Thematic Analysis in Qualitative Research: Common Pitfalls and Practical Insights for Academic Writing

Abstract

This article addresses common pitfalls in the production and presentation of findings in qualitative research, with a focus on thematic analysis. It offers a practical guidance grounded in the theoretical and epistemological foundations of qualitative inquiry, illustrated with concrete examples drawn from the author’s experience mentoring students and reviewing qualitative reports. These examples are used not only to clarify reporting challenges, but also to explain the underlying reasoning behind them. The article advocates a holistic and comprehensive approach to presenting the studied phenomenon, emphasizing the importance of interconnected analysis that goes beyond isolated interview questions. It highlights the significance of context-specific analytical themes that do not resemble generic labels, topic summaries, or mere subtitles. Effective themes are characteristically specific and revealing, capturing nuances, shared meanings, and divergences in participants’ accounts. They function as analytic constructs that link participants’ lived experiences with the abstract concepts and categories of academic analysis. The article also examines the use of participant quotations, emphasizing that quotations are not to be treated as isolated data fragments but as carefully selected and interpreted material embedded in the researcher’s analytic narrative. It explores quotation length, placement, and integration with commentary that reflects the researcher’s interpretive framework. In addition, the article discusses the risk of quantitative overshadowing in qualitative reporting, asserting that meaning takes precedence over numerical proportions. While acknowledging that numbers can support thematic claims, it emphasizes that analytical strength lies in revealing complexities, shared meanings, and variations across experiences. By outlining key principles for systematic, transparent, and reflexive reporting, the article offers a practical roadmap for producing rigorous qualitative outputs. It aims to support researchers in crafting analyses that remain faithful to the meanings co-constructed by participants and researchers and that communicate the depth and complexity of the social phenomena under study.

Introduction

Qualitative research has significantly expanded in recent decades (Allen, 2016; Denzin & Lincoln, 2017). Publications specifically dedicated to qualitative analysis have also thrived (e.g., Flick, 2014a; Järvinen & Mik-Neyer, 2020; Miles et al., 2013). Yet, several issues in qualitative data analysis continue to spark scholarly debate, influencing both how qualitative research is practiced and how its findings are evaluated. Unsettled matters include, but are not limited to methodological discussions around data saturation and adequacy (Naeem et al., 2024; Saunders et al., 2018); researcher positionality in data interpretation (Folkes, 2023); distinctions between classifying data and constructing narratives (Maxwell & Chmiel, 2014); and the potential for secondary analysis–that is reusing a dataset to generate additional academic outputs (Heaton, 2004).

Although qualitative research and methodological discussions have expanded, few published articles offer detailed accounts of how qualitative analysis is actually conducted in practice. As a result, the analysis process remains one of the least explained and least understood stages of qualitative research (Neale, 2016; Raskind et al., 2019). A growing body of scholarship, however, has begun to address this gap by making analytical processes more explicit. For instance, Trainor and Bundon (2020) elaborate on thematic analysis, Neale (2016) discusses iterative categorization, and Bingham (2023) describes the integration of deductive and inductive coding in the methodology sections of their own research articles. Nonetheless, the presentation of findings—particularly the transformation of analysis into a written report—continues to receive significantly less attention than data collection or early analytical stages such as coding and categorization.

This article does not aim to provide an exhaustive account of qualitative analysis or the presentation of findings. Rather, it addresses a persistent gap by examining how thematic analysis is conducted and findings are reported in academic writing, with particular attention to common pitfalls. Although the article concentrates on thematic analysis, key principles—such as the emphasis on holistic and comprehensive analysis and caution against quantitative overshadowing—apply broadly across qualitative approaches. The article’s originality lies not in introducing new methodological frameworks, but in offering concrete guidance—grounded in the theoretical and epistemological foundations of qualitative research and illustrated through practical examples–on how to present findings and enhance analytical reporting in written qualitative outputs.

Given this goal, it is important to recognize the wide diversity of analytical approaches in qualitative research and the lack of a one-size-fits-all formula for analysis and reporting. The article acknowledges that there is no single “right” way to analyze qualitative data (Saldaña & Omasta, 2018), nor is there a universal formula for transforming data into findings (Patton, 2002). Researchers have access to a variety of distinct qualitative analytical methods, such as critical discourse analysis, narrative analysis, content analysis, textual analysis, and thematic analysis (Stevens, 2022). These methods not only differ from one another but also encompass considerable internal variation. Thematic analysis, the primary focus of this article, is one of the most widely used methods and encompasses a family of approaches rather than a singular method–a nuance often overlooked in published articles (Braun & Clarke, 2022, p. 1).

Moreover, different qualitative methods–such as phenomenology, ethnography, and grounded theory–employ distinct analytical strategies, resulting in varied forms of written output. For example, a phenomenological article using thematic analysis differs substantially from an ethnographic monograph. This diversity in qualitative analysis and writing–often underrecognized or misunderstood–can make it challenging for researchers to select, apply, or justify a particular approach.

Despite this diversity within qualitative methods and the risk of reducing them to overly standardized procedures or narrow definitions (Brown-Saracino, 2021), this article argues that qualitative research is nevertheless guided by an underlying logic. While qualitative research may be evaluated using a range of criteria (Tracy, 2010), transparency and systematicity are often emphasized as key benchmarks for establishing analytical rigor (Meyrick, 2006). In this respect, the epistemological foundations of qualitative inquiry aim to balance scientific criteria–such as systematicity, consistency, transparency–with more intuitive orientations, including practicality and the pursuit of meaning. Even when qualitative research is approached as a form of art that values intuition, analysis and reporting need to be “credible” and “communicable” to others (Miles et al., 2013, p. 25). Accordingly, analytical thinking can coexist with intuition and creativity in qualitative research (Janesick, 2001), but researchers are expected to provide a transparent and internally consistent account of how they arrived at their findings, explaining their interpretive decisions, and avoiding fundamental methodological inconsistencies.

This article offers a practical roadmap for novice researchers seeking to improve their reporting of thematic analysis. Unlike broader overviews of qualitative analysis or procedural guides on coding, it focuses on common pitfalls in the transformation of analysis into writing–an underdiscussed but crucial stage in producing credible and rigorous qualitative research. The guidance offered here is informed not only by the existing literature, but also by the author’s ongoing experience mentoring students and reviewing qualitative assignments and papers.

The paper emphasizes two core pillars of qualitative analysis and reporting: (1) understanding and representing participants’ interpretations of the social world through the researcher’s interpretive lens, and (2) achieving analytical depth by providing a comprehensive account supported by rich, nuanced insights. By clarifying common pitfalls and outlining for stronger reporting, the article offers an accessible yet rigorous guide to conducting and presenting thematic findings with clarity, consistency, and analytical rigor.

From Data to Findings: Interpretive Analysis

A strong qualitative analysis reflects the qualitative research paradigm, including its epistemological, ontological, and axiological presuppositions (Neuman, 2006). Despite variations, qualitative research is a “situated activity” in which researchers set aside their preconceptions to understand the social world by observing, interpreting, and exploring individuals’ experiences within the temporal and spatial contexts in which phenomena and perceptions occur (Denzin & Lincoln, 2017).

Being interpretive does not preclude being systematic, empirical, or transparent. In fact, interpretive approaches share these features with positivist and critical social science paradigms (Neuman, 2006). Moreover, the interpretive approach itself is dynamic, with qualitative research often “in transition” and engaging with “post-interpretive paradigms” (Denzin & Lincoln, 2017, p. 31). Although there are multiple complex interpretive practices, interpretation remains a central pillar of most qualitative research (Spencer et al., 2003). The guidance offered in this article is likewise informed by an interpretive approach.

Interpretation is not a simple endeavor; indeed, a specific philosophical tradition—hermeneutics—focuses on the study of interpretation and the development of various theoretical approaches (George, 2021). In qualitative research, interpretive frameworks often fall into two categories: “empathic” and “suspicious” (Willig & Stainton-Rogers, 2017, p. 278). Empathic interpretations seek to understand meaning “from within,” exploring how phenomena are experienced and narrated, while suspicious interpretations look beneath the surface to uncover hidden meanings and underlying structures. For instance, an emphatic interpretation might explore how individuals narrate their healing journeys, whereas a suspicious interpretation might examine how market forces subtly shape the patient-practitioner dynamics. Regardless of the approach, interpretation always adds something to what is already there. Thus, qualitative outputs are to be consciously crafted with an interpretive framework—empathic, suspicious, or both—that reveals both explicit and implicit meanings.

Researchers are encouraged to reflect on the complexity of interpretation and to clearly outline the interpretive acts used in their written report. Although publication constraints may limit the level of detail, it remains important to explain the key steps in the analysis process, including how the data was approached during and after collection, how methodological challenges were addressed, and how findings were generated. Researchers are expected to document not only how data were collected and categorized, but also how key interpretive decisions were made during analysis, as part of establishing transparency and rigor (Spencer et al., 2003, p. 200).

As such, a qualitative research paper is expected to include a substantive discussion of the interpretive practices employed, detailing the interpretative choices made. Ultimately, all qualitative research outputs are rooted in the researcher’s interpretation of participants’ interpretations of the social world. To ensure scientific rigor, the interpretive process is expected to be explicit and transparent.

Achieving Depth and Breadth in Qualitative Analysis

The outcome of qualitative research is not merely a report but a comprehensive examination of a social phenomenon, aimed at capturing its intricacies and rich details through participants’ perspectives. It seeks to provide deep insights into the topic while uncovering the complex and nuanced views of participants in a holistic manner (Patton, 2002). Achieving this depth requires more than analyzing individual questions and their answers; it calls for engaging with the dataset as a whole to produce an in-depth and cohesive interpretation and explanation.

Interconnected Analysis, not Question Based

A common mistake in qualitative research is presenting findings as a collection of participants’ answers to individual interview questions. Treating these responses in isolation undermines the holistic nature of qualitative research, which views data as interconnected and contextually embedded, aiming to understand phenomena in their entirety through relevant aspects and interrelations (Lim, 2024). For example, presenting findings under headings like “Answers to Question 3: Everyday Experience,” followed by a list of individual responses, reflects a fragmented approach. Such presentations fail to synthesize patterns that may emerge across responses to different questions.

While interview questions can help explore the research problem, they do not determine the structure of the analytical output. The analytical report is not merely an aggregation of responses to individual questions. Instead, it reflects the researcher’s effort to identify patterns, connect themes, and uncover the meanings embedded in participants’ responses across the full range of data (Creswell, 2007). This process typically draws on diverse sources—interview transcripts, fieldnotes, written documents, and participant observations—to address the overarching research question.

Reflexivity, a core principle of qualitative research, is often demonstrated by showing how researcher’s roles, assumptions, and perspectives evolve throughout the analysis and writing process (Olmos-Vega et al., 2022). In this regard, findings are expected to move beyond the researcher’s initial assumptions, with interview questions serving as starting points in an evolving analytical journey. Accordingly, interview questions function as tools for data collection, not as the framework for final analysis.

After data collection, researchers typically engage in coding, categorization, and thematization in order to “manage the data for rigorous and trustworthy analysis” (Bingham, 2023). Coding breaks data into smaller, manageable units; categorization groups related codes together; and thematization elevates these codes/categories into higher-order abstract concepts (Merriam & Tisdell, 2015; Saldaña & Omasta, 2018). This layered process links data across sources and makes connections (Dey, 1993), rather than following the linear order of the interview guide. While the design of interview questions may divide a complex phenomenon into relevant and manageable parts, the aim of analysis is to synthesize these parts into a comprehensive understanding of the research topic.

Themes that closely mirror the structure of interview questions often indicate what Connelly and Peltzer (2016) describe as “underdeveloped themes” (p. 52). For example, if an interview guide includes questions about demographic background, individual religiosity, and sources of religious knowledge attainment, a weak analysis may simply reproduce these as headings—“Background,” “Individual Religiosity,” and “Religious Knowledge Sources”—rather than integrating them to explore, for instance, how individual religiosity shapes appeals to particular knowledge sources or, how gender influences both.

Researchers often combine deductive strategies, which align data with pre-established research aims and theoretical concepts, and inductive reasoning, which allows new patterns and themes to surface from within the data itself (Bingham & Witkowsky, 2022). For example, while the interview guide may not have asked directly about intergenerational conflict in child-rearing, repeated references to tensions between older and younger family members in interviews might lead to the emergence of a new theme. Importantly, “emergence” in this context underscores the researcher’s interpretive role; it does not mean that themes simply arise from the data on their own, without analytical intervention (Bingham & Witkowsky, 2022). Ultimately, the unit of analysis in qualitative research is not the individual interview questions, but the dataset as whole, including interviews and any other relevant material.

Layered Explanations for Rich Insights

Qualitative research does not merely confirm the ordinary or reiterate what is already known. Instead, it seeks to uncover depth, generate new understandings, and provide layered explanations that illuminate the complexities and nuances of the phenomenon under study. Achieving this requires the qualitative essay to move beyond surface-level accounts and pursue deeper, more textured interpretations–made possible through nuanced and detailed explanations.

Qualitative research excels in offering diverse forms of explanation, including descriptive, exploratory, and constitutive. Descriptive and exploratory insights emerge from its commitment to “thick description” (Ponterotto, 2006)–for example, detailing bureaucratic administrative regulations governing traditional and complementary therapies. It also offers exploratory insights by investigating phenomena about which little is known (Creswell, 2007), such as the international circulation of training programs in traditional and complementary medicine. Qualitative analysis further contributes to causal inquiry by identifying underlying mechanisms and moving beyond surface-level associations (Miles et al., 2013, p. 199)–for instance, examining how work dissatisfaction, emerging health markets, and the globalization of medical practice influence medical professionals’ engagement with traditional and complementary therapies. Moreover, qualitative analysis can offer “constitutive explanations,” which describe how multiple elements constitute a phenomenon by analyzing their interrelations, interactions, and the specific properties (Packer, 2017)–for example, examining what traditional and complementary therapies mean in a particular national context, the regulations imposed by the state, the main actors involved in their demand and supply, and the circumstances under which patients turn away from modern medicine and seek these treatments.

A qualitative essay may offer any one of these forms of explanation, but regardless of the type employed, it needs to fulfill the four core features of qualitative research: process, closeness, distinction, and improved understanding (Aspers & Corte, 2019). Researchers’ “closeness” to the phenomenon—developed through both data collection and analysis—enables findings that enhance “improved understanding” while revealing the distinctive features of the research topic. Ultimately, qualitative analysis aims to provide a comprehensive and nuanced account of the phenomena studied, capturing its full complexity and contextual richness.

Problems arise when the output of qualitative analysis—whether an essay, article, or thesis—remains shallow and insubstantial, falling short of the standards of “thick description” that supports trustworthiness and validity (Creswell & Miller, 2000). Such work fails to capture the specificities, distinctive features, and contextual dynamics of the research topic. For instance, simply stating that “participants face challenges balancing work and home life” offers little depth, whereas a richer analysis would explore how these challenges vary by gender, occupation, or caregiving roles, and reveal how participants experience, interpret, and emotionally respond to these tensions.

A lack of descriptive detail and an absence of rich individual perspectives undermine the core expectations of qualitative research design. When the written report does not offer an enriched and nuanced exploration of the phenomenon, it fails to meet the core aims of qualitative inquiry. A strong qualitative report provides in-depth accounts from participants’ perspectives, explaining how and why these are embedded in the phenomenon (Merriam & Tisdell, 2015). It moves beyond generic elaborations to uncover the specific properties and the dynamics of the issue under study.

It might be asked, “How much detail is enough?” While no fixed answer can be given, the key lies in the quality rather than the quantity of detail. The scope and character of details are expected to align with the research question and the study’s overall purpose. The analysis offers a strong answer–or at least an improved understanding– of the research question, operationalizing the concept of “data saturation” (Saunders et al., 2018). Ideally, the depth and nuance of the analysis reflect that the researcher was not only close to the data as a “key instrument” in its collection, but also deeply engaged in its interpretation and understanding (Creswell, 2007). The report thus serves as a roadmap, enabling readers to trace participants’ understandings of the world and grasp the relevant specificities of their context.

To avoid producing a generic or surface-level account, the analysis report is expected to guide readers to visualize the studied phenomenon, hear the voices of participants as informants, and perceive its distinct features beyond commonsense or expected narratives. Findings aim to be specific and grounded in the studied context, even as they may hold transferable relevance to other contexts (Lincoln & Guba, 1985). Broader connections, including similarities and differences with other studies, can then be drawn out in the in the discussion section of the qualitative essay.

Connecting Quotes to Analysis

Reporting qualitative findings involves more than documenting outcomes; it entails actively constructing and re-presenting the phenomena explored (White et al., 2003, p. 287). As writers, researchers engage in an interpretive process that incorporates the meanings participants assign to their experiences. This process is situated within the study’s overall framework and oriented toward addressing its central research questions and emergent insights. In this sense, qualitative essays go beyond summarizing, paraphrasing or merely describing data (Braun & Clarke, 2013). They aim to reveal the intertwined understandings of both participants and researchers.

Citing with Commentary

Participants’ voices are often presented through interview quotations, which serve as valuable evidence supporting the paper’s findings and arguments. They are best understood not as isolated data fragments inserted into the text, but as carefully selected and interpreted material that demonstrates how researchers arrived at specific conclusions. As Emerson et al. (2011) emphasize, strong quotations are not simply the “most interesting examples,” but rather instances of “recurring patterns,” “typical situations,” and persuasive evidence aligned with the study’s claims (p. 705).

For instance, a researcher might include a quote such as: “I did not feel welcomed in that religious group. Several members of the congregation kept asking about my background. They questioned my religious training overseas.” While this quotation is rich in detail, it requires framing. The researcher needs to clearly identify the point that the quote is intended to illustrate, and where relevant, further unpack its significance through commentary that follows.

Similarly, researchers are advised to avoid listing quotations back-to-back without commentary. Quotes are not stand-alone evidence; their value emerges through interpretation. It is the researcher’s role to clarify how each quote relates to a broader idea, issue, or theme–highlight nuances or differing perspectives within the data. Rather than leaving readers to make inferences on their own, researchers are expected to articulate the meaning of each quotation, explain why it was selected, and demonstrate how it contributes to the analysis (Naeem et al., 2023).

To achieve this, researchers are encouraged to introduce quotations with brief sentences that clarify their purpose and relevance. Follow-up commentary then situates each quote within the broader context of the study. Emerson et al. (2011) describe this process as offering orienting information before a descriptive excerpt and analytic commentary afterward to ground the interpretation in the excerpt’s details (pp. 727, 732).

Techniques such as transitional phrases, paraphrasing, and highlighting key points help integrate participants’ statements with the researcher’s analysis, ensuring that the researcher’s voice bridges findings, participants’ perspectives, and the scholarly conversation. These connecting phrases need not be long. Even concise expressions such as “as also expressed by another participant,” or “as commented slightly differently by other participants,” or “while some participants emphasized challenges, others pointed to opportunities” can effectively guide the reader. Such cues help signal whether the next quotation reinforces, nuances, or contrasts with the previous ones. Ultimately, it is the researcher’s responsibility to articulate the connections between quotations and the larger analytical narrative they support.

In qualitative essays, balancing detailed empirical material with theoretical relevance is crucial. Researchers typically provide sufficient specificity from fieldnotes, observations, or other sources to ground their arguments, while also linking these details to broader academic frameworks (Emerson et al., 2011, p. 687). This dual focus enables readers to connect the particularities of the case to the study’s broader theoretical contributions. In the discussion section of an article, authors typically situate their findings in relation to previous research, wider scholarly debates and disciplinary contexts.

Building on this, the effective use of quotations in qualitative writing is guided by three principles: Quotations clearly illustrate the author’s point, remain succinct, and represent patterns in the data (Lingard, 2019, p. 360). The “length, relevance, readability, and comprehensibility” of excerpts also need to be carefully assessed in the qualitative essay (Emerson et al., 2011, p. 742). White et al. (2003) discourage lengthy verbatim quotations, advocating for selective use of original passages (p. 290). Similarly, Woods (2006) notes that “quotations enrich a text, but not if they are too lengthy, inappropriate, or numerous, or used out of context” (p. 66).

The type of output—dissertation, monograph, paper, or essay—also influences how excerpts are used. Longer verbatim extracts may be better suited to supplementary materials, even in book-length works, making it important for authors to evaluate their length and relevance accordingly.

Furthermore, while some quotations may be included in full–particularly if they are especially vivid or if breaking them would interrupt the narrative flow–these are best treated as exceptions. By paraphrasing, trimming, or selectively incorporating only the most relevant parts of quotations, researchers as writers enhance the clarity and coherence of the qualitative text. As Ghodsee (2016) highlights, combining direct quotes with paraphrasing often enhances narrative flow and reader engagement (p. 63).

Guarding Against Quantitative Overshadowing

It is crucial to maintain qualitative integrity in the face of quantitative overshadowing. While qualitative and quantitative methods can complement each other –and the binaries between them are more complex than initially assumed (Pilcher & Cortazzi, 2024)– a qualitative essay based on qualitative research is expected to maintain a coherent qualitative orientation to ensure methodological consistency and systematicity (Neuman, 2006).

Themes are intended to reflect the specific features of the data related to the phenomenon under study (Nowell et al., 2017), and it is important that they avoid sounding overly generic or functioning merely as subtitles. Their significance lies in the meanings they bring to light, not in how frequently they appear. Overreliance on numerical measures can obscure the interpretive purpose of qualitative work. This emphasis on meaning over measurement aligns with interpretivist and constructivist approaches in qualitative research, which prioritize understanding, context, and co-construction of meaning over generalizability and mere numerical evidence (Flick, 2014b; Spencer et al., 2014).

Crafting Context-specific Themes, not Subtitles

Unlike quantitative methods designed to test hypotheses, determine causality, or yield generalizable results, qualitative research aims to identify patterns, associations, and connections across different components of the phenomenon under investigation (Palinkas et al., 2015). Writing the findings section of a qualitative essay as though it were based on a random sampling to uncover causes and consequences misrepresents the logic of qualitative inquiry.

For example, in a qualitative study on vegan dietary practices among university students in a particular city, a theme like “socioeconomic factors leading to veganism” reads overly broad and detached. It implies a comprehensive identification of variables– an aim that does not align with the logic of purposive sampling or the interpretive goals of qualitative research. Instead, the focus is on how participants themselves articulate the socioeconomic influences in their own words, as situated within their everyday lives. Rather than isolating variables, qualitative research emphasizes understanding the phenomenon from participants’ standpoints, grounding themes in specific narratives and situated experiences.

Themes are to be “as sensitizing to the data as possible,” meaning that categories are named in ways that allow an outsider to read and grasp their nature (Merriam & Tisdell, 2015, p. 213). Effective themes illuminate the specific features of the social world under study by remaining closely tied to the research topic and its context. Returning to the example of veganism among university students, socioeconomic and cultural influences can indeed be explored, but these discussions need to be grounded in the case-specific details provided by participants. Rather than abstracting into general categories, themes gain validity when anchored in participant’s lived experiences and the sociohistorical context in which those experiences unfold. In this way, qualitative analysis offers a deeper understanding of the phenomenon, grounded in time, place, and subjective meanings.

Themes are not intended to merely summarize topics derived from interview questions or participants’ responses. Nor do they serve as subtitles organizing a report. Rather, they operate as analytical constructs that reveal patterns of meaning within the data. Generic phrases such as “advantages or disadvantages” or “opportunities versus challenges” may appear in a wide range of studies, making them overly broad and insufficiently reflective of the specific social world under investigation. When themes take the form of subtitles, it often signals an underdeveloped stage of analysis, where the richness of participants’ perspectives and the specificity of the context remain unarticulated.

Braun and Clarke (2020) point out that a common pitfall in qualitative analysis is confusing themes with topics. While topics summarize what participants say about specific issues raised in interviews, themes capture patterned responses or shared meanings across the dataset (Braun & Clarke, 2020). As they note, “data topics-as-themes do not have a central concept or a shared meaning—only a shared topic,” whereas themes are analytical constructs built around shared meaning (p. 14). Topic summaries may serve as inputs during coding, but they are generally transformed into conceptual outputs during the final stages of data analysis and writing.

In this frame, a well-crafted theme reflects the distinctive language, emphasis, and experiential textures voiced by participants. For example, in a study exploring university students’ experiences with remote education, participants might raise topics such as lack of motivation, difficulty concentrating, or blurring of personal and academic spaces. While these issues could be grouped under a generic subtitle like “challenges of remote learning” in a standard academic essay, such a label lacks the interpretive depth of a thematic analysis. More evocative theme titles–such as “studying from the bed,” “my bedroom: my classroom”–draw directly from participants’ phrasing, capture embodied experiences, and signal the tension between home and school spaces. These themes invite the reader into the lived realities of participants rather than simply categorizing their complaints.

A researcher might craft another theme that is more abstract, or that is more descriptive. What matters is that each theme illuminates the particularities of a group’s lived experiences within a specific social and historical context. While the essay may later organize these themes under headings like “challenges” or “opportunities,” the strength of thematic analysis lies in its ability to evoke and convey the nuances of those experiences.

Likewise, it is the researcher’s responsibility to develop themes as analytical constructs, rather than treating them as pre-existing topics embedded in the data. This process involves identifying meaningful patterns and carefully crafting conceptual labels that best convey those meanings. Such labels may originate from the researcher’s own interpretation, from participants’ words that succinctly capture a key idea, or from concepts adapted from existing literature (Merriam & Tisdell, 2015). When drawing on academic literature, researchers are expected to modify and contextualize these concepts to suit the specific setting and dataset of their study.

Terms such as “class,” “active aging,” “digitalization,” or “gender roles” are academic concepts, but they do not, on their own, qualify as themes in thematic analysis. Themes are expected to reflect the specific context and findings of a given study. They are not to be treated as subtitles, topic summaries, or theoretical concepts adopted directly from the literature. Rather, themes are crafted to offer immediate and meaningful insights into the phenomenon under investigation–firmly rooted in the data and shaped by the research setting. Rather than functioning as generic subtitles, well-crafted themes serve as windows into the interpretive worlds of both participants and researchers.

Themes: Focus on Meaning, not Numbers

It is misleading to propose that qualitative researchers do not count; numbers can indeed be “integral to qualitative research, as meaning depends, in part, on number” (Sandelowski, 2001, p. 231). Qualitative researchers can benefit from numbers in various ways, such as recognizing patterns by counting similarities or differences. Additionally, quantitative or semi-quantitative information–such as the number of participants or their main demographics—may be included to provide context (Neale et al., 2014). Numbers and percentages can also document or verify conclusions reached, particularly in qualitative content analysis. When used thoughtfully, numbers enhance transparency, add precision, and complement research findings by providing focus or increasing the clarity of statements (Black, 1994; Neale et al., 2014).

Researchers are advised to approach the use of numerical data in their findings with caution to avoid methodological inconsistency and confusion. Significant debate exists over the appropriateness of using numbers in qualitative research (Neale et al., 2014; Wu et al., 2016). Thus, it is crucial to weigh both the benefits and the potential drawbacks when including numerical data in qualitative writing (Hannah & Lautsch, 2011).

Presenting evidence in qualitative research through quantities–such as the number, distribution, or prevalence of participants’ particular views–requires careful consideration. The basis for findings in qualitative research cannot be purely numerical. For example, some authors report that “12 out of 20 participants said yes to this question,” or state that “75% of participants attributed their experience to their family background,” or indicate that “participants talked mostly about the challenges they faced in migrating to another country than the ease,” citing frequencies such as “100 mentions across 20 interviews.”

If such quantitative figures are offered to convince the reader of the “measurable truth” of the findings, it is important to underscore that the validity and rigor of qualitative research are not rooted in quantitative measures. Rather, they lie in its focus on understanding subjective experiences, the meanings participants attribute to them, and the social processes and contextual factors that shape these experiences. Regardless of percentages or prevalence, qualitative research focuses on the meanings conveyed by the participants. In other words, qualitative inquiry prioritizes “how” over “how many.” Even if only a small number of participants voice a particular view, it can still be valuable if it elaborates effectively on how a phenomenon is perceived or experienced. Likewise, even when the majority leans towards a yes/no or positive/negative stance, if the analysis fails to unpack the varied meanings, interpretive positions, or contextual factors behind these responses, it limits the explanatory power of the findings. Using numerical data to suggest a more “objective” or generalizable account risks undermining the foundation of qualitative inquiry.

If researchers decide to include quantitative data–for example, to enhance transparency or complement qualitative findings–it is important to explain their reasoning, elaborate on how these figures contribute to their arguments, and clarify why they are included. This helps ensure that numerical elements serve to strengthen, rather than overshadow, the qualitative narrative. For example, in a study on remote working during the pandemic, a researcher might note that roughly two-thirds of participants referred to work dissatisfaction due to loss of contact with coworkers or the blurring of the boundaries between personal and professional life. The numerical ratio is used to clarify what is meant by statements such as “most participants,” offering some transparency while avoiding overgeneralization.

Yet the goal in qualitative inquiry is not to measure prevalence, nor is the purposive–rather than random–sampling of qualitative research suited for such claims. The researcher, by citing numerical ratios, may simply aim to emphasize how common certain themes were within the specific sample. In this case, they would further elaborate on professional dissatisfaction by providing thick descriptions and detailed, nuanced accounts of how it was experienced and narrated by participants in both similar and distinct ways–some emphasizing isolation, others burnout, and still others the tension between care responsibilities and professional expectations.

Data saturation in qualitative analysis involves recognizing some repetition and frequency, but this pertains to embedded meanings rather than quantitative repetitiveness. Saturation does not require a specific number of repetitions or a minimum threshold for certain views. Instead, it indicates that no new themes or information are emerging and that the data begin to echo what has already been expressed (Saunders et al., 2018).

Qualitative analysis is fundamentally about views, perspectives, and ideas expressed by participants and the researcher’s interpretation of them. It is not about the relative or comparative value of frequencies and proportions. Consequently, themes identified during analysis can—and often do—carry different proportional weights. Continuing with the remote work example, in a study on remote working, the theme of professional burnout might be voiced around twenty times, whereas dissatisfaction is mentioned fifteen times, role tension fifteen times, comfort in working from home ten times, and saving time from traffic five times. Regardless of frequency, all five themes are embedded within the participants’ narratives and represent key aspects of the remote working experience. Each warrants recognition as a theme in thematic analysis–not because of how often it appears, but because it contributed to understanding and explaining the different dimensions of the phenomenon under study.

The significance of themes is not determined by their proportional representation or the percentage of participants expressing them. Each theme contributes meaningfully to the understanding of the phenomenon, regardless of its prevalence. The primary goal of qualitative inquiry is to identify, to interpret, and to understand meanings–not numbers. Thematic qualitative analysis intends to uncover embedded meanings and develop themes that encapsulate the core features of the phenomenon.

Building on this concern, Sandelowski (2001) warns against “acontextual counting” and the risks of “representational or analytic overcounting.” Simply stating what percentage of participants expressed a particular view, without additional contextual information, does not deepen the understanding of the phenomenon. To add interpretive value, it is crucial for the researcher to contextualize numbers. Similarly, consistently reporting how many participants said what can detract from the aesthetic and narrative presentation of findings, as numerical enumeration interrupts the flow of qualitative writing (Sandelowski, 2001). Analytic overcounting is particularly problematic, as it involves counting for its own sake, diverting attention from the core focus of qualitative analysis. What matters most is not how often something was said, but what it reveals. Themes need to capture the essence of the phenomenon, uncover mutually exclusive patterns of response, and illuminate the broader meanings of the study (Sandelowski, 2001, p. 238).

Because the significance of themes does not depend on the proportion of participants expressing them, it is advisable to avoid–or at least use cautiously–terms with quantitative connotations, such as “majority,” “minority,” or “most” (Neale et al., 2014). These terms can be vague and misleading. While frequently repeated expressions often constitute the basis for the conceptualization of themes, less common or minority views can also play a crucial role in enriching the analysis and highlighting complexity (Wu et al., 2016).

These different perspectives reveal the diversity of opinions among participants, illustrating both convergence and divergence. Scholars emphasize the importance of conducting negative case analysis (Hanson, 2017) to explore alternative explanations (Patton, 2002) and to highlight divergences and exceptions (Phoenix & Orr, 2017).

In summary, qualitative research findings are characterized by in-depth descriptions of phenomena and attention to multiple perspectives, capturing both shared and contrasting viewpoints. These findings are grounded not in the proportion or percentage of participants expressing a view, but in the richness, diversity, and interpretive significance of the views conveyed.

Conclusion

Qualitative analysis and its reporting remain among the least discussed yet most essential aspects of qualitative research. Drawing on the author’s practical observations–particularly from supervising student research and reviewing written reports– and building existing scholarship, this article addresses critical gaps in analyzing and presenting qualitative findings, offering practical guidance and clarifying the reasoning behind common pitfalls.

The key takeaways emphasize the importance of interconnected analysis that moves beyond question-by-question reporting, the development of themes that convey nuanced patterns rather than generic labels, and the thoughtful integration of quotations with interpretive commentary. Finally, guarding against quantitative overshadowing reaffirms the distinctive strength of qualitative inquiry: its commitment to meaning, context, and complexity over numeric generalization.

Qualitative research seeks to deepen understanding of complex phenomena, especially those that are not well known. A strong qualitative report illuminates context-specific dynamics through participants’ perspectives, offering both depth and breadth. A robust report typically moves beyond superficial accounts, guiding readers into the interpretive world of participants and the complexities of the social world, capturing nuances, shared patterns, and divergences.

Themes, as presented in qualitative findings, are not mere summaries of interview questions or generic subtitles; nor do they derive their significance from frequency or proportional representation. Rather, themes are analytical constructs carefully crafted by researchers to bridge participants’ lived experiences with scholarly interpretation. They offer a holistic re-reading of participants’ accounts, moving beyond isolated responses and drawing from a range of data sources to capture shared meanings as well as variation.

Researchers are responsible for bringing participants’ worlds to life for readers, showing how participants understand their experiences and how researchers interpret those perspectives—whether empathically, suspiciously, or both. The analysis and its written report belong to the researcher, who is expected to explain transparently and consistently how findings were developed. Quotations serve as powerful forms of evidence and need to be carefully selected, contextually situated, and analytically unpacked. Rather than presenting quotations in succession, researchers are encouraged to provide commentary that reveals their embedded meanings and demonstrates how they illuminate the phenomenon under study.

By integrating systematicity, consistency, transparency, and reflexivity into qualitative analysis and writing, researchers can produce rigorous and impactful outputs that honor the depth and complexity of participants’ social worlds. By reinforcing these principles in both analysis and writing, qualitative researchers not only enhance the credibility of their findings but also contribute to the advancement of qualitative inquiry across disciplines.