Qualitative study design: Narrative inquiry

  • Qualitative study design
  • Phenomenology
  • Grounded theory
  • Ethnography

Narrative inquiry

  • Action research
  • Case Studies
  • Field research
  • Focus groups
  • Observation
  • Surveys & questionnaires
  • Study Designs Home

Narrative inquiry can reveal unique perspectives and deeper understanding of a situation. Often giving voice to marginalised populations whose perspective is not often sought. 

Narrative inquiry records the experiences of an individual or small group, revealing the lived experience or particular perspective of that individual, usually primarily through interview which is then recorded and ordered into a chronological narrative. Often recorded as biography, life history or in the case of older/ancient traditional story recording - oral history.  

  • Qualitative survey 
  • Recordings of oral history (documents can be used as support for correlation and triangulation of information mentioned in interview.) 
  • Focus groups can be used where the focus is a small group or community. 

Reveals in-depth detail of a situation or life experience.  

Can reveal historically significant issues not elsewhere recorded. 

Narrative research was considered a way to democratise the documentation and lived experience of a wider gamut of society. In the past only the rich could afford a biographer to have their life experience recorded, narrative research gave voice to marginalised people and their lived experience. 


“The Hawthorne Effect is the tendency, particularly in social experiments, for people to modify their behaviour because they know they are being studied, and so to distort (usually unwittingly) the research findings.” SRMO  

The researcher must be heavily embedded in the topic with a broad understanding of the subject’s life experience in order to effectively and realistically represent the subject’s life experience. 

There is a lot of data to be worked through making this a time-consuming method beyond even the interview process itself. 

Subject’s will focus on their lived experience and not comment on the greater social movements at work at the time. For example, how the Global Financial Crisis affected their lives, not what caused the Global Financial Crisis. 

This research method relies heavily on the memory of the subject. Therefore, triangulation of the information is recommended such as asking the question in a different way, at a later date, looking for correlating documentation or interviewing similarly related participants. 

Example questions

  • What is the lived experience of a home carer for a terminal cancer patient? 
  • What is it like for parents to have their children die young? 
  • What was the role of the nurse in Australian hospitals in the 1960s? 
  • What is it like to live with cerebral palsy? 
  • What are the difficulties of living in a wheelchair? 

Example studies

  • Francis, M. (2018). A Narrative Inquiry Into the Experience of Being a Victim of Gun Violence. Journal of Trauma Nursing, 25(6), 381–388. https://doi-org.ezproxy-f.deakin.edu.au/10.1097/JTN.0000000000000406 
  •  Kean, B., Oprescu, F., Gray, M., & Burkett, B. (2018). Commitment to physical activity and health: A case study of a paralympic gold medallist. Disability and Rehabilitation, 40(17), 2093-2097. doi:10.1080/09638288.2017.1323234  https://doi-org.ezproxy-f.deakin.edu.au/10.1080/09638288.2017.1323234
  • Liamputtong, P. (2009). Qualitative research methods. Oxford University Press. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b2351301&site=eds-live   
  • Padgett, D. (2012). Qualitative and mixed methods in public health. SAGE. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b3657335&authtype=sso&custid=deakin&site=eds-live&scope=site  
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  • Last Updated: Oct 12, 2023 11:29 AM
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narrative research examples brainly

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….

What Is Narrative Research

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.


narrative research examples brainly

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

narrative research examples brainly

  • 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.

narrative research examples brainly

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.

narrative research examples brainly

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

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

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

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

narrative research examples brainly

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.

narrative research examples brainly

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

narrative research examples brainly

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.

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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.

Simple definition of narrative analysis

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.

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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 .

narrative research examples brainly

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 .

narrative research examples brainly

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

Qualitative content analysis 101

Thanks. I need examples of narrative analysis

Derek Jansen

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.

Yvonne Worrell

Where can I find an example of a narrative analysis table ?


Please i need help with my project,

Mst. Shefat-E-Sultana

how can I cite this article in APA 7th style?


please mention the sources as well.

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How to Write a Systematic Review: A Narrative Review

Ali hasanpour dehkordi.

Social Determinants of Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran

Elaheh Mazaheri

1 Health Information Technology Research Center, Student Research Committee, Department of Medical Library and Information Sciences, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

Hanan A. Ibrahim

2 Department of International Relations, College of Law, Bayan University, Erbil, Kurdistan, Iraq

Sahar Dalvand

3 MSc in Biostatistics, Health Promotion Research Center, Iran University of Medical Sciences, Tehran, Iran

Reza Ghanei Gheshlagh

4 Spiritual Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran

In recent years, published systematic reviews in the world and in Iran have been increasing. These studies are an important resource to answer evidence-based clinical questions and assist health policy-makers and students who want to identify evidence gaps in published research. Systematic review studies, with or without meta-analysis, synthesize all available evidence from studies focused on the same research question. In this study, the steps for a systematic review such as research question design and identification, the search for qualified published studies, the extraction and synthesis of information that pertain to the research question, and interpretation of the results are presented in details. This will be helpful to all interested researchers.

A systematic review, as its name suggests, is a systematic way of collecting, evaluating, integrating, and presenting findings from several studies on a specific question or topic.[ 1 ] A systematic review is a research that, by identifying and combining evidence, is tailored to and answers the research question, based on an assessment of all relevant studies.[ 2 , 3 ] To identify assess and interpret available research, identify effective and ineffective health-care interventions, provide integrated documentation to help decision-making, and identify the gap between studies is one of the most important reasons for conducting systematic review studies.[ 4 ]

In the review studies, the latest scientific information about a particular topic is criticized. In these studies, the terms of review, systematic review, and meta-analysis are used instead. A systematic review is done in one of two methods, quantitative (meta-analysis) and qualitative. In a meta-analysis, the results of two or more studies for the evaluation of say health interventions are combined to measure the effect of treatment, while in the qualitative method, the findings of other studies are combined without using statistical methods.[ 5 ]

Since 1999, various guidelines, including the QUORUM, the MOOSE, the STROBE, the CONSORT, and the QUADAS, have been introduced for reporting meta-analyses. But recently the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement has gained widespread popularity.[ 6 , 7 , 8 , 9 ] The systematic review process based on the PRISMA statement includes four steps of how to formulate research questions, define the eligibility criteria, identify all relevant studies, extract and synthesize data, and deduce and present results (answers to research questions).[ 2 ]

Systematic Review Protocol

Systematic reviews start with a protocol. The protocol is a researcher road map that outlines the goals, methodology, and outcomes of the research. Many journals advise writers to use the PRISMA statement to write the protocol.[ 10 ] The PRISMA checklist includes 27 items related to the content of a systematic review and meta-analysis and includes abstracts, methods, results, discussions, and financial resources.[ 11 ] PRISMA helps writers improve their systematic review and meta-analysis report. Reviewers and editors of medical journals acknowledge that while PRISMA may not be used as a tool to assess the methodological quality, it does help them to publish a better study article [ Figure 1 ].[ 12 ]

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Screening process and articles selection according to the PRISMA guidelines

The main step in designing the protocol is to define the main objectives of the study and provide some background information. Before starting a systematic review, it is important to assess that your study is not a duplicate; therefore, in search of published research, it is necessary to review PREOSPERO and the Cochrane Database of Systematic. Sometimes it is better to search, in four databases, related systematic reviews that have already been published (PubMed, Web of Sciences, Scopus, Cochrane), published systematic review protocols (PubMed, Web of Sciences, Scopus, Cochrane), systematic review protocols that have already been registered but have not been published (PROSPERO, Cochrane), and finally related published articles (PubMed, Web of Sciences, Scopus, Cochrane). The goal is to reduce duplicate research and keep up-to-date systematic reviews.[ 13 ]

Research questions

Writing a research question is the first step in systematic review that summarizes the main goal of the study.[ 14 ] The research question determines which types of studies should be included in the analysis (quantitative, qualitative, methodic mix, review overviews, or other studies). Sometimes a research question may be broken down into several more detailed questions.[ 15 ] The vague questions (such as: is walking helpful?) makes the researcher fail to be well focused on the collected studies or analyze them appropriately.[ 16 ] On the other hand, if the research question is rigid and restrictive (e.g., walking for 43 min and 3 times a week is better than walking for 38 min and 4 times a week?), there may not be enough studies in this area to answer this question and hence the generalizability of the findings to other populations will be reduced.[ 16 , 17 ] A good question in systematic review should include components that are PICOS style which include population (P), intervention (I), comparison (C), outcome (O), and setting (S).[ 18 ] Regarding the purpose of the study, control in clinical trials or pre-poststudies can replace C.[ 19 ]

Search and identify eligible texts

After clarifying the research question and before searching the databases, it is necessary to specify searching methods, articles screening, studies eligibility check, check of the references in eligible studies, data extraction, and data analysis. This helps researchers ensure that potential biases in the selection of potential studies are minimized.[ 14 , 17 ] It should also look at details such as which published and unpublished literature have been searched, how they were searched, by which mechanism they were searched, and what are the inclusion and exclusion criteria.[ 4 ] First, all studies are searched and collected according to predefined keywords; then the title, abstract, and the entire text are screened for relevance by the authors.[ 13 ] By screening articles based on their titles, researchers can quickly decide on whether to retain or remove an article. If more information is needed, the abstracts of the articles will also be reviewed. In the next step, the full text of the articles will be reviewed to identify the relevant articles, and the reason for the removal of excluded articles is reported.[ 20 ] Finally, it is recommended that the process of searching, selecting, and screening articles be reported as a flowchart.[ 21 ] By increasing research, finding up-to-date and relevant information has become more difficult.[ 22 ]

Currently, there is no specific guideline as to which databases should be searched, which database is the best, and how many should be searched; but overall, it is advisable to search broadly. Because no database covers all health topics, it is recommended to use several databases to search.[ 23 ] According to the A MeaSurement Tool to Assess Systematic Reviews scale (AMSTAR) at least two databases should be searched in systematic and meta-analysis, although more comprehensive and accurate results can be obtained by increasing the number of searched databases.[ 24 ] The type of database to be searched depends on the systematic review question. For example, in a clinical trial study, it is recommended that Cochrane, multi-regional clinical trial (mRCTs), and International Clinical Trials Registry Platform be searched.[ 25 ]

For example, MEDLINE, a product of the National Library of Medicine in the United States of America, focuses on peer-reviewed articles in biomedical and health issues, while Embase covers the broad field of pharmacology and summaries of conferences. CINAHL is a great resource for nursing and health research and PsycINFO is a great database for psychology, psychiatry, counseling, addiction, and behavioral problems. Also, national and regional databases can be used to search related articles.[ 26 , 27 ] In addition, the search for conferences and gray literature helps to resolve the file-drawn problem (negative studies that may not be published yet).[ 26 ] If a systematic review is carried out on articles in a particular country or region, the databases in that region or country should also be investigated. For example, Iranian researchers can use national databases such as Scientific Information Database and MagIran. Comprehensive search to identify the maximum number of existing studies leads to a minimization of the selection bias. In the search process, the available databases should be used as much as possible, since many databases are overlapping.[ 17 ] Searching 12 databases (PubMed, Scopus, Web of Science, EMBASE, GHL, VHL, Cochrane, Google Scholar, Clinical trials.gov, mRCTs, POPLINE, and SIGLE) covers all articles published in the field of medicine and health.[ 25 ] Some have suggested that references management software be used to search for more easy identification and removal of duplicate articles from several different databases.[ 20 ] At least one search strategy is presented in the article.[ 21 ]

Quality assessment

The methodological quality assessment of articles is a key step in systematic review that helps identify systemic errors (bias) in results and interpretations. In systematic review studies, unlike other review studies, qualitative assessment or risk of bias is required. There are currently several tools available to review the quality of the articles. The overall score of these tools may not provide sufficient information on the strengths and weaknesses of the studies.[ 28 ] At least two reviewers should independently evaluate the quality of the articles, and if there is any objection, the third author should be asked to examine the article or the two researchers agree on the discussion. Some believe that the study of the quality of studies should be done by removing the name of the journal, title, authors, and institutions in a Blinded fashion.[ 29 ]

There are several ways for quality assessment, such as Sack's quality assessment (1988),[ 30 ] overview quality assessment questionnaire (1991),[ 31 ] CASP (Critical Appraisal Skills Program),[ 32 ] and AMSTAR (2007),[ 33 ] Besides, CASP,[ 34 ] the National Institute for Health and Care Excellence,[ 35 ] and the Joanna Briggs Institute System for the Unified Management, Assessment and Review of Information checklists.[ 30 , 36 ] However, it is worth mentioning that there is no single tool for assessing the quality of all types of reviews, but each is more applicable to some types of reviews. Often, the STROBE tool is used to check the quality of articles. It reviews the title and abstract (item 1), introduction (items 2 and 3), implementation method (items 4–12), findings (items 13–17), discussion (Items 18–21), and funding (item 22). Eighteen items are used to review all articles, but four items (6, 12, 14, and 15) apply in certain situations.[ 9 ] The quality of interventional articles is often evaluated by the JADAD tool, which consists of three sections of randomization (2 scores), blinding (2 scores), and patient count (1 scores).[ 29 ]

Data extraction

At this stage, the researchers extract the necessary information in the selected articles. Elamin believes that reviewing the titles and abstracts and data extraction is a key step in the review process, which is often carried out by two of the research team independently, and ultimately, the results are compared.[ 37 ] This step aimed to prevent selection bias and it is recommended that the chance of agreement between the two researchers (Kappa coefficient) be reported at the end.[ 26 ] Although data collection forms may differ in systematic reviews, they all have information such as first author, year of publication, sample size, target community, region, and outcome. The purpose of data synthesis is to collect the findings of eligible studies, evaluate the strengths of the findings of the studies, and summarize the results. In data synthesis, we can use different analysis frameworks such as meta-ethnography, meta-analysis, or thematic synthesis.[ 38 ] Finally, after quality assessment, data analysis is conducted. The first step in this section is to provide a descriptive evaluation of each study and present the findings in a tabular form. Reviewing this table can determine how to combine and analyze various studies.[ 28 ] The data synthesis approach depends on the nature of the research question and the nature of the initial research studies.[ 39 ] After reviewing the bias and the abstract of the data, it is decided that the synthesis is carried out quantitatively or qualitatively. In case of conceptual heterogeneity (systematic differences in the study design, population, and interventions), the generalizability of the findings will be reduced and the study will not be meta-analysis. The meta-analysis study allows the estimation of the effect size, which is reported as the odds ratio, relative risk, hazard ratio, prevalence, correlation, sensitivity, specificity, and incidence with a confidence interval.[ 26 ]

Estimation of the effect size in systematic review and meta-analysis studies varies according to the type of studies entered into the analysis. Unlike the mean, prevalence, or incidence index, in odds ratio, relative risk, and hazard ratio, it is necessary to combine logarithm and logarithmic standard error of these statistics [ Table 1 ].

Effect size in systematic review and meta-analysis

OR=Odds ratio; RR=Relative risk; RCT= Randomized controlled trial; PPV: positive predictive value; NPV: negative predictive value; PLR: positive likelihood ratio; NLR: negative likelihood ratio; DOR: diagnostic odds ratio

Interpreting and presenting results (answers to research questions)

A systematic review ends with the interpretation of results. At this stage, the results of the study are summarized and the conclusions are presented to improve clinical and therapeutic decision-making. A systematic review with or without meta-analysis provides the best evidence available in the hierarchy of evidence-based practice.[ 14 ] Using meta-analysis can provide explicit conclusions. Conceptually, meta-analysis is used to combine the results of two or more studies that are similar to the specific intervention and the similar outcomes. In meta-analysis, instead of the simple average of the results of various studies, the weighted average of studies is reported, meaning studies with larger sample sizes account for more weight. To combine the results of various studies, we can use two models of fixed and random effects. In the fixed-effect model, it is assumed that the parameters studied are constant in all studies, and in the random-effect model, the measured parameter is assumed to be distributed between the studies and each study has measured some of it. This model offers a more conservative estimate.[ 40 ]

Three types of homogeneity tests can be used: (1) forest plot, (2) Cochrane's Q test (Chi-squared), and (3) Higgins I 2 statistics. In the forest plot, more overlap between confidence intervals indicates more homogeneity. In the Q statistic, when the P value is less than 0.1, it indicates heterogeneity exists and a random-effect model should be used.[ 41 ] Various tests such as the I 2 index are used to determine heterogeneity, values between 0 and 100; the values below 25%, between 25% and 50%, and above 75% indicate low, moderate, and high levels of heterogeneity, respectively.[ 26 , 42 ] The results of the meta-analyzing study are presented graphically using the forest plot, which shows the statistical weight of each study with a 95% confidence interval and a standard error of the mean.[ 40 ]

The importance of meta-analyses and systematic reviews in providing evidence useful in making clinical and policy decisions is ever-increasing. Nevertheless, they are prone to publication bias that occurs when positive or significant results are preferred for publication.[ 43 ] Song maintains that studies reporting a certain direction of results or powerful correlations may be more likely to be published than the studies which do not.[ 44 ] In addition, when searching for meta-analyses, gray literature (e.g., dissertations, conference abstracts, or book chapters) and unpublished studies may be missed. Moreover, meta-analyses only based on published studies may exaggerate the estimates of effect sizes; as a result, patients may be exposed to harmful or ineffective treatment methods.[ 44 , 45 ] However, there are some tests that can help in detecting negative expected results that are not included in a review due to publication bias.[ 46 ] In addition, publication bias can be reduced through searching for data that are not published.

Systematic reviews and meta-analyses have certain advantages; some of the most important ones are as follows: examining differences in the findings of different studies, summarizing results from various studies, increased accuracy of estimating effects, increased statistical power, overcoming problems related to small sample sizes, resolving controversies from disagreeing studies, increased generalizability of results, determining the possible need for new studies, overcoming the limitations of narrative reviews, and making new hypotheses for further research.[ 47 , 48 ]

Despite the importance of systematic reviews, the author may face numerous problems in searching, screening, and synthesizing data during this process. A systematic review requires extensive access to databases and journals that can be costly for nonacademic researchers.[ 13 ] Also, in reviewing the inclusion and exclusion criteria, the inevitable mindsets of browsers may be involved and the criteria are interpreted differently from each other.[ 49 ] Lee refers to some disadvantages of these studies, the most significant ones are as follows: a research field cannot be summarized by one number, publication bias, heterogeneity, combining unrelated things, being vulnerable to subjectivity, failing to account for all confounders, comparing variables that are not comparable, just focusing on main effects, and possible inconsistency with results of randomized trials.[ 47 ] Different types of programs are available to perform meta-analysis. Some of the most commonly used statistical programs are general statistical packages, including SAS, SPSS, R, and Stata. Using flexible commands in these programs, meta-analyses can be easily run and the results can be readily plotted out. However, these statistical programs are often expensive. An alternative to using statistical packages is to use programs designed for meta-analysis, including Metawin, RevMan, and Comprehensive Meta-analysis. However, these programs may have limitations, including that they can accept few data formats and do not provide much opportunity to set the graphical display of findings. Another alternative is to use Microsoft Excel. Although it is not a free software, it is usually found in many computers.[ 20 , 50 ]

A systematic review study is a powerful and valuable tool for answering research questions, generating new hypotheses, and identifying areas where there is a lack of tangible knowledge. A systematic review study provides an excellent opportunity for researchers to improve critical assessment and evidence synthesis skills.

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Handbook of Research Methods in Health Social Sciences pp 411–423 Cite as

Narrative Research

  • Kayi Ntinda 2  
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Narrative research aims to unravel consequential stories of people’s lives as told by them in their own words and worlds. In the context of the health, social sciences, and education, narrative research is both a data gathering and interpretive or analytical framework. It meets these twin goals admirably by having people make sense of their lived health and well-being in their social context as they understand it, including their self-belief-oriented stories. Narrative research falls within the realm of social constructivism or the philosophy that people’s lived stories capture the complexities and nuanced understanding of their significant experiences. This chapter presents a brief overview of the narrative research approaches as forms of inquiry based on storytelling and premised on the truth value of the stories to best represent the teller’s life world. The chapter also discusses data collection, analysis, and presentation utilizing narrative analysis. In doing so, this chapter provides illustrative examples applying narrative-oriented approaches to research in the health and social sciences. The chapter concludes by outlining the importance of narrative research to person-centric investigations in which the teller-informant view matters to the resulting body of knowledge.

  • Collaboration
  • Lived experience
  • Intersubjectivity
  • Narrative inquiry
  • Storytelling
  • Meaning-making

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Research Method

Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

Table of Contents

Narrative Analysis

Narrative Analysis


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.


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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// Resource

Field guide: narrative research methodologies.

The narrative change field is informed by an array of multidisciplinary approaches to craft narratives, test messages, landscape the narrative environment, and measure narrative change efforts. Our field guide presents a map to a number of traditional and emergent research practices in this space.

As part of our Understanding Narrative Research Methodologies project, Narrative Initiative worked with Spitfire Strategies to produce a field guide to narrative research methodologies. Based on nearly 20 interviews with researchers, practitioners and academics, this report explores the landscape of both existing and emergent narrative research methodologies.

We see this guide as a first edition, intended to spark dialogue. We hope researchers and practitioners in the field reach out to exchange learning and help us fill in the gaps. If you’re interested in further conversation, please contact Márquez .

narrative research examples brainly


There are likely thousands of organizations and movements actively at work to promote fair and inclusive societies, trying to win justice and equity on a grand scale. These groups, including nonprofits, tap into our imaginations by organizing and by using visual and verbal language to open new pathways and possibilities. We understand this nexus of efforts as narrative change work. No entity does this work alone. Success is found when work is done in coalition and collaboration. How then do they uncover concepts that will move their audiences to action, build power and stickiness, and lead to lasting change?

Narrative Initiative commissioned Spitfire Strategies to learn more about the research approaches and methods being used to inform and advance the narrative work of social justice organizations. This Field Guide offers lessons from interviews with some narrative change research leaders. Our interviewees presented a snapshot of the field, identified barriers, and offered a starting point to deepening narrative change research.

Due to its emergent nature and the varied traditions feeding into narrative change research, a set of needs arose that we find noteworthy. Interviewees cited the need for boldly embracing equity and diversity, and for collaboration across organizations and disciplines sharing research tools, data, and insights. They also expressed a need for shared research ethics and standards of practice. Both the challenge and the opportunity in this work lies in drawing from multiple sectors that contribute to narrative change practice.

We see this Field Guide as the first edition of a tool for narrative change researchers and those interested in embarking upon the practices detailed below. We also frame this Field Guide as an invitation to dialogue and learning exchange wherein readers help fill in the gaps and point to strong examples of theory and practice informing their own approaches. Ultimately, we want to learn with you how research methodologies are being used to make justice and equity common sense.

This report was written by Inga Skippings, Mark Dessaury, and Alexander (Bob) Boykin at Spitfire Strategies ; in conversation with Márquez Rhyne and Rachel Weidinger at Narrative Initiative.  We want to thank the following for helping to shape the thinking in this Field Guide:

  • Meg Bostrom, Topos Partnership
  • Jeff Chang, Race Forward
  • Brett Davidson, Open Society Foundations
  • Kristen Grimm, Spitfire Strategies
  • Hahrie Han, The P3 Lab
  • Doug Hattaway, Hattaway Communications
  • David Karpf, George Washington University
  • Nat Kendall-Taylor, FrameWorks Institute
  • Martin Kirk, /The Rules
  • Richard Kirsch, Our Story – The Hub for American Narratives
  • Liz Manne, Liz Manne Strategy
  • Felicia Perez, Center for Story-based Strategy
  • Rashid Shabazz, Color of Change
  • Micah Sifry, Civic Hall and Personal Democracy Media
  • Anat Shenker-Osorio, ASO Communications
  • Tracy Van Slyke, Pop Culture Collaborative
  • Brian Waniewski, Harmony Labs
  • Rachel Weidinger, Upwell (closed)

Download the full report to continue reading.

narrative research examples brainly

Resource Metadata


  • Observing Narrative Together

Related Narrative Initiative Project

  • Narrative Research
  • Field of Narrative Change

Produced by

  • Spitfire Strategies
  • Narrative Initiative

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  • Knowledge Base
  • How to write a narrative essay | Example & tips

How to Write a Narrative Essay | Example & Tips

Published on July 24, 2020 by Jack Caulfield . Revised on July 23, 2023.

A narrative essay tells a story. In most cases, this is a story about a personal experience you had. This type of essay , along with the descriptive essay , allows you to get personal and creative, unlike most academic writing .

Table of contents

What is a narrative essay for, choosing a topic, interactive example of a narrative essay, other interesting articles, frequently asked questions about narrative essays.

When assigned a narrative essay, you might find yourself wondering: Why does my teacher want to hear this story? Topics for narrative essays can range from the important to the trivial. Usually the point is not so much the story itself, but the way you tell it.

A narrative essay is a way of testing your ability to tell a story in a clear and interesting way. You’re expected to think about where your story begins and ends, and how to convey it with eye-catching language and a satisfying pace.

These skills are quite different from those needed for formal academic writing. For instance, in a narrative essay the use of the first person (“I”) is encouraged, as is the use of figurative language, dialogue, and suspense.

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Narrative essay assignments vary widely in the amount of direction you’re given about your topic. You may be assigned quite a specific topic or choice of topics to work with.

  • Write a story about your first day of school.
  • Write a story about your favorite holiday destination.

You may also be given prompts that leave you a much wider choice of topic.

  • Write about an experience where you learned something about yourself.
  • Write about an achievement you are proud of. What did you accomplish, and how?

In these cases, you might have to think harder to decide what story you want to tell. The best kind of story for a narrative essay is one you can use to talk about a particular theme or lesson, or that takes a surprising turn somewhere along the way.

For example, a trip where everything went according to plan makes for a less interesting story than one where something unexpected happened that you then had to respond to. Choose an experience that might surprise the reader or teach them something.

Narrative essays in college applications

When applying for college , you might be asked to write a narrative essay that expresses something about your personal qualities.

For example, this application prompt from Common App requires you to respond with a narrative essay.

In this context, choose a story that is not only interesting but also expresses the qualities the prompt is looking for—here, resilience and the ability to learn from failure—and frame the story in a way that emphasizes these qualities.

An example of a short narrative essay, responding to the prompt “Write about an experience where you learned something about yourself,” is shown below.

Hover over different parts of the text to see how the structure works.

Since elementary school, I have always favored subjects like science and math over the humanities. My instinct was always to think of these subjects as more solid and serious than classes like English. If there was no right answer, I thought, why bother? But recently I had an experience that taught me my academic interests are more flexible than I had thought: I took my first philosophy class.

Before I entered the classroom, I was skeptical. I waited outside with the other students and wondered what exactly philosophy would involve—I really had no idea. I imagined something pretty abstract: long, stilted conversations pondering the meaning of life. But what I got was something quite different.

A young man in jeans, Mr. Jones—“but you can call me Rob”—was far from the white-haired, buttoned-up old man I had half-expected. And rather than pulling us into pedantic arguments about obscure philosophical points, Rob engaged us on our level. To talk free will, we looked at our own choices. To talk ethics, we looked at dilemmas we had faced ourselves. By the end of class, I’d discovered that questions with no right answer can turn out to be the most interesting ones.

The experience has taught me to look at things a little more “philosophically”—and not just because it was a philosophy class! I learned that if I let go of my preconceptions, I can actually get a lot out of subjects I was previously dismissive of. The class taught me—in more ways than one—to look at things with an open mind.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

  • Ad hominem fallacy
  • Post hoc fallacy
  • Appeal to authority fallacy
  • False cause fallacy
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  • College Essay Format & Structure
  • Comparing and Contrasting in an Essay

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If you’re not given much guidance on what your narrative essay should be about, consider the context and scope of the assignment. What kind of story is relevant, interesting, and possible to tell within the word count?

The best kind of story for a narrative essay is one you can use to reflect on a particular theme or lesson, or that takes a surprising turn somewhere along the way.

Don’t worry too much if your topic seems unoriginal. The point of a narrative essay is how you tell the story and the point you make with it, not the subject of the story itself.

Narrative essays are usually assigned as writing exercises at high school or in university composition classes. They may also form part of a university application.

When you are prompted to tell a story about your own life or experiences, a narrative essay is usually the right response.

The key difference is that a narrative essay is designed to tell a complete story, while a descriptive essay is meant to convey an intense description of a particular place, object, or concept.

Narrative and descriptive essays both allow you to write more personally and creatively than other kinds of essays , and similar writing skills can apply to both.

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Caulfield, J. (2023, July 23). How to Write a Narrative Essay | Example & Tips. Scribbr. Retrieved December 21, 2023, from https://www.scribbr.com/academic-essay/narrative-essay/

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