thesis in narrative analysis

Narrative Analysis 101

Everything you need to know to get started

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

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

Overview: Narrative Analysis

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

What Is Narrative Analysis?

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

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

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

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

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

The Two Overarching Approaches

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

First up is the inductive approach to narrative analysis.

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

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

Contrasted to this is the deductive approach.

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

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

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

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Strengths & Weaknesses

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

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

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

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

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

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

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

When To Use Narrative Analysis

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

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

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

Let’s recap

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

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

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

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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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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Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

Table of Contents

Narrative Analysis

Definition:

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

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

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

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

Structural Analysis

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

Discourse Analysis

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

Phenomenological Analysis

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

Critical Analysis

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

Autoethnography

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

Thematic Analysis

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

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

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

Applications of Narrative Analysis

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

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

Examples of Narrative Analysis

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

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

Purpose of Narrative Analysis

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

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

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

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

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

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

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

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

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

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

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

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Using narrative analysis in qualitative research

Last updated

7 March 2023

Reviewed by

Jean Kaluza

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

Master narrative analysis

Analyze your qualitative data faster and surface more actionable insights

• What is narrative analysis?

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

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

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

Is narrative analysis qualitative or quantitative?

Narrative analysis is a qualitative research method.

Is narrative analysis a method or methodology?

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

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

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

What are the characteristics of narrative research?

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

• Examples of types of narratives

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

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

• What is the objective of narrative analysis?

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

What is the difference between content analysis and narrative analysis?

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

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

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

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

What is the difference between narrative analysis and thematic analysis?

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

• Capturing narrative data

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

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

• Methods of transcribing narrative data

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

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

Video and audio transcription templates

• How to code narrative analysis

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

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

• Types of narrative analysis

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

Inductive method

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

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

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

Taking cues from entrance and exit talk

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

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

Deductive method

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

Hybrid inductive and deductive narrative analysis

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

• How to analyze data after coding using narrative analysis

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

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

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

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

Step 1 – Code narrative blocks

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

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

Step 2 – Group and read by live-event

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

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

Step 3 – Create a nested story structure

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

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

Step 4 – Delve into the story structure

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

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

Step 5 – Compare across story structure

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

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

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

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

Step 6 – Tell the core narrative

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

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

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

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Narrative Analysis

• Reference work entry
• First Online: 13 January 2019
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• Nicole L. Sharp 2 ,
• Rosalind A. Bye 2 &
• Anne Cusick 3  

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

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

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What is Narrative Analysis?

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

What is narrative analysis in qualitative research?

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

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

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

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

Examples of personal narratives

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

Topical stories

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

Personal narrative 

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

Entire life story

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

Capturing narrative data

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

Transcribing narrative data

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

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

Coding in narrative analysis

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

What is narrative research

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

Download Free Narrative Analysis Guide

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

The Narrative Analysis PDF will be emailed to you

Inductive method for narrative analysis

Learn about inductive narrative method:.

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

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

Entrance and Exit Talk

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

A story may start with the phrases: 

“There was this one time…”, 

“Let me give you an example”, 

and “I’ll always remember when…”

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

“So that’s how that wrapped up…”

“That is a pretty classic example of…”

and “and that was the end of that.”

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

Deductive method for narrative analysis

Learn about deductive narrative method:.

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

Abstract: The core thesis of the story, summary

Orientation: Time, place, situation, and characters

Complicating action: Sequence of events, plot

Evaluation: How the storyteller comments on meaning 

Resolution: Outcome of the story

Coda: Story’s ending 

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

You - A character is in a zone of comfort

Need - But they want something.

Go - They enter an unfamiliar situation,

Search - Adapt to it,

Find - Get what they wanted,

Take/Pay - Pay a heavy price for it,

Return - Then return to their familiar situation,

Change - Having changed.

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

Hybrid Inductive and Deductive Narrative Analysis

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

How to analyze data in a narrative interview

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

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

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

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

Step 1: Code Narrative Blocks

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

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

Step 2: Group and Read By Live-Event

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

Step 3: Create Nested Story Structure Codes

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

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

Abstract (these codes are based on story structure)

Orientation

Complicating action

More generally put:

Life Event Code   

Story Structure Code 1

Story Structure Code 2

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

Step 4: Delve into the Story Structure

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

Deciding to have children

Parent divorce

Parents still together

Step 5: Compare Across Story Structure

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

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

Step 6: Tell the Core Narrative

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

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

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

Try Delve, Narrative Analysis Software

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

References:

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

Cite this blog post:

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

Film Analysis

What this handout is about.

This handout introduces film analysis and and offers strategies and resources for approaching film analysis assignments.

Writing the film analysis essay

Writing a film analysis requires you to consider the composition of the film—the individual parts and choices made that come together to create the finished piece. Film analysis goes beyond the analysis of the film as literature to include camera angles, lighting, set design, sound elements, costume choices, editing, etc. in making an argument. The first step to analyzing the film is to watch it with a plan.

Watching the film

First it’s important to watch the film carefully with a critical eye. Consider why you’ve been assigned to watch a film and write an analysis. How does this activity fit into the course? Why have you been assigned this particular film? What are you looking for in connection to the course content? Let’s practice with this clip from Alfred Hitchcock’s Vertigo (1958). Here are some tips on how to watch the clip critically, just as you would an entire film:

• Give the clip your undivided attention at least once. Pay close attention to details and make observations that might start leading to bigger questions.
• Watch the clip a second time. For this viewing, you will want to focus specifically on those elements of film analysis that your class has focused on, so review your course notes. For example, from whose perspective is this clip shot? What choices help convey that perspective? What is the overall tone, theme, or effect of this clip?
• Take notes while you watch for the second time. Notes will help you keep track of what you noticed and when, if you include timestamps in your notes. Timestamps are vital for citing scenes from a film!

For more information on watching a film, check out the Learning Center’s handout on watching film analytically . For more resources on researching film, including glossaries of film terms, see UNC Library’s research guide on film & cinema .

Brainstorming ideas

Once you’ve watched the film twice, it’s time to brainstorm some ideas based on your notes. Brainstorming is a major step that helps develop and explore ideas. As you brainstorm, you may want to cluster your ideas around central topics or themes that emerge as you review your notes. Did you ask several questions about color? Were you curious about repeated images? Perhaps these are directions you can pursue.

If you’re writing an argumentative essay, you can use the connections that you develop while brainstorming to draft a thesis statement . Consider the assignment and prompt when formulating a thesis, as well as what kind of evidence you will present to support your claims. Your evidence could be dialogue, sound edits, cinematography decisions, etc. Much of how you make these decisions will depend on the type of film analysis you are conducting, an important decision covered in the next section.

After brainstorming, you can draft an outline of your film analysis using the same strategies that you would for other writing assignments. Here are a few more tips to keep in mind as you prepare for this stage of the assignment:

• Make sure you understand the prompt and what you are being asked to do. Remember that this is ultimately an assignment, so your thesis should answer what the prompt asks. Check with your professor if you are unsure.
• In most cases, the director’s name is used to talk about the film as a whole, for instance, “Alfred Hitchcock’s Vertigo .” However, some writers may want to include the names of other persons who helped to create the film, including the actors, the cinematographer, and the sound editor, among others.
• When describing a sequence in a film, use the literary present. An example could be, “In Vertigo , Hitchcock employs techniques of observation to dramatize the act of detection.”
• Finding a screenplay/script of the movie may be helpful and save you time when compiling citations. But keep in mind that there may be differences between the screenplay and the actual product (and these differences might be a topic of discussion!).
• Go beyond describing basic film elements by articulating the significance of these elements in support of your particular position. For example, you may have an interpretation of the striking color green in Vertigo , but you would only mention this if it was relevant to your argument. For more help on using evidence effectively, see the section on “using evidence” in our evidence handout .

Also be sure to avoid confusing the terms shot, scene, and sequence. Remember, a shot ends every time the camera cuts; a scene can be composed of several related shots; and a sequence is a set of related scenes.

Different types of film analysis

As you consider your notes, outline, and general thesis about a film, the majority of your assignment will depend on what type of film analysis you are conducting. This section explores some of the different types of film analyses you may have been assigned to write.

Semiotic analysis

Semiotic analysis is the interpretation of signs and symbols, typically involving metaphors and analogies to both inanimate objects and characters within a film. Because symbols have several meanings, writers often need to determine what a particular symbol means in the film and in a broader cultural or historical context.

For instance, a writer could explore the symbolism of the flowers in Vertigo by connecting the images of them falling apart to the vulnerability of the heroine.

Here are a few other questions to consider for this type of analysis:

• What objects or images are repeated throughout the film?
• How does the director associate a character with small signs, such as certain colors, clothing, food, or language use?
• How does a symbol or object relate to other symbols and objects, that is, what is the relationship between the film’s signs?

Many films are rich with symbolism, and it can be easy to get lost in the details. Remember to bring a semiotic analysis back around to answering the question “So what?” in your thesis.

Narrative analysis

Narrative analysis is an examination of the story elements, including narrative structure, character, and plot. This type of analysis considers the entirety of the film and the story it seeks to tell.

For example, you could take the same object from the previous example—the flowers—which meant one thing in a semiotic analysis, and ask instead about their narrative role. That is, you might analyze how Hitchcock introduces the flowers at the beginning of the film in order to return to them later to draw out the completion of the heroine’s character arc.

To create this type of analysis, you could consider questions like:

• How does the film correspond to the Three-Act Structure: Act One: Setup; Act Two: Confrontation; and Act Three: Resolution?
• What is the plot of the film? How does this plot differ from the narrative, that is, how the story is told? For example, are events presented out of order and to what effect?
• Does the plot revolve around one character? Does the plot revolve around multiple characters? How do these characters develop across the film?

When writing a narrative analysis, take care not to spend too time on summarizing at the expense of your argument. See our handout on summarizing for more tips on making summary serve analysis.

Cultural/historical analysis

One of the most common types of analysis is the examination of a film’s relationship to its broader cultural, historical, or theoretical contexts. Whether films intentionally comment on their context or not, they are always a product of the culture or period in which they were created. By placing the film in a particular context, this type of analysis asks how the film models, challenges, or subverts different types of relations, whether historical, social, or even theoretical.

For example, the clip from Vertigo depicts a man observing a woman without her knowing it. You could examine how this aspect of the film addresses a midcentury social concern about observation, such as the sexual policing of women, or a political one, such as Cold War-era McCarthyism.

A few of the many questions you could ask in this vein include:

• How does the film comment on, reinforce, or even critique social and political issues at the time it was released, including questions of race, ethnicity, gender, and sexuality?
• How might a biographical understanding of the film’s creators and their historical moment affect the way you view the film?
• How might a specific film theory, such as Queer Theory, Structuralist Theory, or Marxist Film Theory, provide a language or set of terms for articulating the attributes of the film?

Take advantage of class resources to explore possible approaches to cultural/historical film analyses, and find out whether you will be expected to do additional research into the film’s context.

Mise-en-scène analysis

A mise-en-scène analysis attends to how the filmmakers have arranged compositional elements in a film and specifically within a scene or even a single shot. This type of analysis organizes the individual elements of a scene to explore how they come together to produce meaning. You may focus on anything that adds meaning to the formal effect produced by a given scene, including: blocking, lighting, design, color, costume, as well as how these attributes work in conjunction with decisions related to sound, cinematography, and editing. For example, in the clip from Vertigo , a mise-en-scène analysis might ask how numerous elements, from lighting to camera angles, work together to present the viewer with the perspective of Jimmy Stewart’s character.

To conduct this type of analysis, you could ask:

• What effects are created in a scene, and what is their purpose?
• How does this scene represent the theme of the movie?
• How does a scene work to express a broader point to the film’s plot?

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The Incident of Emmett Till: a Historical Analysis of Alleged Actions

This essay about the historical figure Emmett Till examines the contested incident where he allegedly whistled at a white woman, Carolyn Bryant, in 1955 Mississippi—an action that purportedly led to his tragic lynching. The essay explores various accounts and testimonies surrounding the incident, highlighting the deep-seated racial tensions of the era. It discusses the implications of whether Till actually whistled and the broader context of systemic racism that facilitated his murder regardless of his actions. The case’s role in energizing the civil rights movement, particularly influencing figures like Rosa Parks, is also analyzed. Ultimately, the essay uses Till’s story to reflect on the persistent issues of racial injustice and the historical struggle for equality in America.

How it works

The civil rights movement was sparked by the horrible murder of a young African American boy named Emmett Till in 1955, and his narrative is ingrained in American history. The contentious question of whether Emmett Till’s whistle at Carolyn Bryant, a white woman, precipitated his alleged lynching, is at the heart of his narrative. This inquiry sheds light on the intense racial tensions of the day in addition to probing the reasoning behind a horrific deed.

When Emmett Till left Chicago to visit family in Money, Mississippi, he was only 14 years old.

He went straight into Bryant’s Grocery and Meat Market after arriving. What happened in those brief minutes inside is still a source of debate and in-depth historical research. In the Jim Crow South, where racial rules were tightly enforced, Carolyn Bryant claimed that Till made inappropriate approaches toward her, including whistling at her—an conduct that was deemed egregiously offensive.

The veracity of the whistle itself has been widely debated. Witnesses at the time provided conflicting testimonies, and decades later, Carolyn Bryant recanted parts of her original story, casting further doubt on the narrative she presented at the trial of Till’s murderers, who were acquitted by an all-white jury.

Analyzing the implications of whether Till whistled or not transcends the specifics of that day. If he did whistle, it would be indicative of a young boy unaware or defiant of the severe racial norms of Mississippi—a stark contrast to his life in the relatively more liberal Chicago. If he did not, it underscores the tragic reality that even an unsubstantiated allegation could lead to violent retribution against African Americans.

Moreover, the focus on the whistle shifts attention away from the more significant issues at hand: the brutality of his murder and the virulent racism that enabled it. Till’s death was not merely a result of his actions, whatever they might have been, but of a societal structure that permitted, and indeed condoned, such violence against African Americans.

The aftermath of Till’s death was a pivotal moment in American history. It galvanized the civil rights movement, with figures like Rosa Parks citing Till’s murder as a catalyst for their activism. The open-casket funeral held by Mamie Till Bradley, Emmett’s mother, showcased the brutality of his murder to the world, further highlighting the dire need for change in the United States.

As historians and cultural analysts continue to study this case, the question of whether Till whistled remains significant not for its factual resolution but for its symbolic weight in American history. It represents the myriad ways in which African Americans were oppressed and punished under Jim Crow laws, often based on mere accusations without any need for proof.

In conclusion, the discussion of Emmett Till’s alleged whistle is more than a query about a boy’s actions; it is a reflection on the historical context of racial injustice in America. Understanding this incident helps to appreciate the complexities of racial relations in the mid-20th century and the long, ongoing struggle for equality and justice in the United States. This story, emblematic of many untold stories, continues to resonate as a powerful reminder of the past and its lingering effects on the present.

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• Published: 08 May 2024

Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

• Mariyamma Philip 1  

BMC Medical Education volume  24 , Article number:  512 ( 2024 ) Cite this article

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Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.

One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.

Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.

The study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

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A PhD degree is a research degree, and research scholars submit a thesis based on original research in their chosen field. Doctor of Philosophy (PhD) degrees are awarded in a wide range of academic disciplines, and the PhD students are usually referred as research scholars. A comprehensive understanding of statistics allows research scholars to add rigour to their research. This approach helps them evaluate the current practices and draw informed conclusions from studies that were undertaken to generate their own hypotheses and to design, analyse and interpret complex clinical decisions. Therefore, intensive training at the beginning of the PhD journey is essential, as intensive training in research methodology and statistics in the early stages of research helps scholars design and plan their studies efficiently.

The University Grants Commission of India has taken various initiatives to introduce academic reforms to higher education institutions in India and mandated in 2009 that coursework be treated as a prerequisite for PhD preparation and that a minimum of four credits be assigned to one or more courses on research methodology, which could cover areas such as quantitative methods, computer applications, and research ethics. UGC also clearly states that all candidates admitted to PhD programmes shall be required to complete the prescribed coursework during the initial two semesters [ 1 ]. National Institute of Mental Health and Neurosciences (NIMHANS) at Bangalore, a tertiary care hospital and medical higher education institute in South India, that trains students in higher education in clinical fields, also introduced coursework in the PhD program for research scholars from various backgrounds, such as basic, behavioral and neurosciences, as per the UGC mandate. Research scholars undertake coursework programs soon after admission, which consist of several modules that include research methodology and statistical software training, among others.

Most scholars approach a course in statistics with the prejudice that statistics is uninteresting, demanding, complex or involve much mathematics and, most importantly, it is not relevant to their career goals. They approach statistics with considerable apprehension and negative attitudes, probably because of their inability to grasp the relevance of the application of the methods in their fields of study. This could be resolved by providing sufficient and relevant examples of the application of statistical techniques from various fields of medical research and by providing hands-on experience to learn how these techniques are applied and interpreted on real data. Hence, research methodology and statistical methods and the application of statistical methods using software have been given much importance and are taught as two modules, named Research Methodology and Statistics and Statistical Software Training, at this institute of medical higher education that trains research scholars in fields as diverse as basic, behavioural and neurosciences. Approximately 50% of the coursework curriculum focused on these two modules. Research scholars were thus given an opportunity to understand the theoretical aspects of the research methodology and statistical methods. They were also given hands-on training on statistical software to analyse the data using these methods and to interpret the findings. The coursework program was designed in this specific manner, as this intensive training would enable the research scholars to design their research studies more effectively and analyse their data in a better manner.

It is important to study attitudes toward statistics because attitudes are known to impact the learning process. Also, most importantly, these scholars are expected to utilize the skills in statistics and research methods to design research projects or guide postgraduate students and research scholars in the near future. Several authors have assessed attitudes toward statistics among various students and examined how attitudes affect academic achievement, how attitudes are correlated with knowledge in statistics and how attitudes change after a training program. There are studies on attitudes toward statistics among graduate [ 2 , 3 , 4 ] and postgraduate [ 5 ] medical students, politics, sociology, ( 6 – 7 ) psychology [ 8 , 9 , 10 ], social work [ 11 ], and management students [ 12 ]. However, there is a dearth of related literature on research scholars, and there are only two studies on the attitudes of research scholars. In their study of doctoral students in education-related fields, Cook & Catanzaro (2022) investigated the factors that contribute to statistics anxiety and attitudes toward statistics and how anxiety, attitudes and plans for future research use are connected among doctoral students [ 13 ]. Another study by Sohrabi et al. (2018) on research scholars assessed the change in knowledge and attitude towards teaching and educational design of basic science PhD students at a Medical University after a two-day workshop on empowerment and familiarity with the teaching and learning principles [ 14 ]. There were no studies that assessed changes in the attitudes or knowledge of research scholars across the PhD training period or after intensive training programmes such as PhD coursework. Even though PhD coursework has been established in institutes of higher education in India for more than a decade, there are no published research on the effectiveness of coursework from Indian universities or institutes of higher education.

This study aimed to determine the effectiveness of PhD coursework and whether intensive training programs such as PhD coursework can influence the knowledge and attitudes toward statistics of research scholars. Additionally, it would be interesting to know if the acquired knowledge could be retained longer, especially 2–3 years after the coursework, the crucial time of PhD data analysis. Hence, this study compares the scores of knowledge in statistics and attitude toward statistics of the research scholars at different time points of their PhD training, i.e., before, soon after and 2–3 years after the coursework.

Participants

This is an observational study of single group with repeated assessments. The institute offers a three-month coursework program consisting of seven modules, the first module is ethics; the fifth is research methodology and statistics; and the last is neurosciences. The study was conducted in January 2020. All research scholars of the institute who had completed PhD coursework in the last three years were considered for this study ( n  = 130). Knowledge and attitudes toward statistics before and soon after the coursework module were assessed as part of the coursework program. They were collected on the first and last day of the program respectively. The author who was also the coordinator of the research methodology and statistics module of the coursework have obtained the necessary permission to use the data for this study. The scholars invited to be part of the study by e-mail. Knowledge and attitude towards statistics 2–3 years after the coursework were assessed online using Google forms. They were also administered a semi structured questionnaire to elicit details about the usefulness of coursework. Participation was voluntary, and consent was also sought online. The confidentiality of the data was assured. Data were not collected from research scholars of Biostatistics or from research scholars who had more than a decade of experience or who had been working in the institute as faculty, assuming that their scores could be higher and could bias the findings. This non funded study was reviewed and approved by the Institute Ethics Committee.

Instruments

Knowledge in Statistics was assessed by a questionnaire prepared by the author and was used as part of the coursework evaluation. The survey included 25 questions that assessed the knowledge of statistics on areas such as descriptive statistics, sampling methods, study design, parametric and nonparametric tests and multivariate analyses. Right answers were assigned a score of 1, and wrong answers were assigned a score of 0. Total scores ranged from 0 to 25. Statistics attitudes were assessed by the Survey of Attitudes toward Statistics (SATS) scale. The SATS is a 36-item scale that measures 6 domains of attitudes towards statistics. The possible range of scores for each item is between 1 and 7. The total score was calculated by dividing the summed score by the number of items. Higher scores indicate more positive attitudes. The SAT-36 is a copyrighted scale, and researchers are allowed to use it only with prior permission. ( 15 – 16 ) The author obtained permission for use in the coursework evaluation and this study. A semi structured questionnaire was also used to elicit details about the usefulness of coursework.

Statistical analysis

Descriptive statistics such as mean, standard deviation, number and percentages were used to describe the socio-demographic data. General Linear Model Repeated Measures of Analysis of variance was used to compare knowledge and attitude scores across assessments. Categorical data from the semi structured questionnaire are presented as percentages. All the statistical tests were two-tailed, and a p value < 0.05 was set a priori as the threshold for statistical significance. IBM SPSS (28.0) was used to analyse the data.

One hundred and thirty research scholars who had completed coursework (CW) in the last 2–3 years were considered for the study. These scholars were sent Google forms to assess their knowledge and attitudes 2–3 years after coursework. 81 scholars responded (62%), and 4 scholars did not consent to participate in the study. The data of 77 scholars were merged with the data obtained during the coursework program (before and soon after CW). Socio-demographic characteristics of the scholars are presented in Table  1 .

The age of the respondents ranged from 23 to 36 years, with an average of 28.7 years (3.01), and the majority of the respondents were females (65%). Years of experience (i.e., after masters) before joining a PhD programme ranged from 0.5 to 9 years, and half of them had less than three years of experience before joining the PhD programme (median-3). More than half of those who responded were research scholars from the behavioural sciences (55%), while approximately 30% were from the basic sciences (29%).

General Linear Model Repeated Measures of Analysis of variance was used to compare the knowledge and attitude scores of scholars before, soon after and 2–3 after the coursework (will now be referred as “later the CW”), and the results are presented below (Table  2 ; Fig.  1 ).

Comparison of knowledge and attitude scores across the assessments. Later the CW – 2–3 years after the coursework

The scores for knowledge and attitude differed significantly across time. Scores of knowledge and attitude increased soon after the coursework; the percentage of change was 77% and 43% respectively. However, significant reductions in knowledge and attitude scores were observed 2–3 years after the coursework compared to scores soon after the coursework. The reduction was higher for attitude scores; knowledge and attitude scores have decreased by 10% and 37% respectively. The change in scores across assessments is evident from the graph, and clearly the effect size is higher for attitude than knowledge.

The scores of knowledge or attitude before the coursework did not significantly differ with respect to gender or age or were not correlated with years of experience. Hence, they were not considered as covariates in the above analysis.

A semi structured questionnaire with open ended questions was also administered to elicit in-depth information about the usefulness of the coursework programme, in which they were also asked to self- rate their knowledge. The data were mostly categorical or narratives. Research scholars’ self-rated knowledge scores (on a scale of 0–10) also showed similar changes; knowledge improved significantly and was retained even after the training (Fig.  2 ).

Self-rated knowledge scores of research scholars over time. Later the CW – 2–3 years after the coursework

The response to the question “ How has coursework changed your attitude toward statistics?”, is presented in Fig.  3 . The responses were Yes, positively, Yes - Negatively, No change – still apprehensive, No change – still appreciate, No change – still hate statistics. The majority of the scholars (70%) reported a positive change in their attitude toward statistics. Moreover, none of the scholars reported negative changes. Approximately 9% of the scholars reported that they were still apprehensive about statistics or hate statistics after the coursework.

How has coursework changed your attitude toward statistics?

Those scholars who reported that they were apprehensive about statistics or hate statistics noted the complexity of the subject, lack of clarity, improper instructions and fear of mathematics as major reasons for their attitude. Some responses are listed below.

“The statistical concepts were not taught in an understandable manner from the UG level” , “I am weak in mathematical concepts. The equations and formulae in statistics scare me”. “Lack of knowledge about the importance of statistics and fear of mathematical equations”. “The preconceived notion that Statistics is difficult to learn” . “In most of the places, it is not taught properly and conceptual clarity is not focused on, and because of this an avoidance builds up, which might be a reason for the negative attitude”.

Majority of the scholars (92%) felt that coursework has helped them in their PhD, and they were happy to recommend it for other research scholars (97%). The responses of the scholars to the question “ How was coursework helpful in your PhD journey ?”, are listed below.

“Course work gave a fair idea on various things related to research as well as statistics” . “Creating the best design while planning methodology, which is learnt form course work, will increase efficiency in completing the thesis, thereby making it faster”. “Course work give better idea of how to proceed in many areas like literature search, referencing, choosing statistical methods, and learning about research procedures”. “Course work gave a good idea of research methodology, biostatistics and ethics. This would help in writing a better protocol and a better thesis”. “It helps us to plan our research well and to formulate, collect and plan for analysis”. “It makes people to plan their statistical analysis well in advance” .

This study evaluated the effectiveness of the existing coursework programme in an institution of higher medical education, and investigated whether the coursework programme benefits research scholars by improving their knowledge of statistics and attitudes towards statistics. The study concluded that the coursework program was beneficial for improving scholars’ knowledge about statistics and attitudes toward statistics.

Unlike other studies that have assessed attitudes toward statistics, the study participants in this study were research scholars. Research scholars need extensive training in statistics, as they need to apply statistical tests and use statistical reasoning in their research thesis, and in their profession to design research projects or their future student dissertations. Notably, no studies have assessed the attitudes or knowledge of research scholars in statistics either across the PhD training period or after intensive statistics training programs. However, the findings of this study are consistent with the findings of a study that compared the knowledge and attitudes toward teaching and education design of PhD students after a two-day educational course and instructional design workshop [ 14 ].

Statistics educators need not only impart knowledge but they should also motivate the learners to appreciate the role of statistics and to continue to learn the quantitative skills that is needed in their professional lives. Therefore, the role of learners’ attitudes toward statistics requires special attention. Since PhD coursework is possibly a major contributor to creating a statistically literate research community, scholars’ attitudes toward statistics need to be considered important and given special attention. Passionate and engaging statistics educators who have adequate experience in illustrating relatable examples could help scholars feel less anxious and build competence and better attitudes toward statistics. Statistics educators should be aware of scholars’ anxiety, fears and attitudes toward statistics and about its influence on learning outcomes and further interest in the subject.

Strengths and limitations

Analysis of changes in knowledge and attitudes scores across various time points of PhD training is the major strength of the study. Additionally, this study evaluates the effectiveness of intensive statistical courses for research scholars in terms of changes in knowledge and attitudes. This study has its own limitations: the data were collected through online platforms, and the nonresponse rate was about 38%. Ability in mathematics or prior learning experience in statistics, interest in the subject, statistics anxiety or performance in coursework were not assessed; hence, their influence could not be studied. The reliability and validity of the knowledge questionnaire have not been established at the time of this study. However, author who had prepared the questionnaire had ensured questions from different areas of statistics that were covered during the coursework, it has also been used as part of the coursework evaluation. Despite these limitations, this study highlights the changes in attitudes and knowledge following an intensive training program. Future research could investigate the roles of age, sex, mathematical ability, achievement or performance outcomes and statistics anxiety.

The study concluded that a rigorous and intensive training program such as PhD coursework was beneficial for improving knowledge about statistics and attitudes toward statistics. However, the significant reduction in attitude and knowledge scores after 2–3 years of coursework indicates that a refresher program might be helpful for research scholars as they approach the analysis stage of their thesis. Statistics educators must develop innovative methods to teach research scholars from nonstatistical backgrounds. They also must be empathetic to understanding scholars’ anxiety, fears and attitudes toward statistics and to understand its influence on learning outcomes and further interest in the subject.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The author would like to thank the participants of the study and peers and experts who examined the content of the questionnaire for their time and effort.

This research did not receive any grants from funding agencies in the public, commercial, or not-for-profit sectors.

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This study used data already collected data (before and soon after coursework). The data pertaining to knowledge and attitude towards statistics 2–3 years after coursework were collected from research scholars through the online survey platform Google forms. The participants were invited to participate in the survey through e-mail. The study was explained in detail, and participation in the study was completely voluntary. Informed consent was obtained online in the form of a statement of consent. The confidentiality of the data was assured, even though identifiable personal information was not collected. This non-funded study was reviewed and approved by NIMHANS Institute Ethics Committee (No. NIMHANS/21st IEC (BS&NS Div.)

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Philip, M. Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework. BMC Med Educ 24 , 512 (2024). https://doi.org/10.1186/s12909-024-05487-y

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