3rd MethodsNET Conference | Abstracts

Natalia Martini, Antje Jacobs, Karin Hannes, Syeda Sidra Idrees, Elien Stouten — KU Leuven

An issue in more conventional research methodology is bodiless abstraction, which privileges a cognitive, mind-dominating process of analytical steps. Supported by scholars who are calling for a return to intimacy and the sensory experience of being in/with the world, we challenge this paradigm for powerful reimaginations of society and academia. In this workshop we redesign research processes as affective and intracorporeal, rooted in a sensorial experience of movement from different methodological perspectives – disrupting the myth of the detached thinker and offering a framework of grounded positionality and sensory awareness. By bringing the body into play, we situate participants’ imagination of what a more compassionate, inclusive society might look like into felt experience. Part one will explore walking methodologies for becoming differently attuned to the living environments through which we move. Drawing on speculative ethnographic walking and walking in/with the dark, we will make ‘sense’ of priorly “known” phenomena, grounded in relational and multisensory engagements with place, reimagining the latter through climate and mobility justice lenses. Instead of observing society from the outside, part two will inquire from within the moving body. Using the internal logics of performative and martial arts, we will examine how felt experience, embodied agency, and relational movement generate insights into societal transformation. The workshop invites investigation into how your body senses, decides, resists, adapts, and produces knowledge, illustrating how the push for change is enacted and rehearsed through the body as an entry-point for alternative understanding and action. Part three (Moving-on with Embodied Self-Expression) engages participants in participatory body-mapping as an arts-based method rooted in Global South traditions. Through collaborative art-making practices, participants will explore how bodies carry movement, memory and relational encounters across time and space. The session explores how embodied self-expression may blur distinctions between inside/outside experience, body/mind, and being/knowing. Art is not treated as representation, but as emergent once bodies have been “moved” physically, relationally, affectively and cognitively, positioning body-mapping as a material process of spatio-temporal embodied self-expression.

Anka Kekez, Ana Petek — University of Zagreb and MethodsNET

The session demonstrates how coding procedures differ across three influential approaches to qualitative data analysis: Qualitative Content Analysis (QConA), Codebook Thematic Analysis and Reflexive Thematic Analysis. Using sample empirical material, participants will observe step-by-step how each approach defines coding units, develops and applies codes, structures analytical categories or themes, and produces different forms of analytical output.Rather than presenting abstract methodological distinctions, the session is organized as a practical comparative demonstration from the researcher’s perspective. Participants will engage directly with key stages of the coding process, including data segmentation, initial coding, codebook development, theme construction, and refinement of coding schemes. Particular attention is given to differences in coding flexibility, the role of researcher subjectivity, the use of codebooks, and the relationship between coding and theory. The demonstration contrasts the structured and category-oriented logic of QConA with the more flexible logic of thematic analysis, while also highlighting important distinctions between codebook and reflexive approaches to TA.The demonstration is designed for methods teachers, early-career researchers, doctoral students, and scholars interested in methodological transparency and qualitative rigor across the human sciences. It is particularly relevant for researchers working with interviews, focus groups, policy documents, organizational texts, or mixed qualitative datasets who seek practical guidance in choosing and applying appropriate qualitative analytical approaches. By making coding procedures explicit and comparable across approaches, the demonstration aims to strengthen methodological reflexivity and support more coherent and transparent qualitative research design.

Tamara Pataki – Verbi Software; Christina Silver – University of Surrey

The integration of GenAI into qualitative and interpretive research has sparked a vigorous methodological debate, with positions ranging from enthusiasm, through cautious exploration, to outright rejection. There is increasing interest in integrating chat-based tools in interpretive methods, including critical discourse analysis (DeJeu 2024) and reflexive thematic analysis (Hitch 2024), despite criticism claiming that GenAI is “incapable of meaning making” (Jowsey et al. 2025). This Methods Lab addresses methodological debates, focusing on the practice of qualitative coding as a core analytic activity across many methods through practical exercises and discussion. We explore under what conditions AI use is appropriate and how to incorporate it with methodological integrity. AI-Assistants embedded in traditional CAQDAS packages such as MAXQDA offer an ideal ground for exploring these questions and considering how to integrate human and AI-Assisted analysis.The session begins with methodological discussions about the foundations of interpretive analysis: What does the practice of meaning-making involve? What does coding qualitative data accomplish? Why does methodological reflexivity matter? From this grounding, we move to a hands-on practice using MAXQDA’s AI coding features, working through core questions researchers face: Where in the analytic process can GenAI meaningfully contribute? How should researchers evaluate and refine AI-generated code suggestions? What instructions can effectively prompt the AI?Throughout we foreground the importance of technological reflexivity, meaning intentionally considering how tools shape methodological choices and knowledge outcomes (Paulus & Lester, 2023). The session closes with guidance on communicating AI-assisted analysis transparently and reflecting the researcher’s positionality in the AI-assisted process.

Ulrike Lühe — University of Geneva

Research in the social science and humanities has often been extractive. This is especially true for research that is concerned with vulnerable populations or where significant power discrepancies exist between those doing the research and those being researched. Participatory approaches to research have been proposed as one remedy. In this training session we will collaboratively explore possibilities for designing participatory research projects going beyond frameworks of Participatory Action Research, where each phase of the research offers opportunities for research participants and communities to co-design the research process. The objectives of the training are to 1) discuss the ethical and conceptual importance and implications of participatory research approaches, 2) explore practical strategies for designing participatory research, 3) discuss the challenges and shortcomings of these strategies. Throughout the training we will consider practical concerns such as the needs and roles of particular participant groups, partnership goals and configurations, ethical principles and how to put them into practice, etc.

Stefan Rädiker — Methoden-Expertise.de

Integration is the defining challenge of mixed methods research – and the point at which many projects fall short. Kuckartz and Rädiker (2022, 2023, in press) distinguish three integration strategies: results-based (comparing findings post-hoc), sequence-based (one strand informing the next), and data-based (linking qualitative and quantitative data on the case level). Data-based integration offers the deepest analytical leverage but demands software that handles both data types in parallel.This hands-on session shows how MAXQDA enables data-based integration through joint displays. Combining demonstrations and guided exercises, participants will learn to:- use quantitative variables to filter and retrieve qualitative material- build joint displays for case and group comparisons (e.g., qualitative statements grouped by quantitative categories)- apply a digital worksheet for planning, conducting, and reporting mixed methods studiesSelected ReferencesGuetterman, T. C., & James, T. G. (2023). A software feature for mixed methods analysis: The MAXQDA Interactive Quote Matrix. Methods in Psychology, 8, 100116.Kuckartz, U., & Rädiker, S. (2023). Using software for mixed methods analysis. In International Encyclopedia of Education (4th ed., pp. 500-512). Elsevier.

Michalis Christodoulou — Aristotle University of Thessaloniki

Retroduction, demi-regularities and the Context-Mechanism-Outcome configuration represent the most prominent conceptual bridges between Mixed-Method Research (MMR) and Critical Realist (CR) metatheory. Yet despite their currency, limited methodological work has addressed how CR principles can be systematically integrated into specific MMR designs. The most intuitive approach assigns quantitative methods the task of identifying demi-regularities and testing how causal mechanisms operate under particular conditions, while qualitative methods illuminate complex concepts and relationships resistant to standardized measurement. Within this logic, SEM and regression techniques serve not as predictive tools but as instruments for surfacing hidden regularities and contextual patterns within which mechanisms are activated. This approach, however, carries two significant shortcomings. First, the methodological distinction between explanatory sequential designs in which qualitative work precedes quantitative analysis and those in which it follows remains underspecified — the sequencing decision lacks a principled CR rationale. Second, since generative mechanisms in CR are not variables, the use of regression techniques risks conflating contexts, variables and mechanisms, undermining the ontological commitments that motivate the CR framework in the first place. This presentation responds to both concerns by developing a methodological argument on two fronts: first, how CR principles can be coherently integrated into the three most commonly used MMR designs; and second, under what conditions case-based quantitative methods — such as Configural Frequency Analysis or Qualitative Comparative Analysis — offer a more epistemologically consistent basis for theorizing within CR-driven MMR.

Judith Schoonenboom — University of Vienna

At the end of their studies, mixed methods researchers arrive at a complex inference, commonly referred to as the metainference. Here is an example: „Providing textbooks to primary schools in rural Kenya raised quantitative grades for pupils with previous high grades, but not for others, because the latter group were unable to read their textbook, written in English, which is not their native language.“ Metainferences are the result of constructing complex chains of inferences within a single study. A discussion of how such complex inference chains are constructed has barely begun. This is unfortunate because mixed methods research allows researchers to advance their field by combining within one study different research purposes and processes, such as exploration, explanation, differentiation among groups and generalization. The specific sequence of these research processes, and hence of deriving inferences, determines the sophistication and quality of the metainference. In this workshop, we will analyze a few real-life examples to see how complex inference chains are developed in each example, in what order inferences are derived, how they connect to and build on each other, and what role each dataset plays in deriving inferences, building the complex inference chains, and developing the metainference. At the end of the workshop, we will formulate guidelines for practice.

Cheryl Poth — University of Alberta

Designing and teaching high-quality mixed methods training requires intentional pedagogical choices that make integration visible across the research process—conceptually, procedurally, analytically, and interpretively. Yet practical guidance on how to design courses that deliberately scaffold the development of integration-specific skills remains limited. This session addresses that gap by guiding participants through eight essential instructional decisions that underpin rigorous, engaging, and developmentally appropriate mixed-methods learning. We begin by examining why centering integration is foundational to effective mixed methods teaching and how to make it explicit in the course design, instruction, and assessment. Participants will explore how integration can serve as both a methodological principle and a pedagogical anchor to strengthen learners’ conceptual understanding and support their ability to plan and justify mixed methods design decisions.Participants will then work through eight instructional decisions that shape effective mixed methods learning, including defining course purposes and learning outcomes; sequencing content; selecting examples and perspectives; designing active learning strategies across delivery formats; aligning assessment with instruction; scaffolding reflection and positionality; supporting individualized learning pathways; and aligning course logistics with learner priorities. Engaging with these decisions in a structured way will help participants better understand the influence of their instructional choices. The session also addresses the realities of teaching mixed methods to diverse learner groups with varied disciplinary backgrounds, methodological experience, and motivations. A collaborative learning environment will support participants in sharing ideas and challenges, drawing on the facilitator’s more than 15 years of experience teaching mixed methods across global contexts.

Sebastian Sünkler, Oliver Koop — Hamburg University of Applied Sciences

Executing independent studies on commercial search engines is a fundamental challenge for the research community. These platforms operate as algorithmic “black boxes”, offering minimal data access while public search APIs are severely limited or entirely discontinued. Furthermore, modern search result pages rely on client-side rendering and complex asynchronous layouts, making traditional scraping methods inadequate for accurate academic study. This demonstration session introduces the Result Assessment Tool (RAT), a free, open-source software toolkit designed to standardize the entire end-to-end research workflow for digital platform auditing.RAT provides a unified environment that empowers researchers to execute a four-stage methodological workflow: Define, Collect, Analyze, and Evaluate. We will walk through this complete workflow during the live session. First, we show how researchers define studies by sampling real-world query sets using the built-in Query Sampler. Second, we demonstrate how RAT automatically collects matching results, including SERP metadata, full HTML source code, and dynamic AI-generated summaries across engines such as Google and Bing. Third, we showcase how the toolkit’s backend automatically analyses data, computing result overlap, domain distributions, and custom SEO or readability scores. Fourth, we demonstrate how researchers use the frontend’s evaluation interface to enable jurors to assess preserved screenshots and collected AI responses via custom questionnaires (e.g., evaluations of relevance, usability, and more).This session provides interdisciplinary researchers with a transparent, accessible software toolkit for auditing complex commercial search systems.

Taimoor Khan, Aleksandra Pawlik, Ahrabhi Kathirgamalingam, Felix Victor Münch — GESIS

Transparent documentation and reproducible results are essential for computational methods in scientific studies, yet many computational methods in published articles fail to reproduce their findings. This is often due to i) inconsistent (code and data) sharing practices, ii) incomplete or inconsistent documentation, iii) unpreserved execution environment, or iv) obsolete code [1]. On the other hand, manually reviewing computational methods for transparency and reproducibility is labor-intensive and susceptible to human errors, causing long delays in feedback and bypassing constraints, respectively. To address this gap, we automate the computational methods review mechanism to highlight the discrepancies hindering computational reproducibility. The review tool is implemented as GitHub workflows that work with computational methods in GitHub repositories. It is customized for the Methods Hub platform [1], offering transparent and reproducible computational methods to social scientists. It ensures the specific method publishing guidelines for the methods submitted to the platform, including the availability of documents such as open license and citation file format, etc., method documentation in a prescribed structure, such as the method README templates. It also verifies that the method execution environment files are available and can be deployed to execute the method. The computational method review tool serves as the first-hand technical review to expedite feedback on technical issues and assist in addressing them by pointing to relevant guidelines until the method is ready for a thorough scientific review. In this session, we will demonstrate the use of our automated method review tool by reviewing sample methods from the Methods Hub platform. [1]https://dl.acm.org/doi/10.1145/3736731.3746161

Aleksandra Pawlik, Muhammad Taimoor Khan, Ahrabhi Kathirgamalingam, Felix Victor Münch, Arnim Bleier — GESIS

This hands-on interactive tutorial will demonstrate how to use Jupyter4NFDI infrastructure to make the computational part of research reproducible, easy to share, and easy to collaborate on. We will use examples from Methods Hub, including repositories on GitHub with Jupyter Notebooks and R Markdown or Quarto documents.Methods Hub is an openly available catalogue of methods and tutorials for computational social science, developed and maintained by the research community. It enables users to explore and try out content via Interactive Environments (IE), which include Jupyter4NFDI and myBinder. The former is infrastructure funded through the German Base4NFDI funding stream, providing access to federated computational resources via JupyterHub. The latter is an open, free, on-demand instance of JupyterLab running on community-supported servers. Both require only a browser and no local software installation, lowering the technical barriers to teaching, collaboration, and reproducible research.In this session we will demonstrate both Interactive Environments in action and discuss the practical trade-offs between Jupyter4NFDI and myBinder to help participants choose the right tool for their needs. We will also walk through what it takes to prepare computational research outputs so they run correctly in either environment, which, for those interested, would be the necessary steps to publish your content on Methods Hub.To follow along, participants will need a laptop with an up-to-date browser. Prior experience with coding or GitHub is useful but not essential.

Benedict Wauters — Flemish Ministry for Labour and Social Economy
Derek Beach — Aarhus University

This session deals with presenting recent innovations in process tracing methods, as well as closely related theory-based evaluation methods (TBEM). The promise of process tracing and similar TBEM is that they enable applied researchers to unpack causal processes linking interventions with outcomes and evidence them empirically. Process tracing can be used to evaluate both macro-level processes, as well as the aggregation of individual level interventions to see how different participants interacted with an intervention. These methods have comparative strengths in studying ‘intangible’ or ‘soft’ interventions such as knowledge, policy dialogue and advocacy and communication campaigns.The session will introduce recent innovations related to theorizing process (process theories of change), and in particular how do we know what aspects of actor interaction in a process can be abstracted away and what needs to be kept to understand the causal structure of the process. The session will also work through innovations related to operationalizing process theories through focusing on activities and interactions that leave empirical traces in cases. The session will conclude with discussions about how to learn from cases, and how processual comparisons can be deployed.

Hilde van Meegdenburg — Leiden University

This training session will discuss the core of Process Tracing (PT) methods with a focus on studying the within-case causal productivity that brings about an outcome. That is, it establishes PT as a method focussed on, and studying, token-level data to derive within-case causal inferences. However, much research is interested in conducting some form of comparative PT: many researchers aim to conduct PT on two to four cases. Although this is methodologically possible, it is not as straightforward as it is at times presented. Put simply, if PT focusses on within-case causal inference, then comparative PT—which has a cross-case component—is inherently a multi-method research approach. With a focus on the PT, and thus the within-case, component, this training session explores this tension and will discuss under which conditions comparative PT is a viable multi-method research approach.

Tshilidzi Catlyn, Marcia Mkansi, Ernest Mnkandla, Ntombhi Mkalipi — University of South Africa

Choosing the right research philosophy is one of the most critical yet under-supported decisions for graduate students, often shaping the success of their entire research journey. Misalignment between a student’s personal attributes and their chosen philosophical stance can hinder research progress and the quality of the resulting knowledge product. The literature presents multiple, sometimes conflicting, perspectives on research paradigms, philosophies, and their links to methodology, creating confusion for students encountering these concepts for the first time. This study presents an adaptive software solution that leverages machine learning and natural language processing to guide postgraduate students in selecting a research philosophy aligned with their personal attributes. The system employs predictive models—including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Classification and Regression Trees (CART)—together with NLP techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and Hashing, to identify relationships between student profiles and philosophical approaches. Cross-validation results demonstrate a confidence level of 0.83, indicating the model’s reliability in recommending suitable research philosophies. The study found that the developed software enhances the curriculum and assists in teaching students about philosophies and paradigms, particularly in the transition from undergraduate to postgraduate education. Participants showed significant improvements in understanding the meaning of research paradigms and philosophies and in differentiating between them, which is essential for recognizing the implications of their research choices in knowledge development. By bridging student characteristics, philosophical theory, and methodological practice, this AI-driven approach not only guides students but also strengthens research education through technology-enhanced learning.

Dharmanand Joshi — Auckland University

This training session provides a practical introduction to conducting narrative inquiry in qualitative research. Situated within Applied Linguistics and related fields, the session presents narrative inquiry as a relational and interpretive approach that enables researchers to explore lived experience, identity, and professional practice through systematic inquiry.Drawing on the presenter’s doctoral research experience, the session guides participants through the key stages of designing and conducting a small-scale narrative inquiry project. It begins by clarifying what narrative inquiry is and how it differs from more conventional forms of qualitative research. Participants will then identify a focus for inquiry grounded in their own context and learn how to generate narrative data using tools such as story prompts, reflective writing, and conversational accounts.The session also introduces practical strategies for working with narrative data. Participants will explore how to read and reread accounts, attend to shifts in voice and perspective, and interpret meaning with sensitivity to context, emotion, and relational positioning. Ethical considerations, including representation and responsibility toward participants’ stories, will be integrated throughout.Combining brief input with guided reflection, pair discussion, and hands-on planning, the session offers a structured yet flexible pathway for participants to begin their own narrative inquiry. By the end, participants will have a clear understanding of how to design, conduct, and interpret a narrative inquiry project that is contextually grounded and professionally meaningful.

Emmanuel Remi Aiyede — University of Ibadan

This participatory training session provides a structured introduction to participatory research methods, focusing on their practical application, having the MethodsNet conference as the fieldwork. The session is designed for researchers interested in integrating participatory approaches into their methodological toolkit, particularly in contexts where rapid, situated data collection and collaborative inquiry are possible. Participants will be guided through the design of a small-scale participatory research protocol, including the formulation of research questions, selection of appropriate methods, and development of sampling strategies that ensure diversity across geographic, disciplinary, and demographic dimensions. The session places particular emphasis on methodological rigor in participatory settings, including transparency of research intent, informed consent procedures, and reflexive awareness of researcher-participant roles. Through guided exercises, participants will collaboratively design a research plan and optionally conduct brief peer-based data collection activities (e.g., semi-structured interviews or structured observations) during the conference. These activities are followed by a structured debrief focused on methodological challenges, data quality considerations, and adaptation of participatory methods in real-world research contexts. The session aims to bridge conceptual understanding and applied practice, offering participants a concrete introduction to participatory research design that can be adapted to their own empirical work.

Christine Gibb — University of Ottawa

Knowledge mobilization refers to the process of moving research evidence into active use to maximize its impact on policy, programs, practice, and public understanding. Increasingly expected in grant applications and academic research, knowledge mobilization seeks to bridge the gap between universities and society by fostering reciprocal exchanges between researchers and knowledge users. Yet many researchers receive limited practical training in how to design meaningful knowledge mobilization strategies aligned with their methodological approach. This participatory session introduces participants to the fundamentals of knowledge mobilization and goes through practical exercises to develop a knowledge mobilization plan for a current or proposed research project. The workshop builds on Vicky Ward’s (2017) framework and guiding questions: “Why is knowledge being mobilized? Whose knowledge is being mobilized? What type of knowledge is being mobilized? How is knowledge being mobilized?” Through hands-on activities and collaborative discussions, participants will identify the key knowledge users of their research, map where these audiences seek information, and assess which communication and engagement strategies are most appropriate for different contexts and communities. The workshop emphasizes that knowledge mobilization is not simply the dissemination of findings at the end of a project, but an ongoing relational process. Participants will also reflect on challenges associated with translating research across academic, community, policy, and practitioner settings. By the end of the participatory session, participants will have drafted a knowledge mobilization plan that can be adapted for their research proposals, ethics applications, partnerships, and future dissemination activities.