coverage . This means that most of the content in your sample should have a code applied to it. If there are large segments of your data that are uncoded, you are potentially missing things. Now, do note that I said most of the time. There are instances when we are using artifacts that may contain a lot of information, only some of which will apply to what we are studying. In these instances, we obviously wouldn’t be expecting the same level of coverage with our codes. As you go about coding you may change, refine and adapt your codebook as you go through your data and compare the information that reflects each code. As you do this, keep your research journal handy and make sure to capture and record these changes so that you have a trail documenting the evolution of your analysis. Also, as suggested earlier, content analysis may also involve some degree of counting as well. You may be keeping a tally of how many times a particular code is represented in your data, thereby offering your reader both a quantification of how many times (and across how many sources) a code was reflected and a narrative description of what that code came to mean.

Representing the findings from your coding scheme

Finally, you need to consider how you will represent the findings from your coding work. This may involve listing out narrative descriptions of codes, visual representations of what each code came to mean or how they related to each other, or a table that includes examples of how your data reflected different elements of your coding structure. However you choose to represent the findings of your content analysis, make sure the resulting product answers your research question and is readily understandable and easy-to-interpret for your audience.

• Much like thematic analysis, content analysis is concerned with breaking up qualitative data so that you can compare and contrast ideas as you look across all your data, collectively. A couple of distinctions between thematic and content analysis include content analysis’s emphasis on more clearly specifying the unit of analysis used for the purpose of analysis and the flexibility that content analysis offers in comparing across different types of data.
• Coding involves both grouping data (after it has been deconstructed) and defining these codes (giving them meaning). If we are using a deductive approach to analysis, we will start with the code defined. If we are using an inductive approach, the code will not be defined until the end of the analysis.

Identify a qualitative research article that uses content analysis (do a quick search of “qualitative” and “content analysis” in your research search engine of choice).

• How do the authors display their findings?
• What was effective in their presentation?
• What was ineffective in their presentation?

Resources for learning more about Content Analysis

Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis .

Colorado State University (n.d.) Writing@CSU Guide: Content analysis .

Columbia University Mailman School of Public Health, Population Health. (n.d.) Methods: Content analysis

Mayring, P. (2000, June). Qualitative content analysis . 

A few exemplars of studies employing Content Analysis

Collins et al. (2018). Content analysis of advantages and disadvantages of drinking among individuals with the lived experience of homelessness and alcohol use disorders .

Corley, N. A., & Young, S. M. (2018). Is social work still racist? A content analysis of recent literature .

Deepak et al. (2016). Intersections between technology, engaged learning, and social capital in social work education .

19.6 Grounded theory analysis

• Explain defining features of grounded theory analysis as a strategy for qualitative data analysis and identify when it is most effectively used
• Formulate an initial grounded theory analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with grounded theory analysis

Just to be clear, grounded theory doubles as both qualitative research design (we will talk about some other qualitative designs in Chapter 22 ) and a type of qualitative data analysis. Here we are specifically interested in discussing grounded theory as an approach to analysis in this chapter. With a grounded theory analysis , we are attempting to come up with a common understanding of how some event or series of events occurs based on our examination of participants’ knowledge and experience of that event. Let’s consider the potential this approach has for us as social workers in the fight for social justice. Using grounded theory analysis we might try to answer research questions like:

• How do communities identity, organize, and challenge structural issues of racial inequality?
• How do immigrant families respond to threat of family member deportation?
• How has the war on drugs campaign shaped social welfare practices?

In each of these instances, we are attempting to uncover a process that is taking place. To do so, we will be analyzing data that describes the participants’ experiences with these processes and attempt to draw out and describe the components that seem quintessential to understanding this process.

Differences in approaches to grounded theory analysis largely lie in the amount (and types) of structure that are applied to the analysis process. Strauss and Corbin (2014) [15] suggest a highly structured approach to grounded theory analysis, one that moves back and forth between the data and the evolving theory that is being developed, making sure to anchor the theory very explicitly in concrete data points. With this approach, the researcher role is more detective-like; the facts are there, and you are uncovering and assembling them, more reflective of deductive reasoning . While Charmaz (2014) [16]  suggests a more interpretive approach to grounded theory analysis, where findings emerge as an exchange between the unique and subjective (yet still accountable) position of the researcher(s) and their understanding of the data, acknowledging that another researcher might emerge with a different theory or understanding. So in this case, the researcher functions more as a liaison, where they bridge understanding between the participant group and the scientific community, using their own unique perspective to help facilitate this process. This approach reflects inductive reasoning .

Coding in grounded theory

Coding in grounded theory is generally a sequential activity. First, the researcher engages in open coding of the data. This involves reviewing the data to determine the preliminary ideas that seem important and potential labels that reflect their significance for the event or process you are studying. Within this open coding process, the researcher will also likely develop subcategories that help to expand and provide a richer understanding of what each of the categories can mean. Next, axial coding will revisit the open codes and identify connections between codes, thereby beginning to group codes that share a relationship. Finally, selective or theoretical coding explores how the relationships between these concepts come together, providing a theory that describes how this event or series of events takes place, often ending in an overarching or unifying idea tying these concepts together. Dr. Tiffany Gallicano [17] has a helpful blog post that walks the reader through examples of each stage of coding. Figure 19.13 offers an example of each stage of coding in a study examining experiences of students who are new to online learning and how they make sense of it. Keep in mind that this is an evolving process and your document should capture this changing process. You may notice that in the example “Feels isolated from professor and classmates” is listed under both axial codes “Challenges presented by technology” and “Course design”. This isn’t an error; it just represents that it isn’t yet clear if this code is most reflective of one of these two axial codes or both. Eventually, the placement of this code may change, but we will make sure to capture why this change is made.

Constant comparison

While ground theory is not the only approach to qualitative analysis that utilizes constant comparison, it is certainly widely associated with this approach. Constant comparison reflects the motion that takes place throughout the analytic process (across the levels of coding described above), whereby as researchers we move back and forth between the data and the emerging categories and our evolving theoretical understanding. We are continually checking what we believe to be the results against the raw data. It is an ongoing cycle to help ensure that we are doing right by our data and helps ensure the trustworthiness of our research. Ground theory often relies on a relatively large number of interviews and usually will begin analysis while the interviews are ongoing. As a result, the researcher(s) work to continuously compare their understanding of findings against new and existing data that they have collected.

Developing your theory

Remember, the aim of using a grounded theory approach to your analysis is to develop a theory, or an explanation of how a certain event/phenomenon/process occurs. As you bring your coding process to a close, you will emerge not just with a list of ideas or themes, but an explanation of how these ideas are interrelated and work together to produce the event you are studying. Thus, you are building a theory that explains the event you are studying that is grounded in the data you have gathered.

Thinking about power and control as we build theories

I want to bring the discussion back to issues of power and control in research. As discussed early in this chapter, regardless of what approach we are using to analyze our data we need to be concerned with the potential for abuse of power in the research process and how this can further contribute to oppression and systemic inequality. I think this point can be demonstrated well here in our discussion of grounded theory analysis. Since grounded theory is often concerned with describing some aspect of human behavior: how people respond to events, how people arrive at decisions, how human processes work. Even though we aren’t necessarily seeking generalizable results in a qualitative study, research consumers may still be influenced by how we present our findings. This can influence how they perceive the population that is represented in our study. For example, for many years science did a great disservice to families impacted by schizophrenia, advancing the theory of the schizophrenogenic mother [18] . Using pseudoscience , the scientific community misrepresented the influence of parenting (a process), and specifically the mother’s role in the development of the disorder of schizophrenia. You can imagine the harm caused by this theory to family dynamics, stigma, institutional mistrust, etc. To learn more about this you can read this brief but informative editorial article by Anne Harrington in the Lancet . [19] Instances like these should haunt and challenge the scientific community to do better. Engaging community members in active and more meaningful ways in research is one important way we can respond. Shouldn’t theories be built by the people they are meant to represent?

• Ground theory analysis aims to develop a common understanding of how some event or series of events occurs based on our examination of participants’ knowledge and experience of that event.
• Using grounded theory often involves a series of coding activities (e.g. open, axial, selective or theoretical) to help determine both the main concepts that seem essential to understanding an event, but also how they relate or come together in a dynamic process.
• Constant comparison is a tool often used by qualitative researchers using a grounded theory analysis approach in which they move back and forth between the data and the emerging categories and the evolving theoretical understanding they are developing.

Resources for learning more about Grounded Theory

Chun Tie, Y., Birks, M., & Francis, K. (2019). Grounded theory research: A design framework for novice researchers .

Gibbs, G.R. (2015, February 4). A discussion with Kathy Charmaz on Grounded Theory .

Glaser, B.G., & Holton, J. (2004, May). Remodeling grounded theory .

Mills, J., Bonner, A., & Francis, K. (2006). The development of Constructivist Grounded Theory .

A few exemplars of studies employing Grounded Theory

Burkhart, L., & Hogan, N. (2015). Being a female veteran: A grounded theory of coping with transitions .

Donaldson, W. V., & Vacha-Haase, T. (2016). Exploring staff clinical knowledge and practice with LGBT residents in long-term care: A grounded theory of cultural competency and training needs .

Vanidestine, T., & Aparicio, E. M. (2019). How social welfare and health professionals understand “Race,” Racism, and Whiteness: A social justice approach to grounded theory .

19.7 Photovoice

• Explain defining features of photovoice as a strategy for qualitative data analysis and identify when it is most effectively used
• Formulate an initial analysis plan using photovoice (if appropriate for your research proposal)

What are you trying to accomplish with photovoice analysis?

Photovoice is an approach to qualitative research that combines the steps of data gathering and analysis with visual and narrative data. The ultimate aim of the analysis is to produce some kind of desired change with and for the community of participants. While other analysis approaches discussed here may involve including participants more actively in the research process, it is certainly not the norm. However, with photovoice, it is. Using an approach that involves photovoice will generally assume that the participants in your study will be taking on a very active role throughout the research process, to the point of acting as co-researchers. This is especially evident during the analysis phase of your work.

As an example of this work, Mitchell (2018) [20] combines photovoice and an environmental justice approach to engage a Native American community around the significance and the implications of water for their tribe. This research is designed to help raise awareness and support advocacy efforts for improved access to and quality of natural resources for this group. Photovoice has grown out of participatory and community-based research traditions that assume that community members have their own expertise they bring to the research process, and that they should be involved, empowered, and mutually benefit from research that is being conducted. This mutual benefit means that this type of research involves some kind of desired and very tangible changes for participants; the research will support something that community members want to see happen. Examples of these changes could be legislative action, raising community awareness, or changing some organizational practice(s).

Training your team

Because this approach involve s participants not just sharing information, but actually utilizing research skills to help collect and interpret data, as a researcher you need to take on an educator role and share your research expertise in preparing them to do so. After recruiting and gathering informed consent, part of the on-boarding process will be to determine the focus of your study. Some photovoice projects are more prescribed, where the researcher comes with an idea and seeks to partner with a specific group or community to explore this topic. At other times, the researcher joins with the community first, and collectively they determine the focus of the study and craft the research question. Once this focus has been determined and shared, the team will be charged with gathering photos or videos that represent responses to the research question for each individual participant. Depending on the technology used to capture these photos (e.g. cameras, ipads, video recorders, cell phones), training may need to be provided.

Once photos have been captured, team members will be asked to provide a caption or description that helps to interpret what their picture(s) mean in relation to the focus of the study. After this, the team will collectively need to seek out themes and patterns across the visual and narrative representations. This means you may employ different elements of thematic or content analysis to help you interpret the collective meaning across the data and you will need to train your team to utilize these approaches.

Converging on a shared story

Once you have found common themes, together you will work to assemble these into a cohesive broader story or message regarding the focus of your topic. Now remember, the participatory roots of photovoice suggest that the aim of this message is to seek out, support, encourage or demand some form of change or transformation, so part of what you will want to keep in mind is that this is intended to be a persuasive story. Your research team will need to consider how to put your findings together in a way that supports this intended change. The packaging and format of your findings will have important implications for developing and disseminating the final products of qualitative research. Chapter 21 focuses more specifically on decisions connected with this phase of the research process.

• Photovoice is a unique approach to qualitative research that combines visual and narrative information in an attempt to produce more meaningful and accessible results as an alternative to other traditional research methods.
• A cornerstone of Photovoice research involves the training and participation of community members during the analysis process. Additionally, the results of the analysis are often intended for some form of direct change or transformation that is valued by the community.

After learning about these different types of qualitative analysis:

• Which of these approaches make the most sense to you and how you view the world?
• Which of them are most appealing and why?
• Which do you want to learn more about?

Decision Point: How will you conduct your analysis?

• What makes this the most effective choice?
• Outline the steps you plan to take to conduct your analysis
• What peer-reviewed resources have you gathered to help you learn more about this method of analysis? (keep these handy for when you write-up your study!)

Resources for learning more about Photovice:

Liebenberg, L. (2018). Thinking critically about photovoice: Achieving empowerment and social change .

Mangosing, D. (2015, June 18). Photovoice training and orientation .

University of Kansas, Community Toolbox. (n.d.). Section 20. Implementing Photovoice in Your Community .

Woodgate et al. (2017, January). Worth a thousand words? Advantages, challenges and opportunities in working with photovoice as a qualitative research method with youth and their families .

A few exemplars of studies employing Photovoice:

Fisher-Borne, M., & Brown, A. (2018). A case study using Photovoice to explore racial and social identity among young Black men: Implications for social work research and practice .

Houle et al. (2018). Public housing tenants’ perspective on residential environment and positive well-being: An empowerment-based Photovoice study and its implications for social work .

Mitchell, F. M. (2018). “Water Is Life”: Using photovoice to document American Indian perspectives on water and health .

Media Attributions

• no black box © Cory Cummings
• iterative v linear © Cory Cummings
• discussing around table © Activités culturelles UdeM is licensed under a Public Domain license
• thematic map © Cory Cummings
• codebook and decision rules
• constant compare © JohannaMarie is licensed under a CC0 (Creative Commons Zero) license
• community meeting © Korean Resource Center 민족학교 is licensed under a CC BY-ND (Attribution NoDerivatives) license
• photo exhibit © University of the Fraser Valley, I Lead Abbey Youth 4 Change is licensed under a CC BY (Attribution) license
• Kleining, G., & Witt, H. (2000). The qualitative heuristic approach: A methodology for discovery in psychology and the social sciences. Rediscovering the method of introspection as an example. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 1 (1). ↵
• Burck, C. (2005). Comparing qualitative research methodologies for systemic research: The use of grounded theory, discourse analysis and narrative analysis. Journal of Family Therapy, 27 (3), 237-262. ↵
• Mogashoa, T. (2014). Understanding critical discourse analysis in qualitative research. International Journal of Humanities Social Sciences and Education, 1 (7), 104-113. ↵
• Contandriopoulos, D., Larouche, C., Breton, M., & Brousselle, A. (2018). A sociogram is worth a thousand words: proposing a method for the visual analysis of narrative data. Qualitative Research, 18 (1), 70-87. ↵
• Elliott, L. (2016, January, 16). Dangers of “damage-centered” research . The Ohio State University, College of Arts and Sciences: Appalachian Student Resources. https://u.osu.edu/appalachia/2016/01/16/dangers-of-damage-centered-research/ ↵
• Smith, L. T. (2012). Decolonizing methodologies: Research and indigenous peoples (2nd ed.). Zed Books Ltd. ↵
• PAR-L. (2010). Introduction to feminist research . [Webpage]. https://www2.unb.ca/parl/research.htm#:~:text=Methodologically%2C%20feminist%20research%20differs%20from,standpoints%20and%20experiences%20of%20women . ↵
• Mafile'o, T. (2004). Exploring Tongan Social Work: Fekau'aki (Connecting) and Fakatokilalo (Humility). Qualitative Social Work, 3 (3), 239-257. ↵
• Duke Mod U Social Science Research Institute. (2016, November 11). How to know you are coding correct: Qualitative research methods. [Video]. YouTube. https://www.youtube.com/watch?v=iL7Ww5kpnIM&feature=youtu.be ↵
• Wolf, H. R. (2010). Growing up in New York City: A generational memoir (1941-1960). American Studies Journal, 54. http://www.asjournal.org/54-2010/growing-up-in-new-york-city/ ↵
• Clarke, V., Braun, V., & Hayfield, N. (2015). Thematic analysis. In J. A. Smith (ed.) Qualitative psychology: A practical guide to research methods , (3rd ed.). 222-248. ↵
• Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15 (3), 398-405. ↵
• Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15 (9), 1277-1288. ↵
• Corbin, J., & Strauss, A. (2014). Basics of qualitative research: Techniques and procedures for developing grounded theory . Sage publications. ↵
• Charmaz, K. (2014). Constructing grounded theory . Sage Publications ↵
• Gallicano, T. (2013, July 22). An example of how to perform open coding, axial coding and selective coding. [Blog post]. https://prpost.wordpress.com/2013/07/22/an-example-of-how-to-perform-open-coding-axial-coding-and-selective-coding/ ↵
• Harrington, A. (2012). The fall of the schizophrenogenic mother. The Lancet, 379 (9823), 1292-1293. ↵
• Mitchell, F. M. (2018). “Water Is Life”: Using photovoice to document American Indian perspectives on water and health. S ocial Work Research, 42 (4), 277-289. ↵

The act of breaking piece of qualitative data apart during the analysis process to discern meaning and ultimately, the results of the study.

The act of putting the deconstructed qualitative back together during the analysis process in the search for meaning and ultimately the results of the study.

Member checking involves taking your results back to participants to see if we "got it right" in our analysis. While our findings bring together many different peoples' data into one set of findings, participants should still be able to recognize their input and feel like their ideas and experiences have been captured adequately.

Rigor is the process through which we demonstrate, to the best of our ability, that our research is empirically sound and reflects a scientific approach to knowledge building.

The idea that researchers are responsible for conducting research that is ethical, honest, and following accepted research practices.

The process of research is record and described in such a way that the steps the researcher took throughout the research process are clear.

A research journal that helps the researcher to reflect on and consider their thoughts and reactions to the research process and how it may be shaping the study

Memoing is the act of recording your thoughts, reactions, quandaries as you are reviewing the data you are gathering.

An audit trail is a system of documenting in qualitative research analysis that allows you to link your final results with your original raw data. Using an audit trail, an independent researcher should be able to start with your results and trace the research process backwards to the raw data. This helps to strengthen the trustworthiness of the research.

Research that portrays groups of people or communities as flawed, surrounded by problems, or incapable of producing change. 

Research methodologies that center and affirm African cultures, knowledge, beliefs, and values. 

Research methods that reclaim control over indigenous ways of knowing and being.

Research methods in this tradition seek to, "remove the power imbalance between research and subject; (are) politically motivated in that (they) seeks to change social inequality; and (they) begin with the standpoints and experiences of women". [1]

Research methods using this approach aim to question, challenge and/or reject knowledge that is commonly accepted and privileged in society and elevate and empower knowledge and perspectives that are often perceived as non-normative.

A research process where you create a plan, you gather your data, you analyze your data and each step is completed before you proceed to the next.

An iterative approach means that after planning and once we begin collecting data, we begin analyzing as data as it is coming in.  This early analysis of our (incomplete) data, then impacts our planning, ongoing data gathering and future analysis as it progresses.

The point where gathering more data doesn't offer any new ideas or perspectives on the issue you are studying.  Reaching saturation is an indication that we can stop qualitative data collection.

These are software tools that can aid qualitative researchers in managing, organizing and manipulating/analyzing their data.

A document that we use to keep track of and define the codes that we have identified (or are using) in our qualitative data analysis.

Part of the qualitative data analysis process where we begin to interpret and assign meaning to the data.

Thematic analysis is an approach to qualitative analysis, in which the researcher attempts to identify themes or patterns across their data to better understand the topic being studied.

An approach to data analysis in which we gather our data first and then generate a theory about its meaning through our analysis.

An approach to data analysis in which the researchers begins their analysis using a theory to see if their data fits within this theoretical framework (tests the theory).

Categories that we use that are determined ahead of time, based on existing literature/knowledge.

A data matrix is a tool used by researchers to track and organize data and findings during qualitative analysis.

A visual representation of how each individual category fits with the others when using thematic analysis to analyze your qualitative data.

An approach to data analysis that seeks to identify patterns, trends, or ideas across qualitative data through processes of coding and categorization.

entity that a researcher wants to say something about at the end of her study (individual, group, or organization)

A decision-rule provides information on how the researcher determines what code should be placed on an item, especially when codes may be similar in nature.

In qualitative data, coverage refers to the amount of data that can be categorized or sorted using the code structure that we are using (or have developed) in our study. With qualitative research, our aim is to have good coverage with our code structure.

A form of qualitative analysis that aims to develop a theory or understanding of how some event or series of events occurs by closely examining participant knowledge and experience of that event(s).

starts by reading existing theories, then testing hypotheses and revising or confirming the theory

a paradigm based on the idea that social context and interaction frame our realities

when a researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences

An initial phase of coding that involves reviewing the data to determine the preliminary ideas that seem important and potential labels that reflect their significance.  

Axial coding is phase of qualitative analysis in which the research will revisit the open codes and identify connections between codes, thereby beginning to group codes that share a relationship.

Selective or theoretical coding is part of a qualitative analysis process that seeks to determine how important concepts and their relationships to each other come together, providing a theory that describes the focus of the study. It often results in an overarching or unifying idea tying these concepts together.

Constant comparison reflects the motion that takes place in some qualitative analysis approaches whereby the researcher moves back and forth between the data and the emerging categories and evolving understanding they have in their results. They are continually checking what they believed to be the results against the raw data they are working with.

Trustworthiness is a quality reflected by qualitative research that is conducted in a credible way; a way that should produce confidence in its findings.

claims about the world that appear scientific but are incompatible with the values and practices of science

Photovoice is a technique that merges pictures with narrative (word or voice data that helps that interpret the meaning or significance of the visual artifact. It is often used as a tool in CBPR.

Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• Methodology
• Research Methodology
• Qualitative Research

What Is Qualitative Research? An Overview and Guidelines

• Australasian Marketing Journal (AMJ)
• This person is not on ResearchGate, or hasn't claimed this research yet.

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

No full-text available

To read the full-text of this research, you can request a copy directly from the author.

• Mina Seraj-Aksit

• J Consum Behav

• Margaret M. Cullen

• Rossella Gambetti

• Luisella Bovera

• Qual Res Psychol

• Weng Marc Lim

• Karyn Cooper
• Gaurav Gupta

• Bronislaw Malinowski
• QUAL HEALTH RES
• Wendy Duggleby

• Edward F. Fern
• J BUS IND MARK

• Kathy Charmaz
• INT J MANAG REV
• Malvina Klag
• Ann Langley
• Roy Suddaby
• INT J INTERCULT REL
• Yvonna S. Lincoln
• Anselm L. Strauss
• Int J Retail Distrib Manag

• J SOC ISSUES
• Egon G. Guba
• LANDSCAPE URBAN PLAN

• Susan Spiggle
• L Cavusoglu
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Qualitative research methods in drug abuse and AIDS prevention research: an overview

• Population and Public Health Sciences

Research output : Contribution to journal › Review article › peer-review

ASJC Scopus Subject Areas

• Medicine (miscellaneous)
• qualitative research methods
• AIDS prevention

Disciplines

• Immune System Diseases
• Preventive Medicine
• Substance Abuse and Addiction
• Virus Diseases

Access to Document

• 157 Final published version, 656 KB

Other files and links

• Link to publication in Scopus
• Link to the citations in Scopus

T1 - Qualitative research methods in drug abuse and AIDS prevention research

T2 - an overview

AU - Carlson, Robert G.

AU - Siegal, Harvey A.

AU - Falck, Russel S.

N2 - Almost two decades ago, at the first workshop/technical review on qualitative research methods and ethnography sponsored by the National Institute on Drug Abuse (NIDA), Siegal (1977, p. 79) remarked that despite the existence of numerous excellent qualitative studies on drug abuse, “Ethnographers have had difficulty explaining precisely what they do.” In the intervening years, qualitative research methods have gained increasing importance as a systematic means of data collection and analysis that have become critical dimensions in drug abuse and AIDS research (Lambert 1990). For example, qualitative and ethnographic research are key components in NIDA’s recent program announcement, “Strategies to Reduce HIV Sexual Risk Practices in Drug Users.” Moreover, through the National AIDS Demonstration Research Program (Brown and Beschner 1993) and the Cooperative Agreement for AIDS Community-Based Outreach/Intervention research initiative, qualitative methodologists, or ethnographers. have worked increasingly on research teams composed of epidemiologists, statisticians, health educators, and psychologists, thereby promoting interdisciplinary cooperation. The recent publication of Denzin and Lincoln’s (1994a) compendium, “Handbook of Qualitative Research,” emphasizes this momentum toward interdisciplinary understanding.

AB - Almost two decades ago, at the first workshop/technical review on qualitative research methods and ethnography sponsored by the National Institute on Drug Abuse (NIDA), Siegal (1977, p. 79) remarked that despite the existence of numerous excellent qualitative studies on drug abuse, “Ethnographers have had difficulty explaining precisely what they do.” In the intervening years, qualitative research methods have gained increasing importance as a systematic means of data collection and analysis that have become critical dimensions in drug abuse and AIDS research (Lambert 1990). For example, qualitative and ethnographic research are key components in NIDA’s recent program announcement, “Strategies to Reduce HIV Sexual Risk Practices in Drug Users.” Moreover, through the National AIDS Demonstration Research Program (Brown and Beschner 1993) and the Cooperative Agreement for AIDS Community-Based Outreach/Intervention research initiative, qualitative methodologists, or ethnographers. have worked increasingly on research teams composed of epidemiologists, statisticians, health educators, and psychologists, thereby promoting interdisciplinary cooperation. The recent publication of Denzin and Lincoln’s (1994a) compendium, “Handbook of Qualitative Research,” emphasizes this momentum toward interdisciplinary understanding.

KW - qualitative research methods

KW - drug abuse

KW - AIDS prevention

UR - http://www.scopus.com/inward/record.url?scp=0029449489&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029449489&partnerID=8YFLogxK

M3 - Review article

C2 - 8684442

SN - 1046-9516

JO - NIDA Research Monograph Series

JF - NIDA Research Monograph Series

• Open access
• Published: 07 August 2024

Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study

• Rebecca Barry   ORCID: orcid.org/0000-0003-2272-4694 1 ,
• Elyce Green   ORCID: orcid.org/0000-0002-7291-6419 1 ,
• Kristy Robson   ORCID: orcid.org/0000-0002-8046-7940 1 &
• Melissa Nott   ORCID: orcid.org/0000-0001-7088-5826 1  

BMC Health Services Research volume  24 , Article number:  908 ( 2024 ) Cite this article

152 Accesses

Metrics details

The use of telehealth has proliferated to the point of being a common and accepted method of healthcare service delivery. Due to the rapidity of telehealth implementation, the evidence underpinning this approach to healthcare delivery is lagging, particularly when considering the uniqueness of some service users, such as those in rural areas. This research aimed to address the current gap in knowledge related to the factors critical for the successful delivery of telehealth to rural populations.

This research used a qualitative descriptive design to explore telehealth service provision in rural areas from the perspective of clinicians and describe factors critical to the effective delivery of telehealth in rural contexts. Semi-structured interviews were conducted with clinicians from allied health and nursing backgrounds working in child and family nursing, allied health services, and mental health services. A manifest content analysis was undertaken using the Framework approach.

Sixteen health professionals from nursing, clinical psychology, and social work were interviewed. Participants mostly identified as female (88%) and ranged in age from 26 to 65 years with a mean age of 47 years. Three overarching themes were identified: (1) Navigating the role of telehealth to support rural healthcare; (2) Preparing clinicians to engage in telehealth service delivery; and (3) Appreciating the complexities of telehealth implementation across services and environments.

Conclusions

This research suggests that successful delivery of telehealth to rural populations requires consideration of the context in which telehealth services are being delivered, particularly in rural and remote communities where there are challenges with resourcing and training to support health professionals. Rural populations, like all communities, need choice in healthcare service delivery and models to increase accessibility. Preparation and specific, intentional training for health professionals on how to transition to and maintain telehealth services is a critical factor for delivery of telehealth to rural populations. Future research should further investigate the training and supports required for telehealth service provision, including who, when and what training will equip health professionals with the appropriate skill set to deliver rural telehealth services.

Peer Review reports

Introduction

Telehealth is a commonly utilised application in rural health settings due to its ability to augment service delivery across wide geographical areas. During the COVID-19 pandemic, the use of telehealth became prolific as it was rapidly adopted across many new fields of practice to allow for healthcare to continue despite requirements for physical distancing. In Australia, the Medicare Benefits Scheme (MBS) lists health services that are subsidised by the federal government. Telehealth items were extensively added to these services as part of the response to COVID-19 [ 1 ]. Although there are no longer requirements for physical distancing in Australia, many health providers have continued to offer services via telehealth, particularly in rural areas [ 2 , 3 ]. For the purpose of this research, telehealth was defined as a consultation with a healthcare provider by phone or video call [ 4 ]. Telehealth service provision in rural areas requires consideration of contextual factors such as access to reliable internet, community members’ means to finance this access [ 5 ], and the requirement for health professionals to function across a broad range of specialty skills. These factors present a case for considering the delivery of telehealth in rural areas as a unique approach, rather than one portion of the broader use of telehealth.

Research focused on rural telehealth has proliferated alongside the rapid implementation of this service mode. To date, there has been a focus on the impact of telehealth on areas such as client access and outcomes [ 2 ], client and health professional satisfaction with services and technology [ 6 ], direct and indirect costs to the patient (travel cost and time), healthcare service provider staffing, lower onsite healthcare resource utilisation, improved physician recruitment and retention, and improved client access to care and education [ 7 , 8 ]. In terms of service implementation, these elements are important but do not outline the broader implementation factors critical to the success of telehealth delivery in rural areas. One study by Sutarsa et al. explored the implications of telehealth as a replacement for face-to-face services from the perspectives of general practitioners and clients [ 9 ] and articulated that telehealth services are not a like-for-like service compared to face-to-face modes. Research has also highlighted the importance of understanding the experience of telehealth in rural Australia across different population groups, including Aboriginal and Torres Strait Islander peoples, and the need to consider culturally appropriate services [ 10 , 11 , 12 , 13 ].

Research is now required to determine what the critical implementation factors are for telehealth delivery in rural areas. This type of research would move towards answering calls for interdisciplinary, qualitative, place-based research [ 12 ] that explores factors required for the sustainability and usability of telehealth in rural areas. It would also contribute to the currently limited understanding of implementation factors required for telehealth delivery to rural populations [ 14 ]. There is a reasonable expectation that there is consistency in the way health services are delivered, particularly across geographical locations. Due to the rapid implementation of telehealth services, there was limited opportunity to proactively identify factors critical for successful telehealth delivery in rural areas and this has created a lag in policy, process, and training. This research aimed to address this gap in the literature by exploring and describing rural health professionals’ experiences providing telehealth services. For the purpose of this research, rural is inclusive of locations classified as rural or remote (MM3-6) using the Modified Monash Model which considers remoteness and population size in its categorisation [ 15 ].

This research study adopted a qualitative descriptive design as described by Sandelowski [ 16 ]. The purpose of a descriptive study is to document and describe a phenomenon of interest [ 17 ] and this method is useful when researchers seek to understand who was involved, what occurred, and the location of the phenomena of interest [ 18 ]. The phenomenon of interest for this research was the provision of telehealth services to rural communities by health professionals. In line with this, a purposive sampling technique was used to identify participants who have experience of this phenomenon [ 19 ]. This research is reported in line with the consolidated criteria for reporting qualitative research [ 20 ] to enhance transparency and trustworthiness of the research process and results [ 21 ].

Research aims

This research aimed to:

Explore telehealth service provision in rural areas from the perspective of clinicians.

Describe factors critical to the successful delivery of telehealth in rural contexts.

Participant recruitment and data collection

People eligible to participate in the research were allied health (using the definition provided by Allied Health Professions Australia [ 22 ]) or nursing staff who delivered telehealth services to people living in the geographical area covered by two rural local health districts in New South Wales, Australia (encompassing rural areas MM3-6). Health organisations providing telehealth service delivery in the southwestern and central western regions of New South Wales were identified through the research teams’ networks and invited to be part of the research.

Telehealth adoption in these organisations was intentionally variable to capture different experiences and ranged from newly established (prompted by COVID-19) to well established (> 10 years of telehealth use). Organisations included government, non-government, and not-for-profit health service providers offering child and family nursing, allied health services, and mental health services. Child and family nursing services were delivered by a government health service and a not-for-profit specialist service, providing health professional advice, education, and guidance to families with a baby or toddler. Child and family nurses were in the same geographical region as the families receiving telehealth. Transition to telehealth services was prompted by the COVID-19 pandemic. The participating allied health service was a large, non-government provider of allied health services to regional New South Wales. Allied health professionals were in the same region as the client receiving telehealth services. Use of telehealth in this organisation had commenced prior to the COVID-19 pandemic. Telehealth mental health services were delivered by an emergency mental health team, located at a large regional hospital to clients in another healthcare facility or location to which the health professional could not be physically present (typically a lower acuity health service in a rural location).

Once organisations agreed to disseminate the research invitation, a key contact person employed at each health organisation invited staff to participate via email. Staff were provided with contact details of the research team in the email invitation. All recruitment and consent processes were managed by the research team to minimise risk of real or perceived coercion between staff and the key contact person, who was often in a supervisory or managerial position within the organisation. Data were collected using semi-structured interviews using an online platform with only the interviewer and participant present. Interviews were conducted by a research team member with training in qualitative data collection during November and December 2021 and were transcribed verbatim by a professional transcribing service. All participants were offered the opportunity to review their transcript and provide feedback, however none opted to do so. Data saturation was not used as guidance for participant numbers, taking the view of Braun and Clarke [ 23 ] that meaning is generated through the analysis rather than reaching a point of saturation.

Data analysis

Researchers undertook a manifest content analysis of the data using the Framework approach developed by Ritchie and Spencer [ 24 ]. All four co-authors were involved in the data analysis process. Framework uses five stages for analysis including (1) familiarisation (2) identifying a thematic framework based on emergent overarching themes, (3) application of the coding framework to the interview transcripts [indexing], (4) reviewing and charting of themes and subthemes, and (5) mapping and interpretation [ 24 , p. 178]. The research team analysed a common interview initially, identified codes and themes, then independently applied these to the remaining interviews. Themes were centrally recorded, reviewed, and discussed by the research team prior to inclusion into the thematic framework. Final themes were confirmed via collaborative discussion and consensus. The iterative process used to review and code data was recorded into an Excel spreadsheet to ensure auditability and credibility, and to enhance the trustworthiness of the analysis process.

This study was approved by the Greater Western NSW Human Research Ethics Committee and Charles Sturt University Human Research Ethics Committee (approval numbers: 2021/ETH00088 and H21215). All participants provided written consent.

Eighteen health professionals consented to be interviewed. Two were lost to follow-up, therefore semi-structured interviews were conducted with 16 of these health professionals, the majority of which were from the discipline of nursing ( n  = 13, 81.3%). Participant demographics and their pseudonyms are shown in Table  1 .

Participants mostly identified as female ( n  = 14, 88%) and ranged in age from 26 to 65 years with a mean age of 47 years. Participants all delivered services to rural communities in the identified local health districts and resided within the geographical area they serviced. The participants resided in areas classified as MM3-6 but were most likely to reside in an area classified MM3 (81%). Average interview time was 38 min, and all interviews were conducted online via Zoom.

Three overarching themes were identified through the analysis of interview transcripts with health professionals. These themes were: (1) Navigating the role of telehealth to support rural healthcare; (2) Preparing clinicians to engage in telehealth service delivery; and (3) Appreciating the complexities of telehealth implementation across services and environments.

Theme 1: navigating the role of telehealth to support rural healthcare

The first theme described clinicians’ experiences of using telehealth to deliver healthcare to rural communities, including perceived benefits and challenges to acceptance, choice, and access. Interview participants identified several factors that impacted on or influenced the way they could deliver telehealth, and these were common across the different organisational structures. Clinicians highlighted the need to consider how to effectively navigate the role of telehealth in supporting their practice, including when it would enhance their practice, and when it might create barriers. The ability to improve rural service provision through greater access was commonly discussed by participants. In terms of factors important for telehealth delivery in rural contexts, the participants demonstrated that knowledge of why and how telehealth was used were important, including the broadened opportunity for healthcare access and an understanding of the benefits and challenges of providing these services.

Access to timely and specialist healthcare for rural communities

Participants described a range of benefits using telehealth to contact small, rural locations and facilitate greater access to services closer to home. This was particularly evident when there was lack of specialist support in these areas. These opportunities meant that rural people could receive timely care that they required, without the burden of travelling significant distances to access health services.

The obvious thing in an area like this, is that years ago, people were being transported three hours just to see us face to face. It’s obviously giving better, more timely access to services. (Patrick)

Staff access to specialist support was seen as an important aspect for rural healthcare by participants, because of the challenges associated with lack of staffing and resources within these areas which potentially increased the risks for staff in these locations, particularly when managing clients with acute mental illnesses.

Within the metro areas they’ve got so many staff and so many hospitals and they can manage mental health patients quite well within those facilities, but with us some of these hospitals will have one RN on overnight and it’s just crappy for them, and so having us able to do video link, it kind of takes the pressure off and we’re happy to make the decisions and the risky decisions for what that person needs. (Tracey)

Participants described how the option to use telehealth to provide specialised knowledge and expertise to support local health staff in rural hospitals likely led to more appropriate outcomes for clients wanting to be able to remain in their community. Conversely, Amber described the implications if telehealth was not available.

If there was some reason why the telehealth wasn’t available… quite often, I suppose the general process be down to putting the pressure on the nursing and the medical staff there to make a decision around that person, which is not a fair or appropriate thing for them to do. (Amber)

Benefits and challenges to providing telehealth in rural communities

Complementing the advantage of reduced travel time to access services, was the ability for clients to access additional support via telehealth, which was perceived as a benefit. For example, one participant described how telehealth was useful for troubleshooting client’s problems rather than waiting for their next scheduled appointment.

If a mum rings you with an issue, you can always say to them “are you happy to jump onto My Virtual Care with me now?” We can do that, do a consult over My Virtual Care. Then I can actually gauge how mum is. (Jade)

While accessibility was a benefit, participants highlighted that rural communities need to be provided with choice, rather than the assumption that telehealth be the preferred option for everyone, as many rural clients want face-to-face services.

They’d all prefer, I think, to be able to see someone in person. I think that’s generally what NSW rural [want] —’cause I’m from country towns as well—there’s no substitute, like I said, for face-to-face assessment. (Adam)

Other, more practical limitations of broad adoption of telehealth raised by the participants included issues with managing technology and variability in internet connectivity.

For many people in the rural areas, it’s still an issue having that regular [internet] connection that works all the time. I think it’s a great option but I still think it’s something that some rural people will always have some challenges with because it’s not—there’s so many black spots and so many issues still with the internet connection in rural areas. Even in town, there’s certain areas that are still having lots of problems. (Chloe)

Participants also identified barriers related to assumptions that all clients will have access to technology and have the necessary data to undertake a telehealth consultation, which wasn’t always the case, particularly with individuals experiencing socioeconomic disadvantage.

A lot of [Aboriginal] families don’t actually have access to telehealth services. Unless they use their phone. If they have the technology on their phones. I found that was a little bit of an issue to try and help those particular clients to get access to the internet, to have enough data on their phone to make that call. There was a lot of issues and a lot of things that we were putting in complaints about as they were going “we’re using up a lot of these peoples’ data and they don’t have internet in their home.” (Evelyn).

Other challenges identified by the participants were related to use of telehealth for clients that required additional support. Many participants talked about the complexities of using an interpreter during a telehealth consultation for culturally and linguistically diverse clients.

Having interpreters, that’s another element that’s really, really difficult because you’re doing video link, but then you’ve also got the phone on speaker and you’re having this three-way conversation. Even that, in itself, that added element on video link is really, really tough. It’s a really long process. (Tracey)

In summary, this theme described some of the benefits and constraints when using telehealth for the delivery of rural health services. The participants demonstrated the importance of understanding the needs and contexts of individual clients, and accounting for this when making decisions to incorporate telehealth into their service provision. Understanding how and why telehealth can be implemented in rural contexts was an important foundation for the delivery of these services.

Theme 2: preparing clinicians to engage in telehealth service delivery

The preparation required for clinicians to engage with telehealth service delivery was highlighted and the participants described the unique set of skills required to effectively build rapport, engage, and carry out assessments with clients. For many participants who had not routinely used telehealth prior to the COVID-19 pandemic, the transition to using telehealth had been rapid. The participants reflected on the implications of rapidly adopting these new practices and the skills they required to effectively deliver care using telehealth. These skills were critical for effective delivery of telehealth to rural communities.

Rapid adoption of new skills and ways of working

The rapid and often unsupported implementation of telehealth in response to the COVID-19 pandemic resulted in clinicians needing to learn and adapt to telehealth, often without being taught or with minimal instruction.

We had to do virtual, virtually overnight we were changed to, “Here you go. Do it this way,” without any real education. It was learned as we went because everybody was in the same boat. Everyone was scrabbling to try and work out how to do it. (Chloe)

In addition to telehealth services starting quickly, telehealth provision requires clinicians to use a unique set of skills. Therapeutic interventions and approaches were identified as being more challenging when seeing a client through a screen, compared to being physically present together in a room.

The body language is hidden a little bit when you’re on teleconference, whereas when you’re standing up face to face with someone, or standing side by side, the person can see the whole picture. When you’re on the video link, the patient actually can’t—you both can’t see each other wholly. That’s one big barrier. (Adam)

There was an emphasis on communication skills such as active listening and body language that were required when engaging with telehealth. These skills were seen as integral to building rapport and connection. The importance of language in an environment with limited visualisation of body language, is further demonstrated by one participant describing how they tuned into the timing and flow of the conversation to avoid interrupting and how these skills were pertinent for using telehealth.

In the beginning especially, we might do this thing where I think they’ve finished or there’s a bit of silence, so I go to speak and then they go to speak at the same time, and that’s different because normally in person you can really gauge that quite well if they’ve got more to say. I think those little things mean that you’ve got to work a bit harder and you’ve got to bring those things to the attention of the client often. (Robyn)

Preparing clinicians to engage in telehealth also required skills in sharing clear and consistent information with clients about the process of interacting via telehealth. This included information to reassure the client that the telehealth appointment was private as well as prepare them for potential interruptions due to connection issues.

I think being really explicitly clear about the fact that with our setups we have here, no one can dial in, no one else is in my room even watching you. We’re not recording, and there’s a lot of extra information, I think around that we could be doing better in terms of delivering to the person. (Amber)

Becoming accustomed to working through the ‘window’

Telehealth was often described as a window and not a view of the whole person which presented limitations for clinicians, such as seeing nuance of expression. Participants described the difficulties of assessing a client using telehealth when you cannot see the whole picture such as facial expressions, movement, behaviour, interactions with others, dress, and hygiene.

I found it was quite difficult because you couldn’t always see the actual child or the baby, especially if they just had their phone. You couldn’t pick up the body language. You couldn’t always see the facial expressions. You couldn’t see the child and how the child was responding. It did inhibit a lot of that side of our assessing. Quite often you’d have to just write, “Unable to view child.” You might be able to hear them but you couldn’t see them. (Chloe)

Due to the window view, the participants described how they needed to pay even greater attention to eye contact and tone of voice when engaging with clients via telehealth.

I think the eye contact is still a really important thing. Getting the flow of what they’re comfortable with a little bit too. It’s being really careful around the tone of voice as well too, because—again, that’s the same for face-to-face, but be particularly careful of it over telehealth. (Amber)

This theme demonstrates that there are unique and nuanced skills required by clinicians to effectively engage in provision of rural healthcare services via telehealth. Many clinicians described how the rapid uptake of telehealth required them to quickly adapt to providing telehealth services, and they had to modify their approach rather than replicate what they would do in face-to-face contexts. Appreciating the different skills sets required for telehealth practice was perceived as an important element in supporting clinicians to deliver quality healthcare.

Theme 3: appreciating the complexities of telehealth implementation across services and environments

It was commonly acknowledged that there needed to be an appreciation by clinicians of the multiple different environments that telehealth was being delivered in, as well as the types of consultations being undertaken. This was particularly important when well-resourced large regional settings were engaging with small rural services or when clinicians were undertaking consultations within a client’s home.

Working from a different location and context

One of the factors identified as important for the successful delivery of services via telehealth was an understanding of the location and context that was being linked into. Participants regularly talked about the challenges when undertaking a telehealth consultation with clients at home, which impacted the quality of the consultation as it was easy to “ lose focus” (Kelsey) and become distracted.

Instead of just coming in with one child, they had all the kids, all wanting their attention. I also found that babies and kids kept pressing the screen and would actually disconnect us regularly. (Chloe)

For participants located in larger regional locations delivering telehealth services to smaller rural hospitals, it was acknowledged that not all services had equivalent resources, skills, and experience with this type of healthcare approach.

They shouldn’t have to do—they’ve gotta double-click here, login there. They’re relying on speakers that don’t work. Sometimes they can’t get the cameras working. I think telehealth works as long as it’s really user friendly. I think nurses—as a nurse, we’re not supposed to be—I know IT’s in our job criteria, but not to the level where you’ve got to have a degree in technology to use it. (Adam)

Participants also recognised that supporting a client through a telehealth consultation adds workload stress as rural clinicians are often having pressures with caseloads and are juggling multiple other tasks while trying to trouble shoot technology issues associated with a telehealth consultation.

Most people are like me, not great with computers. Sometimes the nurse has got other things in the Emergency Department she’s trying to juggle. (Eleanor)

Considerations for safety, privacy, and confidentiality

Participants talked about the challenges that arose due to inconsistencies in where and how the telehealth consultation would be conducted. Concerns about online safety and information privacy were identified by participants.

There’s the privacy issue, particularly when we might see someone and they might be in a bed and they’ve got a laptop there, and they’re not given headphones, and we’re blaring through the speaker at them, and someone’s three meters away in another bed. That’s not good. That’s a bit of a problem. (Patrick)

When telehealth was offered as an option to clients at a remote healthcare site, clinicians noted that some clients were not provided with adequate support and were left to undertake the consultation by themselves which could cause safety risks for the client and an inability for the telehealth clinician to control the situation.

There were some issues with patients’ safety though. Where the telehealth was located was just in a standard consult room and there was actually a situation where somebody self-harmed with a needle that was in a used syringe box in that room. Then it was like, you just can’t see high risk—environment. (Eleanor)

Additionally, participants noted that they were often using their own office space to conduct telehealth consultations rather than a clinical room which meant there were other considerations to think about.

Now I always lock my room so nobody can enter. That’s a nice little lesson learnt. I had a consult with a mum and some other clinicians came into my room and I thought “oh my goodness. I forgot to lock.” I’m very mindful now that I lock. (Jade)

This theme highlights the complexities that exist when implementing telehealth across a range of rural healthcare settings and environments. It was noted by participants that there were variable skills and experience in using telehealth across staff located in smaller rural areas, which could impact on how effective the consultation was. Participants identified the importance of purposely considering the environment in which the telehealth consultation was being held, ensuring that privacy, safety, and distractibility concerns have been adequately addressed before the consultation begins. These factors were considered important for the successful implementation of telehealth in rural areas.

This study explored telehealth service delivery in various rural health contexts, with 16 allied health and nursing clinicians who had provided telehealth services to people living in rural communities prior to, and during the COVID-19 pandemic. Reflections gained from clinicians were analysed and reported thematically. Major themes identified were clinicians navigating the role of telehealth to support rural healthcare, the need to prepare clinicians to engage in telehealth service delivery and appreciating the complexities of telehealth implementation across services and environments.

The utilisation of telehealth for health service delivery has been promoted as a solution to resolve access and equity issues, particularly for rural communities who are often impacted by limited health services due to distance and isolation [ 6 ]. This study identified a range of perceived benefits for both clients and clinicians, such as improved access to services across large geographic distances, including specialist care, and reduced travel time to engage with a range of health services. These findings are largely supported by the broader literature, such as the systematic review undertaken by Tsou et al. [ 25 ] which found that telehealth can improve clinical outcomes and increase the timeliness to access services, including specialist knowledge. Clinicians in our study also noted the benefits of using telehealth for ad hoc clinical support outside of regular appointment times, which to date has not been commonly reported in the literature as a benefit. Further investigation into this aspect may be warranted.

The findings from this study identify a range of challenges that exist when delivering health services within a virtual context. It was common for participants to highlight that personal preference for face-to-face sessions could not always be accommodated when implementing telehealth services in rural areas. The perceived technological possibilities to improve access can have unintended consequences for community members which may contribute to lack of responsiveness to community needs [ 12 ]. It is therefore important to understand the client and their preferences for using telehealth rather than making assumptions on the appropriateness of this type of health service delivery [ 26 ]. As such, telehealth is likely to function best when there is a pre-established relationship between the client and clinician, with clients who have a good knowledge of their personal health and have access to and familiarity with digital technology [ 13 ]. Alternatively, it is appropriate to consider how telehealth can be a supplementary tool rather than a stand-alone service model replacing face-to-face interactions [ 13 ].

As identified in this study, managing technology and internet connectivity are commonly reported issues for rural communities engaging in telehealth services [ 27 , 28 ]. Additionally, it was highlighted that within some rural communities with higher socioeconomic disadvantage, limited access to an appropriate level of technology and the required data to undertake a telehealth consult was a deterrent to engage in these types of services. Mathew et al. [ 13 ] found in their study that bandwidth impacted video consultations, which was further compromised by weather conditions, and clients without smartphones had difficulty accessing relevant virtual consultation software.

The findings presented here indicate that while telehealth can be a useful model, it may not be suitable for all clients or client groups. For example, the use of interpreters in telehealth to support clients was a key challenge identified in this study. This is supported by Mathew et al. [ 13 ] who identified that language barriers affected the quality of telehealth consultations and accessing appropriate interpreters was often difficult. Consideration of health and digital literacy, access and availability of technology and internet, appropriate client selection, and facilitating client choice are all important drivers to enhance telehealth experiences [ 29 ]. Nelson et al. [ 6 ] acknowledged the barriers that exist with telehealth, suggesting that ‘it is not the groups that have difficulty engaging, it is that telehealth and digital services are hard to engage with’ (p. 8). There is a need for telehealth services to be delivered in a way that is inclusive of different groups, and this becomes more pertinent in rural areas where resources are not the same as metropolitan areas.

The findings of this research highlight the unique set of skills required for health professionals to translate their practice across a virtual medium. The participants described these modifications in relation to communication skills, the ability to build rapport, conduct healthcare assessments, and provide treatment while looking at a ‘window view’ of a person. Several other studies have reported similar skillsets that are required to effectively use telehealth. Uscher-Pines et al. [ 30 ] conducted research on the experiences of psychiatrists moving to telemedicine during the COVID-19 pandemic and noted challenges affecting the quality of provider-patient interactions and difficulty conducting assessment through the window of a screen. Henry et al. [ 31 ] documented a list of interpersonal skills considered essential for the use of telehealth encompassing attributes related to set-up, verbal and non-verbal communication, relationship building, and environmental considerations.

Despite the literature uniformly agreeing that telehealth requires a unique skill set there is no agreement on how, when and for whom education related to these skills should be provided. The skills required for health professionals to use telehealth have been treated as an add-on to health practice rather than as a specialty skill set requiring learning and assessment. This is reflected in research such as that by Nelson et al. [ 6 ] who found that 58% of mental health professionals using telehealth in rural areas were not trained to use it. This gap between training and practice is likely to have arisen from the rapid and widespread implementation of telehealth during the COVID-19 pandemic (i.e. the change in MBS item numbers [ 1 ]) but has not been addressed in subsequent years. For practice to remain in step with policy and funding changes, the factors required for successful implementation of telehealth in rural practice must be addressed.

The lack of clarity around who must undertake training in telehealth and how regularly, presents a challenge for rural health professionals whose skill set has been described as a specialist-generalist that covers a significant breadth of knowledge [ 32 ]. Maintaining knowledge currency across this breadth is integral and requires significant resources (time, travel, money) in an environment where access to education can be limited [ 33 ]. There is risk associated with continually adding skills on to the workload of rural health professionals without adequate guidance and provision for time to develop and maintain these skills.

While the education required to equip rural health professionals with the skills needed to effectively use telehealth in their practice is developing, until education requirements are uniformly understood and made accessible this is likely to continue to pose risk for rural health professionals and the community members accessing their services. Major investment in the education of all health professionals in telehealth service delivery, no matter the context, has been identified as critical [ 6 ].

This research highlights that the experience of using telehealth in rural communities is unique and thus a ‘one size fits all’ approach is not helpful and can overlook the individual needs of a community. Participants described experiences of using telehealth that were different between rural communities, particularly for smaller, more remote rural locations where resources and staff support and experience using telehealth were not always equivalent to larger rural locations. Research has indicated the need to invest in resourcing and education to support expansion of telehealth, noting this is particularly important in rural, regional, and remote areas [ 34 ]. Our study recognises that this is an ongoing need as rural communities continue to have diverse experiences of using telehealth services. Careful consideration of the context of individual rural health services, including the community needs, location, and resource availability on both ends of the consultation is required. Use of telehealth cannot have the same outcomes in every area. It is imperative that service providers and clinicians delivering telehealth from metropolitan areas to rural communities appreciate and understand the uniqueness of every community, so their approach is tailored and is helpful rather than hindering the experience for people in rural communities.

Limitations

There are a number of limitations inherent to the design of this study. Participants were recruited via their workplace and thus although steps were taken to ensure they understood the research would not affect their employment, it is possible some employees perceived an association between the research and their employment. Health professionals who had either very positive or very negative experiences with telehealth may have been more likely to participate, as they may be more likely to want to discuss their experiences. In addition to this, only health services that were already connected with the researchers’ networks were invited to participate. Other limitations include purposive sampling, noting that the opinions of the participants are not generalisable. The participant group also represented mostly nursing professionals whose experiences with telehealth may differ from other health disciplines. Finally, it is important to acknowledge that the opinions of the health professionals who participated in the study, may not represent, or align with the experience and opinions of service users.

This study illustrates that while telehealth has provided increased access to services for many rural communities, others have experienced barriers related to variability in connectivity and managing technology. The results demonstrated that telehealth may not be the preferred or appropriate option for some individuals in rural communities and it is important to provide choice. Consideration of the context in which telehealth services are being delivered, particularly in rural and remote communities where there are challenges with resourcing and training to support health professionals, is critical to the success of telehealth service provision. Another critical factor is preparation and specific, intentional training for health professionals on how to transition to manage and maintain telehealth services effectively. Telehealth interventions require a unique skill set and guidance pertaining to who, when and what training will equip health professionals with the appropriate skill set to deliver telehealth services is still to be determined.

Data availability

The qualitative data collected for this study was de-identified before analysis. Consent was not obtained to use or publish individual level identified data from the participants and hence cannot be shared publicly. The de-identified data can be obtained from the corresponding author on reasonable request.

Commonwealth of Australia. COVID-19 Temporary MBS Telehealth Services: Department of Health and Aged Care, Australian Government; 2022 [ https://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/Factsheet-TempBB .

Caffery LA, Muurlink OT, Taylor-Robinson AW. Survival of rural telehealth services post‐pandemic in Australia: a call to retain the gains in the ‘new normal’. Aust J Rural Health. 2022;30(4):544–9.

Article   PubMed   PubMed Central   Google Scholar  

Shaver J. The state of telehealth before and after the COVID-19 pandemic. Prim Care: Clin Office Pract. 2022;49(4):517–30.

Article   Google Scholar  

Australian Digital Health Agency. What is telehealth? 2024 [ https://www.digitalhealth.gov.au/initiatives-and-programs/telehealth .

Hirko KA, Kerver JM, Ford S, Szafranski C, Beckett J, Kitchen C, et al. Telehealth in response to the COVID-19 pandemic: implications for rural health disparities. J Am Med Inform Assoc. 2020;27(11):1816–8.

Nelson D, Inghels M, Kenny A, Skinner S, McCranor T, Wyatt S, et al. Mental health professionals and telehealth in a rural setting: a cross sectional survey. BMC Health Serv Res. 2023;23(1):200.

Butzner M, Cuffee Y. Telehealth interventions and Outcomes Across Rural Communities in the United States: Narrative Review. J Med Internet Res. 2021;23(8):NPAG–NPAG.

Calleja Z, Job J, Jackson C. Offsite primary care providers using telehealth to support a sustainable workforce in rural and remote general practice: a rapid review of the literature. Aust J Rural Health. 2023;31(1):5–18.

Article   PubMed   Google Scholar  

Sutarsa IN, Kasim R, Steward B, Bain-Donohue S, Slimings C, Hall Dykgraaf S, et al. Implications of telehealth services for healthcare delivery and access in rural and remote communities: perceptions of patients and general practitioners. Aust J Prim Health. 2022;28(6):522–8.

Bradford NK, Caffery LJ, Smith AC. Telehealth services in rural and remote Australia: a systematic review of models of care and factors influencing success and sustainability. Rural Remote Health. 2016;16(4):1–23.

Google Scholar  

Caffery LJ, Bradford NK, Wickramasinghe SI, Hayman N, Smith AC. Outcomes of using telehealth for the provision of healthcare to Aboriginal and Torres Strait Islander people: a systematic review. Aust N Z J Public Health. 2017;41(1):48–53.

Warr D, Luscombe G, Couch D. Hype, evidence gaps and digital divides: Telehealth blind spots in rural Australia. Health. 2023;27(4):588–606.

Mathew S, Fitts MS, Liddle Z, Bourke L, Campbell N, Murakami-Gold L, et al. Telehealth in remote Australia: a supplementary tool or an alternative model of care replacing face-to-face consultations? BMC Health Serv Res. 2023;23(1):1–10.

Campbell J, Theodoros D, Hartley N, Russell T, Gillespie N. Implementation factors are neglected in research investigating telehealth delivery of allied health services to rural children: a scoping review. J Telemedicine Telecare. 2020;26(10):590–606.

Commonwealth of Australia. Modified Monash Model: Department of Health and Aged Care Commonwealth of Australia; 2021 [updated 14 December 2021. https://www.health.gov.au/topics/rural-health-workforce/classifications/mmm .

Sandelowski M. Whatever happened to qualitative description? Research in nursing & health. 2000;23(4):334 – 40.

Marshall C, Rossman GB. Designing qualitative research: Sage; 2014.

Caelli K, Ray L, Mill J. Clear as mud’: toward greater clarity in generic qualitative research. Int J Qualitative Methods. 2003;2(2):1–13.

Tolley EE. Qualitative methods in public health: a field guide for applied research. Second edition. ed. San Francisco, CA: Jossey-Bass & Pfeiffer Imprints, Wiley; 2016.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Levitt HM, Motulsky SL, Wertz FJ, Morrow SL, Ponterotto JG. Recommendations for designing and reviewing qualitative research in psychology: promoting methodological integrity. Qualitative Psychol. 2017;4(1):2.

Allied Health Professions Australia. Allied health professions 2024 [ https://ahpa.com.au/allied-health-professions/ .

Braun V, Clarke V. To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Res Sport Exerc Health. 2021;13(2):201–16.

Ritchie J, Spencer L. In: Alan B, Burgess RG, editors. Qualitative data analysis for applied policy research. Analyzing qualitative data: Routledge; 1994. pp. 173–94.

Tsou C, Robinson S, Boyd J, Jamieson A, Blakeman R, Yeung J, et al. Effectiveness of telehealth in rural and remote emergency departments: systematic review. J Med Internet Res. 2021;23(11):e30632.

Pullyblank K. A scoping literature review of rural beliefs and attitudes toward telehealth utilization. West J Nurs Res. 2023;45(4):375–84.

Jonnagaddala J, Godinho MA, Liaw S-T. From telehealth to virtual primary care in Australia? A Rapid scoping review. Int J Med Informatics. 2021;151:104470.

Jonasdottir SK, Thordardottir I, Jonsdottir T. Health professionals’ perspective towards challenges and opportunities of telehealth service provision: a scoping review. Int J Med Informatics. 2022;167:104862.

Clay-Williams R, Hibbert P, Carrigan A, Roberts N, Austin E, Fajardo Pulido D, et al. The diversity of providers’ and consumers’ views of virtual versus inpatient care provision: a qualitative study. BMC Health Serv Res. 2023;23(1):724.

Uscher-Pines L, Sousa J, Raja P, Mehrotra A, Barnett ML, Huskamp HA. Suddenly becoming a virtual doctor: experiences of psychiatrists transitioning to telemedicine during the COVID-19 pandemic. Psychiatric Serv. 2020;71(11):1143–50.

Henry BW, Ames LJ, Block DE, Vozenilek JA. Experienced practitioners’ views on interpersonal skills in telehealth delivery. Internet J Allied Health Sci Pract. 2018;16(2):2.

McCullough K, Bayes S, Whitehead L, Williams A, Cope V. Nursing in a different world: remote area nursing as a specialist–generalist practice area. Aust J Rural Health. 2022;30(5):570–81.

Reeve C, Johnston K, Young L. Health profession education in remote or geographically isolated settings: a scoping review. J Med Educ Curric Dev. 2020;7:2382120520943595.

PubMed   PubMed Central   Google Scholar  

Cummings E, Merolli M, Schaper L, editors. Barriers to telehealth uptake in rural, regional, remote Australia: what can be done to expand telehealth access in remote areas. Digital Health: Changing the Way Healthcare is Conceptualised and Delivered: Selected Papers from the 27th Australian National Health Informatics Conference (HIC 2019); 2019: IOS Press.

Download references

Acknowledgements

The authors would like to acknowledge Georgina Luscombe, Julian Grant, Claire Seaman, Jennifer Cox, Sarah Redshaw and Jennifer Schwarz who contributed to various elements of the project.

The study authors are employed by Three Rivers Department of Rural Health. Three Rivers Department of Rural Health is funded by the Australian Government under the Rural Health Multidisciplinary Training (RHMT) Program.

Author information

Authors and affiliations.

Three Rivers Department of Rural Health, Charles Sturt University, Locked Bag 588, Tooma Way, Wagga Wagga, NSW, 2678, Australia

Rebecca Barry, Elyce Green, Kristy Robson & Melissa Nott

You can also search for this author in PubMed   Google Scholar

Contributions

RB & EG contributed to the conceptualisation of the study and methodological design. RB & MN collected the research data. RB, EG, MN, KR contributed to analysis and interpretation of the research data. RB, EG, MN, KR drafted the manuscript. All authors provided feedback on the manuscript and approved the final submitted manuscript.

Corresponding author

Correspondence to Rebecca Barry .

Ethics declarations

Ethics approval and consent to participate.

Ethics approvals were obtained from the Greater Western NSW Human Research Ethics Committee and Charles Sturt University Human Research Ethics Committee (approval numbers: 2021/ETH00088 and H21215). Informed written consent was obtained from all participants. All methods were carried out in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Barry, R., Green, E., Robson, K. et al. Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study. BMC Health Serv Res 24 , 908 (2024). https://doi.org/10.1186/s12913-024-11233-3

Download citation

Received : 19 March 2024

Accepted : 23 June 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s12913-024-11233-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Service provision
• Rural health
• Allied health
• Rural workforce

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Educ Health Promot

Information challenges of COVID-19: A qualitative research

Golrokh atighechian.

Health Management and Economics Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Fatemeh Rezaei

Nahid tavakoli.

1 Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Mitra Abarghoian

2 Vice-Chancellery for Research and Technology, Isfahan University of Medical Sciences, Isfahan, Iran

BACKGROUND:

At the beginning of the COVID-19 pandemic, the Iranian Ministry of Health and Medical Education set up a 24-h call center, i.e., Center 4030, to mitigate people's worries and anxieties, create composure, increase people's trust, and answer their questions. This qualitative study aimed to identify the challenges of COVID-19-related-information among people in point of experts' views.

MATERIALS AND METHODS:

This qualitative study was conducted to collect the opinions of experts on the identification of the Information challenges of COVID-19 during March–June 2020. The research population included all health professionals and experts. The sampling method was initially purposive and continued to saturate the data as snowball technique. In this study, 19 participants were interviewed. The data were collected using a semi-structured interview. After collecting the data, the audio files of the interviews were written down to extract their external and internal elements. MAXQDA version 12 software was used to organize qualitative analysis and coding data.

The results of this study involved eight themes, i.e., lack of planning, lack of social trust in government, lack of COVID-19-integrated scientific authority in the country, conflicts of interest, lack of integrated information sources, distracting public attention, infodemic, and poor information quality, classified into 16 categories.

CONCLUSIONS:

The main information challenges that people in Iran faced included the lack of a scientific reference source to access accurate information, the existence of a large volume of information in virtual networks, and a huge volume of statistics from various information channels that caused confusion among people.

Introduction

In general, any outbreak will be accompanied by a tsunami of information, which, unfortunately, most often includes misinformation and rumors as well. Moreover, this is significantly intensified in the current century due to the availability and ubiquity of social media. Obviously, getting the right information from a reliable source is a key issue in this type of pandemics.[ 1 ] “Access to the right information can save lives,” argues Zaimova, quoting the head of the World Health Organization (WHO).[ 2 ] In the recent COVID-19 pandemic, besides the challenges exerted upon the health system, the rapid dissemination of information, including false and misleading information about the disease, has had a major impact on the behavioral patterns of people in various communities. Therefore, community leaders and governments must take appropriate measures to ensure that people have access to reliable and relevant information about COVID-19. The head of the Atlantic Consulate, Wedelmann, acknowledges that scientists and other experts are the most reliable source of information, and governments and employers should call on them to obtain the most reliable information.[ 3 ] Evidence suggests that people unintentionally share false information about COVID-19, without thinking about its authenticity, based on various motives such as entertainment and attracting attention and approval on social media. Lack of transparency also leads to rumors, speculation, and misinformation.[ 4 ] Hua and Shaw stated that 44% of people were actively looking for reliable information, following the news, and putting their interests first, while 33% only passively digested information about COVID-19.[ 1 ] In this regard, the dangers of misinformation during the management of COVID-19 outbreak have been introduced with the term “infodemic.” Some experts believe that infodemic, i.e., too much information including right as well as wrong information, is spreading around the world. The worst-case scenario involves the fact that incorrect information is potentially released faster than the virus itself, causing people to make uninformed or misinformed decisions.[ 5 ] Therefore, there is the challenge of how people search for or avoid information. On the other hand, the unprecedented distribution of information on social media has provided people with access to a large amount of information. This has caused the spread of rumors and the dissemination of questionable information. As a result, this information conflict has led to the development of misinformation among people, as well as a negative impact on their behavior.[ 3 ] In addition, the psychological effects of misinformation on social media are significant. Therefore, if people cannot verify the accuracy of a large portion of information in cyberspace and the media, they will be anxious and worried. Therefore, it is necessary to draw their attention to the information that is published by official institutions and government agencies.[ 6 ] The WHO has said that misinformation has hampered the efforts of organizations and governments to control the spread of COVID-19. This makes it difficult to hear the voices of health-care organizations. Therefore, major attention and resources have been allocated to dealing with misinformation. Because the spread of this pandemic has been accompanied by a wide range of useless informational content, it has created new challenges.[ 7 ] While expressing his concern about the publication of false information about COVID-19, the head of the WHO admits that we are not fighting the coronavirus; rather, we are fighting the infodemic. Since false news and misinformation during this time will lead to misguided medical advice worldwide, the question is how to deal with such a serious problem.[ 8 ] In this regard, it is important for traditional and modern social media to help people have a better understanding of what they are looking for information about because these media are sometimes ahead of the evidence.[ 9 ] With the increasing use of social media and communication technologies, the infodemic challenge is growing,[ 10 ] and the sheer volume of online information is increasing people's anxiety. Therefore, it is imperative for the digital media platforms to be environmentally friendly and to create trust and calmness among people, especially when sharing information related to health and life threats.[ 11 ] On the other hand, many reliable sources, such as the WHO, are on social media, which can reduce people's anxiety by giving them access to the correct information while controlling the virus at the same time. Of course, the impact of the response to the infodemic varies depending on people's trust in the authorities and officials from one country to the other.[ 7 ] As the demand for access to reliable and timely information about COVID-19 increases in the community, policymakers need to be aware of the best practices for reducing the risk of the infodemic and turning to knowledge and expertise available in academic settings.[ 12 ] The wrong information is one of the great human challenges in the new COVID-19 crisis. Some people spend a lot of time reading-related information in print and virtual media. However, they are unable to distinguish quality information from false and low-quality information. This information reinforces the challenges, and people need to be equipped with the knowledge and skills of health literacy and media health literacy.[ 13 ] Academics and scientists need to pay attention to two basic aspects to share scientific information. These include filters that have the ability to increase the validity of data and the individual responsibility for creating and distributing information among people.[ 14 ] Information from all sources should be transferred to a dedicated COVID-19 center to discover, diagnose, treat, and most importantly, inform policymakers, investors, resource providers, affected populations, and social media. Reproduction and enhancement of misinformation must be prevented. In all scenarios, information must be at the level of understanding of the relevant community.[ 15 ] Over the past 2 months in Iran, people have faced many challenges due to concerns about the spread of COVID-19 because of the spread of large amounts of scattered and disorganized information on social media from domestic and foreign sources. This has exacerbated their concerns and confusion about conscious decisions on this disease's prevention and care. On the other hand, the repeated recommendations of the media and the retelling of the decisions and actions of the officials, which were sometimes inconsistent and contradictory, led to the intensification of mental fatigue and confusion of the families.[ 16 ] At the beginning of this pandemic, the Iranian Ministry of Health and Medical Education set up a 24-h call center, i.e., Center 4030, to mitigate people's worries and anxieties, create composure, increase people's trust, and answer their questions. The main objective of this call center has been to answer ambiguities and prevent rumors.

However, despite the implementation of this important step, people are still resorting to various sources to obtain information in the face of numerous information challenges related to the coronavirus, and this qualitative study has been designed to identify them.

Materials and Methods

Study Design and Setting: In this study, a qualitative study was conducted to collect the opinions of experts on the identification of the information characteristics and challenges of COVID-19 during March–June 2020.

Study participants and sampling: The research population included all health professionals and experts, including university faculty members, policymakers, university administrators and experts and physicians, nurses working in the infectious diseases unit. The sampling method was initially purposive and continued to saturate the data as snowball technique. First, five participants were selected who had experience or knowledge about the main phenomenon or basic concepts explored. In this regard, to access different opinions about the central phenomenon and the explored concepts, the sampling with maximum diversity was performed, and people with different views were selected. Sampling continued until data saturation. In this study, 19 participants were interviewed. Inclusion criteria consisted of all professionals, policymakers, managers, and experts with at least 5 years of experience. Furthermore, individuals who refused to be interviewed were excluded.

Data Collection Tool and Technique: The data were collected using a semi-structured interview. To verify the validity of the interview guide, the interview questions among the research team were first discussed with the participation of one external expert and revised accordingly. The interview guide was subsequently tested on three nonparticipants to check the number and order of the questions in the study. It is achieved by analyzing and comparing the contents of the interview until no new or appropriate details concerning a theme appear to emerge.

The time and place of the interview were prearranged with the participants, preceded by obtaining their permission through an informed consent form. The interviews were recorded through a voice recorder. Due to the prevalence of corona, some interviews were conducted by phone. After collecting the data, the audio files of the interviews were written down to extract their external and internal elements.

MAXQDA Plus version 12 software (Release 12.3.0, VERBI GmbH Berlin) was used to organize qualitative analysis and coding data. For the evaluation of the reliability of the study data, four criteria were used in Lincoln and Goba, namely, credibility, conformability, dependability, and transferability (Lincoln YS and Guba EG, 1985).

Ethical consideration: This study received the required ethics approval from Isfahan University of Medical Sciences Research Ethics Committee, Isfahan, Iran, with ethics code No. IR.MUI.MED.REC.1398.653.

More than half of the participants were male (63.1%), and the majority had a PhD (42.1%). Furthermore, more than half of the participants (52.6%) had more than 20 years of experience [ Table 1 ].

Basic characteristics of participants

The results of this study involved eight themes, i.e., lack of planning, lack of social trust in government, lack of COVID-19-integrated scientific authority in the country, conflicts of interest, lack of integrated information sources, distracting public attention, infodemic, and poor information quality Table 2 , classified into 16 categories.

Information challenges regarding COVID-19

Lack of planning

Lack of planning involves invalid information and a lack of consistency in informing the public.

Participants believed that invalid information and instability in information-related decisions were indicative of the authorities' lack of planning in the COVID-19 outbreak. The confusion of health policymakers in the decision-making process, the government's different decisions to declare closures for different jobs, and the variable decisions of managers were among the issues that the participants referred to.

The Ministry of Health and Medical Education and the health authorities do not have specific credible channels and entries, so weaknesses and conflicts are transferred to the community, then their authority is destroyed, and people lose confidence in official sources (Interviewee 5).

The reasons for the officials' lack of planning in this pandemic involved managers' changing decisions, people's confusion about whether COVID-19 was getting serious or not, lack of foresight and preparedness of the government to guide people, failure to implement preventive measures to mitigate the confusion of people, and politically ignoring the support and experience of other countries.

Due to the fact that the news and information about the Coronavirus unfortunately reached the people very late, the members of the community partially underestimated the epidemic, and no training was provided (Interviewee 1).

Lack of social trust in government

This involved lack of transparency from the officials in informing people, and a lack of trust on the part of people due to authorities downplaying the seriousness of the crisis. The government's secrecy in providing information about the number of deaths and infections led to people shifting their attention and trust to unofficial channels, which was a sign of their lack of social trust.

People's attention and trust in unofficial channels was expressed as one of the signs of social distrust. People always think that the government is hiding the facts from the foreign channels or from other media, that is, we have a kind of unhealthy atmosphere (Interviewee 1).

Participants cited a lack of transparent information in the early days of the epidemic and lack of timely information as some of the reasons for people's distrust.

The more realistic and transparent we talk to people, the more we can gain people's trust. People traditionally trust centers that have long been among their safe havens. Well, naturally, medical centers are one of these centers (Interviewee 10).

The lack of transparency in the statistics was another reason for people's distrust.

For example, even in the case of statistics, it is not yet clear whether the statistics are real or not. Even if they weren't real, it would definitely be a good reason behind it that I don't want to talk about. There is probably a reason, and I have to admit, they don't want to announce the actual statistics (Interviewee 2).

Lack of COVID-19-integrated scientific authority in the country

This theme includes a lack of consistency of published information and various media and informational sources. The lack of an integrated scientific reference led to parallel work, lack of consensus among experts on some scientific topics, and information confusion when comparing multiple information sources.

Recently, The Islamic Republic of Iran Medical Council has been working for itself, which is, in my opinion, wrong. All of this must be centralized, and in fact we must have a position of information management under the supervision of the Ministry of Health and Medical Education. All material produced must first be approved by the Ministry of Health and Medical Education, and then reach the public (Interviewee 17).

Conflicts of interest

This included disagreement between officials and disregard for different specialties. Lack of consensus among authorities and ignoring the different specialized opinion of experts led to conflicts of interest.

Every organization considers its own interests and does not value us (the Ministry of Health and Medical Education). They do not follow government orders, even if it is to their detriment. Therefore, providing information under these conditions will not be effective (Interviewee 1).

The multiplicity of nontechnical spokespersons in the national media and noncompliance with professional privacy were some of the issues raised by the participants.

Well, I don't know what's behind the scene. But when we hear and compare their official statements, there are all kinds of conflicts in the policies and words of health policymakers (Interviewee 5).

Lack of integrated information sources

This category included parallel information provision streams from the media and a lack of valid information provision channels. The existence of multiple telephone lines multiple websites created by different individuals indicated a lack of an integrated information source in the country.

One organization said we would give people a phone number, another said we would create a website. However, everyone wants to have an information channel (Interviewee 2).

Lack of knowledge about where to go for information and parallel work in informing indicated the lack of an integrated information source in the country.

People don't know where to get information and which information source to trust. Well, the existence of social networks makes information available to the public, but the important thing is to trust our own mass media or a foreign media (Interviewee 11).

Distracting public attention

This category included provoking people through cyberspace, speculations caused by following the cyberspace information sources, easy access to unreliable sources and virtual networks, misuse of virtual networks by profiteers, each person having a tribune in the cyberspace, and increasing public concern by foreign channels.

I spend almost 90% of my time dealing with and denying false news. Sir, this is not true, sir, this is not true, sir, this is not true, and then the one I can say is right is what the Ministry said. So it is better, at least for ourselves, to have the unity of voice as always (Interviewee 3).

The gap between the data reported by the government and that reported by foreign media on the disease has raised concerns.

If people are given regular statistics, their fears will be reduced (Interviewee 3).

This category included a wide dissemination of information and diffusion of false information. From the participants' point of view, the high volume of available information and the anarchy of information in COVID-19 caused anxiety and confusion among people.

The most important problem, in my opinion, is that people are confused about information, that is, they have become so bombarded with information that they can't really decide what to do (Interviewee 10).

The availability of multiple sources of information, the dissemination of false news on virtual networks, and lack of information refining have prevented people from distinguishing between right and wrong information.

Valid and reliable information must be given to people. People receive general information about COVID-19 from various media outlets, but they do not have the same information about necessary actions, such as disinfecting surfaces. One source says make Javelle water and bleaching solution with a ratio of 1:4. Another source says make it with a ratio of 1:49, another says make it with the ratio of 1:100. Individuals and/or organizations give different instructions (Interviewee 17).

Poor information quality

This category included disinformation, contradictory information, limited access to information, and misinformation. Lack of access to accurate and comprehensive information about the disease and the lack of accurate statistics on the number of infected individuals have caused concern.

We do not have accurate statistics. We don't know how many patients we have, how many samples have been sent, how many have been positive and how many have been negative. This causes fear and panic among people (Interviewee 14).

Participants acknowledged that the spread of rumors and false news by virtual networks has accelerated the dissemination of low-quality and misleading information.

At present, the media and social networks in the country have spread false information among people by spreading rumors in the community. Of course, there are reasons why we may have caused this (Interviewee 7).

Appropriate behavioral patterns among authorities and the public in epidemics regarding the production and distribution of information in various media are very helpful in promoting public awareness and knowledge for the prevention of epidemics.[ 17 ] In the present study, participants believed that provided medical information should be organized, simple, and fluent and in a language that is easy to understand by ordinary people to reduce concerns and anxieties of people. As many behavioral fears and reactions naturally arise from a lack of knowledge, rumors, and misinformation, providing clear, concise, and accurate information about COVID-19, and user-friendly ways to access such information reduce the public's focus on rumors.[ 18 ] According to the participants' views, multiple instructions from different universities, the presence of multiple articles and longwinded instructions, and the presence of multiple sources of information that must be compared have led to confusion. Moreover, they emphasized that people must refer to reliable information sources such as the website of the Ministry of Health and Medical Education, doctors' inquiries, the National Broadcasting Media, and trustworthy online news, to reduce their worries about the virus and to prevent being infected with misinformation. The WHO states that insufficient information about the coronavirus increases the likelihood of mistrust in government and authorities. In addition, this organization recommends searching for information from reliable sources, such as radio and television, and national newspapers, once or twice a day instead of once every hour, helping people manage and reduce their stress.[ 19 ] As worst-case scenarios are usually accelerated when there is no information, leaders should provide the most up-to-date information about COVID-19 for health workers to know how to protect themselves and what to do if they encounter it. In addition, the leaders should anticipate what questions might arise and prepare their answers well. In this way, they are empowered with reliable information so that they can help themselves and control their stress.[ 20 ] In their study, Stirling et al . found that 66.4% and 55.3% of medical students depended on the internet, and television and radio for getting coronavirus information, respectively.[ 21 ] Participants in the present study acknowledged that a lack of clear information and normalizing the prevalence and risk of the disease by the radio and television channels at the beginning of the epidemic, lack of transparency in the statistics provided to the public, provision of politically biased information, government secrecy and untruths, rumors, and dissemination of various pieces of false news in virtual media led to people's concern and confusion in obtaining accurate and reliable information. The findings of the present study were consistent with Baines study, showing that a lack of transparency and delay in public urgency led to fears among the health authorities and delays in disclosing information about COVID-19, spreading misinformation and rumors among the public, incorrect public forecasting, ultimately causing the unexpected dissemination of the virus.[ 22 ] Moreover, the findings of the present study were in line with those of Dong's study, showing that downplaying the severity of the epidemic of COVID-19 by the Chinese government in the early days caused people's distrust in the transparency and the decision-making capability of the government.[ 23 ] In the present study, according to the participants' views, the infodemic phenomenon led to people's confusion. In this regard, the presence of numerous information sources, the high volume of available information, people's anxiety caused by information anarchy, information redundancy, lack of information refining and cleaning instruments, and the misrepresentation of news in virtual networks were mentioned as examples. Lu's study showed that infodemic, including incorrect information about COVID-19 on social media and elsewhere, caused a major risk to people's mental health during this crisis.[ 23 ] In his study, Bains emphasized that, in order to fight infodemic, it was necessary to analyze all types of information, to have an integrated scientific approach, to have a clear and scientific definition of all types of information, and to avoid using wrong words.[ 22 ] The findings of the current study showed that provoking people through virtual networks, speculation due to following cyberspace channels, easy access to unreliable sources in virtual networks, and the misuse of virtual networks by profiteers were significant challenges people encountered. Allah Verdi believes that there is a difference between producing and disseminating COVID-19 health messages and disseminated unprofessional messages on social media. Hence, in order to break the chain of disease transmission, it is necessary for the health system to take measures to prevent the spread of misinformation.[ 24 ] Kouzy et al . analyzed 673 tweets and showed that the least amount of unconfirmed information was related to public health accounts and accounts of health-care services, while the most misleading information was related to personal and group accounts. Another noteworthy point in her study was the lower incidence of misinformation when searching the literature using COVID-19 instead of 2019_ncov and corona. She believes that incorrect medical information and a lot of unconfirmed content about COVID-19 are being widely published on social media, and it is necessary to intervene in this process to protect public safety.[ 25 ] The other challenges mentioned in the present study involved promoting people's awareness in an unfair environment structured around mistrust, higher levels of trust on the part of people in social media than mass media, people's distrust of official information sources, failure to take the virus seriously, failure to inform people by the national media, lack of managers' ability to attract public cooperation and trust, failure to provide timely information, and secrecy in reporting the number of COVID-19 deaths and patients. In her study, Sharma emphasized that the health-care organizations and other authorities should develop practical strategies for identifying credible and reliable information sources and disseminating valid information about COVID-19. In addition, she argues that, using scientific methods, such as data mining, for identifying and removing those messages in virtual networks which have no scientific evidence behind them is one of the legal measures that can be taken.[ 26 , 27 ] In the current study, provision of contradicting content from different sources; obtaining information from invalid sources, which creates anxiety; lack of a reputable reference source to answer all relevant questions; contradiction between official media news and social networks; availability of information from multiple and contradictory channels; and lack of knowledge on where to go for reliable information were among other challenges noted by the participants. Hua described the reasons of China's success in controlling COVID-19 as a strong government, implementing restrictions, and people's immediate participation. In the early stages, the highest judicial authority's guidelines on false news constituted an important step toward reducing confusion and panic among people.[ 12 ] In Medford's study, about half of the tweets scared people and about 30% were surprising them, among which the political and economic impacts of COVID-19 were the most important discussion topics.[ 28 ]

Shankar pointed out that one of the challenges for medical staff in dealing with cancer patients, who wanted to find accurate information to adapt to the conditions of COVI-19, was the existence of a large volume of information on virtual networks.[ 29 ] Health ministries and health education specialists in various countries should design an interactive dashboard to deal with the release of huge amounts of inaccurate information and misinformation, provide real-time information, and eliminate rumors related to COVID-19 around the world.[ 24 ] In his study, Bastani emphasized that health department's managers should have practical perspectives on managing public information in the community.[ 30 ]

Conclusions

With the COVID-19 pandemic, information seeking, especially on social media, emerged as one of the major challenges facing the affected communities. In this regard, the large volume of information and the lack of a reliable source to obtain accurate information, especially in the early days, caused concern and anxiety among people. In this study, the main information challenges that people in Iran faced included the lack of a scientific reference source to access accurate information, the existence of a large volume of information in virtual networks, and a huge volume of statistics and detailed news from various information channels that caused confusion among people. Therefore, considering the fact that epidemiological predictions show the high likelihood for the continuation or re-spread of this virus, it is recommended that health leaders identify and/or introduce a scientific authority for information related to COVID-19 in the country; introduce reliable information sources; provide simple, legible, and transparent information; and encourage people to improve their knowledge so that they can correctly interpret the right information, and keep themselves and their families safe from the virus.

Financial support and sponsorship

This study was funded by Isfahan University of Medical Sciences, Isfahan, Iran, with research code No. 198222.

There are no conflicts of interest.

Acknowledgments

We would like to thank all interviewees for their kind contribution. Moreover, the authors cordially appreciate Dr. Hasan Ashrafi-Rizi for his kind help and guidance.