Narrative Analysis

Narrative analysis is a qualitative research method used to examine how people construct and communicate meaning through stories. Rather than treating an interview merely as a collection of statements that can be separated into codes, narrative analysis often preserves the sequence, structure and context of an account because how a story is told can be as analytically important as what the story describes.

Narrative analysis is particularly appropriate when the research question concerns experience over time, identity, significant transitions, turning points or the ways individuals make sense of events through storytelling.

On this page

• Narrative analysis explained simply
• What narrative analysis is
• What makes data narrative
• Types of narrative analysis
• Dudovskiy Narrative Analysis Lens Framework
• Thematic narrative analysis
• Structural narrative analysis
• Dialogic and performance narrative analysis
• Narrative analysis vs thematic analysis
• Narrative analysis vs narrative inquiry
• How to conduct narrative analysis
• Reporting narrative analysis in a dissertation
• Application example
• Advantages and limitations
• Common mistakes
• Narrative analysis in business research
• Narrative analysis in the age of AI
• When to use narrative analysis
• Dissertation example
• Exam tip

Narrative Analysis at a Glance

Narrative Analysis Explained Simply

Imagine interviewing an entrepreneur about the failure of her first company.

She describes starting the business with great confidence, losing an important customer, experiencing serious financial problems, eventually closing the company and later starting another business.

A researcher could break the interview into categories such as financial difficulties, customer relationships, entrepreneurial learning and business strategy.

Narrative analysis allows a different question:

How does the entrepreneur construct the story of failure?

Perhaps she presents the failed company as the event that transformed her from an inexperienced founder into a capable entrepreneur. The sequence becomes important:

confidence → crisis → failure → reflection → learning → new beginning

The failure is therefore not simply an event mentioned in the interview. Its position within the story helps construct the participant’s present identity.

This illustrates the distinctive value of narrative analysis: the organisation of a story can itself carry meaning.

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What Is Narrative Analysis?

Narrative analysis refers to a family of qualitative approaches used to examine stories and other narrative accounts.

The central assumption is that people do not necessarily communicate experience as disconnected facts. They organise events into accounts containing sequences, relationships, explanations, evaluations and interpretations. Through these narratives, individuals can construct meanings about themselves, other people and the social world.

Narrative researchers may therefore examine what happened in a story, how events are organised, why particular events are emphasised, how the narrator positions themselves and how the social context influences the account.

Riessman’s work is particularly influential in demonstrating that narrative analysis can focus on different dimensions of storytelling, including thematic content, narrative structure and the interactional or performative circumstances in which stories are produced.

Narrative analysis is therefore not one rigid analytical technique. It is better understood as a methodological family whose approaches share an interest in story, sequence and meaning, but may analyse different aspects of narrative material.

What Makes Data Narrative?

Not every piece of qualitative data is necessarily a narrative.

A participant answering:

“I prefer working from home because commuting takes too much time.”

provides useful qualitative data, but not necessarily a developed story.

Compare this with:

“Before the pandemic, I thought working from home would make people less productive. Then our office closed and I had to manage my team remotely. During the first few weeks I struggled, but eventually I realised my team was performing better. That completely changed how I think about management.”

The second account contains:

• a situation before change;
• an event or disruption;
• a sequence of experiences;
• evaluation;
• transformation;
• a present interpretation of what happened.

This temporal organisation makes the account particularly suitable for narrative analysis.

Narrative data do not have to follow a perfect beginning-middle-end structure. Stories can be fragmented, contradictory and incomplete. Indeed, such characteristics can themselves be analytically significant.

The key issue is whether the relationship and sequence between events contributes meaning that would be lost if the account were treated only as isolated statements.

Types of Narrative Analysis

Narrative analysis can be conducted in several ways. Three particularly useful analytical orientations are thematic, structural and dialogic/performance narrative analysis.

Thematic narrative analysis concentrates primarily on what is said. Researchers examine the meanings and experiences communicated through stories while generally preserving more of the narrative’s context than would occur in conventional cross-case thematic coding.

Structural narrative analysis concentrates more strongly on how a story is organised. Researchers may examine sequence, turning points, plot development, evaluation, repetition and the linguistic devices used to construct the narrative.

Dialogic/performance narrative analysis considers storytelling as an interaction. A narrative is not treated simply as a neutral account stored inside a participant and extracted by an interviewer. The analysis considers who tells the story, who hears it, why it is being told in that situation and how wider cultural or organisational contexts shape its construction.

These approaches can overlap. The methodological requirement is not to force every study into one label, but to explain which dimensions of narrative are analytically important and why.

Dudovskiy Narrative Analysis Lens Framework

The Dudovskiy Narrative Analysis Lens Framework helps researchers move from the vague statement “I will analyse participants’ stories” towards a more explicit analytical strategy.

These lenses are not necessarily sequential stages. A researcher can use several simultaneously.

The central principle is:

Do not fragment a narrative before deciding whether its sequence, structure and context carry meaning.

This matters because coding can create analytical efficiency while simultaneously destroying relationships between events.

If a participant describes promotion → overwhelming responsibility → burnout → resignation → career change, separating promotion, stress, resignation and career development into unrelated coding categories may obscure the participant’s construction of one event as leading to another.

Narrative analysis asks whether preserving that relationship produces a better answer to the research question.

Thematic Narrative Analysis

Thematic narrative analysis focuses primarily on the content and meaning of stories.

It asks questions such as:

• What experiences does the narrator describe?
• What meanings are attributed to those experiences?
• What issues repeatedly become important within the story?
• How does the narrator understand what happened?

The word thematic can create confusion because thematic narrative analysis and thematic analysis are not identical.

In thematic narrative analysis, the researcher generally retains stronger attention to the individual narrative and its context. A story may be examined as a coherent account before comparisons are made with other narratives.

For example, researchers studying business founders could examine how each founder constructs the experience of entrepreneurial failure. One participant might construct failure as education, another as personal injustice and another as evidence that entrepreneurship was incompatible with family responsibilities.

Comparisons can subsequently be made, but preserving the integrity of each narrative can remain analytically important.

Structural Narrative Analysis

Structural narrative analysis shifts attention from primarily what the narrator says towards how the narrative is organised.

The researcher may examine:

• chronology;
• beginnings and endings;
• turning points;
• disruptions;
• repetition;
• evaluation;
• resolution;
• the positioning of characters and events.

A classic influence on structural approaches is Labov’s work on narrative organisation.

Consider two managers who both experienced redundancy.

The first begins with the redundancy itself and describes everything that follows as a struggle to recover.

The second begins several years earlier with growing dissatisfaction at work and eventually presents redundancy as an unexpected opportunity.

The factual event may be similar, but its position within the narrative structure changes its meaning.

Structural analysis therefore treats narrative organisation not merely as presentation but as potential evidence about how individuals make sense of experience.

Dialogic and Performance Narrative Analysis

Dialogic/performance approaches challenge the assumption that a story belongs exclusively to the participant.

Stories are produced in particular circumstances.

A senior executive describing a failed acquisition to an academic researcher may construct the event differently from how the same executive describes it to colleagues, shareholders or close friends.

The interviewer also participates in the production of the narrative. Questions, reactions, institutional identity and the purpose of the interview can influence what is told and how.

A dialogic/performance analysis may therefore ask:

Who is speaking?

Who is listening?

Why is this story being told now?

What identity is the narrator constructing?

What organisational or cultural narratives are available to the speaker?

This approach is especially useful when identity, legitimacy, power or social positioning are central to the research question.

Narrative Analysis vs Thematic Analysis

Narrative analysis and thematic analysis can both be applied to interview data, but they usually organise the analytical task differently.

Suppose 20 managers are interviewed about implementing artificial intelligence in their organisations.

A thematic analysis might code extracts across all 20 interviews and develop themes such as fear of job displacement, pressure to adopt AI, trust in automated decisions and changing managerial roles.

A narrative analysis might instead examine how particular managers construct their personal journeys from scepticism to adoption, or from enthusiasm to disappointment.

The decision can therefore be framed as:

Are patterns across participants more important, or is meaning contained in how individual accounts unfold?

Neither approach is inherently superior. The correct choice follows from the research question.

Narrative Analysis vs Narrative Inquiry

Narrative analysis and narrative inquiry overlap considerably, but they should not automatically be treated as synonyms.

Narrative analysis usually refers more specifically to approaches used to analyse stories or narrative material.

Narrative inquiry can refer to a broader research methodology in which narrative principles shape the research process itself—from understanding experience and developing relationships with participants to collecting field texts and constructing the final research account.

Clandinin and Connelly’s influential approach to narrative inquiry, for example, emphasises dimensions of temporality, sociality and place.

A study can therefore collect interviews for a broader qualitative project and subsequently use narrative analysis as its analytical method.

In a narrative inquiry, by contrast, narrative may organise the entire methodological orientation of the study.

The distinction is not absolute because terminology varies across qualitative traditions. Researchers should therefore define how they are using the term rather than assuming that all authors use it identically.

How to Conduct Narrative Analysis

Narrative analysis is not a mechanical sequence, but a defensible study usually involves several connected analytical activities.

Researchers frequently move backwards and forwards between these activities as interpretations develop.

The objective is not simply to retell participants’ stories. Narrative analysis requires the researcher to explain why the construction of those stories matters for understanding the research problem.

How to Report Narrative Analysis in a Dissertation

A methodology chapter should specify why narrative analysis was more appropriate than other qualitative analytical approaches.

The researcher should explain what constitutes a narrative in the study, how participants or narratives were selected, how narrative material was generated, what unit of analysis was used and which analytical orientation guided interpretation.

For example, merely writing:

“Interviews were analysed using narrative analysis.”

leaves important methodological decisions unexplained.

A stronger account might state that the study used thematic narrative analysis because the research sought to understand how entrepreneurs constructed the meaning of business failure while preserving the chronology and context of each entrepreneurial journey.

The methodology should also explain how analysis was conducted. Did the researcher first examine each account holistically? Were turning points identified? Was narrative structure analysed? Were stories compared across participants? How was context incorporated?

Where appropriate, reflexivity should be discussed because the researcher’s questions, interpretations and interaction with participants can contribute to the production and interpretation of narratives.

The methodology chapter should therefore demonstrate a coherent chain:

research question → need for narrative data → analytical lens → narrative interpretation → findings

Application of Narrative Analysis: an Example

Consider a study investigating how senior female managers construct their journeys towards executive leadership positions.

The researcher conducts in-depth interviews that encourage participants to narrate their careers rather than simply answer a fixed list of questions about leadership.

Each account is initially examined as a whole.

One participant may construct her career as a gradual progression based on competence and persistence. Another may organise her story around a decisive encounter with a mentor. A third may describe repeated experiences of exclusion before presenting promotion to senior leadership as overcoming institutional barriers.

The researcher identifies important turning points such as first managerial appointments, maternity leave, international assignments, experiences of discrimination, mentoring relationships and promotion to executive roles.

However, the analysis goes beyond listing these events.

It examines how participants connect events to construct leadership identities. For example, an episode initially described as discrimination may later be positioned within the narrative as the event that strengthened a participant’s determination to pursue senior leadership.

The researcher can also examine narrative structure. Some participants may construct highly coherent stories of continuous ambition, whereas others retrospectively connect previously unrelated events into a career narrative that only becomes meaningful from their present executive position.

Comparisons across participants may subsequently reveal broader patterns, but the individual sequence of each career story remains analytically important.

Advantages and Limitations of Narrative Analysis

One of the greatest advantages of narrative analysis is its ability to preserve temporality and context.

Many human experiences unfold over time. Entrepreneurship, careers, organisational change, illness, migration and identity development cannot always be adequately understood by separating individual statements from the sequences in which participants place them.

Narrative analysis can consequently reveal how individuals connect past events with present identities and future expectations. It can expose turning points, contradictions and changes in interpretation that might disappear when interviews are fragmented into thematic categories.

The method also allows researchers to examine how people construct identities through storytelling rather than treating interview statements simply as transparent descriptions of reality.

However, this analytical depth creates limitations.

Narrative studies often involve smaller numbers of participants because analysing whole accounts can be time-intensive. Comparing cases can also be difficult because narratives differ substantially in structure, length and context.

Interpretation is another challenge. Researchers need to distinguish carefully between what participants say happened and how participants currently narrate what happened. A compelling story should not automatically be treated as an objective historical record.

Narrative analysis can also become overly descriptive. Reproducing long participant stories without explaining their methodological significance constitutes storytelling, but not necessarily strong narrative analysis.

Common Mistakes When Using Narrative Analysis

A common mistake is collecting conventional short-answer interviews and later deciding to call the analysis narrative. Narrative analysis requires material sufficiently rich to support examination of stories, sequence, meaning or narrative construction.

Another mistake is fragmenting interviews immediately into hundreds of codes. Doing so may destroy precisely the temporal relationships that justified narrative analysis.

Researchers can also confuse summary with analysis. Retelling what happened to a participant does not explain how the story constructs meaning, identity or relationships between events.

Another problem is assuming that a participant’s narrative provides direct access to historical reality. Narratives are accounts constructed from a particular present position and for a particular audience.

Conversely, researchers should avoid interpreting every linguistic detail if their research question does not require structural or performance analysis. The analytical lens needs to remain proportionate to the purpose of the study.

Finally, narrative analysis should not be selected merely because interviews contain stories. The method is justified when storytelling itself contributes something important to answering the research question.

Narrative Analysis in Business Research

Narrative analysis is particularly useful in business research when researchers need to understand processes and identities that develop over time.

Entrepreneurship provides an obvious application. Founders frequently construct stories explaining why they started businesses, how they responded to failure and what particular events mean for their identities as entrepreneurs.

Leadership research can examine how managers construct leadership identities through career narratives. Organisational-change studies can explore how employees narrate periods before, during and after restructuring. Family-business research can analyse stories of succession and intergenerational responsibility.

Narrative analysis can also contribute to research on mergers, redundancy, professional careers, innovation, organisational crisis and corporate identity.

Its particular value is that organisational experiences are not necessarily understood by participants as isolated variables. People often construct relationships between events:

“Because this happened, I changed.”

“That was the moment I realised…”

“Everything was different after…”

Those connections can become important research data rather than merely conversational detail.

Narrative Analysis in the Age of AI and Digital Research

Generative AI creates substantial opportunities for narrative research. AI systems can assist researchers in organising large collections of interviews, identifying chronological sequences, locating potential turning points, comparing narrative structures and exploring recurring forms of storytelling.

This can make narrative analysis more scalable.

But narrative analysis also exposes one of the deepest methodological problems associated with AI-assisted qualitative research:

If AI reconstructs the meaning of someone’s story, whose interpretation are we reporting?

A language model can produce an elegant summary of an interview while removing hesitation, contradiction, repetition and ambiguity. Yet those apparently inconvenient features may be precisely what matters analytically.

Suppose a manager repeatedly changes how they explain a failed project—first blaming market conditions, later accepting personal responsibility and finally describing the failure as necessary for professional development.

An AI-generated summary might transform these tensions into one coherent account of learning from failure. The summary becomes easier to read while potentially eliminating the narrative instability that deserves analysis.

AI can therefore assist narrative researchers with organisation and analytical exploration, but methodological responsibility remains with the researcher.

Researchers using AI should document significant AI involvement and critically evaluate whether automated processing preserves sequence, voice, context and ambiguity. Particular care is required when sensitive personal narratives are uploaded to external AI systems because confidentiality, consent and data governance may also be implicated.

The strongest use of AI in narrative analysis is therefore not to replace interpretation, but to expand the researcher’s ability to interrogate narrative material while preserving human responsibility for methodological judgement.

When to Use Narrative Analysis

Narrative analysis is particularly appropriate when:

• the research question concerns experiences unfolding over time;
• participants’ stories are important objects of analysis rather than merely sources of coded statements;
• chronology or sequence contributes to meaning;
• turning points and transitions are important;
• the study investigates identity or sense-making;
• researchers need to understand how participants connect past experiences with present circumstances;
• the way a story is constructed is analytically relevant;
• social, organisational or interactional context shapes how accounts are produced;
• preserving individual cases is more important than immediately identifying patterns across a large dataset.

Narrative analysis is less appropriate when the primary objective is to identify broad patterns across many participants without preserving individual stories, quantify predefined categories or analyse linguistic interaction using a more specialised discourse-oriented approach.

Dissertation Example

Consider a dissertation titled “How Technology Entrepreneurs Narrate Business Survival During Financial Crisis.”

The study uses semi-structured narrative interviews with founders whose businesses experienced severe financial difficulties but continued operating. Narrative analysis is selected because the research question concerns how entrepreneurs construct and interpret the process of organisational survival over time, rather than merely identifying common challenges experienced during financial crisis.

In the methodology chapter, the researcher explains that each interview is first analysed as a complete narrative. Particular attention is given to chronology, turning points, explanations of causality and changes in the narrator’s entrepreneurial identity. The researcher then compares narratives while retaining the contextual sequence of each founder’s account.

For example, one founder may construct the loss of a major customer as the crisis that forced strategic reinvention, whereas another may portray the same type of event as evidence of earlier managerial mistakes. The analysis examines not only the events described but how founders retrospectively position those events within broader stories of survival.

The dissertation therefore justifies narrative analysis on methodological grounds: fragmenting the interviews immediately into cross-case themes would risk losing the temporal relationships and identity construction central to the research question.

Exam Tip

If an exam asks you to explain narrative analysis, avoid defining it simply as “the analysis of people’s stories.”

A stronger answer explains that narrative analysis examines how people organise and communicate experience through stories, potentially analysing content, structure, sequence, context and performance.

For a higher-level answer, distinguish narrative analysis from thematic analysis. Thematic analysis typically searches for patterned meanings across a dataset, whereas narrative analysis often preserves individual accounts because the sequence and construction of the story can themselves carry analytical meaning.

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References

Clandinin, D.J. and Connelly, F.M. (2000) Narrative Inquiry: Experience and Story in Qualitative Research. San Francisco, CA: Jossey-Bass.

Labov, W. and Waletzky, J. (1967) ‘Narrative analysis: Oral versions of personal experience’, in Helm, J. (ed.) Essays on the Verbal and Visual Arts. Seattle: University of Washington Press, pp. 12–44.

Riessman, C.K. (2008) Narrative Methods for the Human Sciences. Thousand Oaks, CA: SAGE.