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Narrative Analysis – Types, Methods and Examples

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Narrative Analysis

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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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. 

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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.

what is narrative data analysis in qualitative research

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  • 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 101

Everything you need to know to get started

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

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

Overview: Narrative Analysis

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

What Is Narrative Analysis?

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

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

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

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

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

Simple definition of narrative analysis

The Two Overarching Approaches

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

First up is the inductive approach to narrative analysis.

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

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

Contrasted to this is the deductive approach.

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

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

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

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what is narrative data analysis in qualitative research

Strengths & Weaknesses

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

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

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

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

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

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

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

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When To Use Narrative Analysis

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

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

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

what is narrative data analysis in qualitative research

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 .

what is narrative data analysis in qualitative research

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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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Research aims, research objectives and research questions

Thanks. I need examples of narrative analysis

Derek Jansen

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

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

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

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

Yvonne Worrell

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

Belinda

Please i need help with my project,

Mst. Shefat-E-Sultana

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

Towha

please mention the sources as well.

Bezuayehu

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

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

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

what is narrative data analysis in qualitative research

  • 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.

what is narrative data analysis in qualitative research

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.

what is narrative data analysis in qualitative research

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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.

what is narrative data analysis in qualitative research

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.

what is narrative data analysis in qualitative research

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

Triangulating data

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

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

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

Conduct data analysis for your narrative research with ATLAS.ti.

Dedicated research software like ATLAS.ti helps the researcher catalog, penetrate, and analyze the data generated in any qualitative research project. Start with a free trial today.

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

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

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

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

Structural analysis

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

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

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

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

Functional analysis

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

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

Dialogic analysis

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

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

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

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

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

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

Subjectivity in narratives

what is narrative data analysis in qualitative research

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

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

Connecting narratives to broader phenomena

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

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

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

Turn to ATLAS.ti for your narrative analysis.

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what is narrative data analysis in qualitative research

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What is Narrative Analysis and When to Use It?

  • Watching now: Chapter 1: Narrative Analysis in Qualitative Research Start time: 00:00:00 End time: 00:02:43

Video Type: Tutorial

(2018). What is narrative analysis and when to use it? [Video]. Sage Research Methods. https:// doi. org/10.4135/9781529625332

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Several examples of narrative analysis illustrating when it is best used in qualitative research.

Chapter 1: Narrative Analysis in Qualitative Research

  • Start time: 00:00:00
  • End time: 00:02:43
  • Product: Sage Research Methods Video: Qualitative and Mixed Methods
  • Type of Content: Tutorial
  • Title: What is Narrative Analysis and When to Use It?
  • Publisher: Scholarsight Technologies Private Limited
  • Series: Which Qualitative Method Should I Use and When?
  • Publication year: 2018
  • Online pub date: March 14, 2023
  • Discipline: Sociology , History , Education , Criminology and Criminal Justice , Counseling and Psychotherapy , Public Health , Psychology , Anthropology , Communication and Media Studies , Social Work , Health , Political Science and International Relations , Social Policy and Public Policy , Business and Management , Geography , Nursing
  • Methods: Qualitative measures , Narrative analysis , Qualitative data analysis
  • Duration: 00:02:43
  • DOI: https:// doi. org/10.4135/9781529625332
  • Keywords: mixed methods research , narrative analysis , qualitative data analysis , qualitative research methods Show all Show less
  • Online ISBN: 9781529625332 More information Less information

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what is narrative data analysis in qualitative research

Narrative Analysis: Methods and Examples

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

What Is Narrative Research

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

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

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

What Is Narrative Research?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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.

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Narrative Analysis

“the oldest and most natural form of sense making” are stories or narratives (jonansses & hernandez-serrano, 2002,  p. 66).  stories are how we make sense of our experiences, how we communicate with others, and through which we understand the world around us.  stories, also called “narratives” have become a popular source of data in qualitative research.  the key to this type of qualitative research is the use of stories as data, and more spefically, first-person accounts of experience told in story form having a beginning, middle, and end.  other terms for these stories of experience are biography, life history, oral history, autoethnography, and autobiography., first-person accounts of experience constitute the narrative “text” of this research approach.  whether the account is in the form autobiography, life history, interview, journal letters, or other materials, the text is analyzed for the meaning it has for its author., as with other forms of qualitative research, narrative research makes use of various methodological approaches to analyzing stories.  each approach examines, in some ways, how the story is constructed, what linguistic tools are used, and the cultural context of the story.  for example, a biographical approach can seek to understand the influece of gender, race, family of origin, life events, turning point experiences, and/or other persons in the participants’ life.  as well, a psychological approach concentrates more on the personal, including thoughts and motivations.  a linguistic approach, or discourse analysis, focuses on the language of the story or the spoken text, and and also attends to the speaker’s intonation, pitch, and pauses., this page features narrative analysis tools, resources, and examples to help you analyze your narrative data. below is a brief outline differentiating four types of narrative analysis (adapted from cresswell, 2009 and merriam, 2009):, thematic analysis, thematic analysis in its simplest form is a categorizing strategy for qualitative data. researchers review their data, make notes and begin to sort it into categories. styled as a data analytic strategy, it helps researchers move their analysis from a broad reading of the data towards discovering patterns and developing themes. while researchers debate whether thematic analysis is a complete “method” per se, it is a process that can be used with many kinds of qualitative data, and with many goals in mind. for that reason, thematic analysis is often implicitly and explicitly a part of other types of data analysis including discourse analysis, grounded theory, and case study., you can download more information about the application of thematic analysis below:, fereday, j. & muir­cochrane, e. (2006). demonstrating rigor using thematic analysis: a hybrid  approach of inductive and deductive qualitative methods, 5(1), 80­92., in this article, the authors demonstrate how they used thematic analysis to code qualitative data.  in doing so, they combine inductive and deductive approaches to identity themes and interpret  the data. deductive data analysis is characterized as examining the general data into specific themes and is informally discussed as the ‘top­down’ approach. inductive data analysis begins  from more specific observations to broader generalizations and theories. a deductive approach usually begins with a hypothesis whereas an inductive approach explores new phenomena. oftentimes the analysis of qualitative data can require both inductive and deductive thematic analysis. this article is especially helpful for exemplifying how to organize data, which steps to take during the coding process, and the ways in which utilizing thematic analysis can facilitate the interpretation of data. this approach is useful in narrative analysis because it synthesizes data while recognizing the contributions of the theoretical frameworks utilized, facilitating broader understanding of data collected. ,  structural analysis, structural analysis focuses on the ways in which the narrative is conveyed by the speaker with particular emphasis given to the interaction between speaker and listener.  in this form of analysis, the language that the speaker uses, the pauses in speech, discourse markers, and other similar structural aspects of speech are the focus. when utilizing this approach, oftentimes the narrative is divided into stanzas and each stanza is analyzed by itself and also in the way in which it connects to the other pieces of the narrative., you can download more information about the application of structural analysis below:, gee, j.p. & green, j.l. (1998). discourse analysis, learning, and social practice: a  methodological study. review of research in education, 23, 119­169., in this article, the authors demonstrates how they used structural analysis within an ethnographic approach to study learning in social settings. in doing so, they introduce a conceptual framework to build a logic of inquiry. ethnography is the study of people, customs, and culture, which facilitates an in­depth understanding of meanings in the lives of individuals or community. logic of inquiry begins with the application of a rational model to the raw data, then researcers builds a theory to understand the gaps from this preliminary analysis, and finally determine if the theory is well­formed. this article is helpful for understanding what learning in a local setting is, how and when learning occurs, and how information is transformed into a sociocultural resource. this approach is useful in narrative analysis because it examines the ways in which the narrative is conveyed by the speaker., dialogic analysis, dialogical analysis is an interpretative methodology which closely analyzes spoken or written utterances or actions for their embedded communicative significance. although dialogical analysis tends to focus on discourse, it is distinct from discourse analysis and conversation analysis because its focus goes beyond the question of how people speak and what they achieve by speaking.  dialogical analysis uses dialogue as a metaphor for understanding phenomena beyond communication itself, such as the self, internal dialogues, self-talk, misunderstandings, trust and distrust, the production of knowledge, and relations between groups in society., you can download more information about the application of dialogical analysis below:, gillespe, a. & cornish, f (2009). intersubjectivity: towards a dialogical analysis. journal for the theory of social behavoir, 40(1), 19­46., in this article, the authors review existing methodologies to study intersubjectivity.  the authors  propose a dialogical analysis of intersubjectivity in order to overcome the limitations of existing methodologies. intersubjectivity refers to the variety of possible relations between people’s  perspective. this approach is useful in narrative analysis because it considers the social, historical, and cultural context of the narrative and the audience’s response to the narrative. in doing so, this approach facilitates a contextual understanding of the data that is collected., visual analysis, visual analysis seeks to understand how words and images convey meaning; subsequently exploring the visual message., the researcher is offering their analysis of the visual document in a manner that is clear, concise, and informative.  visual analysis is not describing the data, as the method explains the rhetorical strategies at work and the ways in which the document communicates visually., you can download more information about the application of visual analysis below:, goodwin, c. (2000). practices of seeing: visual analysis: an ethnomethodological approach. handbook of visual analysis , 157-182., in this article, the author demonstrates visual analysis through specific examples, so that the reader can understand how visual images produce meaningful details to the narrative, such as spatial and environmental setting, the progression of the narrative, and the visible depiction of the participants’ bodies . as a result, this approach enhances the narrative analysis by analyzing the nonverbal aspects to build a deeper, comprehensive understanding of the accounts researchers are analyzing.  , information for this page has been has been adapted from these  sources:, merriam, sharan b.merriam, sharan b. (2009)  qualitative research :a guide to design and implementation  san francisco, ca: jossey-bass., creswell, j. w. (2009).  research design: qualitative, quantitative, and mixed methods approaches . los angeles, ca: sage..

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  • What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

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Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

  • Observations: recording what you have seen, heard, or encountered in detailed field notes.
  • Interviews:  personally asking people questions in one-on-one conversations.
  • Focus groups: asking questions and generating discussion among a group of people.
  • Surveys : distributing questionnaires with open-ended questions.
  • Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
  • You take field notes with observations and reflect on your own experiences of the company culture.
  • You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
  • You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

  • Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
  • Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
  • Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
  • Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
  • Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

  • Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

  • Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

  • Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

  • Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

  • Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

  • Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

  • Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

  • Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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What is data saturation in qualitative research.

8 min read A crucial milestone in qualitative research, data saturation means you can end the data collection phase and move on to your analysis. Here we explain exactly what it means, the telltale signs that you’ve reached it, and how to get there as efficiently as possible.

Author:  Will Webster

Subject Matter Expert:  Jess Oliveros

Data saturation is a point in data collection when new information no longer brings fresh insights to the research questions.

Reaching data saturation means you’ve collected enough data to confidently understand the patterns and themes within the dataset – you’ve got what you need to draw conclusions and make your points. Think of it like a conversation where everything that can be said has been said, and now it’s just repetition.

Why is data saturation most relevant to qualitative research? Because qualitative research is about understanding something deeply, and you can reach a critical mass when trying to do that. Quantitative research, on the other hand, deals in numbers and with predetermined sample sizes , so the concept of data saturation is less relevant.

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How to know when data saturation is reached

At the point of data saturation, you start to notice that the information you’re collecting is just reinforcing what you already know rather than providing new insights.

Knowing when you’ve reached this point is fairly subjective – there’s no formula or equation that can be applied. But there are some telltale signs that can apply to any qualitative research project .

When one or multiple of these signs are present, it’s a good time to begin finalizing the data collection phase and move on to a more detailed analysis.

Recurring themes

You start to notice that new data doesn’t bring up new themes or ideas. Instead, it echoes what you’ve already recorded.

This is a sign that you’ve likely tapped into all the main ideas related to your research question.

No new data

When interviews or surveys start to feel like you’re reading from the same script with each participant, you’ve probably reached the limit of diversity in responses. New participants will probably only confirm what you already know.

You’ve collected enough instances and evidence for each category of your analysis that you can support each theme with multiple examples. In other words, your data has become saturated with a depth and richness that illustrates each finding.

Full understanding

You reach a level of familiarity with the subject matter that allows you to accurately predict what your participants will say next. If this is the case, you’ve likely reached data saturation.

Consistency

The data starts to show consistent patterns that support a coherent story. Crucially, inconsistencies and outliers don’t challenge your thinking and significantly alter the narrative you’ve formed.

This consistency across the data set strengthens the validity of your findings.

Is data saturation the goal of qualitative research?

In a word, no. But it’s often a critical milestone.

The true goal of qualitative research is to gain a deep understanding of the subject matter; data saturation indicates that you’ve gathered enough information to achieve that understanding.

That said, working to achieve data saturation in the most efficient way possible should be a goal of your research project.

How can a qualitative research project reach data saturation?

Reaching data saturation is a pivotal point in qualitative research as a sign that you’ve generated comprehensive and reliable findings.

There’s no exact science for reaching this point, but it does consistently demand two things: an adequate sample size and well-screened participants.

Adequate sample size

Achieving data saturation in qualitative research heavily relies on determining an appropriate sample size .

This is less about hitting a specific number and more about ensuring that the range of participants is broad enough to capture the diverse perspectives your research needs – while being focused enough to allow for thorough analysis.

Flexibility is crucial in this process. For example, in a study exploring patient experiences in a hospital, starting with a small group of patients from various departments might be the initial plan. However, as the interviews progress, if new themes continue to emerge, it might indicate the need to broaden the sample size to include more patients or even healthcare providers for a more comprehensive understanding.

An iterative approach like this can help your research to capture the complexity of people’s experiences without overwhelming the research with redundant information. The goal is to reach a point where additional interviews yield little new information, signaling that the range of experiences has been adequately captured.

While yes, it’s important to stay flexible and iterate as you go, it’s always wise to make use of research solutions that can make recommendations on suggested sample size . Such tools can also monitor crucial metrics like completion rate and audience size to keep your research project on track to reach data saturation.

Well-screened participants

In qualitative research, the depth and validity of your findings are of course totally influenced by your participants. This is where the importance of well-screened participants becomes very clear.

In any research project that addresses a complex social issue – from public health strategy to educational reform – having participants who can provide a range of lived experiences and viewpoints is crucial. Generating the best result isn’t about finding a random assortment of individuals, but instead about forming a carefully selected research panel whose experiences and perspectives directly relate to the research questions.

Achieving this means looking beyond surface criteria, like age or occupation, and instead delving into qualities that are relevant to the study, like experiences, attitudes or behaviors. This ensures that the data collected is rich and deeply rooted in real-world contexts, and will ultimately set you on a faster route to data saturation.

At the same time, if you find that your participants aren’t providing the depth or range of insights expected, you probably need to reevaluate your screening criteria. It’s unlikely that you’ll get it right first time – as with determining sample size, don’t be afraid of an iterative process.

To expedite this process, researchers can use digital tools to build ever-richer pictures of respondents , driving more targeted research and more tailored interactions.

Elevate your qualitative research skills

Mastering qualitative research involves more than knowing concepts like data saturation – it’s about grasping the entire research journey. To do this, you need to dive deep into the world of qualitative research where understanding the ‘why’ behind the ‘what’ is key.

‘Qualitative research design handbook’ is your guide through this journey.

It covers everything from the essence of qualitative analysis to the intricacies of survey design and data collection. You’ll learn how to apply qualitative techniques effectively, ensuring your research is both rich and insightful.

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Community Energy in Germany pp 207–239 Cite as

Methodological Considerations for Data Collection and Analysis

  • Jörg Radtke 2  
  • First Online: 01 June 2023

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The essential feature of the chosen research design is a mixed methods approach that combines both quantitative and qualitative survey instruments and different procedures and analytical methodologies (see Kuckartz 2014a). At its core, this approach has the following characteristics:

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The approach is particularly emphasized in the context of community energy research, see Rogers et al. 2008 ; Seyfang et al. 2013 ; also in relation to the sustainability context see Browne et al. 2014 .

Donges ( 2012 : 217) describes from an organisational sociology and systems theory perspective a macro-level as “society and its subsystems”, meso-level composed of the organisations and micro-level as “social action of individuals”. In between he determines a “macro-meso area” in which the organisations move in society as well as a “micro-meso area” in which the individuals and groups act in organisations. See in relation to community energy also the micro-, meso- and macro-division in Devine-Wright 2014 .

Social (emergent or latent) patterns of interaction and interpretation, geneses of meaning, and social dynamics of interaction are not of interest, or recorded (see e.g., Przyborski 2004 ; Deppermann 2008 ; Lamnek 2010 ; Przyborski and Wohlrab-Sahr 2008 ; Medjedović 2014 ; Reichertz 2014 ). A level of content is covered, which mainly concerns procedural-systematic dimensions (e.g., course/process of an assembly or modes and forms of action such as working groups); see on the political science perspective Patzelt 2012 .

Meinefeld sums up by noting: “Qualitative methodology cannot stop at fundamentally rejecting the prior formulation of hypotheses and only allowing ex-post facto hypotheses for the interpretation of the collected material; it must also assign an independent place in its methodological justification to the control of the researcher’s prior knowledge. This does not mean that qualitative social research must always be preceded by elaborate hypotheses oriented towards theories (…). In particular, it cannot be a matter of narrowing social research to the testing of hypotheses. Qualitative methodology, however, cannot continue to adhere to the self-image of having at its disposal a specific access to social phenomena which is free of prior knowledge and which is fundamentally different from the guidance of perception by ex-ante hypotheses. It must develop communicable possibilities to reflect and make explicit the pre-structuring of the object area that also occurs in it” (Meinefeld 1997 : 32).

Porst ( 2014a : 95 f.) mentions here: “use of simple, unambiguous terms that are understood in the same way by all respondents; use of questions with a clear temporal reference; use of answer categories that are exhaustively free of overlap; definition of unclear terms; avoidance of long and complex as well as hypothetical questions; avoidance of double stimuli and negations as well as of insinuations and suggestive questions; avoidance of questions that aim at information that many respondents presumably do not have; the context of a question should not have an (uncontrolled) effect on its answer.”

See Fleiß ( 2011 ) on the use of grounded theory in connection with the typological method.

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