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Narrative Analysis – Types, Methods and Examples

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Narrative Analysis

Definition:

Narrative analysis is a qualitative research methodology that involves examining and interpreting the stories or narratives people tell in order to gain insights into the meanings, experiences, and perspectives that underlie them. Narrative analysis can be applied to various forms of communication, including written texts, oral interviews, and visual media.

In narrative analysis, researchers typically examine the structure, content, and context of the narratives they are studying, paying close attention to the language, themes, and symbols used by the storytellers. They may also look for patterns or recurring motifs within the narratives, and consider the cultural and social contexts in which they are situated.

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

This type of narrative analysis involves examining the content of a narrative in order to identify themes, motifs, and other patterns. Researchers may use coding schemes to identify specific themes or categories within the text, and then analyze how they are related to each other and to the overall narrative. Content analysis can be used to study various forms of communication, including written texts, oral interviews, and visual media.

Structural Analysis

This type of narrative analysis focuses on the formal structure of a narrative, including its plot, character development, and use of literary devices. Researchers may analyze the narrative arc, the relationship between the protagonist and antagonist, or the use of symbolism and metaphor. Structural analysis can be useful for understanding how a narrative is constructed and how it affects the reader or audience.

Discourse Analysis

This type of narrative analysis focuses on the language and discourse used in a narrative, including the social and cultural context in which it is situated. Researchers may analyze the use of specific words or phrases, the tone and style of the narrative, or the ways in which social and cultural norms are reflected in the narrative. Discourse analysis can be useful for understanding how narratives are influenced by larger social and cultural structures.

Phenomenological Analysis

This type of narrative analysis focuses on the subjective experience of the narrator, and how they interpret and make sense of their experiences. Researchers may analyze the language used to describe experiences, the emotions expressed in the narrative, or the ways in which the narrator constructs meaning from their experiences. Phenomenological analysis can be useful for understanding how people make sense of their own lives and experiences.

Critical Analysis

This type of narrative analysis involves examining the political, social, and ideological implications of a narrative, and questioning its underlying assumptions and values. Researchers may analyze the ways in which a narrative reflects or reinforces dominant power structures, or how it challenges or subverts those structures. Critical analysis can be useful for understanding the role that narratives play in shaping social and cultural norms.

Autoethnography

This type of narrative analysis involves using personal narratives to explore cultural experiences and identity formation. Researchers may use their own personal narratives to explore issues such as race, gender, or sexuality, and to understand how larger social and cultural structures shape individual experiences. Autoethnography can be useful for understanding how individuals negotiate and navigate complex cultural identities.

Thematic Analysis

This method involves identifying themes or patterns that emerge from the data, and then interpreting these themes in relation to the research question. Researchers may use a deductive approach, where they start with a pre-existing theoretical framework, or an inductive approach, where themes are generated from the data itself.

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

• Identify the research question: Narrative analysis begins with identifying the research question or topic of interest. Researchers may want to explore a particular social or cultural phenomenon, or gain a deeper understanding of a particular individual’s experience.
• Collect the narratives: Researchers then collect the narratives or stories that they will analyze. This can involve collecting written texts, conducting interviews, or analyzing visual media.
• Transcribe and code the narratives: Once the narratives have been collected, they are transcribed into a written format, and then coded in order to identify themes, motifs, or other patterns. Researchers may use a coding scheme that has been developed specifically for the study, or they may use an existing coding scheme.
• Analyze the narratives: Researchers then analyze the narratives, focusing on the themes, motifs, and other patterns that have emerged from the coding process. They may also analyze the formal structure of the narratives, the language used, and the social and cultural context in which they are situated.
• Interpret the findings: Finally, researchers interpret the findings of the narrative analysis, and draw conclusions about the meanings, experiences, and perspectives that underlie the narratives. They may use the findings to develop theories, make recommendations, or inform further research.

Applications of Narrative Analysis

Narrative analysis is a versatile qualitative research method that has applications across a wide range of fields, including psychology, sociology, anthropology, literature, and history. Here are some examples of how narrative analysis can be used:

• Understanding individuals’ experiences: Narrative analysis can be used to gain a deeper understanding of individuals’ experiences, including their thoughts, feelings, and perspectives. For example, psychologists might use narrative analysis to explore the stories that individuals tell about their experiences with mental illness.
• Exploring cultural and social phenomena: Narrative analysis can also be used to explore cultural and social phenomena, such as gender, race, and identity. Sociologists might use narrative analysis to examine how individuals understand and experience their gender identity.
• Analyzing historical events: Narrative analysis can be used to analyze historical events, including those that have been recorded in literary texts or personal accounts. Historians might use narrative analysis to explore the stories of survivors of historical traumas, such as war or genocide.
• Examining media representations: Narrative analysis can be used to examine media representations of social and cultural phenomena, such as news stories, films, or television shows. Communication scholars might use narrative analysis to examine how news media represent different social groups.
• Developing interventions: Narrative analysis can be used to develop interventions to address social and cultural problems. For example, social workers might use narrative analysis to understand the experiences of individuals who have experienced domestic violence, and then use that knowledge to develop more effective interventions.

Examples of Narrative Analysis

Here are some examples of how narrative analysis has been used in research:

• Personal narratives of illness: Researchers have used narrative analysis to examine the personal narratives of individuals living with chronic illness, to understand how they make sense of their experiences and construct their identities.
• Oral histories: Historians have used narrative analysis to analyze oral histories to gain insights into individuals’ experiences of historical events and social movements.
• Children’s stories: Researchers have used narrative analysis to analyze children’s stories to understand how they understand and make sense of the world around them.
• Personal diaries : Researchers have used narrative analysis to examine personal diaries to gain insights into individuals’ experiences of significant life events, such as the loss of a loved one or the transition to adulthood.
• Memoirs : Researchers have used narrative analysis to analyze memoirs to understand how individuals construct their life stories and make sense of their experiences.
• Life histories : Researchers have used narrative analysis to examine life histories to gain insights into individuals’ experiences of migration, displacement, or social exclusion.

Purpose of Narrative Analysis

The purpose of narrative analysis is to gain a deeper understanding of the stories that individuals tell about their experiences, identities, and beliefs. By analyzing the structure, content, and context of these stories, researchers can uncover patterns and themes that shed light on the ways in which individuals make sense of their lives and the world around them.

The primary purpose of narrative analysis is to explore the meanings that individuals attach to their experiences. This involves examining the different elements of a story, such as the plot, characters, setting, and themes, to identify the underlying values, beliefs, and attitudes that shape the story. By analyzing these elements, researchers can gain insights into the ways in which individuals construct their identities, understand their relationships with others, and make sense of the world.

Narrative analysis can also be used to identify patterns and themes across multiple stories. This involves comparing and contrasting the stories of different individuals or groups to identify commonalities and differences. By analyzing these patterns and themes, researchers can gain insights into broader cultural and social phenomena, such as gender, race, and identity.

In addition, narrative analysis can be used to develop interventions that address social and cultural problems. By understanding the stories that individuals tell about their experiences, researchers can develop interventions that are tailored to the unique needs of different individuals and groups.

Overall, the purpose of narrative analysis is to provide a rich, nuanced understanding of the ways in which individuals construct meaning and make sense of their lives. By analyzing the stories that individuals tell, researchers can gain insights into the complex and multifaceted nature of human experience.

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

• Studying life stories: Narrative analysis can be useful in understanding how individuals construct their life stories, including the events, characters, and themes that are important to them.
• Analyzing cultural narratives: Narrative analysis can be used to analyze cultural narratives, such as myths, legends, and folktales, to understand their meanings and functions.
• Exploring organizational narratives: Narrative analysis can be helpful in examining the stories that organizations tell about themselves, their histories, and their values, to understand how they shape the culture and practices of the organization.
• Investigating media narratives: Narrative analysis can be used to analyze media narratives, such as news stories, films, and TV shows, to understand how they construct meaning and influence public perceptions.
• Examining policy narratives: Narrative analysis can be helpful in examining policy narratives, such as political speeches and policy documents, to understand how they construct ideas and justify policy decisions.

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

• Focus on stories and narratives: Narrative analysis is concerned with analyzing the stories and narratives that people tell, whether they are oral or written, to understand how they shape and reflect individuals’ experiences and identities.
• Emphasis on context: Narrative analysis seeks to understand the context in which the narratives are produced and the social and cultural factors that shape them.
• Interpretive approach: Narrative analysis is an interpretive approach that seeks to identify patterns and themes in the stories and narratives and to understand the meaning that individuals and communities attach to them.
• Iterative process: Narrative analysis involves an iterative process of analysis, in which the researcher continually refines their understanding of the narratives as they examine more data.
• Attention to language and form : Narrative analysis pays close attention to the language and form of the narratives, including the use of metaphor, imagery, and narrative structure, to understand the meaning that individuals and communities attach to them.
• Reflexivity : Narrative analysis requires the researcher to reflect on their own assumptions and biases and to consider how their own positionality may shape their interpretation of the narratives.
• Qualitative approach: Narrative analysis is typically a qualitative research method that involves in-depth analysis of a small number of cases rather than large-scale quantitative studies.

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

• Rich and detailed data : Narrative analysis provides rich and detailed data that allows for a deep understanding of individuals’ experiences, emotions, and identities.
• Humanizing approach: Narrative analysis allows individuals to tell their own stories and express their own perspectives, which can help to humanize research and give voice to marginalized communities.
• Holistic understanding: Narrative analysis allows researchers to understand individuals’ experiences in their entirety, including the social, cultural, and historical contexts in which they occur.
• Flexibility : Narrative analysis is a flexible research method that can be applied to a wide range of contexts and research questions.
• Interpretive insights: Narrative analysis provides interpretive insights into the meanings that individuals attach to their experiences and the ways in which they construct their identities.
• Appropriate for sensitive topics: Narrative analysis can be particularly useful in researching sensitive topics, such as trauma or mental health, as it allows individuals to express their experiences in their own words and on their own terms.
• Can lead to policy implications: Narrative analysis can provide insights that can inform policy decisions and interventions, particularly in areas such as health, education, and social policy.

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

• Subjectivity : Narrative analysis relies on the interpretation of researchers, which can be influenced by their own biases and assumptions.
• Limited generalizability: Narrative analysis typically involves in-depth analysis of a small number of cases, which limits its generalizability to broader populations.
• Ethical considerations: The process of eliciting and analyzing narratives can raise ethical concerns, particularly when sensitive topics such as trauma or abuse are involved.
• Limited control over data collection: Narrative analysis often relies on data that is already available, such as interviews, oral histories, or written texts, which can limit the control that researchers have over the quality and completeness of the data.
• Time-consuming: Narrative analysis can be a time-consuming research method, particularly when analyzing large amounts of data.
• Interpretation challenges: Narrative analysis requires researchers to make complex interpretations of data, which can be challenging and time-consuming.
• Limited statistical analysis: Narrative analysis is typically a qualitative research method that does not lend itself well to statistical analysis.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Narrative Analysis 101

Everything you need to know to get started

By: Ethar Al-Saraf (PhD)| Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to research, the host of qualitative analysis methods available to you can be a little overwhelming. In this post, we’ll  unpack the sometimes slippery topic of narrative analysis . We’ll explain what it is, consider its strengths and weaknesses , and look at when and when not to use this analysis method. 

Overview: Narrative Analysis

• What is narrative analysis (simple definition)
• The two overarching approaches  
• The strengths & weaknesses of narrative analysis
• When (and when not) to use it
• Key takeaways

What Is Narrative Analysis?

Simply put, narrative analysis is a qualitative analysis method focused on interpreting human experiences and motivations by looking closely at the stories (the narratives) people tell in a particular context.

In other words, a narrative analysis interprets long-form participant responses or written stories as data, to uncover themes and meanings . That data could be taken from interviews, monologues, written stories, or even recordings. In other words, narrative analysis can be used on both primary and secondary data to provide evidence from the experiences described.

That’s all quite conceptual, so let’s look at an example of how narrative analysis could be used.

Let’s say you’re interested in researching the beliefs of a particular author on popular culture. In that case, you might identify the characters , plotlines , symbols and motifs used in their stories. You could then use narrative analysis to analyse these in combination and against the backdrop of the relevant context.

This would allow you to interpret the underlying meanings and implications in their writing, and what they reveal about the beliefs of the author. In other words, you’d look to understand the views of the author by analysing the narratives that run through their work.

The Two Overarching Approaches

Generally speaking, there are two approaches that one can take to narrative analysis. Specifically, an inductive approach or a deductive approach. Each one will have a meaningful impact on how you interpret your data and the conclusions you can draw, so it’s important that you understand the difference.

First up is the inductive approach to narrative analysis.

The inductive approach takes a bottom-up view , allowing the data to speak for itself, without the influence of any preconceived notions . With this approach, you begin by looking at the data and deriving patterns and themes that can be used to explain the story, as opposed to viewing the data through the lens of pre-existing hypotheses, theories or frameworks. In other words, the analysis is led by the data.

For example, with an inductive approach, you might notice patterns or themes in the way an author presents their characters or develops their plot. You’d then observe these patterns, develop an interpretation of what they might reveal in the context of the story, and draw conclusions relative to the aims of your research.

Contrasted to this is the deductive approach.

With the deductive approach to narrative analysis, you begin by using existing theories that a narrative can be tested against . Here, the analysis adopts particular theoretical assumptions and/or provides hypotheses, and then looks for evidence in a story that will either verify or disprove them.

For example, your analysis might begin with a theory that wealthy authors only tell stories to get the sympathy of their readers. A deductive analysis might then look at the narratives of wealthy authors for evidence that will substantiate (or refute) the theory and then draw conclusions about its accuracy, and suggest explanations for why that might or might not be the case.

Which approach you should take depends on your research aims, objectives and research questions . If these are more exploratory in nature, you’ll likely take an inductive approach. Conversely, if they are more confirmatory in nature, you’ll likely opt for the deductive approach.

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Strengths & Weaknesses

Now that we have a clearer view of what narrative analysis is and the two approaches to it, it’s important to understand its strengths and weaknesses , so that you can make the right choices in your research project.

A primary strength of narrative analysis is the rich insight it can generate by uncovering the underlying meanings and interpretations of human experience. The focus on an individual narrative highlights the nuances and complexities of their experience, revealing details that might be missed or considered insignificant by other methods.

Another strength of narrative analysis is the range of topics it can be used for. The focus on human experience means that a narrative analysis can democratise your data analysis, by revealing the value of individuals’ own interpretation of their experience in contrast to broader social, cultural, and political factors.

All that said, just like all analysis methods, narrative analysis has its weaknesses. It’s important to understand these so that you can choose the most appropriate method for your particular research project.

The first drawback of narrative analysis is the problem of subjectivity and interpretation . In other words, a drawback of the focus on stories and their details is that they’re open to being understood differently depending on who’s reading them. This means that a strong understanding of the author’s cultural context is crucial to developing your interpretation of the data. At the same time, it’s important that you remain open-minded in how you interpret your chosen narrative and avoid making any assumptions .

A second weakness of narrative analysis is the issue of reliability and generalisation . Since narrative analysis depends almost entirely on a subjective narrative and your interpretation, the findings and conclusions can’t usually be generalised or empirically verified. Although some conclusions can be drawn about the cultural context, they’re still based on what will almost always be anecdotal data and not suitable for the basis of a theory, for example.

Last but not least, the focus on long-form data expressed as stories means that narrative analysis can be very time-consuming . In addition to the source data itself, you will have to be well informed on the author’s cultural context as well as other interpretations of the narrative, where possible, to ensure you have a holistic view. So, if you’re going to undertake narrative analysis, make sure that you allocate a generous amount of time to work through the data.

When To Use Narrative Analysis

As a qualitative method focused on analysing and interpreting narratives describing human experiences, narrative analysis is usually most appropriate for research topics focused on social, personal, cultural , or even ideological events or phenomena and how they’re understood at an individual level.

For example, if you were interested in understanding the experiences and beliefs of individuals suffering social marginalisation, you could use narrative analysis to look at the narratives and stories told by people in marginalised groups to identify patterns , symbols , or motifs that shed light on how they rationalise their experiences.

In this example, narrative analysis presents a good natural fit as it’s focused on analysing people’s stories to understand their views and beliefs at an individual level. Conversely, if your research was geared towards understanding broader themes and patterns regarding an event or phenomena, analysis methods such as content analysis or thematic analysis may be better suited, depending on your research aim .

Let’s recap

In this post, we’ve explored the basics of narrative analysis in qualitative research. The key takeaways are:

• Narrative analysis is a qualitative analysis method focused on interpreting human experience in the form of stories or narratives .
• There are two overarching approaches to narrative analysis: the inductive (exploratory) approach and the deductive (confirmatory) approach.
• Like all analysis methods, narrative analysis has a particular set of strengths and weaknesses .
• Narrative analysis is generally most appropriate for research focused on interpreting individual, human experiences as expressed in detailed , long-form accounts.

If you’d like to learn more about narrative analysis and qualitative analysis methods in general, be sure to check out the rest of the Grad Coach blog here . Alternatively, if you’re looking for hands-on help with your project, take a look at our 1-on-1 private coaching service .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Thanks. I need examples of narrative analysis

Here are some examples of research topics that could utilise narrative analysis:

Personal Narratives of Trauma: Analysing personal stories of individuals who have experienced trauma to understand the impact, coping mechanisms, and healing processes.

Identity Formation in Immigrant Communities: Examining the narratives of immigrants to explore how they construct and negotiate their identities in a new cultural context.

Media Representations of Gender: Analysing narratives in media texts (such as films, television shows, or advertisements) to investigate the portrayal of gender roles, stereotypes, and power dynamics.

Where can I find an example of a narrative analysis table ?

Please i need help with my project,

how can I cite this article in APA 7th style?

please mention the sources as well.

My research is mixed approach. I use interview,key_inforamt interview,FGD and document.so,which qualitative analysis is appropriate to analyze these data.Thanks

Which qualitative analysis methode is appropriate to analyze data obtain from intetview,key informant intetview,Focus group discussion and document.

I’ve finished my PhD. Now I need a “platform” that will help me objectively ascertain the tacit assumptions that are buried within a narrative. Can you help?

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Qual Data Analysis & Narrative Research

Qualitative data analysis varies by methodology, so there is no one approach that fits across different types of studies. Narrative research is focused on the elicitation and interpretation of people's narrative accounts of their experience. A method based on the use of diaries which have been created for the purposes of research. Diaries can include narratives in semi-structured reports on key events or experiences or unstructured written accounts. Articles in this multidisciplinary, open access collection illustrate options for data analysis in narrative or  diary research.

Bentley, A., Salifu, Y., & Walshe, C. (2021). Applying an Analytical Process to Longitudinal Narrative Interviews With Couples Living and Dying With Lewy Body Dementia . International Journal of Qualitative Methods, 20. https://doi.org/10.1177/16094069211060653

Abstract. Narrative research methods invite people to share their experiences via storytelling. There is increasing interest as to how qualitative narrative inquiry can provide greater understanding into the lived experience around health and illness, particularly within the field of dementia. Narrative research is concerned with how humans make sense of and engage with the changes and disruptions of everyday life. However, narrative research is an emerging and evolving field with no single clearly defined approach to data analysis. In this article, we provide a methodological exemplar by applying Murray’s four levels of narrative analysis to longitudinal narrative interviews completed with couples living with Lewy body dementia. We describe how to analyse connections between the four levels and how to articulate this across different interview time points. This analysis process contributes to methodological knowledge by providing a strategy to connect the personal, interpersonal, positional and societal levels of analysis. The time taken for in-depth analysis of a co-created, dyadic longitudinal narrative approach requires careful consideration, but ultimately, it can provide a richer understanding of the lived experience, allowing for deeper social, clinical and academic insight.

Bischoping, K. (2018). Revisiting a Boy Named Jim: Using Narrative Analysis to Prompt Reflexivity . International Journal of QualitativeMethods, 17 (1), 1609406918809167. doi:10.1177/1609406918809167

Abstract. Using examples from qualitative healthresearch and from my childhood experience of reading a poem about a boydevoured by a lion (Belloc, 1907), I expand on a framework for reflexivitydeveloped in Bischoping and Gazso (2016). This framework is unique in firstsynthesizing works from multidisciplinary narrative analysis research in orderto arrive at common criteria for a “good” story: reportability, liveability,coherence, and fidelity. Next, each of these criteria is used to generatequestions that can prompt reflexivity among qualitative researchers, regardlessof whether they use narrative data or other narrative analysis strategies.These questions pertain to a broad span of issues, including appropriation,censorship, and the power to represent, using discomfort to guide insight,addressing vicarious traumatization, accommodating diverse participant populations,decolonizing ontology, and incorporating power and the social into analysesoverly focused on individual meaning-making. Finally, I reflect on theaffinities between narrative – in its imaginatively constructed, expressive,and open-ended qualities – and the reflexive impulse.

Bruce, A., Beuthin, R., Sheilds,L., Molzahn, A., & Schick-Makaroff, K. (2016). Narrative Research Evolving: Evolving Through Narrative Research . International Journal of Qualitative Methods, 15 (1), 1609406916659292.doi:10.1177/1609406916659292

Abstract. Narrative research methodology is evolving, and we contend that the notion of emergent design is vital if narrative inquiry(NI) is to continue flourishing in generating new knowledge. We situate the discussion within the  narrative turn  in qualitative research while drawing on experiences of conducting a longitudinal narrative study. The philosophical tensions encountered are described, as our understanding and application of narrative approaches evolved. We outline challenges in data collection and analysis in response to what we were learning and identify institutional barriers within ethics review processes that potentially impede emergent approaches. We conclude that researchers using NI can, and must, pursue unanticipated methodological changes when in the midst of conducting the inquiry. Understanding the benefits and institutional barriers to emergent aspects of design is discussed in this ever-maturing approach to qualitative research.

Farmer, J. R., Mackinnon, S. P.,& Cowie, M. (2017). Perfectionism and Life Narratives: A Qualitative Study . SAGE Open, 7 (3), 2158244017721733.doi:10.1177/2158244017721733

Abstract. We examined how perfectionistic people conceptualize perfectionism and narrate life events using thematic analysis. Participants included 20 university students who qualified as highly perfectionistic based on cutoffs on the Almost Perfect Scale–Revised ( n  = 6 adaptive,  n  = 14 maladaptive). Participants completed a qualitative interview. Using thematic analysis, we identified five themes regarding participants’ conceptualization of perfectionism. The most common themes supported prior theory (high personal standards, performance is never good enough), along with a few comparatively understudied themes (being neat and orderly, feels superior to others, gets caught up in the details). We also identified five themes in a life narrative interview (relationship success, relationship problems, agentic redemption, agentic contamination, and academic success), which provided insight into how young, perfectionistic university students create meaning and identity through autobiographical narratives. “Relationship success” themes were most central to adaptive perfectionists, whereas “agentic redemption” themes were most central to maladaptive perfectionists.

Jackson, S., Backett-Milburn, K., & Newall, E. (2013). Researching Distressing Topics: Emotional Reflexivity and Emotional Labor in the Secondary Analysis of Children and Young People’s Narratives of Abuse . SAGE Open, 3(2). https://doi.org/10.1177/2158244013490705

Qualitative researchers who explore sensitive topics may expose themselves to emotional distress. Consequently, researchers are often faced with the challenge of maintaining emotional equilibrium during the research process. However, discussion on the management of difficult emotions has occupied a peripheral place within accounts of research practice. With rare exceptions, the focus of published accounts is concentrated on the analysis of the emotional phenomena that emerge during the collection of primary research data. Hence, there is a comparative absence of a dialogue around the emotional dimensions of working with secondary data sources. This article highlights some of the complex ways in which emotions enter the research process during secondary analysis, and the ways in which we engaged with and managed emotional states such as anger, sadness, and horror. The concepts of emotional labor and emotional reflexivity are used to consider the ways in which we “worked with” and “worked on” emotion. In doing so, we draw on our collective experiences of working on two collaborative projects with ChildLine Scotland in which a secondary analysis was conducted on children’s narratives of distress, worry, abuse, and neglect.

Kaun, A. (2010). Open-Ended Online Diaries: Capturing Life as it is Narrated . International Journal of Qualitative Methods, 9 (2), 133-148.doi:10.1177/160940691000900202

Abstract. Weblogs and life journals are popular forms of reflecting and reporting online about one's everyday life. In this article the author examines whether solicited online diaries can be used in qualitative research. She discusses advantages and disadvantages of the online research, diaries as a source of data, and narration as a method. The discussion is exemplified by the presentation of an online diary study conducted in two partsin the spring and autumn of 2009 with students from Tartu, Narva, and Tallinn,Estonia. This article shows the illuminating potential and richness of solicited online diaries applied in an open-ended, qualitative understanding as a way to investigate everyday life. At the same time, the main challenges arepresented and discussed.

Meraz, R. L., Osteen, K., & McGee, J. (2019). Applying Multiple Methods of Systematic Evaluation in Narrative Analysis for Greater Validity and Deeper Meaning . International Journal of Qualitative Methods, 18. https://doi.org/10.1177/1609406919892472

Abstract. Personal narrative is at the heart of how human beings share information, represent identity, and convey ideas. Narrative research is a form of qualitative analysis that assists researchers in gaining insight into the lived experiences of the persons they are studying within their unique life circumstances and contexts. Although many narrative investigations report themes from study data, there is no single, well-defined approach to data analysis in narrative research. In this article, we provide a method for analyzing the data beyond the spoken words by applying Riessman’s thematic, structural, and performance analysis. We describe how applying multiple methods of systematic evaluation to narrative data leads to a deeper and more valid insight into the told stories. The data analysis process outlined in this article contributes to the academic discourse and knowledge supporting the use of multiple methods of systematic evaluation to uncover deeper meaning and thus leading to greater validity of the findings from narrative data.

Moen, T. (2006). Reflections on the Narrative Research Approach . International Journal of Qualitative Methods, 5 (4), 56-69. doi:10.1177/160940690600500405

Abstract. In her reflections on the narrative research approach, the author starts by placing narrative research within the frame work of sociocultural theory, where the challenge for the researcher is to examine and understand how human actions are related to the social context in which they occur and how and where they occur through growth. The author argues that the narrative as a unit of analysis provides the means for doing this. She then presents some of the basic premises of narrative research before she reflects on the process of narrative inquiry and addresses the issue of the “true” narrative. Throughout the article, the author refers to educational research and in the concluding section argues that the results of narrative research can be used as thought-provoking tools within the field of teacher education.

Nasheeda, A., Abdullah, H. B., Krauss, S. E., & Ahmed, N. B. (2019). Transforming Transcripts Into Stories: A Multimethod Approach to Narrative Analysis . International Journal of Qualitative Methods, 18. https://doi.org/10.1177/1609406919856797

Abstract. Stories are essential realities from our past and present. As the primary sources of data in narrative research, interview transcripts play an essential role in giving meaning to the personal stories of research participants. The pragmatic narratives found in transcripts represent human experience as it unfolds. Analyzing the narratives found in interview transcripts thus moves beyond providing descriptions and thematic developments as found in most qualitative studies. Crafting stories from interview transcripts involves a complex set of analytic processes. Building on the first author's personal experience in working on a doctoral thesis employing narrative inquiry, this article presents a multimethod restorying framework to narrative analysis. A step-by-step progression within the framework includes choosing interview participants, transcribing interviews, familiarizing oneself with the transcripts (elements of holistic-content reading), chronologically plotting (elements of the story), use of follow-up interviews as a way to collaborate (an important procedure in narrative inquiry), and developing the story through structural analysis. It is hoped that this article will encourage other researchers embarking on narrative analysis to become creative in presenting participants’ lived experiences through meaningful, collaborative strategies. This article demonstrates the fluidity of narrative analysis and emphasizes that there is no single procedure to be followed in attempting to create stories from interview transcripts.

Saint Arnault D, Sinko L. Comparative Ethnographic Narrative Analysis Method: Comparing Culture in Narratives . Global Qualitative Nursing Research. 2021;8. doi:10.1177/23333936211020722

Abstract. Narrative data analysis aims to understand the stories’ content, structure, or function. However narrative data can also be used to examine how context influences self-concepts, relationship dynamics, and meaning-making. This methodological paper explores the potential of narrative analysis to discover and compare the processes by which culture shapes selfhood and meaning making. We describe the development of the Comparative Ethnographic Narrative Analysis Method as an analytic procedure to systematically compare narrators’ experiences, meaning making, decisions, and actions across cultures. This analytic strategy seeks to discover shared themes, examine culturally distinct themes, and illuminate meta-level cultural beliefs and values that link shared themes. We emphasize the need for a shared research question, comparable samples, shared non-biased instruments, and high-fidelity training if one uses this qualitative method for cross-cultural research. Finally, specific issues, trouble-shooting practices, and implications are discussed.

Tohar, V., Asaf, M., Kainan, A.,& Shahar, R. (2007). An Alternative Approach for Personal Narrative Interpretation: The Semiotics of Roland Barthes . International Journal of Qualitative Methods, 6 (3), 57-70.doi:10.1177/160940690700600306

Abstract. In this paper the authors propose RolandBarthes's analytical method, which appears in his classic work  S/Z (1974), as a new way of analyzing personal stories. The fivecodes that are described in the book are linked to the domains of poetics,language, and culture, and expose facets that are embedded in the deepstructure of narratives. These codes are helpful in revealing findings withregard to the development of the professional careers of teacher educators.

Vindrola-Padros, C., & Johnson, G. A. (2014). The Narrated, Nonnarrated, and the Disnarrated: Conceptual Tools for Analyzing Narratives in Health Services Research . Qualitative Health Research, 24 (11),1603-1611. doi:10.1177/1049732314549019

Abstract. While analyzing the narratives of children receiving pediatric oncology treatment and their parents, we encountered three ways to look at their narratives: what was narrated, nonnarrated, and disnarrated. The narrated refers to the actors (characters) and events (scenes) individuals decided to include in the narration of their experiences, the nonnarrated are everything not included in narration, and the disnarrated are elements that are narrated in the story but did not actually take place. We use our reflection to illustrate how an integrative analysis of these different forms of narration can allow us to produce a holistic interpretation of people’s experiences of illness. This approach is still in the early stages of development, but we hope this article can promote a debate in the field and lead to the refinement of an important tool for narrative analysis.

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Qual Data Analysis & Phenomenology

Qual data analysis & ethnography.

Using narrative analysis in qualitative research

Last updated

7 March 2023

Reviewed by

Jean Kaluza

After spending considerable time and effort interviewing persons for research, you want to ensure you get the most out of the data you gathered. One method that gives you an excellent opportunity to connect with your data on a very human and personal level is a narrative analysis in qualitative research. 

Master narrative analysis

Analyze your qualitative data faster and surface more actionable insights

• What is narrative analysis?

Narrative analysis is a type of qualitative data analysis that focuses on interpreting the core narratives from a study group's personal stories. Using first-person narrative, data is acquired and organized to allow the researcher to understand how the individuals experienced something. 

Instead of focusing on just the actual words used during an interview, the narrative analysis also allows for a compilation of data on how the person expressed themselves, what language they used when describing a particular event or feeling, and the thoughts and motivations they experienced. A narrative analysis will also consider how the research participants constructed their narratives.

From the interview to coding , you should strive to keep the entire individual narrative together, so that the information shared during the interview remains intact.

Is narrative analysis qualitative or quantitative?

Narrative analysis is a qualitative research method.

Is narrative analysis a method or methodology?

A method describes the tools or processes used to understand your data; methodology describes the overall framework used to support the methods chosen. By this definition, narrative analysis can be both a method used to understand data and a methodology appropriate for approaching data that comes primarily from first-person stories.

• Do you need to perform narrative research to conduct a narrative analysis?

A narrative analysis will give the best answers about the data if you begin with conducting narrative research. Narrative research explores an entire story with a research participant to understand their personal story.

What are the characteristics of narrative research?

Narrative research always includes data from individuals that tell the story of their experiences. This is captured using loosely structured interviews . These can be a single interview or a series of long interviews over a period of time. Narrative research focuses on the construct and expressions of the story as experienced by the research participant.

• Examples of types of narratives

Narrative data is based on narratives. Your data may include the entire life story or a complete personal narrative, giving a comprehensive account of someone's life, depending on the researched subject. Alternatively, a topical story can provide context around one specific moment in the research participant's life. 

Personal narratives can be single or multiple sessions, encompassing more than topical stories but not entire life stories of the individuals.

• What is the objective of narrative analysis?

The narrative analysis seeks to organize the overall experience of a group of research participants' stories. The goal is to turn people's individual narratives into data that can be coded and organized so that researchers can easily understand the impact of a certain event, feeling, or decision on the involved persons. At the end of a narrative analysis, researchers can identify certain core narratives that capture the human experience.

What is the difference between content analysis and narrative analysis?

Content analysis is a research method that determines how often certain words, concepts, or themes appear inside a sampling of qualitative data . The narrative analysis focuses on the overall story and organizing the constructs and features of a narrative.

What is the difference between narrative analysis and case study in qualitative research?

A case study focuses on one particular event. A narrative analysis draws from a larger amount of data surrounding the entire narrative, including the thoughts that led up to a decision and the personal conclusion of the research participant. 

A case study, therefore, is any specific topic studied in depth, whereas narrative analysis explores single or multi-faceted experiences across time. ​​

What is the difference between narrative analysis and thematic analysis?

A thematic analysis will appear as researchers review the available qualitative data and note any recurring themes. Unlike narrative analysis, which describes an entire method of evaluating data to find a conclusion, a thematic analysis only describes reviewing and categorizing the data.

• Capturing narrative data

Because narrative data relies heavily on allowing a research participant to describe their experience, it is best to allow for a less structured interview. Allowing the participant to explore tangents or analyze their personal narrative will result in more complete data. 

When collecting narrative data, always allow the participant the time and space needed to complete their narrative.

• Methods of transcribing narrative data

A narrative analysis requires that the researchers have access to the entire verbatim narrative of the participant, including not just the word they use but the pauses, the verbal tics, and verbal crutches, such as "um" and "hmm." 

As the entire way the story is expressed is part of the data, a verbatim transcription should be created before attempting to code the narrative analysis.

Video and audio transcription templates

• How to code narrative analysis

Coding narrative analysis has two natural start points, either using a deductive coding system or an inductive coding system. Regardless of your chosen method, it's crucial not to lose valuable data during the organization process.

When coding, expect to see more information in the code snippets.

• Types of narrative analysis

After coding is complete, you should expect your data to look like large blocks of text organized by the parts of the story. You will also see where individual narratives compare and diverge.

Inductive method

Using an inductive narrative method treats the entire narrative as one datum or one set of information. An inductive narrative method will encourage the research participant to organize their own story. 

To make sense of how a story begins and ends, you must rely on cues from the participant. These may take the form of entrance and exit talks. 

Participants may not always provide clear indicators of where their narratives start and end. However, you can anticipate that their stories will contain elements of a beginning, middle, and end. By analyzing these components through coding, you can identify emerging patterns in the data.

Taking cues from entrance and exit talk

Entrance talk is when the participant begins a particular set of narratives. You may hear expressions such as, "I remember when…," "It first occurred to me when…," or "Here's an example…."

Exit talk allows you to see when the story is wrapping up, and you might expect to hear a phrase like, "…and that's how we decided", "after that, we moved on," or "that's pretty much it."

Deductive method

Regardless of your chosen method, using a deductive method can help preserve the overall storyline while coding. Starting with a deductive method allows for the separation of narrative pieces without compromising the story's integrity.

Hybrid inductive and deductive narrative analysis

Using both methods together gives you a comprehensive understanding of the data. You can start by coding the entire story using the inductive method. Then, you can better analyze and interpret the data by applying deductive codes to individual parts of the story.

• How to analyze data after coding using narrative analysis

A narrative analysis aims to take all relevant interviews and organize them down to a few core narratives. After reviewing the coding, these core narratives may appear through a repeated moment of decision occurring before the climax or a key feeling that affected the participant's outcome.

You may see these core narratives diverge early on, or you may learn that a particular moment after introspection reveals the core narrative for each participant. Either way, researchers can now quickly express and understand the data you acquired.

• A step-by-step approach to narrative analysis and finding core narratives

Narrative analysis may look slightly different to each research group, but we will walk through the process using the Delve method for this article.

Step 1 – Code narrative blocks

Organize your narrative blocks using inductive coding to organize stories by a life event.

Example: Narrative interviews are conducted with homeowners asking them to describe how they bought their first home.

Step 2 – Group and read by live-event

You begin your data analysis by reading through each of the narratives coded with the same life event.

Example: You read through each homeowner's experience of buying their first home and notice that some common themes begin to appear, such as "we were tired of renting," "our family expanded to the point that we needed a larger space," and "we had finally saved enough for a downpayment."

Step 3 – Create a nested story structure

As these common narratives develop throughout the participant's interviews, create and nest code according to your narrative analysis framework. Use your coding to break down the narrative into pieces that can be analyzed together.

Example: During your interviews, you find that the beginning of the narrative usually includes the pressures faced before buying a home that pushes the research participants to consider homeownership. The middle of the narrative often includes challenges that come up during the decision-making process. The end of the narrative usually includes perspectives about the excitement, stress, or consequences of home ownership that has finally taken place. 

Step 4 – Delve into the story structure

Once the narratives are organized into their pieces, you begin to notice how participants structure their own stories and where similarities and differences emerge.

Example: You find in your research that many people who choose to buy homes had the desire to buy a home before their circumstances allowed them to. You notice that almost all the stories begin with the feeling of some sort of outside pressure.

Step 5 – Compare across story structure

While breaking down narratives into smaller pieces is necessary for analysis, it's important not to lose sight of the overall story. To keep the big picture in mind, take breaks to step back and reread the entire narrative of a code block. This will help you remember how participants expressed themselves and ensure that the core narrative remains the focus of the analysis.

Example: By carefully examining the similarities across the beginnings of participants' narratives, you find the similarities in pressures. Considering the overall narrative, you notice how these pressures lead to similar decisions despite the challenges faced. 

Divergence in feelings towards homeownership can be linked to positive or negative pressures. Individuals who received positive pressure, such as family support or excitement, may view homeownership more favorably. Meanwhile, negative pressures like high rent or peer pressure may cause individuals to have a more negative attitude toward homeownership.

These factors can contribute to the initial divergence in feelings towards homeownership.

Step 6 – Tell the core narrative

After carefully analyzing the data, you have found how the narratives relate and diverge. You may be able to create a theory about why the narratives diverge and can create one or two core narratives that explain the way the story was experienced.

Example: You can now construct a core narrative on how a person's initial feelings toward buying a house affect their feelings after purchasing and living in their first home.

Narrative analysis in qualitative research is an invaluable tool to understand how people's stories and ability to self-narrate reflect the human experience. Qualitative data analysis can be improved through coding and organizing complete narratives. By doing so, researchers can conclude how humans process and move through decisions and life events.

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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis

Thematic analysis

• Thematic analysis vs. content analysis
• Introduction

Types of narrative research

Research methods for a narrative analysis, narrative analysis, considerations for narrative analysis.

• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative analysis software

Narrative analysis in research

Narrative analysis is an approach to qualitative research that involves the documentation of narratives both for the purpose of understanding events and phenomena and understanding how people communicate stories.

Let's look at the basics of narrative research, then examine the process of conducting a narrative inquiry and how ATLAS.ti can help you conduct a narrative analysis.

Qualitative researchers can employ various forms of narrative research, but all of these distinct approaches utilize perspectival data as the means for contributing to theory.

A biography is the most straightforward form of narrative research. Data collection for a biography generally involves summarizing the main points of an individual's life or at least the part of their history involved with events that a researcher wants to examine. Generally speaking, a biography aims to provide a more complete record of an individual person's life in a manner that might dispel any inaccuracies that exist in popular thought or provide a new perspective on that person’s history. Narrative researchers may also construct a new biography of someone who doesn’t have a public or online presence to delve deeper into that person’s history relating to the research topic.

The purpose of biographies as a function of narrative inquiry is to shed light on the lived experience of a particular person that a more casual examination of someone's life might overlook. Newspaper articles and online posts might give someone an overview of information about any individual. At the same time, a more involved survey or interview can provide sufficiently comprehensive knowledge about a person useful for narrative analysis and theoretical development.

Life history

This is probably the most involved form of narrative research as it requires capturing as much of the total human experience of an individual person as possible. While it involves elements of biographical research, constructing a life history also means collecting first-person knowledge from the subject through narrative interviews and observations while drawing on other forms of data , such as field notes and in-depth interviews with others.

Even a newspaper article or blog post about the person can contribute to the contextual meaning informing the life history. The objective of conducting a life history is to construct a complete picture of the person from past to present in a manner that gives your research audience the means to immerse themselves in the human experience of the person you are studying.

Oral history

While all forms of narrative research rely on narrative interviews with research participants, oral histories begin with and branch out from the individual's point of view as the driving force of data collection .

Major events like wars and natural disasters are often observed and described at scale, but a bird's eye view of such events may not provide a complete story. Oral history can assist researchers in providing a unique and perhaps unexplored perspective from in-depth interviews with a narrator's own words of what happened, how they experienced it, and what reasons they give for their actions. Researchers who collect this sort of information can then help fill in the gaps common knowledge may not have grasped.

The objective of an oral history is to provide a perspective built on personal experience. The unique viewpoint that personal narratives can provide has the potential to raise analytical insights that research methods at scale may overlook. Narrative analysis of oral histories can hence illuminate potential inquiries that can be addressed in future studies.

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To conduct narrative analysis, researchers need a narrative and research question . A narrative alone might make for an interesting story that instills information, but analyzing a narrative to generate knowledge requires ordering that information to identify patterns, intentions, and effects.

Narrative analysis presents a distinctive research approach among various methodologies , and it can pose significant challenges due to its inherent interpretative nature. Essentially, this method revolves around capturing and examining the verbal or written accounts and visual depictions shared by individuals. Narrative inquiry strives to unravel the essence of what is conveyed by closely observing the content and manner of expression.

Furthermore, narrative research assumes a dual role, serving both as a research technique and a subject of investigation. Regarded as "real-world measures," narrative methods provide valuable tools for exploring actual societal issues. The narrative approach encompasses an individual's life story and the profound significance embedded within their lived experiences. Typically, a composite of narratives is synthesized, intermingling and mutually influencing each other.

Designing a research inquiry

Sometimes, narrative research is less about the storyteller or the story they are telling than it is about generating knowledge that contributes to a greater understanding of social behavior and cultural practices. While it might be interesting or useful to hear a comedian tell a story that makes their audience laugh, a narrative analysis of that story can identify how the comedian constructs their narrative or what causes the audience to laugh.

As with all research, a narrative inquiry starts with a research question that is tied to existing relevant theory regarding the object of analysis (i.e., the person or event for which the narrative is constructed). If your research question involves studying racial inequalities in university contexts, for example, then the narrative analysis you are seeking might revolve around the lived experiences of students of color. If you are analyzing narratives from children's stories, then your research question might relate to identifying aspects of children's stories that grab the attention of young readers. The point is that researchers conducting a narrative inquiry do not do so merely to collect more information about their object of inquiry. Ultimately, narrative research is tied to developing a more contextualized or broader understanding of the social world.

Data collection

Having crafted the research questions and chosen the appropriate form of narrative research for your study, you can start to collect your data for the eventual narrative analysis.

Needless to say, the key point in narrative research is the narrative. The story is either the unit of analysis or the focal point from which researchers pursue other methods of research. Interviews and observations are great ways to collect narratives. Particularly with biographies and life histories, one of the best ways to study your object of inquiry is to interview them. If you are conducting narrative research for discourse analysis, then observing or recording narratives (e.g., storytelling, audiobooks, podcasts) is ideal for later narrative analysis.

Triangulating data

If you are collecting a life history or an oral history, then you will need to rely on collecting evidence from different sources to support the analysis of the narrative. In research, triangulation is the concept of drawing on multiple methods or sources of data to get a more comprehensive picture of your object of inquiry.

While a narrative inquiry is constructed around the story or its storyteller, assertions that can be made from an analysis of the story can benefit from supporting evidence (or lack thereof) collected by other means.

Even a lack of supporting evidence might be telling. For example, suppose your object of inquiry tells a story about working minimum wage jobs all throughout college to pay for their tuition. Looking for triangulation, in this case, means searching through records and other forms of information to support the claims being put forth. If it turns out that the storyteller's claims bear further warranting - maybe you discover that family or scholarships supported them during college - your analysis might uncover new inquiries as to why the story was presented the way it was. Perhaps they are trying to impress their audience or construct a narrative identity about themselves that reinforces their thinking about who they are. The important point here is that triangulation is a necessary component of narrative research to learn more about the object of inquiry from different angles.

Conduct data analysis for your narrative research with ATLAS.ti.

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This brings us to the analysis part of narrative research. As explained above, a narrative can be viewed as a straightforward story to understand and internalize. As researchers, however, we have many different approaches available to us for analyzing narrative data depending on our research inquiry.

In this section, we will examine some of the most common forms of analysis while looking at how you can employ tools in ATLAS.ti to analyze your qualitative data .

Qualitative research often employs thematic analysis , which refers to a search for commonly occurring themes that appear in the data. The important point of thematic analysis in narrative research is that the themes arise from the data produced by the research participants. In other words, the themes in a narrative study are strongly based on how the research participants see them rather than focusing on how researchers or existing theory see them.

ATLAS.ti can be used for thematic analysis in any research field or discipline. Data in narrative research is summarized through the coding process , where the researcher codes large segments of data with short, descriptive labels that can succinctly describe the data thematically. The emerging patterns among occurring codes in the perspectival data thus inform the identification of themes that arise from the collected narratives.

Structural analysis

The search for structure in a narrative is less about what is conveyed in the narrative and more about how the narrative is told. The differences in narrative forms ultimately tell us something useful about the meaning-making epistemologies and values of the people telling them and the cultures they inhabit.

Just like in thematic analysis, codes in ATLAS.ti can be used to summarize data, except that in this case, codes could be created to specifically examine structure by identifying the particular parts or moves in a narrative (e.g., introduction, conflict, resolution). Code-Document Analysis in ATLAS.ti can then tell you which of your narratives (represented by discrete documents) contain which parts of a common narrative.

It may also be useful to conduct a content analysis of narratives to analyze them structurally. English has many signal words and phrases (e.g., "for example," "as a result," and "suddenly") to alert listeners and readers that they are coming to a new step in the narrative.

In this case, both the Text Search and Word Frequencies tools in ATLAS.ti can help you identify the various aspects of the narrative structure (including automatically identifying discrete parts of speech) and the frequency in which they occur across different narratives.

Functional analysis

Whereas a straightforward structural analysis identifies the particular parts of a narrative, a functional analysis looks at what the narrator is trying to accomplish through the content and structure of their narrative. For example, if a research participant telling their narrative asks the interviewer rhetorical questions, they might be doing so to make the interviewer think or adopt the participant's perspective.

A functional analysis often requires the researcher to take notes and reflect on their experiences while collecting data from research participants. ATLAS.ti offers a dedicated space for memos , which can serve to jot down useful contextual information that the researcher can refer to while coding and analyzing data.

Dialogic analysis

There is a nuanced difference between what a narrator tries to accomplish when telling a narrative and how the listener is affected by the narrative. There may be an overlap between the two, but the extent to which a narrative might resonate with people can give us useful insights about a culture or society.

The topic of humor is one such area that can benefit from dialogic analysis, considering that there are vast differences in how cultures perceive humor in terms of how a joke is constructed or what cultural references are required to understand a joke.

Imagine that you are analyzing a reading of a children's book in front of an audience of children at a library. If it is supposed to be funny, how do you determine what parts of the book are funny and why?

The coding process in ATLAS.ti can help with dialogic analysis of a transcript from that reading. In such an analysis, you can have two sets of codes, one for thematically summarizing the elements of the book reading and one for marking when the children laugh.

The Code Co-Occurrence Analysis tool can then tell you which codes occur during the times that there is laughter, giving you a sense of what parts of a children's narrative might be funny to its audience.

Narrative analysis and research hold immense significance within the realm of social science research, contributing a distinct and valuable approach. Whether employed as a component of a comprehensive presentation or pursued as an independent scholarly endeavor, narrative research merits recognition as a distinctive form of research and interpretation in its own right.

Subjectivity in narratives

It is crucial to acknowledge that every narrative is intricately intertwined with its cultural milieu and the subjective experiences of the storyteller. While the outcomes of research are undoubtedly influenced by the individual narratives involved, a conscientious adherence to narrative methodology and a critical reflection on one's research can foster transparent and rigorous investigations, minimizing the potential for misunderstandings.

Rather than striving to perceive narratives through an objective lens, it is imperative to contextualize them within their sociocultural fabric. By doing so, an analysis can embrace the diverse array of narratives and enable multiple perspectives to illuminate a phenomenon or story. Embracing such complexity, narrative methodologies find considerable application in social science research.

Connecting narratives to broader phenomena

In employing narrative analysis, researchers delve into the intricate tapestry of personal narratives, carefully considering the multifaceted interplay between individual experiences and broader societal dynamics.

This meticulous approach fosters a deeper understanding of the intricate web of meanings that shape the narratives under examination. Consequently, researchers can uncover rich insights and discern patterns that may have remained hidden otherwise. These can provide valuable contributions to both theory and practice.

In summary, narrative analysis occupies a vital position within social science research. By appreciating the cultural embeddedness of narratives, employing a thoughtful methodology, and critically reflecting on one's research, scholars can conduct robust investigations that shed light on the complexities of human experiences while avoiding potential pitfalls and fostering a nuanced understanding of the narratives explored.

Turn to ATLAS.ti for your narrative analysis.

Researchers can rely on ATLAS.ti for conducting qualitative research. See why with a free trial.

Narrative Analysis

Cite this chapter.

• Phil Benson 5  

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Narrative analysis is a relatively recent addition to the toolkit of applied linguistics. Its basic premise is that the telling of stories can elucidate the meanings attached to participants’ experiences. These may be stories told by participants during data collection or stories constructed by researchers (sometimes in collaboration with participants) during analysis of a data set. This chapter is mainly concerned with uses of storytelling and narrative writing in data analysis and presentation of research findings.

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Benson, P. (2018). Narrative Analysis. In: Phakiti, A., De Costa, P., Plonsky, L., Starfield, S. (eds) The Palgrave Handbook of Applied Linguistics Research Methodology. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59900-1_26

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Narrative Analysis: Methods and Examples

Narrative analysis is a powerful qualitative research tool. Narrative research can uncover behaviors, feelings and motivations that aren’t expressed explicitly….

Narrative analysis is a powerful qualitative research tool. Narrative research can uncover behaviors, feelings and motivations that aren’t expressed explicitly. It also provides rich linguistic data that may shed light on various aspects of cultural or social phenomena.

Narrative analysis provides researchers with detailed information about their subjects that they couldn’t get through other methods. Narrative analysis in qualitative research reveals hidden motivations that aren’t easy to perceive directly. This is especially true in research conducted with cultural subjects where the researcher must peel the many layers of a culture.

Let’s look at how narrative research is performed, what it can tell us about the subject, and some examples of narrative research.

What Is Narrative Research?

Examples of narrative research, difference between narrative analysis and case study, analyzing results in the narrative method.

Narrative analysis is a form of qualitative research in which the researcher focuses on a topic and analyzes the data collected from case studies, surveys, observations or other similar methods. The researchers write their findings, then review and analyze them.

To conduct narrative analysis, researchers must understand the background, setting, social and cultural context of the research subjects. This gives researchers a better idea of what their subjects mean in their narration. It’s especially true in context-rich research where there are many hidden layers of meaning that can only be uncovered by an in-depth understanding of the culture or environment.

Before starting narrative research, researchers need to know as much about their research subjects as possible. They interview key informants and collect large amounts of text from them. They even use other sources, such as existing literature and personal recollections.

From this large base of information, researchers choose a few instances they feel are good examples of what they want to talk about and then analyze them in depth.

Through this approach, researchers can gain a holistic view of the subject’s life and activities. It can show what motivates people and provide a better view of the society that the subjects live in by enabling researchers to see how individuals interact with one another.

• It’s been used by researchers to study indigenous peoples of various countries, such as the Maori in New Zealand.
• It can be used in medicine. Researchers, for instance, can study how doctors communicate with their patients during end-of-life care.
• The narrative model has been used to explore the relationship between music and social change in East Africa.
• Narrative research is being used to explore the differences in emotions experienced by different generations in Japanese society.

Through these examples of narrative research, we can see its nature and how it fills a gap left by other research methods.

Many people confuse narrative analysis in qualitative research with case studies. Here are some key differences between the two:

• A case study examines one context in depth, whereas narrative research explores how a subject has acted in various contexts across time
• Case studies are often longer and more detailed, but they rarely provide an overview of the subject’s life or experiences
• Narrative analysis implies that researchers are observing several instances that encompass the subject’s life, which is why it provides a richer view of things

Both tools can give similar results, but there are some differences that lead researchers to choose one or the other or, perhaps, even both in their research design.

Once the narratives have been collected, researchers notice certain patterns and themes emerging as they read and analyze the text. They note these down, compare them with other research on the subject, figure out how it all fits together and then find a theory that can explain these findings.

Many social scientists have used narrative research as a valuable tool to analyze their concepts and theories. This is mainly because narrative analysis is a more thorough and multifaceted method. It helps researchers not only build a deeper understanding of their subject, but also helps them figure out why people act and react as they do.

Storytelling is a central feature of narrative research. The narrative interview is an interactive conversation. This process can be very intimate and sometimes bring about powerful emotions from both parties. Therefore, this form of qualitative research isn’t suitable for everyone. The interviewer needs to be a good listener and must understand the interview process. The interviewee also needs to be comfortable to be able to provide authentic narratives.

Understanding what kind of research to use is a powerful tool for a manager. We can use narrative analysis in many ways. Narrative research is a multifaceted method that has the potential to show different results based on the researcher’s intentions for their study.

Learning how to use such tools will improve the productivity of teams. Harappa’s Thinking Critically course will show you the way. Learners will understand how to better process information and consider different perspectives in their analysis, which will allow for better-informed decision making. Our faculty will provide real-world insights to ensure an impactful learning experience that takes professionals at every stage of their careers to the next level.

Explore Harappa Diaries to learn more about topics such as Phenomenological Research , Types Of Survey Research , Examples Of Correlational Research and Tips to Improve your Analytical Skills to upgrade your knowledge and skills.

What is Narrative Analysis?

This is part of our Essential Guide to Coding Qualitative Data | Start a Free Trial | Free Qualitative Data Analysis Course

What is narrative analysis in qualitative research?

Researchers use narrative analysis to understand how research participants construct story and narrative from their own personal experience. That means there is a dual layer of interpretation in narrative analysis. First the research participants interpret their own lives through narrative. Then the researcher interprets the construction of that narrative.

Narratives can be derived from journals, letters, conversations, autobiographies, transcripts of in-depth interviews, focus groups, or other types of narrative qualitative research and then used in narrative research.

This post is in part a summary of our interpretation of Catherine Kohler Riessman’s Narrative Analysis . 

Learn about other methods of qualitative analysis on Delve’s YouTube channel.

Examples of personal narratives

Personal narratives come in a variety of forms and can all be used in narrative research.

Topical stories

A restricted story about one specific moment in time with a plot, characters, and setting, but doesn’t encompass the entirety of a person’s life. Example: a research participant’s answer to a single interview question

Personal narrative 

Personal narratives come from a long interview or a series of long narrative interviews that give an extended account of someone’s life. Example: a researcher conducting an in-depth interview, or a series of in-depth interviews with an individual over an extended period of time.

Entire life story

Constructed from a collection of interviews, observations, and documents about a person’s life. Example: a historian putting together the biography of someone’s life from past artifacts.

Capturing narrative data

While humans naturally create narratives and stories when interpreting their own lives, certain data collection methods are more conducive to understanding your research participants' sense of self narrative. Semi-structured interviews, for example, give the interviewee the space to go on narrative tangents and fully convey their internal narratives. Heavily structured interviews that follow a question answer format or written surveys, are less likely to capture narrative data. 

Transcribing narrative data

As mentioned earlier, narrative analysis has dual layers of interpretation. Researchers should not take narrative interviews at face value because they are not just summarizing a research participant's self-narrative. Instead, researchers should actively interpret how the interviewee created that self-narrative. Thus narrative analysis emphasizes taking verbatim transcription of narrative interviews, where it is important to include pauses, filler words, and stray utterances like “um….”.

For more information on transcription options, please see our guide on how to transcribe interviews.

Coding in narrative analysis

There are many methods for coding narrative data. They range from deductive coding where you start with a list of codes, and inductive coding where you do not. You can also learn about many other ways to code in our Essential Guide to Coding Qualitative Data or take our Free Course on Qualitative Data Analysis .

What is narrative research

In addition to narrative analysis, you can also practice narrative research, which is a type of study that seeks to understand and encapsulate the human experience by using in depth methods to explore the meanings associated to people’s lived experiences. You can utilize narrative research design to learn about these concepts. Narrative analysis can be used in narrative research as well as other approaches such as grounded theory , action research , ethnology and more.

Download Free Narrative Analysis Guide

Want to learn how to do narrative analysis? Submit your email to request our free narrative analysis guide with tips on how to get started with your own narrative analysis. You will get a narrative analysis in qualitative research PDF emailed to you.

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Inductive method for narrative analysis

Learn about inductive narrative method:.

It is common for inductive methods of narrative analysis to code much larger blocks of text than traditional coding methods. Narrative analysis differs from other qualitative analysis methods , in that it attempts to keep the individual narratives intact. In many coding methods, it is common to split up an interviewee’s narrative into smaller pieces and group them by theme with other interviewee’s statements. This breaks up the individual’s personal narrative. 

Narrative analysis treats a complete story as the individual piece of datum that you are analyzing. So in the inductive method of narrative analysis, you should code the entire block of text for each of your research participants' stories. This section of text is called a “narrative block”

Entrance and Exit Talk

There are tricks to identifying narrative blocks in your research participants’ narrative interviews. Riesssman recommended looking for “entrance and exit talk”. Your participants may give you verbal hints when they begin and end a story. 

A story may start with the phrases: 

“There was this one time…”, 

“Let me give you an example”, 

and “I’ll always remember when…”

Likewise, you can detect the end of stories with exit talk such as:

“So that’s how that wrapped up…”

“That is a pretty classic example of…”

and “and that was the end of that.”

You can’t always depend on “entrance and exit talk”, as they will not always be used. Furthermore, semi-structured interviews are not screenplays. Narratives won’t always exist as nice neat narrative blocks. Participants may meander and go on tangents. But the narrative through-line may still exist. And using coding you group together a narrative that is spread across an interview.

Deductive method for narrative analysis

Learn about deductive narrative method:.

There are many existing story structure frameworks. With a deductive method of narrative analysis, researchers can use a story structure framework and as their initial set of codes. This can be as simple as “Beginning”, “Middle” and “End”. In “Doing Narrative Research”, Patterson used the following codes for his narrative structure.

Abstract: The core thesis of the story, summary

Orientation: Time, place, situation, and characters

Complicating action: Sequence of events, plot

Evaluation: How the storyteller comments on meaning 

Resolution: Outcome of the story

Coda: Story’s ending 

At Delve, when we conduct narrative analysis we prefer the “Story Circle” for our initial set of codes:

You - A character is in a zone of comfort

Need - But they want something.

Go - They enter an unfamiliar situation,

Search - Adapt to it,

Find - Get what they wanted,

Take/Pay - Pay a heavy price for it,

Return - Then return to their familiar situation,

Change - Having changed.

When utilizing the deductive method, you may want to keep track of the existing framework in a codebook. See our guide on “ How to Create a Qualitative Codebook” .

Hybrid Inductive and Deductive Narrative Analysis

As is common in other methods of qualitative analysis, combining inductive and deductive can be helpful. For narrative analysis, this involves first coding inductively the narrative blocks in your transcripts. Then within those narrative blocks, code deductively using a story structure framework. We will delve deeper into this in the following sections.

How to analyze data in a narrative interview

Narrative analysis, like many qual methods, takes a set of data like interviews and reduces it to abstract findings. The difference is that while many popular qualitative methods aim to reduce interviews to a set of core themes or findings, narrative analysis aims to reduce interviews to a set of core narratives.

A core narrative is a generalized narrative grounded in your research participants’ stories. This is not implying that all stories in your narrative study will be perfectly encapsulated by one core narrative. There will be outliers and nuance. And as in all qualitative analysis, embracing and communicating this is an important part of the process.

A step by step approach to narrative analysis and finding the core narratives

There is no one agreed-upon method of narrative analysis or narrative research method. There are many types of narrative research designs. That being said, we thought it would be helpful to provide a step-by-step narrative approach to at least one method of narrative analysis that will help you find core narratives in research.

Step 1: Code Narrative Blocks

Inductively code the narrative blocks you find in your interviews. You should code narrative blocks about similar “life events” with the same code. 

For example, stories about how someone decided to have children could be coded as “Narratives about deciding to have children”.

Step 2: Group and Read By Live-Event

Read over all the narratives that you coded with the same “life event” code. As you do so, note their similarities and differences. This is the beginning of your analysis!

Step 3: Create Nested Story Structure Codes

For every “life event” code, create and nest codes based on your story structure framework of choice. For example:

Narratives about deciding to have children (this is your inductively created life-event code)

Abstract (these codes are based on story structure)

Orientation

Complicating action

More generally put:

Life Event Code   

Story Structure Code 1

Story Structure Code 2

Now break up your narrative blocks, by applying these story structure codes. 

Step 4: Delve into the Story Structure

Now you can collate each life event by its story structure code. For example within “narratives about deciding to have children'', you can focus on “Orientation”. In all the stories about deciding to have children, you can compare and contrast how different research participants oriented their stories. The similarities and differences can be written down as you observe them. Differences can be further coded to help with later analysis. For example, if it was common for your participants to talk about their parent’s marital status, you may end up with the following code structure.

Deciding to have children

Parent divorce

Parents still together

Step 5: Compare Across Story Structure

As you break up your narrative blocks by story structure, do not lose sight of the overarching narrative. Switch between reading your narrative blocks as a whole, and diving into each individual story structure code. Pay attention to how story structure codes relate across a life event. 

For example, participants who talked about their parents’ divorce, may construct meaning differently than those whose parents remained together. You may discover this finding by comparing “Orientation” with “Evaluation”.

Step 6: Tell the Core Narrative

At the end of these steps, you will have fully explored each narrative block. You will have a deep understanding of how your research participants self-narrate their lives. You will have observed how your participants' stories relate, but also how they diverge. And through the process, you may have a theory why these stories diverge. 

For each life-event take the structure you used (in our example Patterson’s Abstract, Orientation, etc…) and write a core narrative that encapsulates the commonalities between your participants. If you have found fundamental differences within your research base, you can capture that nuance in a single core narrative. Alternatively, you can break a life event into two core narratives and compare them. In our example above we may write one core narrative from the perspective of participants whose parents divorced and another perspective of participants whose parents stayed together.

Now that you’ve learned about various models of narrative analysis, take the next step by seeing how to code the data that you collect from these methods. Check out our Essential Guide to Coding Qualitative Data or take our Free Online Course on Qualitative Data Analysis .

Try Delve, Narrative Analysis Software

Online software such as Delve can help streamline how you’re coding your qualitative coding. Try a free trial or watch a demo of the Delve.

References:

Riessman, Catherine Kohler. (©1993) Narrative analysis /Newbury Park, CA : Sage Publications,

Cite this blog post:

Delve, Ho, L., & Limpaecher, A. (2020b, September 15). What is Narrative Analysis? Essential Guide to Coding Qualitative Data. https://delvetool.com/blog/narrativeanalysis

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Finding a path in a methodological jungle: a qualitative research of resilience

Elīna zelčāne.

Department of Health Psychology and Paedagogy, Riga Stradiņš University, Riga, Latvia

Anita Pipere

Qualitative research provides an in-depth understanding of lived experiences. However, these experiences can be hard to apprehend by using just one method of data analysis. A good example is the experience of resilience. In this paper, the authors describe the chain of the decision-making process in the research of the construct of “resilience”. s The authors justify the implications of a multi-method, pluralistic approach, and show how the triangulation of two or more qualitative methods and integration of several qualitative data analysis methods can improve a deeper understanding of the resilience among people with chronic pain. By combining the thematic analysis, narrative analysis, and critical incident technique, lived experiences can be seen from different perspectives.Therefore, the thematic analysis describes the content and answers to “what” regarding resilience, the narrative analysis describes the dynamics of resilience, and answers to “how”, while the critical incident technique clarifies the most significant experience and the answers to “why” changes happen. This integrative approach could be used in the analysis of other psychological constructs and can serve as an example of how the rigour of qualitative research could be provided.

Introduction

Just a few decades ago, qualitative researchers put a lot of effort into discussions with quantitative researchers to prove that a qualitative research strategy can also be viewed as a scientific inquiry and can provide valid and significant knowledge. Today, qualitative research is no longer just “not quantitative research” but has developed an identity or maybe multiple identities of its own (Flick, 2018 ). Qualitative research is especially appropriate to study complex constructs and experiences holistically. It allows one to acquire a deeper understanding of people’s lived experiences in diverse contexts (Hong & Cross Francis, 2020 ) and deals primarily with an intensive rather than extensive examination of these experiences (Gough & Deatrick, 2015 ).

The wider use of different qualitative approaches has led to new methodological challenges. One such challenge is to support methodological integrity in keeping with a diversity of researchers’ goals and approaches (Levitt, 2021 ). Although qualitative research is an approach rather than a particular set of techniques, it does not mean that a researcher can choose any design or combine any methods without justification. The inconsistency between the research question and the methodology, insufficient methodological knowledge, and the lack of attention to a philosophical foundation of qualitative methodology can be mentioned as some important challenges (Khankeh et al., 2015 ). To overcome this challenge, a researcher must become familiar with traditional approaches and recently developed ones in qualitative research and choose the most appropriate for the given research problem and research questions.

Another challenge is how to present the findings of qualitative research in a way that they can be comprehended by both academic and non-academic readers. Therefore, the researchers need to render the qualitative research findings more “friendly” to people who may not have academic or professional backgrounds or interests, provided that the findings are still faithful (Holloway & Todres, 2007 ). Besides, the findings of qualitative research often make sense in a very narrow context, while outside the academic environment there is a demand for practical and more general benefits that could promote change in a wider context. Thus, researchers must provide a “thick description” of the participants and the research process, to enable the reader to assess whether these findings are transferable to their own setting (Korstjens & Moser, 2018 ).

Qualitative researchers often use well-trodden paths. Svend Brinkmann ( 2015 ) calls this process a “McDonaldization” of qualitative research. To cope with this trend, it is recommended to also use innovative methods to explore psychological issues in health and illness (Chamberlain & Murray, 2017 ) and learn from artists how to capture peoples’ attention in a more creative way (Holloway & Todres, 2007 ). Innovative practices in qualitative research can involve pluralisms of various kinds, creative ways of collecting and analysing data, disseminating findings, and participation in some of the ethical and practical challenges involved in qualitative research (Lamarre & Chamberlain, 2021 ).

Today, qualitative research is widely used in different social sciences, and psychology is one of the areas where it is expanding rapidly. The proportion of qualitative research has grown especially in the field of health psychology. One of the reasons for the current popularity of qualitative health research is the growing emphasis of policy and practitioners on patient/client experiences and practices related to prevention, illness, and use of services (Gough & Deatrick, 2015 ). Qualitative research design is consistent with the Chronic care model (CCM), which is a widely-used framework for organizing and providing care for people with chronic disease (Wagner et al., 2001 ). The CCM aims to improve the quality of care and patient outcomes by providing proactive, patient-centred, and integrated care (Spoorenberg et al., 2015 ). Qualitative research can provide a deeper understanding of patients’ perspectives, experiences, and treatment needs and could promote patient-centred care (O’Reilly et al., 2021 ; Renjith et al., 2021 ). When patients feel respected, are included in the decision-making process, and can express their needs and emotions without feeling judged, they report a stronger sense of alliance with the care providers (Youssef et al., 2020 ). Qualitative research “gives voice” to patients (Braun & Clarke, 2019 ; Stein & Mankowski, 2004 ), allowing researchers and practitioners to observe health-related issues from several perspectives and analyse qualitative data with multiple methods.

One example of such a construct that can be qualitatively studied from different points of view is the experience of resilience while living with chronic musculoskeletal pain (CMP). In this paper, we describe the chain of the decision-making process in the research of the mentioned topic, starting from the dilemma between quantitative and qualitative research strategies to the decision to combine different data analysis methods. This article focuses specifically on the discussion of how the integration of several qualitative data analysis methods can improve a deeper understanding of the formation and maintenance of resilience among people with chronic pain.

Resilience in chronic pain: A rationale for qualitative research

The American Psychological Association defines resilience as a process of adapting well in the face of adversity, trauma, tragedy, threats, or significant sources of stress (APA, 2012 ). Resilience can be defined as the process of effectively negotiating, adapting to, or managing significant sources of stress or trauma. Assets and resources within the individual, their life and environment facilitate this capacity for adaptation and “bouncing back” in the face of adversity (Windle, 2011 ).

In previous studies, resilience has been viewed as a personality trait (Block & Kremen, 1996 ; Connor & Davidson, 2003 , Wagnild & Young, 1993 ), or a dynamic process, that can lead to a positive outcome (Bonanno & Mancini, 2008 ; Luthar & Cicchetti, 2000 ; Masten, 2011 ; Rutter, 2006 ). Although there are several definitions of resilience, most of them are based on two core concepts—adversity and positive adaptation. The notion of risk and positive adaptation are fundamental to both personal characteristics and process-based conceptualizations of resilience (Vella & Pai, 2019 ). Some researchers use the term “adaptation” meaning both the process of adjustment and its outcome (Luthar & Cicchetti, 2000 ; Rutter, 2006 ) but recently many scholars have emphasized the three pillars of resilience—adversity, the process of adaptation, and the preservation of health functioning or positive outcome (Hiebel et al., 2021 ; Kunzler et al., 2018 ; Stainton et al., 2019 ).

In recent studies, researchers offer an integrative view of resilience, describing it as a multifactorial, multisystemic and context dependent construct (Miller-Graff, 2022 ; Sisto et al., 2019; Ungar, 2021 ). Individual resilience is influenced by biological, psychological, social, and ecological factors and can manifest itself in different ways, like maintaining healthy functioning despite adversity, recovering from adversity and bouncing back to homoeostasis or even bouncing forward and experiencing personal growth (Ungar, 2021 ).

In the context of health psychology last few years there has been a shift away from disease-focused to health-focused research (Denckla et al., 2020 ). Resilience is viewed not only as the absence of psychopathology but as a presence of psychological, mental, social, and spiritual capital that help to maintain the quality of life despite the illness (Babić et al., 2020 ). Since people with chronic pain or other chronic conditions are not able to recover fully and return to homoeostasis, resilience in this context is defined as the ability to live fulfilling life in the presence of pain (Goubert & Trompetter, 2017 ; Sturgeon & Zautra, 2016 ).Chronic diseases, especially chronic pain, can negatively affect the physical, mental, and social aspects of a person’s life. However, chronically ill people, who have higher resilience scores, tend to have less depression and anxiety. Instead, they have a better quality of life and health behaviour (Cal et al., 2015 ; Gheshlagh et al., 2016 ). The effect of resilience can manifest itself in faster recovery from the negative effects of pain, through effective preservation of positive functioning despite the presence of pain (Sturgeon & Zautra, 2010 ).

Although previous studies (Gonzalez et al., 2019 ; Hemington et al., 2017 ; Ramírez-Maestre et al., 2019 ) have confirmed that resilience plays a key role in one’s adaptation to chronic pain, several questions still need to be answered. Why some people with chronic pain are more resilient than others? What factors influenced the development of their resilience? What are people with chronic pain doing to improve and maintain their long-term resilience?

The nature of these questions has inevitably led us to the exploration of experience related to the resilience of a specific population, alluded to by the qualitative research approach. We combined all these questions into one main research question, as is often done in qualitative studies: What is the experience of developing and maintaining resilience in people with CMP?

The next step after formulating the research question was to choose the right research paradigm or perspective on how a researcher sees and interprets the world. In recent studies, resilience has been seen as a context-dependent construct (Gentili et al., 2019 ; Hayman et al., 2017 ; Ungar, 2018 ). Resilience can be understood differently when we discuss, for example, adaptation to chronic pain, the experience of divorce, domestic violence, or childhood trauma. In different contexts, the opportunities for individuals are different, the needs are different, and the extent to which individuals can make use of these opportunities is different (Pooley & Cohen, 2010 ). Considering that there is no such thing as “common resilience for all”, we decided to ground our research on the paradigm of social constructivism. Constructivists acknowledge that individuals construct their own perceptions of the world, but social constructionists go one step further, arguing that those individual constructions are developed in a social world (Harper & Thomson, 2011 ). A fundamental assumption of the social constructivism paradigm is that there is no universal reality. Meanings, knowledge, and truth are created by the interactions of individuals within a society (Andrews, 2012 ; Creswell, 2013 ).

The choice of the social constructivism paradigm, along with the research question, confirmed the use of a qualitative research strategy, as it is more appropriate to study mental facts, such as experiences, feelings, and attitudes, which are ontologically subjective phenomena. In contrast, a quantitative research strategy is more suitable for studying brute facts or external reality (Silva, 2008 ). Quantitative studies have made a major contribution to resilience research in healthcare by demonstrating that resilience is positively correlated with social and physical functioning, adaptation to illness and better health outcomes (Kim et al., 2019 ; Musich et al., 2022 ; Schäfer et al., 2022 ; Seiler & Jenewein, 2019 ), but quantitative studies can’t provide a sufficiently deep and comprehensive understanding of how resilience is formed and how the resilience dynamic is influenced by the general context of life.

Resilience is a multidimensional, contextually specific, and culturally biased construct (Ungar, 2013 ). The meaning we put in the words “being resilient” is not the same for all of us. Global resilience is at best quite rare, if not non-existent because it changes in different situations and at different times (Vanderbilt-Adriance & Shaw, 2008 ). For example, a person can cope effectively with stressors at work but shows very low resilience in the face of disease. These differences can be explained by the fact that resilience is influenced not only by internal but also by external risk and protective factors. Resilience of an individual depends on resilience of interconnected systems. Resilience develops and changes because all of the systems accounting for resilience are dynamic (Masten, 2021 ). Many authors (Bonanno & Mancini, 2008 ; Davydov et al., 2010 ; Geard et al., 2018 ) admit that resilience in encountering short-term stressors differs from the resilience we experience when living with long-term adversity. Strategies that help in the short term may not be helpful in the long term; besides, we can experience several ups and downs.

Using resilience questionnaires and scales, we can determine some general characteristics or manifestations of resilience. Longitudinal studies allow to measure resilience in different periods of time, but quantitative studies are unable to answer the question of why changes in resilience at different stages of life and in specific situations happen. Qualitative research methods (especially, interviews) could help to understand the meanings, beliefs, and values of the participants, which play a critical role in explaining their behaviour and its consequences and understanding the effect of social and cultural contexts on these meanings, behaviours, processes, and results (Maxwell, 2021 ).

Although a mixed methods design is often used to study common and unique aspects of resilience (Ungar & Liebenberg, 2011 ) and initially we considered using the mixed methods research in this study, we came to the conclusion that our research question is related to the deep understanding of participants’ unique experience of resilience and can best be answered by using the qualitative research design. Taking into account the aspects mentioned above, it appears that a qualitative research strategy would be the most appropriate choice to study resilience in people with chronic pain. Furthermore, we have provided arguments for why we have chosen the particular research design.

Multiple case study design

The case study design was selected as the most relevant to investigate the resilience of people with CMP. Creswell defines a case study as an in-depth exploration of a bounded system or multiple bounded systems in their real-life setting (Creswell et al., 2007 ). In our research, each case (each participant’s experience of resilience while living with chronic pain) has its limits in time (the duration of the illness) and its unique context or real-life context (environment, available resources, etc.).

In contrast to experimental designs, which seek to test a specific hypothesis through manipulating the environment, the case study approach lends itself well to capturing information on more explanatory questions “how”, “what”, and “why” (Crowe et al., 2011 ). Case study research is an increasingly popular approach among qualitative researchers, providing methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods (Hyett et al., 2014 ).

There are two key approaches to case study research. Those researchers whose philosophical assumptions are grounded in postpositivism usually use Robert Yin’s approach (Yin, 2003 ), but researchers whose philosophical assumptions are grounded in constructivism mostly use the approach by Robert Stake ( 1995 ) or Sharan B. Merriam ( 2009 ).

Since we grounded our research in the paradigm of social constructivism, the approach to the case study by Stake was chosen. He emphasizes that a case study is not a methodological choice but rather a choice of what is to be studied (Stake, 2008 ). In our research, this is the subjective experience of the resilience of each research participant.

Stake and other representatives of constructivism claim that reality is not available to us in an objective way; it is possible to study only the meaning people attach to what has happened because each of us interprets reality differently (Yazan, 2015 ). In our research, we are not studying resilience as an objective reality that can be measured, but as a subjective perception of this experience over time.

Stake speaks about three types of case studies: intrinsic, instrumental, and multiple case studies (Stake, 1995 ). An intrinsic case study allows one to explore a unique phenomenon. An instrumental case study is used if a researcher wants to gain a broader understanding of some issue through this particular case, but the collective or multiple case study involves multiple cases being studied simultaneously or sequentially to gain an even broader understanding of the issue. In our study, we apply multiple case design.

Multiple case studies are often used in health psychology (Boblin et al., 2013 ; Breet & Bantjes, 2017 ; Fearon et al., 2021 ), because these studies allow a researcher to analyse within each setting and between settings (Baxter & Jack, 2008 ). In our investigation, we were interested in individual stories and the unique resilience experience of each participant, but we also wanted to know whether people with chronic pain have used similar strategies to adapt to the disease and if they have mentioned any common factors that helped them develop resilience. In light of the arguments mentioned above, a multiple case study seemed to be the most relevant design.

In the following paragraphs, we will substantiate the selection of specific methods for data collection and analysis and how multi-method and pluralistic approaches can enhance research rigour.

Multi-method qualitative approach as methodological triangulation

Similarly as in quantitative research, qualitative research has its criteria to ensure the rigour of the research. One such criterion is triangulation. Triangulation means being able to look at the same phenomenon or research topic through more than one source of data (Abdalla et al., 2018 ). It refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999 ). Triangulation is not only a strategy for the validation of the research procedures and results (Flick, 2018 ) but also a strategy that allows adding depth to the data that are collected and gives a more complete picture of the phenomenon that is studied (Fusch et al., 2018 ). Abdalla et al. suggest several functions of triangulation. Information from different angles can be used to confirm, develop, or illuminate the research problem (Abdalla et al., 2018 ).

For more than three decades, qualitative researchers have used multiple forms of triangulation in a study: data triangulation, methodological triangulation, theory or perspective triangulation, and investigator triangulation, following the suggestions of Denzin (Denzin, 1989 ). By data triangulation, Denzin meant different data points (people, time, space) that represent different data of the same event. By methodological triangulation, he meant multiple data collection methods, for example, interviews, focus groups, and observations. The theory triangulation designated viewing data through the lens of different theories, while the investigator triangulation meant that more than one investigator was observing the same data.

In the study described in this article, we combined two data collection methods that provide methodological triangulation. A combination of several qualitative data collection methods to investigate a research question or phenomenon is usually called the “multiple method(s)” approach (McDonnell et al., 2017 ) or “multimethod(s)” (Anguera et al., 2018 ; Mik-Meyer, 2020 ; Roller & Lavrakas, 2015 ) approach. Some authors, like Janice M. Morse (Morse, 2003 , 2009 ) have used both concepts. In our research, we use the term “multimethod approach”, which is also used by American Psychological Association (APA, n.d ).

The combination of different data-gathering methods allows us to overcome each method’s weaknesses and limitations, contributes to a better understanding of a research problem compared to research that is based on only one methodological approach, and provides knowledge that otherwise is inaccessible to the researcher (Creswell, 2015 ).

However, some authors admit that multi-method research also has some challenges. One such challenge is how to synthesize the findings of two separate methods if they are not complementary but conflicting (Nepal, 2010 ). In our study, data gathering methods are complementary, but any contradicting results, if such appear, are analysed assuming that the contradictions may not exist simultaneously but emerge at different time points. In the following paragraphs, we will explain how the combination of several data analysis methods can help to solve these contradictions.

Another challenge is to compare the weight of the data obtained by different methods. For example, does a focus group interview with six participants carry the same weight as an individual interview? (Carter et al., 2014 ). In addition, this challenge in more detail will be described further.

In our qualitative study, we combine individual semi-structured interviews with focus groups conducted with interviewed participants. In the following sections of this paper, we will explain our considerations for combining these methods and justify why we took both methods onboard with the same participants.

Combination of semi-structured individual interviews and focus groups

A semi-structured interview (SSI) is the most common format of data collection in qualitative research. It employs a relatively detailed interview guide and is designed to determine subjective responses from people regarding a particular situation or phenomenon they have experienced (McIntosh & Morse, 2015 ). Although SSI has a pre-planned structure, it differs from a structured interview with more openness. SSI is often accompanied by follow-up “why” or “how” questions (Adams, 2015 ) and gives the interviewer the opportunity to elaborate and explain particular issues through the use of open questions (Alsaawi, 2014 ). It also differs from an unstructured interview, where the interviewer asks only some general questions and is mainly a listener (Brinkmann, 2014 ). SSI is useful when a researcher works with a complex issue because he can use probes and spontaneous questions to explore, deepen understanding, and clarify answers to questions (Wilson, 2014 ).

We selected SSI as the main data collection method for several reasons: 1) from the main qualitative data collection methods (observations, textual or visual analysis, individual and group interviews) only individual or focus group interviews could give enough information to answer the research question, 2) in a one-to-one interview format, the interviewer can create a safe environment and adjust to every participant; 3) we had a set of specific research subquestions ( How do people with chronic pain describe the development of resilience? How do they describe factors that have contributed to or hindered resilience at the beginning of their illness? How do they describe the manifestation of resilience in the long term? How do they describe factors that have contributed to or hindered resilience in the long term? How does resilience change over time? ), so we needed a fairly structured interview protocol that allowed us to answer these questions. But we also did not want to lose in-depth data and unexpected disclosures, which is why we did not select a structured interview.

Although individual interviews have many advantages, they have some disadvantages as well, such as the hierarchical position and the power of the interviewer over the participant. The participant is reduced to the role of a passive provider of data, while the interviewer is the one who uses skilled rapport promotion technology (Nunkoosing, 2005 ). Another disadvantage is a lack of group dynamics, which could bring new themes into discussions (Lambert & Loiselle, 2008 ).

To enhance research rigour, we decided to use one more data collection method and combine individual interviews with focus groups. The focus group approach is a qualitative method for collecting data on the selected topic with a structured and focused discussion in a small group of people (Gundumogula, 2020 ). Focus groups create open lines of communication between individuals and rely on the dynamic interaction between participants to produce data that would be impossible to gather via other approaches, such as one-on-one interviewing (Jarvis & Barberena, 2008 ). A significant role in focus groups is played by a moderator. The involvement of a good moderator can ensure that the conversation is always on track and encourage the participation of participants without one individual dominating the discussion (Sagoe, 2012 ).

For some participants, it could be easier to disclose personal and sensitive information through individual interviews (Kaplowitz, 2000 ; Kruger et al., 2019 ), but for others, the focus group format could be more appropriate. Listening to other participants’ experience stories can encourage self-disclosure and stimulate memory (Guest et al., 2017 ; Kitzinger, 1994 ).

The limitation of focus groups is the possibility of bias and manipulation through leading or dominating participants, as well as tendencies towards normative discourses, conflicts, and arguments within focus groups (Gundumogula, 2020 ; Smithson, 2000 ). Using these methods together, it could be possible to find a balance between looking for a diversity of topics and a deeper investigation of each topic.

Janice Morse argues that in situations where a researcher uses multiple qualitative methods, one of them is usually a core method and the rest methods are supplementary methods. A second qualitative component can identify gaps or holes, “pick up” what the first method missed and allow discussing some parts of the findings that had not been on the researcher’s screen earlier (Morse, 2010 ).

In our study, a semi-structured interview is a core method that was used to collect data from all participants, while focus group discussions were used as a supplementary method to obtain feedback from the part of research participants who were interviewed and to clarify whether our interpretation of the interview data coincides with the views of the participants. Focus group discussions as a complementary method are also valuable because due to the dynamics of the group, participants could recall important information they did not mention during the interviews. Interaction between participants can promote discussions and bring new perspectives to the investigated problem. Participants can influence each other through their presence and their reactions to what other people say (Mack et al., 2005 ).

In the first phase of the study, we developed a protocol for the semi-structured interview consistent with the research questions. Because of our decision to use an inductive approach to data analysis, our questions weren’t grounded in the literature and we didn’t have an intention to test hypothesis through the answers to these questions. Instead, we were open to whatever emerged from the data. To avoid the situation where participants could be influenced to give certain answers or very short answers, we formulated only open-ended interview questions aligning with research questions, thus aiming for richer data.

The interviews were approximately 60 to 90 minutes long and provided us with main data on the lived experiences of the participants. Since we were interested in the dynamics of resilience, the interviewer spent a lot of time listening to stories about different periods in the life of the participants. If the participants wanted to share more information than asked, the interviewer allowed them to speak because additional information would help to understand the context of the story and give a deeper understanding of the different factors that have influenced the resilience of the participants.

Our strategy was to analyse the interview data and find out which themes appeared in the participants’ responses more frequently, speaking about each research question. We were also looking for contradictory ideas and trying to understand what influences specific beliefs and values. For example, why do some participants accept the disease as something they will have to live with all their lives, but others still have the hope to eliminate the disease? More information about the data analysis process will be presented in the following chapters of this paper.

After drawing the first conclusions, we organized two focus groups. In the theoretical literature, there is a suggestion to conduct at least two focus groups to ensure data saturation. (Hennink et al., 2019 ). The more focus groups are organized, the more different themes and perspectives can arise, and the researchers can find ideas that are common in all groups. Since focus groups in our study are only an additional method and the sample is quite small (17 participants), it was agreed that two groups would be enough to get feedback from participants about our interpretations of the research results.

Before moving on to data analysis, we must answer the question of why we stopped collecting data at the point that we did and what our arguments were for determining the sample size.

Criteria for determining sample size

Samples in qualitative research tend to be small to support the depth of case-oriented analysis, that is fundamental to this mode of inquiry, but at the same time large enough to allow the unfolding of a new and richly textured understanding of the phenomenon under study (Sandelowski, 1996 ; Vasileiou et al., 2018 ).

Although qualitative researchers still have discussions about the number of interviews, that would be enough to ensure the research rigour and provide the answers to the research questions, there are several criteria that help to define an optimal sample size. In the thematic analysis, that is used in our research, one of the most significant criteria to determine sample size is saturation. Saturation can be defined as the point at which additional data do not lead to any new emerging themes (Given, 2016 ). Even if some new codes arise, these data change a little or do not change the coding result at all. According to this criterion, the researcher can stop conducting interviews at the moment when saturation is reached (Bryman, 2012 ). But this approach, as emphasized by Bryman ( 2012 ), is a very demanding one, because it forces the researcher to combine sampling, data collection, and data analysis, rather than treating them as separate stages in a linear process. Another suggestion is that a researcher must be sure that the data he/she has and what he/she wants to say coincide, that data support his/her conclusions, and conclusions are not going beyond what data can support (Becker, 2012 ).

Hennik et al. acknowledge that saturation can be understood as code saturation and meaning saturation. Code saturation can be defined as the point where no additional issues are identified and the codebook begins to stabilize but meaning saturation can be defined as the point where we fully understand issues and when no further dimensions, nuances, or insights of issues can be found (Hennink et al., 2017 ). It is easier to reach code saturation than meaning saturation because people can put different meanings in the same codes, and some codes, especially abstract ones, can have multiple dimensions. Focusing on codes alone is a deficient measure of saturation; codes can be saturated, but vital information remains unconsidered (Sebele-Mpofu, 2020 ). It is important not only to look at the frequency of the data but also to interpret the data and to see what is in it (McIntosh & Morse, 2015 ).

Saturation is influenced by multiple parameters or criteria that determine how large a sample must be. One such criterion is accessibility. The more specific and harder to access the population, the smaller could be the minimal number of participants (Adler & Adler, 2012 ; Brannen, 2012 ). Another criterion is the homogeneity or heterogeneity of the population. In a homogeneous population, the sample size could be smaller; in a heterogeneous population with more different subgroups, the sample must be larger (Adler & Adler, 2012 ; Brannen, 2012 ; Hennink et al., 2017 ). The theoretical background can also influence the sample size (Bryman, 2012 ; Hennink et al., 2017 ). For example, life story research is likely to involve a smaller sample size than research aiming to develop some theory. The sample size will most likely be smaller if the data is thick or richer and larger than if the data are thin (Hennink et al., 2017 ). And, of course, available resources can also play an important role in a sample size (Flick, 2018 ).

Maltreud and collegues (Malterud et al., 2016 ) have proposed the concept of “information power” to guide adequate sample size for qualitative studies. Information power depends on the aim of the study, sample specificity, use of established theory, quality of dialogue, and analysis strategy. The more information the sample holds, relevant to the actual study, the lower amount of participants is needed.

By evaluating the criteria mentioned above, we realized that our sample must be rather small, than big, because of quite a narrow and specific aim of our study. The aim of this study is to capture themes, not to develop theories. Although the population under study has subgroups, it is still quite homogeneous. The interviews would produce thick data. The only argument that indicated the need for a larger sample was the multidimensional concept of resilience, which could determine the longer time to move from code saturation to meaning saturation.

In our study, we interviewed 17 people with CMP. To answer our main research question “What is the experience of developing resilience in people with CMP?” we purposely looked for working-age participants with different types and different intensities of musculoskeletal pain, such as back pain, joint pain, pain after spinal cord injury, etc., who are 18–65 years old and have been living with pain for five years or more. We approached participants through patient associations, Facebook groups, and personal contacts. There were seven men and ten women among the participants aged 29 to 64 years. Four participants had chronic pain after spinal cord injury and used a wheelchair. Six participants had rheumatoid arthritis or other rheumatoid disease and seven participants had other diagnoses that caused neck or back pain, like spondylosis, osteoporosis, and disk herniation. Three participants didn’t do paid work. Two of them were women at pre-retirement age who looked after their grandchildren and one was a man with a spinal cord injury. The other participants worked despite the limitations caused by pain.

The decision to stop data collection after 17 interviews was based on several considerations. First of all, we reached a code and meaning saturation. In our study, thematic analysis was the instrument to examine saturation. During the first stage of the inductive thematic analysis, we developed a codebook and applied it to the rest of the interviews. Having analysed 13 interviews, we found central codes that are repeated in each interview and that less than 5% of the new codes appear. After we found central codes and reached code saturation, we went through all interviews and analysed what participants mean by each code. Fully understanding all dimensions of conceptual codes requires much more data than fully understanding concrete codes (Hennink et al., 2017 ). In our study, the category that was described bythe largest diversity of meanings was “adapting to the disease”. For some participants, it meant the ability to handle everything by themselves, but for others—the ability to use available social resources. We continued to conduct interviews and after analysing 17 interviews, we reached meaning saturation because no new code dimensions appeared.

By studying theoretical literature and analysing the criteria mentioned above, we found that sample size, starting from 12 interviews, can be sufficient for data saturation in a thematic analysis (Ando, Cousins, & Young, 2014 ) and 16 interviews can lead to meaning saturation (Hennink et al., 2017 ). It matched our conclusion that 17 interviews would give enough information to answer the research question.

After analysing 17 interviews, we obtained sufficient information power, that allowed us to provide a thick description of each case as well as find commonalities and differences between cases.

In the next paragraphs, we will provide more detailed information on the process of data analysis and justify the necessity for a pluralistic approach.

The pluralistic approach to qualitative data analysis

Previously, we described our assumptions for choosing a qualitative research strategy and considerations for using two data collection methods. In this paragraph, we’ll continue to describe the data analysis process and will demonstrate why the development of resilience as a dynamic process should not be understood as applying only a single method of data analysis.

To describe different aspects of qualitative data, we use the pluralistic data analysis approach. In research, methodology pluralism has been approached using a range of conceptual labels (Frost & Nolas, 2011 ). In a broader sense, pluralism means combining a range of different data modes in a single research project, for example, quantitative and qualitative methods, but in a more narrow sense, it refers to the combination of several qualitative data analysis methods.

Pluralism in qualitative research is defined as the application of more than one qualitative analytical method to a single data set (Clarke et al., 2014 ) or, as specified by Frost, as the interpretation of one interview transcript with different qualitative analysis techniques (Frost et al., 2010 ). The aim of pluralist analyses is to produce rich, multilayered, multiperspective readings of any qualitative data set through the application of diverse ways of seeing and maximizing holistic understanding (Dewe & Coyle, 2014 ).

According to the literature, multiple analytical approaches are appropriate for understanding a plural and complex world, and the variety of human expression cannot always be adequately represented by one framework alone (Chamberlain et al., 2011 ; Frost et al., 2010 ; Kincheloe, 2001 , 2001 ). The data set can tell us several different things, depending on the questions we ask. Analysing the same data from different analytical lenses can reveal more meanings than analysing these data just from one analytical lens (Frost et al., 2010 ; Willig, 2013 ). The pluralistic approach not only enhances a deeper understanding of the phenomenon but, if each analysis method is performed by different researchers, it also reduces subjectivity and increases transparency in a study (Frost et al., 2010 ).

The pluralistic approach is widely used in social sciences; in recent years, it has also gained popularity in health psychology research (Dempsey et al., 2019 ; Dewe & Coyle, 2014 ; Madill et al., 2018 ; Rosas et al., 2019 ). The pluralistic approach has several advantages but combining different data analysis methods can also be challenging.

Researchers must find ways to demonstrate coherent links between theory, method, and findings and explain how findings produced from multiple analyses can remain commensurate or complementary (Braun & Clarke, 2019 ; Clarke et al., 2014 ). There must be a clear rationale for the theories and methods being used so that the researchers demonstrate reflexivity and document their research process in an accessible manner (Frost & Nolas, 2011 ). The use of methods without justification can lead to disjointed and fragmented findings (Chamberlain et al., 2011 ). Another challenge is the willingness of researchers to use a pluralistic approach. Pluralism requires researchers to be competent in all methods they apply (Clarke et al., 2014 ), which could be challenging, especially for new researchers.

In our study, we investigate both the content and dynamics of the experience of resilience in people living with chronic pain. Therefore, we are interested not only in resilience development strategies and factors that positively or negatively influence resilience but also in changes over time—how these strategies and factors change if we compare short-term and long-term resilience.

Upon starting this research, our main focus was on strategies that help to improve resilience. We considered that thematic analysis could be the best data analysis method for finding the most common strategies. After conducting the first pilot interviews, we were surprised by the richness of the available data. The participants shared different stories of their lives and acknowledged that the way they perceive pain has changed over time. We realized that we must broaden our research question and focus not only on common themes but also on the life of each participant in its unique context and dynamic. Therefore, we decided to apply both thematic and narrative analysis to analyse our data. Then, after conducting the third pilot interview, we noticed an interesting nuance—all participants were speaking about specific turning points in their lives, which dramatically changed their attitudes and resilience. From this, we understood that we need one more method that could be appropriate for analysing those changes. Studying the literature, we found that the critical incident technique (CIT) could be valuable to define critical incidents or experiences that contributed, positively or negatively, to resilience.

The pluralistic approach was not our strategy at the beginning of the investigation, but we came to this decision during the analysis of the pilot interviews. It confirms once again that conducting pilot interviews is an especially important step that allows for identifying “holes” and flaws in research questions and methods. The combination of thematic analysis, narrative analysis, and critical incident technique could provide answers to all research questions that we are interested in. More detailed considerations of the use of each method will be illustrated in the next paragraphs.

Combining methods: thematic analysis, narrative analysis, and critical incident technique

At the beginning of the research, our focus was mainly on strategies that help improve resilience. We decided that a thematic analysis would be an appropriate method to find common themes and to find out which strategies to improve resilience would be the most helpful. The interview protocol was created, and three pilot interviews were conducted and analysed with reflexive thematic analysis approach created by Braun and Clarke (Braun & Clarke, 2006 ). Pilot studies allow researchers to practice and assess the effectiveness of their planned data collection and analysis techniques (Doody & Doody, 2015 ). The piloting of interviews was set up to find out whether the interview questions are understandable and provide answers to the research question.

After conducting and analysing three pilot interviews, we realized that qualitative data provide more comprehensive material than we initially expected. We observed that interviewees not only answered the questions but spoke about their life as a whole, bringing up significant experiences from their past, like other traumatic experiences (such as divorce or losing their job), important people in their lives that influenced their values and attitudes, the brightest childhood memories, etc.

We concluded that we must revise the interview protocol and, for further interviews, include more questions about the dynamics of experience in different stages of the disease. This was the first time we noticed that short-term and long-term strategies differ, so the questions should be modified from more general to more specific. Creswell et al. ( 2007 ) has emphasized that qualitative research questions could change during the entire research process. Initial provisional questions can become more focused because researchers gain a deeper or broader understanding. That is why the qualitative study could not be fully planned in advance.

Since we added new research questions, we also needed new methods for data analysis. We realized that it is impossible to answer all research questions by using only thematic analysis. The thematic analysis allows one to find common themes between cases (Braun & Clarke, 2006 ; Joffe, 2012 ) but the narrative analysis could be more appropriate for analysing differences in cases and describing the dynamics of individual narratives in their unique context (Floersch et al., 2010 ; Simons et al., 2008 ).

Pilot interviews gave us rich qualitative data, including information about events that dramatically changed participants’ attitudes and resilience. So, we concluded that in addition to thematic and narrative analysis, CIT could be valuable for defining critical incidents or experiences that made a contribution, either positively or negatively, to resilience. Finally, we decided to combine reflexive thematic analysis (Braun & Clarke, 2006 ), narrative analysis (Crossley, 2000 ), and the enhanced critical incident technique (ECIT) (Butterfield et al., 2009 ). In what follows, the use of each method is explained in detail.

Reflexive thematic analysis

Thematic analysis (TA) can be seen as an umbrella term, used for sometimes quite different approaches, rather than a single qualitative analytic approach. The three main approaches in TA are the coding reliability approach, the codebook approach, and the reflexive approach (Braun & Clarke, 2019 ). TA has been widely used in recent qualitative health research designs (e.g., Lyng et al., 2022 ; Opsomer et al., 2019 ; Zarotti et al., 2019 ), because it is not strictly connected with a particular methodology and is quite flexible.

Since our research is based on the paradigm of social constructivism, we decided to use a reflexive thematic analysis. An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case (Hyett et al., 2014 ). Of all TA approaches, reflexive TA fits best with the paradigm of social constructivism because it emphasizes the active role of the researcher in coding and theme generation. The researcher not only identifies semantic themes and summarizes the content of the data, but also looks for latent themes, revealing the underlying ideas within the data (Braun & Clarke, 2019 ). The subjectivity of a researcher is the primary “tool” for reflexive TA. Subjectivity is not a problem to be managed or controlled, it is a resource for research (Braun & Clarke, 2019 ; Gough & Madill, 2012 as cited by).

The described investigation focuses not on objective reality but on the way participants perceive and, together with the researcher, interpret their subjective experiences. It should also be acknowledged that the previous experiences, biases, and research position of researchers impact the way they look at the data. Subjectivity without reflexivity could be a limitation, but if researchers are aware of their role and impact, subjectivity could become a resource. In this study, the researcher, who conducted the interviews, is an insider to the study population. The researcher’s personal experience of living with chronic pain helped stimulate a dialogue with interviewees and increase mutual trust.

In recent years reflexive TA has been used more often in health psychology (Bose & L Elfström, 2022 ; D’Souza et al., 2022 ; McKenna-Plumley et al., 2021 ), since it is a theoretically flexible method and could be adapted to different research designs.

By using a classic six-step process (Braun & Clarke, 2006 ): 1) familiarizing oneself with the data, 2) generating codes, 3) constructing themes, 4) reviewing potential themes, 5) defining and naming themes, and 6) producing the report, we gradually moved through the data several times until we constructed final themes. The thematic analysis allowed us to answer “what” questions about the content of resilience. What strategies do people with chronic pain use to promote resilience? What are the main obstacles and contributing factors?

Narrative analysis

After identifying central themes with TA, we assumed the narrative analysis of each case. Just like thematic analysis, narrative analysis is an umbrella term, not a single method. The narrative method allows us to look at the story from a holistic perspective without the need of breaking it down into themes (Riessman, 2008 ). Narrative not only brings order and meaning to our daily life but, reflexively, it also provides structure to our very sense of self-hood (Murray, 2015 ). The narrative analysis helped us answer questions that start with “how”, for example, how people see the impact of disease on their lives and how they describe changes in their habits, attitudes, and life as a whole while living with chronic pain.

We based our analysis on the Michelle Crossley’s ( 2000 ) framework that includes six steps:

1) reading and familiarizing, 2) identifying important concepts to look for, 3) identifying “narrative tone”, 4) identifying the “imagery” and themes, 5) weaving it all together, and 6) writing a research report.

Since we study resilience in the context of chronic pain, the Crossley’s framework seemed to be the most appropriate one, as the author has developed this framework to analyse stories of illness and trauma. In health psychology, the Crossley’s framework is frequently used (Manning, 2015 ; Winslow et al., 2005 ; Wong & Breheny, 2021 ). Crossley has admitted that when people talk or write about their experiences of chronic or serious illness, they often characterize themselves as becoming totally different people (Crossley, 2000 ). Resilience often means not just bouncing back or returning to a status quo but bouncing forward or becoming even stronger than before illness (Hynes et al., 2020 ). This change could also be perceived as becoming a totally different person. In our research, we were interested in this process of change. The narrative analysis allowed us to answer “how” questions about the resilience process. How does disease change our attitudes towards ourselves and others? How does time influence these changes?

We applied narrative analysis for each research question in each interview and analysed responses for different stages of the disease. For example, asking about strategies people used to overcome or accept pain, we looked at what the strategies were and how they changed in the first months after diagnosis, in the first years after diagnosis, and in the long term, five or more years after diagnosis. This timeline provided an opportunity to study the dynamics of resilience. The creation of an approximate timeline helped to understand why particular themes appear in the specific moment after diagnosis and how they are related to other life events.

The critical incident technique

Finally, we applied CIT to qualitative data to describe the ups and downs that significantly changed people’s lives. The founder of CIT is John Flanagan ( 1954 ), who developed this method for the Aviation psychology program of the US army. The purpose of the CIT was to gather information on behaviours that contribute to the success or, in contrast, lead to failure.

Flanagan’s technique was rooted in the positivist paradigm and was more suitable for studying job performance in the field of organizational psychology. After more than 50 years Lee D. Butterfield and colleagues (Butterfield et al., 2009 ) modified this method so that it could meet the needs of researchers from multiple perspectives and could be used in different fields, and named this method ECIT.

In our research, we apply the ECIT which is methodologically more flexible than Flanagan’s technique and could be adjusted to the paradigm of social constructivism. ECIT allows us to study critical incidents from the perspective of the participants and explore their perception of the main turning points, without the expectation that we are studying the objective reality. Compared to other methods, ECIT is a relatively rarely used method in qualitative research, but several recent studies prove that this method could be a good research tool in psychology (Klarare et al., 2018 ; Kwee et al., 2020 ; Nitkin & Buchanan, 2020 ; Springer & Bedi, 2021 ).

ECIT involves five main steps: 1) determining the general goals of the activity being studied, 2) making plans and setting specifications, 3) collecting the data, 4) analysing the data, and 5) interpreting the data and reporting the results. Although the main steps are defined very generally, Butterfield describes in detail how to perform each step. For example, he illustrates how to identify critical incidents (something that helped or hindered a particular experience or activity) and wish list items (those people, support, information, programs, etc., that were not present at the time of the participant’s experience, but those involved believe would have been helpful) (Butterfield et al., 2009 ).

To ensure credibility and rigour, Butterfield also developed nine credibility checks for ECIT—audiotaping interviews, interview fidelity, independent extraction of critical incidents, exhaustiveness, participation rates, placing incidents into categories by an independent judge, cross-checking by participants, expert opinions, and theoretical agreement (Butterfield et al., 2009 ).

When analysing critical incidents, we also looked at the approximate timeline to find out whether critical incidents were related to the time since diagnosis.

To conclude, we can say that all three methods allowed us to answer different research questions, complement each other, and help achieve the research objectives (see Table I ). In the next chapter, we will describe how we integrated all three data analysis methods and how the within-case and across—case approach helped to achieve a balance between generalization and an in-depth understanding of the particular case.

Research questions and data analysis methods.

Within-case and across-case approach in the data analysis process

Case study research has sometimes been criticized for lacking scientific rigour and providing little basis for generalization (Crowe et al., 2011 ; Hammersley et al., 2000 ; Kyburz-Graber, 2004 ). Although Stake ( 1995 ) argues that the purpose of case study research is particularization, not a generalization, the goal of researchers who are doing multiple case research is not only an in-depth understanding of particular cases but willingness to provide findings that could be applied to other similar contexts.

Considering that generalizability due to a small sample size could be a problem, qualitative researchers instead speak about qualitative generalization or transferability as one of the trustworthiness criteria (Anney, 2014 ; Levitt, 2021 ; Maxwell, 2021 ). Qualitative generalization or transferability means that findings are described in a thick way or in such detail that readers can see both constancy and variation within a phenomenon and transfer data from the study to their own context (Levitt, 2021 ). The researcher must provide enough information on the meanings, contexts, and processes operating in the study setting or population that the reader can adequately judge (Maxwell, 2021 ).

To ensure that findings are reported widely and transparently enough, in the beginning, the researcher should create a system of how he/she will integrate all data analysis methods and notice common elements in a rich material of data, gathered from individual cases.

In our research, we applied within-case and across-case analysis, described by Lyoness Ayres et al. (Ayres et al., 2003 ) as an approach that helps to achieve qualitative generalization and find a balance between uniqueness and differences from one side and commonalities from the other. Across-case analysis means looking for common themes in all accounts, within-case analysis means in-depth exploration of a single account, considering contextual richness. In multiple case studies, integration of across-case, and within-case analysis is often used (Banerjee & Dixit, 2016 ; Chung, 2019 ; Fearon et al., 2021 ; Glette & Wiig, 2022 ; Starks et al., 2010 ), because it allows producing contextually grounded, generalizable findings (Ayres et al., 2003 ).

Within-case methods are less useful in the development of generalizations about the experience of health and disease drawn from multiple cases, but they provide contextual richness. Neither across-case nor within-case approaches alone enable the researcher to interpret an experience both through its parts and as a whole so that readers can recognize individual experiences in a generalizable way (Ayres et al., 2003 ).

For example, if we look only at cases and analyse common themes, we could find several controversial themes, such as denial of the disease and acceptance of the disease. But if we look at the cases and each person’s story as a whole, we can see that in the first months after diagnosis the person can deny the disease and avoid talking about health problems, but after a while, the disease could become part of his daily life.

The within-case and across-case approach also allows for the investigation of situations where most of the cases have similarities, but some cases differ from others. Looking across and within cases, we can identify possible factors that could influence these differences (past experience, social factors, thinking patterns, religiosity, etc.). For example, if we analyse the acceptance process, we can see that most patients have accepted their condition, but in some cases, the participants do not accept the fact that they will have to live with this diagnosis for the rest of their lives. By examining these diverse cases in more detail, we can see that these people believe in God’s healing.

By combining the within-case and across-case approach, we could find a balance between generalization and an in-depth understanding of the experience of resilience while living with chronic pain.

Conclusions

The purpose of this paper was to describe the decision-making chain of a qualitative research process and, specifically, to discuss how the integration of several methods of data collection and analysis can improve a deeper understanding of the formation and maintenance of resilience among people with CMP.

Although qualitative researchers have many methodological freedoms, sometimes this freedom can become a pitfall. If a researcher lacks tacit knowledge of different approaches and their theoretical basis, he/she may choose methods that are inconsistent with each other or inappropriate for answering the research questions. In this paper, we provide an example of how to avoid these pitfalls. We briefly describe each step we were doing and provide transparency for the readers so that they can follow the analysis process.

At the beginning, we formulated the research question: What is the experience of developing and maintaining resilience in people with chronic musculoskeletal pain (CMP)?

Considering that resilience can be understood differently in different contexts and that we can explore only subjective interpretations of resilience, but not resilience as such, we decided to ground our research on the paradigm of social constructivism. A fundamental assumption of the social constructivism paradigm is that meanings, knowledge, and truth are created by the interactions of individuals within a society.

When we had chosen the paradigm or perspective of how we will look at the experience of resilience, we decided to use a qualitative research strategy that is more appropriate for studying subjective constructs, such as experiences, feelings, and attitudes at different stages of life and in specific situations. This article approves that the qualitative research strategy can provide a significant contribution to health psychology. It allows analysing of complex constructs and helps not only to identify the problem but also to reveal the causality and influence of various factors on the situation.

The next step was to choose a research design. Since we were interested not only in the unique resilience experience of each participant but also wanted to know if people with chronic pain have used similar strategies to adapt to the disease, we concluded that multiple case study designs will allow us to analyse within each setting and between settings.

In this paper, we have provided arguments on how a multimethod approach can promote research rigour. We combined two data collection methods, semi-structured interviews and focus groups. Semi-structured interviews gave us rich material of data and allowed us to answer concrete subquestions but focus group discussions were a supplementary method for getting feedback from participants and clarifying our interpretations.

We also described the process of determining the sample size. The decision to stop data collection after 17 interviews were based on several considerations. We got enough information to answer the research question and reached code and meaning saturation.

The data analysis process is the most time-consuming part of qualitative research, especially if researchers have chosen a pluralistic data analysis approach and interpreted an interview transcript with different qualitative analysis techniques. In this paper, we argue why it is worth doing it. Analysing the same data from different analytical lenses can enhance a deeper understanding of the construct, reveal more meanings, and give a holistic understanding compared to analysing these data from only one analytical lens.

It is very important to conduct pilot interviews to see if the chosen data analysis method can provide answers to the research questions. At the beginning of our research, we considered that in our study thematic analysis could be the best data analysis method to find the most common strategies. However, after conducting the first pilot interviews, we were surprised by how rich the data was. Participants shared the dynamics of their experience while living with chronic pain, as well as information about events that dramatically changed their attitudes and resilience. We came to the conclusion that we must revise the interview protocol and include more questions and additional data analysis methods.

Finally, we decided to combine three methods, thematic analysis, narrative analysis, and CIT. The thematic analysis allowed us to find common themes between cases, narrative analysis was more appropriate for analysing differences in cases and describing the dynamics of individual narratives in their unique context, while the critical incident technique was valuable for defining critical incidents or experiences that made a contribution, either positively or negatively, to resilience.

To find a balance between uniqueness and differences, on the one hand, and commonalities, on the other hand, we applied within-case and across-case approach in the data analysis process. This allowed us to explain controversial topics and identify possible factors that could influence differences between cases, as well as give contextual richness.

The decision-making chain described in this article can serve as an example for qualitative researchers interested in health research, especially those who study lived experiences of resilience or other constructs in its dynamics and unique context, like dynamics of health behaviour, changes in professional health, self-regulation in the context of chronic diseases etc.

It’s important to justify and make transparent every decision during the process of qualitative research not only because it increases the quality of the research in the eyes of other researchers, but also because it helps to convince policymakers and stakeholders that qualitative research just like quantitative research could be well-grounded and can give a significant contribution to society. To engage in dialogue with decision-makers and wider society, findings should be presented in an easily understandable way by putting an emphasis on practical solutions this research can promote. The strength of this paper is the strong connection between theory and practice. Examples of specific studies can be helpful to better understand the theoretical assumptions and recommendations. The limitation of this study is the small sample size and heterogeneity of participants who have different kinds of musculoskeletal pain, such as back pain, joint pain, or spastic pain. For further studies, it would be valuable to analyse the results in different subgroups of participants to see whether strategies to improve resilience differ depending on the severity of the disease and the type of pain.

Ethical approval

This study was approved by the Riga Stradiņš University Research Ethics Committee.

Biographies

Elīna Zelčāne , MPhil., is a PhD student and a lecturer of communication psychology at the Faculty of Public Health and Social Welfare at the Rīga Stradiņš University, Latvia. Earned her MPhil. in philosophy in 2006 at the University of Latvia (Riga, Latvia) and now is studying psychology at the Rīga Stradiņš University, Latvia. Current research interests: health psychology, qualitative research, resilience interventions. https://orcid.org/0000-0002-2186-2115

Anita Pipere , Dr. psych., is an acting professor of psychology at the Faculty of Public Health and Social Welfare at the Rīga Stradiņš University, Latvia, and a professor and senior researcher at the Institute of Humanities and Social Sciences at Daugavpils University, Latvia. Earned her Ph.D. in psychology in 1993 at the University of Latvia (Riga, Latvia). Work experience: from 1993 until now occupies positions starting from lecturer to professor and senior researcher at Daugavpils University, from 2019 until now works as an acting professor at the Riga Stradiņš University. Experience in academic work as a university teacher, researcher, editor, and reviewer of journals and books, leader and participant in projects in psychology and education. Current research interests: philosophy of science, health psychology, qualitative research. Member of the International Society for Dialogical Science. https://orcid.org/0000-0003-2238-7026

Funding Statement

This work was not supported by external funding.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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Data as narrative: contesting the right to the word

Nick Couldry

Nick Couldry investigates the fundamental processes that underly data activism.

"If like everything else, social movements are being datafied, this operates on at least four levels: a change in the general conditions under which all social movements operate; data becoming either the specific or general object of activism; and finally, data becoming crucial to practices of movement resistance. Underlying this is a further pattern, that data as a narrative are increasingly an important aspect of contestation in contemporary politics."

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Violent crime is a key midterm voting issue, but what does the data say?

Political candidates around the United States have released thousands of ads focusing on violent crime this year, and most registered voters see the issue as very important in the Nov. 8 midterm elections. But official statistics from the federal government paint a complicated picture when it comes to recent changes in the U.S. violent crime rate.

With Election Day approaching, here’s a closer look at voter attitudes about violent crime, as well as an analysis of the nation’s violent crime rate itself. All findings are drawn from Center surveys and the federal government’s two primary measures of crime : a large annual survey from the Bureau of Justice Statistics (BJS) and an annual study of local police data from the Federal Bureau of Investigation (FBI).

This Pew Research Center analysis examines the importance of violent crime as a voting issue in this year’s congressional elections and provides the latest available government data on the nation’s violent crime rate in recent years.

The public opinion data in this analysis is based on a Center survey of 5,098 U.S. adults, including 3,993 registered voters, conducted Oct. 10-16, 2022. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology . Here are the questions used in the survey , along with responses, and its methodology .

The government crime statistics cited here come from the National Crime Victimization Survey , published by the Bureau of Justice Statistics, and the National Incident-Based Reporting System , published by the Federal Bureau of Investigation. For both studies, 2021 is the most recent year with available data.

Around six-in-ten registered voters (61%) say violent crime is very important when making their decision about who to vote for in this year’s congressional elections. Violent crime ranks alongside energy policy and health care in perceived importance as a midterm issue, but far below the economy , according to the Center’s October survey.

Republican voters are much more likely than Democratic voters to see violent crime as a key voting issue this year. Roughly three-quarters of Republican and GOP-leaning registered voters (73%) say violent crime is very important to their vote, compared with around half of Democratic or Democratic-leaning registered voters (49%).

Conservative Republican voters are especially focused on the issue: About eight-in-ten (77%) see violent crime as very important to their vote, compared with 63% of moderate or liberal Republican voters, 65% of moderate or conservative Democratic voters and only about a third of liberal Democratic voters (34%).

Older voters are far more likely than younger ones to see violent crime as a key election issue. Three-quarters of registered voters ages 65 and older say violent crime is a very important voting issue for them this year, compared with fewer than half of voters under 30 (44%).

There are other demographic differences, too. When it comes to education, for example, voters without a college degree are substantially more likely than voters who have graduated from college to say violent crime is very important to their midterm vote.

Black voters are particularly likely to say violent crime is a very important midterm issue. Black Americans have consistently been more likely than other racial and ethnic groups to express concern about violent crime, and that remains the case this year.

Some 81% of Black registered voters say violent crime is very important to their midterm vote, compared with 65% of Hispanic and 56% of White voters. (There were not enough Asian American voters in the Center’s survey to analyze independently.)

Differences by race are especially pronounced among Democratic registered voters. While 82% of Black Democratic voters say violent crime is very important to their vote this year, only a third of White Democratic voters say the same.

Annual government surveys from the Bureau of Justice Statistics show no recent increase in the U.S. violent crime rate. In 2021, the most recent year with available data , there were 16.5 violent crimes for every 1,000 Americans ages 12 and older. That was statistically unchanged from the year before, below pre-pandemic levels and far below the rates recorded in the 1990s, according to the National Crime Victimization Survey .

For each of the four violent crime types tracked in the survey – simple assault, aggravated assault, robbery and rape/sexual assault – there was no statistically significant increase either in 2020 or 2021.

The National Crime Victimization Survey is fielded each year among approximately 240,000 Americans ages 12 and older and asks them to describe any recent experiences they have had with crime. The survey counts threatened, attempted and completed crimes, whether or not they were reported to police. Notably, it does not track the most serious form of violent crime, murder, because it is based on interviews with surviving crime victims.

The FBI also estimates that there was no increase in the violent crime rate in 2021. The other major government study of crime in the U.S., the National Incident-Based Reporting System from the Federal Bureau of Investigation, uses a different methodology from the BJS survey and only tracks crimes that are reported to police.

The most recent version of the FBI study shows no rise in the national violent crime rate between 2020 and 2021. That said, there is considerable uncertainty around the FBI’s figures for 2021 because of a transition to a new data collection system . The FBI reported an increase in the violent crime rate between 2019 and 2020, when the previous data collection system was still in place.

The FBI estimates the violent crime rate by tracking four offenses that only partly overlap with those tracked by the National Crime Victimization Survey: murder and non-negligent manslaughter, rape, aggravated assault and robbery. It relies on data voluntarily submitted by thousands of local police departments, but many law enforcement agencies do not participate.

In the latest FBI study, around four-in-ten police departments – including large ones such as the New York Police Department – did not submit data, so the FBI estimated data for those areas. The high nonparticipation rate is at least partly due to the new reporting system, which asks local police departments to submit far more information about each crime than in the past. The new reporting system also makes it difficult to compare recent data with data from past years.

While the total U.S. violent crime rate does not appear to have increased recently, the most serious form of violent crime – murder – has risen significantly during the pandemic. Both the FBI and the Centers for Disease Control and Prevention (CDC) reported a roughly 30% increase in the U.S. murder rate between 2019 and 2020, marking one of the largest year-over-year increases ever recorded. The FBI’s latest data , as well as provisional data from the CDC , suggest that murders continued to rise in 2021.

Despite the increase in the nation’s murder rate in 2020, the rate remained well below past highs, and murder remains the least common type of violent crime overall.

There are many reasons why voters might be concerned about violent crime, even if official statistics do not show an increase in the nation’s total violent crime rate. One important consideration is that official statistics for 2022 are not yet available. Voters might be reacting to an increase in violent crime that has yet to surface in annual government reports. Some estimates from nongovernmental organizations do point to an increase in certain kinds of violent crime in 2022: For example, the Major Cities Chiefs Association, an organization of police executives representing large cities, estimates that robberies and aggravated assaults increased in the first six months of this year compared with the same period the year before.

Voters also might be thinking of specific kinds of violent crime – such as murder, which has risen substantially – rather than the total violent crime rate, which is an aggregate measure that includes several different crime types, such as assault and robbery.

Some voters could be reacting to conditions in their own communities rather than at the national level. Violent crime is a heavily localized phenomenon , and the national violent crime rate may not reflect conditions in Americans’ own neighborhoods.

Media coverage could affect voters’ perceptions about violent crime , too, as could public statements from political candidates and elected officials. Republican candidates, in particular, have emphasized crime on the campaign trail this year.

More broadly, the public often tends to believe that crime is up, even when the data shows it is down. In 22 of 26 Gallup surveys conducted since 1993, at least six-in-ten U.S. adults said there was more crime nationally than there was the year before, despite the general downward trend in the national violent crime rate during most of that period.

• Criminal Justice
• Election 2022

John Gramlich is an associate director at Pew Research Center

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#blacklivesmatter turns 10, support for the black lives matter movement has dropped considerably from its peak in 2020, fewer than 1% of federal criminal defendants were acquitted in 2022, before release of video showing tyre nichols’ beating, public views of police conduct had improved modestly, most popular.

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