types of narrative analysis in qualitative research

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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Narrative Analysis In Qualitative Research

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What Is Narrative Analysis?

Narrative analysis is a qualitative research method used to understand how individuals create stories from their personal experiences.

There is an emphasis on understanding the context in which a narrative is constructed, recognizing the influence of historical, cultural, and social factors on storytelling.

It differs from other qualitative methods like interpretive phenomenological analysis (IPA) and discourse analysis by specifically examining how individuals use stories to make sense of their experiences and the world around them.

Narrative analysis is not applicable to all research topics; it is best suited when the focus of the analysis is narratives or stories.

Examples of topics that are well-suited to narrative analysis include: various aspects of identity, individual experiences of psychological processes, interpersonal and intimate relationships, and experiences of body, beauty and health

Assumptions of Narrative Analysis

• Stories are interpretations of the world and experiences: Narrative analysis assumes that stories are not accurate representations of reality. People use stories to explain or normalize what has occurred in their lives and make sense of why things are the way they are. People make sense of their lives through the stories they tell.
• Language is an object for close investigation: A structural analysis of a narrative focuses on the way a story is told, treating language as an object for investigation in itself, not just as it refers to content. This kind of analysis attends to the linguistic phenomena of a story and its overall composition.
• Meaning is created through narrative: Narrative inquiry is the study of how stories unfold over time and is useful for understanding how people perceive reality, make sense of their worlds, and perform social actions. Researchers and participants are co-authors of stories because they collaborate to create meaning. Narrative analysts show how the tools (e.g. its structure and style) used to build a story create the meaning of the experience being shared
• Stories do not speak for themselves: Narratives do not speak for themselves, and they require interpretation when used as data in social research. Researchers must interpret a story by deciding what constitutes a story, collecting stories, identifying stories within data, and identifying narrative themes and relationships.

Key Concepts in Narrative Analysis

Narrative analysis is concerned with more than just  what  is said (the content). It also considers  how  the story is constructed (the structure) and the context or situation in which the story is told (the performance)

• Defining “Story” and “Narrative” : A story is a structured account of events, while a narrative is a story that has been shaped and given meaning by a storyteller. The process of transforming events into a narrative involves selecting, organizing, and interpreting those events in a way that conveys a particular message or understanding.
• Content:  While narrative analysis values how a story is told, the content ( what is said ) remains significant. The themes, events, and characters in a story provide insights into the storyteller’s experiences, beliefs, and values. Therefore, narrative analysis sees content as inseparable from structure and performance. All three work together to create the meaning of a story.
• Narrative Structure: Narrative analysis examines how elements like plot, setting, and characterization are used to construct a story. For example, a researcher might study how the sequence of events, the choice of words, or the use of metaphors shapes the meaning of a story.
• Narrative as Performance: Narratives are not simply neutral accounts of events but are performed and co-constructed through interactions between the storyteller and the audience. This means that understanding a narrative involves paying attention to how it is told, who is telling it, and to whom it is being told. For instance, a researcher might study how a story changes depending on who is telling it, or how the same story is received by different audiences.

Approaches to Narrative Analysis

There are different models and approaches to narrative analysis, and the type that is used depends on the research problem.

• Thematic Analysis : Thematic analysis assumes language is a direct and unambiguous route to meaning. In this approach, researchers collect many stories and then inductively create conceptual groupings from the data. One of the assumptions of thematic analysis is that everyone in the group means the same thing by what they say, even when grouped into a similar thematic category.
• Structural Analysis: This approach views language as a resource and an object for investigation, moving beyond the referential content. Structural analysis assumes the way a story is told is as important as the content of the story. Following Labov’s Narrative Model, the researcher may focus on identifying and examining the key elements of narrative structure, such as the abstract, orientation, complicating action, evaluation, resolution, and coda.
• Interactional Analysis: Interactional analysis looks at how narratives are created and understood within the context of social interactions. This approach acknowledges that narratives are not created in isolation but are shaped by the listener’s responses, the social context of the storytelling, and the relationship between the storyteller and the listener. E.g. Mishler’s Model.
• Performance Analysis : Examining the performative elements of storytelling such as the use of language, nonverbal communication, and audience engagement provides further insights into how stories are constructed and the effects they create. Researchers are interested in how the narrator positions themselves in relation to the audience.

Pratical Steps: Conducting Narrative Analysis

The steps involved in conducting narrative analysis are often iterative and non-linear, rather than following a strict sequential order.

While the steps provide a general framework and guidance for the research process, in practice, researchers may move back and forth between different stages, or engage in multiple steps simultaneously, as new insights and questions emerge from the data.

The iterative nature of narrative analysis reflects the complex and dynamic nature of human experience and meaning-making.

1. Situate the Epistemological Approach

Determine whether to use a naturalist or constructivist approach. The research questions and theoretical framework inform this decision.

Situating the epistemological approach at the outset of the study helps ensure consistency and coherence throughout the research process, guiding methodological choices and the interpretation of findings.

If the research questions focus on understanding the subjective experiences and meaning-making processes of participants, a constructivist approach may be more appropriate.

Conversely, if the research aims to identify common patterns or themes across narratives and assumes a more objective reality, a naturalist approach may be suitable.

Naturalist Approach :

• Assumes that narratives reflect an objective reality or truth
• Seeks to capture and understand the “real” experiences and perspectives of participants
• Aims to minimize the researcher’s influence on the data collection and interpretation process
• Aligns with a more positivist or realist paradigm

Constructivist Approach :

• Assumes that narratives are constructed and shaped by the interaction between the narrator and the listener (researcher)
• Acknowledges that multiple realities or truths can exist, as individuals interpret and make sense of their experiences differently
• Recognizes the researcher’s role in co-creating meaning during the data collection and analysis process
• Aligns with an interpretivist or social constructionist paradigm

2. Select the Analytical Model(s)

Decide which model(s) to use in analyzing narrative data. Different models focus on different features of narratives and raise distinct questions during analysis.

Research design, informed by the chosen epistemological approach, will guide decisions regarding the use of single or multiple models.

• Structural Model:  Examines the structure of stories and the ways in which they are told. Considers elements such as plot, characters, setting, and narrative arc
• Thematic Model:  Analyzes the content of stories, focusing on the themes around which stories are told. May involve coding the data to identify recurrent themes and organizing them into categories or hierarchies
• Interactional/Performative Model:  Investigates the contextual features that shape the construction of narratives and how meaning is collaboratively created through interaction between storytellers and listeners.

3. Select Narratives to Analyze

In conducting narrative analysis involves selecting specific narratives to analyze within the larger dataset. Even when the aim is to analyze the data holistically, researchers often choose to focus on particular narratives for close scrutiny.

This selection process is guided by the research questions, theoretical framework, and the analytical strategy employed in the study.

When selecting narratives to analyze, researchers may consider the following:

• Representativeness : Choosing narratives that are representative of the broader dataset or the phenomena under investigation. This may involve selecting narratives that exemplify common themes, patterns, or experiences shared by multiple participants.
• Uniqueness : Identifying narratives that stand out as unique, unusual, or deviant cases. These narratives may offer valuable insights into the diversity of experiences or challenge dominant patterns or assumptions.
• Theoretical relevance : Selecting narratives that are particularly relevant to the theoretical framework or concepts guiding the study. These narratives may help illuminate or expand upon key theoretical ideas.
• Richness of data : Choosing narratives that are rich in detail, providing thick descriptions and in-depth insights into the participants’ experiences, thoughts, and emotions.

4. Identifying Narrative Blocks

A narrative block refers to a complete, self-contained story or narrative within a larger dataset, such as an interview transcript.

It is a segment of the data that has a clear beginning, middle, and end, and that conveys a specific experience, event, or perspective of the participant.

This involves looking for cues like “entrance and exit talk”, which signal the beginning and end of a distinct narrative within a conversation.

For instance, phrases like, “There was this one time…” or “Let me give you an example…” may signal the beginning of a narrative block.

Similarly, phrases like, “So that’s how that wrapped up…” or “That is a pretty classic example of…” can help researchers pinpoint the end of a narrative block

It is important to note that the selection of narratives and units of analysis is an iterative process, and researchers may revisit and refine their choices as they delve deeper into the data and their analysis progresses.

Researchers should be transparent about their selection criteria and process, and should reflect on how their choices may impact the interpretation and findings of the study.

Here’s an example of what a narrative block might look like:

“I remember when I first started college. I was so nervous and excited at the same time. I didn’t know anyone on campus, and I was worried about fitting in. But during orientation week, I met this group of people who were just as lost and nervous as I was. We bonded over our shared experiences and became fast friends. That group of friends made all the difference in my college experience. We supported each other through the ups and downs, and I don’t think I would have made it through without them.”

This narrative block has a clear beginning (starting college), middle (meeting friends during orientation week), and end (reflecting on the importance of those friendships throughout college).

It conveys a specific experience and perspective of the participant, making it a suitable unit for narrative analysis.

5. Code Narrative Blocks

In conducting narrative analysis involves coding the narrative blocks using one or multiple analytical models.

Coding is the process of assigning labels or categories to segments of data, allowing researchers to organize, retrieve, and interpret the information in a systematic manner.

The coding process may involve several rounds or iterations, as researchers refine their codes and categories based on their deepening understanding of the data.

There are two main approaches to coding narrative blocks:

It’s important to note that these classifications are not always clear-cut, and researchers may use a combination of inductive and deductive approaches in their analysis.

For example, a researcher might start with a deductive structural analysis, using a predefined model of narrative structure, but then switch to an inductive thematic analysis to identify emergent themes within each structural element.

Inductive Coding

This approach, starting with the data and allowing themes and categories to emerge from the narratives aligns with a constructivist approach, where meaning is viewed as co-created between the researcher and the participant.

Researchers using inductive coding might identify emergent themes in the narratives about “life events” and code these narrative blocks accordingly.

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

• Also known as “open coding” or “data-driven coding”
• Involves allowing themes and categories to emerge from the data itself, rather than imposing pre-existing frameworks or theories
• Researchers immerse themselves in the narrative data, identifying patterns, similarities, and differences across the stories
• Codes are developed based on the researcher’s interpretation of the data and are refined iteratively throughout the analysis process
• Aligns with a constructivist approach, acknowledging the researcher’s role in co-creating meaning and the possibility of multiple interpretations

Deductive Coding

This approach, using pre-existing frameworks or theories to guide the coding process, aligns with a naturalist approach, where the researcher seeks to objectively identify and categorize elements of the narratives.

One such framework is the one proposed by Labov (1997), which identifies six key elements of a story:

• Abstract : A summary or overview of the story, often provided at the beginning
• Orientation : The setting or context of the story, including information about the time, place, characters, and situation
• Complicating Action : The main plot or sequence of events that drive the story forward, often involving a problem, challenge, or conflict
• Evaluation : The storyteller’s commentary on the meaning or significance of the events, revealing their attitudes, opinions, or emotions
• Resolution : The outcome or conclusion of the story, often resolving the complicating action or providing a sense of closure
• Coda : An optional element that brings the story back to the present or reflects on its broader implications

When using this framework for deductive coding, researchers would analyze each narrative block, looking for segments that correspond to these six elements. They would then assign the appropriate code to each segment, such as “Abstract,” “Orientation,” “Complicating Action,” and so on.

Here’s an example of how this might be applied to a narrative block:

“I remember my first day at my new job [Orientation]. I was so nervous and excited at the same time [Evaluation]. As soon as I walked in, I realized I had forgotten my employee ID [Complicating Action]. I panicked and thought I would be fired on the spot [Evaluation]. But then my manager came over, laughed, and said, ‘Don’t worry, it happens to everyone. We’ll get you a new one.’ [Resolution] That moment taught me that it’s okay to make mistakes and that my new workplace was actually pretty understanding [Coda].”

By applying Labov’s story structure framework, researchers can systematically analyze the narrative data, identifying patterns in how stories are structured and told.

This can provide insights into the way individuals make sense of their experiences and construct meaning through storytelling.

Step 6: Delve into the Story Structure

This step involves a deep and systematic examination of the coded narrative data, with a focus on understanding how the narrators use story structure elements (e.g., abstract, orientation, complicating action, evaluation, resolution, and coda) to construct meaning and convey their experiences.

By delving into the story structure, researchers can identify patterns, themes, and variations across different narratives, and gain insights into the ways in which individuals make sense of their lives through storytelling.

It allows researchers to move beyond the surface level of the narratives and to gain a deeper understanding of how individuals use storytelling to make sense of their lives and multifaceted nature of human experience.

This involves:

• Researchers organize the coded narrative data by grouping together segments that belong to the same story structure element (e.g., all “orientation” segments, all “complicating action” segments, etc.).
• This allows researchers to compare and contrast how different narrators use each story structure element, and to identify patterns, themes, and variations across the narratives.
• Researchers closely examine the content of each coded segment, paying attention to the specific details, descriptions, and evaluations provided by the narrators.
• They also consider the function of each story structure element, i.e., how it contributes to the overall meaning and coherence of the narrative.
• For example, researchers might analyze how narrators use the “orientation” element to set the scene, introduce characters, and provide context for their stories, or how they use the “evaluation” element to convey their attitudes, emotions, and reflections on the events being narrated.
• Researchers seek to understand how narrators make sense of their experiences and construct meaning through the way they structure and tell their stories.
• This involves considering the interplay between story structure, content, and context, and how these elements shape the overall meaning and significance of the narratives.
• Researchers may also consider the narrator’s perspective, the audience and social context of the storytelling, and the broader cultural and historical frameworks that inform the narratives.

Throughout this process, researchers need to be aware of the challenges and complexities of interpretation, such as the fact that narrators may not always follow a linear or coherent story structure, or that different individuals may attribute different meanings to similar experiences.

Researchers should aim to provide nuanced and contextualized descriptions of their findings, supported by relevant examples and quotes from the narratives.

Step 7: Compare Across Story Structure

This step involves a comparative analysis of the narrative data, looking for patterns, similarities, and differences in how story structure elements are used across different narratives.

In the previous step (Step 6: Delve into the Story Structure), researchers examined each story structure element in depth, analyzing its content, function, and meaning within individual narratives.

In Step 7, the focus shifts to a higher-level analysis, where researchers compare and contrast the use of story structure elements across the entire dataset.

The goal is to provide a comprehensive and integrative understanding of the narrative data, one that goes beyond the analysis of individual stories and reveals the broader patterns, meanings, and significance of storytelling in human experience.

This comparative analysis can be done in several ways:

• Researchers look for similarities and differences in how different individuals use each story structure element (e.g., orientation, complicating action, resolution) to construct their narratives.
• This can reveal patterns in how people from different backgrounds, experiences, or perspectives structure and tell their stories.
• Researchers may also compare the use of story structure elements across different types of narratives, such as life stories, event narratives, or turning point narratives.
• This can help identify genre-specific patterns or conventions in how stories are structured and told.
• Researchers may consider how the social, cultural, or historical context in which narratives are produced influences the way story structure elements are used.
• For example, they may compare narratives told in different settings (e.g., interviews, social media, public speeches), or at different points in time, to see how context shapes the structure and content of stories.

Throughout this comparative analysis, researchers should remain attentive to the overarching narrative and the broader themes and meanings that emerge from the data.

While breaking down narratives into specific story structure elements can provide valuable insights, it’s important not to lose sight of the holistic nature of narratives and the way in which different elements work together to create meaning.

Researchers should also be reflexive about their own role in the analysis process, acknowledging how their own backgrounds, assumptions, and interpretive frameworks may shape their understanding of the narratives.

They should strive to provide a balanced and nuanced account of their findings, highlighting both the commonalities and the variations in how story structure elements are used across different narratives.

By comparing story structure elements across the dataset, researchers can generate new insights and theories about the ways in which individuals use storytelling to make sense of their lives and experiences.

They may identify common patterns or structures that underlie different types of narratives, or they may discover how particular social, cultural, or historical factors shape the way stories are told.

Step 8: Tell the Core Narrative

This step involves synthesizing the insights and findings from the previous steps into a coherent and compelling narrative account that captures the essence of the research participants’ experiences and the key themes and meanings that emerged from the analysis.

At this stage, researchers have thoroughly examined the narrative data, coding and analyzing it at various levels, from the specific story structure elements to the broader patterns and comparisons across narratives.

They have gained a deep understanding of how participants use storytelling to make sense of their lives and experiences, and how different factors (such as social, cultural, or historical context) shape the way stories are told.

In Step 8, researchers aim to distill this complex and multifaceted understanding into a clear and concise narrative that conveys the core insights and conclusions of the study.

The goal is to provide a powerful and insightful narrative account that captures the richness and complexity of the research participants’ experiences, and that contributes to a deeper understanding of the ways in which storytelling shapes and reflects human lives and meanings.

By telling the core narrative, researchers can communicate the significance and relevance of their findings to a wider audience, and contribute to ongoing conversations and debates in their field and beyond.

• Researchers review the findings from the previous steps and identify the most salient and significant themes and meanings that emerged from the analysis.
• These themes may relate to the content of the narratives (e.g., common experiences, challenges, or turning points), the structure of the narratives (e.g., common patterns or variations in how stories are told), or the broader social and cultural factors that shape the narratives.
• Researchers organize the key themes and findings into a logical and compelling narrative that tells the “core story” of the research participants’ experiences.
• This may involve selecting illustrative examples or quotes from the narratives to support and enrich the main points, and providing interpretive commentary to guide the reader’s understanding.
• Researchers should aim to create a narrative that is both faithful to the complexity and diversity of the participants’ experiences and clear and accessible to the intended audience.
• In telling the core narrative, researchers should also consider the broader implications and significance of their findings, both for the specific field of study and for understanding human experience more generally.
• This may involve discussing how the findings relate to existing theories or debates in the field, identifying new questions or directions for future research, or highlighting the practical applications or social relevance of the study.

Ethical Considerations in Narrative Analysis

Researchers face the challenge of balancing the need to provide faithful accounts of participant stories with the ethical obligation to interpret those stories in a way that respects the participants and avoids misrepresentation.

This requires nuance and sensitivity, acknowledging the ambiguities inherent in narrative data.

Reflexivity and Positionality

Researchers should acknowledge their role in shaping all aspects of the research process, including the interpretation of narratives.

Researchers need to be aware of their own subjectivity and how their experiences, assumptions, and perspectives could influence their interpretations of participants’ narratives.

This awareness, often referred to as reflexivity, involves critically examining one’s own assumptions and being conscious of potential biases throughout every stage of the research process.

Researchers are encouraged to maintain field journals to track their thoughts and experiences, which can provide valuable insights into their influence on the research.

• Transparency is Crucial: Researchers must be transparent about their positionality, clearly articulating how their background and perspectives have shaped their understanding of the data.
• Reflexive Journals: Researchers can utilize reflexive journals to document feelings and thoughts throughout the research process, particularly during data analysis, helping to distinguish personal biases from participant perspectives.
• Team-Based Reflexivity: In team-based research, researchers should engage in open communication with their colleagues, sharing their reflexive insights and perspectives to ensure a well-rounded understanding of the data.

Respecting Participants’ Voices

Ethical narrative analysis emphasizes the importance of representing participants’ stories in a way that is true to their experiences.

Ethical narrative analysis prioritizes representing participants’ stories in a manner that accurately reflects their lived experiences, ensuring their voices are heard and their perspectives are not misrepresented.

This can include involving participants in the interpretation of their narratives and giving them a voice in how their stories are shared.

This can involve:

• Participant Involvement: Researchers can involve participants in the interpretation of their narratives, giving them a voice in deciding how their stories are shared [VI, 15].
• Member Checking: Sharing transcripts, analyses, and publications with research participants is a common practice in narrative research, allowing for further dialogue and ensuring accurate representation.
• Collaborative Meaning-Making: Researchers should approach interviews as opportunities for collaborative meaning-making, recognizing that interviewees have their own agendas and interpretations of the interactions. Researchers should validate participant experiences without judgment, encouraging them to tell their stories authentically.
• Ethical Interviewing: Researchers must adopt ethical interviewing practices, gaining informed consent, guaranteeing anonymity, and being sensitive to potential distress caused by interview questions.

Strengths of Narrative Analysis

Narrative analysis is a powerful tool for qualitative research, offering several strengths.

• Rich Insights into Human Experience : Narrative analysis stands out for its ability to generate rich, nuanced insights into the complexities of human experience. Unlike other methods that might overlook individual perspectives, narrative analysis centers on personal stories, capturing the unique ways individuals perceive, interpret, and make sense of their lives and experiences.
• Exploring Underlying Meanings : This method enables researchers to go beyond superficial descriptions, uncovering the underlying meanings, motivations, and interpretations embedded within personal narratives. By examining the stories people tell, researchers can gain a deeper understanding of the beliefs, values, and cultural contexts that shape those experiences.
• Versatility and Broad Applications : Narrative analysis offers flexibility in its application, proving valuable for a wide range of research topics, particularly those focused on social, personal, cultural, or ideological phenomena. This approach proves particularly well-suited for exploring topics where individual perspectives and experiences are central to understanding the phenomenon under investigation.
• Democratizing Data Analysis : By focusing on the narratives of individuals, narrative analysis offers a democratizing approach to research. This method values the insights and interpretations individuals have about their own experiences, often contrasting with broader societal, cultural, and political factors. This approach acknowledges that individuals possess valuable understandings of their own lives, contributing to a more comprehensive and inclusive research process.

Let’s illustrate these strengths with a specific research example. Imagine investigating the experiences and beliefs of individuals facing social marginalization.

Narrative analysis, in this context, would allow researchers to closely examine the stories told by people within marginalized groups.

By identifying recurring patterns, symbols, or motifs within their narratives, researchers could shed light on how these individuals make sense of their experiences, revealing the often-hidden impacts of social marginalization.

Weaknesses of Narrative Analysis

• It can be time-consuming: Narrative analysis can require a significant time investment to analyze source data, especially when long-form stories are involved. Researchers must also be knowledgeable about the author’s cultural context and consider other interpretations of the narrative.
• Reliability and generalizability are limited: Because narrative analysis relies heavily on subjective interpretation of the narrative, the findings cannot usually be generalized to larger populations or empirically verified. Although conclusions about the cultural context might be drawn, they are based on anecdotal data, making them unsuitable as a basis for theory development.
• Labov’s model is not appropriate for all types of narratives: While Labov’s model can be useful for analyzing monological narratives, it is not suitable for conversational narratives, interactional discourses, or co-constructed stories. This is because the model primarily focuses on analyzing monological narratives collected through interviews like oral histories or life stories, rather than conversational interviews.
• Timelines may oversimplify life stories: While timelines can be a useful tool for organizing large amounts of narrative data, they have limitations. Summarizing and quantifying narrative data in this way risks reducing the complexity and oversimplifying the stories of individuals. Additionally, timelines may not fully capture the episodic nature of narratives, which often unfold non-linearly.

Further Information

For narrative analysis.

• Bamberg, M. (2006) Stories: Big or small. Why do we care? Narrative Inquiry, 16(1):139–147.
• Bamberg, M. (2012) Narrative analysis, in H. Cooper, P.M. Camic, D.L. Long, A.T. Panter, D. Rindskopf and K. Sher (eds), APA Handbook of Research Methods in Psychology, Vol. 2. Washington, DC: American Psychological Association, pp. 85–102.
• De Fina, A., & Georgakopoulou, A. (2012). Analyzing narrative Discourse and sociolinguistic perspectives Cambridge, UK: Cambridge University Press
• Gee, P. (2011). An introduction to discourse analysis: Theory and method (3rd ed.). New York, NY: Routledge.
• Holstein, J., & Gubrium, J. (Eds.). (2012). Varieties of narrative analysis. Thousand Oaks, CA: Sage
• Riessman, C. K. (2008). Narrative methods for the human sciences. Thousand Oaks, CA: Sage

LABOVIAN MODEL

Labov’s Narrative Model, developed by sociolinguist William Labov, is a structural approach to analyzing narratives that focuses on the formal properties and organizational features of stories.

Labov identified six key elements that he argued are present in fully-formed oral narratives: abstract, orientation, complicating action, evaluation, resolution, and coda.

• Labov, W. (1997). Further steps in narrative analysis. Journal of Narrative and Life History (7 ),395–415.
• Labov, W. and Waletzky J. (1997) Narrative analysis: Oral versions of personal experience. Journal of Narrative and Life History, 7 (1–4): 3–38.
• McCormack, C. (2004). Storying stories: a narrative approach to in-depth interview conversations.  International journal of social research methodology ,  7 (3), 219-236.
• Patterson, W. (2008). Narratives of events: Labovian narrative analysis and its limitations.  Doing narrative research , 22-40.

POLKINGHORNE MODEL

The Polkinghorne Model, developed by psychologist Donald Polkinghorne, is a narrative approach to understanding human experience and meaning-making.

According to Polkinghorne, narratives are not simply a way of representing or communicating experience, but are the primary means through which we construct and make sense of our lives.

He argued that narratives are a fundamental form of human cognition, and that we use stories to organize and interpret our experiences, to create coherence and continuity in our sense of self, and to navigate the social and cultural worlds we inhabit.

One of the key features of the Polkinghorne Model is its emphasis on the interpretive and constructivist nature of narrative analysis.

Polkinghorne argued that narratives are not simply a reflection of an objective reality, but are always shaped by the social, cultural, and historical contexts in which they are told, as well as by the individual’s own perspective and meaning-making processes.

• Polkinghorne, D. E. (1995). Narrative configuration in qualitative analysis.  International journal of qualitative studies in education ,  8 (1), 5-23.
• Polkinghorne, D. (1988).  Narrative knowing and the human sciences . Suny Press.
• Polkinghorne, D. E. (2007). Validity issues in narrative research.  Qualitative inquiry ,  13 (4), 471-486.

MISHLER MODEL

Elliot Mishler, a social psychologist and professor at Harvard Medical School, developed an influential model for analyzing narratives in the context of medical encounters.

The Mishler Model, also known as the “Narrative Functions Model,” focuses on the interactive and collaborative nature of storytelling in medical interviews, and examines how patients and healthcare providers co-construct meaning through their dialogue.

• Mishler, E. G. (1995). Models of narrative analysis: A typology.  Journal of narrative and life history ,  5 (2), 87-123.
• Mishler, E. G. (1986).  The analysis of interview-narratives  (pp. 233-255). TR Sarbin (Ed.), Narrative psychology: The storied nature of human conduct.
• Mishler, E. G. (2009).  Storylines . Harvard University Press.
• Mishler, E. G. (1991).  Research interviewing: Context and narrative . Harvard university press.

FOR VISUAL NARRATIVE ANALYSIS

• Bell, 5. E. (2002), Photo images: Jo Spence’s narratives of Journal for the Social Study of Health, Illness and with illness. Health An Interdisciplinary by post, 6 (1), 5-30.
• Pink, 5. (2004) Visual methods in C. Seale, G. Gobo, obrium, & D. Silverman (Eds), [Special issue) Qualitative Research Practice (pp. 361-378). London: Sage
• Adams, H. L. (2015). Insights into processes of posttraumatic growth through narrative analysis of chronic illness stories.  Qualitative Psychology ,  2 (2), 111.
• Ehsan, N., Riaz, M., & Khalily, T. (2019). Trauma of terror and displacement: A narrative analysis of mental health of women IDPS in KPK (Pakistan).  Peace and Conflict: Journal of Peace Psychology ,  25 (2), 140.
• Fewings, E., & Quinlan, E. (2023). “It hasn’t gone away after 30 years.” late-career Australian psychologists’ experience of uncertainty throughout their career .  Professional Psychology: Research and Practice, 54 (3), 221–230. 
• Skopp, N. A., Holland, K. M., Logan, J. E., Alexander, C. L., & Floyd, C. F. (2019). Circumstances preceding suicide in US soldiers: A qualitative analysis of narrative data.  Psychological services ,  16 (2), 302.

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 .

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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 ...

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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Using narrative analysis in qualitative research

Last updated

7 March 2023

Reviewed by

Jean Kaluza

Short on time? Get an AI generated summary of this article instead

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. 

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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.

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Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.

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 data 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.

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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.

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Essentials of Narrative Analysis

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• Table of contents
• Contributor bios
• Book details

The brief, practical texts in the Essentials of Qualitative Methods series introduce social science and psychology researchers to key approaches to qualitative methods, offering exciting opportunities to gather in-depth qualitative data and to develop rich and useful findings.

In this book, Ruthellen Josselson and Phillip L. Hammack introduce readers to narrative analysis, a qualitative method that investigates how people make meaning of their lives and experiences in both social and cultural contexts. This method offers researchers a window into how individuals’ stories are shaped by the categories they inhabit, such as gender, race, class, and sexual identity, and it preserves the voice of the individual through a close textual analysis of their storytelling.

About the Essentials of Qualitative Methods book series

Even for experienced researchers, selecting and correctly applying the right method can be challenging. In this groundbreaking series, leading experts in qualitative methods provide clear, crisp, and comprehensive descriptions of their approach, including its methodological integrity, and its benefits and limitations.

Each book includes numerous examples to enable readers to quickly and thoroughly grasp how to leverage these valuable methods.

Series Foreword Clara E. Hill and Sarah Knox

• Contextual Foundations for the Method
• Study Design and Data Collection
• Data Analysis
• Writing the Manuscript
• Variations on the Method
• Methodological Integrity and Ethics
• Summary and Conclusions

Appendix: Exemplar Studies

About the Authors

About the Series Editors

Ruthellen Josselson, PhD, is professor of clinical psychology at The Fielding Graduate University, Santa Barbara, California. She was formerly a professor at The Hebrew University of Jerusalem, a visiting professor at Harvard University School of Education, and a visiting fellow at Cambridge University.

She is a cofounder of the Society for Qualitative Inquiry in Psychology and editor of the APA journal Qualitative Psychology .

She received the Henry A. Murray Award and the Theodore R. Sarbin Award from APA and the Distinguished Contribution to Qualitative Research Award from APA Division 5.

Based on interviews she has conducted over 35 years, she has written three books exploring women’s identity longitudinally, Finding Herself, Revising Herself , and most recently, Paths to Fulfillment: Women’s Search for Meaning and Identity .

She has authored many journal articles and book chapters that explore the theory and practice of qualitative inquiry, as well as the recent book,  Interviewing for Qualitative Inquiry: A Relational Approach .

She has conducted workshops on qualitative inquiry internationally as well as in the U.S. She was a member of the APA Task Force that produced the “Journal Article Reporting Standards for Qualitative Research.”

Phillip L. Hammack is professor and chair of psychology and director of the Sexual and Gender Diversity Laboratory at the University of California, Santa Cruz.

For over a decade, he has been a leader in the movement to promote narrative theory and methods and to legitimize qualitative inquiry in psychology. Among his widely cited work is the 2008 landmark paper, “Narrative and the Cultural Psychology of Identity,” published in Personality and Social Psychology Review and the 2011 book Narrative and the Politics of Identity published by Oxford University Press. Hammack is also editor of The Oxford Handbook of Social Psychology and Social Justice (Oxford, 2018).

He is the recipient of several early career awards and prestigious fellowships, including a William T. Grant Foundation Scholar Award and a fellowship from the Center for Advanced Study in the Behavioral Sciences at Stanford University.

His current research focuses on gender, sexual, and intimate diversity, centering the use of narrative and other qualitative methods.

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