Narrative Analysis In Qualitative Research

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

1. 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.
2. 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.
3. 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
4. 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:

1. 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.
2. 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.
3. 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.
4. 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:

1. Collating each life event by its story structure code:
   • 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.
2. Analyzing the content and function of each story structure element:
   • 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.
3. Interpreting the meaning-making processes:
   • 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:

1. Comparing story structure elements across different narrators:
   • 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.
2. Comparing story structure elements across different types of narratives:
   • 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.
3. Comparing story structure elements across different contexts:
   • 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.

This involves:

1. Identifying the central themes and meanings:
   • 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.
2. Constructing a coherent narrative account:
   • 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.
3. Reflecting on the implications and significance of the findings:
   • 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

Examples

• 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.