The Sage Research Methods editorial team is pleased to announce that we are now accepting proposals for our next collection, Using AI for Research.

This collection will provide social and behavioural science researchers and students with urgently needed clear, critical, practical guidance for using AI across the research lifecycle. It will address Understanding & Evaluating AI Use in Research, and the use of AI tools for Literature Review and Evidence Synthesis, Research Design and Planning, Data Collection and Management, Analysis and Interpretation, and Writing and Dissemination.

Using AI for Research will provide learners with resources that help them use AI critically and effectively. This includes understanding when not to use AI and how to recognise and manage its limitations, alongside its potential to enhance and expand what researchers can do, opening new ways to generate insights and work more efficiently across the research lifecycle.

If you have used AI in your research, we’d like you to share your advice and experience through one of our articles. Your article could be a case study, guide, teaching dataset, or video interview or tutorial. Each text-article will bepeer-reviewed prior to acceptance for publication.

• Case Study (2,000-5,000 words):Write about the experience of conducting a specific research project. Focus on the methodological and practical challenges, key decisions, and lessons learned. You can find an example here: Learning Engagement in Academic Writing Classes Via Translanguaging Among English Majors: A Convergent Mixed-Methods Study

• Guide (2,000-4,000 words):Provide an accessible overview of key research methods, principles, skills, and practices, drawing on your research (and, where relevant, teaching) experience. You can find an example here: How to Critically Evaluate Information Sources

• Video interview or tutorial (10-15 minutes): In a remotely recorded interview or tutorial – with support from a producer, videographer, and editor – explain and demonstrate a method, principle, skill, or practice. You can find an example here: Null Hypothesis Significance Testing: An Introduction to P-Hacking

• Teaching Dataset (2,000 – 4,000 words): Share a real research dataset with an accompanying instructional guide, giving students hands-on practice working with and analysing data (guide up to 4,000 words). You can find an example here: From Data to Decisions: Using Large Language Models as Analytic Partners to Analyze Qualitative Data

There are many advantages to publishing in Sage Research Methods:

• Pedagogical impact: shape how AI is used in research practice

• It reaches a global audience through academic library subscriptions worldwide

• It is a peer-reviewed and award-winning resource

• No fee is required to publish

• An honorarium is paid for published articles

• You will receive two year’s free access to the resource

We will be accepting submissions through till October 2026. For further information, you can visit our website or complete our online form to register your interest.

We look forward to hearing from you!

In the next few weeks and months, students will be completing final year research projects as part of their degree programme. For many, this is a highly weighted assessment and the longest piece of academic writing they have undertaken to date. These projects are not only substantial in length; they are also deeply personal. Students often invest significant time, care and enthusiasm into topics that reflect their interests, career aspirations or favourite areas of study. As a result, the final stages of the research process are often accompanied by a mix of anticipation, relief and anxiety about “letting go” of something they have worked on for so long. What does this mean for supervisors, and how can you best support students in overcoming this final hurdle?

This advice has been curated to accompany our book, Research Methods in Psychology: An Essential Guide, where you can find more guidance for each stage of the research process.

‍Agree a writing schedule and set deadlines

At this stage, students can easily feel overwhelmed by the volume of work still to be completed. Working together to agree a realistic writing schedule can help restore a sense of structure and control. When developing the schedule, be mindful of other assessment deadlines and key personal commitments. It is often helpful to work backwards from the final submission date and, depending on whether you are able to read a draft of some or all of the report, to identify when they should submit any drafts and when feedback on these will be returned. We recommend ensuring students have at least one week before the final deadline to implement your last round of feedback. Building in this buffer can significantly reduce last-minute stress and improve the overall quality of the final submission.

‍Encourage students to look at examples

We encourage final year students to look at published research and previous students’ reports as examples to help them along, and so that they can visualise what a finished report should look like. At this final stage it is important to consider that students will have different motivations for wanting to use examples. Whereas before they may have been looking at these examples for substantive knowledge and ideas on content, now they are looking at them for writing style, the order of subsections and formatting style. Encourage them to pay attention to factors including how the writer hooks the attention of the reader in the introduction, how the writer ensures that the reader can identify the relevant evidence which provides an answer to each of the research questions posed and the additional content beyond the main chapters such as an abstract and what the purpose of these are.

‍Allow time to reflect

Strong research projects are those where the student is able to take a step back and think reflexively about what they have done. Encourage your student to review why they chose the topic of their research and their specific method and to consider whether other students would have approached the research in the same way. They should write in their research project how their own experiences, positionality and beliefs have influenced the research process. In these final weeks, when confidence can fluctuate, it is also helpful to remind students of how far they have come. Completing a research project develops resilience, project management, critical thinking and analytical skills; all of which will benefit them in their future careers and further study.

Our book, Research Methods in Psychology: An Essential Guide is an accessible text walking students through the research process. It draws on the experiences of real students who we have supervised to demonstrate the steps needed to successfully complete a research project. Chapters share top tips, activities for students to test their knowledge and understanding as well as questions for them to reflect on whilst they read. Part 4 of the book Finishing off your Research is recommended for students currently approaching the latter stages of their research project.

Join us on June 23rd at 11am BST for a free webinar where we will share more information about our new book and top tips for helping students develop feasible research projects. Register here.

We are proud to announce that the Research Integrity Collection is now complete! In our recent webinar, we explored the value, purpose practical implementation of the collection to serve your entire research community.

Looking to strengthen your institution’s approach to research integrity? From core compliance to a truly embedded culture of good practice, the Research Integrity Collection offers a comprehensive training solution designed to support researchers at every stage. It’s your one stop, sector aligned hub for all things research integrity.

The collection supports researchers at every stage of their career and of the research cycle, ensuring that every researcher has the knowledge needed to uphold research integrity principles. The collection offers foundational teaching for early career researchers, refresher training for those needing to stay current with the latest integrity requirements but lacking time for a full-length programme, and specialised training for senior researchers, principal investigators, and those with leadership and management responsibilities.  

Catch up on the Research Integrity Collection launch webinar - an in-depth session unpacking the value, purpose and real-world application of the programmes and how can support your research community in meeting sector expectations - including the REF.  

You'll hear directly from contributors, see the collection in action, and gain clarity on implementation, including hearing directly from Queen Mary University of London on their plans for the Collection. 

The Research Integrity Collection is available now. Request a free trial or speak to a member of the team to unlock your access.

Sage Research Methods continues to evolve to meet the changing needs of researchers, educators, and students worldwide. We’re pleased to share that our latest updates are now live, bringing fresh resources, new perspectives, and expanded methodological coverage across the resource. These updates are designed to help users stay current, work more inclusively and ethically, and confidently navigate research at every stage — from foundational learning to advanced, real‑world application.

What’s new in Sage Research Methods?

The latest updates introduce new books, case studies, datasets, and practical tools that reflect how research is being conducted today, while anticipating emerging approaches and challenges. Here are some of our highlights:

• Expanded case studies and applied research examples: new case studies explore topics such as trauma‑informed interviews, prison ethnography, and rural health research, offering practical insight into conducting research in complex, sensitive, and real‑world contexts. These resources support both teaching and applied learning across disciplines.

• Read for free: Doing Prison Ethnography from a Distance

• Greater focus on ethics, inclusion, and representation: the updates place strong emphasis on ethical decision‑making and inclusive research practice, with guidance on politically sensitive contexts and work with marginalised communities. This reflects SRM’s ongoing commitment to supporting responsible and representative research.

• Read for free: An Introduction to Ethical Considerations in Data Interpretation

• New and updated datasets for hands‑on learning: fresh datasets support mixed‑methods and data‑driven projects, enabling students and researchers to practise coding, visualisation, and interpretation across quantitative and qualitative approaches. Several pieces of content also focus on visual storytelling and analytics to help users present complex findings clearly and accessibly.

• Read for free: Learn About Thematic Narrative Analysis Using Data from Student Interviews on Racialized Experiences in Higher Education

• Coverage of digital, remote, and AI‑enabled research: as research methods continue to evolve post‑pandemic, SRM now includes expanded guidance on digital and remote methodologies. Updated content also addresses the responsible use of AI tools, social media data, and virtual ethnography within robust ethical frameworks.

• Read for free: Affective-Discursive Approach in Digital Activism Research

You can learn more about all new content, browse the title lists, or request free trial access here.

You are teaching a methods course in Politics and International Relations. Perhaps it is a course that has ‘research methods’ in the title. Perhaps the final assignment for your course is a research project. Perhaps you are convening a dissertation module. What challenges do you expect?

We might know some of these challenges, having taught research methods – many times, in fact, at the London School of Economics and Political Science, University of Chicago, University of Oxford and Durham University. For Anouk, this often involved teaching causal inference and statistics in departments where a good share of her colleagues conducted research nowhere near such approaches, and many students do not tend to think of themselves as statistics people. For Mareike, this often involved conveying to students how to research people’s experiences, how to interview and observe, and how to draw themes from text, in a department where research with statistics was the only type of research students had encountered. Challenges abound!

And yet, despite our very different approaches to research, we have collaborated for well over a decade, talking about research methods while sitting around a fire in South Sudan, watching goats and chickens roam and eating our rice and beans. At times, we idly speculated what it would look like if we were to take the teaching challenges we encountered and our experience of doing research to write a research methods textbook together.  

Fast-forward 15 or so years, and we have done it: Research Design in Politics and International Relations. What specific challenges inspired us to write this textbook? Here are five scenarios. They might be familiar to you, too.  

1. Seminar challenge: “I just want to study [insert topic here]”

Students don’t typically come to Politics and International Relations for the methods. They are passionate about topics like climate change, equality or political polarisation. Methods can seem to them as something abstract, standing in the way of their interests—rather than something that allows them to pursue these.  

How we dealt with this challenge

To show that methods are the pathway to interesting topics, we made a good portion of the textbook topic-led, covering the themes mentioned above as well as and war and violence, voting, identity, and social movements and protests. This shows students how they might ask different research questions about these topics and study them using different research designs (including experiments, process tracing, discourse analysis, or ethnography, among others). We don’t just show this in the abstract, but make it concrete by using recently published articles about each.

2. Grading an assignment challenge: tick-box theory/literature/methods section that does not connect to the rest of the assignment

It can be difficult for students to see how it all fits together: reading in the assessment instructions that they must engage with literature or theory and must describe their methods, does not always result in a coherent assessment. Maybe teaching is not entirely blameless, here:  the most logical way to plan our lectures is also to disconnect the pieces of the research puzzle by discussing literature one week, theory the next. Bit by bit, but often unconnected.

How we dealt with this challenge

To bring it together, we are summarising the entire research process in a single chapter, a single figure even, to help students keep track of the bigger picture. We were not entirely sure about this: is there a better way to attract scathing critique than to claim to have summarised the research process not just for ourselves, but across the discipline of Politics and International Relations? However, we have presented this approach several times now. We think of it not just as a teaching tool, but also as a tool to facilitate giving better feedback on research outside of one’s methodological wheelhouse. It is also a tool to understand knowledge hierarchies. Being somewhat pleasantly surprised, we have received overwhelmingly positive reactions. Researchers and teachers from many methodological backgrounds recognise themselves in the research process as we present it.

3. In lecture challenge: “But [your colleague] said something different about what makes for good research!”

That we do not approach research in the exact same way as all of our colleagues is a good thing. Diversity is crucial in knowledge production. However, for students who hear from multiple sides about what good research is, this can be confusing.

How we dealt with this challenge

We present the research process as a series of interdependent choices. Some research questions go better with particular research designs. How we use theory affects how we select the cases we want to study. This affects how we sample, and what data we use. And all of this is shaped by our epistemology: the knowledge we think we can create through research. It is not the case that some choices are better than others, but they are choices that researchers must make, coherently. In that, students are no different from experienced researchers.  

4. In office hours challenge: “Doing research is difficult, what am I doing wrong?”

Of course! D­­­­oing research is difficult, for us and students alike. But this is not always visible to students. Because when we write a book or article, it is not an account of all the stumbling blocks across our research journey, or our distractions and doubts when journeying. It is a polished account of what we ended up doing after overcoming them.

How we dealt with this challenge

We are so grateful to seven authors of articles that became examples of how to implement a research design. They offered an incredibly generous behind-the-scenes look, making visible how the research question we start out with is not always the one with which we end up; how not all data collection goes to plan; how hypotheses are unexpectedly not supported by evidence; how we grapple with knowledge hierarchies, and much more. They also shared practical tips on how they stay motivated, and how they dealt with inevitable difficulties, distractions, doubts and demotivation. Because we all have them—and it helps students a lot to know that!

5. That nagging voice in the back of students’ (and no-longer students’) heads challenge: “Can I really produce good research?”

Teaching research methods is about empowering students to become researchers, at least for the duration of their dissertation or research assignment.  Many of us can relate to the occasional or not-so-occasional flash of imposter syndrome that comes with this: is what I’m doing actually good research?

How we dealt with this challenge

By offering a way through the research process, imagined as a set of choices, we show the diversity of coherent choices that are possible and give examples for these. By equipping students to make these choices, including by being honest about the challenges most researchers encounter, we hope to have helped you and other instructors to empower students to become confident and considered researchers.

Free webinar: Teaching research design in Politics and International Relations: Wednesday, 25th March 5pm GMT / 9am PDT

Join Anouk S. Rigterink and Mareike Schomerus as they introduce ways in which teaching research methods can empower students to think like researchers, help overcome common student anxieties and reignite students’ curiosity for political questions. Register here

You can find out also more about their new key textbook Research Design in Politics and International Relations (2026) here.

Looking for fresh ways to enrich your methods curriculum? Dive into Sage’s new Research Methods Resources Hub for free resources that harness insights from our leading authors, and introduce strategies to improve research teaching, learning and practice. The resources include highlights from the Sage Research Methods Community blog, and a wealth of further materials for methods teaching. 

Whether you’re updating a syllabus or sharpening your own toolkit, visit the Hub today to access: 

• Free webinars: Practical guidance from renowned experts on navigating the research journey. 

• Must-read articles: Critical perspectives on contemporary research hurdles. 

• Syllabus support: Suggested reading lists and seamless inspection copy ordering. 

This post originally appeared on the Epigeum blog here.

Professor Matthew Campbell explores how research integrity is shaped through everyday leadership decisions and highlights three key leadership levers that strengthen and influence research culture.

What shapes research integrity more: formal rules or everyday leadership decisions?

When most people hear “research integrity”, they often think of extreme cases such as fabrication, falsification or plagiarism. In reality, the integrity of a research environment is built, or eroded, through far more ordinary moments: how decisions are made under pressure, how teams communicate uncertainty, how credit is allocated, and how concerns are handled when the answers are uncomfortable.

In the UK, research integrity is framed with the context of honest, rigorous, transparent, respectful and accountable practice. For experienced researchers, that means research integrity is not simply a set of rules to comply with. It is a leadership practice. The practical implication is straightforward: experienced researchers, research leaders shape culture whether they intend to or not. The question is whether we do so deliberately.

Below are three leadership “levers” that reliably influence research culture, each translated into practical actions emphasised within Research Integrity for Experienced Researchers (part of the Research Integrity Collection, an online course designed specifically for those leading people, projects, and partnerships across the research lifecycle.  

1) Make mentoring a ‘system’

Mentoring is sometimes treated as discretionary – something you do when time allows. In reality, it is one of the most effective mechanisms for preventing problems before they start, because it shapes how early-career researchers learn what “good” looks like when no one is watching. Mentoring should be treated as a formal, organisation-wide system-embedded tool in workloading, capability development, and performance expectations, not an informal, optional act of individual goodwill. 

Practical strategies experienced researchers can implement immediately include:

• Set expectations early and explicitly. Don’t assume shared understanding of standards for record-keeping, version control, lab notebooks, preregistration, or authorship criteria. If it matters, write it down.

• Normalise uncertainty and questioning. Make it safe to say “I don’t know” and “I think we should check”. These statements are not weakness; they are markers of rigour.

• Mentor for judgement, not just technique. The most consequential decisions in research are rarely technical. They are ethical and contextual (trade-offs, borderline cases, conflicts, and incentives).

A key point here is that mentoring is not only for large research groups. Leadership can, and should, also be exercised through less formal mentoring and professional development, particularly in smaller teams and disciplines where leadership is less hierarchical.  

2) Use openness to reduce friction and prevent disputes

Openness is often framed as a principle. Experienced researchers know it is also a management and leadership tool.

Many integrity issues emerge from misaligned assumptions: who owns which tasks, what counts as “finished”, what quality threshold is acceptable, or who will be included on outputs. Openness reduces ambiguity, and ambiguity is where disputes thrive.

Try embedding these habits into your normal routines:

• Run “pre-mortems” before projects begin. Ask: where could this collaboration fail (data access, roles, timelines, authorship, IP, approvals), and what will we do if it does?

• Make decisions traceable. Ensure key decisions (method changes, exclusions, analytic choices, authorship revisions) are recorded, dated, and accessible.

• Share learning not just success. Leaders who disclose setbacks and limitations set a tone that prioritises integrity over image; if you’re open and honest with you team, they’re more likely to be open and honest with you.

This is not about performative transparency. It is about creating a workflow in which doing the right thing is the path of least resistance.

3) Model ethical practice through communication, feedback, and what you reward

Culture is shaped less by what leaders say and more by what they do, and especially what they reward, tolerate, and ignore.

Responsible leadership is strongly tied to communication behaviours: active listening, timely feedback, fairness, and respect, particularly when the message is difficult.  

Concrete actions include:

• Give feedback that protects standards without humiliating people. “This needs improving” can coexist with psychological safety if the critique is specific, constructive, and focused on the work.

• Address poor practice early. Small deviations become “how we do things here” if leaders look away.  

• Be explicit about fairness and credit. Authorship and contribution decisions should be predictable, evidenced, and revisited when scope changes not negotiated at the end when incentives are highest.

• Respond well when concerns are raised. The first response matters. If people learn that raising an issue triggers defensiveness or retaliation, you will not hear about problems until they become crises.

In short: leaders are always teaching. The question is what lesson the team is absorbing?

Research Integrity for Experienced Researchers is built around a practical premise: experienced researchers do not need generic reminders that integrity matters. Instead they need structured, scenario-led support to apply principles in real leadership contexts. The course focuses on the responsibilities of experienced researchers across professional behaviours, the research process, and collaboration, using interactive exercises that translate values into decisions, conversations, and repeatable habits.  

For institutions, the benefits are equally tangible: fewer avoidable disputes, stronger supervisory practice, better team communication, and a clearer, more consistent research culture driven from the top and bottom and experienced by everyone. If research integrity is the outcome, leadership is the mechanism. Research Integrity for Experienced Researchers is designed to help experienced researchers strengthen that mechanism, deliberately, consistently, and in ways that teams can feel day to day.  

Research Integrity for Experienced Researchers  - part of the Research Integrity Collection - is coming out soon. Request a free trial to explore the course for your institution.

This post originally appeared on the Epigeum blog here.

To mark International Mentorship Month, Audrey J. Murrell, Ph.D. — lead advisor of the Epigeum’s upcoming ‘Mastering Mentoring’ program — explores the impact of mentoring and how intentional, well‑supported approaches can be beneficial for learning.

Mentoring has clearly been shown to be beneficial for career, leadership, and personal development.  It is also a powerful tool for learning, knowledge sharing, and fostering supportive and inclusive environments.  I have been studying, writing about, and working with organizations to enhance their use and impact of mentoring for almost three decades.  During that time, I have been consistently impressed by the remarkable effect of mentoring across all types of relationships and within diverse organizations. Substantial research and my own work show that mentoring is a critical tool for effective personal, career, and leadership development.  Mentoring is essential for providing opportunities to enhance personal growth, share knowledge, develop innovative solutions, and promote diversity and inclusion across different views and disciplines.  It is also vital within educational environments as mentoring enhances learning, career development, knowledge creation, and innovation.   

However, one aspect of mentoring that is sometimes overlooked is that there is no single, perfect type of mentoring. Mentoring relationships are as diverse as the people who are engaged in them.  Our focus should be on creating “Intelligent Mentoring” that includes clarity of purpose, authentic commitment the work of mentoring and adequate resources that go beyond one-time programs that may lack the resources and commitment to sustain mentoring efforts and their impact.  This begins with focusing on the diverse array of mentoring tools, structures and functions in order to select the specific approach to mentoring that will support your purpose and achieve your objectives. 

Each type of mentoring can serve as a tool to support what is important to people and leaders to achieve impact.  Traditional one-on-one mentoring is essential for individuals’ development and career effectiveness. Group mentoring often supports engagement and builds a sense of community among individuals with diverse backgrounds. Mentoring can also involve role modeling, in which leaders and other individuals serve as examples for others to learn from and emulate.  Interestingly, reverse mentoring is an effective tool for knowledge sharing and innovation. 

My recent work has focused extensively on the importance and effectiveness of peer mentoring.  This involves individuals who may be at the same career stage or age, and such lateral relationships can help buffer status and power differences that would otherwise make mentoring relationships challenging.  Peer mentors are valuable resources for sharing knowledge and ideas, providing leadership, offering support and encouragement, and building partnerships for collaboration.  Another impact of peer mentoring is that it can serve as a valuable tool for bridging differences that may impede collaboration, thereby fostering a supportive and inclusive environment.  

Peer mentoring is particularly effective for fostering collaborations across disciplines or areas of expertise.  Throughout my career and leadership experience, access to knowledgeable individuals from diverse backgrounds, expertise, disciplines, and functional areas has been highly impactful.  Sharing ideas, receiving feedback, and collaborating on solutions are uniquely facilitated by peer mentoring relationships. Clearly, there is much to learn and access across these diverse mentoring relationships. 

We are continuing to learning more about the impact of mentoring through research, educational programs, and personal experience.  Mastering Mentoring is designed to support the development of meaningful relationships and create a supportive and inclusive environment, which is especially important for researchers in the early stages of their careers. Mentoring opens doors and offers opportunities for growth by providing access to knowledge, resources, and networks, thereby helping to build equity across research environments.  We hope this tool will be an important and effective resource to assist individuals throughout their journeys.  

As we recognize the importance of mentoring this month, let us renew our focus on how this powerful tool can be most effective for supporting organizations and their stakeholders. It’s time that we practice “Intelligent Mentoring” which means three key commitments: developing clarity of purpose and desired outcomes, providing support that includes investing resources (people, time, and financial support) for the ongoing work for mentoring that is sustainable beyond one-time efforts. 

Mastering Mentoring will be published in 2026. Visit Epigeum website to find out more.

In the below guest post, Kyra van Weenen, Commissioning Editor for Sage Research Methods explores the unique concept behind Sage’s flagship Research Methods Case Studies, and how they support researchers and students in conducting their own research.

What do you do when you need to suddenly redesign your field research in rural Cambodia, realizing that participants with limited literacy are not able to complete your consent forms? How do you conduct meaningful interviews with participants inside a prison when the only available spaces turn out to be noisy corridors and cramped cells? How did a researcher go about tracking down 100+ former participants from a cohabitation study, 15 years later, when many of them moved across states, changed careers or changed their names after marriage?

Since their release back in 2014, Sage Research Methods Case Studies have concerned themselves with all the different types of on-the-ground challenges and realities that emerge in social science research. These firsthand narratives, written with other researchers in mind, pass on lessons about how a chosen research method or design worked out in practice – and what did not go to plan at all. The structure is simple, the content all the more rich: authors describe the original idea behind their research, break down the research design while sharing valuable detail on the unexpected hurdles and obstacles they faced and how they decided to handle them. Case Studies have remained a core component of Sage Research Methods (SRM) multi-media collection releases throughout the years and earned SRM a place in the list of Choice Magazine’s Outstanding Academic Titles of 2018. What makes these real-world, ‘warts and all’ narratives of past research projects such a valuable learning resource?

Sage’s Vice President for Books & Social Science Innovation Katie Metzler was responsible for the content strategy and innovation of Sage Research Methods at the time that the first Case Studies collection was launched. She recalls a clear demand for Research Methods materials that would prepare students for the realities of the research process in practice, to complement the more ‘sanitised’ or ‘how-to’-presentation of research methods in conventional publications like textbooks and journal articles. ‘Working with academics who authored books at Sage, I often heard stories about the practical problems arising in their research, like the difficulties they faced once they were in the field or times they lost all their research data. It showed that research really isn’t a linear, smooth process at all, but a messy, trial-and-error endeavour with regular unexpected challenges’. Their true stories felt like an underused source of lessons to pass on to students learning about - and applying - research methods in their own real-life projects.

Kelly Leahy, Assistant Professor at Syracuse University and author of the upcoming SRM Case Study Qualitative Corroboration: Interviewing, Inventorying, Observing, and Surveying to Understand Preschool Parents’ Meaning Making of Emerging Digital Enrichment Products echoes that value in giving the ‘unvarnished’, first-hand narratives of researchers’ experience a dedicated place in scholarly publishing, saying: ‘when we're conducting research, so many interesting puzzles arise, leading to conversations, revisions, trials, and new ideas. The richness and nuances of this discovery process are part of what makes our research so interesting! But these moments are often neatly summarized in our methods or findings chapters. Sage Research Methods Cases provides a unique opportunity to consider the surprises and decision points within research, and explicate the process of decision making. [It] gives us the chance to share these discovery moments with students, faculty, and scholars’.

Eleven years and more than 3,000 case studies later, that value is still echoed by libraries and faculty, and SRM’s sales- and marketing teams receive regular feedback that materials which show students how research methods and statistics apply to real-world scenarios are invaluable to teaching. Where case-study narratives are not normally a focus in conventional learning materials on methods, faculty would often end up having to write their own based on their past research experiences. Having access to a large selection of vetted case studies at once offered a real solution to that. As Case Studies rapidly took their place as one of its core content types, Sage Research Methods soon expanded its suite of resources with new, theme-focused resources including SRM: Doing Research Online and SRM: Diversifying and Decolonizing Research. In each new release, case studies have taken a prominent place in its content offering.

An important reason for this continuous interest in case studies is the constantly-evolving research landscape of the social sciences, where new methods and technology call for fresh reflections on their application in real-time. ‘There is an ongoing need for SRM Case Studies as they reflect important methodological trends’, says one of SRM’s most experienced peer reviewers. ‘I have been a reviewer for many years and have seen how the content has shifted in waves across this time; the dramatic need to pivot methods during the COVID-19 lockdowns, the move to more digital and online data collection, increasing participant involvement and collaborative approaches, and now the current rapid rise of AI. Whilst research methods generally stay the same, the way in which they are conducted is constantly evolving, and it is these nuances that this collection showcases.’

In reaffirming the speed at which experienced researchers and students are expected to grapple with the challenges of applying established and novel research methods in an ever-changing, diverse society, SRM cases present real-world reminders that the hurdles along the way are an unavoidable part of the process. ‘The stories told within them acknowledge these moments, not as methodological failings, but as valuable learning opportunities’, writes SRM’s peer reviewer.   Continuing to bring those lessons ‘from the field’ to other researchers is how we ensure responsible social-science research for the future.

Explore a sample of Sage Research Methods case studies with exclusive free access:

• Recruitment Challenges Using Online Methods: A Qualitative Study of Young Adult Care Partners of Young Adults with Advanced Cancer

• Challenges In Analyzing Cross-Cultural Qualitative Social Science Data: Attitudes about Fathers’ Child Support in South Korea and the United States

• Seeking John Smith: Finding Qualitative Interviewees for Follow-Up and Pivoting Data Collection During the COVID-19 Pandemic

If you are interested in contributing a research methods case study of your own research project, don’t hesitate to get in touch with Kyra: kyra.vanweenen@sagepub.co.uk

This blog post was originally published on the Sage Perspectives blog here

Silvia Gutiérrez is the Senior Program Officer for Libraries at the Wikimedia Foundation, where she supports global library partnerships drawing on her background as a Wikimedian, research librarian, and computational humanist. Previously, she was a Digital Humanities academic at the Daniel Cosío Villegas Library, leading Wikimedia campaigns focused on social movements, linguistic diversity, and gender equity. She is a Ph.D. student at the University of Leipzig researching Wikipedia references with AI and data visualization, and a co-founder of R-Ladies CDMX.

You’ve probably heard it before: “Never cite Wikipedia in your research paper.” And it’s good advice. But maybe you haven’t heard about how using Wikipedia (especially its citations) actually strengthens your work and even helps others discover it later. And this post is all about that!

Start Here: Wikipedia as The Research Launchpad

Many academic librarians actually encourage students to begin with Wikipedia, not as a source to cite, but as a map to credible sources. For example, the CMU Libraries guide to web research explicitly recommends Wikipedia as a starting point for understanding a topic:

“You can see that the sources cited include books (with ISBNs), information from college or university websites (ending in .edu), and articles (with DOIs). These are all sources you could safely use in a research paper, all located in one place.”
— LibGuides at Central Michigan University

Wikipedia is a curated bibliography of thousands of topics, constantly updated by a global community of ~300,000 volunteers!

Wikipedia Citations as an Altmetric Signal

Beyond being a source-finding tool, Wikipedia is increasingly recognized as a meaningful Altmetric indicator. A 2020 study published in PLOS ONE  analyzed over 1.4 million Wikipedia citations to scientific articles. The authors found that being cited on Wikipedia is a sign of societal impact—it means your work is being integrated into public knowledge.

“Wikipedia references to scientific articles can provide highly valuable altmetric information given that the inclusion of references is not a trivial activity and is usually subject to community scrutiny.”

In fact, Altmetric.com gives Wikipedia citations a weight of 3—higher than mentions on X (1) or Facebook (0.25). This reflects the careful, community-vetted process behind adding citations to Wikipedia.

But Not All Wikipedias Are Created Equal

While English Wikipedia is often rich in citations, the same isn’t true for every language edition. A recent study (May 2025) analyzed 55 language editions. They found stark disparities in citation quality across topics and languages.

For example:

• In English Wikipedia, business-related articles scored an average quality of ~70%.

• In Chinese Min Nan Wikipedia, the same topic scored only ~12%.

This highlights an important caveat: citation quality varies widely. The good news is that if you speak one of these lesser-developed languages, you could be the change you want to see!

How You Can Contribute (Ethically!)

If you're a researcher, you might wonder: Can I add citations to my own work on Wikipedia? The answer is yes, you can, and maybe you should? But as you would do with any self-citation, you should do it with care.

Wikipedia has clear guidelines on conflicts of interest. You're allowed to cite your own published work if it's relevant, conforms to content policies, and isn't excessive. The key is to:

1. Write in the third person – don't use "my research shows," but rather cite the paper objectively.

2. Avoid over-citing yourself – balance your citations with other relevant research.

3. When in doubt, propose edits on the article's talk page and let the community review them first.

Adding your work alongside other researchers' contributions doesn't just boost your visibility—it enriches free knowledge and helps build a more complete, credible resource for everyone.

See Your Impact in Real Time

Want to know how many readers benefit from your contributions? Activate your Newcomer Homepage! This personalized dashboard shows you exactly how many people read the articles you've edited, giving you a tangible sense of your public impact.

Watch this quick explainer to learn more: YouTube: The Newcomer Homepage

The Bottom Line

Wikipedia isn’t the endpoint of research: it’s a dynamic, collaborative bridge between public knowledge and academic work. By using it strategically, and contributing ethically, you can:

• Quickly locate authoritative sources, curated by a global community

• Understand how your research fits into public discourse

• Gain Altmetric recognition if your work is cited

• Directly contribute to open knowledge!

So next time you’re starting a new project, use the world’s top read online reference work, and consider giving back by adding well-sourced, balanced citations (including your own relevant work!) where they’re needed most. Your research (and the world’s readers) will thank you.

Join Silvia from the Wikimedia Foundation at Sage’s informative webinar coming up soon!

"Beyond Publication: How to Increase Citations and Academic Reach"
February 4th, 2026, 11am EST

Register here.

This post was originally published on the Sage blog here

With 2025 ending, you may be thinking about reviewing your research progress and preparing for the upcoming year. If you don’t know where to start, here are some suggestions on organizing your work and planning for 2026.

Reflection and celebration

• Conduct a year-end reflection: Take time to evaluate your achievements and challenges from 2025. Documenting key lessons learned can help you identify strengths to leverage any areas for growth in the coming year. Consider seeking feedback from peers or mentors to gain additional perspectives on your progress.

• Celebrate milestones: Acknowledge your progress by celebrating completed projects, accepted papers, or new skills acquired. Recognizing achievements, both big and small, helps maintain motivation throughout the year.

Professional profiles and organization

• Update academic profiles: Ensure all relevant profiles, including LinkedIn, ResearchGate, faculty profiles, and your CV, reflect your 2025 research focus and publications.

• Organize references and data: Streamline your reference library (using tools such as Zotero, EndNote, or Mendeley) and verify that research data is securely stored and accurately labeled.

Goal setting and planning

• Set clear research goals for 2026: Define specific projects, topics, or questions to address next year. Clear objectives can guide efforts and promote effective collaboration with contributors, affiliates, or co-authors.

• Conference planning: Identify field-related conferences to attend, determine formats (in-person or virtual), and examine funding options. Record deadlines for department or travel funding applications.

• Monitor special issues or collections: Note submission deadlines for collections and special issues to facilitate timely contributions.

• Plan for publication: Select relevant journals or conferences and design a submission timeline. Draft outlines or abstracts for future papers to begin the process.

Skill development

• Enhance skills: Enroll in courses or training related to data analysis, writing, or presentation. Continuous development may provide further research opportunities and improve project quality. Schedule necessary trainings and consider early registration for 2026 How to Do Research and Get Published webinars.

Time management and well-being

• Balance multiple roles: As an author, professor, reviewer, or mentor, it's crucial to set boundaries and prioritize tasks according to deadlines and impact. Consider using digital calendars or task management tools to allocate dedicated time blocks for writing, teaching, reviewing, and administrative responsibilities.

• Practice self-care and maintain equilibrium: Allocate time for rest and personal activities. Maintaining balance supports sustained productivity.

Funding and grants

• Seek funding and grant opportunities: Investigate forthcoming grant deadlines and start applications early to broaden potential funding sources.

Although these suggestions may seem minor, small, regular actions will build long-term accomplishments in research. Wishing you a happy, prosperous, and productive 2026.

Whether you’re tackling a systematic review for your own research project, or supporting students, this recent Sage Campus webinar is an essential watch.

Dr Gemma Cherry and Dr Michelle Maden, co-authors of Doing a Systematic Review: A Student’s Guide, break down the rigorous process into 10 clear, manageable steps. You’ll learn the difference between systematic and scoping reviews, the critical need for a registered protocol, and how to craft a focused question using frameworks like PICO.

The session guides you through key methodologies, from comprehensive multi-database searching and managing results with tools like Rayyan, to transparently reporting your findings via the PRISMA Flow Diagram. The speakers also offer crucial advice on dealing with "empty" reviews, team size considerations, and responsibly using AI in the review process.

Watch the full webinar recording below to set your research on the right track.

Rapid advancements in Artificial Intelligence have instigated introspection across the data visualisation field. Though swathes of AI discourse are characterised by hype and gimmickry, if you cut through the noise, it’s clear we are facing a significant new era of technological progress. It’s time to evolve, or die, as the saying goes.

But what should our relationship with AI be, particularly with generative AI? To what extent could our authentic craft be usefully augmented by an artificial one? How will it help and with what activities? In what ways might it pose risks?

I’ve spent time considering the elements of the data visualisation process, as detailed in my book, where AI could helpfully collaborate with data visualisers, augmenting our efforts to make tedious tasks more efficient and creative acts more ambitious. Rather than focus on the concerns I have about AI, I’m choosing to frame this list around a more positive, perhaps hopeful, perspective about ways I would appreciate enhanced support:

DATA

1. Data foraging: Receiving smarter suggestions for places to source bespoke data, especially unstructured or non-digitised data, perhaps for niche topics for which data may be fragmented and requires extensive searching.

2. Qualitative sources: Unlocking greater potential in the extracting of unstructured qualitative data from compelling material like text, printed artefacts, imagery, audio, or video sources.

3. Data handling: Having an auto-generated transcript of a given data handling process would be invaluable, noting all the relevant steps from initial gathering, to cleaning, to calculating, and through to transforming it ready for charting.

CREATIVITY

1. Colour themes: Although there are specific rules for certain colour-data associations, there is plenty of scope for subjectively selecting specific colours across all aspects of a visualisation. These could be inspired by suggestions based on subject matter and/or tone, with mockup variations to enable you to consider how they’d look.

2. Thematic inspiration: Using smarter searching, I would appreciate surgically sourcing relevant examples of visualisation designs for inspiration about a similar subject matter or to find other approaches to tackling common analysis challenges (eg. “how have others shown changes over time and location simultaneously but a static/printed output?”)

3. Signature style: Could AI learn your design techniques, style, or preferences, in order to suggest refinements each time you work through a visualisation process? This would be especially relevant when working in a corporate or commercial context with formalised design guidelines/branding rules to observe.

DESIGN

1. No-code interactivity: Enabling the creation of interactive visualisations without the need to code. Tools like ‘Make real‘, offer a promising sketchy-point-and-click interface to automate interface designs. Alternatively, using language-led prompts to describe the features you’d want - skipping even the step of vibe-coding - would be game-changing.

2. Text annotation: Smart-generated chart titles and captions, and suggestions for their styling and positioning within a chart’s spatial constraints - even if only as a first draft text - would be a huge time saver. Refining would be much quicker than doing from scratch. Extending this to generate copy for user guides, chart-reading instructions, data definitions, method statements, and FAQs - as well as auto-translating into other languages - could be invaluable.

3. Story building: AI assistance for developing designs to aid amplifying certain narratives/insights, not just in helping to select the right chart type, but in the broader design and styling of the overall communication. As demonstrated impressively in this video from Andy Cotgreave.

EVALUATING

1. Output previews: An enhanced way to envision a visualisation design as previewed in a wide range of different output formats, dimensions, and settings would be very useful, to see how outputs look on mobile/tablet, laptop/desktop, meeting room screen, home TV screen, through to conference halls. Extending this to simulating how they might look - or suggesting how they should look - in different settings like a printed document on a moving train, a newspaper graphic read outside a sunny cafe, a poster-sized map being viewed in a busy under-lit station at night.

2. Functional evaluation: Evaluating a nearly-finished work by occupying the mindset of a target audience to judge its effectiveness, perhaps with different viewer profiles - from vis-expert-level, domain-expert-level, and to lay-person. This could assess the design’s perceived trustworthiness, to surface any potential issues about the reliability of the data material (sources, assumptions, inclusions or exemptions?) and check the integrity of design (any distortions, misleading chart constructions?). It could also perform an accessibility check to assess suitability for different levels of visual, physical, technical, perceptual, and interpretive accessibility.

3. Stylistic evaluation: This will always be extremely subjective, even for humans, but a further valuable assessment would be formed around perceived visual appeal: is it beautiful, is it visually unified, is it memorable, is it sufficiently attention-grabbing, does it feel authoritative? All matters of contextual variation, and utterly subjective, but worthy of assistance if available.

Andy Kirk is an Independent Data Visualisation Expert delivering a range of professional services to clients around the world as a data visualisation design consultant and prolific trainer. He is a sought-after speaker, a four-times published author, the editor of visualisingdata.com, and host of the ‘Explore Explain’ video and podcast series. He is the author of Data Visualisation: A Handbook for Data Driven Design, now in its 3rd edition.

Publishing your research Open Access (OA) ensures that it reaches the widest possible audience — freely and immediately. This guide walks you through what OA means, its key benefits, and how Sage’s flexible publishing options help researchers share their work responsibly, increase visibility, and meet funder or institutional requirements.

What Is Open Access?

Open Access is an international movement to make scientific research — including publications and data — freely available to everyone. Through this model, research is accessible online immediately upon publication, without paywalls or subscription barriers.

Anyone is free to read, download, copy, distribute, print, or use OA content for educational and research purposes within the terms of its license. This approach expands the reach and influence of scholarship while encouraging collaboration and innovation across borders.

Sage’s Flexible Routes to Open Access

At Sage, we strive to make publishing OA as straightforward as possible. On our Open Access page, we outline several publishing paths to suit different author needs.

1. Fully (Gold) Open Access Journals

All articles in these journals are published openly and are immediately accessible to everyone upon publication. There are no paywalls, and reuse is permitted under the selected Creative Commons license.

2. Hybrid Open Access (Sage Choice)

Many Sage journals operate under a subscription model but offer authors the option to make individual articles open access by paying an Article Processing Charge (APC). This provides flexibility for authors and funders — enabling OA publication where desired, while the journal remains available through subscriptions for others.

3. Green Open Access (Self-Archiving)

Authors can share specific versions of their manuscripts — such as the accepted version — in institutional repositories or on personal websites, in line with journal policies. Sage supports responsible sharing through clear guidance on embargo periods, formats, and licensing.

4. Subscribe to Open (S2O)

An emerging and collaborative model, S2O allows subscription journals to make content open access when enough libraries renew their subscriptions. This approach transforms existing funding into support for open publishing, benefiting the entire research community.

Why Choose Open Access — What You Gain

Broader Reach & Impact

Open Access makes your work available to anyone — researchers in well-funded institutions, independent scholars, policymakers, practitioners, and the public. Greater accessibility often translates to higher citations, wider readership, and greater real-world relevance.

Author Rights & Control

OA empowers authors to retain more control over how their work is used. Through transparent licensing, you can determine the ways others may reuse or build upon your research.

Equity & Inclusion

Open Access helps level the playing field, particularly for researchers in low- and middle-income countries and institutions with limited resources. It turns knowledge into a truly shared global resource.

Compliance & Visibility

Many funders and institutions now require OA publication or deposition. Publishing with Sage helps you meet these mandates while maintaining rigorous editorial standards and long-term sustainability.

How Sage Supports Open Access Authors

Sage is committed to enabling responsible and accessible open publishing. Here’s how we support our authors:

• Open Access Agreements & Institutional Support
  Many institutions have agreements with Sage that fully or partially cover APCs. Authors affiliated with these institutions can publish OA at reduced or no cost.

• Waivers and Discounts
  Sage partners with Research4Life to offer APC waivers or discounts for authors in eligible countries, ensuring global participation in OA publishing.

• Transparent Policies & Author Guidance
  Our Open Access page provides detailed information about OA routes, licensing, and self-archiving. We also offer clear FAQs and rights guidance to support authors throughout the process.

• Quality and Integrity
  All Sage OA journals maintain the same rigorous peer review, editorial oversight, and production standards as our subscription journals — ensuring credibility and quality across every format.

Tips for Authors Who Want to Publish Open Access

• Check your funder or institution’s OA policies — Some require specific licenses or deposition practices.

• Explore your institution’s OA agreement with Sage — You may be eligible for a waiver or discount.

• Select your license carefully — Creative Commons licenses (e.g., CC BY) define how others can use your work.

• Plan ahead for APCs — Where applicable, include them in grant or institutional budgets.

• Deposit your manuscript — If using Green OA, upload your accepted version to your institutional repository once permitted.

• Promote your article — Share links through academic networks, social media, or institutional channels to increase readership and engagement.

Conclusion

Open Access is more than just removing paywalls — it represents a shift toward sharing, collaboration, and inclusivity in research.

At Sage, we believe in empowering authors, institutions, and communities to disseminate their work widely while upholding quality and sustainability.

If you’re ready to explore open access publishing or learn more about Sage’s OA options, visit our Sage Journals Open Access page. Together, we can advance a more open and connected research ecosystem.

We’re excited to share that Research Methods - part of the 'Research Skills Toolkit' - has been updated to reflect today’s research landscape. Discover brand new content on Gen AI, Data Literacy and more – designed to support researchers with the skills they need in their careers.

Developed with real-world relevance at its core, the Research Methods  - part of the Research Skills Toolkit- update reflects our commitment to delivering support for researchers that is up to date and tackles the key skills they need for success. With insights from our expert panel, the programme remains academically robust and relevant, especially in light of rapid developments in AI and research practice. Research Methods has been restructured to offer a more intuitive learning experience. 

New additions include: 

• A brand new module on Gen AI for researchers, authored by Dr Martin Compton. Explore topics including AI in research design, data collection, literature review and writing. This concise, practical introduction helps researchers to navigate ethical and effective use of Gen AI in their work. 

• Expanded content on data, Gen AI, and indigenous methodologies integrated throughout the programme 
  

• A new module in the Data Literacy course, exploring data literacy within the context of research methods

You will also benefit from:

• Restructured programme for improved user experience and clearer navigation 
  

• Updated content and edited videos to enhance quality and presentation 

If you would like to explore and learn more, sign up for a free trial where you can explore more.  

Artificial Intelligence (AI) technologies are evolving at a remarkable pace; what was cutting-edge yesterday may be obsolete today. In 2014 I led a study that took months to develop an AI-type method for analysing text that could only be applied to healthcare surveys. That approach, once innovative, is now significantly surpassed by freely available AI tools that can respond instantly to a typed question in normal language on any topic or data.  

The fast-changing nature of AI shaped our editorial decisions for the 5th edition of Researching Society and Culture; we chose not to explore AI in depth, focusing on pragmatic advice for navigating real-world research complexities. AI was mentioned briefly in chapters on ethics, literature reviews, writing, and analysis, acknowledging its relevance, without attempting to capture a moving target. But here we give a short overview of current AI capabilities for researching society and culture.

Origins of GenAI and social science research

Back in 2014, the term AI was reserved for Artificial General Intelligence (AGI), a still-unrealised form of adaptable, human-like intelligence. What is nowadays colloquially called AI is more accurately termed ‘generative AI’ or GenAI. This refers to computer models trained to find patterns in data presented to them (predominantly using machine learning) and to then generate new content from new data or from simple written requests by mimicking those patterns using statistical techniques. Understanding this distinction is crucial; although powerful, GenAI systems are narrowly task-specific and rely heavily on human input and statistical approaches, lacking the adaptable intelligence of AGI.

There are two common forms of GenAI.  Large language models (LLMs) such as ChatGPT are most frequently used to generate text or analyse data including images. Diffusion models are typically used to modify and generate images, video and audio.

GenAI was first adopted within social sciences research for its ability to process vast amounts of data, such as text, images, and social media; this is known as Big Data analytics. GenAI has a synergistic relationship with Big Data, which is used to train it to analyse more Big Data.  Ironically, technologies like GenAI, wearable sensors and the Internet of Things (IoT) have accelerated Big Data growth, creating a feedback loop that increases reliance on GenAI.

GenAI capabilities

The processing power of GenAI can give us rapid insights into cultural shifts, public opinion, and emerging trends, and can be time-saving, for example when summarising or coding large datasets. The auto-coding within Computer Assisted Qualitative Data Analysis (CAQDAS) software, an early form of this, looked for keywords; nowadays ‘natural language processing’ (NLP) systems give the illusion of actually interpreting complex text. For example, they can distinguish between a trial run, a medication trial, and a court trial in complex sentences through linguistic context (word patterns and parts-of-speech, such as verbs), using statistical techniques to group the data. We humans then name these groups using our own conceptualisations; if we encode those names within the GenAI NLP programming and they don’t include the original words, it can seem as if GenAI exhibits real understanding. 

Beyond analysis, GenAI can automate mundane tasks, such as producing contents lists for reports, or correcting grammar, freeing researchers for more conceptual or creative thinking.  GenAI also creates new possibilities for digitising and analysing cultural artifacts, graphics (including medical scans) and paper-based materials (including handwritten documents). 

Its capabilities have been described as transformative by researchers with disabilities or neurodivergence, supporting them with tasks they find challenging; for example, AI-generated summaries of reading lists can help prioritise sources. The Model Context Protocol (MCP), using GenAI like Claude can even engage people in seemingly natural conversations, tailored to their needs, including accent and voice.

Challenges of using GenAI in research

Despite its impressive outputs, GenAI is still learning. It can produce convincing results, but these often contain errors or ‘give-aways’ that reveal their non-human origin. More concerning are the biases embedded in GenAI systems, through every decision made in their development and use. If data used to train GenAI reflect existing societal biases, GenAI algorithms can perpetuate and amplify those. This can result in "vanilla-ization", where nuanced insights are flattened and generic or centrist conclusions normalised. The risk is heightened by GenAI’s algorithmic monoculture, that is, a reliance on just a few GenAI systems with similar or identical underlying algorithms or training data. At worst, this can lead to serious discrimination.

Ethical concerns also arise around intellectual property. Some GenAI models are trained on research outputs and artwork without permission, raising issues of misrepresentation, fraud, and loss of earnings or recognition. These unapproved uses can also disseminate sensitive information without the ethical safeguards researchers typically apply. While you might consider some of these misuses amusing or easy to spot, they can enter the IoT ecosystem, where future GenAI use incorporates them as legitimate data, a phenomenon known as ‘AI hallucination’. In other words, AI-generated outputs feed back into the system, reinforcing errors at scale.

To address these challenges, SAGE, along with universities and research institutions, have developed guidelines for responsible AI use. This requires collaboration across disciplines (e.g. AI, social sciences, humanities, and ethics). AI can and does make mistakes, though they tend to be systematic and so detectable, and human oversight remains essential. Ultimately you should aim to use AI as an assistant, to augment, not replace, human judgment and critical thinking.  AI is only as good as the data and humans behind it.

Further resources on the use of AI in social science

• de Manuel, A., Delgado, J., Parra Jounou, I., Ausín, T., Casacuberta, D., Cruz, M., Guersenzvaig, A., Moyano, C., Rodríguez-Arias, D., Rueda, J., & Puyol, A. (2023). Ethical assessments and mitigation strategies for biases in AI-systems used during the COVID-19 pandemic. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231179199 (Original work published 2023)

• Gillespie, T. (2024). Generative AI and the politics of visibility. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241252131 (Original work published 2024)

• Milana, F., Costanza, E., Musolesi, M., & Ayobi, A. (2025). Understanding Interaction with Machine Learning through a Thematic Analysis Coding Assistant: A User Study. Proceedings of the ACM on Human-Computer Interaction, 9(2), Article CSCW197. https://doi.org/10.1145/3711095  

• Riedl, C., & De Cremer, D. (2025). AI for collective intelligence. Collective Intelligence, 4(2). https://doi.org/10.1177/26339137251328909 (Original work published 2025)

Order your copy of Researching Society and Culture (2025) by Clive Seale and Carol Rivas here

There are some perennial challenges to doing a research project that all of us have faced: formulating a research question, wrestling with paradigmatic assumptions, keeping on track of a multi-year project. The Essential Guide to Doing Your Research Project has been helping students (and supervisors) for more than 20 years. But in a post-pandemic world with the transformations being wrought by artificial intelligence, datafication, and the rise and fall of social media platforms, there are new challenges facing researchers.

I was delighted to be invited by Zina O’Leary to join as an author for the fifth edition. I’m passionate about improving students’ research literacy, challenging assumptions about the quantitative-qualitative ‘divide,’ and empowering students be critical consumers and producers of research. Living and working in Aotearoa New Zealand, I am sensitive to the ways research is implicated in systems of imperialism and colonisation. These ‘big picture’ forces can sometimes seem quite distant for students embarking on a research project – particularly if their topic or approach is not critical in orientation. Yet these dynamics structure many underlying assumptions about what research is, where it happens, and how we should engage with it.

The fifth edition foregrounds power and politics across the text. This includes a substantially revised chapter, “Undertaking Credible and Ethical Research”, which encourages students to develop reflexivity about their own positionality and their approach to research. We have included new critical thinking challenges to encourage reflection about whose voices are represented in the ‘canon’ of scholarly literature and how this might shape students’ research questions and literature reviews.

One rapidly changing area of research practice concerns digital data. There are new ways to access, collect, store, and share data that have their own ethical considerations and challenges. Many widely used techniques of just a few years ago (e.g. scraping tools from Twitter) are already out of date – and the platforms themselves have changed almost unrecognisably. Our “Online Generated Data” chapter has been updated with a focus on transferrable skills and overarching principles that apply across software and platforms. We situate the technical decisions involved in data collection within the wider context – where debates about the energy demands of data storage, shifts towards open access publishing, and indigenous data sovereignty pose no easy answers. Instead, we give students tools to critically weigh their decisions and figure out the stance that best suits them and their projects.

Another crucial skill for students – within their research journeys and beyond – is communicating their findings to a range of audiences. Higher education institutions around the world are emphasising the importance of research translation and knowledge transfer. This is an important obligation to avoid the ‘ivory tower’ stereotype of academia. Yet it’s not always straightforward to know how best to engage in such work. Our updated chapters on “The Challenge of Writing Up” and “Final Hurdles” provide practical tips including the importance of storytelling, visual communication, and accessible design. These skills matter more than ever in an increasingly precarious job market where many of our students will not go on to academic careers. And they offer important reminders (to all of us) of the importance of bringing our research stories alive for the people who matter.

Find out more and order your copy of The Essential Guide to Doing Your Research Project (2025) by Zina O’Leary and Emma Tennent here

This post originally appeared on Sage Perspectives blog

Publishing is just the start. Promote your article the year it’s released to boost visibility, citations, media coverage, and career impact. From CV updates to conference invites, early promotion ensures your work stays relevant and reaches the right audience.

You’ve published your article—congratulations! The first year of publication is your golden window to make noise, build momentum, and maximize impact. Here’s why timing is everything:

Time-Sensitive Wins You Don’t Want to Miss

• Add It to Your CV This Year

Your CV is a living document, and a new publication is a major milestone. Including it in this year’s version keeps your academic profile current and competitive—especially if you're applying for grants, fellowships, jobs, or promotions.

Be sure to add your paper to your researcher profiles, and if you haven't already, create a research profile to begin establishing your online presence early in your promotion journey.

• Year-End Wrap-Ups and Highlights

Many institutions, departments, and labs compile annual research highlights. Recently published articles could be eligible for inclusion in:

• Department newsletters

• University press releases

• Annual reports

• Social media roundups

Don’t miss the chance to be part of your institution’s narrative for the year. Get in touch with their press or publications office to discover opportunities to promote your work.

• Grant and Funding Applications

Most grant applications ask for recent publications. Disseminating your research now ensures it’s top-of-mind and easily accessible when reviewers assess your research output.

• Conference Season Relevance

Conference organizers often seek panel speakers who can present timely research. By promoting your recent work, you can build a reputation and stay relevant, thereby increasing your chances of being invited to join a panel. Additionally, if you are seeking funding to attend a conference, showcasing your recent scholarship can highlight your active engagement in the field and strengthen your case for support.

• Media and Public Engagement

Journalists and scholarly communicators are always looking for groundbreaking research. Promoting your article early increases the chance of media coverage while your findings are still novel. Submit pitches to publication outlets yourself or get in touch with journalists/communicators directly. Be sure to note any policy or practical implications of your research and any calls to action for researchers or the public when pitching or discussing your article. Building a story around your article will further its appeal.

Long-Term Benefits Start Now

• Early Citations

The sooner your peers read your work, the sooner they can cite it. Citations often snowball from initial visibility, and early ones count toward journal impact metrics. Encourage early citations by promoting your paper as soon as it’s published.

• Community Engagement

Sharing your article on platforms like LinkedIn, X (formerly Twitter), Bluesky, and ResearchGate invites discussion, feedback, and collaboration—especially while the topic is still hot. Get in touch with the journal’s editorial team to discover opportunities to collaborate on promotion.

• Search Engine Boost

Search engines and academic databases prioritize recent uploads. Promoting your article early helps it get indexed and ranked, increasing discoverability.

• Don’t Let Your Work Fade Quietly

Reader attention spans can be short. If you wait too long, your article risks being buried under newer publications. Promotion in the year of publication keeps your work visible, relevant, and part of the conversation.

Bottom line

Publication is just the beginning of your article’s journey. Promotion can turn your article into a citation, a collaboration, or a career opportunity. So don’t wait—share your work while it’s fresh and let the world know why it matters.

To discover promotion strategies, check out our resources on promoting your article & building your network.

Each year, Sage Research Methods (SRM) evolves to reflect the shifting landscape of research practice. The 2025 updates are no exception—bringing fresh perspectives, timely topics, and practical tools to support researchers at every stage of their journey. This year’s updates span across seven SRM collections, each responding to emerging trends and long-standing challenges in the research community. Here’s a look at what’s new, and why it matters:

•  The Cases collection continues to offer a rich mix of qualitative, quantitative, and mixed-methods case studies. New additions explore ethical research boundaries, AI-related methodologies and unconventional interviews – reflecting the growing interest in responsible research practices and the integration of technology into fieldwork

•  Doing Research Online builds on its digital-first foundation with new content on digital methods for a post-pandemic world, such as drone visuals and netnography. As remote and tech-enabled research becomes the norm, these updates offer timely support for navigating new tools and platforms

•  Decolonizing and Diversifying Research continues its mission to challenge dominant paradigms. Highlights from 2025 include content on empowering marginalized voices in focus groups, and a dataset featuring participant drawings from a migration study using the “River of Life” metaphor — a powerful example of visual elicitation in qualitative research

•  A fresh batch of titles join the Books & Reference library, offering more core titles with enduring value – such as a new edition of Reflexive Methodology by Mats Alvesson, or Writing a Winning Thesis, by Randy Joyner. These foundational texts continue to shape how research is taught and practiced across disciplines

The 2025 updates reaffirm Sage Research Methods’ commitment to relevance, diversity, and methodological rigor. Whether you're teaching, learning, or conducting research, these new resources are designed to meet you where you are—and help you go further.

By Leandro S. Pongeluppe

Leandro S. Pongeluppe is the winner of Administrative Science Quarterly's 2025 Dissertation Award

From Dissertation to Publication: Seven Lessons from the Field

Transforming a dissertation into a journal article is not just about running regressions, citing classic papers, and trimming the word count – it involves clarifying your work’s contribution, sharpening the methods, and speaking to a broad community aiming to create societal good. I am honored to share lessons I learned – some more painful than others – while getting my dissertation published in Administrative Science Quarterly.

1. Focus on what you are passionate about.

The PhD journey is long, and sustained motivation is difficult, especially when you are away from home, family, and friends. Transform these hardships into something meaningful to you! I anchored my research on issues I genuinely care about: the UN’s Sustainable Development Goals. My research question – whether business capabilities training can promote the socioeconomic advancement of disenfranchised individuals living in Brazilian slums, or favelas (Pongeluppe, 2024: 619) – was interesting to me not only as a scholar but as someone seeking to understand how people face socioeconomic inequality and can move out of poverty.

2. Follow strong role models.

Rather than trying an entirely new approach, learn from rigorous studies in your field. I benefited from authors (e.g., Attanasio, Kugler, & Meghir [2011]) who had performed randomized control trials (RCTs) and made their data and analysis code publicly available. This helped me understand not only the final product but also how data was collected and analyzed. Modeling best practices from high-quality research helped me elevate the design of my dissertation and refine its narrative for publication.

3. Collaborate.

Instead of designing an intervention from scratch, I partnered with a long-standing NGO, now called Instituto da Providência, which had over 15 years of experience delivering business training programs in Rio de Janeiro’s favelas. They brought institutional memory, a well-established intervention design, and organized historical data; I contributed an impact evaluation of their program. This collaboration saved time, reduced costs, and embedded the research in a vivid context.

4. Mix your methods.

I get annoyed with some economist friends who claim that they understand the world by only running regressions. Take the opportunity to go to the field, talk to people, and get your hands a bit dirty (Hirsch, Michaels, and Friedman, 1987). While the backbone of my study was a stratified RCT, I complemented it with computational text analysis of participant feedback, field visits, and interviews with community leaders and entrepreneurs. This triangulation helped me not only to understand my results better but also to explore the mechanisms behind them. Multiple methods enrich the data and your story.

5. Know your data and do your job.

Working with primary data brings both freedom and responsibility. I was meticulous in documenting each step of the process, from pre-registration to making data, code, and legal documents available. Knowing your data means being able to respond confidently to reviewers and make adjustments during revision. Scientific rigor is not optional – it is your best ally in the publication process.

6. Count on your scholarly community.

It takes a village to get to publication. Supervisors (Anita McGahan in my case) and committee members will guide you. Peer doctoral students will help you with ideas and execution. Editors (Chris Rider in my case) and referees will support you in crafting your contribution and making the most of your work. Their feedback, while sometimes daunting, can clarify your paper’s contribution and improve its quality. More importantly, they force you to refine your ideas and grow as a scholar.

7. Think beyond academia.

Do not limit yourself to publishing in a scientific journal. The program I evaluated was later expanded into public policy across 23 municipalities in São Paulo State, reaching over 12,500 women who lead single-parent households. The published article may have been the academic endpoint, but it was not the end of the work. We have a world to care for. Science can and should contribute to it.

A dissertation is a personal and intellectual adventure full of ups and downs. My hope is that I’ve offered encouragement – and practical guidance – as you take the next step. Keep working hard, and good things will happen!

References

Attanasio, O., Kugler, A. D., & Meghir, C. (2011). Subsidizing vocational training for disadvantaged youth in developing countries: Evidence from a randomized trial. American Economic Journal: Applied Economics, 3, 188–220.

Hirsch, P., Michaels, S., & Friedman, R. (1987). “Dirty hands” versus “clean models”: Is sociology in danger of being seduced by economics? Theory and Society, 16, 317–336.

Pongeluppe, L. S. (2024). The allegory of the favela: The multifaceted effects of socieconomic mobility. Administrative Science Quarterly, 69, 619–654.

Read the article here

Article Details
The Allegory of the Favela: The Multifaceted Effects of Socioeconomic Mobility
Leandro S. Pongeluppe
First published: March 29, 2024
DOI: 10.1177/00018392241240469
Administrative Science Quarterly