narrative analysis case study

Narrative Analysis 101

Everything you need to know to get started

By: Ethar Al-Saraf (PhD)| Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to research, the host of qualitative analysis methods available to you can be a little overwhelming. In this post, we’ll  unpack the sometimes slippery topic of narrative analysis . We’ll explain what it is, consider its strengths and weaknesses , and look at when and when not to use this analysis method. 

Overview: Narrative Analysis

• What is narrative analysis (simple definition)
• The two overarching approaches  
• The strengths & weaknesses of narrative analysis
• When (and when not) to use it
• Key takeaways

What Is Narrative Analysis?

Simply put, narrative analysis is a qualitative analysis method focused on interpreting human experiences and motivations by looking closely at the stories (the narratives) people tell in a particular context.

In other words, a narrative analysis interprets long-form participant responses or written stories as data, to uncover themes and meanings . That data could be taken from interviews, monologues, written stories, or even recordings. In other words, narrative analysis can be used on both primary and secondary data to provide evidence from the experiences described.

That’s all quite conceptual, so let’s look at an example of how narrative analysis could be used.

Let’s say you’re interested in researching the beliefs of a particular author on popular culture. In that case, you might identify the characters , plotlines , symbols and motifs used in their stories. You could then use narrative analysis to analyse these in combination and against the backdrop of the relevant context.

This would allow you to interpret the underlying meanings and implications in their writing, and what they reveal about the beliefs of the author. In other words, you’d look to understand the views of the author by analysing the narratives that run through their work.

The Two Overarching Approaches

Generally speaking, there are two approaches that one can take to narrative analysis. Specifically, an inductive approach or a deductive approach. Each one will have a meaningful impact on how you interpret your data and the conclusions you can draw, so it’s important that you understand the difference.

First up is the inductive approach to narrative analysis.

The inductive approach takes a bottom-up view , allowing the data to speak for itself, without the influence of any preconceived notions . With this approach, you begin by looking at the data and deriving patterns and themes that can be used to explain the story, as opposed to viewing the data through the lens of pre-existing hypotheses, theories or frameworks. In other words, the analysis is led by the data.

For example, with an inductive approach, you might notice patterns or themes in the way an author presents their characters or develops their plot. You’d then observe these patterns, develop an interpretation of what they might reveal in the context of the story, and draw conclusions relative to the aims of your research.

Contrasted to this is the deductive approach.

With the deductive approach to narrative analysis, you begin by using existing theories that a narrative can be tested against . Here, the analysis adopts particular theoretical assumptions and/or provides hypotheses, and then looks for evidence in a story that will either verify or disprove them.

For example, your analysis might begin with a theory that wealthy authors only tell stories to get the sympathy of their readers. A deductive analysis might then look at the narratives of wealthy authors for evidence that will substantiate (or refute) the theory and then draw conclusions about its accuracy, and suggest explanations for why that might or might not be the case.

Which approach you should take depends on your research aims, objectives and research questions . If these are more exploratory in nature, you’ll likely take an inductive approach. Conversely, if they are more confirmatory in nature, you’ll likely opt for the deductive approach.

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Strengths & Weaknesses

Now that we have a clearer view of what narrative analysis is and the two approaches to it, it’s important to understand its strengths and weaknesses , so that you can make the right choices in your research project.

A primary strength of narrative analysis is the rich insight it can generate by uncovering the underlying meanings and interpretations of human experience. The focus on an individual narrative highlights the nuances and complexities of their experience, revealing details that might be missed or considered insignificant by other methods.

Another strength of narrative analysis is the range of topics it can be used for. The focus on human experience means that a narrative analysis can democratise your data analysis, by revealing the value of individuals’ own interpretation of their experience in contrast to broader social, cultural, and political factors.

All that said, just like all analysis methods, narrative analysis has its weaknesses. It’s important to understand these so that you can choose the most appropriate method for your particular research project.

The first drawback of narrative analysis is the problem of subjectivity and interpretation . In other words, a drawback of the focus on stories and their details is that they’re open to being understood differently depending on who’s reading them. This means that a strong understanding of the author’s cultural context is crucial to developing your interpretation of the data. At the same time, it’s important that you remain open-minded in how you interpret your chosen narrative and avoid making any assumptions .

A second weakness of narrative analysis is the issue of reliability and generalisation . Since narrative analysis depends almost entirely on a subjective narrative and your interpretation, the findings and conclusions can’t usually be generalised or empirically verified. Although some conclusions can be drawn about the cultural context, they’re still based on what will almost always be anecdotal data and not suitable for the basis of a theory, for example.

Last but not least, the focus on long-form data expressed as stories means that narrative analysis can be very time-consuming . In addition to the source data itself, you will have to be well informed on the author’s cultural context as well as other interpretations of the narrative, where possible, to ensure you have a holistic view. So, if you’re going to undertake narrative analysis, make sure that you allocate a generous amount of time to work through the data.

When To Use Narrative Analysis

As a qualitative method focused on analysing and interpreting narratives describing human experiences, narrative analysis is usually most appropriate for research topics focused on social, personal, cultural , or even ideological events or phenomena and how they’re understood at an individual level.

For example, if you were interested in understanding the experiences and beliefs of individuals suffering social marginalisation, you could use narrative analysis to look at the narratives and stories told by people in marginalised groups to identify patterns , symbols , or motifs that shed light on how they rationalise their experiences.

In this example, narrative analysis presents a good natural fit as it’s focused on analysing people’s stories to understand their views and beliefs at an individual level. Conversely, if your research was geared towards understanding broader themes and patterns regarding an event or phenomena, analysis methods such as content analysis or thematic analysis may be better suited, depending on your research aim .

Let’s recap

In this post, we’ve explored the basics of narrative analysis in qualitative research. The key takeaways are:

• Narrative analysis is a qualitative analysis method focused on interpreting human experience in the form of stories or narratives .
• There are two overarching approaches to narrative analysis: the inductive (exploratory) approach and the deductive (confirmatory) approach.
• Like all analysis methods, narrative analysis has a particular set of strengths and weaknesses .
• Narrative analysis is generally most appropriate for research focused on interpreting individual, human experiences as expressed in detailed , long-form accounts.

If you’d like to learn more about narrative analysis and qualitative analysis methods in general, be sure to check out the rest of the Grad Coach blog here . Alternatively, if you’re looking for hands-on help with your project, take a look at our 1-on-1 private coaching service .

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Thanks. I need examples of narrative analysis

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

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

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

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

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

Please i need help with my project,

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

please mention the sources as well.

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

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

I’ve finished my PhD. Now I need a “platform” that will help me objectively ascertain the tacit assumptions that are buried within a narrative. Can you help?

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Using narrative analysis in qualitative research

Last updated

7 March 2023

Reviewed by

Jean Kaluza

Short on time? Get an AI generated summary of this article instead

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

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• What is narrative analysis?

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

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

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

Is narrative analysis qualitative or quantitative?

Narrative analysis is a qualitative research method.

Is narrative analysis a method or methodology?

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

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

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

What are the characteristics of narrative research?

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

• Examples of types of narratives

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

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

• What is the objective of narrative analysis?

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

What is the difference between content analysis and narrative analysis?

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

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

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

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

What is the difference between narrative analysis and thematic analysis?

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

• Capturing narrative data

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

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

• Methods of transcribing narrative data

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

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

Video and audio transcription templates

• How to code narrative analysis

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

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

• Types of narrative analysis

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

Inductive method

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

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

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

Taking cues from entrance and exit talk

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

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

Deductive method

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

Hybrid inductive and deductive narrative analysis

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

• How to analyze data after coding using narrative analysis

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

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

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

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

Step 1 – Code narrative blocks

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

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

Step 2 – Group and read by live-event

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

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

Step 3 – Create a nested story structure

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

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

Step 4 – Delve into the story structure

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

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

Step 5 – Compare across story structure

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

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

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

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

Step 6 – Tell the core narrative

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

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

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

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• > The Case for Case Studies
• > Analytic Narratives and Case Studies

Book contents

• The Case for Case Studies
• Strategies for Social Inquiry
• Copyright page
• Contributors
• Preface and Acknowledgments
• 1 Using Case Studies to Enhance the Quality of Explanation and Implementation
• Part I Internal and External Validity Issues in Case Study Research
• Part II Ensuring High-Quality Case Studies
• Part III Putting Case Studies to Work: Applications to Development Practice
• 9 Process Tracing for Program Evaluation
• 10 Positive Deviance Cases: Their Value for Development Research, Policy, and Practice
• 11 Analytic Narratives and Case Studies
• 12 Using Case Studies for Organizational Learning in Development Agencies
• 13 Connecting Case Studies to Policy and Practice

11 - Analytic Narratives and Case Studies

from Part III - Putting Case Studies to Work: Applications to Development Practice

Published online by Cambridge University Press:  05 May 2022

Margaret Levi and Barry Weingast focus on a particular type of case in which the subject is an outcome that results from strategic interaction, when one person’s decision depends on what another does. “A weakness of case studies per se is that there typically exist multiple ways to interpret a given case,” they begin. “How are we to know which interpretation makes most sense? What gives us confidence in the particular interpretation offered?” An analytic narrative first elucidates the principal players, their preferences, key decision points and possible choices, and the rules of the game. It then builds a model of the sequence of interaction including predicted outcomes and evaluates the model through comparative statics and the testable implications the mode generates. Most analytic narratives model situations as extensive form games. However, although game theory is useful, there is no hard rule that requires us to formalize. In this kind of case study, the findings do not generalize to other contexts, but instead point to the characteristics of situations to which a similar strategic logic applies.

11.1 Introduction

Analytic narratives ( Reference Bates, Greif, Levi, Rosenthal and Weingast Bates et al., 1998 , Reference Bates, Greif, Levi, Rosenthal and Weingast 2000 ; Reference Levi and Mayntz Levi, 2002 , Reference Levi, Shapiro, Smith and Masoud 2004 ) involve selecting a problem or puzzle, then building a model to explicate the logic of an explanation for the puzzle or problem, often in the context of a unique case. The method involves several steps. First, the use of narrative to elucidate the principal players, their preferences, the key decision points and possible choices, and the rules of game, all in a textured and sequenced account. Second, building a model of the sequence of interaction, including predicted outcomes. And, third, the evaluation of the model through comparative statics and the testable implications the model generates. The analytic narrative approach is most useful to scholars who seek to evaluate the strength of parsimonious causal mechanisms in the context of a specific and often unique case. The requirement of explicit formal theorizing (or at least theory that could be formalized) compels scholars to make causal statements and to identify a small number of variables as central to understanding the case.

Case studies abound in the study of development. A weakness of case studies per se is that there typically exist multiple ways to interpret a given case. How are we to know which interpretation makes most sense? What gives us confidence in the particular interpretation offered? This problem is particularly difficult where the uniqueness of the situation precludes the collection of a data set that encompasses multiple cases. Many scholars augment their case study with a model. The model adds some discipline to the account. For example, observed choices must be consistent with the assumption about preferences in the model. A model is necessary, but not sufficient: that is, a model alone does not an analytic narrative make.

The analytic narrative approach provides a means to help get around these questions. The essence of many cases is unique, including the French Revolution, the American Civil War, or the surprising and quick development of Spain following the death of long-time dictator, Francisco Franco, in 1975. Analytic narratives deal with these cases by building a model that has multiple implications, and then testing an implication of the model that provides the possibility for both confidence in the claims and comparison across cases.

For example, Reference Weingast, Bates, Greif, Levi, Rosenthal and Weingast Weingast’s (1998) case study of the American Civil War builds on a unique feature of American institutions to explain long-term political stability – namely, the “balance rule”: the idea that both Northern free states and Southern slave states would be admitted in pairs, giving each set of states a veto over national policy. This institution fell apart in the 1850s. Weingast tests his account of this failure by using game theory to reveal a path not taken, given the interest calculations of those making choices about what path to take. This enabled him to estimate a counterfactual involving what would have happened had a contingency in the case study not occurred. Thus, the main thesis of the case – the balance rule – is unique and cannot be tested directly, but other implications of the approach can be tested so as to give confidence in the overall account.

A second example addresses the effect on public goods provision of a 2004 decentralization reform in postconflict Sierra Leone ( Reference Clayton, Noveck and Levi Clayton et al. 2015 ). The specificities of most laws are unique; even when the words are replicated, implementation varies across and within countries. In this instance, the narrative reveals the key stakeholders at the local level: elected councilors and paramount chiefs. The interests of the former should lead them to prefer successful implementation, ceteris paribus; but in some localities they conceded to the paramount chiefs, who preferred the status quo. Given other reforms that increased the power of the councilors and reduced that of the chiefs, the question becomes why the councilors deferred. What are the relevant comparative statics? This question produced a series of testable implications. The case, although unique, sheds light on the more general problem of the variation in the impact of decentralization on the delivery of health and education services.

This chapter proceeds as follows. In Section 11.2 , we discuss criteria for case section. Section 11.3 discusses identifying processes and mechanisms, while Section 11.4 discusses the limitations of the approach. In Section 11.5 , we briefly discuss the implications of the approach for the development context. Our take-aways follow.

11.2 Criteria for Case Selection

The analytic narrative approach combines a commitment to rational choice, a deep interest in a particular case, a method for devising a generalizable model of the case, and a means of providing empirical evidence, even in unique cases.

The combination also entails an aim most area specialists lack: to go beyond detailing the case to elaborate more general conditions for the problem or puzzle. This exercise requires criteria for selection of cases other than their intellectual appeal as puzzles demanding solutions. Standard approaches to case selection emphasize the bases for choice among a sample of cases which are informative about the causal chain of interest, because of the absence, presence, or extreme values of key variables. One traditional method advocates pairs of cases that are either “most similar,” hopefully allowing the analyst to identify similar mechanisms in the two cases, or “most different,” hopefully allowing the analyst the ability to isolate a mechanism that accounts for the differences. These traditional methods fail when more than one causal variable is relevant.

Bearing similarities to the analytic narratives approach is process tracing ( Reference George and Bennett George and Bennett 2005 ; Reference Collier Collier 2011 ; Bennett, Chapter 4 , this volume), which shares an emphasis on both sequencing and fine-grained description as means for making causal inferences. Process tracing also shares a concern with generating testable implications, but its emphasis is on key variables rather than the key actors, their interaction, and their strategies. This makes game-theoretic analysis largely irrelevant to process tracing.

Analytic narratives include features that make the cases amenable to modeling, which not all puzzles or problems are. Essential to the model building is the choice of cases in which the key actors interact strategically. That is, the choices of one actor depend on the choices of the other. In addition, analytic narratives consider situations that can be modeled as an extensive-form game, which generates a subgame perfect equilibrium.

Another necessary feature of an analytic narrative is the opportunity to get at an important process or mechanism not easily accessible through other means. For example, the extensive-form game allows the analysis to demonstrate the existence of a self-enforcing institution that often solves an important economic or political problem through creating a credible commitment. The advantage of the game is that it reveals the logic of why, in equilibrium, it is in the interest of the players to fulfill their threats or promises against those who leave the equilibrium path.

The formalization itself is not a requirement of a successful analytic narrative; indeed, in some cases, there are too many actors and no benefit from reducing the multiple players to the small number required for a game-theoretical model. Levi’s case on conscription ( Reference Bates, Greif, Levi, Rosenthal and Weingast Bates et al. 1998 ) illustrates how one can still use the logic of extended form games to assess the strategies and actions – and paths not taken – without formalization. Another example is Reference Ferrara Ferrara’s (2003) analytic narrative of the Burmese uprising in 1988 as a means to understand both a particular historical event and the more general question of the relationship between coercion and protest.

The final expectation of an analytic narrative is that the causal mechanisms and the structures or relationships must be generalizable to other cases under specifiable conditions. We deal with this issue below.

11.3 Identifying Sequence and Mechanisms

Analytics, in this approach, refer to the building of models derived from rational choice, particularly the theory of extensive-form games. Footnote 1 The steps toward building the model include:

First, extracting from the narratives the key actors, their goals, the sequence of options available to an actor at a given moment, and the effective rules that influence actors’ behaviors.

Second, elaborating the strategic interactions that produce an equilibrium that constrains some actions and facilitates others. By making clear and explicit the assumptions about who the key actors and their preferences are, it is possible to challenge the assumptions to produce new insights and competitive interpretations of the data.

Third, the equilibrium analysis leads to comparative static predictions that produce testable implications even if they’re not the main assertion of the case.

We emphasize this third criterion. An important advantage of relying on game theory is that this method often produces comparative statics – that is, predictions about how the equilibrium shifts in response to changes in the exogenous variables. This approach allows the analyst to identify the reasons for the shift from one equilibrium to another. It therefore produces expectations of behaviors in the form of testable implications if the key actors are staying on the equilibrium path and if they are not. A case study that includes a model may involve the first two criteria, but generally not the third. This is especially true for unique cases where it is hard to test the model directly. Both authors have written many case studies of this type. These cases may provide insights, but they are not analytical narratives (see, e.g., Reference Levi Levi 1988 ; Reference Weingast, Morris, Oppenheimer and Soltan Weingast 2004 ). Analytic narratives require testable implications derived from the comparative statics that the narrative helps reveal.

The narrative of analytic narratives establishes the principal players, their goals, and their preferences while also illuminating the effective rules of the game, constraints, and incentives. Narrative is the story being told but as a detailed and textured account of context and process, with concern for sequence, temporality, and key events. By meeting these criteria, the narrative offers a means to arbitrate among possible explanations for observational equivalences – that is, two distinct processes that lead to the same outcome.

Comparative statics are crucial for comparative research because they generate hypotheses of what could have taken place under different conditions. Comparative statics therefore clarify the relationship between the key endogenous and exogenous variables. Moreover, the consideration of “off-the-equilibrium-path” behavior typically reveals reasons and reasoning for why actors took one path and not another. Indeed, what actors believe will happen were they to make a different choice typically influences the choices they do make. As Niall Reference Ferguson Ferguson (1999) observed in his study of the causes of World War I, to understand why Britain entered what would otherwise have been a continental war, we need to know what the British believed would happen had they not entered the war. Another important aspect of the game-theoretic approach is that the off-the-path behavior of an equilibrium disciplines each player’s beliefs, for they must be consistent with all the other player’s strategies (see Reference Weingast, Tetlock and Belkin Weingast 1996 ).

For example, consider the illustrative “deterrence game” in the appendix of Analytic narratives ( Reference Bates, Greif, Levi, Rosenthal and Weingast Bates et al. 1998 ). Two countries interact: the home country and an opponent. The home country maintains a large, expensive army; the opponent does not attack. Is the large army the reason for peace as it deters the opponent from attacking? Or is it a waste of resources because the opponent has no interest in attacking? These two hypotheses are observationally equivalent: both offer explanations for why the opponent does not attack, yet they differ dramatically as to the reason for the observation. Different people have different beliefs that can only be understood contextually:

[T]he observationally equivalent interpretations rest on markedly different theories of behavior. To settle upon an explanation, we must move outside the game and investigate empirical materials. We must determine how the opponent’s beliefs shape their behavior. This blend of strategic reasoning and empirical investigation helps to define the method of analytic narratives.

This approach provides the researcher with some discipline. As the deterrence game illustrates, absent a game and an equilibrium structure, it is possible to posit a wide range of beliefs that motivate action. How do we choose among these different accounts? In the context of a game, beliefs about another player’s actions are part of the equilibrium. Not just any sets of beliefs will work. In the deterrence game, the opponent must have a belief about how the home country will react to an attack; and, in equilibrium, this belief must hold in practice.

This form of explicit theory provides criteria to enable the researcher to distill the narrative and ensure that the explanation need not rely too much on factors outside the model.

11.4 Overcoming the Limits of Analytic Narratives

The analytic narrative approach, at least in its original formulation, had several potential limitations, some recognized by the authors and others revealed by various critiques.

11.4.1 Generalizing

The Achilles’ heel of analytic narratives – as with any approach to case studies – is in the capacity to generalize, given that each narrative represents an effort to account for a particular puzzle in a particular place and time with a model and theory tailored to that situation. Even so, it is possible to use the cases to make some more general points.

Although the approach is not straightforwardly deductive, it nonetheless relies on rational choice, which is a general theory of how structures shape individual choices and, consequently, collective outcomes. Rational choice, particularly in its game-theoretic form, highlights certain properties of the structure and strategic choices that arise. Although the specific game may not be portable, it may yield explanations that can be tested in the form of collective action problems, principal–agent issues, credible commitments, veto points, and the like. Analytic narratives provide a way to suggest the characteristics of situations to which these apply and in what ways. For example, the models of federalism, as initially developed by William Reference Riker Riker (1964) and further developed by Weingast and his collaborators ( Reference Weingast Weingast 1995 ; Reference Weingast Montinola, Qian, and Weingast 1995 ), are useful in explicating a large number of problems in a wide range of countries, including the case Reference Weingast, Bates, Greif, Levi, Rosenthal and Weingast Weingast (1998) addresses in his Analytic narratives chapter.

Moreover, the analytic narrative approach also demands identification of causal mechanisms. A wide range of mechanisms, such as emotions, resentment, and other aspects of behavioral economics, can offer a fine-grained explanation of the link between actions and alternatives ( Reference Elster Elster 1998 , Reference Elster 1999 ). Others have fruitfully made these links in such situations as insurgency in El Salvador ( Reference Wood, Goodwin, Jasper and Polletta Wood 2001 ) and violence in Eastern Europe and the Balkans ( Reference Petersen Petersen 2002 , Reference Petersen 2011 ) while meeting the requirement that they “generate new predictions at the aggregate or structural level” ( Reference Stinchcombe Stinchcombe 1991 : 385).

11.4.2 Surprise, Contingency, and Conjunction

Daniel Reference Carpenter Carpenter’s (2000) critique of Analytic narratives raised several issues that the approach needed to confront to fulfill its promise. Carpenter worried that we narrowed the conceptualization of narrative in a way that was likely to neglect the surprises history offers, the contingencies that affect outcomes, and the conjunctures that make parsimony so difficult. The first and last are easiest to address since nothing about the method precludes either. The approach actually makes it possible to take surprises into account since they often take the form of events that would change comparative static outcomes. Reference de Figueiredo, Rakove and Weingast De Figueiredo, Rakove, and Weingast (2006) illustrate one means by which game-theoretic models can be generalized to encompass surprises. Those American colonists already suspicious of Britain were apt to believe the worst interpretation of any British act and to believe that large-scale rebellion was inevitable. The result was a self-confirming equilibrium to explain the surprise element in the eruption of the American Revolution. Nothing is foreordained by an analytic narrative, which, on the contrary, often reveals factors as significant that we might not otherwise have noted. For example, in Gretchen Reference Helmke Helmke’s (2005) analysis of courts in autocratic regimes, her counterintuitive finding is that a nonindependent judiciary has the power, under certain circumstances, to rule against its government.

Carpenter also raises conjunctural analysis: the idea that multiple, interlacing factors occur at once – say, a war and a depression – so that the causal factors are difficult to disentangle. Reference Carpenter Carpenter’s concern (2000 : 657–658) is that “[i]f one changes the values of two variables at once, or renders the values of one variable dependent on those of another – precisely as historians who rely on conjunctures tend to do – then the embedded independence assumption comes, well, with high costs.” Reference Skocpol Skocpol (2000) and Reference Katznelson and Milner Katznelson and Milner (2002) share Carpenter’s concern.

Conjunctures are a problem for every form of analysis, not just analytic narratives. Moreover, by relying on game theory, analytic narratives may be uniquely suited to addressing conjunctures. By providing a specific model of events, a game-theoretic model helps disentangle conjunctures by potentially making predictions about what would have happened had only one of the conjoining events occurred instead.

Carpenter claims that contingency disappears from the analytic narrative approach because, as he perceives it, there is less likelihood of multiple equilibria – that is, alternative stable states of the world. He goes on to say that “[i]t would have been theoretically appealing for the authors to give examples where history in some way ‘selects’ some equilibria and makes others impossible (kind of like a trembling hand, or stability, or coalition-proofness criterion)” Reference Carpenter Carpenter (2000 , 657).

But this criticism reflects a misreading of the analytic narrative approach. The use of game theory means that in many instances multiple equilibria will arise. Hence, the existence of multiple equilibria is part of the analytic narrative approach even if the case studies in the original volume do not make that evident. Contingency in the form of multiple equilibria is therefore a feature of the approach.

Even when there are clear focal points and strategies, factors in the situation can change unexpectedly. Some contextual changes may have clear and significant consequences, others have butterfly effects, and still others have little or no effect. The narrative is crucial here for sorting out what matters for what. In Rosenthal’s Analytic narratives chapter, the potential birth of a Catholic heir to James II affects the calculations of both monarch and elites, but its importance lies in how it changes the strategies of the elites even unto the point of revolution ( Reference Rosenthal, Bates, Greif, Levi, Rosenthal and Weingast Rosenthal 1998 : 92). Why elites resorted to revolution rather than peaceful institutional change becomes apparent through the narrative and the associated model.

Uncertainty and lack of information are prevalent features of the unraveling of events in history, and they are major bases of contingency. Ahlquist and Levi’s work on leadership illustrates the effect of uncertainty ( Reference Ahlquist and Levi Ahlquist and Levi 2011 , Reference Ahlquist and Levi 2013 ). They find that followers, members, and citizens are very concerned to have competent representation; followers, members, and citizens therefore do their best to figure out who will be a good leader based on the track record of potential candidates. Nonetheless, unknowns remain, often in the form of other variables that are uncertain. For example, no one can know for sure how opponents will react to a given leader, what the economy will do, or how leaders will respond under circumstances distinctive from those in which they were selected. This uncertainty has direct consequences for other facets of the organization, such as its governance arrangements and mechanisms of accountability. If members knew and understood all the implications of their original choice, they might make a different one – if they could. Yet, uncertainty instead leads them to coordinate around a specific leader and leadership style, and they may well continue to maintain that person in office for years.

Analytic narratives must include problems of randomness or contingency, but not if they are too extreme. The example of unions makes the point. Members address their leadership problem in the face of uncertainty about the occurrence of strikes and only partial information about the reaction of employers to their demands. Because the interactions between unions and managers involve unpredictable elements, and because leaders cannot always deliver what they promise, leadership turnover may result. However, as Reference Ahlquist and Levi Ahlquist and Levi (2013) show, this turnover is not only relatively rare but also highly delimited by the organizational culture and governance arrangements that ensure new leaders will share many of the characteristics of their predecessors.

The analytic narrative approach rests on cases where there is some, but hardly complete, contingency in the path of history, cases that the model helps in understanding what was likely to happen. Nothing about the approach, however, limits it to cases of determinateness or low contingency. Extensive-form games have long proved useful in studying settings of high uncertainty and contingency.

11.5 Analytic Narratives for Use in Development Policy and Practice

In this section we turn, briefly, to suggest the implications of the analytic narrative approach to problems of development.

Economists have long proposed an economic role for political institutions, such as the market infrastructure embodied in the provision of secure property rights, enforcement of contracts, and, generally, the provision of justice and the rule of law ( Reference Weingast Weingast 1995 ). Governments that use violence against minorities and opponents, confiscate citizens’ wealth, and create economic privileges (such as dispensing monopoly rights) fail to provide adequate market infrastructure. As Adam Smith recognized more than two centuries ago, the risk of violence and of plunder leads men to avoid hard work, initiative, and investment. In discussing settings in which “the occupiers of land in the country were exposed to every sort of violence,” Smith argued that “men in this defenceless state naturally content themselves with their necessary subsistence; because to acquire more might only tempt the injustice of their oppressors” ( Reference Smith Smith 1776 : III.iii.12:405). Further, a “person who can acquire no property, can have no other interest but to eat as much, and to labour as little as possible” ( Reference Smith Smith 1776 : III.ii.9:387–88).

But, if secure property rights, enforcement of contracts, and the provision of justice are necessary for economic development, how are such institutions built and, especially, sustained?

Reference North and Weingast North and Weingast (1989 ) developed the hypothesis of credible commitments to answer this question. Governments seeking to implement the economists’ prescriptions for political institutions had to commit to honoring rights of citizens and to use agreed upon political procedures to make political decisions. They developed their hypothesis in the context of a unique case: the English Glorious Revolution of 1688–1689. Although this revolution and its institutional consequences were unique to that case, North and Weingast provided some important evidence favoring their larger, general argument about credible commitments. Focusing on public finance, they showed that the ability of the English government to borrow money changed dramatically. Government debt had never been much above 5 percent of estimated GDP in the seventeenth century. But in the eight years following the Glorious Revolution, it rose by nearly an order of magnitude, to 40 percent of estimated GDP. Because debt repayment depends critically on credible commitments, the massive increase in debt in a short time suggests that a new mechanism for making credible commitments had emerged.

Sure enough, subsequent studies have identified some of the devices used to create credible commitments and have leant support to the hypothesis. Footnote 2 First, Reference Cox Cox (2012 ) has shown that a number of other variables also increased dramatically, consistent with the credible commitment hypothesis. Specifically, per the North and Weingast narrative, Cox demonstrates that parliament gained control over taxation and the issuance of public debt. Similarly, the ministerial responsibility system emerged: while parliament faced difficulties in holding the king accountable for public decisions, they could hold the king’s ministers accountable, forcing them to honor parliament’s interests. Second, scholars have undertaken a range of studies of public debt at similar events. For example, Reference Summerhill Summerhill (2015) has shown that nineteenth-century imperial Brazil provided the institutions for credible commitment to public debt, yet it failed to provide the institutional foundations for private financial markets and hence this fundamental basis for economic development. Reference Mo and Weingast Mo and Weingast (2013 : ch 4) reveal the means by which the South Korean regime under President Park Chung provided credibility to its promises to honor property rights and a range of other programs, such as education, underpinning that country’s economic development.

As a second illustration, consider political stability, another element widely agreed as important for economic development. Coups, civil wars, ethnic conflict, and other forms of disorder cripple a country’s ability to develop. Reference Cox, North and Weingast Cox, North, and Weingast (2019) show that disorder in the form of violent takeover of regimes occurs surprisingly often in the developing world: the median regime of the poorest half of countries lasts only seven years. Just how do a minority of countries provide for political stability?

Reference Mittal and Weingast Mittal and Weingast (2012) provide three conditions for political stability, one of which they call the “limit condition”: the idea that all successful constitutions reduce the stakes of power, for example, by providing incentives for political officials to honor a range of citizen rights. Limited government does not imply small government (as modern political debate suggests), but a government that can honor restrictions on its behavior, such as abiding by election results, refraining from the use of violence to repress enemies, and, generally, honoring citizen rights.

The logic of the limit condition is that high stakes make it much more likely that people who feel threatened by the government will support coups. For example, landowners in Chile under the presidency of Salvadore Allende supported the military coup in 1973 to protect themselves. Similar events led to disorder in Spain (1936–1939) and Kenya (2007–2008). One way that constitutions reduce the stakes of politics is through various forms of countermajoritarian institutions.

Mittal and Weingast develop their hypothesis in the context of the American case, where the institutional features of the US Constitution are unique. Subsequent work has revealed similar features in a range of cases of stable constitutions. Countermajoritarian provisions serve two valuable roles in preserving political stability. First, they often aid in the instantiation of democracy. When groups see themselves as potentially worse off under democracy, they are likely to resist democratization. The reality is that powerful – and sometimes inimical – groups often have the power to hold up democratization, such as slaveholders in the early American Republic, Whites in South Africa in the 1990s, the supporters of the authoritarian regime of Francisco Franco in Spain following the death of the dictator in 1975, the military dictatorship in Chile in the late 1980s and early 1990s, and the communist regime in Poland in 1989. In each of these cases, countermajoritarian provisions to protect powerful groups aided democratization and, eventually, the lightening of the relevant constraints. Similarly, electoral laws often bias elections in favor of constituencies that favor the previous regime (Chile and Spain). These provisions also become part of the limit condition once democracy has been initiated.

Others have also used analytic narratives to discuss the difficulties of achieving transitions to democracy. Reference Ferrara Ferrara (2003) suggests conditions under which widespread protest and uprising has little effect, given the strategic use of coercion. His case is Burma, but the implications are more general. On the other hand, Reference Nalepa Nalepa (2010) considers the conditions under which pacts and negotiated settlements among elites facilitate the transition from autocracy to more open access regimes. She finds that the transition will prove unstable (if it even takes place) unless a specific type of limit condition holds: namely, that key players receive credible commitments that the “skeletons in their closets” will not be revealed or that they will receive amnesty for politically problematic behavior during the old regime. Her analytic narrative focuses on Eastern Europe but is applicable to a wide range of cases where transitional justice is at issue.

Analytic narrative approaches are also useful in understanding why some reforms succeed while others do not in countries experiencing development. Methodologically, this demands explicit recognition of the comparative statics, on the one hand, and the off-the-path-behavior, on the other. These features distinguish analytic narratives from other case studies, enabling them to reveal processes and causal mechanisms that might otherwise go undetected and to provide the bases for generalizations that might otherwise not be possible. Some authors already self-consciously attempt analytic narratives (e.g., Reference Hosman Hosman 2009 on Nigeria’s failed oil policies), but many do not. Even so, we could get additional leverage on reform by transforming existing case studies into analytic narratives.

Among the many accounts of reform, we have selected two categories of cases where analytic narratives clearly increase explanatory power for the particular instance as well as making the findings transportable to other situations.

The first set is where the same laws have different effects in different places. The study of the Sierra Leone decentralization reforms ( Reference Clayton, Noveck and Levi Clayton, Noveck, and Levi 2015 ) display not only varying impact but also reasons for that variation. Sierra Leone has a long history of tension among elites at different levels of government and a more recent history of tensions among key local elites. This case explores the consequences of the latter for effective public service delivery once decentralization is introduced.

A rich literature (cited in the case study) reveals contradictory expectations of the effects of local interelite dynamics. The narrative reveals considerable county-level variations in power sharing between the traditional power-holders, the paramount chiefs, and the newer power-holders, the elected councilors. This variation provides an opportunity to derive expectations specific to the case and then assess their plausibility. Indeed, from the narrative the authors hypothesized that competitive relationships among the two improve services while collusion reduces their quality.

The first challenge was to offer a measure of elite dynamics to be used in a statistical investigation of the implications of differences. To test the impact of this relationship required, first, a measure of the nature of their interaction. The probability of collusion was operationalized by using data that reports on the following direct relationships: the median number of times councilors report having contacted a chief in the previous month, the percentage of councilors that report having had a dispute with a chief during the past month, and the percentage of councilors that report that they are related to a Paramount Chief either through blood or marriage.

But what accounts for the distinctiveness of power-sharing arrangements? The strategic interaction underlying the implementation of the reforms reveals two possible equilibria of collusion and competition. Digging deeper uncovers factors that possibly change the strategic interactions among the key actors and, thus, the comparative statics. The authors considered the gender, age, and party of the councilors as well as the degree of electoral competition. But the most telling explanatory factor was one that could only be known by knowing the case in depth: the proportion of councilors who were in the Civilian Defense Force (CDF) during the war. Former CDF councilors were likely to have forged deep ties with the paramount chiefs who were the primary sources of funding.

The next step was to determine the extent to which these different kinds of power-sharing arrangements – and the probable causes of them – actually influenced service delivery. Drawing out testable implications that could in fact be explored with the available survey material enabled the authors to provide additional confidence in the hypotheses they had derived. Further statistical tests indeed suggested that collusion, particularly that produced by the proportion of councilors who had been in the CDF, leads to far poorer service delivery than does competition.

Bangladesh, Honduras, China, and the United States all have similar labor laws on their books but very distinctive actual protection and enforcement of labor protections in the supply chains of global brands. Reference Berliner, Greenleaf, Lake, Levi and Noveck Berliner et al. (2015a , Reference Berliner, Greenleaf, Lake, Levi and Noveck 2015b ) investigate the clusters of stakeholders and what transforms the relations of power among them. To do this, they consider the strategic interactions among key players and what transforms the current equilibrium or status quo. Using the logic of game theory but not formalizing it, they are aware that it is off the equilibrium path for workers to organize and make demands unless they are assured that they will not be punished for their actions by losing their jobs or being sent to jail. Footnote 3 That only happens when brands find it in their interest to improve worker rights and benefits, and this only occurs if government is upholding its laws or the reputation of brands among consumers is being threatened.

Unfortunately, both of these circumstances are most likely to occur when there is an unexpected (if predictable) catastrophe such as a major fire or building collapse where workers’ lives are tragically lost. Reflecting comparative statics, such a shift leads to reform, but whether the commitments are credible depends on the creation of legal institutions that are hard to change and that incorporate sufficient administrative capacity to implement the rules. The testable implications may differ among the cases, but they are the organizing principles of the cases. The findings are not promising for labor rights. Honduras and Bangladesh lack the government capacity to maintain a positive labor rights regime over a long period of time. In China, the government has the capacity but not the will to establish meaningful labor rights, although it does ensure some protections. The United States, which once had both the will and capacity, now lacks the first and possibly the second and so has undergone a reversal. The result in all these instances is that the pressure on brands to discipline their supply chains is episodic or nonexistent.

The second set of cases document instances where societal interests come to trump private interests, making it possible to actually implement policies that will serve the population as a whole. As we saw with the labor illustration, it is difficult enough to ensure the protection of the interests of a neglected group within the society. It is arguably harder to protect general interests, as the case of corruption in Indonesia ( Reference Kuris Kuris 2012a , Reference Kuris 2012b ) documents. And it is arguably harder still to implement policies where the interests of the world at large are at issue, as the case of deforestation in Brazil ( Reference Jackson Jackson 2014 , Reference Jackson 2015 ) details.

Of course, in each of these cases a range of stakeholders are the beneficiaries or losers from policy change. To transform past practice required some combination of leadership, interests, expert knowledge disseminated widely, trust relationships, monitoring, new forms of direct enforcement, credible commitments, and mobilizations that changed the incentives of both government officials and recalcitrant stakeholders. All of these features are documented in these cases, and documented well. Lacking is a structure to the accounts that makes it possible to observe the causal mechanisms and derive testable implications. The comparative statics are not sufficiently explicit. While the Sierra Leone decentralization and labor standards cases do not provide an actual formalization of the game, the presentation of the material makes it possible not only to derive but also to test implications that enhance confidence in the claims of the authors and make them generalizable to other cases.

11.6 Creating Take-Aways

Multiple interpretations are inherent in the traditional case study method. Moving beyond traditional approaches, analytic narratives provide two methods for establishing the generalizability of findings from case studies. First, the model in an analytic narrative often affords a range of explanations and predictions. Although the main account of a unique case may not be testable, the model may yield other predictions that can be tested, either in this case or in other cases. Second, as with other methods, out-of-sample tests constitute an important route to generalization. The presumption today in social science research is that the authors will provide those tests themselves. However, seldom does the level of knowledge for the out of sample case rival the detailed understanding of the original case that puzzled the author. The demonstration of generalizability must rest on a larger community of scholars who take the findings applicable to one place and time to illuminate a very different place and time. Each case then becomes a case among many that are grist for the mill of scholars, experts on particular countries and sectors, and policy-makers who must work collaboratively to sort out the lessons learned.

In this chapter, we have outlined the analytic narrative approach and, in Section 11.5 , suggested the potential value of the approach for problems of development. Reflecting the interest of the authors employing the approach, the applications tend to focus on political issues, such as political stability and violence. The approach also applies to case studies of particular economic reforms, and we believe it will produce valuable results in this area.

In summary, the goal of analytic narratives is to provide several forms of discipline on the structure of case studies, such as a game, with emphasis on comparative statics and on off-the-path-behavior, and on predictions that can be tested on aspects of the case even if the main assertion about the case cannot.

1 In principle, the rational choice component can be replaced with decision-making criteria from behavioral economics, although we have not pursued that path.

2 While a number of studies have criticized the North and Weingast thesis and evidence ( Reference Sussman and Yafeh Sussman and Yafeh, 2007 ; Reference Pincus, Robinson, Galieni and Sened Pincus and Robinson, 2014 ), none have argued against the debt-credibility hypothesis; and, further, we believe Reference Cox Cox (2012) and related work provides the latest review and statement of the evidence.

3 Reference Golden Golden (1997) makes similar observations in the European context.

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• Analytic Narratives and Case Studies
• By Margaret Levi , Barry R. Weingast
• Edited by Jennifer Widner , Princeton University, New Jersey , Michael Woolcock , Daniel Ortega Nieto
• Book: The Case for Case Studies
• Online publication: 05 May 2022
• Chapter DOI: https://doi.org/10.1017/9781108688253.012

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Home » Narrative Analysis – Types, Methods and Examples

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.

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Muhammad Hassan

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Article Contents

Introduction, narrative, storytelling and program evaluation, the context: a community intervention trial to promote the health of recent mothers, illustrating the analytic approach: the unique insights from narrative, two examples of stories from the cdo data set, concluding remarks.

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Researching practice: the methodological case for narrative inquiry

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Therese Riley, Penelope Hawe, Researching practice: the methodological case for narrative inquiry, Health Education Research , Volume 20, Issue 2, April 2005, Pages 226–236, https://doi.org/10.1093/her/cyg122

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Research interest in the analysis of stories has increased as researchers in many disciplines endeavor to see the world through the eyes of others. We make the methodological case for narrative inquiry as a unique means to get inside the world of health promotion practice. We demonstrate how this form of inquiry may reveal what practitioners value most in and through their practice, and the indigenous theory or the cause-and-consequence thinking that governs their actions. Our examples draw on a unique data set, i.e. 2 two years' of diaries being kept by community development officers in eight communities engaged in a primary care and community development intervention to reduce postnatal depression and promote the physical health of recent mothers. Narrative inquiry examines the way a story is told by considering the positioning of the actor/storyteller, the endpoints, the supporting cast, the sequencing and the tension created by the revelation of some events, in preference to others. Narrative methods may provide special insights into the complexity of community intervention implementation over and above more familiar research methods.

When preventive intervention programs are described, they tend to focus on the technology of the intervention without informing us about how the context in which it was implemented affected the technology. We learn little about the many compromises, choice points and backroom conversations that allowed it to take the form it took. [( Trickett, 1998 ), p. 329].

The history of health promotion has been one of developing and testing increasingly sophisticated theories to inform and strengthen the effectiveness of actions taken by the front-line workers. Theories of health promotion have been developed for multiple levels of analysis (individual, group, organizational, community, etc.) ( Glanz et al. , 1990 ) and for a variety of settings (schools, workplaces, hospitals, etc.) ( Poland et al. , 2000 ). Large-scale, whole-community prevention trials have been conducted purporting to test particular state-of-the-art theories in cancer control and heart disease prevention ( Thompson et al. , 2003 ). Studies of interventions typically include process evaluations, which allow investigators to comment on the extent to which what took place actually matched what was planned ( Flora et al. , 1993 ).

What we hear less about, however, is the private contexts of practice as Trickett describes above and ways of viewing the ‘problem’ at hand other than those preconceived by the intervention's designers. Evaluators who use qualitative methods may get closer to this ( Patton, 1990 ). ‘Key informant’ interviews have become increasingly used to gain insight into the factors that have helped or hindered program development or might explain why programs appear to work in some contexts, but not in others ( Goodman et al. , 1993 ). Even so, this literature contains examples of studies where interviews held at the end of the program still have failed to give investigators confidence about what really happened and why ( Tudor-Smith et al. , 1998 ). Investigators who have engaged practitioners in interviews about the nature of their practice have also commented on how difficult it is for people, in retrospect, to articulate aspects of what they do and think ( Hawe et al. , 1998 ). Thus, many aspects of practice remain elusive.

In this paper we suggest that narrative methods may give new and deeper insights into the complexity of practice contexts. By narrative inquiry, we mean the use of personal journals by and serial interviews with fieldworkers during their implementation of a health promotion intervention. Narrative methods may also allow us to better understand the mechanisms through which health programs are transported and translated. In doing so, the natural or indigenous theory of an intervention may be revealed, i.e. the cause-and-consequence thinking of practitioners, which may or may not match the theory supposed to be tested by the intervention. We use a case study from a whole-community intervention trial to illustrate how we are using these methods. The results of the analysis are not presented here.

Narrative inquiry has a long, strong and contested tradition. There are a range of approaches to narrative inquiry, emanating from diverse disciplines such as psychology, sociology, medicine, literature and cultural studies ( Riessman, 1993 ; Mishler, 1995 ). As a result, the process of interpreting stories is now a point of scholarly investigation in itself, because there is no one unifying method ( Riessman, 1993 ; Mishler, 1995 ; Schegloff, 1997 ; Manning et al. , 1998 ). Approaches differ on the core questions of why and how stories are told. That is, the nature of the storytelling occasion and therefore the knowledge claims that can be made about the problem under investigation.

‘Story’ and ‘narrative’ are words often used interchangeably, but they are analytically different. The difference relates to where the primary data ends and where the analysis of that data begins. Frank ( Frank, 2000 ) points out that people tell stories, but narratives come from the analysis of stories. Therefore, the researcher's role is to interpret the stories in order to analyze the underlying narrative that the storytellers may not be able to give voice to themselves. For example, in a narrative study of people who are unemployed, Ezzy ( Ezzy, 2000 ) explored the role that broader social forces play in how people tell stories about their job loss. He described two narratives: the heroic and tragic job loss narratives. The heroic narrative gives prominence to the role of a person's individual agency and autonomy, whereas the tragic job loss narrative is one is which the person is a victim of institutional or social forces beyond their control. These narrative structures provide insights into how people come to understand their unemployment and the type of action or inaction they take as a result.

The word ‘narrative’ is used extensively in health research. It commonly refers to the field of illness narratives, such as accounts of cancer from the patient's perspective ( Frank, 1998 ). The use of words like ‘narrative’ and ‘story’ became more popular in health promotion in the early 1990s as part of an increased emphasis on reflective practice and methods of program evaluation which gave more control to research participants. For example, Dixon argued that storytelling methods were ideally suited to community development projects because the creation of the project's meaning or public representation is placed more in the control of participants, as opposed to external researchers ( Dixon, 1995 ). Storytelling has developed as a training and practice development technique for knowledge development in health promotion ( Centre for Community Development in Health, 1993 ; Labonte et al. , 1999 ).

Thus health promotion was part of what Chamberlayne et al. ( Chamberlayne et al. , 2000 ) referred to as the ‘biographical turn’ in the social sciences. That is, they were part of the larger move towards methods that tap into the personal and social meanings that are considered to be the basis of people's actions. Incorporated within these methods are mechanisms for critical reflection which conceive the individual as the primary sense-making agent in the construction of his/her own identity ( Blumer, 1969 ; Giddens, 1984 , 1991 ; Schwandt, 1998 ). Reflective writing also became a feature within the context of professional development literature ( Schon, 1999 ), and also in education ( Orem, 2001 ), business ( Hartog, 2002 ) and medicine ( Webster, 2002 ).

In our case, narrative inquiry is providing insight into the mechanisms by which community development officers facilitate transformative change among people and organizations, as part of their role to implement a new community-level intervention. We are using narrative inquiry alongside a fleet of methods including self-completed questionnaires, interviews, observation, document analysis and network analysis of inter organizational collaboration patterns ( Hawe et al. , 2004 ).

The intervention, PRISM (Program of Resources, Information and Support for Mothers), is a coordinated and comprehensive primary care and community-based strategy to promote maternal health after childbirth. The study involved 16 local government areas in the state of Victoria, Australia and approximately 20 000 women. The rationale for the intervention and the evidence on which it is based are described by the PRISM designers ( Gunn et al. , 2003 ; Lumley et al. , 2003 ). The intervention is anchored and facilitated in each of the eight intervention communities by a full-time community development officer (CDO) working with a local steering committee for 2 years.

The diaries and interviews

The data are in the form of field diaries and in-depth interviews. Each CDO maintained a field diary over the 2 years of their employment. CDOs were invited to record in it their feelings, thoughts, frustrations, plans and hopes. Agreement to be involved in program documentation was a part of their employment contract with the PRISM research team. Nevertheless the CDOs' agreement to write diaries with the authors (the ‘EcoPRISM team’) was confidential and entirely independent of the PRISM research team. The average field diary consists of approximately 40 000 words of verbatim reflection.

The interview data comprise 34 interviews (in total) undertaken at strategic points of intervention implementation with each CDO. The interviews provided the opportunity for CDOs to talk about what they may have found tedious or difficult to write down. The interviews explored emerging themes within the data. The interviews were tape recorded and transcribed. They were undertaken both over the telephone and face-to-face.

Creating and sustaining the right research conditions for collecting this data was paramount. Unless we could create the right conditions, the CDOs may tell us only part of their story, what they think we want to hear or indeed nothing at all. These conditions encompassed:Creating these conditions in order to gather data in an ethical and principled manner required the researcher (T. R.) to position herself closely with the CDOs. CDOs spent approximately 90 min a week working on program documentation.

Flexibility in how the data were recorded. Some CDOs had electronic diaries. Some were hand written. Some were emails and others were a combination of the three. A couple of CDOs changed recording methods over time.

Adjusting recording methods to suit field conditions.

Empathy to the challenges CDOs faced in implementing the intervention and in their research relationship with us.

Participation in project dissemination. Co-authoring of papers and conference presentations about the project with CDOs.

Trust within the research relationship. By this we mean trust that we would maintain confidentiality and trust that we would represent the CDOs' story accurately.

How narrative analysis differs from thematic analysis

Thematic analysis is common in health promotion research. It involves the open coding of data, i.e. the building of a set of themes to describe the phenomenon of interest by putting ‘like with like’ ( Morse and Field, 1995 ). The researcher looks for patterns in the data, labels them and groups them accordingly ( Strauss, 1987 ). This approach to analysis can stop at the stage of simple listing of themes [e.g. ( Gordon and Turner, 2003 )]. If the development of themes is led by the researcher's a priori interests, some researchers have preferred to use the term ‘template’ analysis ( Crabtree and Miller, 1999 ). On the other hand, if the themes are derived inductively from the data itself then the thematic analysis may be considered to be more close to a grounded theory analysis [e.g. ( Kalnins et al. , 2002 )]. In practice, many researchers in health promotion conduct thematic analyses that reflect both the ideas they bring to the data set beforehand (from the research questions) as well as being open to ‘new’ themes in the data.

Narrative analysis differs from thematic analysis in two interconnected ways. First, narrative analysis focuses more directly on the dynamic ‘in process’ nature of interpretation ( Ezzy, 2002 ). That is, how the interpretations of the CDOs might change with time, with new experiences, and with new and varied social interactions. So, integration of time and context in the construction of meaning is a distinctly narrative characteristic ( Simms, 2003 ). This is something that Ricoeur calls the ‘threefold present’ in which the past and the future co-exist with the present in the mind of the narrator, through memory in the first case and expectation in the second. A thematic analysis might document different themes arising at different stages of the intervention. However, how time drives or potentially transforms the interpretation is integral to the construction of narratives. It is central to the development of narrative types ( Schutz, 1963a , b ), as we describe later.

Second, narrative analysis begins from the stand point of storyteller, or in our case CDO. From here we analyze how people, events, norms and values, organizations, and past histories and future possibilities, are made sense of and incorporated into the storyteller's interpretations and subsequent actions. That is, narrative analysis contextualizes the sense-making process by focusing on the person, rather than a set of themes. This is an important methodological distinction. In analyzing the CDO diaries we attempt to stand in the CDOs' shoes and experience events as they do. As situations, people and events change over time, our vantage point remains the same. In this way we gain unique insights into how they interpret the world. Thematic analysis, in contrast, de-contextualizes the data (e.g. by ‘cutting and pasting’ themes together) to examine the meta or broader issues. Narrative inquiry shares with discourse analysis both a concern for how broader institutional values and cultural norms are expressed in language, and the belief that language is a form of action ( Potter and Wetherell, 1987 ). However, narrative analysis adds further insights into ‘contexts of practice’ because it studies the world through the eyes of one storyteller and applies a theory of time.

Key features of narrative inquiry

Narrative inquiry attempts to understand how people think through events and what they value. We learn this through a close examination of how people talk about events and whose perspectives they draw on to make sense of such events. This may reveal itself in how and when particular events or activities are introduced, how tension is portrayed, and in how judgments are carried out (e.g. the portrayal of right and wrong).

A narrative approach looks closely at the sentences constructed by the storyteller and the information and meaning they portray. The following categories have been adapted from Young ( Young, 1984 ). Are the sentences descriptive? That is, a sentence or paragraph that sets the scene, but has no temporal role in the story. Are they consecutive ? Is there a logic to where the sentence fits into the story? Are they consequential to the story? That is, they have causal implications. If the sentences are evaluative , do they show something of the attitude of the CDO? These sentences give meaning to the story. If they are transformative , they express a change in how the storyteller evaluates something, such as an epiphany.

Narrative inquiry captures how people make sense of the world. This ‘thinking through’ events is presented in the recording of events, such as the extent of detail given. It is also captured in the form of internalized soliloquies ( Athens, 1994 ; Ezzy, 1998 ). These are the conversations one has with oneself or imagined others.

Narrative analysis focuses on who is mentioned in the telling of events (and who is absent) and the role they have in the telling of events. Gergen and Gergen ( Gergen and Gergen, 1984 ) refer to these people as the supporting cast of a person's narrative. As a supporting cast member, they have a purpose or reason for existing in the story. The manner in which the supporting cast are discussed in the field diaries may range from factual accounting of events, to theorizing what that supporting cast member is thinking or doing. Most importantly, who is mentioned in the field diary reveals the people or organizations that are most significant to the CDO in their practice.

Thinking about the context of the storytelling is another important feature of narrative inquiry. Frank ( Frank, 2000 ) refers to the storytelling relation . By this he means that data emerges from within the relation between the teller, the listener and the context of the telling of the story. Storytelling can be a political occasion. Narrative inquiry takes as a given that people may exclude details of events or exaggerate aspects of stories ( Ezzy, 2000 ). What is of analytical interest to the narrative researcher is why these exclusions or exaggerations exist.

On the basis of careful examination of the data, why and how the story is being told, who the supporting cast are and the nature of the storytelling occasion, one can determine the narrative's plot or what the story is about. The plot of a persons' narrative is the organizing theme ( Ezzy, 1998 ) that brings coherence to the telling of events. Events are understood according to the plot. As a result, we can see and understand how a person makes sense of the world.

Finally, the point of the story considers both the organizing theme and the form of the narrative. Form refers to the flow of the narrative over time. Common prototypes are stable, progressive and regressive narratives ( Gergen and Gergen, 1988 ). A stable narrative is one in which the person's evaluations of situations and events remains the same over the course of time. A regressive narrative is one in which these evaluations get worse with time. A progressive narrative is one in which the person's evaluations improve over time. These broad narrative forms are represented in Frye's ( Frye, 1957 ) forms of literary narrative: the tragedy, the comedy, the happy ending, the satire, the romantic saga, etc. It is the inter-relationship of the organizing theme and form that creates what is called ‘coherent directionality’ in the narrative. This means how it makes sense over time.

A complete narrative analysis takes all CDOs and all their stories. It is beyond the scope of this paper to present this in totality here. Instead, to illustrate the insights we are gaining through narrative inquiry, we present two examples of stories below. Table I outlines the narrative approach applied to these two examples. We have also demonstrated the type of themes we could derive from the same quotations if we were to undertake two kinds of thematic analysis, either guided by an a priori interest in program implementation or not. This is presented in Tables II and III .

Example of narrative analysis of two stories

Thematic analysis of two stories: example led by a priori interests

Thematic analysis of two stories: example based on text (free codes) for both stories

The cinema story

I do a lot of my best project work after hours in the supermarket. Friday evening after work was very fruitful in this way. Good conversations with three young mums interested in the project, one who inspired me weeks ago to set up classes at the swimming pool—and then I bumped into the local cinema owner. I had asked him some time ago to think about piloting a Cry Baby program at his cinema, but hadn't got back to him to check. At the bakery counter he said yes! So next week we'll get together to discuss upcoming films, a launch for the first Cry Baby session…

Catching the hairdresser

Ring Sally the hairdresser—catch her at last. She seems interested (though privately always consider that when people are hard to catch and not returning calls it suggests that they may well end up not contributing— my personal theory that, in the end, people contribute to any activity in inverse proportion to the amount of effort involved in contacting them in the first place) so send her again details of Project and [voucher] contract.

As demonstrated in Table I narrative analysis can be applied to short, very specific stories. We have applied these steps to the entire CDO data set in order to identify the main plots to each of the CDO narratives. Then, through a process of comparison between each of the narratives, a narrative typology or model of ideal types (of narratives) has been created, understood from a phenomenological point of view ( Schutz, 1963a , b ). This means comparing each of the organizing themes for similarities and differences regarding their interpretative framework. By placing each narrative theme under scrutiny, we find that some plots are very similar in nature (form and theme), while others stand out as different. In this way we hope to be able to put forward some of the defining characteristics of practice in the context we have researched, that is, experienced community development practitioners working within the context of a community intervention trial.

The assumption that we bring to this work is that a better understanding of intervention dynamics and indigenous theory may lead to fewer failed community interventions ( Thompson et al. , 2003 ). Because our PRISM trial collaborators are conducting a traditional process evaluation ( Lumley et al. , 2003 ), focused on the program elements, we will be able to determine how a different way of describing intervention unfolding sheds additional light on the ‘black box’ of the intervention. Our interpretations will also be linked to the burgeoning field of implementation analysis ( Ottoson et al. , 1987 ; Bauman, 1991 ; Bammer, 2003 ). This field argues that we need to move beyond mechanistic ways of viewing interventions [e.g. ( Flora et al. , 1993 )] to encompass new methods better suited to the complexity of the personal, organizational and community change processes that interventions purport to bring about.

A primary weakness of narrative inquiry is that it is retrospective. So the length of time required for analysis and presentation of results can be a disincentive. For this reason, fine-tuning narrative methods is a major challenge for future work. Hence, we relied on thematic analysis in order to feedback data that might be timely and important for fine-tuning the intervention in progress ( Riley et al. , 2004 ). However, the narrative analysis takes us much further into the private world of the practitioner and helps us (re)think what the intervention represents. It helps us understand the intensely personal investments being made by CDOs in the project. This is revealed in the CDO's placement of ‘self’ in the narrative. We learn about the progressive or regressive trade-offs, risks and rewards. This provides the social context to allow us to better interpret project dynamics and tensions. For example, the stakes involved when different opinions arose regarding how far PRISM could be adapted to suit local context ( Riley et al. , 2004 ).

Riger ( Riger, 1989 ) argues that some of the most important (but typically untold) stories within community interventions are about the power dynamics, i.e. what gets said publicly about the intervention and why. Our analysis thus far privileges the perspective of the CDO. However, another data set in our study, key informant interviews held in each community at the end of the intervention, will allow us to challenge or confirm these views. This includes members of the steering committees (i.e. some of the ‘supporting cast’).

Narrative analysis requires an in-depth engagement with and understanding of the participant's experience. As a result, there is a blurring of interpretive boundaries between the analyst and the research participant. Such a blurring results in two distinct criticisms of narrative analysis. One is that the analyst can play too strong an interpretative role without sufficient links back to empirical data ( Atkinson, 1997 ). The other criticism is that the analyst plays too weak an interpretive role. Atkinson ( Atkinson, 1997 ) argues that within some forms of narrative analysis there is a lack of analytical attention to social context and interaction, subsequently celebrating, rather than analyzing, the research participant's stories. Researchers are likely to be open to such criticism when unable to define and defend the interpretive framework that is being applied to interrogate the data.

Narrative inquiry encourages the analyst to consider what is in the data set and also what is not there, such as missing characters or alternative viewpoints. This makes the systematic ‘coding’ of data extremely difficult ( Rice and Ezzy, 1999 ) and affirms the importance of a guiding set of analytical principles with which to interrogate the data. Introspective reflexivity is critical in this regard ( Finlay, 2003 ). By this we mean that researchers must interrogate the dynamic created between the researcher and ‘the researched’ and devise accountability mechanisms. In this way the researchers' location and representation within the study is a key component of both data collection and analysis and we have drawn on insights from ethnography in this regard ( Michalowski, 1997 ; Reinharz, 1997 ; McCorkel and Myers, 2003 ). The challenges arising from our research context have been explored in a series of presentations and publications we have pursued with CDOs ( Riley and Hawe, 2000 , 2001 , 2002 ; Riley et al. , 2001 ; Sanders et al. , 2001 ). For an exploration of the ethical challenges we faced, see Riley et al. ( Riley et al. , 2004 ).

Our data set is unique. We know of no other large-scale intervention studies using narrative methods to understand practice contexts. CDOs told us that, overall, writing about their experience helped. It enabled their viewpoints to be articulated and better heard. We hope that by describing our narrative approach we will encourage other researchers to investigate the opportunity provided by narrative inquiry in everyday practice and in intervention study contexts.

We are indebted to the CDOs (Wendy Arney, Deborah Brown, Kay Dufty, Serena Everill, Annie Lanyon, Melanie Sanders, Leanne Skipsey, Jennifer Stone and Scilla Taylor) for their willingness to engage with us and to share their reflections on their use of diaries. The PRISM research trial team is Judith Lumley, Rhonda Small, Stephanie Brown, Lyn Watson, Wendy Dawson, Jane Gunn and Creina Mitchell. Our thanks to them for the opportunity to participate as collaborators in the trial. The EcoPRISM study is funded by the National Health and Medical Research Council, Australia. P. H. is a Senior Scholar of the Alberta Heritage Foundation for Medical Research, Canada and holds the Markin Chair in Health and Society at the University of Calgary.

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Author notes

1VicHealth Centre for the Promotion of Mental Health and Social Wellbeing, School of Population Health, University of Melbourne, Carlton, Victoria 3053, Australia (Formerly at the Centre for the Study of Mothers' and Children's Health, LaTrobe University, Bundoora, Victoria 3086, Australia), 2Department of Community Health Sciences, University of Calgary, Calgary, Alberta T2N 4N1, Canada and 3School of Public Health, LaTrobe University, Bundoora, Victoria 3086, Australia

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Case studies synthesis: a thematic, cross-case, and narrative synthesis worked example

• Published: 03 August 2014
• Volume 20 , pages 1634–1665, ( 2015 )

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• Daniela S. Cruzes 1 ,
• Tore Dybå 1 ,
• Per Runeson 2 &
• Martin Höst 2  

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Case studies are largely used for investigating software engineering practices. They are characterized by their flexible nature, multiple forms of data collection, and are mostly informed by qualitative data. Synthesis of case studies is necessary to build a body of knowledge from individual cases. There are many methods for such synthesis, but they are yet not well explored in software engineering. The objective of this research is to demonstrate the similarities and differences of the results and conclusions when applying three different methods of synthesis, and to discuss the challenges of synthesizing evidence from reported case studies in SE. We describe a worked example of three such methods where three independent teams synthesized two studies that investigated critical factors of trust in outsourced projects through thematic synthesis and cross-case analysis, and compared these to each other and also to an already published narrative synthesis. In addition, despite that the primary studies were well presented for synthesis, we identified challenges in the use of case studies synthesis methods related to the goals and research questions of the synthesis, the types and number of case studies, variations in context, limited access to raw data, and quality of the case studies. Thus, we recommend that the analysts should be aware of these challenges and try to account for them during the execution of the synthesis. We also recommend that analysts consider using more than one method of synthesis for sake of reliability of the results and conclusions.

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Cruzes, D.S., Dybå, T., Runeson, P. et al. Case studies synthesis: a thematic, cross-case, and narrative synthesis worked example. Empir Software Eng 20 , 1634–1665 (2015). https://doi.org/10.1007/s10664-014-9326-8

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Narrative Analysis: Methods and Examples

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

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

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

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

What Is Narrative Research?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Tip #509: Directions for Writing a Narrative Case Study

• March 17, 2014
• case study , conversation , decisions , detail , development , dialogue , instructions , questions , success , thinking

“If written directions alone would suffice, libraries wouldn’t need to have the rest of the universities attached.” Judith Martin

I created these instructions for writing a narrative case study, for the benefit of three health professionals in Kenya who were contracted to create case studies for a US Agency for Industrial Development training program. Please see what you think:

A narrative case study is a story of a real life problem or situation that provides sufficient background data so that the problem can be analyzed and solved.

• A good case study is written in the form of a story.
• It has a problem for the readers to solve.
• It has characters who have names and use authentic dialogue.
• It is descriptive, with realistic details.
• The flow is easy to follow.
• There is sufficient information so that the readers can understand what the problem is and, after thinking about it and analyzing the information, come up with a proposed solution.
• It has pertinent questions that focus the readers on the key points.

Directions:

• Draw your information from real situations that were either resolved successfully or unsuccessfully.
• Your case study should include:
• A decision maker who is dealing with some question or problem that needs to be resolved;
• A description of the problem;
• An explanation of the context in which the problem occurred; and
• Sufficient supporting data.
• Your case study should provide the answers to these questions:
• What is the issue?
• Who is involved?
• When did the situation occur?
• Where did the situation take place?
• Why did the issue/problem arise?
• What key facts should be considered?
• What questions do the key characters need to resolve?
• What alternatives are available to the decision-maker?
• Your case study should have five sections:
• Introduction
• Answers to Questions [identify what the actual owner did in the situation]

If you would like a copy of the Narrative Case Study Template that accompanied these directions, just contact Deborah Laurel .

May your learning be sweet.

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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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