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Narrative Analysis – Types, Methods and Examples

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Narrative Analysis

Definition:

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

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

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

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

Structural Analysis

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

Discourse Analysis

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

Phenomenological Analysis

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

Critical Analysis

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

Autoethnography

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

Thematic Analysis

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

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

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

Applications of Narrative Analysis

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

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

Examples of Narrative Analysis

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

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

Purpose of Narrative Analysis

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

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

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

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

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

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

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

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

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

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

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

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

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

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Using narrative analysis in qualitative research

Last updated

7 March 2023

Reviewed by

Jean Kaluza

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

Master narrative analysis

Analyze your qualitative data faster and surface more actionable insights

• What is narrative analysis?

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

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

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

Is narrative analysis qualitative or quantitative?

Narrative analysis is a qualitative research method.

Is narrative analysis a method or methodology?

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

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

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

What are the characteristics of narrative research?

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

• Examples of types of narratives

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

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

• What is the objective of narrative analysis?

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

What is the difference between content analysis and narrative analysis?

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

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

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

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

What is the difference between narrative analysis and thematic analysis?

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

• Capturing narrative data

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

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

• Methods of transcribing narrative data

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

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

Video and audio transcription templates

• How to code narrative analysis

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

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

• Types of narrative analysis

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

Inductive method

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

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

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

Taking cues from entrance and exit talk

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

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

Deductive method

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

Hybrid inductive and deductive narrative analysis

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

• How to analyze data after coding using narrative analysis

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

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

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

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

Step 1 – Code narrative blocks

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

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

Step 2 – Group and read by live-event

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

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

Step 3 – Create a nested story structure

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

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

Step 4 – Delve into the story structure

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

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

Step 5 – Compare across story structure

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

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

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

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

Step 6 – Tell the core narrative

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

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

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

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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,

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

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

Thematic analysis

• Thematic analysis vs. content analysis
• Introduction

Types of narrative research

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

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

Narrative analysis in research

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

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

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

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

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

Life history

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

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

Oral history

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

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

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

Whatever your research, get it done with ATLAS.ti.

From case study research to interviews, turn to ATLAS.ti for your qualitative research. Click here for a free trial.

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

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

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

Designing a research inquiry

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

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

Data collection

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

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

Triangulating data

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

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

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

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

Dedicated research software like ATLAS.ti helps the researcher catalog, penetrate, and analyze the data generated in any qualitative research project. Start with a free trial today.

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

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

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

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

Structural analysis

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

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

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

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

Functional analysis

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

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

Dialogic analysis

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

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

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

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

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

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

Subjectivity in narratives

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

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

Connecting narratives to broader phenomena

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

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

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

Turn to ATLAS.ti for your narrative analysis.

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

• What is Narrative Analysis in Research? Methods & Applications

• Uncategorized

Introduction

Narratives have been an integral part of human communication since time immemorial. In the world of research, narrative analysis offers a unique window into the lived experiences and perspectives of individuals. 

Narrative Analysis is a research approach that focuses on exploring the stories people tell. These stories encompass personal experiences, events, emotions, and cultural contexts, providing invaluable insights into how individuals perceive and make sense of their worlds.

The narrative analysis serves as a bridge, connecting researchers to the human narratives behind the numbers. They help you grasp the nuances, contradictions, and underlying meanings that quantitative data might miss.

This article discusses the terrain of Narrative Analysis – its methods, applications, and significance in research.

Understanding Narrative Analysis

Narrative data refers to the stories, accounts, or personal experiences shared by individuals. These narratives can take various forms, such as oral interviews, written texts, autobiographies, diaries, and even visual materials like photographs or artwork. Unlike quantitative data, which focuses on measurable variables and statistical analysis, narrative data captures the depth and complexity of human experiences, emotions, and perspectives.

When you encounter a narrative, you’re not just reading or listening to a sequence of events. You’re engaging with a layered account that carries within it the nuances of the narrator’s thoughts, feelings, and perceptions. This makes narrative data a rich source of qualitative information, which can provide invaluable insights into the social, cultural, and psychological dimensions of a particular phenomenon.

The Distinction between Quantitative and Qualitative Research

In research, two major paradigms guide the study design and data analysis: quantitative and qualitative. While quantitative research relies on numerical data and statistical analyses to draw conclusions, qualitative research delves into the underlying meanings, interpretations, and context of human experiences. Narrative analysis falls within the realm of qualitative research, as it focuses on understanding the stories people tell and the meanings embedded within those narratives.

Quantitative research seeks to measure and quantify relationships between variables, often resulting in generalizable findings. On the other hand, qualitative research, and by extension narrative analysis, emphasizes depth over breadth. It seeks to capture the unique perspectives of individuals, acknowledging that human experiences are complex and cannot always be neatly categorized into numerical data points.

Read Also: 15 Reasons to Choose Quantitative over Qualitative Research

Role of Narratives in Qualitative Research

• Unveiling Personal Meaning: Narratives provide researchers with direct access to the inner world of participants. By exploring their stories, researchers can uncover the personal meanings, motivations, and interpretations that shape individuals’ lives.
• Contextualizing Experiences: Human experiences are inherently shaped by social, cultural, and historical contexts. Through narrative analysis, researchers can gain insights into how these contexts influence and shape individuals’ experiences and identities.
• Creating Empathy: Engaging with narratives allows researchers to develop a deeper sense of empathy and connection with participants. This connection is crucial for understanding the human aspects of research beyond just data points.
• Exploring Change and Development: Narratives are powerful tools for tracing the evolution of experiences over time. Researchers can analyze how individuals’ stories change, develop, or transform, shedding light on personal growth or shifts in identity.

Key Approaches to Narrative Analysis

Structuralist approach.

• Focus on Narrative Structure and Components: The structuralist approach to narrative analysis delves into the architecture of a narrative. Instead of focusing solely on the content, this approach highlights the way a narrative is constructed and organized.
• Identification of Key Elements: Within this framework, researchers identify and dissect key elements that contribute to the narrative’s structure and coherence. These elements include the plot, characters, and setting.

The structuralist approach enables researchers to uncover how narratives are crafted and how these structural components interact to create a coherent and meaningful whole.

Functional Approach

• Emphasis on the Functions of Narratives: The functional approach takes a step beyond structure and focuses on the purpose and functions of narratives. Narratives are not just told for the sake of recounting events; they serve specific functions in communication and meaning-making.
• Understanding Narratives as a Way to Convey Meaning: This approach views narratives as tools for conveying complex meanings, emotions, and experiences. Researchers analyze how narratives function as vehicles for transmitting cultural values, personal beliefs, and emotional states.

The functional approach helps researchers uncover the deeper layers of significance that narratives carry, shedding light on the underlying motives behind storytelling.

Contextual Approach

• Analyzing Narratives within their Sociocultural Context: The contextual approach acknowledges that narratives are not isolated entities but are embedded within specific sociocultural contexts. This approach emphasizes the importance of understanding the cultural, historical, and social backdrop against which narratives are told.
• Uncovering Hidden Social and Cultural Dimensions: Researchers employing the contextual approach seek to uncover the hidden dimensions of culture, society, and power dynamics that influence the narratives. By analyzing how narratives reflect and shape these contexts, researchers gain insights into broader societal trends and norms.

The contextual approach enriches narrative analysis by revealing how individual stories are woven into the fabric of larger cultural narratives.

Steps in Conducting Narrative Analysis

To embark on a successful narrative analysis journey, you’ll need to follow a structured process. Let’s break down the key steps involved:

A. Data Collection 

• Gathering Narratives through Interviews, Texts, or Observations: At the heart of narrative analysis is the collection of narratives. Depending on your research goals, you can gather narratives through interviews, written texts, or even observational data. Interviews allow for an in-depth exploration of personal experiences, while texts (such as diaries, letters, or online posts) can provide valuable insights into the narrators’ thoughts and emotions. Observational data, on the other hand, can offer a more unfiltered view of people’s actions and behaviors.
• Ensuring Diverse and Representative Samples: It’s crucial to ensure that your sample is diverse and representative of the population you’re studying. This diversity helps capture a wide range of perspectives and experiences, contributing to the richness of your analysis.

Read More – 7 Data Collection Methods & Tools for Research

B. Data Transcription and Organization

• Transcribing Narratives Accurately: Transcription involves converting audio or visual data, such as interview recordings, into written form. Accurate transcription is paramount as it forms the basis for your analysis. Pay attention to tone, pauses, and nonverbal cues, as these can add layers of meaning to the narratives.
• Organizing Data for Analysis: Once your narratives are transcribed, organize them in a systematic manner. You could use software or tools designed for qualitative data analysis to tag and categorize different themes, characters, and events in the narratives. This organization sets the stage for in-depth exploration.

C. Initial Reading and Immersion

• Developing Familiarity with the Narratives: Before diving into detailed analysis, immerse yourself in the narratives. Read through them multiple times to familiarize yourself with the content. This process allows you to engage with the stories and get a sense of the narrators’ perspectives.
• Preliminary Insights and Observations: As you read and immerse yourself, you will start noticing initial patterns, themes, and recurring motifs within the narratives. These preliminary insights provide a foundation for the deeper analysis that follows.

Techniques for Analyzing Narratives A. Thematic Analysis

• Identifying recurring themes and patterns: The first step in thematic analysis involves closely reading and immersing yourself in the narratives. By doing so, you can identify recurring themes and patterns that emerge across different stories. These themes might be emotional states, cultural motifs, or even societal issues. For example, if you are analyzing narratives about personal experiences with mental health, you might identify themes like stigma, resilience, and support networks.
• Creating a coding framework: Once you’ve identified the recurring themes, the next step is to create a coding framework. This framework involves systematically categorizing different segments of the narratives under relevant themes. This process helps you organize the data, making it easier to compare and contrast different stories. As you progress, you will refine your coding framework, ensuring that it accurately captures the nuances of the narratives.

B. Structural Analysis

• Mapping narrative components: Structural analysis focuses on the elements that constitute a narrative. This includes identifying characters, settings, events, and conflicts within the stories. By mapping out these components, you can gain insights into how narratives are constructed and how they influence the overall message. For instance, if you are studying travel narratives, you might analyze how the depiction of different locations impacts the narrative’s tone and meaning.
• Analyzing narrative progression and development: In addition to mapping the components, you should also analyze the progression and development of the narratives. How do stories unfold over time? What pivotal moments shape the trajectory of the narrative? Answering these questions can help you uncover the underlying dynamics that drive the stories. For example, in analyzing narratives about career success, you might examine how setbacks and triumphs contribute to the overall narrative arc.

C. Discourse Analysis

• Exploring language use and meaning construction: Discourse analysis delves into the language used within narratives. Words, phrases, and rhetorical devices are not merely tools of expression; they shape the meaning and interpretation of the stories. By examining language use, you can uncover hidden nuances and perspectives. If you are studying political narratives, for instance, you might analyze how certain linguistic choices influence public opinion.
• Uncovering underlying ideologies and power dynamics: Beyond surface-level language, discourse analysis helps you reveal the underlying ideologies and power dynamics present in narratives. Who has the authority to tell their story? Whose voices are marginalized or silenced? These questions shed light on the social, cultural, and political context within which narratives are constructed. When studying gender dynamics, for example, you might analyze how gendered language perpetuates certain stereotypes.

Addressing Challenges in Narrative Analysis

Subjectivity and researcher bias.

• Acknowledging Researcher Perspective: It’s important to recognize that researchers bring their own perspectives and biases to the analysis. These biases can influence interpretation and potentially skew findings.
• Strategies for Minimizing Bias: Researchers should engage in reflexivity, acknowledging their own biases and beliefs. Employing a diverse team for analysis can help mitigate individual biases. Clear documentation of analytical decisions and interpretations also enhances transparency.

Read – Research Bias: Definition, Types + Examples

Ensuring Rigor and Reliability

• Establishing Intercoder Reliability: In collaborative analysis, ensuring consistency among coders is essential. Intercoder reliability tests can quantify the agreement between coders and improve the robustness of findings.
• Triangulation of Findings: To enhance the credibility of narrative analysis, researchers can triangulate findings by comparing them with data from other sources or methods. This approach strengthens the validity of the interpretations.

Ethical Considerations

• Respecting Participants’ Confidentiality and Privacy: Researchers must prioritize the protection of participants’ identities and sensitive information when presenting narratives. Anonymization techniques and pseudonyms can be employed to maintain confidentiality.
• Informed Consent and Transparent Reporting: Obtaining informed consent from participants is crucial, especially when sharing personal stories. Researchers should provide clear information about the study’s purpose and potential consequences. Transparent reporting ensures the ethical handling of data.

Read More: What Are Ethical Practices in Market Research?

Applications of Narrative Analysis

• Psychology and Mental Health Research: Narrative analysis finds extensive use in psychology and mental health research. It allows researchers to explore individual experiences of trauma, coping mechanisms, and personal growth. By analyzing narratives, researchers can gain insights into the subjective realities of individuals and how they construct their own identities in the face of challenges.
• Sociological Studies: In sociological research, narrative analysis helps unveil the ways individuals navigate social structures and norms. It provides a window into how people perceive their roles in society, their interactions with institutions, and the impact of societal changes on their lives.
• Anthropological Research: Anthropologists employ narrative analysis to study cultural practices, rituals, and traditions. Researchers can better understand the collective identity, historical memory, and cultural values that shape the group’s worldview through their stories.
• Educational Research: Narrative analysis is invaluable in educational research, as it sheds light on students’ learning experiences, challenges, and perspectives. It allows educators to tailor teaching methods to students’ needs and adapt curricula to better resonate with their experiences.
• Healthcare and Patient Narratives: In healthcare, narrative analysis plays a crucial role in understanding patient experiences, illness narratives, and the doctor-patient relationship. Healthcare professionals analyzing patient narratives can improve patient-centered care and enhance communication between patients and medical practitioners.

In conclusion, narrative analysis is a versatile and insightful qualitative research method that enables you to explore the rich tapestry of human experiences. Through its diverse methods and applications across psychology, sociology, anthropology, education, and healthcare, narrative analysis empowers you to unlock the stories that drive our understanding of the world around us. So, as you embark on your research journey, consider integrating narrative analysis to delve deeper into the narratives that define us all.

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• data collection methods
• narrative analysis
• qualitative research
• quantitative research
• research bias
• thematic analysis
• Olayemi Jemimah Aransiola

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Narrative Analysis

• Reference work entry
• First Online: 13 January 2019
• Cite this reference work entry

• Nicole L. Sharp 2 ,
• Rosalind A. Bye 2 &
• Anne Cusick 3  

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13 Citations

Narrative inquiry methods have much to offer within health and social research. They have the capacity to reveal the complexity of human experience and to understand how people make sense of their lives within social, cultural, and historical contexts. There is no set approach to undertaking a narrative inquiry, and a number of scholars have offered interpretations of narrative inquiry approaches. Various combinations have also been employed successfully in the literature. There are, however, limited detailed accounts of the actual techniques and processes undertaken during the analysis phase of narrative inquiry. This can make it difficult for researchers to know where to start (and stop) when they come to do narrative analysis. This chapter describes in detail the practical steps that can be undertaken within narrative analysis. Drawing on the work of Polkinghorne (Int J Qual Stud Educ. 8(1):5–23, 1995), both narrative analysis and paradigmatic analysis of narrative techniques are explored, as they offer equally useful insights for different purposes. Narrative analysis procedures reveal the constructed story of an individual participant, while paradigmatic analysis of narratives uses both inductive and deductive means to identify common and contrasting themes between stories. These analysis methods can be used separately, or in combination, depending on the aims of the research. Details from narrative inquiries conducted by the authors to reveal the stories of emerging adults with cerebral palsy, and families of adolescents with acquired brain inquiry, are used throughout the chapter to provide practical examples of narrative analysis techniques.

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Narrative Research

Concluding Comments: Challenges, Opportunities and Future Directions in Narrative Inquiry

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Sharp, N.L., Bye, R.A., Cusick, A. (2019). Narrative Analysis. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_106

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Research Guides

Narrative Analysis

Nicole Ayers; Alexandra Fields; and Michelle Koehler

Description

Narrative analysis is a research methodology that is primarily used in qualitative research with the goal of understanding research participants’ “self-generated meanings” (Flick, 2014, p. 204). Narrative analysis uses participants’ voices and the events that participants describe as occurring in their lives in order to construct a chronological story from the data (Franzosi, 1998). Narrative analysis is seen as particularly helpful in conveying how the participants’ lived experiences, including their self-perceptions, perceptions of events, and perceptions of others, informs their understanding of themselves and the world, and it is rooted in a variety of narrative theories that help those engaging in narrative analysis identify different structures for generating stories out of data (Herman & Vervaeck, 2005). Not only does narrative analysis lend itself well to critical and interpretivist paradigms, but it is also seen as a particularly useful tool for ethnographers. The majority of researchers who employ narrative analysis methodologies do so because they want to understand the many contradictions and layers of meaning found in narratives as well as to understand how “narratives operate dialogically between the personal and the surrounding social worlds that produce, consume, silence and contest them” (Flick, 2014, p. 204). Therefore, narrative analysis offers researchers the opportunity to deconstruct participants’ stories and to recontextualize them within the larger social world, which can prove helpful to both interpretivist and critical paradigms that hope to explore and, potentially, contend misperceptions about those being studied.

Not only does narrative analysis lend itself well to critical and interpretivist paradigms, but it is also seen as a particularly useful tool for ethnographers. Specifically, since ethnographers frequently employ participant interviews as the tool for constructing an understanding of social phenomena and social locations, narrative analysis can provide a unique lens for ethnographers to place participants’ stories at the center of their research (Franzosi, 1998). Moreover, ethnographers have often been criticized for reifying existing stereotypes and misperceptions of their research participants. Narrative analysis, therefore, is seen as a potential strategy for ensuring that participants are the ones sharing their stories as opposed to the researchers sharing their interpretations of participants’ experiences (Gubrium & Holstein, 1999; Kim, 2016).

Flick, U. (2014). The SAGE handbook of qualitative data analysis . London, England: SAGE.

Franzosi, R. (1998). Narrative analysis: Or why (and how) sociologists should be interested in narrative. Annual Review of Sociology, 24, 517-554. http://dx.doi.org/10.1146/annurev.soc.24.1.517

Gubrium, J. F., & Holstein, J. A. (1999). At the border of narrative and ethnography. Journal of  Contemporary Ethnography , 28 (5), 561–573. https://dx.doi-org/10.1177/089124199129023550

Herman, L., & Vervaeck, B. (2005). Handbook of narrative analysis . Lincoln, NE: University of Nebraska Press.

Kim, J.-H. (2016). Understanding narrative inquiry: The crafting and analysis of stories as research. Thousand Oaks, CA: SAGE.

Key Research Books and Articles on Narrative Analysis Methodology

In this paper, Franzosi makes the case for why sociologists should consider narrative analysis methodologies, suggesting that narrative analysis naturally aligns with the field of sociology. Franzosi asserts that since much of the empirical data that sociologists collect is inherently written as narrative, it is only natural for sociologists to utilize narrative analysis as a methodological approach to their research. Moreover, because Franzosi provides a clear working definition of narrative analysis, then walks readers through analysis of a narrative text, this paper is a useful tool not just for sociologists but for all academics interested in narrative analysis and looking for clarity on how one might engage in the narrative analysis of text.

In this article, Gubrium and Holstein assert that researchers often exist between the borders of ethnographic and narrative methodologies, and that, in the future, rather than delineating clear borders between these methodologies, researchers should instead become comfortable existing within the tensions of this border. Specifically, the argument is made that ethnographic research has been criticized for often reifying existing stereotypes or misunderstandings of those being studied rather than presenting an interpretation of the participants and their spaces/places through the eyes of those existing within them. Therefore, the suggestion is that narrative analysis could provide a tool for ethnographers to better understand the role of incorporating participants’ stories and understandings of their spaces and places within the ethnographic study. This paper is helpful then in demonstrating a rationale as well as a means for ethnographers to incorporate narrative analysis into their methodologies.

In this handbook, the authors define a variety of narrative theories and illuminate the potential benefits and limitations of each. The authors divide the book into three chapters based upon major narrative theoretical constructs: “Before and Surrounding Structuralism,” “Structuralism,” and “Post-Classical Narratology”. Within each chapter, the authors begin by providing the history and development of each theory as well as concrete understandings of how academics, researchers, and theorists alike would approach narrative analysis from their varied perspectives depending upon their narrative theory alignment. For example, the authors explain how classical structuralists and post-classicists approach narrative analysis differently, and they use two stories as models for demonstrating the different nuanced approaches to narrative analysis (p. 103). This text serves as a useful tool for those looking to engage in narrative analysis but struggling to understand its varied theoretical underpinnings and how they inform one’s approach to narrative analysis. however, for those looking for a basic definition and understanding of approaches to narrative analysis, this predominantly theoretical text may prove cumbersome.

Josselson, R. and Lieblich, A. (1999). Making meaning of narratives. Thousand Oaks, CA: Sage.

In this book, the authors present readers with ten essays that explore the use of narrative analysis within a variety of disciplines, including literary studies, nursing, criminology, sociology, and psychology. The first essay, unlike the other nine, begins by elucidating the issues, both methodological and ethical, that researchers may face by using people’s stories as their primary and/or only source of data, and it helps readers understand the notion of narratives telling many different truths. The other nine essays provide examples of narrative analysis research within specific disciplines. The strengths of this book are that it helps researchers conceptualize the varied ways in which narrative analysis can be applied and to think critically about the “multiple truths” that can be explored through narrative analysis. Thus, if one is less interested in the history of narrative analysis or multiple definitions of narrative analysis, but instead wants to see examples of narrative analysis in action, this book will prove useful.

Kim, J.-H. (2016). Understanding narrative inquiry: The crafting and analysis of stories as research . Thousand Oaks, CA: SAGE.

This textbook provides both a theoretical and methodological understanding of narrative inquiry as a qualitative research theory and methodology. The book begins by exploring the many disciplines in which narrative inquiry can be employed and the theoretical underpinnings behind narrative inquiry. After providing a wealth of theoretical lenses for which researchers might employ narrative inquiry, Dr. Kim then provides explicit feedback on how one should engage in data collection and analysis using narrative inquiry; the book ends by addressing critical issues to consider as narrative researchers and including examples of narrative inquiry in action. Therefore, this textbook provides a thorough examination of narrative inquiry through both theoretical and methodological lenses, and it is highly recommended for any qualitative researcher interested in engaging in narrative research.

Recent Dissertations Using Narrative Analysis Methodology

Njoku, N. R. (2017). Woman in the making: The impact of the constructed campus environment of Xavier University of Louisiana on the construction of Black womanhood . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 10637092)

This study adopts a narrative analysis approach as a means for giving voice to African American woman attending Xavier University of Louisiana. Through a narrative analysis approach, participants’ perspectives were not contrasted to others, but rather highlighted individually. The narrative inquiry approach is centered within Black feminist epistemology and works toward telling the stories of each participant. The research questions guiding this research are:

• How do African American women construct Black womanhood?
• What role does the HBCU [historically Black colleges and universities] campus environment play in facilitating these constructions of Black womanhood? (p. 6)

Participants were alumni of Xavier University who identified as both African-American and cisgender women. The data were initially gathered through in-depth interviews to establish a timeline and develop a relationship between researcher and participant. For the second aspect of data collection, participants were asked to compose a timeline of their lives, combining pictures with the narrative. This then was used as a prompt for further reflection as each participant shared stories about the pictures along the timeline. One implication of this study is that research that conflates Black men muffles the voices of the women, who have their own narratives and experiences to share. The lack of nuance between groups lessens the chances that the needs of these women will be met in their academic endeavors.

Petrone, D. (2016). A narrative analysis of women’s desires and contributions to community, sentience, agency and transformation: A narrative analysis . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 10146171)

The goal of this dissertation is to explore the ways that women and their community develop agency. A perspective of critical literacy and narrative inquiry create a space where participants explore and grow; the assumption remains that “humanity is not finished” (p. ii), which allows for continued growth and development.

Within this study, narrative analysis is utilized along with a critical approach to disrupt ideas of power. Within a narrative analysis view, the narrative is seen as data, and a stance of embracing change that connects the words to the world is adopted. Additionally, the idea of highlighting the connection or collaboration between researcher and participant is important throughout this study. Data were gathered through a focus group comprised largely of friends or acquaintances of the researcher who shared a sense of “unfinishedness” (p. 51), which then allowed for a connection based on common sharing and support. Interviews were the primary source of data, both within the larger focus group and then with individuals. The implications of this study are in the possibility for human development, specifically in relation to internal growth, as individuals work to read, and interact with, the world.

Wingfield, M. V. (2018). Becoming all that I can be: Narrative analysis of African-American students’ literacy perceptions and experiences in an urban Title I school . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 10784392)

Within in this study, students’ writing, specifically poetry, is analyzed for its narrative connections to the students’ own lives. This allows for students’ narratives to disrupt the deficit approach frequently connected with research around Title I schools by acknowledging their “culturally situated literacies, opinions, and academic potential for success” (p. 72). More specifically, the purpose of this study is to explore students’ perceptions of literacy experiences through high school. The research questions guiding this study are:

• How do African-American high school graduates from a low-income urban community school describe their high school literacy experiences?
• How do African-American students perceive the ways in which their literacy experiences were culturally responsive by addressing their varied literacy practices? (p. 16)

Narrative analysis was adopted to explore a critical approach and culturally responsive pedagogy. Data were gathered through interviews and artifacts that included books, photos, and the senior portfolio. These data were analyzed as points within a story, or as part of the participants’ narrative of their experience. The implications of this study are support of culturally responsive pedagogy and critical literacies in Title I schools.

Internet Resources

Centre for Narrative Research’s Blog ( https://centrefornarrativeresearch.wordpress.com/2018/02/16/centre-for-narrative-research-spring-summer-2018-events/ )

The Centre for Narrative Research Blog offers an up-to-date blog from The University of East London’s School of Social Sciences with events around the world, which narrative researchers could attend.

The Australian Department of Defense: “A Review of Narrative Methodology” Bibliography PDF ( http://www.webpages.uidaho.edu/css506/506%20readings/review%20of%20narritive%20methodology%20australian%20gov.pdf )

The Australian Department of Defense: Defense, Science and Technology Organisation (DSTO) published an annotated bibliography titled “A Review of Narrative Methodology.” The DTSO cites many publications of narrative methodology research that study human action. The executive summary that starts the bibliography provides a clear definition of narrative inquiry and its historical background.

Narrative Inquiry: What’s Your Story? ( http://qualitativeresearchontario.openetext.utoronto.ca/chapter/video-module-3-doing-qualitative-research/ )

A research guide from The University of Western Ontario provides video lectures pertaining to qualitative research.  Scroll down to a video lecture, entitled, “Narrative Inquiry: What’s Your Story?” from Dr. Debbie Laliberte Rudman of The University of Western Ontario. The resource also includes a list of suggested readings.

Professional Organizations and Conferences

The following associations and conferences have a focus on Narrative Inquiry. They serve as a venue for presenting current research.  They also serve as additional points for researchers to develop their understanding of and collaboration within the field of Narrative Inquiry.

The American Educational Resource Association (AERA) has a specific webpage for narrative research resources, which includes a YouTube Video of Vivian Gussin Paley’s discussion “How can we study the narrative of play when the children are given so little time to play?”, book suggestions with annotations, resources sorted by journals, books, teachers, multicultural, feminism, identity, qualitative books that include narrative research, specific journal articles, websites, and notes and comments from our members.

• AERA Narrative Research SIG Website ( https://sites.google.com/site/aeranarrativeresearchsig/home/resources-1 )
• The International Society for the Study of Narrative is an organization with an annual conference. http://narrative.georgetown.edu/conferences/
• Narrative Matters is a biannual conference on narrative analysis. The 2018 conference was held at the University of Twente in the Netherlands. https://www.utwente.nl/en/bms/narrativematters2018/

Narrative Analysis Copyright © 2019 by Nicole Ayers; Alexandra Fields; and Michelle Koehler is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Narrative Analysis

Narrative analysis is a valuable data analysis technique in qualitative research. It is typically used in those studies which have already employed narrative inquiry as a qualitative method. Narrative knowledge is created and constructed through the stories of lived experience and sense-making, the meanings people afford to them, and therefore offers valuable insight into the complexity of human lives, cultures, and behaviours. Narrative analysis uses the ‘story’ as the unit of analysis, in contrast to thematic and other forms of qualitative analysis.

Narrative analysis is useful for practitioners and researchers who wish to focus on individual experiences, e.g. via collected stories and unstructured interviews.  Examples of possible applications include case studies; patients’ experiences of health care services or illness; life stories and experiences of social care clients; victims’ experiences of the criminal justice system.

This training benefits participants who wish to advance their knowledge of qualitative research methods. It explores the opportunities that narrative analysis offers in a range of applied and policy settings and contexts. It is relevant to researchers who have narrative data (or plan to collect narrative data) ready for analysis.

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Narrative Analysis: Methods and Examples

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

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

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

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

What Is Narrative Research?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What is Narrative Analysis?

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

What is narrative analysis in qualitative research?

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

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

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

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

Examples of personal narratives

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

Topical stories

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

Personal narrative 

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

Entire life story

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

Capturing narrative data

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

Transcribing narrative data

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

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

Coding in narrative analysis

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

What is narrative research

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

Download Free Narrative Analysis Guide

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

The Narrative Analysis PDF will be emailed to you

Inductive method for narrative analysis

Learn about inductive narrative method:.

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

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

Entrance and Exit Talk

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

A story may start with the phrases: 

“There was this one time…”, 

“Let me give you an example”, 

and “I’ll always remember when…”

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

“So that’s how that wrapped up…”

“That is a pretty classic example of…”

and “and that was the end of that.”

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

Deductive method for narrative analysis

Learn about deductive narrative method:.

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

Abstract: The core thesis of the story, summary

Orientation: Time, place, situation, and characters

Complicating action: Sequence of events, plot

Evaluation: How the storyteller comments on meaning 

Resolution: Outcome of the story

Coda: Story’s ending 

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

You - A character is in a zone of comfort

Need - But they want something.

Go - They enter an unfamiliar situation,

Search - Adapt to it,

Find - Get what they wanted,

Take/Pay - Pay a heavy price for it,

Return - Then return to their familiar situation,

Change - Having changed.

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

Hybrid Inductive and Deductive Narrative Analysis

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

How to analyze data in a narrative interview

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

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

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

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

Step 1: Code Narrative Blocks

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

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

Step 2: Group and Read By Live-Event

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

Step 3: Create Nested Story Structure Codes

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

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

Abstract (these codes are based on story structure)

Orientation

Complicating action

More generally put:

Life Event Code   

Story Structure Code 1

Story Structure Code 2

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

Step 4: Delve into the Story Structure

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

Deciding to have children

Parent divorce

Parents still together

Step 5: Compare Across Story Structure

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

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

Step 6: Tell the Core Narrative

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

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

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

Try Delve, Narrative Analysis Software

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

References:

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

Cite this blog post:

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

Qualitative Research: Narrative

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What is Narrative Analysis?

Narrative research  is a term that subsumes a group of approaches that in turn rely on the written or spoken words or visual representation of individuals. These approaches typically focus on the lives of individuals as told through their own stories. Clandinin and Connelly define it as "a way of understanding and inquiring into experience through “collaboration between researcher and participants, over time, in a place or series of places, and in social interaction with milieus” ( Clandinin  & Connelly, 2000, p. 20)."

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• Narrative Analysis Survey of the science of Narrative Analysis by Catherine Kohler Riessman, a leading voice in the field.
• The state of the art in Narrative Inquiry Reflections on narrative inquiry and the status of the field.
• Stories of Experience and Narrative Inquiry This paper briefly surveys forms of narrative inquiry in educational studies and outline certain criteria, methods, and writing forms, which are described in terms of beginning the story, living the story, and selecting stories to construct and reconstruct narrative plots.
• Validity in Issues of Narrative Research Examines the question of validity in narrative studies.
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Narrative Reviews: Flexible, Rigorous, and Practical

Javeed sukhera.

Javeed Sukhera, MD, PhD, FRCPC , is Chair/Chief, Department of Psychiatry, Institute of Living and Hartford Hospital

Introduction

Narrative reviews are a type of knowledge synthesis grounded in a distinct research tradition. They are often framed as non-systematic, which implies that there is a hierarchy of evidence placing narrative reviews below other review forms. 1 However, narrative reviews are highly useful to medical educators and researchers. While a systematic review often focuses on a narrow question in a specific context, with a prespecified method to synthesize findings from similar studies, a narrative review can include a wide variety of studies and provide an overall summary, with interpretation and critique. 1 Examples of narrative review types include state-of-the-art, critical, and integrative reviews, among many others.

Foundations

Narrative reviews are situated within diverse disciplines in the social sciences and humanities. Most forms of narrative reviews align with subjectivist and interpretivist paradigms. These worldviews emphasize that reality is subjective and dynamic. They contrast with the positivist and post-positivist worldviews that are the foundations of systematic reviews: a single reality can be known through experimental research. Unlike systematic reviews, narrative reviews offer researchers the ability to synthesize multiple points of view and harness unique review team perspectives, which will shape the analysis. Therefore, insights gained from a narrative review will vary depending on the individual, organizational, or historical contexts in which the review was conducted. 1 - 5

Why Choose a Narrative Review?

Narrative reviews allow researchers to describe what is known on a topic while conducting a subjective examination and critique of an entire body of literature. Authors can describe the topic's current status while providing insights on advancing the field, new theories, or current evidence viewed from different or unusual perspectives. 3 Therefore, such reviews can be useful by exploring topics that are under-researched as well as for new insights or ways of thinking regarding well-developed, robustly researched fields.

Narrative reviews are often useful for topics that require a meaningful synthesis of research evidence that may be complex or broad and that require detailed, nuanced description and interpretation. 1 See Boxes 1 and 2 for resources on writing a narrative review as well as a case example of a program director's use of a narrative review for an interprofessional education experience. This Journal of Graduate Medical Education (JGME) special review series will continue to use the Case of Dr. Smith to consider the same question using different review methodologies.

Box 1 The Case of Dr. Smith

Dr. Smith, a program director, has been tasked to develop an interprofessional education (IPE) experience for the residency program. Dr. Smith decides that conducting a literature review would be a savvy way to examine the existing evidence and generate a publication useful to others. Using PubMed and a general subject search with “interprofessional education,” Dr. Smith identifies 24 000 matches. Dr. Smith begins to randomly sample the papers and notes the huge diversity of types and approaches: randomized trials, qualitative investigations, critical perspectives, and more.

Dr. Smith decides to do a meta-narrative review, because she notes that there are tensions and contradictions in the ways in which IPE is discussed by different health professions education communities, such as in nursing literature vs in medical journals.

Box 2 Resources

Ferrari R. Writing narrative style literature reviews. Med Writing . 2015;24(4):230-235. doi: 10.1179/2047480615Z.000000000329

Green BN, Johnson CD, Adams A. Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. J Chiropr Med . 2006;5(3):101-117. doi: 10.1016/S0899-3467(07)60142-6

Gregory AT, Denniss AR. An introduction to writing narrative and systematic reviews—tasks, tips and traps for aspiring authors. Heart Lung Circ . 2018;27(7):893-898. doi: 10.1016/j.hlc.2018.03.027

Murphy CM. Writing an effective review article. J Med Toxicol . 2012;8(2):89-90. doi: 10.1007/s13181-012-0234-2

Process and Rigor

While each type of narrative review has its own associated markers of rigor, the following guidelines are broadly applicable to narrative reviews and can help readers critically appraise their quality. These principles may also guide researchers who wish to conduct narrative reviews. When engaging with a narrative review as a reader or a researcher, scholars are advised to be conversant with the following 5 foundational elements of narrative reviews.

Rationale for a Narrative Review

First, scholars should consider the framing of the research question. Does the topic being studied align with the type of knowledge synthesis performed through a narrative review? Authors should have a clear research question and a specific audience target. Authors should also provide a rationale for why a narrative review method was chosen. 6 The manuscript should include the initial research question as well as details about any iterative refinements to the question.

Clarity of Boundaries, Scope, and Definitions

Second, although narrative reviews do not typically involve strict predetermined inclusion or exclusion criteria, scholars should explicitly demarcate the boundaries and scope of their topic. They should also clearly define key terms related to the topic and research question and any definitions used. Authors should elaborate why they chose a particular definition if others were available. As narrative reviews are flexible, the initial scope may change through the review process. In such circumstances, authors should provide reasonable justification for the evolution of inclusion and exclusion criteria and a description of how this affected the literature search.

Justification for Inclusion and Exclusion Criteria

Third, authors of narrative reviews should explain which search terms and databases were included in the synthesis and why. For example, did authors include research studies from a particular database, time frame, or study design? Did they include commentaries or empirical articles? Did they include grey literature such as trade publications, reports, or digital media? Each of the authors' choices should be outlined with appropriate reasoning. 7 Narrative reviews tend to be iterative and involve multiple cycles of searching, analysis, and interpretation. High-quality narrative reviews usually include pivotal or seminal papers that address the phenomenon of interest and other manuscripts that are relevant to the research question.

Reflexivity and a Saturation/Sufficiency Statement

Fourth, narrative reviews should clearly specify any factors that may have shaped the authors' interpretations and analysis. One fundamental distinction between narrative and non-narrative reviews is that narrative reviews explicitly recognize that they may not include all relevant literature on a topic. Since narrative reviews do not aim to be inclusive of all literature addressing the phenomenon of interest, a justification for the selection of manuscripts must be included. Authors should carefully outline how researchers conducted analyses and how they determined that sufficient analysis and interpretation was achieved. This latter concept is similar to considerations of saturation or thematic sufficiency in primary qualitative research. 8

Details on Analysis and Interpretation

Lastly, since several different categories of reviews fall under the narrative review umbrella, the analysis conducted in a narrative review varies by type. Regardless of the type of narrative review carried out, authors should clearly describe how analyses were conducted and provide justification for their approach. Narrative reviews are enhanced when researchers are explicit about how their perspectives and experiences informed problem identification, interpretation, and analysis. Given that authors' unique perspectives shape the selection of literature and its interpretation, narrative reviews may be reproduced, but different authors will likely yield different insights and interpretations.

Distinctive Methods and Subtypes

The narrative review has been commonly framed as an umbrella term that includes several different subtypes of reviews. These narrative medicine subtypes share the goals of deepening an understanding of a topic, while describing why researchers chose to explore and analyze the topic in a specific way.

There are several subtypes of narrative reviews with distinctive methodologies; each offers a unique way of approaching the research question and analyzing and interpreting the literature. This article will describe some common narrative review types that will also be discussed in upcoming JGME special articles on reviews: state-of-the-art , meta-ethnographic , critical , and theory integration reviews.

A state-of-the-art review attempts to summarize the research concerning a specific topic along a timeline of significant changes in understanding or research orientations. By focusing on such turning points in the history of evolving understandings of a phenomenon, state-of-the-art reviews offer a summary of the current state of understanding, how such an understanding was developed, and an idea of future directions. A state-of-the art review seeks to offer a 3-part description: where are we now in our understanding, how did we get here, and where should we go next?

A meta-ethnographic review involves choosing and interpreting qualitative research evidence about a specific topic. Working exclusively with qualitative data, this type of knowledge synthesis aims to generate new insights or new conclusions about a topic. It draws together insights and analyses from existing publications of qualitative research to construct new knowledge that spans across these individual, and often small scale, studies.

A meta-narrative review seeks to explore and make sense of contradictions and tensions within the literature. A meta-narrative review maps how a certain topic is understood in distinct ways, conducts a focused search to describe and compare narratives, and then seeks to make sense of how such narratives are interpreted across different disciplines or historical contexts, as part of the analysis. 9

A critical review is a narrative synthesis of literature that brings an interpretative lens: the review is shaped by a theory, a critical point of view, or perspectives from other domains to inform the literature analysis. Critical reviews involve an interpretative process that combines the reviewer's theoretical premise with existing theories and models to allow for synthesis and interpretation of diverse studies. First, reviewers develop and outline their interpretive theoretical position, which is informed by individual knowledge and experience. Next, a noncomprehensive search is completed to capture and identify dominant themes focused on a research question. 8 , 10

An integrative review typically has 1 of 2 different orientations. Empirical integrative reviews analyze and synthesize publications of evidence-based studies with diverse methodologies. In contrast, theoretical integrative reviews conduct an analysis of the available theories addressing a phenomenon, critically appraise those theories, and propose an advancement in the development of those theories. Both types of integrative reviews follow a multistage approach including problem identification, searching, evaluation, analysis, and presentation. 11

Strengths and Weaknesses

Narrative reviews have many strengths. They are flexible and practical, and ideally provide a readable, relevant synthesis of a diverse literature. Narrative reviews are often helpful for teaching or learning about a topic because they deliver a general overview. They are also useful for setting the stage for future research, as they offer an interpretation of the literature, note gaps, and critique research to date.

Such reviews may be useful for providing general background; however, a more comprehensive form of review may be necessary. Narrative reviews do not offer an evidence-based synthesis for focused questions, nor do they offer definitive guideline statements. All types of narrative reviews offer interpretations that are open to critique and will vary depending on the author team or context of the review.

Conclusions

Well-done narrative reviews provide a readable, thoughtful, and practical synthesis on a topic. They allow review authors to advance new ideas while describing and interpreting literature in the field. Narrative reviews do not aim to be systematic syntheses that answer a specific, highly focused question; instead, they offer carefully thought out and rigorous interpretations of a body of knowledge. Such reviews will not provide an exhaustive, comprehensive review of the literature; however, they are useful for a rich and meaningful summary of a topic.

Methodologies and Inequities Participatory and Narrative Approaches to Research with Marginalized Communities

Article sidebar, main article content.

In this commentary, we reflect on a study investigating how young people living with HIV navigated the COVID-19 pandemic and offer concrete methodological approaches to studying health inequity. We describe how participatory and narrative-based methods helped us develop five specific study protocols that reflected our commitments to equity in research: revising questions to account for local conditions of risk; intervening in histories of extractive research practices leveraged against communities at the margins; phrasing demographic questions to account for the complexity of identity; incorporating consent iteratively across the study; and offering incentives that were consistent with participants’ expertise of their own lived experiences. We use these reflections to further ongoing conversations about integrating equity into rhetorically inflected health research. 

Article Details

Mckinley green, george mason university.

McKinley Green is an Assistant Professor of English at George Mason University, where he researches technical and professional communication, queer theory, and rhetorics of health and medicine.

Val Crutcher, Youth and AIDS Projects

Val Crutcher is the Executive Director of the Youth and AIDS Projects. Val graduated from Concordia University with a B.A. in Political Science, and she began her youth-work career in 1989 and has worked with various shelters and youth service in the Minneapolis/St. Paul area. She has been with YAP since 1998.

Océane Lune, Youth and AIDS Projects

Océane Lune, YAP’s Community Engagement Coordinator, is a Black, Queer, Non-Binary, HIV+, Louisiana native. They were diagnosed with HIV in 2005 as a homeless youth and sex worker. Since then, Océane has been a fierce, unapologetic advocate for those impacted by HIV, especially young people, Trans*/Non-Binary folks, and Black communities.

Munira Mutmainna, George Mason University

Munira Mutmainna is a Doctoral candidate in Writing and Rhetoric and George Mason University. Her current research focuses on immigrant health rhetoric and communication in the U.S. settings.

Raquelle Lenoir, Youth and AIDS Projects

Racquelle Lenoir, the Lead Case Manager at YAP, has been working in public health for 5 years. However, most of her reward for public health is with her work in HIV. Raquelle has been a part of many different forums as well as being an educator on other health disparities.

Andrew Schuster, Youth and AIDS Projects

Andrew Schuster is the Director of Operations at YAP. With a B.A. in Communications/ Journalism and Justice/Peace Studies from the University of St. Thomas (St. Paul, MN), has gained experience in HIV advocacy, project management, community organizing, and he strives to eliminate stereotypes, stigma, and barriers to care.

Gage Urvina, Youth and AIDS Projects

Gage Urvina is the client services coordinator at YAP. Gage has worked in the field of HIV for seven years, where he strives to meet clients where they are and to help folks to establish and maintain healthy lifestyles.

Calla Brown, University of Minnesota, Department of Pediatrics and Youth and AIDS Projects

Calla Brown is an internist and pediatrician and practices primary care at a community health center in Minneapolis. She is an Assistant Professor in the Department of Pediatrics at the University of Minnesota and joined YAP in 2020. Calla is passionate about community health, human rights, and health justice.

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Qualitative Data Analysis Methodologies and Methods

Qualitative data analysis involves interpreting non-numerical data to identify patterns, themes, and insights. There are several methodologies and methods used in qualitative data analysis.

In this article, we will explore qualitative data analysis techniques in great detail, with each method providing a different perspective on how to interpret qualitative data.

Table of Content

Types of Qualitative Data Analysis Methodologies

1. content analysis, 2. thematic analysis, 3. narrative analysis, 4. discourse analysis, 5. grounded theory analysis, 6. text analysis, 7. ethnographic analysis, advantages and disadvantages of different qualitative data analysis methodologies, best practices for qualitative data analysis, qualitative data analysis methods- faq’s.

Lets weigh the benefits and disadvantages of each:

Content analysis involves systematically reading textual content or other types of communication to perceive patterns, themes, and meanings within the content. It provides a dependent technique to inspecting huge volumes of records to discover insights or trends. Researchers categorize and code the content material based on predetermined criteria or emergent themes, taking into consideration quantitative and qualitative interpretation of the facts. Content analysis is regularly an iterative procedure, with researchers revisiting and refining the coding scheme, collecting additional facts, or accomplishing in addition analysis as needed to deepen know-how or cope with new studies questions.

There are 3 fundamental techniques to content analysis:

• Conventional Content Analysis : In conventional content analysis, researchers technique the records with out preconceived categories or theoretical frameworks. Instead, they allow classes and themes to emerge evidently from the statistics through an iterative system of coding and analysis. This technique is exploratory and bendy, allowing for the discovery of latest insights and styles inside the content material.
• Directed Content Analysis : Directed content material analysis entails studying the statistics based totally on existing theories or principles. Researchers start with predefined categories or subject matters derived from theoretical frameworks or previous research findings. The analysis is focused on confirming, refining, or extending present theories in place of coming across new ones. Directed content analysis is specifically beneficial whilst researchers intention to test hypotheses or explore particular concepts in the statistics.
• Summative Content Analysis : Summative content material analysis focuses on quantifying the presence or frequency of precise content within the information. Researchers expand predetermined classes or coding schemes primarily based on predefined criteria, after which systematically code the statistics in line with those classes. The emphasis is on counting occurrences of predefined attributes or topics to provide a numerical summary of the content. Summative content material analysis is frequently used to track modifications over time, examine unique assets of content material, or verify the superiority of specific subject matters inside a dataset.

When to Use Content Analysis?

• Exploratory Research : Content analysis is appropriate for exploratory research in which the goal is to uncover new insights, discover emerging developments, or recognize the breadth of communique on a particular subject matter.
• Comparative Analysis: It is useful for comparative analysis, permitting researchers to compare conversation throughout extraordinary sources, time periods, or cultural contexts.
• Historical Analysis : Content analysis can be carried out to historical research, allowing researchers to analyze ancient files, media content, or archival substances to apprehend conversation styles over the years.
• Policy Analysis: It is valuable for policy analysis, supporting researchers look at the portrayal of problems in media or public discourse and informing coverage-making methods.
• Market Research: Content analysis is usually utilized in market research to investigate advertising and marketing substances, social media content, and customer critiques, presenting insights into patron perceptions and possibilities.

Thematic analysis is a method for identifying, analyzing, and reporting styles or topics within qualitative records. It entails systematically coding and categorizing information to become aware of not unusual issues, styles, or ideas that emerge from the dataset. Researchers interact in a method of inductive reasoning to generate topics that capture the essence of the facts, making an allowance for interpretation and exploration of underlying meanings.

Thematic analysis is appropriate when researchers are seeking for to become aware of, analyze, and document patterns or issues inside qualitative records. It is especially beneficial for exploratory studies where the intention is to find new insights or recognize the breadth of studies and views associated with a specific phenomenon.

Thematic analysis offers a bendy and systematic approach for identifying and reading styles or topics within qualitative statistics, making it a treasured method for exploring complex phenomena and producing insights that inform concept, exercise, and policy.

When to use Thematic analysis?

• Psychology : Thematic analysis is used to explore mental phenomena, which include coping mechanisms in reaction to strain, attitudes towards mental fitness, or stories of trauma.
• Education : Researchers practice thematic analysis to apprehend student perceptions of getting to know environments, teaching methods, or academic interventions.
• Healthcare : Thematic analysis enables take a look at affected person reports with healthcare offerings, attitudes towards treatment alternatives, or obstacles to gaining access to healthcare.
• Market Research: Thematic analysis is applied to research purchaser remarks, perceive product options, or recognize emblem perceptions in marketplace research research.

Narrative analysis entails analyzing and interpreting the memories or narratives that people use to make feel of their stories. It focuses on the shape, content, and which means of narratives to apprehend how people construct and speak their identities, values, and ideals via storytelling. It is especially beneficial for exploring how people assemble and communicate their identities, values, and beliefs through storytelling.

When to use Narrative Analysis?

It’s extensively used throughout numerous disciplines, which includes sociology, psychology, anthropology, literary research, and verbal exchange studies. Some applications of narrative analysis in qualitative statistics analysis methodologies are:

• Understanding Identity Construction : Narrative analysis can be used to explore how people construct their identities through the tales they tell approximately themselves. Researchers can examine the issues, plot systems, and language utilized in narratives to uncover how individuals perceive themselves and their place inside the world.
• Exploring Life Experiences : Researchers frequently use narrative analysis to research the lived reports of people or groups. By inspecting the narratives shared by using members, researchers can advantage insights into the demanding situations, triumphs, and extensive events that shape people’s lives.
• Examining Cultural Meanings and Practices: Narrative analysis can provide treasured insights into cultural meanings and practices. By studying the stories shared within a selected cultural context, researchers can find shared values, ideals, and norms that influence behavior and social interactions.
• Exploring Trauma and Healing : Narrative analysis is usually utilized in studies on trauma and restoration tactics. By studying narratives of trauma survivors, researchers can explore how individuals make experience of their studies, deal with adversity, and embark on trips of restoration and resilience.
• Analyzing Media and Popular Culture : Narrative analysis also can be applied to analyze media texts, inclusive of films, tv suggests, and literature. Researchers can have a look at the narratives constructed within these texts to understand how they reflect and shape cultural beliefs, ideologies, and norms.

Narrative analysis offers a powerful technique for exploring the structure, content, and that means of narratives or stories instructed by people, providing insights into their lived reports, identities, and perspectives. However, researchers need to navigate the interpretive subjectivity, time-extensive nature, and moral concerns related to reading narratives in qualitative studies.

Discourse analysis examines the approaches wherein language is used to construct that means, form social interactions, and reproduce electricity members of the family inside society. It makes a speciality of studying spoken or written texts, in addition to the wider social and cultural contexts in which communique happens. Researchers explore how language displays and shapes social norms, ideologies, and power dynamics.

Discourse analysis is employed when researchers are seeking to investigate social interactions, power dynamics, and identity creation through language. It is applied to take a look at how language shapes social relations, constructs identities, and reflects cultural norms and values.

When to use Discourse Analysis?

• Linguistics and Language Studies : Discourse analysis is foundational to linguistics and language research, where it’s miles used to study language use, communique patterns, and discourse structures. Linguists behavior discourse analysis to investigate how language shapes social interactions, constructs identities, and reflects cultural norms. Discourse analysis facilitates uncover the underlying meanings, ideologies, and energy dynamics embedded in language.
• Media and Communication : Discourse analysis is applied in media and conversation research to have a look at media representations, discursive practices, and ideological frameworks. Researchers conduct discourse analysis to analyze media texts, information coverage, and political speeches, exploring how language constructs and disseminates social meanings and values. Discourse analysis informs media literacy efforts, media grievance, and media coverage debates.
• Political Science : Discourse analysis is applied in political science to look at political rhetoric, public discourse, and policymaking tactics. Researchers behavior discourse analysis to research political speeches, party manifestos, and coverage files, analyzing how language constructs political identities, legitimizes authority, and shapes public opinion. Discourse analysis informs political verbal exchange techniques, political campaigning, and policy advocacy.

Grounded theory analysis is an inductive studies approach used to broaden theories or causes based on empirical data. It includes systematically studying qualitative information to perceive ideas, categories, and relationships that emerge from the statistics itself, rather than testing preconceived hypotheses. Researchers have interaction in a procedure of constant assessment and theoretical sampling to refine and increase theoretical insights.

Grounded theory analysis is hired whilst researchers are seeking for to find styles, relationships, and tactics that emerge from the records itself, with out implementing preconceived hypotheses or theoretical assumptions.

When to use Grounded Theory Analysis?

Grounded concept analysis is applied throughout various disciplines and studies contexts, such as:

• Social Sciences Research : Grounded Theory Analysis is significantly used in sociology, anthropology, psychology, and related disciplines to discover diverse social phenomena together with organization dynamics, social interactions, cultural practices, and societal structures.
• Healthcare Research : In healthcare, Grounded Theory can be implemented to apprehend affected person reviews, healthcare provider-patient interactions, healthcare delivery procedures, and the impact of healthcare guidelines on individuals and communities.
• Organizational Studies : Researchers use Grounded Theory to examine organizational conduct, leadership, place of work subculture, and worker dynamics. It enables in knowledge how groups function and the way they may be advanced.
• Educational Research : In training, Grounded Theory Analysis can be used to discover teaching and getting to know processes, scholar studies, educational regulations, and the effectiveness of educational interventions.

Text analysis involves examining written or verbal communique to extract meaningful insights or styles. It encompasses numerous techniques which includes sentiment analysis, subject matter modeling, and keyword extraction. For instance, in a have a look at on patron opinions of a eating place, textual content analysis is probably used to become aware of established topics along with food first-class, service enjoy, and atmosphere. Key additives and strategies worried in text analysis:

• Sentiment Analysis : This approach includes determining the sentiment expressed in a piece of textual content, whether or not it is high quality, bad, or impartial. Sentiment analysis algorithms use natural language processing (NLP) to analyze the words, phrases, and context within the text to deduce the overall sentiment. For instance, in customer reviews of a eating place, sentiment analysis could be used to gauge purchaser delight levels based totally on the emotions expressed within the critiques.
• Topic Modeling : Topic modeling is a statistical technique used to become aware of the underlying topics or issues present within a group of documents or text statistics. It entails uncovering the latent patterns of co-occurring phrases or terms that constitute awesome topics. Techniques like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) are normally used for topic modeling. In the context of eating place opinions, subject matter modeling should assist identify not unusual subject matters inclusive of meals excellent, provider revel in, cleanliness, etc., across a large corpus of opinions.
• Keyword Extraction : Keyword extraction includes figuring out and extracting the most applicable phrases or phrases from a bit of text that seize its essence or major topics. This technique enables to summarize the important thing content material or subjects mentioned within the textual content. For instance, in eating place analysiss, key-word extraction ought to identify often referred to terms like “scrumptious meals,” “friendly group of workers,” “lengthy wait times,” etc., presenting a quick analysis of customer sentiments and concerns.

When to use Text Analysis?

Text analysis has numerous programs throughout diverse domain names, including:

• Business and Marketing: Analyzing purchaser remarks, sentiment analysis of social media posts, brand monitoring, and market fashion analysis.
• Healthcare: Extracting scientific statistics from scientific notes, analyzing patient comments, and detecting unfavorable drug reactions from textual content information.
• Social Sciences: Studying public discourse, political communique, opinion mining, and discourse analysis in social media.
• Academic Research: Conducting literature analysiss, analyzing studies articles, and identifying rising studies topics and trends.
• Customer Experience : Understanding purchaser sentiments, identifying product or service problems, and improving client satisfaction via text-based totally comments analysis.

Ethnographic analysis involves immersing in a selected cultural or social setting to understand the views, behaviors, and interactions of the human beings within that context. Researchers conduct observations, interviews, and participant observations to gain insights into the culture, practices, and social dynamics of the community under study. It is is suitable when researchers aim to gain an in-depth understanding of a particular cultural or social setting, including the perspectives, behaviors, and interactions of the people within that context. Particularly beneficial for reading complex social phenomena of their natural environment, wherein observations and interactions arise organically.

When to use Ethnographic Analysis?

• Cultural Understanding : Ethnographic analysis is right whilst researchers goal to gain deep insights into the lifestyle, ideals, and social practices of a selected institution or community.
• Behavioral Observation : It is beneficial while researchers want to observe and apprehend the behaviors, interactions, and each day activities of individuals within their natural surroundings.
• Contextual Exploration : Ethnographic analysis is valuable for exploring the context and lived stories of individuals, presenting wealthy, exact descriptions of their social and cultural worlds.
• Complex Social Dynamics: It is suitable whilst analyzing complex social phenomena or phenomena which might be deeply embedded within social contexts, including rituals, traditions, or network dynamics.
• Qualitative Inquiry: Ethnographic analysis is desired while researchers are seeking for to conduct qualitative inquiry targeted on know-how the subjective meanings and perspectives of individuals inside their cultural context.

Ethnographic analysis gives a effective method for analyzing complex social phenomena of their herbal context, offering rich and nuanced insights into the cultural practices, social dynamics, and lived experiences of individuals inside a particular community. However, researchers need to cautiously bear in mind the time commitment, ethical considerations, and potential biases associated with ethnographic studies.

• Clearly Defined Research Question : Ground analysis in a clear and targeted research question. This will manual for information series and preserve you on the right track at some point of analysis.
• Systematic Coding : Develop a coding scheme to categorize facts into significant topics or concepts. Use software gear to assist in organizing and dealing with codes.
• Constant Comparison : Continuously examine new facts with current codes and subject matters to refine interpretations and make sure consistency.
• Triangulation : Validate findings by the use of a couple of records sources, strategies, or researchers to corroborate consequences and beautify credibility.

Refine subject matters and interpretations through engaging in repeated cycles of gathering, coding, and analysis.

Qualitative data analysis techniques are effective means of revealing deep insights and comprehending intricate phenomena in both practice and study. Through the use of rigorous analytical approaches, researchers may convert qualitative data into significant ideas, interpretations, and narratives that further knowledge and support evidence-based decision-making.

Is it possible to mix quantitative and qualitative methodologies for data analysis?

A: In order to triangulate results and get a thorough grasp of study concerns, researchers do, in fact, often use mixed methods techniques.

How can I choose the best approach for analyzing qualitative data for my study?

A: To choose the best approach, take the research topic, the properties of the data, and the theoretical framework into consideration.

What are some tactics I might do to improve the reliability and validity of my qualitative data analysis?

Aim for peer debriefing and member verification to improve validity, and maintain transparency, reflexivity, and methodological coherence throughout the analytic process.

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Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation

• Baoqiang He 1 , 2   na1 ,
• Yehui Liao 1   na1 ,
• Minghao Tian 1 ,
• Chao Tang 1 ,
• Qiang Tang 1 ,
• Wenyang Zhou 1 ,
• Yebo Leng 1 , 3 &
• Dejun Zhong 1 , 2  

Arthritis Research & Therapy volume  26 , Article number:  100 ( 2024 ) Cite this article

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Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment.

Materials and methods

Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein–protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations.

A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations.

Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.

Introduction

As a degenerative disease that is difficult to reverse, osteoarthritis (OA) is often accompanied by joint pain, stiffness, joint swelling, restricted movement, and joint deformity, all of which seriously affect daily life activities. The structural changes in OA mainly involve the articular cartilage, subchondral bone, ligaments, capsule, synovium, and periarticular muscles [ 1 ]. The prevalence of OA is steadily rising due to the aging population and the obesity epidemic [ 1 ], and it has placed a significant burden on society [ 2 ]. Currently, the main treatments for OA remain nonsteroidal anti-inflammatory drugs (NSAIDs), pain medications, and joint replacement surgery. However, these treatments cannot reduce the incidence of the early stages of the disease [ 3 ], prevent further cartilage degeneration, or promote cartilage regeneration [ 4 ]. Therefore, further understanding of the pathophysiological mechanisms of OA could aid in the development of additional approaches for more effective diagnosis and treatment.

Ferroptosis is a specific type of programmed cell death driven by iron-dependent lipid peroxidation characterized by an abnormal accumulation of lipid reactive oxygen species (ROS) [ 5 , 6 ]. This programmed cell death was first reported and named by Dixon in 2012 [ 7 ]. Many studies have demonstrated that ferroptosis and the development of OA are closely related [ 8 , 9 , 10 , 11 ], and ferroptosis-related genes (FRGs) can help in the diagnosis of OA, as well as in predicting the immune status of patients with OA [ 12 , 13 ].

Copper is an indispensable trace element involved in a wide range of biological reactions. A small study reported elevated plasma and synovial copper concentrations in patients with OA compared with healthy controls [ 14 ], and another study also found that elevated levels of copper were associated with an increased risk of OA [ 15 ]. When the oxidizing capacity of copper ions in the body exceeds the antioxidant capacity of the body, joints can be destroyed [ 16 ]. Cuproptosis is a novel form of programmed cell death during which copper binds directly to the fatty acylated components of the tricarboxylic acid (TCA) cycle, thereby leading to an increase in toxic proteins and ultimately to cell death [ 17 ]. Ferroptosis is an iron-dependent programmed cell death caused by lipid peroxidation and the massive accumulation of reactive oxygen radicals[ 7 ]. Furthermore, copper and iron are closely related; copper is essential for iron absorption, meaning that copper deficiency or overload can impair the balance of iron metabolism [ 18 ]. When the balance of iron metabolism is disturbed, lipid peroxidation and oxidative stress may be induced, which in turn leads to ferroptosis and alters the expression of FRGs [ 19 , 20 , 21 ]. However, it has not yet been reported whether new signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with FRGs are beneficial for the diagnosis and treatment of OA.

In this study, we explored and analyzed the immune characteristics and biological functions of c-FRGs in patients with OA. In addition, we screened key ferroptosis-related biomarkers associated with cuproptosis in OA, constructed ceRNA networks, and predicted potential drugs for OA treatment. Our results suggest that c-FRGs may play an important role in the pathophysiological process of OA and provide new directions and ideas for OA research.

Data collection

The US National Center for Biotechnology Information (NCBI) gene expression omnibus (GEO) is the world's largest international public repository of high-throughput molecular information. Using “osteoarthritis” as a search term, the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) was searched for appropriate datasets, and four datasets that met the study requirements were downloaded. These four datasets were GSE55235, GSE169077, GSE55457, and GSE55584, and the chip type was Affymetrix Human Genome U133a. We eventually obtained 25 normal human synovial samples and 32 OA synovial samples from the four datasets as samples for the follow-up study. To assess the accuracy of the analysis, the GSE82107 dataset was used as validation sets. In addition, the FRGs and CRGs were obtained from the published literature [ 6 ] and the FerrDb website ( http://www.zhounan.org/ferrdb/ ).

Extraction of c-FRGs and obtaining differentially expressed c-FRGs

Inter-batch differences between the four groups (GSE55235, GSE169077, GSE55457, and GSE55584) were eliminated using “affy” packet merging and the “sva” packet. We performed a Pearson correlation analysis of CRGs with FRGs to obtain particular FRGs (c-FRGs) that were highly correlated with CRGs (|r| > 0.5, adj. p value < 0.05). Differentially expressed genes (DEGs) and differentially expressed c-FRGs (c-FDEGs) were obtained using the “limma” package ( p value < 0.05).

Function enrichment analysis and protein–protein interaction (PPI) networks

To acquire disease-related biological functions and signaling pathways, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of c-FDEGs were performed. GO enrichment analysis was used to describe the molecular functions (MF), cellular components (CC), and biological processes (BP) involved in the target genes ( p -value < 0.05). KEGG analysis was used to systematically analyze gene functions and to link genomic information and functional information ( p -value < 0.05). The results of the gene set enrichment analysis (GSEA), GO enrichment analysis, and KEGG pathway enrichment analysis of the c-FDEGs were visualized using the “ClusterProfiler” package in R. GSEA was based on the gene set (h. all. v7. 5. 1. symbols. gmt), which was downloaded from MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). The STRING database is used for searching interactions between known proteins and for predicting interactions between proteins and is one of the most data-rich and widely used databases for studying protein interactions. Protein interaction analysis was performed on all c-FDEGs through the STRING website ( https://string-db.org/ ) and visualized using Cytoscape software. The degree values of the c-FDEGs were calculated using the cytoHubba plugin, and the top seven genes were used as hub genes.

Acquisition and validation of biomarkers

In this research, we used three machine learning algorithms: support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest analysis (RF). First, we used the “e1071” R package for SVM-RFE analysis. Subsequently, the “glmnet” package was used to perform LASSO regression analysis. In addition, RF was conducted adopting the “randomForest” package, and genes with importance > 1 were retained. The crossover genes obtained by these three methods were regarded as prospective biomarkers for OA.

Construction and validation of disease model (nomogram)

In addition, a nomogram based on characteristic biomarkers was structured using the “rms” R package. Receiver operating characteristic (ROC) analysis was performed on the biomarkers and the obtained models, and the area under the curve (AUC) values were calculated with the “pROC” package to assess the diagnostic efficacy of the potential biomarkers. In addition, the four biomarkers and the obtained disease nomogram were validated on the GSE82107 validation set.

Collection of clinical samples

Synovial tissue collection and all experimental procedures were approved by the Institutional Review Board of the Affiliated Hospital of Southwest Medical University (KY2023293) in accordance with the guidelines of the Chinese Health Sciences Administration, and written informed consent was obtained from the donors. Synovial tissue from the suprapatellar bursa was collected as OA synovial samples and normal control samples, respectively, from patients who met the American College of Rheumatology criteria for the diagnosis of primary symptomatic knee OA (n=6; men: 3, women: 3; age: 55-70 years) and from patients who underwent trauma-related lower extremity amputation but did not have osteoarthritis or rheumatoid arthritis (n=6; men: 4, women: 2; age: 50-67 years). All samples were collected within two hours of arthroplasty or lower limb amputation and were divided into two portions for subsequent immunofluorescence staining and western blot experiments, respectively.

Immunofluorescence staining

Mid-sagittal sections (4-μm thick) of paraffin-embedded clinical synovial specimens were incubated for 1 hour at room temperature, after which the slides were closed with 10% bovine serum (Solarbio, Beijing, China) for 1 hour at room temperature and then incubated with primary antibodies for 16 hours at 4°C. The fluorescent dye was incubated for 1 hour at room temperature, and the slides were subsequently sealed with DAPI Sealer (Thermo Fisher Scientific, Waltham, MA, USA).

Western blot analysis

Protein lysates were extracted from synovial tissue samples and lysed with RIPA buffer to extract the total protein. After conducting a BCA protein assay (Beyotime, Shanghai, China), 5 × sample buffer (Servicebio, Wuhan, China) was added to the protein lysates. Equal amounts of lysates were then separated through SDS-PAGE and transferred to a 0.22-um PVDF microporous membrane (Merck Millipore, Burlington, MA, USA). Next, the membrane was sealed with 5% skimmed milk and incubated with the primary antibody for 16 hours at 4°C, after which the membrane was incubated with the secondary antibody for 60 minutes at room temperature. Target protein bands were visualized using FDbio-Dura ECL (Merck Millipore, Burlington, MA, USA). The antibodies used for immunofluorescence and western blot in this study were as follows: rabbit anti-FZD7 (Cat. #: DF8657, 1:1,000; AFFBIOTECH, USA), rabbit anti-SLC39A14 (ZIP14) (Cat. #: 26540-1-AP, 1:1,000, Proteintech, Rosemont, IL, USA), rabbit anti-CDKN1A (p21) (Cat. #: 2947T, 1:1,000, Cell Signaling Technology, Danvers, MA, USA), rabbit anti-GABARAPL2 (Cat. #: 14256T, 1:1,000, Cell Signaling Technology), anti-GAPDH (Cat. #: 60004 -1-Ig, 1:1,000, Proteintech, USA), and species-matched HRP-conjugated secondary antibody (Cat. #: SA00001-1, 1:1,000; Proteintech, USA).

ssGSEA, GSEA, and GSVA for differentially expressed c-FRGs

The gene set (h.all.v2022.1.Hs.symbols.gmt), a collection of 50 symbolic gene sets for humans, was downloaded from MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). The 50 symbolic human gene set scores were calculated for each sample using single-sample GSEA (ssGSEA), and differential scores were obtained for the non-OA and OA groups. The “corrplot” package was used to perform correlation analysis between biomarkers and ssGSEA gene sets. Next, GSEA and gene set variation analysis (GSVA) were performed for the four biomarkers, the seven hub genes, and the remaining 29 differentially expressed c-FRGs.

Prediction of therapeutic drugs

The gene–drug interaction database (DGIDB, http://www.dgidb.org ) [ 22 ] can help researchers annotate known pharmacogenetic interactions and potential drug accessibility–related genes. In this research, we used DGIdb to filter potential drugs targeted to biomarkers so as to identify new therapeutic targets. The obtained drug prediction results were also imported into Cytoscape (v3.9.1) software for visualization.

Construction of ceRNA network

The miRanda, TargetScan, and miRDB databases are authoritative databases used for predicting miRNA–target gene regulatory relationships, and spongeScan is a web tool designed for sequence-based complementary detection of miRNA-binding elements in lncRNA sequences. Biomarkers of common mRNA–miRNA interactions were identified in miRanda ( http://www.microrna.org/microrna/home.do ), TargetScan ( http://www.targetscan.org ), and miRDB ( https://mirdb.org ). miRNA–lncRNA interactions were obtained from Spongescan ( http://spongescan.rc.ufl.edu ). These interactions were imported into Cytoscape to construct the ceRNA network.

Immune infiltration analysis

To better understand the changes that occur in the immune system of patients with OA, the “CIBERSORT” R package was used to describe the basic expression of 22 immune cell subtypes. Next, we analyzed the correlation between potential biomarkers, hub genes, and the 22 immune cell types.

scRNA‑seq analysis

The OA synovial scRNA-seq data (GSE152805) from three patients were obtained from the GEO database and analyzed using the "Seurat" software package. To ensure high quality of the data, we removed low-quality cells (cells with <200 or >10,000 detected genes, >10% of mitochondrial genes, or <300 or >30,000 expressed genes) and low-expressed genes (any gene expressed in less than three cells). We used the "NormalizeData" function to normalize the gene expression of the included cells and performed principal component analysis (PCA) using the top 2000 highly variable genes to extract the top 12 principal components (PCs), which were retained for further analysis using the "FindVariableFeatures" function. To perform unsupervised and unbiased clustering of cell subpopulations, the "FindNeighbors," "FindClusters" (resolution = 0.6), and "RunUMAP" functions were applied. Each cell cluster was manually annotated according to the cell-specific marker genes. These marker genes were obtained from previously published literature[ 23 , 24 ] and from the CellMarker website ( http://xteam.xbio.top/CellMarker/ ). Finally, we used CellChat (1.6.1) for the inference and analysis of cell–cell communication.

Figure 1 describes the entire flow of the study.

A graphical flowchart of the study design

Extracting c-FRGs and obtaining differentially expressed c-FRGs

After merging the GSE55235, GSE169077, GSE55457, and GSE55584 datasets (Table 1 ), the newly produced gene expression matrices were subjected to normalization and presented as bidimensional PCA plots prior to and after processing (Fig. 2 a and b), indicating that the final sample data obtained were plausible. A total of 63 FRGs were found to be closely associated with 11 CRGs (Fig. 2 e, Supplementary Table 1 ). A total of 4167 DEGs were determined and identified (Fig. 2 c). There were a total of 40 c-FDEGs, including 13 upregulated genes and 27 downregulated genes (Fig. 2 d, Supplementary Table 2 ). The correlations between the 40 c-FDEGs are shown in Supplementary Figure 1 . The expression patterns of the 40 c-FDEGs are visualized in the heatmap (Fig. 2 f).

Extraction of particular ferroptosis-related genes (c-FRGs) and obtainment of differentially expressed c-FRGs (c-FDEGs). a, b Two-dimensional PCA cluster plot of GSE55235, GSE169077, GSE55457, and GSE55584 datasets before and after normalization. c Volcano plot of DEGs. Red spots represent upregulated genes and green spots represent downregulated genes. d Overall expression landscape of c-FRGs in osteoarthritis (OA). * P < 0.05; ** P < 0.01; *** P < 0. 001. OA represents the OA group and Normal represents the normal control group. e Extraction of c-FDEGs. f  Heatmap of c-FDEGs. The redder the color, the higher the expression; conversely, the bluer the color, the lower the expression

Function enrichment analysis

Understanding the signaling pathways, biological processes, and interrelationships involved in c-FDEGs is of great importance in revealing the pathogenesis of OA. GO enrichment analysis showed that c-FDEGs were significantly enriched in the regulation of the inflammatory response (BP), the positive regulation of cellular catabolic process (BP), the autophagosome membrane (CC), the recycling endosome (CC), and NF-κB binding (MF) (Fig. 3 a, Supplementary Table 3 ). KEGG pathway analysis showed that these c-FDEGs were mainly involved in the IL-17 signaling pathway, NOD-like receptor signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway (Fig. 3 b, Supplementary Table 4 ). GSEA suggested that the development of OA may be associated with hypoxia, MYC targets v2, the P53 pathway, the inflammatory response, TNFα signaling via NF-κB, the interferon-α response, and peroxisome (Fig. 3 c and d).

Functional analyses: ( a ) Gene Ontology (GO) enrichment analysis showed that the 40 c-FDEGs were significantly enriched in the regulation of the inflammatory response, the positive regulation of cellular catabolic process, the autophagosome membrane, the recycling endosome, and NF-κB binding. b Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that these c-FDEGs were mainly involved in the IL-17 signaling pathway, NOD-like receptor signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway. c Gene set enrichment analysis (GSEA) in the normal control group and (d) GSEA in the OA group based on the core set of 50 human genes suggested that the development of OA may be associated with hypoxia, MYC targets v2, the P53 pathway, the inflammatory response, TNFα signaling via NF-κB, the interferon-α response, and peroxisome

Building PPI networks

The String database is a database that can be used to retrieve interactions between known and predicted proteins. To explore the interactions between each c-FDEG, all of the abovementioned 40 c-FDEGs were imported into the STRING database. The PPI network of c-FDEGs after deleting isolated c-FDEGs and adding the six related CRGs (without CDKN2A) is shown in Fig. 4 a. The cytoHubba plugin in Cytoscape software was used to calculate the degree values (degrees) of the top seven genes (IL6, IL1B, RELA, PTGS2, EGFR, CDKN2A, and SOCS1) as the PPI network’s hub genes (Fig. 4 b).

Protein–protein interaction (PPI) network and core gene screening. a PPI network constructed from 40 c-FDEGs; red triangles represent c-FDEGs, green triangles represent CRGs that are closely related to them, and the correlation between c-FDEGs and CRGs is indicated by dashed lines. b The top seven core gene interaction networks calculated using the cytoHubba plugin: the darker the color, the more powerful the critical degree

Machine learning algorithm–based biomarker screening for patients with OA

In this study, 40 c-FDEGs were further analyzed for potential biomarkers associated with OA using multiple machine learning methods. SVM-RFE analysis showed that the model containing 24 genes had the best accuracy (Fig. 5 a). LASSO regression analysis showed that the model was able to accurately predict OA when λ was equal to 12. Thus, the LASSO regression model generated 12 candidate genes (Fig. 5 b). We retained the candidate biomarkers with RF results importance > 1 (Fig. 5 c). Lastly, the results of these three methods were integrated, and CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as the final potential biomarkers for OA (Fig. 5 d).

Machine learning-based potential biomarker screening. a SVM-RFE model with the optimal error rate when the number of signature genes was 58. b LASSO regression model. c Random forest model and the top 20 genes in terms of importance. d The final biomarkers screened using three machine learning algorithms

Experimental validation of four biomarkers

To validate the results of the bioinformatics analysis, we collected OA samples (n=6) and normal group samples (n=6), respectively, and performed western blot analysis and immunofluorescence staining (Fig. 6 ). Both results were consistent with the bioinformatics analysis, i.e., higher expression of FZD7 and GABARAPL2 and lower expression of CDKN1A (p21) and SLC39A14 (ZIP14) in the OA group compared with the normal group.

Experimental validation of four biomarkers. a Representative immunofluorescence staining images of the four biomarker proteins (p21, FZD7, GABARAPL2, and ZIP14) in the normal and OA groups, with nuclei stained blue with 4’,6-diamidino-2-phenylindole. Scale bar = 25 µm. b Semi-quantitative analysis of mean fluorescence intensity of the four biomarker proteins in the normal and OA groups ( n = 6). (c, d) Representative western blotting and statistical comparisons of the four biomarker proteins in the normal and OA groups ( n = 6). * p < 0.05, ** p < 0.01, all by independent samples t-test

To better capture the function of the four biomarkers in OA, GSEA, GSVA, and ssGSEA were conducted on each of the above biomarkers (Fig. 7 ). The ssGSEA showed that the OA group was significantly enriched in Notch signaling, interferon alpha (IFN-α) response, the Wnt/β-catenin pathway, bile acid metabolism, and peroxisome, while the non-OA group was mainly enriched in TNFα signaling via NF-κB, hypoxia, MYC targets v2, the P53 pathway, the inflammatory response, PI3K AKT mTOR signaling, and IL6 JAK STAT3 signaling (Fig. 7 i). Correlation analysis showed that CDKN1A and SLC39A14 were significantly positively correlated with the gene sets of hypoxia, TNF-α signaling via NF-κB, the P53 pathway, and mTORC1 signaling. Meanwhile, GABARAPL2 and FZD7 showed significant negative correlations with the gene sets of TNF-α signaling via NF-κB, PI3K AKT mTOR signaling, and mTORC1 signaling (Fig. 7 j). The single-gene GSEA results for the seven hub genes are shown in Supplementary Figure 2 (a–g). The remaining 29 differentially expressed c-FRGs are shown in Supplementary Figure 3 .

GSEA, GSVA, and ssGSEA results of four potential biomarkers. a–d Single-gene GSEA-KEGG pathway analysis of four potential biomarkers. We show the top six pathways with the smallest p -value. e–h High- and low-expression groups based on the expression levels of each potential biomarker combined with gene set variation analysis (GSVA). Red means the pathway is significantly upregulated, green means the pathway is significantly downregulated, and gray means the pathway is not statistically significant. i ssGSEA of OA and normal controls based on the h.all.v7.5.1.symbols.gmt gene set. * P < 0.05; ** P < 0.01; *** P < 0. 001. Treat represents the OA group, and control represents the normal group. (j) Correlation of four biomarkers with 50 human symbolic gene sets from the h.all.v7.5.1.symbols.gmt gene set

Using the above four biomarkers, a disease nomogram was constructed. The AUC values of the individual genes CDKN1A, FZD7, GABARAPL2, and SLC39A4 were 0.931, 0.879, 0.989, and 0.850, respectively, all of which were greater than 0.85 (Fig. 8 a), further indicating that the above genes had good diagnostic ability (Fig. 8 b). The AUC value of this model was 0. 996, which was significantly greater than the AUC value of individual biomarkers, indicating that this model had good diagnostic value (Fig. 8 c and d). To verify whether the above model is diagnostically meaningful, validation was performed on the GSE8207 dataset. The results showed that the AUC values of the four biomarkers were all greater than 0.7, and the AUC value of the model was 1 for the validation set (Fig. 8 f). These results indicate that CDKN1A, FZD7, GABARAPL2, and SLC39A4 are effective disease biomarkers for OA and that the model has high diagnostic efficacy.

Validation of four biomarkers. a ROC analysis of the four biomarkers. b ROC analysis of the disease model constructed from the four biomarkers. c, d Nomograms based on the disease model: we obtained the corresponding scores for each genetic variable, drew a vertical line above the “points” axis, summed the scores of all predictor variables, found the final value on the “total score” axis, and then drew a straight line on the “probability” axis to determine the patient’s risk of osteoarthritis. e, f Validation of the disease model and four biomarkers on the GSE82107 validation dataset

Construction of drug prediction network and lncRNA–miRNA–mRNA network

The corresponding drug prediction network was constructed using the database based on the four biomarkers (Supplementary Figure 4 a). The predicted drugs were celecoxib, paclitaxel, carboplatin, acetaminophen, vantictumab, and nortriptyline. Based on the competitive endogenous RNA hypothesis, an lncRNA–miRNA–mRNA competitive endogenous RNA (ceRNA) network was constructed to explore the function of lncRNA as an miRNA sponge in OA. We obtained 150 target miRNAs based on these biomarkers. Then, 48 lncRNAs were obtained based on these miRNA predictions. The four biomarkers with predicted miRNAs and lncRNAs were introduced into Cytoscape, and constituted a ceRNA network containing 48 lncRNA nodes, 150 miRNA nodes, 4 hub gene nodes, and 198 edges (Supplementary Figure 4 b).

The immune microenvironment plays an important role in the progression of OA. Therefore, with the help of CIBERSORT, we summarized the differences in immune infiltration by immune cell subpopulations between OA samples and non-OA tissues (Fig 9 a). The OA samples contained a higher proportion of memory B cells, M0 macrophages, M2 macrophages, and resting mast cells than the control group, as well as a lower proportion of resting CD4 memory T cells and activated mast cells. Correlation analysis showed that activated mast cells showed positive correlations with PTGS2, IL6, and IL1B, and the correlation between activated mast cells and PTGS2 was the highest (0. 686) (Fig. 9 b). There were positive correlations between IL1B, PTGS2, and M1 macrophages, resting CD4 memory T cells and PTGS2, and regulatory T cells (Tregs) and RELA. There were significant negative correlations between follicular helper T cells and RELA, as well as between plasma cells and SLC39A14 (Fig. 9 c and d).

Results of immune infiltration by CIBERSORTx. a Bar plot showing the composition of 22 types of immune cells. b Box plot presenting the difference of immune infiltration of 22 types of immune cells. Treat represents the OA group, and Control represents the normal group. c Heatmap showing the correlation between seven hub genes and 22 types of immune cells in osteoarthritis. d Correlation between the four biomarkers and 22 types of immune cells in osteoarthritis

Single‑cell analysis

The scRNA-seq data from three OA synovial samples were obtained from the GSE152805 dataset. After initial quality control, we finally retained 10,194 cells for cell annotation (Supplementary Figure 5 ). The top 2000 highly variable genes were selected for further analysis (Supplementary Figure 5 b). We used the "RunPCA" function to reduce the dimensionality and obtained 14 clusters (Supplementary Figures 6 d and e); the first five DEGs of each cluster are shown in Supplementary Table 5 . Later, we performed cellular annotation using marker genes and annotated seven cell populations: fibroblasts (77.7%), macrophages (8.8%), dendritic cells (DCs) (3.6%), endothelial cells (ECs) (3.5%), smooth muscle cells (SMCs) (3.4%), T cells (1.8%), and mast cells (1.2%) (Fig. 10 a). Next, we performed differential gene expression analysis on these seven cell populations to verify the accuracy of the cell annotation (Fig. 10 b). Figures 10 c and d show the distribution and expression of seven hub genes and four biomarker genes in different cell populations. We found that 11 c-FRGs were significantly different in macrophages, DCs, mast cells, and NK cells. For example, IL1B, PTGS2, and SLC39A4 were significantly highly expressed in some cells, whereas they were significantly less expressed, or even absent, in other cells. We used CellChat to identify differentially overexpressed ligands and receptors for each cell population. In total, 254 significant ligand–receptor pairs were detected, which were further classified into 62 signaling pathways (Table 2 ). We found that the immune cells interacted weakly with each other; however, the non-immune cells had extensive communication interactions with other cells and were involved in various paracrine and autocrine signaling interactions (Fig. 10 e to g).

Analysis of single-cell RNA sequencing data from three OA synovial samples. a UMAP plot of scRNA-seq showing unsupervised clusters colored according to putative cell types among a total of 10,194 cells in OA synovial samples. The percentages of total acquired cells were as follows: 77.7% fibroblasts, 8.8% macrophages, 3.6% dendritic cells (DCs), 3.5% endothelial cells (ECs), 3.4% smooth muscle cells (SMCs), 1.8% T cells, and 1.2% mast cells. b Heatmap depicting the expression levels of the top five marker genes among seven detected cell clusters. c, d UMAP plots and violin plots showing the expression of the selected seven hub c-FRGs and four potential biomarkers for each cell type. e Interaction net count plot of OA synovial cells. The thicker the line, the greater the number of interactions. f Interaction weight plot of synovial cells. The thicker the line, the stronger the interaction weights/strength between the two cell types. g Detailed network of cell–cell interactions among seven cell subsets

Copper is an irreplaceable trace metal element that participates in a variety of biological processes. When copper ions accumulate in excess, they eventually lead to cell death, and this new form of programmed cell death is known as cuproptosis [ 17 ]. A recent report has demonstrated that copper levels are significantly higher in the serum and synovial tissue of patients with OA than in controls [ 14 ]. Evidence from several studies suggests that the development of OA is closely related to ferroptosis in articular cartilage and synovium [ 25 , 26 , 27 , 28 , 29 ], and that OA can be treated to some extent by modulation of ferroptosis [ 29 , 30 ]. Additionally, previous studies have reported that copper and iron levels are closely correlated with each other in patients with OA [ 14 , 15 , 31 ].

In this study, we identified transcriptional alterations and expression of c-FRGs based on the GSE55235, GSE169077, GSE55457, and GSE55584 datasets. Forty c-FDEGs were identified in 63 c-FRGs. GO enrichment analysis showed that these 40 c-FDEGs were mainly associated with the inflammatory response, cellular response to external stimulus, and autophagy. The KEGG enrichment analysis showed that these genes were highly enriched mainly in the IL-17 signaling pathway, NOD-like receptor signaling pathway, HIF-1 signaling pathway, and TNFα signaling pathway. For both OA and non-OA groups, GSEA and ssGSEA showed that OA was mainly associated with the enrichments in Notch signaling, adipogenesis, xenobiotic metabolism, fatty acid metabolism, peroxisome, TNFα signaling via NF-κB, the inflammatory response, PI3K AKT mTOR signaling, and IL6 JAK STAT3 signaling. This indicates that the mechanism of OA development is closely related to fatty acid metabolism, the inflammatory response, immune regulation, and cell adhesion.

We analyzed the PPI results using the cytoHubba plugin in Cytoscape, revealing seven key c-FDEGs, including IL6, IL1B, RELA, PTGS2, EGFR, CDKN2A, and SOCS1. GSEA and GSVA of the seven genes revealed that IL6, IL1B, RELA, PTGS2, SOCS1, and EGFR were closely associated with inflammation, immune regulation, extracellular matrix, and cell adhesion pathways in OA, which is consistent with previous findings [ 32 , 33 ]. Interestingly, we also found that they were closely associated with lipid metabolism and fatty acid metabolism in OA. Considering that increased iron accumulation, free radical production, fatty acid supply, and increased lipid peroxidation are key to the induction of ferroptosis [ 5 , 6 , 7 ], it is possible that they affect the development of OA by regulating lipid metabolism and fatty acid metabolism, which affects ferroptosis; however, this needs to be further investigated.

Notably, CDKN2A acts as both a cuproptosis-related gene and a ferroptosis-related gene simultaneously. CDKN2A is often considered an important gene in cellular senescence and aging [ 34 ], and it is used as a molecular marker of cellular senescence [ 35 ]. Our study showed that CDKN2A expression was higher in patients with OA, suggesting that CDKN2A may contribute to the development of OA by affecting cellular senescence and thereby promoting the development of OA.

This is the first study to use the new signature genes combining CRGs with FRGs to reveal the pathogenesis of OA and aid in its treatment. We executed three machine learning algorithms using the 40 c-FDEGs mentioned above and eventually identified four biomarkers: CDKN1A, FZD7, GABARAPL2, and SLC39A14.

Frizzled7 (FZD7) is known to be a receptor of the Wnt pathway. Fzl receptors are usually classified as belonging to the G protein receptor family and are rich in cysteine, which can directly interact with Wnt proteins and thus activate downstream responses [ 36 , 37 , 38 ]. Numerous studies have shown that excessive upregulation or downregulation of Wnt signaling pathways in OA may lead to cartilage damage and ultimately accelerate the progression of OA. Therefore, it is necessary and important to maintain a balance in the biological activity of Wnt-related pathways [ 39 , 40 , 41 ]. In the present study, FZD7 was significantly increased in the OA group compared with the non-OA group. Therefore, we speculate that an excess of FZD7 may lead to the abnormal activation of Wnt-related pathways and ultimately accelerate the development of OA.

ZIP14 (SLC39A14) is a metal transporter [ 42 ] that affects the metabolic balance of zinc, manganese, iron, copper, and other metals [ 43 ]. For example, ZIP14 can transport non-transferrin-bound iron (NTBI) [ 44 ] and ZIP14 can transport cadmium and manganese through metal/bicarbonate symbiotic activity [ 45 ]. It has been shown that OA is closely related to the metabolic balance of metals such as iron, copper, and manganese [ 14 , 15 , 31 , 46 , 47 , 48 ]. In this study, we found that ZIP14 was greatly reduced in the OA group compared with the non-OA group. Furthermore, scRNA-seq analysis showed that the distribution of SLC39A14 in OA patients varied significantly among cell populations, with low or even no expression in some cells, which is likely to disrupt the metal metabolic balance in the joints and eventually cause the accumulation of metals such as iron and copper. Therefore, SLC39A14 (ZIP14) may be a very important therapeutic target for OA treatment in the future.

ssGSEA showed that CDKN1A significantly positively correlated with TNF-α signaling via NF-κB, the TGF-β signaling pathway, hypoxia, the P53 pathway, apoptosis, mTORC1 signaling, and other gene sets, suggesting that CDKN1A may affect OA by regulating inflammation, apoptosis, and hypoxia. Although both the CDKN1A and GABARAPL2 genes have been reported previously [ 49 , 50 , 51 , 52 ], their relationship with ferroptosis and cuproptosis in OA is not yet known. This suggests that these genes may be targets not only for immunotherapy, inflammation, and autophagy but also for the treatment of cuproptosis and ferroptosis in OA. Notably, we found that melphalan, paclitaxel, vinblastine, and vantictumab may serve as potential drugs for the treatment of OA. Previous studies have reported that they act therapeutically by regulating CDKN1A or FZD7 [ 53 , 54 , 55 ], thus affecting processes such as the cell cycle, cell proliferation, and apoptosis, which also validates our prediction. We then constructed a disease model of OA based on these four biomarkers that could significantly improve our ability to recognize OA at an early stage. Thus, our findings suggest that CDKN1A, FZD7, GABARAPL2, and SLC39A14 are excellent disease biomarkers and potential therapeutic targets for OA, and the disease model constructed based on them has good diagnostic efficacy.

Recently, an increasing number of studies have shown that immune cell infiltration is essential for OA onset and development and cartilage repair [ 56 , 57 , 58 ]. Our study showed a close relationship between the seven hub genes and immune cells. Notably, there were significant positive correlations of PTGS2, IL6, and IL1B with M1 macrophages and activated mast cells. Previous studies have demonstrated that the activation of macrophages and mast cells may significantly accelerate the progression of OA [ 58 , 59 , 60 ]. Therefore, we speculate that PTGS2, IL6, and IL1B may influence the onset and progression of OA by regulating these cells. Interestingly, scRNA-seq analysis further revealed that PTGS2 was significantly highly expressed in mast cells, leading us to speculate that PTGC2 may influence the progression of OA by regulating the activation of mast cells and thus the progression of OA. Surprisingly, we found weak interactions between immune cells in the synovial tissue of patients with OA, whereas there were complex communication networks between immune and non-immune cells (fibroblasts, SMCs, and ECs). These hypotheses and questions require more studies to reveal intricate interrelationships between these c-FRGs, immune cells, and OA.

In addition, we found that C10orf91 could regulate CDKN1A and SLC39A14 by regulating hsa-miR-149-3p, hsa-miR-423-5p, hsa-miR-31-5p, and hsa-miR-30b-3p. Both hsa-miR-513a-3p and has-miR-548c-3p can regulate both CDKN1A and GABARAPL2; however, no related study has been reported yet, so this needs to be further investigated and validated in the future.

This study was conducted mainly using bioinformatics analysis, and despite the combination of scRNA-seq analysis and the use of powerful machine learning algorithms, such as RF and SVM-RFE, there are still some limitations to our study. First, the small sample size of the analysis may have led to inaccuracies in the determination of hub genes, CIBERSORT analysis, and single-cell analysis. Second, although the disease model nomogram was well validated, the data was obtained retrospectively from public databases, meaning that inherent selection bias may have affected their accuracy. In addition, while our data can show the correlation between OA and immune cells, they cannot reveal causality. Extensive prospective studies, as well as complementary in vivo and in vitro experimental studies, are necessary to validate the accuracy of potential therapeutic targets and biomarkers.

Conclusions

Our study showed that four genes—CDKN1A, FZD7, GABARAPL2, and SLC39A14—are good disease biomarkers and potential therapeutic targets for OA. Our study provides a theoretical basis and research direction for understanding the role of c-FRGs in the pathophysiological process and for potential therapeutic intervention in OA.

Availability of data and materials

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Osteoarthritis

Nonsteroidal anti-inflammatory drugs

Reactive oxygen species

Ferroptosis-related genes

Tricarboxylic acid

Cuproptosis-related genes

The new signature genes combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs)

National Center for Biotechnology Information

Gene expression omnibus

Differentially expressed genes

Differentially expressed c-FRGs

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

Gene set enrichment analysis

Support vector machine recursive feature elimination

Random forest analysis

Least absolute shrinkage and selection operator

Receiver operating characteristic

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Acknowledgments

This study was a re-analysis based on published data from the GEO database. We would like to thank the GEO database for sharing the data.

This study was supported by Sichuan Medical Association (No. S17075, Q22008, Q21005), the Sichuan Science and Technology Program(No. 24NSFSC2177), the Science and Technology Strategic Cooperation Project between the People's Government of Luzhou City and Southwest Medical University (No. 2020LZXNYDJ22), the Doctoral Research Initiation Fund of Affiliated Hospital of Southwest Medical University (No. 22155), and Sichuan Student Innovation and Entrepreneurship Training Program Project (No. S202010632174).

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Baoqiang He and Yehui Liao are contributed equally.

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Department of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China

Baoqiang He, Yehui Liao, Minghao Tian, Chao Tang, Qiang Tang, Fei Ma, Wenyang Zhou, Yebo Leng & Dejun Zhong

Southwest Medical University, Lu Zhou City, China

Baoqiang He & Dejun Zhong

Meishan Tianfu New Area People’s Hospital, Meishan City, China

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HBQ, LYB, LYH and ZDJ designed the study. Data analysis was performed by HBQ, TC, TQ and MF. HBQ, TMH and ZWY carried out the experiments. HBQ, LYB, and ZDJ wrote the first draft. ZDJ critically revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yebo Leng or Dejun Zhong .

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Synovial tissue collection and all experimental procedures were approved by the Institutional Review Board of the Affiliated Hospital of Southwest Medical University (KY2023293) in accordance with the guidelines of the Chinese Health Sciences Administration, and written informed consent was obtained from the donors.

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He, B., Liao, Y., Tian, M. et al. Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation. Arthritis Res Ther 26 , 100 (2024). https://doi.org/10.1186/s13075-024-03328-3

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The Pill Makes Some Women Miserable. But Are They Really Quitting It en Masse?

The internet is awash with stories of women throwing out their oral contraception. New data suggests a different narrative.

Credit... Eric Helgas for The New York Times

Supported by

By Alisha Haridasani Gupta

• May 16, 2024

The woman in the video looks resolute, and a little sad, as she cuts up a pack of birth control pills. “These silly little pills have literally ruined me as a person,” reads the caption. The clip , which is on TikTok, has 1.1 million likes. It’s one of thousands that have proliferated on social media in recent years with virtually the same message: The pill causes terrible, sometimes irreversible side effects, and women should free themselves from it.

Anecdotal reports from news outlets have suggested that women are quitting the pill in large numbers because of this type of online post. “We’ve known for a long time that people really rely on their social circles to help them with medical decision making as it relates to contraception,” said Dr. Deborah Bartz, an obstetrician-gynecologist at Brigham and Women’s Hospital. Against a backdrop of increasingly restrictive abortion access, the idea that women might be giving up a reliable form of contraception because of social media hype has concerned researchers and doctors.

But, according to initial data, prescriptions for the birth control pill are not actually declining at all. An analysis by Trilliant Health, an analytics firm that provides health care companies with industry insights, found that usage has been steadily trending upward in the United States; 10 percent of women had prescriptions in 2023, up from 7.1 percent in 2018. The analysis looked at prescriptions for the pill that were written and picked up. Even among those aged 15 to 34, who would be most likely to see negative social media posts, Trilliant found prescriptions had increased.

The analysis was done at the request of The New York Times, and drew on Trilliant’s database of medical and pharmacy claims. It looked at a nationally representative sample of roughly 40 million women, aged 15 to 44, who used either Medicaid or commercial insurance. It doesn’t account for people who might get their birth control from telehealth providers that don’t take insurance, but that group most likely represents a small slice of the American population, said Sanjula Jain, chief research officer at Trilliant. Several of those telehealth companies also reported double-digit increases in birth control pill purchases in the past two years. The data also doesn’t include sales of the over-the-counter birth control pill, Opill , which has been available in stores in the U.S. since March .

Ten percent of women had prescriptions for the pill in 2023, up from 7.1 percent in 2018. Source: Trilliant Health

The pill has a reputation as a reliable, if flawed, form of birth control. Its known side effects — including blood clots, weight gain, a loss of libido and mood disruptions — have in fact been the main reason that some women do eventually quit the pill, Dr. Bartz said. When patients raise those concerns with physicians, they are often dismissed, she added, which can erode people’s trust in their doctors, and in health care institutions.

Online, that mistrust has bloomed. In two separate papers, published in 2021 and 2024 , Dr. Bartz analyzed the tone of birth control-related posts on Twitter. In the first study, researchers found that almost a third of posts about the pill from 2006 to 2019 were negative. In the second study, the team found that one of the major focus points of posts about the pill was its side effects. Another analysis from 2023 found that 74 percent of a sample of YouTube videos posted between 2019 and 2021 discussed discontinuing hormonal birth control methods because of side effects.

But the side effects of the pill don’t override its utility for many women. It is often seen as an easy point of entry for people newly considering continuous birth control because it can be started and stopped at any point, rather than requiring a painful procedure , said Dr. Cherise Felix, an obstetrician and gynecologist at Planned Parenthood’s south, east and north Florida chapters.

It is also more than 90 percent effective at preventing pregnancies, and can be used to help manage a range of health conditions, like endometriosis and polycystic ovarian syndrome.

What the analysis from Trilliant also underscores is that perhaps women are not so easily swayed by what they see online, said Dr. Felix, who reviewed the findings but was not involved in the analysis. If anything, they end up discussing it with their doctors to make more informed decisions. “I have not once had a patient start a conversation with ‘I stopped using my birth control because I saw this on TikTok,’” Dr. Felix said. “But I can tell you that just over the course of my career, I am having better-quality discussions with my patients.”

Nine states with some of the most restrictive abortion laws had bigger-than-average growth in pill prescriptions. Source: Trilliant Health

Several experts also pointed to increasingly restrictive abortion laws as a reason for the pill’s staying power. Trilliant’s analysis found that nine states with some of the most restrictive abortion laws saw bigger-than-average growth in prescriptions. For example, in Alabama, where abortion is completely banned with few exceptions, and South Carolina, which restricts abortions after six weeks, prescriptions increased by almost 5 percentage points between 2018 and 2023, compared with a national increase of 3 percentage points in that same time frame.

Women began stocking up on the birth control pill after the June 2022 Supreme Court ruling that ended the constitutional right to abortion, said Julia Strasser, director of the Jacobs Institute of Women’s Health at George Washington University and co-author of a recent study looking at contraception use. In 2019, roughly 32 percent of initial prescriptions were for more than one month; by 2022, more than half of initial prescriptions were for a greater supply of “two months, three months, six months and sometimes even 12,” Dr. Strasser said.

So if more women are relying on the pill, why does social media seem to tell a different story? One explanation, Dr. Bartz said, is what’s known as a negativity bias. Consumers are “much more inclined to complain and say ‘oh my gosh, let me tell you about all this bleeding that I’m having on my pill’ or ‘let me tell you about my weight gain,’ ” she said, and far less likely to post positive reviews.

She’s seen something very different in her clinical practice: Patients valuing their birth control options more than ever. “Post-Dobbs,” Dr. Bartz said, “there has been a heightened recognition of the need to be very proactive in preventing pregnancy.”

Alisha Haridasani Gupta is a Times reporter covering women’s health and health inequities. More about Alisha Haridasani Gupta

Birth Control Methods

A medication called Opill will soon become the most effective birth control method  available over the counter . Here’s what to know .

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