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Narrative Analysis – Types, Methods and Examples

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Narrative Analysis

Definition:

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

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

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

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

Structural Analysis

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

Discourse Analysis

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

Phenomenological Analysis

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

Critical Analysis

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

Autoethnography

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

Thematic Analysis

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

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

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

Applications of Narrative Analysis

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

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

Examples of Narrative Analysis

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

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

Purpose of Narrative Analysis

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

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

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

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

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

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

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

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

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

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

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

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

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

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Muhammad Hassan

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Narrative Analysis 101

Everything you need to know to get started

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

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

Overview: Narrative Analysis

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

What Is Narrative Analysis?

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

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

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

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

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

The Two Overarching Approaches

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

First up is the inductive approach to narrative analysis.

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

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

Contrasted to this is the deductive approach.

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

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

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

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Strengths & Weaknesses

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

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

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

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

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

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

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

When To Use Narrative Analysis

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

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

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

Let’s recap

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

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

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

Psst… there’s more (for free)

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

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

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

Thematic analysis

• Thematic analysis vs. content analysis
• Introduction

Types of narrative research

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

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

Narrative analysis in research

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

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

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

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

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

Life history

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

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

Oral history

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

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

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

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

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

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

Designing a research inquiry

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

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

Data collection

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

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

Triangulating data

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

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

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

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

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

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

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

Structural analysis

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

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

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

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

Functional analysis

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

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

Dialogic analysis

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

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

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

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

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

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

Subjectivity in narratives

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

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

Connecting narratives to broader phenomena

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

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

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

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Using narrative analysis in qualitative research

Last updated

7 March 2023

Reviewed by

Jean Kaluza

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

Master narrative analysis

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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.

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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Qualitative study design: Narrative inquiry

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography

Narrative inquiry

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

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

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

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

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

Can reveal historically significant issues not elsewhere recorded. 

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

Limitations

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

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

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

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

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

Example questions

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

Example studies

• Francis, M. (2018). A Narrative Inquiry Into the Experience of Being a Victim of Gun Violence. Journal of Trauma Nursing, 25(6), 381–388. https://doi-org.ezproxy-f.deakin.edu.au/10.1097/JTN.0000000000000406 
•  Kean, B., Oprescu, F., Gray, M., & Burkett, B. (2018). Commitment to physical activity and health: A case study of a paralympic gold medallist. Disability and Rehabilitation, 40(17), 2093-2097. doi:10.1080/09638288.2017.1323234  https://doi-org.ezproxy-f.deakin.edu.au/10.1080/09638288.2017.1323234
• Liamputtong, P. (2009). Qualitative research methods. Oxford University Press. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b2351301&site=eds-live   
• Padgett, D. (2012). Qualitative and mixed methods in public health. SAGE. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b3657335&authtype=sso&custid=deakin&site=eds-live&scope=site  
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• Last Updated: Apr 8, 2024 11:12 AM
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Narrative Inquiry, Phenomenology, and Grounded Theory in Qualitative Research

• First Online: 27 October 2022

Cite this chapter

• Rabiul Islam 4 &
• Md. Sayeed Akhter 5  

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Narrative inquiry, phenomenology, and grounded theory are the basic types of qualitative research. This chapter discusses the three major types of qualitative research—narrative inquiry, phenomenology, and grounded theory. Firstly, this chapter briefly discusses the issue of qualitative research and types. Secondly, it offers a conceptual understanding of narrative inquiry, phenomenology, and grounded theory including their basic characteristics. Finally, the chapter provides an outline of how these three types of qualitative research are applied in the field.

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Islam, R., Sayeed Akhter, M. (2022). Narrative Inquiry, Phenomenology, and Grounded Theory in Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_8

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Methods for Conducting and Publishing Narrative Research With Undergraduates

Azriel grysman.

1 Psychology Department, Hamilton College, Clinton, NY, United States

Jennifer Lodi-Smith

2 Department of Psychological Sciences and Institute for Autism Research, Canisius College, Buffalo, NY, United States

Introduction

Narrative research systematically codes individual differences in the ways in which participants story crucial events in their lives to understand the extent to which they create meaning and purpose (McAdams, 2008 ). These narrative descriptions of life events address a diverse array of topics, such as personality (McAdams and Guo, 2015 ), development (Fivush et al., 2006 ), clinical applications (Banks and Salmon, 2013 ), well-being (Adler et al., 2016 ), gender (Grysman et al., 2016 ), and older adult memory decline (Levine et al., 2002 ).

Narrative research is an ideal way to involve undergraduate students as contributors to broader projects and often as co-authors. In narrative or mixed method research, undergraduates have the opportunity to think critically about methodology during study construction and implementation, and then by engaging with questions of construct validity when exploring how different methods yield complementary data on one topic. In narrative research in psychology, students collect data, as in many traditional psychology laboratories, but they collect either typed or spoken narratives and then extensively code narratives before quantitative data analysis can occur. Narrative research thus provides a unique opportunity to blend the psychological realities captured by qualitative data with the rigors of quantitative methods.

Narrative researchers start by establishing the construct of interest, deciding when coding narratives for this construct is the most effective form of measurement, rather than a questionnaire or some other form of assessment. A coding manual is developed or adopted, and all coders study the manual, practice implementing it, and discuss the process and any disagreements until the team is confident that all coders are implementing the rules in a similar way. A reliability set is then initiated, such that coders assess a group of narratives from the data of interest independently, compare their codes, and conduct reliability statistics (e.g., Intraclass coefficient, Cohen's kappa). When a predetermined threshold of agreement has been reached and a sufficient percentage of the narrative data has been coded, the two raters are deemed sufficiently similar, disagreements are resolved (by conversation or vote), and one coder completes the remainder of the narrative data. Readers are directed to Syed and Nelson ( 2015 ) and to Adler et al. ( 2017 ) for further details regarding this process, as these papers provide greater depth regarding best practices coding.

Narrative Coding in an Undergraduate Laboratory: Common Challenges and Best Practices

When are students co-authors.

Narrative coding requires heavy investment of time and energy from the student, but time and energy are not the only qualities that matter when deciding on authorship. Because students are often shielded from hypotheses for the duration of coding in order to maintain objectivity and to not bias them in their coding decisions, researchers may be in a bind when data finally arrive; they want to move toward writing but students are not yet sufficiently knowledgeable to act as co-authors. Kosslyn ( 2002 ) outlines six criteria for establishing authorship (see also Fine and Kurdek, 1993 ), and includes a scoring system for the idea, design, implementation (i.e., creation of materials), conducting the experiment, data analysis, and writing. A student who puts countless hours into narrative coding has still only contributed to conducting the experiment or data analysis. If the goal is including students as authors, researchers should consider these many stages as entry points into the research process. After coding has completed, students should read background literature while data are analyzed and be included in the writing process, as detailed below (see “the route to publishing”). In addition, explicit conversations with students about their roles and expectations in a project are always advised.

Roadblocks to Student Education

One concern of a researcher managing a narrative lab is communicating the goals and methods of the interrater process to student research assistants, who have likely never encountered a process like this before. Adding to this challenge is the fact that often researchers shield undergraduates from the study's hypotheses to reduce bias and maintain their objectivity, which can serve as a roadblock both for students' education and involvement in the project and for their ability to make decisions in borderline cases. Clearly communicating the goals and methods involved in a coding project are essential, as is planning for the time needed to orient students to the hypotheses after coding if they are to be included in the later steps of data analysis and writing. In the following two sections, we expand on challenges that arise in this vein and how we have addressed them.

Interpersonal Dynamics

A critical challenge in the interrater process addresses students' experience of power relationships, self-esteem, and internalization of the coding process. In the early stages, students often disagree on how to code a given narrative. Especially when the professor mediates these early disagreements, students might feel intimidated by a professor who sides with one student more consistently than another. Furthermore, disagreeing with a fellow student may be perceived as putting them down; students often hedge explanations with statements like “I was on the fence between those two,” and “you're probably right.” These interpersonal concerns must be addressed early in the coding process, with the goal of translating a theoretical construct into guidelines for making difficult decisions with idiosyncratic data. In the course of this process, students make the most progress by explaining their assumptions and decision process, to help identify points of divergence. Rules-of-thumb that are established in this process will be essential for future cases, increasing agreement but also creating a shared sense of coding goals so that it can be implemented consistently in new circumstances. Thus, interpersonal concerns and intimidation undermine the interrater process by introducing motivations for picking a particular code, ultimately creating a bias in the name of saving face and achieving agreement rather than leading toward agreement because of a shared representation of micro-level decisions that support the coding system.

Clearly communicating the goal of the interrater process is key to establishing a productive coding environment, mitigating the pitfalls described above. One of us (AG) begins coding meetings by discussing the goals of the interrater process, emphasizing that disagreeing ultimately helps us clarify assumptions and prevents future disagreements. If the professor agrees with one person more than another, it is not a sign of favoritism or greater intelligence. Given the novelty of the coding task and undergraduate students' developmental stage, students sometimes need reassurance emphasizing that some people are better at some coding systems than others, or even that some are better coders, and that these skills should not be connected to overall worth.

The next set of challenges pertains to students' own life settings. Depending on the structure of research opportunities in a given department, students work limited hours per week on a project, are commonly only available during the academic semester, and are often pulled by competing commitments. Researchers should establish a framework to help students stay focused on the coding project and complete a meaningful unit of coding before various vacations, semesters abroad, or leaving the laboratory to pursue other interests. This paper discusses best practices that help circumvent these pitfalls, but we recommend designing projects with them in mind. Some coding systems are better suited to semester-long commitments of 3 h per week whereas others need larger time commitments, such as from students completing summer research. It is helpful to identify RAs' long-term plans across semesters, knowing who is going abroad, who expects to stay in the lab, and assigning projects accordingly.

Building a robust collaborative environment can shape an invested team who will be engaged in the sustained efforts needed for successful narrative research. In one of our labs (JLS), general lab meetings are conducted to discuss coding protocols and do collaborative practice. Then an experienced coder is paired with a new lab member. The experienced coder codes while walking the new coder through the decision process for a week's worth of assigned coding. The new coder practices on a standard set of practice narratives under the supervision of the experienced coder, discussing the process throughout. The new coder's work is checked for agreement with published codes and years of other practice coders. The new coder then codes new narratives under the supervision of the experienced coder for 2 weeks or until comfortable coding independently. The most experienced and conscientious junior applies for an internal grant each year to be the lab manager during senior year. This lab manager assigns weekly coding and assists with practical concerns. Coding challenges are discussed at weekly lab meetings. More experienced coders also lead weekly “discrepancy meetings” where two or three trained coders review discrepancies in a coded data set and come to a consensus rating. Such meetings give the students further learning and leadership opportunities. These meetings are done in small teams to accommodate the students' differing schedules and help build understanding of the constructs and a good dynamic in the team.

The Route to Publishing With Undergraduates in Narrative Psychology

When coding has successfully been completed, researchers then have the opportunity to publish their work with undergraduates. When talented students are involved on projects, the transition to writing completes their research experience. A timeline should be established and a process clearly identified: who is the lead author? Is that person writing the whole manuscript and the second author editing or are different sections being written? We have considered all these approaches depending on the abilities and circumstances of the undergraduate. In one example Grysman and Denney ( 2017 ), AG sent successive sections to the student for editing throughout the writing process. In another, because of the student's ability in quantitative analysis and figure creation (Grysman and Dimakis, 2018 ), the undergraduate took the lead on results, and edited the researcher's writing for the introduction and discussion. In a third (Meisels and Grysman, submitted), the undergraduate more centrally designed the study as an honors thesis, and is writing up the manuscript while the researcher edits and writes the heavier statistics and methodological pieces. In another example, Lodi-Smith et al. ( 2009 ) archival open-ended responses were available to code for new constructs, allowing for a shorter project time frame than collecting new narrative data. The undergraduate student's three-semester honors thesis provided the time, scope, and opportunity to code and analyze archival narratives of personality change during college. As narrative labs often have a rich pool of archival data from which new studies can emerge, they can be a rich source of novel data for undergraduate projects.

In sum, there isn't one model of how to yield publishable work, but once the core of a narrative lab has been established, the researcher can flexibly include undergraduates in the writing process to differing degrees. As in other programs of research, students have the opportunity to learn best practices in data collection and analysis in projects they are not actively coding. Because of the need to keep coders blind to study hypotheses it is often helpful to maintain multiple projects in different points of development. Students can gain experience across the research process helping collect new data, coding existing narratives, and analyzing and writing up the coding of previous cohorts of students.

Most importantly, narrative research gives students an opportunity to learn about individuals beyond what they learn in the systematic research process and outcomes of their research. The majority of undergraduate research assistants are not going on to careers as psychologists conducting academic research on narrative identity. Many undergraduate psychology students will work in clinical/counseling settings, in social work, or in related mental health fields. The skills learned in a narrative research lab can generalize far beyond the specific goals of the research team. By reading individual narratives, students and faculty have the opportunity to learn about the lived life, hearing the reality in how people story trauma, success, challenges, and change. They can begin to see subtlety and nuance beyond their own experience and come to appreciate the importance of asking questions and learning from the answers.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. Funding for this article is supported by an internal grant from Hamilton College.

• Adler J. M., Dunlop W. L., Fivush R., Lilgendahl J. P., Lodi-Smith J., McAdams D. P., et al. (2017). Research methods for studying narrative identity: a primer . Soc. Psychol. Pers. Sci. 8 , 519–527. [ Google Scholar ]
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• Qualitative Narrative Inquiry Research

What is a Qualitative Narrative Inquiry Design?

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

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

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

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

• Example Qualitative Narrative Inquiry Design

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

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

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

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

From Haydon and van der Riet (2017)

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

Sampling and Sample Size

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

Data Collection (Methodology)

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

Narrative Data Collection Essentials

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

Instrumentation

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

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

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

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

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

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

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

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

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

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

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Qualitative Research: Narrative

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What is Narrative Analysis?

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

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• Narrative Analysis Survey of the science of Narrative Analysis by Catherine Kohler Riessman, a leading voice in the field.
• The state of the art in Narrative Inquiry Reflections on narrative inquiry and the status of the field.
• Stories of Experience and Narrative Inquiry This paper briefly surveys forms of narrative inquiry in educational studies and outline certain criteria, methods, and writing forms, which are described in terms of beginning the story, living the story, and selecting stories to construct and reconstruct narrative plots.
• Validity in Issues of Narrative Research Examines the question of validity in narrative studies.
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• Published: 12 April 2024

Determinants of hospital readmissions in older people with dementia: a narrative review

• Bria Browne 1 ,
• Khalid Ali 2 , 3 ,
• Elizabeth Ford 4 &
• Naji Tabet 1  

BMC Geriatrics volume  24 , Article number:  336 ( 2024 ) Cite this article

Metrics details

Introduction

Over 50% of hospitalised older people with dementia have multimorbidity, and are at an increased risk of hospital readmissions within 30 days of their discharge. Between 20-40% of these readmissions may be preventable. Current research focuses on the physical causes of hospital readmissions. However, older people with dementia have additional psychosocial factors that are likely to increase their risk of readmissions. This narrative review aimed to identify psychosocial determinants of hospital readmissions, within the context of known physical factors.

Electronic databases MEDLINE, EMBASE, CINAHL and PsychInfo were searched from inception until July 2022 and followed up in February 2024. Quantitative and qualitative studies in English including adults aged 65 years and over with dementia, their care workers and informal carers were considered if they investigated hospital readmissions. An inductive approach was adopted to map the determinants of readmissions. Identified themes were described as narrative categories.

Seventeen studies including 7,194,878 participants met our inclusion criteria from a total of 6369 articles. Sixteen quantitative studies included observational cohort and randomised controlled trial designs, and one study was qualitative. Ten studies were based in the USA, and one study each from Taiwan, Australia, Canada, Sweden, Japan, Denmark, and The Netherlands. Large hospital and insurance records provided data on over 2 million patients in one American study. Physical determinants included reduced mobility and accumulation of long-term conditions. Psychosocial determinants included inadequate hospital discharge planning, limited interdisciplinary collaboration, socioeconomic inequalities among ethnic minorities, and behavioural and psychological symptoms. Other important psychosocial factors such as loneliness, poverty and mental well-being, were not included in the studies.

Poorly defined roles and responsibilities of health and social care professionals and poor communication during care transitions, increase the risk of readmission in older people with dementia. These identified psychosocial determinants are likely to significantly contribute to readmissions. However, future research should focus on the understanding of the interaction between a host of psychosocial and physical determinants, and multidisciplinary interventions across care settings to reduce hospital readmissions.

Peer Review reports

Dementia is a known public health priority due to the increasing amount of people living with the condition worldwide and the subsequent demands placed on health and social care systems, the economy and society [ 1 ]. The World Alzheimer’s Report estimated that over 46 million people were living with dementia worldwide in 2015, and this figure is estimated to increase to 131.5 million by 2050 [ 2 ]. In the United Kingdom, there are currently over 910,000 people living with dementia (PLWD), and this number is projected to increase to over 2 million by 2051, with dementia care costs estimated to have an overall economic impact of £26 billion per annum [ 3 , 4 ]. Dementia has a complex relationship with long-term conditions, with adverse impacts on older PLWD [ 5 ]. This health and social care challenge is demonstrated by UK Hospital Episode Statistics (HES) data in research by Age UK and the National Institute for Health and Care Research (NIHR), where 53% of hospitalised PLWD have three or more long-term conditions [ 6 ]. Additionally, the negative impact of psychosocial factors such as social isolation, poor dementia-friendly environments and behavioural and psychological symptoms of dementia (BPSD), may also contribute towards increased hospital readmissions in PLWD. People with dementia who live in areas of social and economic deprivation or live with depression or anxiety, are at a greater risk of avoidable healthcare outcomes including hospital readmissions [ 7 , 8 ]. Recurrent admissions could lead to faster deterioration, poor quality of life and increased mortality risk for PLWD [ 9 ].

Analysis from the UK HES data commissioned by Alzheimer’s Research UK, found that the number of PLWD aged 65 and over being admitted into hospitals increased by 93% from 210,000 admissions in 2010/11 to 405,000 in 2017/18 [ 10 ]. These hospitalisations could be related to the increased severity of long-term conditions, care needs at the point of discharge, and inadequate resources in post-discharge care [ 11 ]. These figures escalate pressure on healthcare services in caring for PLWD. Additionally, analysis of NHS data by the Alzheimer’s Society found that older people with dementia remain in hospital for up to seven times longer than their age-matched groups without dementia [ 12 ].

Hospital readmission in older adults is recognised as an unplanned return admission to an acute care hospital, within 30 days of their previous admission [ 13 , 14 ]. International research studies indicate that PLWD are more likely to be readmitted to hospital within 30 days of their index admission, where 20–40% of these readmissions are avoidable [ 11 , 15 , 16 , 17 ]. Previous literature reviews focused on the physical determinants of hospital readmissions, such as multimorbidity [ 16 , 18 , 19 ]. However, PLWD also require support with their psychosocial needs in addition to their physical needs. Throughout the dementia trajectory, biomedical deterioration such as frailty and multimorbidity progress in conjunction with psychosocial deterioration, including depression and poor social support [ 20 ]. Cohen-Mansfield [ 21 ] proposed one of the first biopsychosocial models of dementia, where dementia manifests from predisposing factors, life-long events and current biological, psychological and environmental factors. It was suggested that these factors affect the trajectory of dementia through cognitive, behavioural, self-maintenance and affective functioning. For example, pain is a common physical condition with psychosocial impact on cognitive, behavioural and affective functioning for PLWD [ 22 ]. Spector and Orrell [ 23 ] adapted the biopsychosocial model of dementia, where they proposed the impact of biopsychosocial factors varying along the dementia trajectory from normal ageing to the end of life. Hence, the same biopsychosocial factors may have different effects depending on the cognitive status of PLWD [ 23 ]. This is evident as hospital readmissions have been found to increase towards the end of life for PLWD, with various factors including poor community support, pain and multimorbidity [ 24 ]. Therefore, focusing on a few factors for hospital readmissions is problematic, as the dementia trajectory and health outcomes for PLWD are based on the interaction of a myriad of biomedical and psychosocial determinants [ 25 ]. Understanding the psychosocial determinants of hospital readmissions in PLWD may provide individual benefits as well as service implications in reducing this problem.

This narrative review aims to identify the psychosocial determinants of hospital readmissions in older PLWD, within the context of the known physical determinants.

A review was conducted to develop a holistic understanding of the determinants of hospital readmissions among older PLWD. The methodology of this review followed the framework outlined by Arksey and O’Malley [ 26 ]. The reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), to provide a scientific approach to this review [ 27 ]. A protocol was registered with the Zenodo repository (DOI number:  https://doi.org/10.5281/zenodo.10044172 ).

Step one: identifying the research question

This narrative review was guided by the following research question:

What are the psychosocial determinants for acute hospital readmissions for older people with dementia, within 30 days of an index admission?

Step two: identifying relevant studies

A defined search strategy was used for the electronic databases MEDLINE, EMBASE, CINAHL and PsychInfo, which were searched from inception to July 2022. A follow-up search of the electronic databases was carried out in February 2024, to retrieve the latest publications. Search terms for dementia and hospital readmissions were used in combination with truncation and Boolean operators, including AND and OR, to yield results. The search terms used were followed by the dementia literature search strategies guidance, by the National Institute for Health and Care Excellence (NICE) [ 28 ]. To ensure that further relevant articles were identified within the search, the citations of all included studies were searched and screened for inclusion. An initial pilot search was conducted in June 2022, to refine the search strategy and ensure that the search terms produced relevant results. This technique was used to ensure that the search was specific enough to answer the research question, and broad enough to obtain the relevant literature, including non-psychological determinants for readmissions. No restrictions were placed on the publication year. The full search applied to the MEDLINE database is outlined in supplementary data 1 .

Step three: study selection

Publications were included in the review if they involved:

Adults aged 65 years and over living with a dementia diagnosis.

Health and social care workers who work in dementia care, within hospital and community settings.

Family carers of people with dementia who have experienced hospital readmissions.

All empirical studies including quantitative and qualitative research.

Publications were excluded for the following reasons:

Hospital readmissions that do not include people with dementia.

Adults aged under 65 years living with dementia.

Conference abstracts, theses, editorials and opinion pieces.

Articles not in English language.

All publications retrieved from the search strategy were imported into Endnote 20 software to remove duplications and manage references. All titles and abstracts were screened against the eligibility criteria by two independent reviewers (BB and NT). Eligible titles and abstracts then had full-text screening against the eligibility criteria, by the two independent reviewers (BB and NT). Where there was any disagreement among the reviewers, one reviewer (BB) re-read the full-text publication against the eligibility criteria and a consensus was reached by the reviewers.

Step four: charting the data

A data extraction form was developed on Microsoft Excel and pilot-tested on five publications by random selection, to ensure that the relevant data were extracted from the publications. Details on the data extraction form for included publications included study title, author and year, country, study design and aim, research setting, data source, study period, sample characteristics, readmission measure and rate, and reasons for hospital readmissions (Table  1 ). One reviewer (BB) completed the data extraction for all included publications, which was reviewed and discussed with a second reviewer (NT), for overall agreement of data extraction.

Step five: collating, summarising, and reporting the results

The data were cross-tabulated and compiled on a spreadsheet in Microsoft Excel for narrative synthesis to be conducted. As the measurements of the determinants of hospital readmissions in older PLWD were variable such as all-cause 30 and 180-day readmission rates, frequencies of rehospitalisations and successful discharge-to-community rates, methods of statistical pooling data were not feasible.

A narrative synthesis approach was used to allow the integration of quantitative and qualitative data, to identify patterns related to hospital readmissions in older PLWD. Two elements of narrative synthesis outlined by Popay et al. [ 29 ] were adopted. The two elements used were ‘ developing a preliminary synthesis’ and ‘ exploring relationships in the data’ . The elements ‘ developing a theory of how and why interventions work’ and ‘ assessing the robustness of the synthesis’ were not used, as this narrative review was exploratory in nature and assessing the quality of studies was not required to map the relevant evidence available. The summarising of data was completed by one reviewer (BB) and discussed further with a second reviewer (NT), to gain agreement on the developed narrative categories and to report the findings.

Summary of results

The search strategy yielded 4757 articles. After duplicates were removed and the inclusion of five additional studies from citation searching, 4736 titles and abstracts were screened. Thirty articles underwent full-text screening, and fourteen studies were excluded because they did not meet the inclusion criteria. Reasons for exclusion included the age of people with dementia being as young as 30 years old [ 30 ], a study based on hospital discharge issues with no relation to hospital readmissions [ 31 ], studies investigating initial hospitalisations instead of recurrent admissions [ 7 , 24 , 32 , 33 , 34 , 35 , 36 , 37 ], and the research being published as PhD theses [ 38 , 39 ]. The full-text versions of two articles could not be located [ 40 , 41 ]. After the follow-up search, 1633 titles and abstracts were screened. Three articles underwent full-text screening. Among this subgroup, two studies did not meet the inclusion criteria as they included PLWD aged under 65 years old [ 42 , 43 ].

In total, 17 articles were included in this review. A flow chart of the screened studies is shown in Fig.  1 .

PRISMA flowchart of the study selection process

Study characteristics

Table  1 provides the characteristics of all 17 studies included in this narrative review, namely 15 cohort studies [ 16 , 17 , 18 , 19 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ], one qualitative study [ 55 ] and one randomised controlled trial [ 56 ]. A total of 7,194,878 participants were included in this review. The high number of participants is explained by the fact that large hospital and insurance records were used in 15 out of 17 studies [ 16 , 17 , 18 , 19 , 44 , 45 , 46 , 47 , 49 , 50 , 51 , 52 , 53 , 56 , 57 ], with three studies each having over 1.5 million participants [ 18 , 51 , 57 ]. The studies were undertaken in eight countries: one in Taiwan [ 44 ], ten in the USA [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 , 54 , 55 ], one in Australia [ 52 ], and one each in Canada [ 17 ], Sweden [ 56 ], Japan [ 18 ], the Netherlands [ 19 ], and Denmark [ 16 ].

Table  2 summarises the mapping of included studies according to the main categories and sub-categories of the determinants of hospital readmissions in older PLWD. Regarding the psychosocial determinants, the majority of studies were included in the category inadequate discharge planning ( n  = 5), followed by interdisciplinary collaboration ( n  = 4), and ethnic differences in dementia ( n  = 1). The category behavioural and psychological symptoms was recognised to connect both psychosocial and physical determinants simultaneously ( n  = 2). Categories within the physical determinants included long-term conditions ( n  = 5), and functional ability ( n  = 1).

Psychosocial determinants of hospital readmissions in dementia

Inadequate hospital discharge planning.

Five studies highlighted patterns of inadequate hospital discharge planning in older PLWD, where 30-day hospital readmission rates increased when older adults were discharged to live at home alone or after short-term domiciliary care [ 16 , 45 , 46 , 50 , 52 ]. The studies by Knox et al. [ 46 ] and Callahan et al. [ 50 ] identified that the primary reasons for unsuccessful discharges were due to unplanned hospital readmissions during and following domiciliary care, where PLWD were living at home alone. Tropea et al. [ 52 ] demonstrated similar results, where their cohort study identified increased 2, 7 and 28-day hospital readmission rates when people with dementia were discharged to their homes. This increase in hospital readmission rates resulted in a 47% increase in healthcare utilisation costs, compared to hospital readmissions among older people without cognitive impairment [ 52 ]. In a study where discharge plans developed by social workers were assessed for adequacy by primary care providers, Cummings et al. [ 45 ] identified factors that predicted hospital readmission for PLWD. Inadequate discharge plans included factors related to complex care ( p  < 0.01), problematic behaviours ( p  < 0.05), unrealistic family beliefs in the ability to provide care ( p  < 0.001), and unavailable resources ( p  < 0.01) [ 45 ]. In addition, research conducted by Graversen et al. [ 16 ] compared the risk of 30-day hospital readmission after a hospital discharge for pneumonia in PLWD and people without dementia. Graversen et al. [ 16 ] identified a 7% increase in hospital readmissions for PLWD compared to people without dementia, corresponding to an overall adjusted incidence rate ratio (aIRR) of 1.07 (95% CI 1.04–1.10). The highest hospital readmission rates were found within the first few days of hospital discharge for PLWD hospitalised with pneumonia [ 16 ]. The short time spent at home before hospital readmissions suggests inadequate hospital factors for PLWD, which include a lack of dementia-friendly hospital environments and poorly updated discharge planning [ 16 ].

Interdisciplinary collaboration

Three studies [ 17 , 55 , 56 ] highlighted contrasting examples of interdisciplinary collaboration between healthcare staff and older PLWD, which could act as a facilitator or barrier towards hospital readmissions in dementia. Gilmore-Bykovskyi et al. [ 55 ] interviewed nursing home staff about the needs of PLWD during hospital-to-nursing home transitions. Nursing home staff reported feeling ill-equipped with inadequate information sharing from hospital ward staff, short discharge timeframes, and limited control over nursing home admission discussions regarding the individuals’ care needs [ 55 ]. The nursing home staff acknowledged that hospital and nursing home staff may have different perceptions of the physical and psychosocial care needs for PLWD, such that these differences may contribute towards increased hospital readmissions [ 55 ].

As a corollary, a randomised controlled trial by Gustafsson et al. [ 56 ] examined whether clinical pharmacist participation in hospital ward rounds would reduce drug-related hospital readmission rates in PLWD. In the intervention group, the 30-day and 180-day hospital readmission rates were 5% and 11% compared to the control group readmission rates of 11% and 20%, when adjusted for patients with heart failure [ 56 ]. It was thought that such reduced drug-related hospital readmissions in PLWD were due to the close collaboration between the pharmacists and hospital ward medical team, where both areas of expertise worked together to reduce the occurrence of avoidable drug-related hospital readmissions [ 56 ].

Godard-Sebillotte et al. [ 17 ] also demonstrated adequate interdisciplinary collaboration as a key factor in reducing readmission, where they estimated the association between primary care continuity and avoidable hospital readmissions in PLWD. The results identified that when PLWD and their family carers attended regular primary care visits with the same physician, there was a reduced risk of 30-day hospital readmissions within the following year for PLWD (RR = 0.81; 95% CI 0.72–0.92; p  < 0.001) [ 17 ].

Ethnic differences in dementia

An American cohort study by Gilmore-Bykovskyi et al. [ 57 ] examined the association between race and 30-day hospital readmissions among Medicare beneficiaries of Black and Non-Hispanic White individuals with dementia. The unadjusted analysis demonstrated that the odds of hospital readmission among Black beneficiaries were 37% greater compared to non-Hispanic White beneficiaries (OR 1.37, CI 1.35–1.39) [ 57 ]. Disability among Black beneficiaries was twice as high compared to the disability among non-Hispanic White beneficiaries, and a disproportionate number of Black beneficiaries lived in the most disadvantaged neighbourhoods across the USA (28.6% Black vs. 8.9% non-Hispanic White) [ 57 ]. After adjusting for all measured factors which included but were not limited to neighbourhood disadvantage, education level and long-term conditions, Black beneficiaries remained to have greater odds of hospital readmissions by 16% (OR 1.16, CI 1.14–1.17) [ 57 ]. Gilmore-Bykovskyi et al. [ 57 ] recognised the reduction of hospital readmission odds from 37% to 16% among Black beneficiaries, indicating that approximately 50% of the observed excess risk was attributable to exposures associated with racial differences that may include unmeasured clinical factors.

Behavioural and psychological symptoms

A cohort study conducted by Tannenbaum et al. [ 54 ] investigated the association of clinical outcomes with behavioural symptoms including wandering, agitation, aggression and psychosis in hospitalised PLWD. Within the cohort of hospitalised PLWD, 40.6% had behavioural symptoms [ 54 ]. Among this subgroup, 43.1% received a documented diagnosis of delirium, and 30.9% were prescribed antipsychotic medication [ 54 ]. Notably, only 0.2% of PLWD with behavioural symptoms had a formal diagnosis of ‘ dementia with behavioural symptoms’ recorded in their medical notes. Tannenbaum et al. [ 54 ] observed that hospitalised PLWD exhibiting behavioural symptoms were more likely to be admitted from a care facility (26.6% vs. 23.7%, p  < 0.05). This specific cohort of patients demonstrated a higher tendency for readmission to acute medical services, compared to surgical services (92.7% vs. 91%, p  = 0.003). Notably, hospital factors with this group included the presence of an indwelling catheter (11.1% vs. 6%, p  < 0.001), an elevated falls risk (46.7% vs. 42.3%, p  < 0.002), bed alarms attached to their hospital beds (81.6% vs 77.4%, p  < 0.001), and Do Not Resuscitate (DNR) orders (40.6% vs 33.1%, p  < 0.001) [ 54 ]. As a result, this study concluded that hospitalised PLWD with behavioural symptoms exhibited a moderate increase in their odds of having 30-day hospital readmissions (OR = 1.14, CI 95% 1.014–1.289) [ 54 ].

The frequent use of antipsychotic medication for behavioural and psychological symptoms in older PLWD may act as a determinant of hospital readmissions, which was identified in the cohort study by Daiello et al. [ 53 ]. Daiello et al. [ 53 ] investigated the association of dementia with early hospital readmissions among a cohort of Medicare health insurance beneficiaries. This study highlighted that PLWD with 30-day hospital readmissions had more frequent prescriptions for antipsychotic medication, having at least two prescriptions within the study period of one year (12.7% readmission vs. 9.2% no readmission p  < 0.001) [ 53 ]. Additionally, frequent use of antipsychotic medication in the 6 months before or after the index hospitalisation was associated with higher odds of 30-day hospital readmissions for PLWD [ 53 ]. Although not investigated in the study, Daiello and Colleagues [ 53 ] acknowledged that recurrent use of antipsychotic medication in PLWD may be a marker of unmeasured psychosocial factors including behavioural symptoms, agitation and delirium, which increases the risk of 30-day hospital readmissions. Nevertheless, this acknowledgement is supported by the results of Tannenbaum et al’s study [ 54 ].

Physical determinants of hospital readmissions in dementia

Functional ability.

A cohort study by Knox et al. [ 47 ] outlined functional ability in PLWD as a determinant for hospital readmissions, as they examined the association between mobility, self-care and caregiver support with 30-day hospital readmissions in PLWD. The study revealed that PLWD with domiciliary care who were most dependent on mobility (OR 1.59, 95% CI 1.47–1.71) and self-care (OR 1.73, 95% CI 1.61–1.87), had the highest odds for 30-day hospital readmissions when adjusted for caregiver support [ 47 ]. In the most dependent quartiles for mobility and self-care, the two most common conditions resulting in hospital readmissions were sepsis and urinary tract infections [ 47 ]. This finding suggests that regardless of the level of caregiver support and dementia severity, the individuals’ ability to function in the form of mobility and self-care, determines their risk of 30-day hospital readmissions.

Long-term conditions

Long-term conditions that affect PLWD were commonly listed as reasons for hospital readmissions in five studies [ 18 , 19 , 44 , 49 , 51 ]. The long-term conditions included cardiovascular disease (CVD), pneumonia, urinary tract infections and falls [ 18 , 19 , 44 , 49 , 51 ]. The retrospective cohort studies by Lin et al. [ 51 ] and Sakata et al. [ 18 ] retrieved similar results, where over half of the most common hospital admission reasons which included various long-term conditions, increased the hospital readmission rate for PLWD. Lin et al. [ 51 ] highlighted a 73% all-cause 30-day readmission rate, where PLWD were readmitted to hospital with different comorbidity problems from their index admission. When investigating the impact of CVD on hospital readmissions, Van de Vorst et al. [ 19 ] recognised that 37% of hospitalised PLWD and 50% of day clinic outpatients with dementia both with CVD, experienced hospital readmissions within one year. A longitudinal study by Rudolph et al. [ 49 ] demonstrated that the cumulative risk for rehospitalisation increased with the number of long-term conditions that older PLWD live with [ 49 ]. The authors reported that the risk of rehospitalisation was 37% with no long-term conditions, 57% with one long-term condition, 70% with two or three long-term conditions, and 80% with four or five long-term conditions [ 49 ]. However, when Chang et al. [ 44 ] explored the roles of systemic diseases and hospital admission aetiologies for predicting hospital readmissions in dementia, they outlined contrasting results. Hospital admission aetiologies were found to have more clinical weighting than co-existing medical conditions when predicting hospital readmission in older PLWD [ 44 ]. Nevertheless, a significantly smaller sample of 203 PLWD participated in this study, which may reflect the difference in comorbidity-related hospital readmissions in the previously mentioned studies, which included larger samples of 50,000 to 2 million PLWD [ 18 , 19 , 49 , 51 ].

This narrative review provided an overview of some of the psychosocial determinants that contribute towards hospital readmissions in older PLWD, in addition to the known physical determinants. Over half of the publications in this review highlighted psychosocial determinants that are likely to increase the risk of hospital readmissions in dementia [ 11 , 17 , 45 , 16 , 46 , 50 , 52 , 55 , 56 , 57 ]. These psychosocial determinants included inadequate hospital discharge planning particularly when PLWD were discharged to their homes [ 46 , 50 , 52 ], insufficient information sharing of care needs between healthcare staff in hospital and community settings [ 55 ], and socioeconomic disadvantages among certain ethnic minority groups [ 57 ]. Six of the 17 articles in this review outlined the physical determinants that are known to increase the risk of hospital readmission for PLWD, which included living with common long-term conditions such as cardiovascular disease and living with an accumulation of long-term conditions throughout the dementia diagnosis [ 18 , 19 , 44 , 47 , 49 , 51 ]. Consequently, reduced mobility and the functional ability to perform everyday activities such as self-care tasks, are also known to increase hospital readmissions in older PLWD [ 47 ]. While this review highlighted the elevated risk of hospital readmissions associated with the coexistence of multiple physical conditions, reduced mobility and function, the contribution of frailty in older individuals with dementia was overlooked in the included studies. However, frailty in PLWD is associated with an increased risk of hospital readmissions, as demonstrated by an observational study conducted by Briggs et al. [ 58 ]. Some articles that highlighted physical determinants of hospital readmissions acknowledged the importance of psychosocial determinants that may increase the risk of rehospitalisation in older PLWD [ 18 , 47 , 51 , 53 ]. The authors discussed that where common acute conditions may increase the risk of hospital readmissions, these readmissions could also be indicative of problems in the continuity of care, inefficient resource use, and the unrecognised need for special discharge planning in acute care [ 18 , 47 , 51 , 53 ].

Psychosocial and physical connection of hospital readmissions in dementia

The frequent use of antipsychotic medication in older PLWD was recognised to explain the interplay between the psychosocial and physical determinants of hospital readmissions [ 53 ]. Antipsychotic medication is primarily used for psychosis in mental health conditions including schizophrenia and bipolar disorder [ 59 ]. However, antipsychotic medication was additionally licensed in the 1950s for short-term use to manage behavioural and psychological symptoms of dementia (BPSD), following ineffective non-pharmacological treatments [ 59 , 60 , 61 ]. Our review raises concerns about the frequent use of antipsychotic medication in older PLWD, as they are sensitive to the extrapyramidal side effects which include Parkinson-like tremors, involuntary muscle contractions and QT prolongation (interference with conduction in the heart) [ 59 , 60 ]. These side effects of antipsychotic medication in dementia increase the risk of other common side effects including falls, sedation, and cognitive decline, which may increase the length of hospital stay and consequently accumulate additional care needs [ 61 ]. Daiello et al. [ 53 ] demonstrated the consequences of the side effects that follow repeated use of antipsychotic medication, as older PLWD with 30-day hospital readmissions were more likely to have at least two prescriptions for antipsychotic medication within one year. Even though an association between psychosocial and physical determinants of hospital readmissions in dementia exists with a plausible underlying theory, there is still limited evidence to support this statement.

Fragmented health and social care

This review highlights fragmented care between health and social care professionals leading to poor distribution of care responsibilities, which results in increased rates of hospital readmission for older PLWD [ 17 , 18 , 55 ]. The Global Action Plan for Dementia by the World Health Organization emphasises the need for all countries to shift the focus of care from hospitals towards community-based settings, that integrate health and social care systems to provide evidence-based care for PLWD [ 62 ]. This pledge was reiterated in the Prime Minister’s Challenge on Dementia 2020 , where the UK Government committed to improving health and social care provision in the community to reduce avoidable hospital readmissions in PLWD [ 63 ]. In the UK, access to NHS healthcare is free, whereas access to social care is means-tested and limited [ 64 ]. This can result in certain populations having difficulty in accessing adequately funded social care, including older PLWD [ 64 ]. Sir Marmot’s review titled Health Inequity in England: The Marmot Review 10 Years On , found increasingly deprived areas in the North, Midlands and Southern coastal towns of England [ 65 ]. Older PLWD living in these socioeconomically deprived areas do not live in environments that support social connectedness, physical health and activity, or mental stimulation [ 64 , 65 ].

For many individuals living with dementia, adjusting to the evolving circumstances during the Covid-19 pandemic involved limited or no access to in-person community healthcare and social activities. A qualitative study conducted by Giebel et al. [ 66 ] showed that multiple lockdowns and temporary closures of routine care resulted in a decline in mental stimulation, physical deterioration and heightened dependency among PLWD. Moreover, family carers experienced a decline in both mental and physical health, as they were compelled to provide caregiving responsibilities that would have typically been handled by paid carers [ 66 ].

However, the wider health and social care ramifications of Covid-19 are currently being realised. Since in-person healthcare services reopened, PLWD were found to have more advanced dementia with additional physical and psychological care needs, and increased risk of further hospital admissions [ 67 , 68 ]. Digital healthcare was widely implemented to mitigate hospital readmissions such as using smart home devices and telemedicine. Determining the types of technology that are both suitable and safe for PLWD requires further investigation [ 69 , 70 ].

Strengths and limitations

A major strength of this review is that to our knowledge, this is the first narrative review to focus on the psychosocial determinants of hospital readmissions in PLWD. Although hospital readmissions in PLWD have been previously addressed in research, the physical determinants have gained more attention than other determinants of hospital readmissions [ 11 , 18 , 53 , 71 ]. Our review demonstrated the importance of some of the psychosocial impacts of dementia in addition to the known physical impact.

This review also adopted the methodological framework devised by Arksey and O’Malley [ 26 ], to guide a systematic approach and promote scientific rigour. Arksey and O’Malley were the first scholars to develop an internationally recognised framework that follows an iterative process, to clarify the usefulness and methodology of reviews of an exploratory nature. This review also utilised the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [ 27 ], to provide a guided report of the narrative review.

Limitations of this review include that the majority of publications were conducted within Western society. Only two studies in this review were from Japan and Taiwan [ 18 , 44 ], compared to eight studies from the USA [ 45 , 46 , 47 , 49 , 50 , 51 , 53 , 55 ]. As healthcare systems operate differently around the world, future research should be conducted for non-Western societies with rapidly ageing populations living with dementia. Secondly, it is important to note that the data collected in this review span the period from 1991 to 2019, despite studies being published up to 2023. Hence, such data may not provide a comprehensive reflection of the current understanding of hospital readmissions in PLWD, considering that the data were collected before the onset of the Covid-19 pandemic. Lastly, there was heterogeneity within the studies including different study samples, study aims and outcome measures. Hence, a meta-analysis was not conducted in this review.

Future recommendations for research

Future recommendations for research should highlight the voices of healthcare staff and family carers of older PLWD, within different care settings. This review only managed to obtain the experiences and perceptions of care transitions for PLWD from nursing home staff [ 55 ]. Although the interviews highlighted problems resulting in 30-day hospital readmissions for older PLWD, there was a lack of varied voices representing dementia care. Many of the publications in this review discuss the barriers of access to care, limited care resources within hospital and community settings, and problems with hospital discharge planning [ 16 , 18 , 46 , 51 , 55 ]. However, little is known about why these problems exist. Therefore, future discussions with healthcare staff and family carers involved in dementia care could highlight the underlying issues of hospital readmissions among older PLWD.

Future research should focus on providing evidence to explain the interactions between the psychosocial and physical determinants of hospital readmissions in older PLWD, to address the root cause of this problem. Despite attempting to identify and investigate emerging links between psychosocial and physical determinants, the literature contained a dearth of data to allow us to draw conclusions. One would assume that significant synergistic or at least additive interactions exist between physical and psychosocial factors, perpetuating high rates of hospital readmissions for PLWD. However, this will need to await studies to be specifically designed to answer such important questions. Only by understanding such potential interactions would one be able to deliver a holistic intervention approach to decrease rates of hospital readmissions for PLWD. Unfortunately at this time, there is limited evidence to demonstrate how managing both the psychosocial and physical determinants of dementia can help reduce hospital readmissions in older PLWD. This is to recognise that health outcomes according to each determinant of health are nuanced and complex [ 72 ]. Nevertheless, effective interventions to help manage these determinants together could support the multi-factorial problem of hospital readmissions for older PLWD. Future research should highlight appropriate approaches to help tackle this problem.

Despite our systematic approach to the literature search, only few psychosocial factors were identified. One can assume that several other psychosocial factors must exist that could have an impact on hospital readmission rates for PLWD. The list of such factors includes but is not limited to loneliness, poverty, education, mental wellbeing, willingness to accept help and family support. Many psychosocial factors have been suspected as potentially being implicated in hospital readmissions in PLWD, which include being an immigrant, male sex, having behavioural symptoms and frequent use of antipsychotic medication [ 11 ]. Interpersonal factors of dementia care were also highlighted, as living with fewer cohabitants, problems with discharge care transitions, high caregiver stress and inadequate caregiver support were reported to contribute to increased hospital readmissions. Future research needs to assess these and other psychosocial factors individually, in combination with each other as well as the other implicated physical factors.

This review provides insight into how few psychosocial determinants may result in increased hospital readmissions for PLWD. The determinants include inadequate integrated care and insufficient care planning between care settings and healthcare professionals. Future research should try to identify the impact of a myriad of other psychosocial factors and focus on the interaction between the psychosocial and physical determinants, and multidisciplinary interventions across care settings to reduce acute recurrent admissions in dementia.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

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Browne, B., Ali, K., Ford, E. et al. Determinants of hospital readmissions in older people with dementia: a narrative review. BMC Geriatr 24 , 336 (2024). https://doi.org/10.1186/s12877-024-04905-6

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Received : 27 October 2023

Accepted : 20 March 2024

Published : 12 April 2024

DOI : https://doi.org/10.1186/s12877-024-04905-6

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• Hospital readmissions
• Hospitalisations
• Determinants
• Narrative review
• Older adults
• Psychosocial

BMC Geriatrics

ISSN: 1471-2318

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