case study using narrative analysis

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 .

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:

Thanks. I need examples of narrative analysis

Here are some examples of research topics that could utilise narrative analysis:

Personal Narratives of Trauma: Analysing personal stories of individuals who have experienced trauma to understand the impact, coping mechanisms, and healing processes.

Identity Formation in Immigrant Communities: Examining the narratives of immigrants to explore how they construct and negotiate their identities in a new cultural context.

Media Representations of Gender: Analysing narratives in media texts (such as films, television shows, or advertisements) to investigate the portrayal of gender roles, stereotypes, and power dynamics.

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

Please i need help with my project,

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

please mention the sources as well.

My research is mixed approach. I use interview,key_inforamt interview,FGD and document.so,which qualitative analysis is appropriate to analyze these data.Thanks

Which qualitative analysis methode is appropriate to analyze data obtain from intetview,key informant intetview,Focus group discussion and document.

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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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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 – Types, Methods and Examples

Table of Contents

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Int J Qual Stud Health Well-being
• v.18(1); 2023

Finding a path in a methodological jungle: a qualitative research of resilience

Elīna zelčāne.

Department of Health Psychology and Paedagogy, Riga Stradiņš University, Riga, Latvia

Anita Pipere

Qualitative research provides an in-depth understanding of lived experiences. However, these experiences can be hard to apprehend by using just one method of data analysis. A good example is the experience of resilience. In this paper, the authors describe the chain of the decision-making process in the research of the construct of “resilience”. s The authors justify the implications of a multi-method, pluralistic approach, and show how the triangulation of two or more qualitative methods and integration of several qualitative data analysis methods can improve a deeper understanding of the resilience among people with chronic pain. By combining the thematic analysis, narrative analysis, and critical incident technique, lived experiences can be seen from different perspectives.Therefore, the thematic analysis describes the content and answers to “what” regarding resilience, the narrative analysis describes the dynamics of resilience, and answers to “how”, while the critical incident technique clarifies the most significant experience and the answers to “why” changes happen. This integrative approach could be used in the analysis of other psychological constructs and can serve as an example of how the rigour of qualitative research could be provided.

Introduction

Just a few decades ago, qualitative researchers put a lot of effort into discussions with quantitative researchers to prove that a qualitative research strategy can also be viewed as a scientific inquiry and can provide valid and significant knowledge. Today, qualitative research is no longer just “not quantitative research” but has developed an identity or maybe multiple identities of its own (Flick, 2018 ). Qualitative research is especially appropriate to study complex constructs and experiences holistically. It allows one to acquire a deeper understanding of people’s lived experiences in diverse contexts (Hong & Cross Francis, 2020 ) and deals primarily with an intensive rather than extensive examination of these experiences (Gough & Deatrick, 2015 ).

The wider use of different qualitative approaches has led to new methodological challenges. One such challenge is to support methodological integrity in keeping with a diversity of researchers’ goals and approaches (Levitt, 2021 ). Although qualitative research is an approach rather than a particular set of techniques, it does not mean that a researcher can choose any design or combine any methods without justification. The inconsistency between the research question and the methodology, insufficient methodological knowledge, and the lack of attention to a philosophical foundation of qualitative methodology can be mentioned as some important challenges (Khankeh et al., 2015 ). To overcome this challenge, a researcher must become familiar with traditional approaches and recently developed ones in qualitative research and choose the most appropriate for the given research problem and research questions.

Another challenge is how to present the findings of qualitative research in a way that they can be comprehended by both academic and non-academic readers. Therefore, the researchers need to render the qualitative research findings more “friendly” to people who may not have academic or professional backgrounds or interests, provided that the findings are still faithful (Holloway & Todres, 2007 ). Besides, the findings of qualitative research often make sense in a very narrow context, while outside the academic environment there is a demand for practical and more general benefits that could promote change in a wider context. Thus, researchers must provide a “thick description” of the participants and the research process, to enable the reader to assess whether these findings are transferable to their own setting (Korstjens & Moser, 2018 ).

Qualitative researchers often use well-trodden paths. Svend Brinkmann ( 2015 ) calls this process a “McDonaldization” of qualitative research. To cope with this trend, it is recommended to also use innovative methods to explore psychological issues in health and illness (Chamberlain & Murray, 2017 ) and learn from artists how to capture peoples’ attention in a more creative way (Holloway & Todres, 2007 ). Innovative practices in qualitative research can involve pluralisms of various kinds, creative ways of collecting and analysing data, disseminating findings, and participation in some of the ethical and practical challenges involved in qualitative research (Lamarre & Chamberlain, 2021 ).

Today, qualitative research is widely used in different social sciences, and psychology is one of the areas where it is expanding rapidly. The proportion of qualitative research has grown especially in the field of health psychology. One of the reasons for the current popularity of qualitative health research is the growing emphasis of policy and practitioners on patient/client experiences and practices related to prevention, illness, and use of services (Gough & Deatrick, 2015 ). Qualitative research design is consistent with the Chronic care model (CCM), which is a widely-used framework for organizing and providing care for people with chronic disease (Wagner et al., 2001 ). The CCM aims to improve the quality of care and patient outcomes by providing proactive, patient-centred, and integrated care (Spoorenberg et al., 2015 ). Qualitative research can provide a deeper understanding of patients’ perspectives, experiences, and treatment needs and could promote patient-centred care (O’Reilly et al., 2021 ; Renjith et al., 2021 ). When patients feel respected, are included in the decision-making process, and can express their needs and emotions without feeling judged, they report a stronger sense of alliance with the care providers (Youssef et al., 2020 ). Qualitative research “gives voice” to patients (Braun & Clarke, 2019 ; Stein & Mankowski, 2004 ), allowing researchers and practitioners to observe health-related issues from several perspectives and analyse qualitative data with multiple methods.

One example of such a construct that can be qualitatively studied from different points of view is the experience of resilience while living with chronic musculoskeletal pain (CMP). In this paper, we describe the chain of the decision-making process in the research of the mentioned topic, starting from the dilemma between quantitative and qualitative research strategies to the decision to combine different data analysis methods. This article focuses specifically on the discussion of how the integration of several qualitative data analysis methods can improve a deeper understanding of the formation and maintenance of resilience among people with chronic pain.

Resilience in chronic pain: A rationale for qualitative research

The American Psychological Association defines resilience as a process of adapting well in the face of adversity, trauma, tragedy, threats, or significant sources of stress (APA, 2012 ). Resilience can be defined as the process of effectively negotiating, adapting to, or managing significant sources of stress or trauma. Assets and resources within the individual, their life and environment facilitate this capacity for adaptation and “bouncing back” in the face of adversity (Windle, 2011 ).

In previous studies, resilience has been viewed as a personality trait (Block & Kremen, 1996 ; Connor & Davidson, 2003 , Wagnild & Young, 1993 ), or a dynamic process, that can lead to a positive outcome (Bonanno & Mancini, 2008 ; Luthar & Cicchetti, 2000 ; Masten, 2011 ; Rutter, 2006 ). Although there are several definitions of resilience, most of them are based on two core concepts—adversity and positive adaptation. The notion of risk and positive adaptation are fundamental to both personal characteristics and process-based conceptualizations of resilience (Vella & Pai, 2019 ). Some researchers use the term “adaptation” meaning both the process of adjustment and its outcome (Luthar & Cicchetti, 2000 ; Rutter, 2006 ) but recently many scholars have emphasized the three pillars of resilience—adversity, the process of adaptation, and the preservation of health functioning or positive outcome (Hiebel et al., 2021 ; Kunzler et al., 2018 ; Stainton et al., 2019 ).

In recent studies, researchers offer an integrative view of resilience, describing it as a multifactorial, multisystemic and context dependent construct (Miller-Graff, 2022 ; Sisto et al., 2019; Ungar, 2021 ). Individual resilience is influenced by biological, psychological, social, and ecological factors and can manifest itself in different ways, like maintaining healthy functioning despite adversity, recovering from adversity and bouncing back to homoeostasis or even bouncing forward and experiencing personal growth (Ungar, 2021 ).

In the context of health psychology last few years there has been a shift away from disease-focused to health-focused research (Denckla et al., 2020 ). Resilience is viewed not only as the absence of psychopathology but as a presence of psychological, mental, social, and spiritual capital that help to maintain the quality of life despite the illness (Babić et al., 2020 ). Since people with chronic pain or other chronic conditions are not able to recover fully and return to homoeostasis, resilience in this context is defined as the ability to live fulfilling life in the presence of pain (Goubert & Trompetter, 2017 ; Sturgeon & Zautra, 2016 ).Chronic diseases, especially chronic pain, can negatively affect the physical, mental, and social aspects of a person’s life. However, chronically ill people, who have higher resilience scores, tend to have less depression and anxiety. Instead, they have a better quality of life and health behaviour (Cal et al., 2015 ; Gheshlagh et al., 2016 ). The effect of resilience can manifest itself in faster recovery from the negative effects of pain, through effective preservation of positive functioning despite the presence of pain (Sturgeon & Zautra, 2010 ).

Although previous studies (Gonzalez et al., 2019 ; Hemington et al., 2017 ; Ramírez-Maestre et al., 2019 ) have confirmed that resilience plays a key role in one’s adaptation to chronic pain, several questions still need to be answered. Why some people with chronic pain are more resilient than others? What factors influenced the development of their resilience? What are people with chronic pain doing to improve and maintain their long-term resilience?

The nature of these questions has inevitably led us to the exploration of experience related to the resilience of a specific population, alluded to by the qualitative research approach. We combined all these questions into one main research question, as is often done in qualitative studies: What is the experience of developing and maintaining resilience in people with CMP?

The next step after formulating the research question was to choose the right research paradigm or perspective on how a researcher sees and interprets the world. In recent studies, resilience has been seen as a context-dependent construct (Gentili et al., 2019 ; Hayman et al., 2017 ; Ungar, 2018 ). Resilience can be understood differently when we discuss, for example, adaptation to chronic pain, the experience of divorce, domestic violence, or childhood trauma. In different contexts, the opportunities for individuals are different, the needs are different, and the extent to which individuals can make use of these opportunities is different (Pooley & Cohen, 2010 ). Considering that there is no such thing as “common resilience for all”, we decided to ground our research on the paradigm of social constructivism. Constructivists acknowledge that individuals construct their own perceptions of the world, but social constructionists go one step further, arguing that those individual constructions are developed in a social world (Harper & Thomson, 2011 ). A fundamental assumption of the social constructivism paradigm is that there is no universal reality. Meanings, knowledge, and truth are created by the interactions of individuals within a society (Andrews, 2012 ; Creswell, 2013 ).

The choice of the social constructivism paradigm, along with the research question, confirmed the use of a qualitative research strategy, as it is more appropriate to study mental facts, such as experiences, feelings, and attitudes, which are ontologically subjective phenomena. In contrast, a quantitative research strategy is more suitable for studying brute facts or external reality (Silva, 2008 ). Quantitative studies have made a major contribution to resilience research in healthcare by demonstrating that resilience is positively correlated with social and physical functioning, adaptation to illness and better health outcomes (Kim et al., 2019 ; Musich et al., 2022 ; Schäfer et al., 2022 ; Seiler & Jenewein, 2019 ), but quantitative studies can’t provide a sufficiently deep and comprehensive understanding of how resilience is formed and how the resilience dynamic is influenced by the general context of life.

Resilience is a multidimensional, contextually specific, and culturally biased construct (Ungar, 2013 ). The meaning we put in the words “being resilient” is not the same for all of us. Global resilience is at best quite rare, if not non-existent because it changes in different situations and at different times (Vanderbilt-Adriance & Shaw, 2008 ). For example, a person can cope effectively with stressors at work but shows very low resilience in the face of disease. These differences can be explained by the fact that resilience is influenced not only by internal but also by external risk and protective factors. Resilience of an individual depends on resilience of interconnected systems. Resilience develops and changes because all of the systems accounting for resilience are dynamic (Masten, 2021 ). Many authors (Bonanno & Mancini, 2008 ; Davydov et al., 2010 ; Geard et al., 2018 ) admit that resilience in encountering short-term stressors differs from the resilience we experience when living with long-term adversity. Strategies that help in the short term may not be helpful in the long term; besides, we can experience several ups and downs.

Using resilience questionnaires and scales, we can determine some general characteristics or manifestations of resilience. Longitudinal studies allow to measure resilience in different periods of time, but quantitative studies are unable to answer the question of why changes in resilience at different stages of life and in specific situations happen. Qualitative research methods (especially, interviews) could help to understand the meanings, beliefs, and values of the participants, which play a critical role in explaining their behaviour and its consequences and understanding the effect of social and cultural contexts on these meanings, behaviours, processes, and results (Maxwell, 2021 ).

Although a mixed methods design is often used to study common and unique aspects of resilience (Ungar & Liebenberg, 2011 ) and initially we considered using the mixed methods research in this study, we came to the conclusion that our research question is related to the deep understanding of participants’ unique experience of resilience and can best be answered by using the qualitative research design. Taking into account the aspects mentioned above, it appears that a qualitative research strategy would be the most appropriate choice to study resilience in people with chronic pain. Furthermore, we have provided arguments for why we have chosen the particular research design.

Multiple case study design

The case study design was selected as the most relevant to investigate the resilience of people with CMP. Creswell defines a case study as an in-depth exploration of a bounded system or multiple bounded systems in their real-life setting (Creswell et al., 2007 ). In our research, each case (each participant’s experience of resilience while living with chronic pain) has its limits in time (the duration of the illness) and its unique context or real-life context (environment, available resources, etc.).

In contrast to experimental designs, which seek to test a specific hypothesis through manipulating the environment, the case study approach lends itself well to capturing information on more explanatory questions “how”, “what”, and “why” (Crowe et al., 2011 ). Case study research is an increasingly popular approach among qualitative researchers, providing methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods (Hyett et al., 2014 ).

There are two key approaches to case study research. Those researchers whose philosophical assumptions are grounded in postpositivism usually use Robert Yin’s approach (Yin, 2003 ), but researchers whose philosophical assumptions are grounded in constructivism mostly use the approach by Robert Stake ( 1995 ) or Sharan B. Merriam ( 2009 ).

Since we grounded our research in the paradigm of social constructivism, the approach to the case study by Stake was chosen. He emphasizes that a case study is not a methodological choice but rather a choice of what is to be studied (Stake, 2008 ). In our research, this is the subjective experience of the resilience of each research participant.

Stake and other representatives of constructivism claim that reality is not available to us in an objective way; it is possible to study only the meaning people attach to what has happened because each of us interprets reality differently (Yazan, 2015 ). In our research, we are not studying resilience as an objective reality that can be measured, but as a subjective perception of this experience over time.

Stake speaks about three types of case studies: intrinsic, instrumental, and multiple case studies (Stake, 1995 ). An intrinsic case study allows one to explore a unique phenomenon. An instrumental case study is used if a researcher wants to gain a broader understanding of some issue through this particular case, but the collective or multiple case study involves multiple cases being studied simultaneously or sequentially to gain an even broader understanding of the issue. In our study, we apply multiple case design.

Multiple case studies are often used in health psychology (Boblin et al., 2013 ; Breet & Bantjes, 2017 ; Fearon et al., 2021 ), because these studies allow a researcher to analyse within each setting and between settings (Baxter & Jack, 2008 ). In our investigation, we were interested in individual stories and the unique resilience experience of each participant, but we also wanted to know whether people with chronic pain have used similar strategies to adapt to the disease and if they have mentioned any common factors that helped them develop resilience. In light of the arguments mentioned above, a multiple case study seemed to be the most relevant design.

In the following paragraphs, we will substantiate the selection of specific methods for data collection and analysis and how multi-method and pluralistic approaches can enhance research rigour.

Multi-method qualitative approach as methodological triangulation

Similarly as in quantitative research, qualitative research has its criteria to ensure the rigour of the research. One such criterion is triangulation. Triangulation means being able to look at the same phenomenon or research topic through more than one source of data (Abdalla et al., 2018 ). It refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999 ). Triangulation is not only a strategy for the validation of the research procedures and results (Flick, 2018 ) but also a strategy that allows adding depth to the data that are collected and gives a more complete picture of the phenomenon that is studied (Fusch et al., 2018 ). Abdalla et al. suggest several functions of triangulation. Information from different angles can be used to confirm, develop, or illuminate the research problem (Abdalla et al., 2018 ).

For more than three decades, qualitative researchers have used multiple forms of triangulation in a study: data triangulation, methodological triangulation, theory or perspective triangulation, and investigator triangulation, following the suggestions of Denzin (Denzin, 1989 ). By data triangulation, Denzin meant different data points (people, time, space) that represent different data of the same event. By methodological triangulation, he meant multiple data collection methods, for example, interviews, focus groups, and observations. The theory triangulation designated viewing data through the lens of different theories, while the investigator triangulation meant that more than one investigator was observing the same data.

In the study described in this article, we combined two data collection methods that provide methodological triangulation. A combination of several qualitative data collection methods to investigate a research question or phenomenon is usually called the “multiple method(s)” approach (McDonnell et al., 2017 ) or “multimethod(s)” (Anguera et al., 2018 ; Mik-Meyer, 2020 ; Roller & Lavrakas, 2015 ) approach. Some authors, like Janice M. Morse (Morse, 2003 , 2009 ) have used both concepts. In our research, we use the term “multimethod approach”, which is also used by American Psychological Association (APA, n.d ).

The combination of different data-gathering methods allows us to overcome each method’s weaknesses and limitations, contributes to a better understanding of a research problem compared to research that is based on only one methodological approach, and provides knowledge that otherwise is inaccessible to the researcher (Creswell, 2015 ).

However, some authors admit that multi-method research also has some challenges. One such challenge is how to synthesize the findings of two separate methods if they are not complementary but conflicting (Nepal, 2010 ). In our study, data gathering methods are complementary, but any contradicting results, if such appear, are analysed assuming that the contradictions may not exist simultaneously but emerge at different time points. In the following paragraphs, we will explain how the combination of several data analysis methods can help to solve these contradictions.

Another challenge is to compare the weight of the data obtained by different methods. For example, does a focus group interview with six participants carry the same weight as an individual interview? (Carter et al., 2014 ). In addition, this challenge in more detail will be described further.

In our qualitative study, we combine individual semi-structured interviews with focus groups conducted with interviewed participants. In the following sections of this paper, we will explain our considerations for combining these methods and justify why we took both methods onboard with the same participants.

Combination of semi-structured individual interviews and focus groups

A semi-structured interview (SSI) is the most common format of data collection in qualitative research. It employs a relatively detailed interview guide and is designed to determine subjective responses from people regarding a particular situation or phenomenon they have experienced (McIntosh & Morse, 2015 ). Although SSI has a pre-planned structure, it differs from a structured interview with more openness. SSI is often accompanied by follow-up “why” or “how” questions (Adams, 2015 ) and gives the interviewer the opportunity to elaborate and explain particular issues through the use of open questions (Alsaawi, 2014 ). It also differs from an unstructured interview, where the interviewer asks only some general questions and is mainly a listener (Brinkmann, 2014 ). SSI is useful when a researcher works with a complex issue because he can use probes and spontaneous questions to explore, deepen understanding, and clarify answers to questions (Wilson, 2014 ).

We selected SSI as the main data collection method for several reasons: 1) from the main qualitative data collection methods (observations, textual or visual analysis, individual and group interviews) only individual or focus group interviews could give enough information to answer the research question, 2) in a one-to-one interview format, the interviewer can create a safe environment and adjust to every participant; 3) we had a set of specific research subquestions ( How do people with chronic pain describe the development of resilience? How do they describe factors that have contributed to or hindered resilience at the beginning of their illness? How do they describe the manifestation of resilience in the long term? How do they describe factors that have contributed to or hindered resilience in the long term? How does resilience change over time? ), so we needed a fairly structured interview protocol that allowed us to answer these questions. But we also did not want to lose in-depth data and unexpected disclosures, which is why we did not select a structured interview.

Although individual interviews have many advantages, they have some disadvantages as well, such as the hierarchical position and the power of the interviewer over the participant. The participant is reduced to the role of a passive provider of data, while the interviewer is the one who uses skilled rapport promotion technology (Nunkoosing, 2005 ). Another disadvantage is a lack of group dynamics, which could bring new themes into discussions (Lambert & Loiselle, 2008 ).

To enhance research rigour, we decided to use one more data collection method and combine individual interviews with focus groups. The focus group approach is a qualitative method for collecting data on the selected topic with a structured and focused discussion in a small group of people (Gundumogula, 2020 ). Focus groups create open lines of communication between individuals and rely on the dynamic interaction between participants to produce data that would be impossible to gather via other approaches, such as one-on-one interviewing (Jarvis & Barberena, 2008 ). A significant role in focus groups is played by a moderator. The involvement of a good moderator can ensure that the conversation is always on track and encourage the participation of participants without one individual dominating the discussion (Sagoe, 2012 ).

For some participants, it could be easier to disclose personal and sensitive information through individual interviews (Kaplowitz, 2000 ; Kruger et al., 2019 ), but for others, the focus group format could be more appropriate. Listening to other participants’ experience stories can encourage self-disclosure and stimulate memory (Guest et al., 2017 ; Kitzinger, 1994 ).

The limitation of focus groups is the possibility of bias and manipulation through leading or dominating participants, as well as tendencies towards normative discourses, conflicts, and arguments within focus groups (Gundumogula, 2020 ; Smithson, 2000 ). Using these methods together, it could be possible to find a balance between looking for a diversity of topics and a deeper investigation of each topic.

Janice Morse argues that in situations where a researcher uses multiple qualitative methods, one of them is usually a core method and the rest methods are supplementary methods. A second qualitative component can identify gaps or holes, “pick up” what the first method missed and allow discussing some parts of the findings that had not been on the researcher’s screen earlier (Morse, 2010 ).

In our study, a semi-structured interview is a core method that was used to collect data from all participants, while focus group discussions were used as a supplementary method to obtain feedback from the part of research participants who were interviewed and to clarify whether our interpretation of the interview data coincides with the views of the participants. Focus group discussions as a complementary method are also valuable because due to the dynamics of the group, participants could recall important information they did not mention during the interviews. Interaction between participants can promote discussions and bring new perspectives to the investigated problem. Participants can influence each other through their presence and their reactions to what other people say (Mack et al., 2005 ).

In the first phase of the study, we developed a protocol for the semi-structured interview consistent with the research questions. Because of our decision to use an inductive approach to data analysis, our questions weren’t grounded in the literature and we didn’t have an intention to test hypothesis through the answers to these questions. Instead, we were open to whatever emerged from the data. To avoid the situation where participants could be influenced to give certain answers or very short answers, we formulated only open-ended interview questions aligning with research questions, thus aiming for richer data.

The interviews were approximately 60 to 90 minutes long and provided us with main data on the lived experiences of the participants. Since we were interested in the dynamics of resilience, the interviewer spent a lot of time listening to stories about different periods in the life of the participants. If the participants wanted to share more information than asked, the interviewer allowed them to speak because additional information would help to understand the context of the story and give a deeper understanding of the different factors that have influenced the resilience of the participants.

Our strategy was to analyse the interview data and find out which themes appeared in the participants’ responses more frequently, speaking about each research question. We were also looking for contradictory ideas and trying to understand what influences specific beliefs and values. For example, why do some participants accept the disease as something they will have to live with all their lives, but others still have the hope to eliminate the disease? More information about the data analysis process will be presented in the following chapters of this paper.

After drawing the first conclusions, we organized two focus groups. In the theoretical literature, there is a suggestion to conduct at least two focus groups to ensure data saturation. (Hennink et al., 2019 ). The more focus groups are organized, the more different themes and perspectives can arise, and the researchers can find ideas that are common in all groups. Since focus groups in our study are only an additional method and the sample is quite small (17 participants), it was agreed that two groups would be enough to get feedback from participants about our interpretations of the research results.

Before moving on to data analysis, we must answer the question of why we stopped collecting data at the point that we did and what our arguments were for determining the sample size.

Criteria for determining sample size

Samples in qualitative research tend to be small to support the depth of case-oriented analysis, that is fundamental to this mode of inquiry, but at the same time large enough to allow the unfolding of a new and richly textured understanding of the phenomenon under study (Sandelowski, 1996 ; Vasileiou et al., 2018 ).

Although qualitative researchers still have discussions about the number of interviews, that would be enough to ensure the research rigour and provide the answers to the research questions, there are several criteria that help to define an optimal sample size. In the thematic analysis, that is used in our research, one of the most significant criteria to determine sample size is saturation. Saturation can be defined as the point at which additional data do not lead to any new emerging themes (Given, 2016 ). Even if some new codes arise, these data change a little or do not change the coding result at all. According to this criterion, the researcher can stop conducting interviews at the moment when saturation is reached (Bryman, 2012 ). But this approach, as emphasized by Bryman ( 2012 ), is a very demanding one, because it forces the researcher to combine sampling, data collection, and data analysis, rather than treating them as separate stages in a linear process. Another suggestion is that a researcher must be sure that the data he/she has and what he/she wants to say coincide, that data support his/her conclusions, and conclusions are not going beyond what data can support (Becker, 2012 ).

Hennik et al. acknowledge that saturation can be understood as code saturation and meaning saturation. Code saturation can be defined as the point where no additional issues are identified and the codebook begins to stabilize but meaning saturation can be defined as the point where we fully understand issues and when no further dimensions, nuances, or insights of issues can be found (Hennink et al., 2017 ). It is easier to reach code saturation than meaning saturation because people can put different meanings in the same codes, and some codes, especially abstract ones, can have multiple dimensions. Focusing on codes alone is a deficient measure of saturation; codes can be saturated, but vital information remains unconsidered (Sebele-Mpofu, 2020 ). It is important not only to look at the frequency of the data but also to interpret the data and to see what is in it (McIntosh & Morse, 2015 ).

Saturation is influenced by multiple parameters or criteria that determine how large a sample must be. One such criterion is accessibility. The more specific and harder to access the population, the smaller could be the minimal number of participants (Adler & Adler, 2012 ; Brannen, 2012 ). Another criterion is the homogeneity or heterogeneity of the population. In a homogeneous population, the sample size could be smaller; in a heterogeneous population with more different subgroups, the sample must be larger (Adler & Adler, 2012 ; Brannen, 2012 ; Hennink et al., 2017 ). The theoretical background can also influence the sample size (Bryman, 2012 ; Hennink et al., 2017 ). For example, life story research is likely to involve a smaller sample size than research aiming to develop some theory. The sample size will most likely be smaller if the data is thick or richer and larger than if the data are thin (Hennink et al., 2017 ). And, of course, available resources can also play an important role in a sample size (Flick, 2018 ).

Maltreud and collegues (Malterud et al., 2016 ) have proposed the concept of “information power” to guide adequate sample size for qualitative studies. Information power depends on the aim of the study, sample specificity, use of established theory, quality of dialogue, and analysis strategy. The more information the sample holds, relevant to the actual study, the lower amount of participants is needed.

By evaluating the criteria mentioned above, we realized that our sample must be rather small, than big, because of quite a narrow and specific aim of our study. The aim of this study is to capture themes, not to develop theories. Although the population under study has subgroups, it is still quite homogeneous. The interviews would produce thick data. The only argument that indicated the need for a larger sample was the multidimensional concept of resilience, which could determine the longer time to move from code saturation to meaning saturation.

In our study, we interviewed 17 people with CMP. To answer our main research question “What is the experience of developing resilience in people with CMP?” we purposely looked for working-age participants with different types and different intensities of musculoskeletal pain, such as back pain, joint pain, pain after spinal cord injury, etc., who are 18–65 years old and have been living with pain for five years or more. We approached participants through patient associations, Facebook groups, and personal contacts. There were seven men and ten women among the participants aged 29 to 64 years. Four participants had chronic pain after spinal cord injury and used a wheelchair. Six participants had rheumatoid arthritis or other rheumatoid disease and seven participants had other diagnoses that caused neck or back pain, like spondylosis, osteoporosis, and disk herniation. Three participants didn’t do paid work. Two of them were women at pre-retirement age who looked after their grandchildren and one was a man with a spinal cord injury. The other participants worked despite the limitations caused by pain.

The decision to stop data collection after 17 interviews was based on several considerations. First of all, we reached a code and meaning saturation. In our study, thematic analysis was the instrument to examine saturation. During the first stage of the inductive thematic analysis, we developed a codebook and applied it to the rest of the interviews. Having analysed 13 interviews, we found central codes that are repeated in each interview and that less than 5% of the new codes appear. After we found central codes and reached code saturation, we went through all interviews and analysed what participants mean by each code. Fully understanding all dimensions of conceptual codes requires much more data than fully understanding concrete codes (Hennink et al., 2017 ). In our study, the category that was described bythe largest diversity of meanings was “adapting to the disease”. For some participants, it meant the ability to handle everything by themselves, but for others—the ability to use available social resources. We continued to conduct interviews and after analysing 17 interviews, we reached meaning saturation because no new code dimensions appeared.

By studying theoretical literature and analysing the criteria mentioned above, we found that sample size, starting from 12 interviews, can be sufficient for data saturation in a thematic analysis (Ando, Cousins, & Young, 2014 ) and 16 interviews can lead to meaning saturation (Hennink et al., 2017 ). It matched our conclusion that 17 interviews would give enough information to answer the research question.

After analysing 17 interviews, we obtained sufficient information power, that allowed us to provide a thick description of each case as well as find commonalities and differences between cases.

In the next paragraphs, we will provide more detailed information on the process of data analysis and justify the necessity for a pluralistic approach.

The pluralistic approach to qualitative data analysis

Previously, we described our assumptions for choosing a qualitative research strategy and considerations for using two data collection methods. In this paragraph, we’ll continue to describe the data analysis process and will demonstrate why the development of resilience as a dynamic process should not be understood as applying only a single method of data analysis.

To describe different aspects of qualitative data, we use the pluralistic data analysis approach. In research, methodology pluralism has been approached using a range of conceptual labels (Frost & Nolas, 2011 ). In a broader sense, pluralism means combining a range of different data modes in a single research project, for example, quantitative and qualitative methods, but in a more narrow sense, it refers to the combination of several qualitative data analysis methods.

Pluralism in qualitative research is defined as the application of more than one qualitative analytical method to a single data set (Clarke et al., 2014 ) or, as specified by Frost, as the interpretation of one interview transcript with different qualitative analysis techniques (Frost et al., 2010 ). The aim of pluralist analyses is to produce rich, multilayered, multiperspective readings of any qualitative data set through the application of diverse ways of seeing and maximizing holistic understanding (Dewe & Coyle, 2014 ).

According to the literature, multiple analytical approaches are appropriate for understanding a plural and complex world, and the variety of human expression cannot always be adequately represented by one framework alone (Chamberlain et al., 2011 ; Frost et al., 2010 ; Kincheloe, 2001 , 2001 ). The data set can tell us several different things, depending on the questions we ask. Analysing the same data from different analytical lenses can reveal more meanings than analysing these data just from one analytical lens (Frost et al., 2010 ; Willig, 2013 ). The pluralistic approach not only enhances a deeper understanding of the phenomenon but, if each analysis method is performed by different researchers, it also reduces subjectivity and increases transparency in a study (Frost et al., 2010 ).

The pluralistic approach is widely used in social sciences; in recent years, it has also gained popularity in health psychology research (Dempsey et al., 2019 ; Dewe & Coyle, 2014 ; Madill et al., 2018 ; Rosas et al., 2019 ). The pluralistic approach has several advantages but combining different data analysis methods can also be challenging.

Researchers must find ways to demonstrate coherent links between theory, method, and findings and explain how findings produced from multiple analyses can remain commensurate or complementary (Braun & Clarke, 2019 ; Clarke et al., 2014 ). There must be a clear rationale for the theories and methods being used so that the researchers demonstrate reflexivity and document their research process in an accessible manner (Frost & Nolas, 2011 ). The use of methods without justification can lead to disjointed and fragmented findings (Chamberlain et al., 2011 ). Another challenge is the willingness of researchers to use a pluralistic approach. Pluralism requires researchers to be competent in all methods they apply (Clarke et al., 2014 ), which could be challenging, especially for new researchers.

In our study, we investigate both the content and dynamics of the experience of resilience in people living with chronic pain. Therefore, we are interested not only in resilience development strategies and factors that positively or negatively influence resilience but also in changes over time—how these strategies and factors change if we compare short-term and long-term resilience.

Upon starting this research, our main focus was on strategies that help to improve resilience. We considered that thematic analysis could be the best data analysis method for finding the most common strategies. After conducting the first pilot interviews, we were surprised by the richness of the available data. The participants shared different stories of their lives and acknowledged that the way they perceive pain has changed over time. We realized that we must broaden our research question and focus not only on common themes but also on the life of each participant in its unique context and dynamic. Therefore, we decided to apply both thematic and narrative analysis to analyse our data. Then, after conducting the third pilot interview, we noticed an interesting nuance—all participants were speaking about specific turning points in their lives, which dramatically changed their attitudes and resilience. From this, we understood that we need one more method that could be appropriate for analysing those changes. Studying the literature, we found that the critical incident technique (CIT) could be valuable to define critical incidents or experiences that contributed, positively or negatively, to resilience.

The pluralistic approach was not our strategy at the beginning of the investigation, but we came to this decision during the analysis of the pilot interviews. It confirms once again that conducting pilot interviews is an especially important step that allows for identifying “holes” and flaws in research questions and methods. The combination of thematic analysis, narrative analysis, and critical incident technique could provide answers to all research questions that we are interested in. More detailed considerations of the use of each method will be illustrated in the next paragraphs.

Combining methods: thematic analysis, narrative analysis, and critical incident technique

At the beginning of the research, our focus was mainly on strategies that help improve resilience. We decided that a thematic analysis would be an appropriate method to find common themes and to find out which strategies to improve resilience would be the most helpful. The interview protocol was created, and three pilot interviews were conducted and analysed with reflexive thematic analysis approach created by Braun and Clarke (Braun & Clarke, 2006 ). Pilot studies allow researchers to practice and assess the effectiveness of their planned data collection and analysis techniques (Doody & Doody, 2015 ). The piloting of interviews was set up to find out whether the interview questions are understandable and provide answers to the research question.

After conducting and analysing three pilot interviews, we realized that qualitative data provide more comprehensive material than we initially expected. We observed that interviewees not only answered the questions but spoke about their life as a whole, bringing up significant experiences from their past, like other traumatic experiences (such as divorce or losing their job), important people in their lives that influenced their values and attitudes, the brightest childhood memories, etc.

We concluded that we must revise the interview protocol and, for further interviews, include more questions about the dynamics of experience in different stages of the disease. This was the first time we noticed that short-term and long-term strategies differ, so the questions should be modified from more general to more specific. Creswell et al. ( 2007 ) has emphasized that qualitative research questions could change during the entire research process. Initial provisional questions can become more focused because researchers gain a deeper or broader understanding. That is why the qualitative study could not be fully planned in advance.

Since we added new research questions, we also needed new methods for data analysis. We realized that it is impossible to answer all research questions by using only thematic analysis. The thematic analysis allows one to find common themes between cases (Braun & Clarke, 2006 ; Joffe, 2012 ) but the narrative analysis could be more appropriate for analysing differences in cases and describing the dynamics of individual narratives in their unique context (Floersch et al., 2010 ; Simons et al., 2008 ).

Pilot interviews gave us rich qualitative data, including information about events that dramatically changed participants’ attitudes and resilience. So, we concluded that in addition to thematic and narrative analysis, CIT could be valuable for defining critical incidents or experiences that made a contribution, either positively or negatively, to resilience. Finally, we decided to combine reflexive thematic analysis (Braun & Clarke, 2006 ), narrative analysis (Crossley, 2000 ), and the enhanced critical incident technique (ECIT) (Butterfield et al., 2009 ). In what follows, the use of each method is explained in detail.

Reflexive thematic analysis

Thematic analysis (TA) can be seen as an umbrella term, used for sometimes quite different approaches, rather than a single qualitative analytic approach. The three main approaches in TA are the coding reliability approach, the codebook approach, and the reflexive approach (Braun & Clarke, 2019 ). TA has been widely used in recent qualitative health research designs (e.g., Lyng et al., 2022 ; Opsomer et al., 2019 ; Zarotti et al., 2019 ), because it is not strictly connected with a particular methodology and is quite flexible.

Since our research is based on the paradigm of social constructivism, we decided to use a reflexive thematic analysis. An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case (Hyett et al., 2014 ). Of all TA approaches, reflexive TA fits best with the paradigm of social constructivism because it emphasizes the active role of the researcher in coding and theme generation. The researcher not only identifies semantic themes and summarizes the content of the data, but also looks for latent themes, revealing the underlying ideas within the data (Braun & Clarke, 2019 ). The subjectivity of a researcher is the primary “tool” for reflexive TA. Subjectivity is not a problem to be managed or controlled, it is a resource for research (Braun & Clarke, 2019 ; Gough & Madill, 2012 as cited by).

The described investigation focuses not on objective reality but on the way participants perceive and, together with the researcher, interpret their subjective experiences. It should also be acknowledged that the previous experiences, biases, and research position of researchers impact the way they look at the data. Subjectivity without reflexivity could be a limitation, but if researchers are aware of their role and impact, subjectivity could become a resource. In this study, the researcher, who conducted the interviews, is an insider to the study population. The researcher’s personal experience of living with chronic pain helped stimulate a dialogue with interviewees and increase mutual trust.

In recent years reflexive TA has been used more often in health psychology (Bose & L Elfström, 2022 ; D’Souza et al., 2022 ; McKenna-Plumley et al., 2021 ), since it is a theoretically flexible method and could be adapted to different research designs.

By using a classic six-step process (Braun & Clarke, 2006 ): 1) familiarizing oneself with the data, 2) generating codes, 3) constructing themes, 4) reviewing potential themes, 5) defining and naming themes, and 6) producing the report, we gradually moved through the data several times until we constructed final themes. The thematic analysis allowed us to answer “what” questions about the content of resilience. What strategies do people with chronic pain use to promote resilience? What are the main obstacles and contributing factors?

Narrative analysis

After identifying central themes with TA, we assumed the narrative analysis of each case. Just like thematic analysis, narrative analysis is an umbrella term, not a single method. The narrative method allows us to look at the story from a holistic perspective without the need of breaking it down into themes (Riessman, 2008 ). Narrative not only brings order and meaning to our daily life but, reflexively, it also provides structure to our very sense of self-hood (Murray, 2015 ). The narrative analysis helped us answer questions that start with “how”, for example, how people see the impact of disease on their lives and how they describe changes in their habits, attitudes, and life as a whole while living with chronic pain.

We based our analysis on the Michelle Crossley’s ( 2000 ) framework that includes six steps:

1) reading and familiarizing, 2) identifying important concepts to look for, 3) identifying “narrative tone”, 4) identifying the “imagery” and themes, 5) weaving it all together, and 6) writing a research report.

Since we study resilience in the context of chronic pain, the Crossley’s framework seemed to be the most appropriate one, as the author has developed this framework to analyse stories of illness and trauma. In health psychology, the Crossley’s framework is frequently used (Manning, 2015 ; Winslow et al., 2005 ; Wong & Breheny, 2021 ). Crossley has admitted that when people talk or write about their experiences of chronic or serious illness, they often characterize themselves as becoming totally different people (Crossley, 2000 ). Resilience often means not just bouncing back or returning to a status quo but bouncing forward or becoming even stronger than before illness (Hynes et al., 2020 ). This change could also be perceived as becoming a totally different person. In our research, we were interested in this process of change. The narrative analysis allowed us to answer “how” questions about the resilience process. How does disease change our attitudes towards ourselves and others? How does time influence these changes?

We applied narrative analysis for each research question in each interview and analysed responses for different stages of the disease. For example, asking about strategies people used to overcome or accept pain, we looked at what the strategies were and how they changed in the first months after diagnosis, in the first years after diagnosis, and in the long term, five or more years after diagnosis. This timeline provided an opportunity to study the dynamics of resilience. The creation of an approximate timeline helped to understand why particular themes appear in the specific moment after diagnosis and how they are related to other life events.

The critical incident technique

Finally, we applied CIT to qualitative data to describe the ups and downs that significantly changed people’s lives. The founder of CIT is John Flanagan ( 1954 ), who developed this method for the Aviation psychology program of the US army. The purpose of the CIT was to gather information on behaviours that contribute to the success or, in contrast, lead to failure.

Flanagan’s technique was rooted in the positivist paradigm and was more suitable for studying job performance in the field of organizational psychology. After more than 50 years Lee D. Butterfield and colleagues (Butterfield et al., 2009 ) modified this method so that it could meet the needs of researchers from multiple perspectives and could be used in different fields, and named this method ECIT.

In our research, we apply the ECIT which is methodologically more flexible than Flanagan’s technique and could be adjusted to the paradigm of social constructivism. ECIT allows us to study critical incidents from the perspective of the participants and explore their perception of the main turning points, without the expectation that we are studying the objective reality. Compared to other methods, ECIT is a relatively rarely used method in qualitative research, but several recent studies prove that this method could be a good research tool in psychology (Klarare et al., 2018 ; Kwee et al., 2020 ; Nitkin & Buchanan, 2020 ; Springer & Bedi, 2021 ).

ECIT involves five main steps: 1) determining the general goals of the activity being studied, 2) making plans and setting specifications, 3) collecting the data, 4) analysing the data, and 5) interpreting the data and reporting the results. Although the main steps are defined very generally, Butterfield describes in detail how to perform each step. For example, he illustrates how to identify critical incidents (something that helped or hindered a particular experience or activity) and wish list items (those people, support, information, programs, etc., that were not present at the time of the participant’s experience, but those involved believe would have been helpful) (Butterfield et al., 2009 ).

To ensure credibility and rigour, Butterfield also developed nine credibility checks for ECIT—audiotaping interviews, interview fidelity, independent extraction of critical incidents, exhaustiveness, participation rates, placing incidents into categories by an independent judge, cross-checking by participants, expert opinions, and theoretical agreement (Butterfield et al., 2009 ).

When analysing critical incidents, we also looked at the approximate timeline to find out whether critical incidents were related to the time since diagnosis.

To conclude, we can say that all three methods allowed us to answer different research questions, complement each other, and help achieve the research objectives (see Table I ). In the next chapter, we will describe how we integrated all three data analysis methods and how the within-case and across—case approach helped to achieve a balance between generalization and an in-depth understanding of the particular case.

Research questions and data analysis methods.

Within-case and across-case approach in the data analysis process

Case study research has sometimes been criticized for lacking scientific rigour and providing little basis for generalization (Crowe et al., 2011 ; Hammersley et al., 2000 ; Kyburz-Graber, 2004 ). Although Stake ( 1995 ) argues that the purpose of case study research is particularization, not a generalization, the goal of researchers who are doing multiple case research is not only an in-depth understanding of particular cases but willingness to provide findings that could be applied to other similar contexts.

Considering that generalizability due to a small sample size could be a problem, qualitative researchers instead speak about qualitative generalization or transferability as one of the trustworthiness criteria (Anney, 2014 ; Levitt, 2021 ; Maxwell, 2021 ). Qualitative generalization or transferability means that findings are described in a thick way or in such detail that readers can see both constancy and variation within a phenomenon and transfer data from the study to their own context (Levitt, 2021 ). The researcher must provide enough information on the meanings, contexts, and processes operating in the study setting or population that the reader can adequately judge (Maxwell, 2021 ).

To ensure that findings are reported widely and transparently enough, in the beginning, the researcher should create a system of how he/she will integrate all data analysis methods and notice common elements in a rich material of data, gathered from individual cases.

In our research, we applied within-case and across-case analysis, described by Lyoness Ayres et al. (Ayres et al., 2003 ) as an approach that helps to achieve qualitative generalization and find a balance between uniqueness and differences from one side and commonalities from the other. Across-case analysis means looking for common themes in all accounts, within-case analysis means in-depth exploration of a single account, considering contextual richness. In multiple case studies, integration of across-case, and within-case analysis is often used (Banerjee & Dixit, 2016 ; Chung, 2019 ; Fearon et al., 2021 ; Glette & Wiig, 2022 ; Starks et al., 2010 ), because it allows producing contextually grounded, generalizable findings (Ayres et al., 2003 ).

Within-case methods are less useful in the development of generalizations about the experience of health and disease drawn from multiple cases, but they provide contextual richness. Neither across-case nor within-case approaches alone enable the researcher to interpret an experience both through its parts and as a whole so that readers can recognize individual experiences in a generalizable way (Ayres et al., 2003 ).

For example, if we look only at cases and analyse common themes, we could find several controversial themes, such as denial of the disease and acceptance of the disease. But if we look at the cases and each person’s story as a whole, we can see that in the first months after diagnosis the person can deny the disease and avoid talking about health problems, but after a while, the disease could become part of his daily life.

The within-case and across-case approach also allows for the investigation of situations where most of the cases have similarities, but some cases differ from others. Looking across and within cases, we can identify possible factors that could influence these differences (past experience, social factors, thinking patterns, religiosity, etc.). For example, if we analyse the acceptance process, we can see that most patients have accepted their condition, but in some cases, the participants do not accept the fact that they will have to live with this diagnosis for the rest of their lives. By examining these diverse cases in more detail, we can see that these people believe in God’s healing.

By combining the within-case and across-case approach, we could find a balance between generalization and an in-depth understanding of the experience of resilience while living with chronic pain.

Conclusions

The purpose of this paper was to describe the decision-making chain of a qualitative research process and, specifically, to discuss how the integration of several methods of data collection and analysis can improve a deeper understanding of the formation and maintenance of resilience among people with CMP.

Although qualitative researchers have many methodological freedoms, sometimes this freedom can become a pitfall. If a researcher lacks tacit knowledge of different approaches and their theoretical basis, he/she may choose methods that are inconsistent with each other or inappropriate for answering the research questions. In this paper, we provide an example of how to avoid these pitfalls. We briefly describe each step we were doing and provide transparency for the readers so that they can follow the analysis process.

At the beginning, we formulated the research question: What is the experience of developing and maintaining resilience in people with chronic musculoskeletal pain (CMP)?

Considering that resilience can be understood differently in different contexts and that we can explore only subjective interpretations of resilience, but not resilience as such, we decided to ground our research on the paradigm of social constructivism. A fundamental assumption of the social constructivism paradigm is that meanings, knowledge, and truth are created by the interactions of individuals within a society.

When we had chosen the paradigm or perspective of how we will look at the experience of resilience, we decided to use a qualitative research strategy that is more appropriate for studying subjective constructs, such as experiences, feelings, and attitudes at different stages of life and in specific situations. This article approves that the qualitative research strategy can provide a significant contribution to health psychology. It allows analysing of complex constructs and helps not only to identify the problem but also to reveal the causality and influence of various factors on the situation.

The next step was to choose a research design. Since we were interested not only in the unique resilience experience of each participant but also wanted to know if people with chronic pain have used similar strategies to adapt to the disease, we concluded that multiple case study designs will allow us to analyse within each setting and between settings.

In this paper, we have provided arguments on how a multimethod approach can promote research rigour. We combined two data collection methods, semi-structured interviews and focus groups. Semi-structured interviews gave us rich material of data and allowed us to answer concrete subquestions but focus group discussions were a supplementary method for getting feedback from participants and clarifying our interpretations.

We also described the process of determining the sample size. The decision to stop data collection after 17 interviews were based on several considerations. We got enough information to answer the research question and reached code and meaning saturation.

The data analysis process is the most time-consuming part of qualitative research, especially if researchers have chosen a pluralistic data analysis approach and interpreted an interview transcript with different qualitative analysis techniques. In this paper, we argue why it is worth doing it. Analysing the same data from different analytical lenses can enhance a deeper understanding of the construct, reveal more meanings, and give a holistic understanding compared to analysing these data from only one analytical lens.

It is very important to conduct pilot interviews to see if the chosen data analysis method can provide answers to the research questions. At the beginning of our research, we considered that in our study thematic analysis could be the best data analysis method to find the most common strategies. However, after conducting the first pilot interviews, we were surprised by how rich the data was. Participants shared the dynamics of their experience while living with chronic pain, as well as information about events that dramatically changed their attitudes and resilience. We came to the conclusion that we must revise the interview protocol and include more questions and additional data analysis methods.

Finally, we decided to combine three methods, thematic analysis, narrative analysis, and CIT. The thematic analysis allowed us to find common themes between cases, narrative analysis was more appropriate for analysing differences in cases and describing the dynamics of individual narratives in their unique context, while the critical incident technique was valuable for defining critical incidents or experiences that made a contribution, either positively or negatively, to resilience.

To find a balance between uniqueness and differences, on the one hand, and commonalities, on the other hand, we applied within-case and across-case approach in the data analysis process. This allowed us to explain controversial topics and identify possible factors that could influence differences between cases, as well as give contextual richness.

The decision-making chain described in this article can serve as an example for qualitative researchers interested in health research, especially those who study lived experiences of resilience or other constructs in its dynamics and unique context, like dynamics of health behaviour, changes in professional health, self-regulation in the context of chronic diseases etc.

It’s important to justify and make transparent every decision during the process of qualitative research not only because it increases the quality of the research in the eyes of other researchers, but also because it helps to convince policymakers and stakeholders that qualitative research just like quantitative research could be well-grounded and can give a significant contribution to society. To engage in dialogue with decision-makers and wider society, findings should be presented in an easily understandable way by putting an emphasis on practical solutions this research can promote. The strength of this paper is the strong connection between theory and practice. Examples of specific studies can be helpful to better understand the theoretical assumptions and recommendations. The limitation of this study is the small sample size and heterogeneity of participants who have different kinds of musculoskeletal pain, such as back pain, joint pain, or spastic pain. For further studies, it would be valuable to analyse the results in different subgroups of participants to see whether strategies to improve resilience differ depending on the severity of the disease and the type of pain.

Ethical approval

This study was approved by the Riga Stradiņš University Research Ethics Committee.

Biographies

Elīna Zelčāne , MPhil., is a PhD student and a lecturer of communication psychology at the Faculty of Public Health and Social Welfare at the Rīga Stradiņš University, Latvia. Earned her MPhil. in philosophy in 2006 at the University of Latvia (Riga, Latvia) and now is studying psychology at the Rīga Stradiņš University, Latvia. Current research interests: health psychology, qualitative research, resilience interventions. https://orcid.org/0000-0002-2186-2115

Anita Pipere , Dr. psych., is an acting professor of psychology at the Faculty of Public Health and Social Welfare at the Rīga Stradiņš University, Latvia, and a professor and senior researcher at the Institute of Humanities and Social Sciences at Daugavpils University, Latvia. Earned her Ph.D. in psychology in 1993 at the University of Latvia (Riga, Latvia). Work experience: from 1993 until now occupies positions starting from lecturer to professor and senior researcher at Daugavpils University, from 2019 until now works as an acting professor at the Riga Stradiņš University. Experience in academic work as a university teacher, researcher, editor, and reviewer of journals and books, leader and participant in projects in psychology and education. Current research interests: philosophy of science, health psychology, qualitative research. Member of the International Society for Dialogical Science. https://orcid.org/0000-0003-2238-7026

Funding Statement

This work was not supported by external funding.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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The Palgrave Handbook of Applied Linguistics Research Methodology pp 595–613 Cite as

Narrative Analysis

• Phil Benson 5  

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8 Citations

Narrative analysis is a relatively recent addition to the toolkit of applied linguistics. Its basic premise is that the telling of stories can elucidate the meanings attached to participants’ experiences. These may be stories told by participants during data collection or stories constructed by researchers (sometimes in collaboration with participants) during analysis of a data set. This chapter is mainly concerned with uses of storytelling and narrative writing in data analysis and presentation of research findings.

• Autobiography
• Qualitative research
• Storytelling

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Benson, P. (2018). Narrative Analysis. In: Phakiti, A., De Costa, P., Plonsky, L., Starfield, S. (eds) The Palgrave Handbook of Applied Linguistics Research Methodology. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59900-1_26

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Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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