narrative analysis in qualitative research sample

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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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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• 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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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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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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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.

Essentials of Narrative Analysis

Available formats, also available from.

• 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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Chapter 24: Narrative analysis

Darshini Ayton and Heather Craig

Learning outcomes

Upon completion of this chapter, you should be able to:

• Define a narrative.
• Explain the process of conducting narrative analysis and describe the elements of a story produced from narrative data.
• Describe the advantages and challenges of narrative analysis.

What is narrative analysis?

In simple terms, a narrative is a story with a beginning, middle and end or outcome. Bruner 1 suggests that narratives (stories) are ways of knowing:

Telling stories is an astonishing thing. We are a species whose main purpose is to tell each other about the expected and the surprises that upset the expected, and we do that through the stories we tell. 1 (p8)

In narrative analysis, the stories (narratives) participants tell are analysed and then ‘re-storied’, or retold, based on the research questions and frameworks of the research. 2 Narratives may be unique, or they may be representative. However, when writing a ‘group’ story, the narrative should partially describe the individuals while not specifically describing each individual. 3 Narrative analysis may focus on what the story is about (the thematic content of the story) or on the structural components of the story (structural analysis). 3

Constructing a narrative from data

The following 2 processes can assist the researcher to identify the structure and content of the narrative 4 :

• Read and re-read the transcripts.
• Identify events in the transcripts as told by the participants – these are events that have happened.
• Identify the experiences of the participants and note any images, feelings, reactions or meanings ascribed to the experience by the participants.
• Note accounts, explanations and excuses as told by the participants.
• Identify the sequences of events, key characters, plot lines and imagery used in the narrative, and explore what this might represent (e.g. metaphors).
• Identify the beginning, middle and end of the story.

Coding for narrative analysis identifies the sequence or plot of the story. This process involves six codes that are applied to the narrative:

Abstract – a few sentences to introduce the story and give an indication of the topic.

Orientation – the who (people involved), what (situation or activity), when (time) and where (location) of the story.

Complicating action – the event that starts the story and sets up the plot.

Evaluation – sometimes called ‘reflection’; how the participant interprets the plot and their commentary on why (for example) the complicating action is important or not resolved.

Resolution – the final part of the plot that resolves the complicating action.

Coda – a section that ends the story and completes the plot.

Following is a narrative sequence based on the author’s story of being diagnosed with multiple sclerosis. When interviewing participants for the purpose of narrative analysis, the researcher needs to ask a question that elicits a narrative from the participant. For example, in this narrative, the question from the researcher could have been: ‘Tell me the story of how you were diagnosed with multiple sclerosis’. This invites the participant to give a narrative response with a beginning (symptoms), middle (seeking help) and (hopefully) end (the diagnosis). In the example, the author (Ayton) has coded the 6 elements of her story.

We were at the movies on a Saturday night – watching The Simpsons movie with some friends. I remember sitting there feeling like I had sand in my left shoe. My toes felt gritty and weird. It was a weird sensation but I thought it would pass. My husband and I went home and when I woke up the next morning, the sensation was up to my waist. The sensation was like pins and needles – the type you get when you fall asleep on your arm and limit blood circulation. My skin was buzzing slightly. I was starting to freak out at this stage. I was wondering if I had a brain tumour. [ Orientation] [ Abstract] I went to work on Monday morning and, by this stage, the sensation was quite strong and was in both my legs and my torso up to my chest. It was uncomfortable but not painful. It was alarming, though. I was working at a university and my boss was a general practitioner. I told her about my symptoms and she said it could be stress, and to have a bath and breath in and out into a paper bag. When I got home that night I tried doing what she told me, but it didn’t help. I had made an appointment with my normal GP for the next day. When I saw her she thought it could be neurological and gave me a referral to see a private neurologist. She also asked me to get more bloods done. However, the earliest appointment was in 3 weeks. I was starting to feel like I was going crazy and didn’t feel like I could wait that long. [ Complicating action] The next day I went to work but walked over to the hospital that was next to the university. I sat in the emergency department for a few hours and was seen by a registrar, who did the same blood tests as my GP. He didn’t seem to think there was anything wrong with me and said while my white blood-cell count was slightly elevated, it wasn’t enough to suggest that anything was wrong. He sent me home with instructions to come back if it got worse. Over the next few days, the sensation kept getting stronger. It got to the point where I felt I could cut off my leg and not feel it. I felt like some crazy marshmallow person and felt spongy and weird even though my body looked the same. I couldn’t stand it any longer and so I stumbled back to the emergency department on Monday morning. I was seen by a different registrar and remember thinking that he was very good looking. He looked like he could be a character on Grey’s Anatomy . He started doing some tests on me, including ones that tested my skin sensation. While he was doing the tests, I was recounting my experiences of the last week and said that I felt like I was going crazy. He looked me in the eye and said, “I believe you”, and I promptly burst into tears. He called a neuro registrar – a lovely young woman. I remember she asked me to stand with my feet together and to close my eyes. I fell backwards straight away and was so shocked! She also asked me to walk as though I was balancing on a tightrope. I put one foot in front of the other but I couldn’t swing my other leg around without falling over. I was amazed and scared at the same time. She admitted me straight away. I got my first MRI – a very traumatic experience. [ Evaluation] My husband stayed with me that night as I was so freaked out. I had never been admitted to a hospital before. We huddled together on the hospital bed. Needless to say, we didn’t get much sleep. I just kept hearing all the different beeps and alarms that are typical of a hospital ward. At 7 am the next morning the neuro registrar I had met the night before entered with a senior consultant. They explained that I had transverse myelitis and that I had lesions on my spine and brain that were causing the symptoms. They were going to start me on methylprednisolone and I was going to stay in the hospital until the end of the week. [Resolution]

I was so relieved that it wasn’t a brain tumour. However, both my husband and I were trained in biomedicine. When the neurologist was describing the lesions my husband asked – is this multiple sclerosis? The neurologist looked a bit surprised and then said – it could be, but I needed another “attack” before a diagnosis could be made. There was no way to tell if, and when, this would happen. And so the waiting game began. [Coda]

Another example can be found in Wang 5 , who applied narrative analysis to Hannah Gadsby’s Nanette (a show on Netflix).

Narrative analysis can be used to put together the various elements of data in a coherent and interesting narrative, and to provide explanations. 6 The researcher recursively moves between the data and the emerging narrative. As the story is written, the researcher examines the data and develops or refines the story if the events or actions do not align with the plot that is developing. 6

The story that is developed by the researcher would not be the same as one the participant(s) would construct if they wrote their own story. The story is constructed by the researcher and is therefore shaped by the researcher’s personal views, experiences and priorities (i.e. the research objectives). Furthermore, in the case of in-depth interviews, the story will be influenced by the dynamic and collaborative interactions between the researcher and participants as data is collected. 6 The narrative will also differ from that which a different researcher might put together if they conducted a similar study. However, despite this subjectivity, the narrative analysis does provide a credible interpretation of participants’ experiences, with quotes providing examples in the participants’ own ‘voice’. 6 So, while the analysis and subsequent story should be close to the data and ‘fit’ it well, the narrative also adds meaning and order that is not evident from the raw data. 6

Advantages and challenges of narrative analysis

Narrative analysis provides the stories of participants in narrative form to enable comparison between and across key story elements. These narratives provide a holistic understanding of lived experience and can be a powerful way to create policy change. However, narrative analysis can be a slow process and the researcher needs to be able to pay attention to subtle details and interpret the story overall. 6

Table 24.1 provides 2 examples of research using narrative analysis.

Table 24.1. Examples of narrative analysis

Narrative analysis, broadly speaking, is the process of making sense of stories. The coding process seeks to identify key elements of the sequence of the story to identify the beginning (abstract and orientation), middle (complicating action and evaluation) and end (resolution and coda). Although highly interpretive, narrative analysis is a valid method of providing a credible interpretation of the participants’ experiences.

• Bruner J. Narratives of human plight: A conversation with Jerome Bruner. In: Montello M, Charon R, eds. Stories matter – the role of narrative in medical ethics . Routledge; 2002.
• Liamputtong P. Qualitative data analysis: conceptual and practical considerations. Health Promot J Austr . 2009;20(2):133-9. doi:10.1071/he09133
• Figgou L, Pavlopoulos V. Social Psychology: Research Methods. International Encyclopedia of Social & Behavioural Sciences . 2015;22:554-552 doi:10.1016/B978-0-08-097086-8.24028-2
• Gibbs GR. Analyzing Biographies and Narratives. In: Gibbs GR, ed. Analysing Qualitative Data . SAGE Publications, Ltd; 2012:56-72.
• Wang Y. Narrative structure analysis: A story from “Hannah Gadsby: Nanette”. Journal of Language Teaching and Research . 2020;11(5):682-687. doi:10.17507/jltr.1105.03
• Sharp NL, Bye RA, Cusick A. Narrative Analysis. In: Liamputtong P, ed. Handbook of Research Methods in Health Social Sciences . Springer; 2019:861-880.
• Smith B, Sparkes AC. Changing bodies, changing narratives and the consequences of tellability: a case study of becoming disabled through sport. Sociology of Health & Illness . 2007;30(2):217-236 doi:10.1111/j.1467-9566.2007.01033.x
• Irvine L. Animals as Lifechanges and Lifesavers: Pets in the Redemption Narratives of Homeless People. Journal of Contemporary Ethnography . 2013;42(1):3-30 doi:10.1177/0891241612456550

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton and Heather Craig is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Narrative Analysis: Methods and Examples

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

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

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

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

What Is Narrative Research?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Section 2: Home
• Developing the Quantitative Research Design
• Qualitative Descriptive Design
• Design and Development Research (DDR) For Instructional Design
• Qualitative Narrative Inquiry Research

What is a Qualitative Narrative Inquiry Design?

Tips for using narrative inquiry in an applied manuscript, summary of the elements of a qualitative narrative inquiry design, sampling and data collection, resource videos.

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• Case Study Design in an Applied Doctorate
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Narrative inquiry is relatively new among the qualitative research designs compared to qualitative case study, phenomenology, ethnography, and grounded theory. What distinguishes narrative inquiry is it beings with the biographical aspect of C. Wright Mills’ trilogy of ‘biography, history, and society’(O’Tolle, 2018). The primary purpose for a narrative inquiry study is participants provide the researcher with their life experiences through thick rich stories. Narrative inquiry was first used by Connelly and Calandinin as a research design to explore the perceptions and personal stories of teachers (Connelly & Clandinin, 1990). As the seminal authors, Connelly & Clandinin (1990), posited:

Although narrative inquiry has a long intellectual history both in and out of education, it is increasingly used in studies of educational experience. One theory in educational research holds that humans are storytelling organisms who, individually and socially, lead storied lives. Thus, the study of narrative is the study of the ways humans experience the world. This general concept is refined into the view that education and educational research is the construction and reconstruction of personal and social stories; learners, teachers, and researchers are storytellers and characters in their own and other's stories. In this paper we briefly survey forms of narrative inquiry in educational studies and outline certain criteria, methods, and writing forms, which we describe in terms of beginning the story, living the story, and selecting stories to construct and reconstruct narrative plots. 

Attribution: Reprint Policy for Educational Researcher: No written or oral permission is necessary to reproduce a tale, a figure, or an excerpt fewer that 500 words from this journal, or to make photocopies for classroom use. Copyright (1990) by the American Educational Research Association; reproduced with permission from the publisher. 

• Example Qualitative Narrative Inquiry Design

First, the applied doctoral manuscript narrative inquiry researcher should recognize that they are earning a practical/professional based doctorate (Doctor of Education), rather than a research doctorate such as a Ph.D. Unlike a traditional Ph.D. dissertation oral defense where the candidates focus is on theory and research, the NU School of Education applied doctoral candidate presents their finding and contributions to practice to their doctoral committee as a conceptual professional conference level presentation that centers on how their study may resolve a complex problem or issue in the profession. When working on the applied doctoral manuscript keep the focus on the professional and practical benefits that could arise from your study. If the Applied Doctoral Experience (ADE) student is unsure as to whether the topic fits within the requirements of the applied doctoral program (and their specialization, if declared) they should reach out to their research course professor or dissertation chair for guidance. This is known as alignment to the topic and program, and is critical in producing a successful manuscript. Also, most applied doctoral students doing an educational narrative inquiry study will want to use a study site to recruit their participants. For example, the study may involve teachers or college faculty that the researcher will want to interview in order to obtain their stories. Permission may be need from not only the NU Institutional Review Board (IRB), but also the study site. For example, conducting interviews on campus, procuring private school district or college email lists, obtaining archival documents, etc. 

The popularity of narrative inquiry in education is increasing as a circular and pedagogical strategy that lends itself to the practical application of research (Kim, 2016). Keep in mind that by and large practical and professional benefits that arise from a narrative inquiry study revolve around exploring the lived experiences of educators, education administrators, students, and parents or guardians. According to Dunne (2003), 

Research into teaching is best served by narrative modes of inquiry since to understand the teacher’s practice (on his or her own part or on the part of an observer) is to find an illuminating story (or stories) to tell of what they have been involved with their student” (p. 367).

• Temporality – the time of the experiences and how the experiences could influence the future;
• Sociality – cultural and personal influences of the experiences; and;
• Spatiality – the environmental surroundings during the experiences and their influence on the experiences. 

From Haydon and van der Riet (2017)

• Narrative researchers collect stories from individuals retelling of their life experiences to a particular phenomenon. 
• Narrative stories may explore personal characteristics or identities of individuals and how they view themselves in a personal or larger context.
• Chronology is often important in narrative studies, as it allows participants to recall specific places, situations, or changes within their life history.

Sampling and Sample Size

• Purposive sampling is the most often used in narrative inquiry studies. Participants must meet a form of requirement that fits the purpose, problem, and objective of the study
• There is no rule for the sample size for narrative inquiry study. For a dissertation the normal sample size is between 6-10 participants. The reason for this is sampling should be terminated when no new information is forthcoming, which is a common strategy in qualitative studies known as sampling to the point of redundancy.

Data Collection (Methodology)

• Participant and researcher collaborate through the research process to ensure the story told and the story align.
• Extensive “time in the field” (can use Zoom) is spent with participant(s) to gather stories through multiple types of information including, field notes, observations, photos, artifacts, etc.
• Field Test is strongly recommended. The purpose of a field study is to have a panel of experts in the profession of the study review the research protocol and interview questions to ensure they align to the purpose statement and research questions.
• Member Checking is recommended. The trustworthiness of results is the bedrock of high-quality qualitative research. Member checking, also known as participant or respondent validation, is a technique for exploring the credibility of results. Data or results are returned to participants to check for accuracy and resonance with their experiences. Member checking is often mentioned as one in a list of validation techniques (Birt, et al., 2016).

Narrative Data Collection Essentials

• Restorying is the process of gathering stories, analyzing themes for key elements (e.g., time, place, plot, and environment) and then rewriting the stories to place them within a chronological sequence (Ollerenshaw & Creswell, 2002).
• Narrative thinking is critical in a narrative inquiry study. According to Kim (2016), the premise of narrative thinking comprises of three components, the storyteller’s narrative schema, his or her prior knowledge and experience, and cognitive strategies-yields a story that facilitates an understanding of the others and oneself in relation to others.

Instrumentation

• In qualitative research the researcher is the primary instrument.
• In-depth, semi-structured interviews are the norm. Because of the rigor that is required for a narrative inquiry study, it is recommended that two interviews with the same participant be conducted. The primary interview and a follow-up interview to address any additional questions that may arise from the interview transcriptions and/or member checking.

Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: A tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26 (13), 1802-1811. http://dx.doi.org./10.1177/1049732316654870

Cline, J. M. (2020). Collaborative learning for students with learning disabilities in inclusive classrooms: A qualitative narrative inquiry study (Order No. 28263106). Available from ProQuest Dissertations & Theses Global. (2503473076). 

Connelly, F. M., & Clandinin, D. J. (1990). Stories of Experience and Narrative Inquiry. Educational Researcher, 19 (5), 2–14. https://doi.org/10.1080/03323315.2018.1465839

Dunne, J. (2003). Arguing for teaching as a practice: A reply to Alasdair Macintyre. Journal of Philosophy of Education . https://doi.org/10.1111/1467-9752.00331 

Haydon, G., & der Riet, P. van. (2017). Narrative inquiry: A relational research methodology suitable to explore narratives of health and illness. Nordic Journal of Nursing Research , 37(2), 85–89. https://doi.org/10.1177/2057158516675217

Kim, J. H. (2016). Understanding Narrative Inquiry: The crafting and analysis of stories as research. Sage Publications. 

Kim J. H. (2017). Jeong-Hee Kim discusses narrative methods [Video]. SAGE Research Methods Video https://www-doi-org.proxy1.ncu.edu/10.4135/9781473985179

O’ Toole, J. (2018). Institutional storytelling and personal narratives: reflecting on the value of narrative inquiry. Institutional Educational Studies, 37 (2), 175-189. https://doi.org/10.1080/03323315.2018.1465839

Ollerenshaw, J. A., & Creswell, J. W. (2002). Narrative research: A comparison of two restorying data analysis approaches. Qualitative Inquiry, 8 (3), 329–347. 

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You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).

• Look around on the home page. Does anything seem interesting to you?
• How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
• What do the reviews say about the restaurant you've chosen?
• What was the most important factor for you in choosing this spot?
• You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
• Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
• Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

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What is Thematic Analysis in Qualitative Research? Definition, Process and Examples and Best Practices

What is Thematic Analysis in Qualitative Research?

Thematic analysis is a widely used method in qualitative research that involves identifying, analyzing, and reporting patterns, themes, or recurring ideas within a dataset. It is a flexible and systematic approach that allows researchers to uncover meaningful insights and understandings from the rich, often narrative data collected in qualitative studies. The goal of thematic analysis is to distill and organize the data into coherent themes that capture the essence of participants’ experiences, perceptions, or perspectives.

The first step in thematic analysis involves familiarizing oneself with the data through a process known as data immersion. Researchers immerse themselves in the raw data, which can include transcripts from interviews, focus group discussions, or any other qualitative material. This immersion helps researchers gain a holistic understanding of the content and identify initial patterns or notable observations.

Once familiarized with the data, the researcher generates initial codes, which are labels or tags attached to segments of the data that represent specific ideas, concepts, or recurring patterns. These codes are then organized into potential themes. Thematic analysis emphasizes a bottom-up, data-driven approach, allowing themes to emerge organically from the data rather than being imposed based on preconceived notions. Through an iterative process of reviewing, refining, and defining themes, researchers aim to create a coherent and internally consistent representation of the underlying patterns within the dataset.

Finally, thematic analysis involves interpreting and reporting the identified themes in a way that captures the essence of the participants’ experiences or perspectives. This process often involves selecting representative quotes or excerpts from the data to illustrate each theme, providing a rich and vivid portrayal of the findings. Thematic analysis is valued for its adaptability across various research designs and its ability to offer valuable insights into the complexities of qualitative data, making it a widely employed method in the social sciences.

Key Characteristics of Thematic Analysis in Qualitative Research

Thematic analysis is a qualitative research method characterized by several key features that guide the systematic examination and interpretation of textual data. These characteristics contribute to the method’s flexibility and applicability across diverse research contexts. Here are key characteristics of thematic analysis:

• Thematic analysis is primarily inductive, meaning that it allows themes to emerge from the data rather than imposing pre-existing theoretical frameworks. It is driven by the participants’ voices and experiences, emphasizing a bottom-up process that captures the richness of the data.
• Thematic analysis is known for its flexibility, making it applicable to various research questions, study designs, and data types. Researchers can tailor the approach to suit the specific needs of their study, whether it involves interviews, focus groups, or other qualitative data sources.
• Before identifying themes, researchers engage in a process of data immersion and familiarization. This involves a thorough review of the raw data to gain a deep understanding of its content. By immersing themselves in the data, researchers can identify patterns, nuances, and potential areas of interest.
• Thematic analysis involves the systematic coding of data, where researchers assign labels or codes to segments of text representing specific ideas or patterns. These codes are then organized into potential themes. The process is iterative, allowing for constant refinement and development of themes as the analysis progresses.
• Thematic analysis encourages reflexivity, prompting researchers to be aware of their own perspectives, biases, and potential influences on the interpretation of data. This self-awareness contributes to transparency and helps ensure that the analysis is grounded in the participants’ experiences rather than the researchers’ preconceptions.
• Themes in thematic analysis are often emergent, arising from the data rather than being predetermined. Additionally, thematic analysis can involve hierarchical organization of themes, where overarching themes encompass sub-themes, providing a layered and nuanced understanding of the data.
• Transparency is a key characteristic of thematic analysis. Researchers are encouraged to document and report the decision-making process, including how themes were identified, refined, and interpreted. This documentation enhances the rigor and credibility of the research.
• Thematic analysis aims to provide a rich and contextual presentation of the findings. This often involves using participants’ own words or representative quotes to illustrate each theme, allowing readers to connect with the experiences and perspectives being portrayed.
• Thematic analysis is an iterative process that involves multiple rounds of coding, theme generation, and refinement. Researchers continuously revisit the data, codes, and themes to ensure a comprehensive and accurate representation of the dataset.
• Thematic analysis is well-suited for a wide range of research questions, making it applicable in disciplines such as psychology, sociology, education, and health sciences. Its adaptability allows researchers to explore diverse phenomena and capture the complexity of human experiences.

These key characteristics collectively make thematic analysis a versatile and robust qualitative research method, providing researchers with a systematic yet adaptable approach for exploring and understanding the nuances embedded in textual data.

Types of Thematic Analysis in Qualitative Research with Examples

Thematic analysis is a flexible qualitative research method, and there are different types or approaches within thematic analysis. Here are three commonly recognized types with corresponding definitions and examples:

• Inductive thematic analysis involves a bottom-up approach where themes emerge directly from the data. Researchers refrain from using pre-existing theoretical frameworks or prior knowledge to guide the analysis, allowing patterns and themes to surface organically through close examination of the data.
• Example: In a study exploring the experiences of cancer survivors, an inductive approach might involve thoroughly reading interview transcripts, coding segments that stand out, and gradually identifying themes that encapsulate common experiences such as resilience, support systems, and coping strategies.
• Deductive thematic analysis takes a more top-down approach, utilizing pre-existing theories or frameworks to guide the identification and interpretation of themes. Researchers start with predefined categories or concepts and then analyze the data with these predetermined themes in mind.
• Example: In a research project informed by a specific psychological theory, such as Maslow’s Hierarchy of Needs, deductive thematic analysis might involve coding data according to categories derived from Maslow’s theory, such as physiological needs, safety, belongingness, esteem, and self-actualization.
• Framework thematic analysis involves combining elements of both inductive and deductive approaches. Researchers begin with a broad coding framework based on the research question or existing literature but remain open to emergent themes as they delve into the data. The initial framework provides a structure that is flexible enough to evolve through the analysis process.
• Example: In a study examining attitudes toward technology use in education, a framework thematic analysis might start with predefined categories like access, pedagogical integration, and student engagement, but also allow for new themes to emerge during the coding process based on unanticipated insights from the participants.
• Critical thematic analysis goes beyond describing patterns in the data and aims to uncover power structures, social inequalities, and ideologies. It involves questioning assumptions, examining discourses, and exploring how language and representations may perpetuate or challenge existing power dynamics.
• Example: In a study on media representations of a marginalized community, critical thematic analysis might involve examining how specific language choices in news articles contribute to the stereotyping or marginalization of that community. Themes could include instances of linguistic bias, stigmatization, or resistance.
• Narrative thematic analysis focuses on the stories people tell and emphasizes the narrative structure of the data. It involves identifying key plot points, character development, and the ways in which individuals construct and convey meaning through storytelling.
• Example: In a research project exploring personal narratives of overcoming adversity, narrative thematic analysis might involve identifying themes related to the story arc, such as challenges faced, turning points, personal growth, and resolutions. This approach allows researchers to understand how individuals make sense of their experiences through storytelling.

These additional types of thematic analysis reflect the method’s adaptability to various research goals and theoretical orientations. Researchers can choose the type of thematic analysis that aligns with their research questions, epistemological stance, and the depth of analysis required to address the complexities inherent in qualitative data.

Best Practices for Thematic Analysis in Qualitative Research

Thematic analysis is a valuable qualitative research method, and employing best practices enhances the rigor, reliability, and validity of the study findings. Here are some best practices for conducting thematic analysis:

• Begin with well-defined research questions or objectives. Clearly articulate what you aim to explore, ensuring that your thematic analysis remains focused and purposeful.
• Develop a systematic and transparent process for conducting thematic analysis. This process should include distinct stages such as data familiarization, coding, theme generation, reviewing, and reporting. A systematic approach enhances the replicability of your study.
• Immerse yourself in the data to gain a deep understanding of its content. Read and re-read the data to identify patterns, recurring ideas, or potential themes. This initial data immersion phase is crucial for generating meaningful codes and themes.
• Conduct in-depth coding of the data. Code segments that capture meaningful concepts or patterns. Ensure that your coding captures both manifest (explicit) and latent (underlying) content in the data.
• Thematic analysis is an iterative process. Refine and revise codes and themes as you progress through the analysis. Regularly revisit the data to ensure that your emerging themes accurately reflect the complexity of the dataset.
• Be reflexive about your role as a researcher. Acknowledge your preconceptions, biases, and potential influence on the analysis. Document your reflexivity in research notes to enhance transparency.
• Establish clear coding guidelines and maintain consistency in coding across the entire dataset. Consistency enhances the reliability of your analysis, especially if multiple researchers are involved.
• Pay attention to negative or deviant cases that may challenge emerging themes. Ensure that your analysis accounts for variations and exceptions in the data, adding nuance to your interpretations.
• Collaboration can improve the credibility of thematic analysis. If possible, involve other researchers in the process, and seek feedback from peers or experts in qualitative research. This external perspective can enhance the robustness of your findings.
• Maintain a comprehensive audit trail documenting your decision-making processes, from coding to theme generation. This trail serves as a record of your analytical choices and enhances the transparency and trustworthiness of your study.
• Consider using qualitative data analysis software to organize and manage your data. Software tools such as NVivo, MAXQDA, or ATLAS.ti can facilitate efficient coding, retrieval, and organization of thematic data.
• Adhere to ethical standards throughout the research process. Obtain informed consent, protect participant confidentiality, and consider the ethical implications of your analysis, especially when exploring sensitive topics.

By adhering to these best practices, researchers can conduct a robust thematic analysis that contributes meaningful insights to the qualitative research literature. These practices enhance the reliability, validity, and transparency of the research process and findings.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

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