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Research Methodology: Writing a Scientific Research Proposal

Oct 14, 2014

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Research Methodology: Writing a Scientific Research Proposal. Dean Sherzai MD, MAS Ayesha Z. Sherzai, MD, MAS. Writing a research proposal. Formulating a research problem. Conceptualize research design. Selecting a sample. Collecting and processing data. Introduction. A Quick Glance.

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Research Methodology: Writing a Scientific Research Proposal • Dean Sherzai MD, MAS • Ayesha Z. Sherzai, MD, MAS

Writing a research proposal Formulating a research problem Conceptualize research design Selecting a sample Collecting and processing data Introduction A Quick Glance

A research proposal has three main points: • Explanation of proposed research (what will be done) • Methods and techniques to be employed (how it will be done) • Novelty and importance of the study (why it should be done)

Organization • Title • Abstract • Introduction and Review • Research Hypothesis • Material and Methods • Conclusion and Justification • Bibliography

Title • Provide a specific summary of the proposed work • Minimal words, clear language

Abstract • One paragraph • Description of the hypothesis and the goals of the experiment • Readers can quickly assess the basic premise of your proposal

Introduction • Say it in the first paragraph! Readers can be impatient • “In the proposed study, we seek to examine...”

Review of the literature • Lengthiest part of proposal • Begin with basics, narrow the focus to pertinent proposed work • Use plenty of primary sources of information: Textbooks, journal articles, website (with caution!) • Cite appropriately

Identification of the knowledge gap • State what we do not know from reading the existing fund of knowledge • Justification for starting the project

Research Hypothesis • What is the hypothesis that you are testing? • What are the questions that you seek to answer? • Based on what is known in this field, explain what you expect to see and hope to show through your result

Material and Methods • Dictated by the nature of your research • Describe your data source, process of collection in detail • Describe the statistical analysis to be used, limitations

Conclusion and Justification • Explicitly state how your proposed research will advance knowledge • What are the far-reaching effects? Will your study potentially change practices or policies? Why is it that your research deserves funding?

Bibliography • Include all the resources that were used in the writing of the paper • Follow your guidelines for formatting

Bibliography • Example: • Agosti C, Borroni B, Akkawi NM, Bordonali T, Padovani A. Acute myocardial infarction presenting with transient global amnesia. J Am Geriatr Soc. 2006; 6: 1004. • Jain S, Ton TG, Boudreau RM, et al. The Risk of Parkinson Disease Associated with Urate in a Community-Based Cohort of Older Adults. Neuroepidemiology. 2011; 4: 223-229.

A note on sources, paraphrasing and citations • Terse, clearcut, no artistic enhancement • Avoid quoting directly • Read the article, put it down, write it in your own words • Citations: Author, year

A note on voice • Active voice (“I” or “We”) • Switch between active and passive voice to avoid repetition

Important Points • Organized, well-written, concise, complete proposal = easier to conduct experiment • Good writing when paired with a thorough understanding of the subject matter is a valuable skill to possess

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on September 5, 2024.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Introduction

Literature review.

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
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See an example

Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
• Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. & George, T. (2024, September 05). How to Write a Research Proposal | Examples & Templates. Scribbr. Retrieved October 11, 2024, from https://www.scribbr.com/research-process/research-proposal/

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Other students also liked, how to write a problem statement | guide & examples, writing strong research questions | criteria & examples, how to write a literature review | guide, examples, & templates, what is your plagiarism score.

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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Lecture Notes on Research Methodology

Published by Eileen Garrison Modified over 6 years ago

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• VIVA Grant Recipients
• Vgr-social-work-research

Education Standards

Radford university.

Learning Domain: Social Work

Standard: Basic Research Methodology

Lesson 10: Sampling in Qualitative Research

Lesson 11: qualitative measurement & rigor, lesson 12: qualitative design & data gathering, lesson 1: introduction to research, lesson 2: getting started with your research project, lesson 3: critical information literacy, lesson 4: paradigm, theory, and causality, lesson 5: research questions, lesson 6: ethics, lesson 7: measurement in quantitative research, lesson 8: sampling in quantitative research, lesson 9: quantitative research designs, powerpoint slides: sowk 621.01: research i: basic research methodology.

The twelve lessons for SOWK 621.01: Research I: Basic Research Methodology as previously taught by Dr. Matthew DeCarlo at Radford University. Dr. DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant.

The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at Radford University. 

Version History

Chapter 20. Presentations

Introduction.

If a tree falls in a forest, and no one is around to hear it, does it make a sound? If a qualitative study is conducted, but it is not presented (in words or text), did it really happen? Perhaps not. Findings from qualitative research are inextricably tied up with the way those findings are presented. These presentations do not always need to be in writing, but they need to happen. Think of ethnographies, for example, and their thick descriptions of a particular culture. Witnessing a culture, taking fieldnotes, talking to people—none of those things in and of themselves convey the culture. Or think about an interview-based phenomenological study. Boxes of interview transcripts might be interesting to read through, but they are not a completed study without the intervention of hours of analysis and careful selection of exemplary quotes to illustrate key themes and final arguments and theories. And unlike much quantitative research in the social sciences, where the final write-up neatly reports the results of analyses, the way the “write-up” happens is an integral part of the analysis in qualitative research. Once again, we come back to the messiness and stubborn unlinearity of qualitative research. From the very beginning, when designing the study, imagining the form of its ultimate presentation is helpful.

Because qualitative researchers are motivated by understanding and conveying meaning, effective communication is not only an essential skill but a fundamental facet of the entire research project. Ethnographers must be able to convey a certain sense of verisimilitude, the appearance of true reality. Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them. And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important. This chapter will address how to organize various kinds of presentations for different audiences so that your results can be appreciated and understood.

In the world of academic science, social or otherwise, the primary audience for a study’s results is usually the academic community, and the primary venue for communicating to this audience is the academic journal. Journal articles are typically fifteen to thirty pages in length (8,000 to 12,000 words). Although qualitative researchers often write and publish journal articles—indeed, there are several journals dedicated entirely to qualitative research [1] —the best writing by qualitative researchers often shows up in books. This is because books, running from 80,000 to 150,000 words in length, allow the researcher to develop the material fully. You have probably read some of these in various courses you have taken, not realizing what they are. I have used examples of such books throughout this text, beginning with the three profiles in the introductory chapter. In some instances, the chapters in these books began as articles in academic journals (another indication that the journal article format somewhat limits what can be said about the study overall).

While the article and the book are “final” products of qualitative research, there are actually a few other presentation formats that are used along the way. At the very beginning of a research study, it is often important to have a written research proposal not just to clarify to yourself what you will be doing and when but also to justify your research to an outside agency, such as an institutional review board (IRB; see chapter 12), or to a potential funder, which might be your home institution, a government funder (such as the National Science Foundation, or NSF), or a private foundation (such as the Gates Foundation). As you get your research underway, opportunities will arise to present preliminary findings to audiences, usually through presentations at academic conferences. These presentations can provide important feedback as you complete your analyses. Finally, if you are completing a degree and looking to find an academic job, you will be asked to provide a “job talk,” usually about your research. These job talks are similar to conference presentations but can run significantly longer.

All the presentations mentioned so far are (mostly) for academic audiences. But qualitative research is also unique in that many of its practitioners don’t want to confine their presentation only to other academics. Qualitative researchers who study particular contexts or cultures might want to report back to the people and places they observed. Those working in the critical tradition might want to raise awareness of a particular issue to as large an audience as possible. Many others simply want everyday, nonacademic people to read their work, because they think it is interesting and important. To reach a wide audience, the final product can look like almost anything—it can be a poem, a blog, a podcast, even a science fiction short story. And if you are very lucky, it can even be a national or international bestseller.

In this chapter, we are going to stick with the more basic quotidian presentations—the academic paper / research proposal, the conference slideshow presentation / job talk, and the conference poster. We’ll also spend a bit of time on incorporating universal design into your presentations and how to create some especially attractive and impactful visual displays.

Researcher Note

What is the best piece of advice you’ve ever been given about conducting qualitative research?

The best advice I’ve received came from my adviser, Alford Young Jr. He told me to find the “Jessi Streib” answer to my research question, not the “Pierre Bourdieu” answer to my research question. In other words, don’t just say how a famous theorist would answer your question; say something original, something coming from you.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Writing about Your Research

The journal article and the research proposal.

Although the research proposal is written before you have actually done your research and the article is written after all data collection and analysis is complete, there are actually many similarities between the two in terms of organization and purpose. The final article will (probably—depends on how much the research question and focus have shifted during the research itself) incorporate a great deal of what was included in a preliminary research proposal. The average lengths of both a proposal and an article are quite similar, with the “front sections” of the article abbreviated to make space for the findings, discussion of findings, and conclusion.

Figure 20.1 shows one model for what to include in an article or research proposal, comparing the elements of each with a default word count for each section. Please note that you will want to follow whatever specific guidelines you have been provided by the venue you are submitting the article/proposal to: the IRB, the NSF, the Journal of Qualitative Research . In fact, I encourage you to adapt the default model as needed by swapping out expected word counts for each section and adding or varying the sections to match expectations for your particular publication venue. [2]

You will notice a few things about the default model guidelines. First, while half of the proposal is spent discussing the research design, this section is shortened (but still included) for the article. There are a few elements that only show up in the proposal (e.g., the limitations section is in the introductory section here—it will be more fully developed in the conclusory section in the article). Obviously, you don’t have findings in the proposal, so this is an entirely new section for the article. Note that the article does not include a data management plan or a timeline—two aspects that most proposals require.

It might be helpful to find and maintain examples of successfully written sections that you can use as models for your own writing. I have included a few of these throughout the textbook and have included a few more at the end of this chapter.

Make an Argument

Some qualitative researchers, particularly those engaged in deep ethnographic research, focus their attention primarily if not exclusively on describing the data. They might even eschew the notion that they should make an “argument” about the data, preferring instead to use thick descriptions to convey interpretations. Bracketing the contrast between interpretation and argument for the moment, most readers will expect you to provide an argument about your data, and this argument will be in answer to whatever research question you eventually articulate (remember, research questions are allowed to shift as you get further into data collection and analysis). It can be frustrating to read a well-developed study with clear and elegant descriptions and no argument. The argument is the point of the research, and if you do not have one, 99 percent of the time, you are not finished with your analysis. Calarco ( 2020 ) suggests you imagine a pyramid, with all of your data forming the basis and all of your findings forming the middle section; the top/point of the pyramid is your argument, “what the patterns in your data tell us about how the world works or ought to work” ( 181 ).

The academic community to which you belong will be looking for an argument that relates to or develops theory. This is the theoretical generalizability promise of qualitative research. An academic audience will want to know how your findings relate to previous findings, theories, and concepts (the literature review; see chapter 9). It is thus vitally important that you go back to your literature review (or develop a new one) and draw those connections in your discussion and/or conclusion. When writing to other audiences, you will still want an argument, although it may not be written as a theoretical one. What do I mean by that? Even if you are not referring to previous literature or developing new theories or adapting older ones, a simple description of your findings is like dumping a lot of leaves in the lap of your audience. They still deserve to know about the shape of the forest. Maybe provide them a road map through it. Do this by telling a clear and cogent story about the data. What is the primary theme, and why is it important? What is the point of your research? [3]

A beautifully written piece of research based on participant observation [and/or] interviews brings people to life, and helps the reader understand the challenges people face. You are trying to use vivid, detailed and compelling words to help the reader really understand the lives of the people you studied. And you are trying to connect the lived experiences of these people to a broader conceptual point—so that the reader can understand why it matters. ( Lareau 2021:259 )

Do not hide your argument. Make it the focal point of your introductory section, and repeat it as often as needed to ensure the reader remembers it. I am always impressed when I see researchers do this well (see, e.g., Zelizer 1996 ).

Here are a few other suggestions for writing your article: Be brief. Do not overwhelm the reader with too many words; make every word count. Academics are particularly prone to “overwriting” as a way of demonstrating proficiency. Don’t. When writing your methods section, think about it as a “recipe for your work” that allows other researchers to replicate if they so wish ( Calarco 2020:186 ). Convey all the necessary information clearly, succinctly, and accurately. No more, no less. [4] Do not try to write from “beginning to end” in that order. Certain sections, like the introductory section, may be the last ones you write. I find the methods section the easiest, so I often begin there. Calarco ( 2020 ) begins with an outline of the analysis and results section and then works backward from there to outline the contribution she is making, then the full introduction that serves as a road map for the writing of all sections. She leaves the abstract for the very end. Find what order best works for you.

Presenting at Conferences and Job Talks

Students and faculty are primarily called upon to publicly present their research in two distinct contexts—the academic conference and the “job talk.” By convention, conference presentations usually run about fifteen minutes and, at least in sociology and other social sciences, rely primarily on the use of a slideshow (PowerPoint Presentation or PPT) presentation. You are usually one of three or four presenters scheduled on the same “panel,” so it is an important point of etiquette to ensure that your presentation falls within the allotted time and does not crowd into that of the other presenters. Job talks, on the other hand, conventionally require a forty- to forty-five-minute presentation with a fifteen- to twenty-minute question and answer (Q&A) session following it. You are the only person presenting, so if you run over your allotted time, it means less time for the Q&A, which can disturb some audience members who have been waiting for a chance to ask you something. It is sometimes possible to incorporate questions during your presentation, which allows you to take the entire hour, but you might end up shorting your presentation this way if the questions are numerous. It’s best for beginners to stick to the “ask me at the end” format (unless there is a simple clarifying question that can easily be addressed and makes the presentation run more smoothly, as in the case where you simply forgot to include information on the number of interviews you conducted).

For slideshows, you should allot two or even three minutes for each slide, never less than one minute. And those slides should be clear, concise, and limited. Most of what you say should not be on those slides at all. The slides are simply the main points or a clear image of what you are speaking about. Include bulleted points (words, short phrases), not full sentences. The exception is illustrative quotations from transcripts or fieldnotes. In those cases, keep to one illustrative quote per slide, and if it is long, bold or otherwise, highlight the words or passages that are most important for the audience to notice. [5]

Figure 20.2 provides a possible model for sections to include in either a conference presentation or a job talk, with approximate times and approximate numbers of slides. Note the importance (in amount of time spent) of both the research design and the findings/results sections, both of which have been helpfully starred for you. Although you don’t want to short any of the sections, these two sections are the heart of your presentation.

Fig 20.2. Suggested Slideshow Times and Number of Slides

Should you write out your script to read along with your presentation? I have seen this work well, as it prevents presenters from straying off topic and keeps them to the time allotted. On the other hand, these presentations can seem stiff and wooden. Personally, although I have a general script in advance, I like to speak a little more informally and engagingly with each slide, sometimes making connections with previous panelists if I am at a conference. This means I have to pay attention to the time, and I sometimes end up breezing through one section more quickly than I would like. Whatever approach you take, practice in advance. Many times. With an audience. Ask for feedback, and pay attention to any presentation issues that arise (e.g., Do you speak too fast? Are you hard to hear? Do you stumble over a particular word or name?).

Even though there are rules and guidelines for what to include, you will still want to make your presentation as engaging as possible in the little amount of time you have. Calarco ( 2020:274 ) recommends trying one of three story structures to frame your presentation: (1) the uncertain explanation , where you introduce a phenomenon that has not yet been fully explained and then describe how your research is tackling this; (2) the uncertain outcome , where you introduce a phenomenon where the consequences have been unclear and then you reveal those consequences with your research; and (3) the evocative example , where you start with some interesting example from your research (a quote from the interview transcripts, for example) or the real world and then explain how that example illustrates the larger patterns you found in your research. Notice that each of these is a framing story. Framing stories are essential regardless of format!

A Word on Universal Design

Please consider accessibility issues during your presentation, and incorporate elements of universal design into your slideshow. The basic idea behind universal design in presentations is that to the greatest extent possible, all people should be able to view, hear, or otherwise take in your presentation without needing special individual adaptations. If you can make your presentation accessible to people with visual impairment or hearing loss, why not do so? For example, one in twelve men is color-blind, unable to differentiate between certain colors, red/green being the most common problem. So if you design a graphic that relies on red and green bars, some of your audience members may not be able to properly identify which bar means what. Simple contrasts of black and white are much more likely to be visible to all members of your audience. There are many other elements of good universal design, but the basic foundation of all of them is that you consider how to make your presentation as accessible as possible at the outset. For example, include captions whenever possible, both as descriptions on slides and as images on slides and for any audio or video clips you are including; keep font sizes large enough to read from the back of the room; and face the audience when you are.

Poster Design

Undergraduate students who present at conferences are often encouraged to present at “poster sessions.” This usually means setting up a poster version of your research in a large hall or convention space at a set period of time—ninety minutes is common. Your poster will be one of dozens, and conference-goers will wander through the space, stopping intermittently at posters that attract them. Those who stop by might ask you questions about your research, and you are expected to be able to talk intelligently for two or three minutes. It’s a fairly easy way to practice presenting at conferences, which is why so many organizations hold these special poster sessions.

A good poster design will be immediately attractive to passersby and clearly and succinctly describe your research methods, findings, and conclusions. Some students have simply shrunk down their research papers to manageable sizes and then pasted them on a poster, all twelve to fifteen pages of them. Don’t do that! Here are some better suggestions: State the main conclusion of your research in large bold print at the top of your poster, on brightly colored (contrasting) paper, and paste in a QR code that links to your full paper online ( Calarco 2020:280 ). Use the rest of the poster board to provide a couple of highlights and details of the study. For an interview-based study, for example, you will want to put in some details about your sample (including number of interviews) and setting and then perhaps one or two key quotes, also distinguished by contrasting color background.

Incorporating Visual Design in Your Presentations

In addition to ensuring that your presentation is accessible to as large an audience as possible, you also want to think about how to display your data in general, particularly how to use charts and graphs and figures. [6] The first piece of advice is, use them! As the saying goes, a picture is worth a thousand words. If you can cut to the chase with a visually stunning display, do so. But there are visual displays that are stunning, and then there are the tired, hard-to-see visual displays that predominate at conferences. You can do better than most presenters by simply paying attention here and committing yourself to a good design. As with model section passages, keep a file of visual displays that work as models for your own presentations. Find a good guidebook to presenting data effectively (Evergreen 2018 , 2019 ; Schwabisch 2021) , and refer to it often.

Let me make a few suggestions here to get you started. First, test every visual display on a friend or colleague to find out how quickly they can understand the point you are trying to convey. As with reading passages aloud to ensure that your writing works, showing someone your display is the quickest way to find out if it works. Second, put the point in the title of the display! When writing for an academic journal, there will be specific conventions of what to include in the title (full description including methods of analysis, sample, dates), but in a public presentation, there are no limiting rules. So you are free to write as your title “Working-Class College Students Are Three Times as Likely as Their Peers to Drop Out of College,” if that is the point of the graphic display. It certainly helps the communicative aspect. Third, use the themes available to you in Excel for creating graphic displays, but alter them to better fit your needs . Consider adding dark borders to bars and columns, for example, so that they appear crisper for your audience. Include data callouts and labels, and enlarge them so they are clearly visible. When duplicative or otherwise unnecessary, drop distracting gridlines and labels on the y-axis (the vertical one). Don’t go crazy adding different fonts, however—keep things simple and clear. Sans serif fonts (those without the little hooks on the ends of letters) read better from a distance. Try to use the same color scheme throughout, even if this means manually changing the colors of bars and columns. For example, when reporting on working-class college students, I use blue bars, while I reserve green bars for wealthy students and yellow bars for students in the middle. I repeat these colors throughout my presentations and incorporate different colors when talking about other items or factors. You can also try using simple grayscale throughout, with pops of color to indicate a bar or column or line that is of the most interest. These are just some suggestions. The point is to take presentation seriously and to pay attention to visual displays you are using to ensure they effectively communicate what you want them to communicate. I’ve included a data visualization checklist from Evergreen ( 2018 ) here.

Ethics of Presentation and Reliability

Until now, all the data you have collected have been yours alone. Once you present the data, however, you are sharing sometimes very intimate information about people with a broader public. You will find yourself balancing between protecting the privacy of those you’ve interviewed and observed and needing to demonstrate the reliability of the study. The more information you provide to your audience, the more they can understand and appreciate what you have found, but this also may pose risks to your participants. There is no one correct way to go about finding the right balance. As always, you have a duty to consider what you are doing and must make some hard decisions.

The most obvious place we see this paradox emerge is when you mask your data to protect the privacy of your participants. It is standard practice to provide pseudonyms, for example. It is such standard practice that you should always assume you are being given a pseudonym when reading a book or article based on qualitative research. When I was a graduate student, I tried to find information on how best to construct pseudonyms but found little guidance. There are some ethical issues here, I think. [7] Do you create a name that has the same kind of resonance as the original name? If the person goes by a nickname, should you use a nickname as a pseudonym? What about names that are ethnically marked (as in, almost all of them)? Is there something unethical about reracializing a person? (Yes!) In her study of adolescent subcultures, Wilkins ( 2008 ) noted, “Because many of the goths used creative, alternative names rather than their given names, I did my best to reproduce the spirit of their chosen names” ( 24 ).

Your reader or audience will want to know all the details about your participants so that they can gauge both your credibility and the reliability of your findings. But how many details are too many? What if you change the name but otherwise retain all the personal pieces of information about where they grew up, and how old they were when they got married, and how many children they have, and whether they made a splash in the news cycle that time they were stalked by their ex-boyfriend? At some point, those details are going to tip over into the zone of potential unmasking. When you are doing research at one particular field site that may be easily ascertained (as when you interview college students, probably at the institution at which you are a student yourself), it is even more important to be wary of providing too many details. You also need to think that your participants might read what you have written, know things about the site or the population from which you drew your interviews, and figure out whom you are talking about. This can all get very messy if you don’t do more than simply pseudonymize the people you interviewed or observed.

There are some ways to do this. One, you can design a study with all of these risks in mind. That might mean choosing to conduct interviews or observations at multiple sites so that no one person can be easily identified. Another is to alter some basic details about your participants to protect their identity or to refuse to provide all the information when selecting quotes . Let’s say you have an interviewee named “Anna” (a pseudonym), and she is a twenty-four-year-old Latina studying to be an engineer. You want to use a quote from Anna about racial discrimination in her graduate program. Instead of attributing the quote to Anna (whom your reader knows, because you’ve already told them, is a twenty-four-year-old Latina studying engineering), you might simply attribute the quote to “Latina student in STEM.” Taking this a step further, you might leave the quote unattributed, providing a list of quotes about racial discrimination by “various students.”

The problem with masking all the identifiers, of course, is that you lose some of the analytical heft of those attributes. If it mattered that Anna was twenty-four (not thirty-four) and that she was a Latina and that she was studying engineering, taking out any of those aspects of her identity might weaken your analysis. This is one of those “hard choices” you will be called on to make! A rather radical and controversial solution to this dilemma is to create composite characters , characters based on the reality of the interviews but fully masked because they are not identifiable with any one person. My students are often very queasy about this when I explain it to them. The more positivistic your approach and the more you see individuals rather than social relationships/structure as the “object” of your study, the more employing composites will seem like a really bad idea. But composites “allow researchers to present complex, situated accounts from individuals” without disclosing personal identities ( Willis 2019 ), and they can be effective ways of presenting theory narratively ( Hurst 2019 ). Ironically, composites permit you more latitude when including “dirty laundry” or stories that could harm individuals if their identities became known. Rather than squeezing out details that could identify a participant, the identities are permanently removed from the details. Great difficulty remains, however, in clearly explaining the theoretical use of composites to your audience and providing sufficient information on the reliability of the underlying data.

There are a host of other ethical issues that emerge as you write and present your data. This is where being reflective throughout the process will help. How and what you share of what you have learned will depend on the social relationships you have built, the audiences you are writing or speaking to, and the underlying animating goals of your study. Be conscious about all of your decisions, and then be able to explain them fully, both to yourself and to those who ask.

Our research is often close to us. As a Black woman who is a first-generation college student and a professional with a poverty/working-class origin, each of these pieces of my identity creates nuances in how I engage in my research, including how I share it out. Because of this, it’s important for us to have people in our lives who we trust who can help us, particularly, when we are trying to share our findings. As researchers, we have been steeped in our work, so we know all the details and nuances. Sometimes we take this for granted, and we might not have shared those nuances in conversation or writing or taken some of this information for granted. As I share my research with trusted friends and colleagues, I pay attention to the questions they ask me or the feedback they give when we talk or when they read drafts.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

Final Comments: Preparing for Being Challenged

Once you put your work out there, you must be ready to be challenged. Science is a collective enterprise and depends on a healthy give and take among researchers. This can be both novel and difficult as you get started, but the more you understand the importance of these challenges, the easier it will be to develop the kind of thick skin necessary for success in academia. Scientists’ authority rests on both the inherent strength of their findings and their ability to convince other scientists of the reliability and validity and value of those findings. So be prepared to be challenged, and recognize this as simply another important aspect of conducting research!

Considering what challenges might be made as you design and conduct your study will help you when you get to the writing and presentation stage. Address probable challenges in your final article, and have a planned response to probable questions in a conference presentation or job talk. The following is a list of common challenges of qualitative research and how you might best address them:

• Questions about generalizability . Although qualitative research is not statistically generalizable (and be prepared to explain why), qualitative research is theoretically generalizable. Discuss why your findings here might tell us something about related phenomena or contexts.
• Questions about reliability . You probably took steps to ensure the reliability of your findings. Discuss them! This includes explaining the use and value of multiple data sources and defending your sampling and case selections. It also means being transparent about your own position as researcher and explaining steps you took to ensure that what you were seeing was really there.
• Questions about replicability. Although qualitative research cannot strictly be replicated because the circumstances and contexts will necessarily be different (if only because the point in time is different), you should be able to provide as much detail as possible about how the study was conducted so that another researcher could attempt to confirm or disconfirm your findings. Also, be very clear about the limitations of your study, as this allows other researchers insight into what future research might be warranted.

None of this is easy, of course. Writing beautifully and presenting clearly and cogently require skill and practice. If you take anything from this chapter, it is to remember that presentation is an important and essential part of the research process and to allocate time for this as you plan your research.

Data Visualization Checklist for Slideshow (PPT) Presentations

Adapted from Evergreen ( 2018 )

Text checklist

• Short catchy, descriptive titles (e.g., “Working-class students are three times as likely to drop out of college”) summarize the point of the visual display
• Subtitled and annotations provide additional information (e.g., “note: male students also more likely to drop out”)
• Text size is hierarchical and readable (titles are largest; axes labels smallest, which should be at least 20points)
• Text is horizontal. Audience members cannot read vertical text!
• All data labeled directly and clearly: get rid of those “legends” and embed the data in your graphic display
• Labels are used sparingly; avoid redundancy (e.g., do not include both a number axis and a number label)

Arrangement checklist

• Proportions are accurate; bar charts should always start at zero; don’t mislead the audience!
• Data are intentionally ordered (e.g., by frequency counts). Do not leave ragged alphabetized bar graphs!
• Axis intervals are equidistant: spaces between axis intervals should be the same unit
• Graph is two-dimensional. Three-dimensional and “bevelled” displays are confusing
• There is no unwanted decoration (especially the kind that comes automatically through the PPT “theme”). This wastes your space and confuses.

Color checklist

• There is an intentional color scheme (do not use default theme)
• Color is used to identify key patterns (e.g., highlight one bar in red against six others in greyscale if this is the bar you want the audience to notice)
• Color is still legible when printed in black and white
• Color is legible for people with color blindness (do not use red/green or yellow/blue combinations)
• There is sufficient contrast between text and background (black text on white background works best; be careful of white on dark!)

Lines checklist

• Be wary of using gridlines; if you do, mute them (grey, not black)
• Allow graph to bleed into surroundings (don’t use border lines)
• Remove axis lines unless absolutely necessary (better to label directly)

Overall design checklist

• The display highlights a significant finding or conclusion that your audience can ‘”see” relatively quickly
• The type of graph (e.g., bar chart, pie chart, line graph) is appropriate for the data. Avoid pie charts with more than three slices!
• Graph has appropriate level of precision; if you don’t need decimal places
• All the chart elements work together to reinforce the main message

Universal Design Checklist for Slideshow (PPT) Presentations

• Include both verbal and written descriptions (e.g., captions on slides); consider providing a hand-out to accompany the presentation
• Microphone available (ask audience in back if they can clearly hear)
• Face audience; allow people to read your lips
• Turn on captions when presenting audio or video clips
• Adjust light settings for visibility
• Speak slowly and clearly; practice articulation; don’t mutter or speak under your breath (even if you have something humorous to say – say it loud!)
• Use Black/White contrasts for easy visibility; or use color contrasts that are real contrasts (do not rely on people being able to differentiate red from green, for example)
• Use easy to read font styles and avoid too small font sizes: think about what an audience member in the back row will be able to see and read.
• Keep your slides simple: do not overclutter them; if you are including quotes from your interviews, take short evocative snippets only, and bold key words and passages. You should also read aloud each passage, preferably with feeling!

Supplement: Models of Written Sections for Future Reference

Data collection section example.

Interviews were semi structured, lasted between one and three hours, and took place at a location chosen by the interviewee. Discussions centered on four general topics: (1) knowledge of their parent’s immigration experiences; (2) relationship with their parents; (3) understanding of family labor, including language-brokering experiences; and (4) experiences with school and peers, including any future life plans. While conducting interviews, I paid close attention to respondents’ nonverbal cues, as well as their use of metaphors and jokes. I conducted interviews until I reached a point of saturation, as indicated by encountering repeated themes in new interviews (Glaser and Strauss 1967). Interviews were audio recorded, transcribed with each interviewee’s permission, and conducted in accordance with IRB protocols. Minors received permission from their parents before participation in the interview. ( Kwon 2022:1832 )

Justification of Case Selection / Sample Description Section Example

Looking at one profession within one organization and in one geographic area does impose limitations on the generalizability of our findings. However, it also has advantages. We eliminate the problem of interorganizational heterogeneity. If multiple organizations are studied simultaneously, it can make it difficult to discern the mechanisms that contribute to racial inequalities. Even with a single occupation there is considerable heterogeneity, which may make understanding how organizational structure impacts worker outcomes difficult. By using the case of one group of professionals in one religious denomination in one geographic region of the United States, we clarify how individuals’ perceptions and experiences of occupational inequality unfold in relation to a variety of observed and unobserved occupational and contextual factors that might be obscured in a larger-scale study. Focusing on a specific group of professionals allows us to explore and identify ways that formal organizational rules combine with informal processes to contribute to the persistence of racial inequality. ( Eagle and Mueller 2022:1510–1511 )

Ethics Section Example

I asked everyone who was willing to sit for a formal interview to speak only for themselves and offered each of them a prepaid Visa Card worth $25–40. I also offered everyone the opportunity to keep the card and erase the tape completely at any time they were dissatisfied with the interview in any way. No one asked for the tape to be erased; rather, people remarked on the interview being a really good experience because they felt heard. Each interview was professionally transcribed and for the most part the excerpts are literal transcriptions. In a few places, the excerpts have been edited to reduce colloquial features of speech (e.g., you know, like, um) and some recursive elements common to spoken language. A few excerpts were placed into standard English for clarity. I made this choice for the benefit of readers who might otherwise find the insights and ideas harder to parse in the original. However, I have to acknowledge this as an act of class-based violence. I tried to keep the original phrasing whenever possible. ( Pascale 2021:235 )

Further Readings

Calarco, Jessica McCrory. 2020. A Field Guide to Grad School: Uncovering the Hidden Curriculum . Princeton, NJ: Princeton University Press. Don’t let the unassuming title mislead you—there is a wealth of helpful information on writing and presenting data included here in a highly accessible manner. Every graduate student should have a copy of this book.

Edwards, Mark. 2012. Writing in Sociology . Thousand Oaks, CA: SAGE. An excellent guide to writing and presenting sociological research by an Oregon State University professor. Geared toward undergraduates and useful for writing about either quantitative or qualitative research or both.

Evergreen, Stephanie D. H. 2018. Presenting Data Effectively: Communicating Your Findings for Maximum Impact . Thousand Oaks, CA: SAGE. This is one of my very favorite books, and I recommend it highly for everyone who wants their presentations and publications to communicate more effectively than the boring black-and-white, ragged-edge tables and figures academics are used to seeing.

Evergreen, Stephanie D. H. 2019. Effective Data Visualization 2 . Thousand Oaks, CA: SAGE. This is an advanced primer for presenting clean and clear data using graphs, tables, color, font, and so on. Start with Evergreen (2018), and if you graduate from that text, move on to this one.

Schwabisch, Jonathan. 2021. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks . New York: Columbia University Press. Where Evergreen’s (2018, 2019) focus is on how to make the best visual displays possible for effective communication, this book is specifically geared toward visual displays of academic data, both quantitative and qualitative. If you want to know when it is appropriate to use a pie chart instead of a stacked bar chart, this is the reference to use.

• Some examples: Qualitative Inquiry , Qualitative Research , American Journal of Qualitative Research , Ethnography , Journal of Ethnographic and Qualitative Research , Qualitative Report , Qualitative Sociology , and Qualitative Studies . ↵
• This is something I do with every article I write: using Excel, I write each element of the expected article in a separate row, with one column for “expected word count” and another column for “actual word count.” I fill in the actual word count as I write. I add a third column for “comments to myself”—how things are progressing, what I still need to do, and so on. I then use the “sum” function below each of the first two columns to keep a running count of my progress relative to the final word count. ↵
• And this is true, I would argue, even when your primary goal is to leave space for the voices of those who don’t usually get a chance to be part of the conversation. You will still want to put those voices in some kind of choir, with a clear direction (song) to be sung. The worst thing you can do is overwhelm your audience with random quotes or long passages with no key to understanding them. Yes, a lot of metaphors—qualitative researchers love metaphors! ↵
• To take Calarco’s recipe analogy further, do not write like those food bloggers who spend more time discussing the color of their kitchen or the experiences they had at the market than they do the actual cooking; similarly, do not write recipes that omit crucial details like the amount of flour or the size of the baking pan used or the temperature of the oven. ↵
• The exception is the “compare and contrast” of two or more quotes, but use caution here. None of the quotes should be very long at all (a sentence or two each). ↵
• Although this section is geared toward presentations, many of the suggestions could also be useful when writing about your data. Don’t be afraid to use charts and graphs and figures when writing your proposal, article, thesis, or dissertation. At the very least, you should incorporate a tabular display of the participants, sites, or documents used. ↵
• I was so puzzled by these kinds of questions that I wrote one of my very first articles on it ( Hurst 2008 ). ↵

The visual presentation of data or information through graphics such as charts, graphs, plots, infographics, maps, and animation.  Recall the best documentary you ever viewed, and there were probably excellent examples of good data visualization there (for me, this was An Inconvenient Truth , Al Gore’s film about climate change).  Good data visualization allows more effective communication of findings of research, particularly in public presentations (e.g., slideshows).

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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This article explains and illustrates an approach to the design of qualitative inquiry in education using a conceptual methodology informed by a realist ontology. It is written with research novices in mind, based on two observations we have made while supervising postgraduate students. The first is that the methodology literature education students often engage with tends to focus on interpretivist-constructivist approaches. The second observation is that it can be challenging for students to find appealing and accessible material which employs realist informed methodology. To counter this we offer a general introduction to undertaking educational research informed by a realist ontology drawing on a simplified account of critical realism (CR). We illustrate the key concepts of this approach using examples from postgraduate studies in education. It is our hope that this article may stimulate wider discussion about the possibilities, development, and use of realist approaches.

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Introduction

This article explains and illustrates the design of qualitative inquiry in education using a conceptual methodology informed by a realist ontology. We believe that realism is useful for analysing social problems because of its focus on explanation and causal analysis. These two key aspects can be especially useful in education research where they can offer a way to tease out the different components of a complex social phenomenon and offer an explanation of causal relationships. Being able to identify causal mechanisms creates opportunities to offer solutions.

To begin, we note that when we use the term realism, we are not referring to the traditional empirical realist or positivist stance. Instead we use realism as an umbrella term for approaches that are influenced by the critical realism associated with Bhaskar ( [1975]/2008 ), [1979]/2015 ).While acknowledging that there is debate both within and between different realist approaches Footnote 1 (Wheelahan, 2023 ), the aim of this article is not to engage with these variants but instead to offer a more general introduction to undertaking educational research informed by a realist ontology, focusing on the design of what we term a conceptual methodology. To this end we draw on a simplified account of critical realism (Danermark et al., 2002 ; Fletcher, 2017 ; Maxwell, 2012 ; Sayer, 2000 )

We write particularly with postgraduate students in mind, having observed the frustration some students experience when they are unable to find a research approach that fits with the issue they are interested in. Previously we have argued that realism is not widely acknowledged or used in educational research (McPhail & Lourie, 2017 ) and this continues to be the case. The research methodology literature that students often engage with tends to focus on interpretivist-constructivist approaches (McPhail & Lourie, 2017 ) and it can be challenging for students to find appealing and accessible material which employs realist informed methodology (Ackroyd & Karlsson, 2014 ). While there is a large body of literature which explores the philosophical underpinnings of realism, there are fewer examples of realist informed social science research which give a detailed description of the research methodology employed (Fletcher, 2017 ). This article aims to contribute to a body of accessible literature by focusing on the way a realist conceptual methodology can be designed and developed.

The article begins with a brief explanation of the ontological and epistemological basis of realism and highlights the usefulness of employing realism in educational research. This is followed by a discussion about the way disciplinary concepts are understood within realism and how they are put to work to create a conceptual methodology. The latter part of the article draws on Fletcher’s ( 2017 ) work to illustrate and explain the design of conceptual methodology and includes examples from postgraduate research studies informed by realism that were undertaken in New Zealand and that we are familiar with.

The meaning of realism as we use it here can be encapsulated by three propositions. The first is that the real world exists independently of our knowledge of it; second, our knowledge of the real world is fallible; and third, there are grounds for judging some accounts as better or more ‘truthful’ than others (Bhaskar, [1975]/2008 ). The first of these propositions establishes the ontological basis of realism. Ontological realism is the view that there is a mind-independent reality that can be known. That is, “entities exist independently of being perceived, or independently of our theories about them” (Phillips, 1987 , p. 205). While this may be a straightforward proposition when considering the physical and natural world, that is, we can accept the existence of a tree whether we perceive that tree or what our theories about trees are, it is a more difficult proposition to understand when we consider social phenomena. Social realists accept that societies, capitalism, nations, class, and conspiracy theories also exist regardless of whether we believe they exist (Wheelahan, 2023 ). In the case of social phenomena, reality “refers to whatever it is in the universe (i.e., forces, structures and so on) that causes the phenomena we perceive with our senses” (Schwandt, 1997 , p. 133).

Realism distinguishes clearly between our knowledge about the world and the world to which our theories and concepts refer (critical realists refer to these worlds as the transitive and intransitive dimension respectively). Humans produce transitive knowledge about intransitive phenomena. In the case of social science, intransitive phenomena include social processes and structures that we are unable to know directly, although we can perceive the effects of the phenomena. For example, we cannot directly know racism as a social phenomenon with our senses but we can see the effects of racism in the words and actions of people either as individuals or as members of groups or institutions. Importantly, in contrast to strong social constructionism, the phenomena to which our concepts and theories refer is neither a product of, nor constituted by, our theories about it (Carter & New, 2004 ).

The second of the three propositions acknowledges that our knowledge of the real is fallible. This is one of the markers that separates realism from positivism. Perhaps ironically, it is the “evident fallibility of our knowledge– the experience of getting things wrong, of having our expectations confounded, and of crashing into things– that justifies us in believing that the world exists regardless of what we happen to think about it” (Sayer, 2000 , p. 2). Most importantly, our knowledge of the world is concept-dependent, that is, we use concepts to make sense of the world. These concepts in part, shape what we see and how we understand what we see (Wheelahan, 2023 ). This explains why, for realists, our knowledge is considered fallible and provisional. Our experience is shaped by our theories (Sayer, 1992 ) so “we ‘see’ the world through our conceptual lenses. For example, “two teachers, each with contrasting understandings about class and disadvantage, may view the same group of students differently and respond to them differently” (Wheelahan, 2023 , p. 88).

This takes us to the third proposition, that there are grounds for judging some explanations of the social world to be better than others. This is sometimes referred to as judgmental rationality (Moore, 2013 ). While our attempts at explanation are always considered fallible, processes such as public scrutiny of knowledge production (academic peer review for example) help us to progress towards understandings and explanations that are closer to the ‘truth’ than others. While our theories are not a mirror of the world, we can, at any point in time, suggest a good fit between theory and empirical data (Nola, 2001 ). Realist approaches aim to find the best explanation of reality through engagement with existing (fallible) theories about that reality.

How is Critical Realism Different from Other Approaches or Paradigms?

This is the question that those new to research paradigms often ask. Here we offer some responses to that question in relation to two other paradigms that social science research methodology literature suggests are favoured in qualitative research (Cohen et al., 2018 ; Doyle & Loveridge, 2023 ; Merriam, 1998 ). The first is what can be referred to as the interpretivist-constructivist paradigm (Cohen et al., 2018 ; Creswell, 2012 ). Researchers working in this paradigm are interested in finding out how individuals and groups construct meanings of experiences through interaction with others and their environments. From the interpretivist-constructivist world view, reality is constructed through our human interactions with others. If a researcher wants to understand the social world, they must try to understand the processes through which meanings have been or are being constructed. Those working from a critical realist paradigm are also interested in explaining the social meaning of a phenomenon but want to go beyond a focus on the meaning of participants to explaining the phenomenon itself and its causes. Sayer ( 2000 ), for example, notes that one limitation of interpretivism is that it tends to ‘‘reduce social life wholly to the level of meaning, ignoring material change and what happens to people regardless of their understanding’’ (p. 6).

The other broad paradigm commonly used in social science research is the critical paradigm (Doyle & Loveridge, 2023 ). In common with critical realism the critical paradigm assumes that there is a real world that exists independently of our beliefs and constructions, and that this real world makes a material difference to peoples’ lives. However, broadly speaking, researchers working within this paradigm want to uncover or reveal the power relations embedded in social structures and relationships. They are particularly interested in explaining inequality and oppression in society and how individuals and groups might be empowered to challenge social groups and structures (Doyle & Loveridge, 2023 ). Critical realism shares the same view of reality and a similar interest in exploring processes that might be obscured within a phenomenon. However, critical realism differs from critical theory in that critical theory tends to advance a perspective that research should operate within a framework of political assumptions and should be geared towards serving political goals of emancipation (Hammersley & Campbell, 2012 ). While not wanting to claim that CR is ‘apolitical’, the design of a CR study does not by necessity begin by establishing a framework of political assumptions.

The Explanatory Power of Realism

Now that we have set out the ontological and epistemological propositions associated with critical realism, we move on to discuss the usefulness of realism. We agree with Maxwell ( 2012 ) who notes that realism is not the “correct” or “right” paradigm for qualitative research, but that it can be “a useful perspective for qualitative researchers, one that provides insights and strategies that other perspectives ignore or misrepresent” (p. ix, italics in original). Put very simply, realism enables the researcher to explain the meaning that participants make of their social world and identify possible or likely causes of the phenomenon under investigation. This is because realist approaches draw on a ‘depth’ ontology. Bhaskar (2008) identifies three levels of reality in the natural and social worlds; the domain of the real, which consists of structures and generative mechanisms, the domain of the actual, in which events take place, and the domain of the empirical where experiences occur. The domain of the real shapes the domain of the actual. This depth ontology allows realists to examine the interplay between different objects with a focus on causal analysis.

Because of its focus on explanation and causal analysis, realism is useful for analysing social problems and offering solutions for social change (Fletcher, 2017 ). Many of the educational research projects undertaken by postgraduate students in education seem to favour approaches that aim to contribute to a better understanding of how individuals or groups experience and understand a particular issue or challenge in education. Realism offers a way to investigate the causes of those issues or challenges and to offer solutions. Realism does not look for a positivist version of causation, that is realists do not look for laws, but rather tendencies (Danermark et al., 2002 ). Realist explanations “depend on identifying causal mechanisms and how they work, and discovering if they have been activated and under what conditions” (Sayer, 2000 , p. 14).

One of the challenges in undertaking educational research is that schools and classrooms exist in open and complex systems in which it can be difficult to isolate components for analysis (Sayer, 2000 ). Realism addresses this challenge by making use of disciplinary concepts as a means of abstracting from the particular. Realist approaches

rely on abstraction and careful conceptualization, on attempting to abstract out the various components or influences in our heads, and only when we have done this and considered how they combine and interact can we expect to return to the concrete, any-sided object and make sense of it (Sayer, 2000 , p. 19).

A realist methodology often includes empirical data in the form of participants’ accounts of their social world. Realists take participants’ experiences and explanations of reality as a point from which they can begin to identify structures and mechanisms in people’s lives (Shipway, 2011 ). In some cases, participant’s explanations may prove the most accurate in explaining the phenomenon (Fletcher, 2017 ) but researchers acknowledge that in other cases participants’ accounts may be limited in terms of their explanatory power or they may reflect an inaccurate understanding of the causes of the phenomenon. This is because for realists, “how things are in reality is a different question from how people take them to be” (Carter & New, 2004 , p. 6). However, it is worth remembering at this point that for realists, all explanations of reality, including the explanations of both theorists and research participants, are regarded as fallible (Bhaskar, 1979 ).

Concepts and Conceptual Frameworks

When using realism, concepts guide the entire research process, starting from the identification of the problem or phenomenon to investigate, as well as being used as analytical and explanatory tools (Sayer, 2000 ). Carey ( 2009 ) defines concepts as “units of thought”, “mental representations”, and “the constituents of beliefs and theories…” (p. 5). Swanwick ( 1988 ) suggests that concepts provide the means for us to “cluster, classify and categorize experience, seeing certain things as similar or different in a particular way. Conceptualization is the beginning of theoretical description and organization” (p. 146).

Here we want to clarify that we are referring to disciplinary concepts, that is, concepts developed in the disciplines which are likely to be a part of wider conceptual systems of meaning or an epistemic structure; “epistemic structure refers to the system of meaning generated by the interrelationship of abstract concepts in a disciplinary domain” McPhail, 2023 . The utilisation of a concept will often require the understanding of other related concepts possibly by from one theorist or across a discipline’s conceptual webs, or both. Generally, these are concepts within theories that will have stood the test of critique and that have been found to be useful in their explanatory power, for example the concepts of structure and agency from sociology. Disciplinary concepts are essential to a realist analysis because they are used to identify and explain causation. Most of the time causal mechanisms are not observable and can only be ‘known’ through their effects. Therefore, specialised concepts developed in the disciplines are needed to do theoretical and explanatory work for the researcher (Rata, 2012 ). Concepts can also be regarded as real as they have effects in the world (Barrett, 2024 ; Popper, 1978 , 1981 ). For example once the concept of women’s rights gained universal currency its effects in the world could be seen. This is an example of transitive knowledge having ‘real’ effects on the intransitive domain of social processes. The examples we use below in our discussion below are realist in the sense that the use disciplinary concepts in central to the methodology.

Researchers often group concepts together to form a framework that is bespoke for their study. Concepts can be drawn from disparate sources or a key concept might infer others more related to a main concept. For example, Bernstein’s ( 2000 ) concept of recontextualisation infers the concept of ‘recontextualising fields’, the symbolic spaces in which in which the process occurs. While the use of a conceptual framework is certainly not unique to realism, it arguably plays a more significant role than it does in other approaches. Realism requires a researcher to draw appropriate concepts together to enable a theoretical explanation of the social meaning participants make of events and to provide a means of exploring possible or likely causes and processes obscured or less obvious within the phenomenon being investigated. Each researcher’s conceptual framework is unique, because it is the result of the selection of the concepts that are most apposite to explain the object of inquiry. A study employing a conceptual methodology cannot be done by replicating a particular analysis in a different context (Couch, 2020 ).

Developing a Conceptual Methodology

In this section we move away from a general discussion to illustrate and explain the way a conceptual methodology could be developed. We draw on the work of social scientist Fletcher ( 2017 ) who provides an excellent exemplar for researchers wishing to employ critical realism (CR) in their work. We include some insights and examples from her article but primarily we offer examples from realist informed postgraduate studies in education. We have tried to avoid using the more specialised vocabulary associated with CR to offer an accessible introduction to a realist methodology focusing on the role that concepts play throughout the research process. We begin by explaining the different parts of a conceptual methodology and illustrate these using a range of different postgraduate studies. After explaining and illustrating each of the ‘parts’, we offer a summary of one ‘whole’ study.

Identifying Social Phenomenon and Explaining them with Disciplinary Concepts

Like many researchers, realists are drawn to problems or questions related to social phenomenon. Realists usually begin by identifying initial disciplinary concepts that may appear to explain the phenomenon. These initial concepts may be modified or rejected later in the analysis stage as the researcher attempts to find the most accurate explanation of reality that the theory offers. In other words, throughout their analysis researchers aim to find a good fit between an empirical phenomenon and the disciplinary concepts used to explain it. Initial concepts or theories are identified by the researcher as they engage with academic literature guided by their own hunches and experiences as described in the examples below.

Fletcher’s ( 2017 ) research investigated how farm women in Saskatchewan were being affected by major changes in agricultural policy and their responses to those changes. The social phenomenon in this case was the changing nature of farm women’s work in Saskatchewan. Fletcher engaged with literature about trends in Canadian farm women’s work patterns, in particular their off-farm employment. She was especially interested in the changing political-economic context of family farming and changes to farm women’s work. The concepts of gender and political economy are brought together in feminist political economy theory which is a theory Fletcher was familiar with. She used this theory to guide her initial research design because it offered a way to explore the gender dimensions of political-economic structures affecting farm work in Canada.

Sometimes the initial concepts a researcher uses may be fairly self-evident, or already part of an established theory as in the case above. In other cases identifying initial explanatory concepts may be more challenging as in Tian’s ( 2021 ) doctoral study in education which was concerned with why and how the Core Socialist Values (CSV) of the Chinese government are included in school curriculum. After engaging with literature, Tian identified an ideological tension between market socialism and the development of individualistic and materialistic values in Chinese society and began to think about how the CSV might be theorised as a response to that ideological tension. Durkheim’s sociological concept of collective representations (Durkheim, [1912]/2001 ), which refers to the “collective ideas, values, and sentiments” (Durkheim, [1912]/2001 , p. 18) of a group, appeared to be a good way to begin explaining the government’s aims for the CSV. Tian noted that collective representations can also be a mechanism for creating ideology. Here he found Althusser’s ( 2001 ) conceptualisation of ideology useful. Althusser ( 2001 ) describes ideology as a structured “configuration” (p. 119) hidden deep in the thoughts and consciousness of individuals, which allows a “ruling ideology” (p. 112) to be realised. These two concepts, ideology and collective representations, provided Tian with a way to begin to explain why the CSV are included in the school curriculum in China.

Data Collection

In the section above we have shown how concepts assist with both the identification of the phenomenon as well as its possible explanation. Data from the empirical level of reality, which is the realm of events as we experience them (Fletcher, 2017 ), provides evidence of the effects of the social phenomena under investigation. Data is often in the form of interview material but may also include relevant statistics - which might indicate a widespread trend - or policies, and in the case of educational research, curriculum documents.

Data Analysis

There are three key steps involved in data analysis when using CR. The first step is to identify tendencies (Danermark et al., 2002 ), or broad trends found in the empirical data. Demi-regularities could also include the absence of something you might reasonably predict to find (see the author example below). The next step is theoretical redescription. Theoretical redescription refers to the process of describing the tendencies found in the empirical data using disciplinary concepts. The third step, involves identifying causal mechanisms usinga strategy of inference utilising the explanatory possibilities of available concepts (Fletcher, 2017 ).

Below we explain each of the three steps of the data analysis process we have just outlined using one of the author’s doctoral study in education to illustrate each step.

Identifying Tendencies

Data analysis begins with the search for tendencies or broad trends in the empirical data. A realist approach as we are outlining it, is open to various methods in the process of coding and data analysis although the significance of disciplinary concepts means there is likely to be a greater emphasis on deductive rather than inductive coding. However both approaches are used to determine if the chosen concepts are the best fit with the phenomenon under investigation. In summary, a flexible coding and theme development process is useful (Braun & Clarke, 2012 ; Hsieh & Shannon, 2005 ). This involves developing codes and themes from existing theory and literature, initially drawing on the disciplinary concepts that the researcher first identified as possible explanations for the social phenomenon under investigation. Changing, eliminating, and developing new codes and themes typically happens during the process of thematic analysis. This is often an iterative process, a to-ing and fro-ing, between what the data indicates and establishing the most apposite conceptual fit with the data.

Lourie’s ( 2013 ) doctoral study explored the effects of ambiguity in education policy about the purpose of Māori language education in secondary schools for non-Māori students and provides an example of the identification of tendencies in the data. Lourie had noted that while non-Māori students learning Māori language at secondary school was small, there was almost no available research about their beliefs and experiences. New Zealand education policy espoused a commitment to biculturalism Footnote 2 (Ministry of Education, 2007 ) but there appeared to be an ambivalence about the purpose of Māori language learning for non-Māori students. Lourie was curious about why non-Māori students were engaged in Māori language learning at school. One of her initial concepts when thinking about this phenomenon was biculturalism which she thought could potentially explain why some non-Māori students had chosen to learn te reo Māori. Lourie’s empirical data consisted of interviews and one of her questions to participants was why they had chosen to learn te reo Māori. When she began coding the data, she expected to find frequent references to wanting to learn to communicate in another language in some form or another. However, this was a tendency that was almost entirely absent from the data. She used a range of codes using the words and phrases that were frequently appearing. These included “care”, “respect”, “appropriate”, “nice”, and “breaking stereotypes”. Longer descriptions also seemed to be related to these initial codes. For example, one participant said learning Māori was a way to “be more respectful and we’ll bond more”. Lourie ( 2013 ) eventually decided to code these in one category which she called “relationship building”. Guided by her engagement with academic literature and her understanding of her initial concept of biculturalism, she deduced that students were referring to a particular kind of relationship, a bicultural relationship. One tendency she identified in the data related to the concept of biculturalism and allowed her to identify a key finding in the study; that engaging in Māori language learning was a way for students to be bicultural.

Theoretical Redescription

Theoretical redescription is a key part of the realist process where the main empirical findings are interpreted and re-described using disciplinary concepts. Theoretical redescription ‘gets below the surface’ of social experience and is a way of identifying something ‘real’ going on, that cannot be seen, although its effects can be seen. In Lourie’s study, thinking about how students were responding to ideas about biculturalism led her to consider the concepts of ideology and ideological effects in the theoretical redescription stage of her analysis. She theorised that a particular ideology was affecting the way students were viewing the purpose of Māori language learning. Having a concept or theory, to explain an effect helps identify causation (which is the final step in the process). If, as in this case, a group of people are identified as experiencing ideological effects, the next question is, what is the mechanism that enables this to happen?

Identification of Causal Mechanisms

In this stage of the analysis the aim is to identify what social conditions caused the trends observed in the empirical data to appear as they do. This involves a reasoning process that moves back and forward between the empirical and a “deeper” level of reality. Disciplinary concepts are needed to contribute to a (fallible) explanation of the relationship between the social phenomena as it is experienced by the research participants and the ‘real’ structures and mechanisms that shape that experience. It is difficult to describe this step in detail because it involves a strategy of inference unique to each research phenomenon and researcher. This reasoning process requires the researcher to be able to utilise the explanatory possibilities of a concept or groups of concepts. As explained earlier, groups of concepts might be part of an existing theory, or they might be drawn from across a discipline’s conceptual web.

Lourie ( 2013 ) theorised that bicultural education policy was having ideological effects on the way non-Māori students viewed the purpose of learning Māori, so the policy itself was identified as the causal mechanism. This was done by drawing on Gramsci’s ( 1971 ) concept of ideology and theorising the way that policy can work ideologically to hold diverse elements of society together in a relation of consent. Ideology provided the overarching concept to explain the relationship between bicultural education policy and the experiences of non-Māori language learners in schools. However, this explanation is only made possible by utilising other related concepts. Gramsci’s view of ideology and how it works also requires an understanding of ‘hegemony’ and the ‘collective will’. The explanation also requires a particular conceptualisation of policies and how they can work to produce different kinds of relations (Shore & Wright, 1997 ).

An Example of a Conceptual Methodology

In the previous sections we have used different examples to illustrate the way concepts are used during different stages of a conceptual methodology. In this section we use a single research study from education to provide a complete example of the use of a conceptual methodology. The Master’s level research described below was carried out by Wang ( 2021 ) and is summarised in (Wang & McPhail, 2023 ).

Wang set out to investigate how teachers understand and utilise Musical Futures as an approach for developing student autonomy in classroom music teaching. Musical Futures (MF) is an internationally recognised approach to classroom music aimed at developing high levels of student autonomy and engagement through utilisation of several ‘informal music learning principles’ developed by Green ( 2002 , 2008 ). The concept of student autonomy, which guided Wang’s initial research design, emerged primarily from the aim and principles of the MF programme. These principles include students deciding what music they want to play and what instruments they want to learn. Students develop covers of songs entirely through aural means and in friendship groups with minimal instruction from the teacher (referred to as discovery learning group projects). In this way the programme flips traditional approaches to learning music so that the locus of control over what and how of learning sits with the student. This approach is underpinned by a belief that increasing student autonomy over their own learning contributes towards better student engagement and achievement (Ryan & Deci, 2000 ).

Data Analysis: Identifying Tendencies

Once Wang began her data analysis it became clear that some form of adaptation or change of MF had occurred and that “the New Zealand teachers were utilising an approach that they termed MF, derived from training offered in New Zealand by Musical Futures International (MFI) based in Australia” but that this “appeared to bear little resemblance to the structure developed and used by Green in her initial study” (Wang & McPhail, 2023 , p.128). She found that the materials and the teachers’ descriptions of what they did in class indicated a pattern of ‘sequenced skill development’ through ‘scaffolded skill acquisition’ which was then followed by ‘class-based music making’. This was not preceded by any ‘discovery learning group projects’ which was the focus of Green’s original design.

Wang’s supervisor was familiar with the sociological concept of ‘recontextualisation’ (Bernstein, 2000 ) which describes the changes that occur as knowledge moves from its site of production to sites of reproduction. Recontextualisation appeared to be a good fit for explaining what was present in the data. The initial finding appeared to be a clear example of recontextualisation so it was utilised as the overarching concept for the study. In the process of recontextualisation Bernstein ( 2000 ) theorises that ideology always comes into play as new ‘recontextualisation principles’ refocus the original discourse to constitute its own, new recontextualised order. This recontextualisation goes on in in various symbolic sites Bernstein terms ‘fields’, such as ministries of education where policy is made (the official recontextualising field or ORF) or more informal sites where teachers adapt knowledge themselves for teaching (the pedagogic recontextualising field or PRF). Once it was clear there was a gap between the original form of MF in the site of production (i.e. Green, 2008 ) and the site of reproduction being investigated (i.e. Wang’s case studies) the research task involved theorising these changes and the possible causes of the changes.

Data Analysis: Theoretical Redescription

The concept of recontextualisation enabled Wang to identify that an unexpected change had occurred. She then needed to think about what the MF programme represented in terms of educational philosophy so that she could begin to theoretically redescribe the tendencies she had noticed in the data.

Wang used the concept of competence (Bernstein, 2000 ) to theoretically redescribe Green’s original design of MF. Bernstein argues that approaches to education concerned with emancipatory ideals such as student autonomy and pedagogies that challenge traditional educational hierarchies such as discovery learning can be encapsulated within the broader concept of competence . The identification of MF as a competence approach enabled Wang to theorise that what she found in the case studies was not a competence approach but something much more systemised and structured - what Bernstein terms a performance mode of pedagogy. In a performance mode there is an emphasis “upon a specific output of the acquirer, upon a particular text the acquirer is expected to construct and upon the specialised skills necessary to the production of this specific output, text or product” (Bernstein, 2000 , p. 44). At this stage of analysis Wang was able to identify that a performance interpretation of the MF programme had replaced its original competence approach.

Data Analysis: Identification of Causal Mechanisms

The concept of recontextualisation enabled a theoretical redescription of the change that had occurred in a musical teaching strategy, while the concepts of competence and performance helped explain the nature of the change. What remained was to theorise causation. As discussed in previous sections making determinations about causation can only ever be a process of advancing revisable propositions, since the complexities of a layered, or depth, ontology acknowledge that causal mechanisms can be hard to identify and may not always be activated (Sayer, 2000 ).

Thinking about the nature of the changes she had observed helped Wang explain why and how those changes were occurring. For example, (author) describe the approach taken by the teachers in the study as a “guided learning approach, where clear and achievable goals are identified and cognitive overload is avoided”. This contrasts with Green’s original ‘discovery’ approach where learning was intended to be holistic and haphazard (Green, 2008 ). This is a reflection of a shift away from the pure discovery learning characterised by MF at the beginning of the 20th century and several factors can be identified that appear likely to be contributing to this shift. But what is the cause of this shift?

At a deep societal level neo-liberal ideology is a major force at play effecting educational structures such as curriculum, pedagogy, and assessment (author) and its effects can be theorised as influencing the changes observed by Wang. This influence is seen particularly in outcome approaches to education which have become normalised internationally (Sinnema & Aitken, 2013 ; Sinnema et al., 2020 ) and that stand in some tension with discovery type pedagogies because outcomes create compliance demands on teachers in terms of assessment. This shift towards more explicit learning outcomes is seen in the recontextualisation of the MF materials and the approach by the course designers who deliver the materials in New Zealand (MFI). The interview data with both the course designers and the teachers provide ‘clues’ for the researchers about the causal mechanisms. For example the designers wanted to create resources that could be used at lower levels of the curriculum than the original MF which they argued required a more structured approach.

There is also what might be termed ‘a knowledge strand’ in the data that indicates teachers and designers appear to be cognisant of ideas present in the educational firmament from a variety of sources (e.g. cognitive science and knowledge discourses) that leads them to the conclusion that structured approaches are more effective than discovery modes of pedagogy. Again it was the data itself that pointed towards these deeper factors related to knowledge and learning. For example, one teacher noted:

I think that some kids like to be taught a little bit first. I do the ‘throw them in a room to do a song’ after I’ve given them some skills. I did try it the other way but it was little bit too messy, took too long. For my juniors, I didn’t have a lot of time, I want to get them playing but I needed to show them some stuff first (Wang & McPhail, 2023 , p.131).

In conclusion (Wang&McPhail, 2023 ) suggest that the recontextualisation has occurred “through the confluence of a number of causal mechanisms ranging from the views and experiences of the particular recontextualisers themselves (i.e. the design staff at MFI), the way teachers interpret the workshops and materials provided for them, and changes in the ethos or zeitgeist about teaching and learning…” (p. 135) away from discovery learning towards a more structured approach. This research illustrates how the researchers found a good fit between concepts and the phenomenon. However, researchers using realist approaches acknowledge the fallibility of their theorising.

This article has offered a general introduction to undertaking research informed by a realist ontology, outlining the way that a conceptual methodology might be designed and put into action. We believe that realism is useful for analysing social problems because of its focus on explanation and causal analysis. These two aspects can be especially useful in educational research where they can offer a way to tease out the different components of a complex social phenomena and offer an explanation of causal relationships. Being able to identify causal mechanisms creates opportunities to offer solutions.

We drew on a number of examples to illustrate a series of steps (that may not always follow a linear sequence) highlighting the way disciplinary concepts are used during each of the steps. Because realist analyses rely on the explanatory power of concepts, realist approaches employ a conceptual methodology; that is, a methodology that employs a conceptual framework unique to each study that frames and guides the research process. Our hope is that other researchers find our account accessible enough to engage in using or critiquing our modest proposal for the design of CR informed conceptual methodology.

Data availability

The authors have no competing interests to declare that are relevant to the content of this article. All data is drawn from published material.

See for example social realism (Young, 2008 , Maton and Moore, 2010 , Moore, 2013 , Muller, 2000 , Wheelahan, 2010 ), Archer’s ( 1995 , 2000 ) morphogenetic realist social theory, and transactional realism (Biesta, 2014 ; Carlgren, 2020 ), scientific realism (Nola, 1997 , 2001 ; Matthews, 2000 ).

In New Zealand the term biculturalism refers to a partnership between Māori and non-Māori people which was established when the Treaty of Waitangi was signed by Māori and the British Crown in 1840. 

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Lourie, M., McPhail, G. ‘A Realist Conceptual Methodology for Qualitative Educational Research: A Modest Proposal’. NZ J Educ Stud (2024). https://doi.org/10.1007/s40841-024-00344-4

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DOI : https://doi.org/10.1007/s40841-024-00344-4

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