narrative report about quantitative research

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• Volume 8, Issue 2
• Improving Conduct and Reporting of Narrative Synthesis of Quantitative Data (ICONS-Quant): protocol for a mixed methods study to develop a reporting guideline
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• Mhairi Campbell 1 ,
• Srinivasa Vittal Katikireddi 1 ,
• Amanda Sowden 2 ,
• Joanne E McKenzie 3 ,
• Hilary Thomson 1
• 1 MRC/CSO Social and Public Health Sciences Unit , University of Glasgow , Glasgow , UK
• 2 Centre for Reviews and Dissemination , University of York , York , UK
• 3 School of Public Health and Preventive Medicine , Monash University , Melbourne , Victoria , Australia
• Correspondence to Ms Mhairi Campbell; Mhairi.Campbell{at}glasgow.ac.uk

Introduction Reliable evidence syntheses, based on rigorous systematic reviews, provide essential support for evidence-informed clinical practice and health policy. Systematic reviews should use reproducible and transparent methods to draw conclusions from the available body of evidence. Narrative synthesis of quantitative data (NS) is a method commonly used in systematic reviews where it may not be appropriate, or possible, to meta-analyse estimates of intervention effects. A common criticism of NS is that it is opaque and subject to author interpretation, casting doubt on the trustworthiness of a review’s conclusions. Despite published guidance funded by the UK’s Economic and Social Research Council on the conduct of NS, recent work suggests that this guidance is rarely used and many review authors appear to be unclear about best practice. To improve the way that NS is conducted and reported, we are developing a reporting guideline for NS of quantitative data.

Methods We will assess how NS is implemented and reported in Cochrane systematic reviews and the findings will inform the creation of a Delphi consensus exercise by an expert panel. We will use this Delphi survey to develop a checklist for reporting standards for NS. This will be accompanied by supplementary guidance on the conduct and reporting of NS, as well as an online training resource.

Ethics and dissemination Ethical approval for the Delphi survey was obtained from the University of Glasgow in December 2017 (reference 400170060). Dissemination of the results of this study will be through peer-reviewed publications, and national and international conferences.

• evidence synthesis
• health policy

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1136/bmjopen-2017-020064

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Strengths and limitations of this study

This study will be the first to develop a consensus-based reporting guideline for narrative synthesis of quantitative data (NS) in systematic reviews.

The study follows the recommended methodology for developing reporting standards.

The online Delphi survey of international experts in NS will be an effective method of gaining reliable consensus from a group of experts.

The reporting guideline and the supplementary materials developed to support use of existing guidance will aid the implementation of best practice conduct and reporting of NS.

Introduction 

Well-conducted systematic reviews are important for informing clinical practice and health policy. 1 In some reviews, meta-analysis of effect estimates may not be possible or sensible. For example, data may be insufficient to allow calculation of standardised effect estimates, the effect metrics arising from different study designs may not be amenable to synthesis (eg, those arising from interrupted time series and randomised trials), or high levels of statistical heterogeneity may mean that presenting an average effect is misleading. For reviews of quantitative data where statistical synthesis is not possible, narrative synthesis of quantitative data (NS) is often the alternative method of choice. A major concern about NS is that it lacks transparency and therefore introduces bias into the synthesis. 2 3 This is an important criticism, which raises questions about the validity and utility of reviews using NS, and ultimately increases the risk of adding to research waste. 4 NS involves collating study findings into a coherent textual narrative, with descriptions of differences in characteristics of the studies including context and validity, often using tables and graphs to display results. 5 6 Published guidance for NS funded by the UK’s Economic and Social  Research Council (ESRC) describes techniques for promoting transparency between review level data and conclusions; these include graphical and structured tabulation of the data. 5 However, a recent analysis of systematic reviews of public health interventions suggests that this guidance is rarely used. 7

Relative to developments in meta-analysis or statistical synthesis, and synthesis of qualitative data in the past decade, work to support improved conduct and transparent reporting in NS has been scarce. While a reporting guideline has been developed for systematic reviews and meta-analysis, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), 8 the focus of the synthesis items is on meta-analysis of effect estimates, with no items for alternative approaches to synthesis. The Cochrane Methodological Expectations of Cochrane Intervention Reviews (MECIR) standards for conducting and reporting Cochrane reviews specify one general item referring to non-quantitative synthesis or non-statistical synthesis, and do not have any items specifically for NS. 2 Reporting guidelines have had some impact on improving the reporting for randomised trials and may have similar benefits for improving the reporting of methods and results from NS. 9

There is a growing demand for reviews addressing complex questions, and which incorporate diverse sources of data. Cochrane, a global leader in evidence synthesis of health and public health interventions, has recognised this. 10 Following the prioritisation of relevance and breadth of coverage in the Cochrane strategy, 10 it is likely that the proportion of Cochrane reviews addressing complex questions will increase; this may result in increased use of NS methods. Realising the need for improved implementation and reporting of NS methods, the Cochrane Strategic Methods Fund has funded the ICONS-Quant project: Improving Conduct and Reporting of Narrative Synthesis of Quantitative Data. This paper presents the protocol for the work that will be undertaken.

ICONS-Quant

The ICONS-Quant project aims to improve the implementation of NS methods through enhancing existing guidance on the conduct of NS and developing a reporting guideline. Provision of reporting guidelines alone will not necessarily lead to improved research conduct; provision of explanatory guidance, dissemination, endorsement and support for adherence is also necessary. 11 We will produce materials to support the implementation of best practice in the application of NS methods, and improved reporting. While our focus is on Cochrane reviews, the key outputs of the project will be of use for reviews published elsewhere and will be made freely available. We will:

describe current practice in conduct and reporting of NS in Cochrane reviews;

achieve expert consensus on reporting standards for NS;

provide support for those involved in NS through the provision of enhanced guidance on NS conduct and online training resources.

We intend ICONS-Quant guideline to be used in combination with the PRISMA guidelines. 8 The PRISMA guidelines provide items relating to the various stages of review conduct, for example, providing a clear abstract, explaining the literature search strategy, reporting methods to assess risk of bias. The ICONS-Quant reporting guideline will focus on the methods of synthesis, relating most closely to expanding on PRISMA Item 14 ‘synthesis of results’, outlining details that require to be reported to promote transparency in NS.

Methods and analysis

The ICONS-Quant project will be conducted over a period of 24 months from May 2017. Here, we outline the development of a reporting guideline for NS and supporting materials for existing guidance. In line with recommendations for best practice in developing reporting guidelines, 11 we will:

identify the need for the ICONS-Quant guideline (Work Programme One);

conduct a Delphi survey and consensus meeting (Work Programme Two);

enhance existing guidance on NS (Work Programme Three);

develop learning materials for implementation of NS (Work Programme Four).

Below we outline the Project Advisory Group (PAG) and the research that will be conducted within each Work Programme. Details of the ICONS-Quant project have been registered with the Enhancing the Quality and Transparency of Health Research Network, which provides a database of reporting guidelines in development ( http://www.equator-network.org/library/reporting-guidelines-under-development/ #74).

Project Advisory Group

We have established an ICONS-Quant PAG which will provide governance for the project as well as expert advice. The ICONS PAG includes named project collaborators from Cochrane Review Groups (Effective Practice and Organisation of Care, Consumers and Communication, and Tobacco Addiction), a representative with experience of NS from the Campbell Collaboration Methods Group and a user representative from the National Institute for Health and Care Excellence.

Work Programme One: assessment of current reporting and conduct of NS in Cochrane reviews

Previously we investigated current practice in the conduct and reporting of NS in systematic reviews of public health interventions. 7 Work Programme One will extend this exercise to assess use of NS methods and their reporting across all Cochrane Review Groups. We will identify all Cochrane reviews published between April 2016 and April 2017 and screen them to determine the method of synthesis for the primary outcome. Reviews will be included for further examination if the method for reporting the synthesis of the primary outcomes relies on text. We will identify those that use NS or that synthesise studies using text only, whether or not the authors refer to the use of NS or textual methods for synthesis. Reviews will be excluded if they are empty, include only one study, report on diagnostic test accuracy, or are a review of methodology. We will record how the synthesis has been conducted and reported. We will use the existing data extraction template designed for our previous assessment of NS in public health reviews. This template is based on key sources of best practice for NS, 12–15 including the ESRC guidance on the conduct of NS. 5 Questions relate to use of theory; investigation of differences across included studies and reported findings; transparency of links between data and text (including data visualisation tools used); assessment of robustness of the synthesis; and adequacy of description of NS methods. 5 Using a similar format to our review of NS in public health reviews, 16 we will tabulate the extracted data. This will allow description of:

the extent of reporting of NS methods: the amount and type of detail included;

the range of approaches and tools used to narratively synthesise data;

how conceptual and methodological heterogeneity is managed;

review authors’ reflection on robustness of synthesis.

The results of this exercise will be used to inform development of the initial checklist for inclusion in the Delphi survey.

Work Programme Two: Delphi survey

A Delphi consensus survey will be conducted. This is the standard approach to elicit expert opinion for the purposes of developing consensus-based reporting guidelines. 17 18 The results of the assessment exercise in Work Programme One, in conjunction with key texts on NS, 12–15 19 20 findings from the previous assessment of reporting NS in public health reviews 16 and input from the ICONS PAG, will be used to develop the initial items for Round One of the Delphi survey. An expert panel will then be consulted to inform the development of the Delphi survey. The panel will be identified by the project team and members of the ICONS PAG, and will comprise 15–20 authors and methodologists experienced in or familiar with the purpose and conduct of NS. A videoconference with the expert panel will be used to present findings from Work Programme One and a draft of the proposed Delphi survey. Participants’ input will be recorded and used to refine the Delphi survey.

The Delphi online survey will use a questionnaire to achieve consensus on the content and wording of reporting items considered to capture the pertinent details of NS. The online platform will be created by the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, using a web-based platform recently developed for this purpose. The platform facilitates personalised invitations to participate, password-protected logins and personalised reminders, and enables data collation for quantitative and qualitative analyses. There will be two rounds of the survey, with a third version conducted if necessary to gain consensus among participants.

Participants will include members of the ICONS PAG and others experienced in NS. Suitable participants will be identified by the project team, through recommendations from the ICONS PAG and through the data extraction exercise described in Work Programme One. The data extraction exercise will help articulate identified gaps in reporting of methods and findings of NS where transparency is particularly lacking. The identified gaps will be used when drafting reporting item questions for the Delphi consensus exercise, to improve transparency in NS. We will invite a maximum of 100 individuals to participate and they will be recruited via their workplace email address. We will ask for their professional opinion on the content of a draft reporting guideline. The invitation will outline the aim of the Delphi survey, the process involved and the time commitment, and include a participant information sheet. Individuals who accept the invitation will be asked to take part in each round of the survey. It will be clearly stated that at any stage, a respondent can opt out of the Delphi survey. The survey will ask participants to provide details of their job category; no personal information will be collected. Respondents will be asked to use their email address to log in to the survey. This information will be used only to verify the appropriate use of the survey and will not be used in the analysis. The Delphi survey will involve implied consent: it will be made clear to participants that by responding to the survey, they are consenting to participate in the study. It will be explained to respondents that: their responses will not be linked to their identity (deidentified); only researchers will have access to the data; and the data will be stored on a password-encrypted computer and stored and destroyed in accordance with Medical Research Council guidelines.

The Delphi survey will consist of closed and open-ended questions. Round One of the survey will provide an introduction to the project and instructions for the survey. The participants will be invited to rank each of the proposed guideline items on a 4-point Likert scale (essential, desirable, possible, omit, used in previous Delphi surveys for developing reporting guidelines 21 22 ). For each item, the participants will be invited to provide comments. A reminder email will be sent approximately 2 weeks after the initial invitation. Round One will close approximately 4 weeks after the first invitations are issued. Responses to Round One of the Delphi will be exported verbatim into a Microsoft Excel spreadsheet and collated. Responses to the scale rating will be summarised as counts and percentage frequencies. The free-text content will be collated and summarised. The results from both the quantitative and qualitative data collation will be used to inform the development of Round Two of the Delphi and the content of the final guideline checklist. Redrafting of the Delphi survey items will be conducted in discussion with all study group members within 1 month of closure of the round.

All participants from Round One will be invited to take part in Round Two. In Round Two, the proposed checklist items will be presented in three sections:

Items that reached high consensus in Round One and that are expected to be included in the final checklist. These items will have an a priori agreement of >70% approval, as recommended by Diamond et al . 23 Participants will not be asked to rate these items again but will be asked to comment on whether they agree with the inclusion of each item in the checklist and to provide comments if they disagree or with suggestions to clarify the wording of the items.

Items that have been significantly altered or are additional as a result of Round One. The participants will be invited to rate these items on the 4-point scale and provide comments on each item.

Items that were rated as ‘omit’ in Round One and that are not expected to be included in the final checklist. Participants will not be asked to rate these items; they will be invited to provide their opinion on the removal of these items from the final checklist.

If there is a substantial lack of consensus remaining following Round Two of the Delphi, a third round will be prepared and conducted. Round Three will follow the same format as Round Two, providing the reporting guideline items in three sections: items that are expected to be included in the final guideline; those significantly altered; and items that will be removed from the final checklist.

Consensus meeting

An expert panel of individuals experienced in NS methods will be invited to participate in the consensus meeting to finalise the content of the guideline. It is anticipated that this will be held as a face-to-face meeting at the Cochrane colloquium in 2018 in Edinburgh, UK. If this is not possible, an online consensus meeting will be conducted using webinar software. If necessary, an additional virtual meeting will be held to accommodate different time zones of invitees. At the consensus meeting the reporting guideline items developed from the Delphi survey will be discussed, with priority given to establishing consensus on the content and wording of items for which the level of consensus is less clear.

Work Programme Three: enhancement of existing guidance on NS methods

We will produce materials to support the current guidance that includes information on the rationale for, as well as implementation of each stage of NS. This will be developed as a supplement to the reporting guideline items. The enhanced guidance will be accessible to novice reviewers and will provide examples of good practice to illustrate how methods of NS may be used. The findings of Work Programmes One and Two, the assessment of current reporting of NS and the Delphi consensus will be used to inform development of enhanced guidance on NS. 5 Cochrane Review Groups who publish reviews incorporating NS will be identified through the process of Work Programme One. We anticipate that these will include a range of Cochrane Review Groups and examples will be developed which are relevant to all groups. An overview of methodological tools which can be used to support NS and which have been developed since publication of the ESRC guidance in 2006 will also be incorporated. The PAG will be asked for comments on the draft guidance before it is piloted.

Work Programme Four: development of learning materials on implementation of NS

Training materials based on the guidance developed in Work Programmes Two and Three will be produced to promote improved use of NS methods. We have secured support from Cochrane Training to collaborate in Work Programme Four. We will deliver two to three live participatory webinars (to allow for different time zones) to present the agreed guidance developed in Work Programme Two. One webinar will be recorded and provided on a web page, along with a record of the questions raised in the webinar, and any other frequently asked questions that emerge.

In addition, an online training module on NS will be developed in collaboration with Cochrane Training and a specialist e-learning company. The module will include a mix of didactic and participatory teaching methods involving assessment and interpretation of data and syntheses. We will work with Cochrane colleagues to incorporate the reporting items into the MECIR standards, and offer to update the relevant chapters of the Cochrane Handbook.

Ethics and dissemination

Dissemination of the results of this study will be through peer-reviewed publications, and national and international conferences. In addition, the objectives of Work Programmes Three and Four are to distribute and encourage use of the ICONS-Quant guideline through webinars and an online training module.

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Contributors HT conceived the idea of the study. HT, SVK, AS, JEM and MC designed the study methodology. MC prepared the first draft of the protocol manuscript and all authors critically reviewed and approved the final manuscript.

Funding This project was supported by funds provided by the Cochrane Strategic Methods Fund. MC, HT and SVK receive funding from the UK Medical Research Council (MC_UU_12017-13 and MC_UU_12017-15) and the Scottish Government Chief Scientist Office (SPHSU13 and SPHSU15). SVK is supported by an NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02). JEM is supported by a National Health and Medical Research Council (NHMRC) Australian Public Health Fellowship (1072366).

Disclaimer The views expressed in the protocol are those of the authors and not necessarily those of Cochrane or its registered entities, committees or working groups.

Competing interests HT and SVK are Cochrane editors. JEM is a co-convenor of the Cochrane Statistical Methods Group.

Patient consent Not required.

Ethics approval University of Glasgow College of Social Sciences Ethics Committee (reference number400170060)

Provenance and peer review Not commissioned; externally peer reviewed.

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OPINION article

Methods for conducting and publishing narrative research with undergraduates.

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

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

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.

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

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Keywords: narrative research, autobiographical memory, undergraduate, content coding, publishing with undergraduates

Citation: Grysman A and Lodi-Smith J (2019) Methods for Conducting and Publishing Narrative Research With Undergraduates. Front. Psychol . 9:2771. doi: 10.3389/fpsyg.2018.02771

Received: 20 November 2018; Accepted: 24 December 2018; Published: 17 January 2019.

Reviewed by:

Copyright © 2019 Grysman and Lodi-Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Azriel Grysman, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Cochrane Colloquium Abstracts

Narrative synthesis of qualitative and quantitative evidence: an analysis of tools and techniques.

Article type Oral Year 2004 Ottawa Authors Roen K, Rodgers M, Arai L, Petticrew M, Popay J, Roberts H, Sowden A Abstract Background: Statistical approaches to synthesising quantitative evidence are well developed; however these techniques are not always appropriate, either because the quantitative data are not adequate or because the findings to be synthesised are both quantitative and qualitative. Where meta-analysis is considered inappropriate, narrative or qualitative synthesis is usually recommended. Narrative synthesis may also be appropriate for other reasons (ie it is not only a recourse for when meta-analysis cannot be carried out). Narrative synthesis, we believe, is more likely to result in reports which are more accessible to those who might use the findings. Objectives: This presentation will report on methodological work in progress and provide an overview of the kinds of tools and techniques that are available in the literature to guide the narrative synthesis of qualitative and quantitative data. Methods: The work to date involved identifying, selecting and appraising i) methodological texts about reviewing and ii) systematic reviews where narrative synthesis has been used. The aim of this stage of the project is to gather information about the process of narrative synthesis. Results: A range of tools and techniques that have been used in narrative synthesis has been identified. We will present an overview of these tools and techniques and discuss their potential for those undertaking narrative synthesis in the context of systematic reviews. Conclusions: There is increasing demand for reliable research evidence to inform public policies and professional practice. Guidance which enables the production of sound narrative synthesis is likely to contribute to this enterprise. Acknowledgements: This research is funded by the Economic and Social Research Council's (ESRC, U.K.) Methods Research Programme.

Narrative Research

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• Kayi Ntinda 2  

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Narrative research aims to unravel consequential stories of people’s lives as told by them in their own words and worlds. In the context of the health, social sciences, and education, narrative research is both a data gathering and interpretive or analytical framework. It meets these twin goals admirably by having people make sense of their lived health and well-being in their social context as they understand it, including their self-belief-oriented stories. Narrative research falls within the realm of social constructivism or the philosophy that people’s lived stories capture the complexities and nuanced understanding of their significant experiences. This chapter presents a brief overview of the narrative research approaches as forms of inquiry based on storytelling and premised on the truth value of the stories to best represent the teller’s life world. The chapter also discusses data collection, analysis, and presentation utilizing narrative analysis. In doing so, this chapter provides illustrative examples applying narrative-oriented approaches to research in the health and social sciences. The chapter concludes by outlining the importance of narrative research to person-centric investigations in which the teller-informant view matters to the resulting body of knowledge.

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Quantitative Narrative Analysis

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"This is a thought-provoking, timely book because of the increasing interest in narrative techniques and approaches to scholarly management, entrepreneurship and leadership circles. It is a must-read item for serious entrepreneurship, family business and leadership researchers, whether of a quantitative or qualitative volition."

• Statistical analyses show how to examine words using different statistical approaches.
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Narrative Analysis 101

Everything you need to know to get started

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

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

Overview: Narrative Analysis

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

What Is Narrative Analysis?

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

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

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

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

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

The Two Overarching Approaches

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

First up is the inductive approach to narrative analysis.

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

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

Contrasted to this is the deductive approach.

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

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

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

Need a helping hand?

Strengths & Weaknesses

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

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

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

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

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

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

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

When To Use Narrative Analysis

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

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

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

Let’s recap

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

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

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

Psst... there’s more!

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

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Thanks. I need examples of narrative analysis

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

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

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

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

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

Please i need help with my project,

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

please mention the sources as well.

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

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

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

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Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

Table of Contents

Narrative Analysis

Definition:

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

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

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

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

Structural Analysis

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

Discourse Analysis

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

Phenomenological Analysis

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

Critical Analysis

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

Autoethnography

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

Thematic Analysis

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

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

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

Applications of Narrative Analysis

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

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

Examples of Narrative Analysis

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

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

Purpose of Narrative Analysis

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

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

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

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

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

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

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

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

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

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

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

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

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

Thematic analysis

• Thematic analysis vs. content analysis
• Introduction

Types of narrative research

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

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

Narrative analysis in research

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

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

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

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

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

Life history

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

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

Oral history

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

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

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

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

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

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

Designing a research inquiry

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

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

Data collection

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

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

Triangulating data

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

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

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

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

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

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

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

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

Structural analysis

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

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

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

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

Functional analysis

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

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

Dialogic analysis

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

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

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

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

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

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

Subjectivity in narratives

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

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

Connecting narratives to broader phenomena

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

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

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

Turn to ATLAS.ti for your narrative analysis.

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