This article describes a visual and participatory study exploring postsecondary students’ conceptualisations of feminism. We conducted the study in two phases: the first involved individual object elicitation interviews and the second involved group participatory data analysis sessions. Here, we focus on the participatory data analysis phase and the ‘amazing space’ some participants felt it engendered. We discuss two features that we see as related to this interpretation of the research process. The participatory data analysis sessions were, on the one hand, encounters suffused with intimacy and vulnerability, and on the other hand, dialogues that exposed participants to interpretive disagreements and tensions. We also reflect on how the study’s culminating research exhibit worked to extend the ‘amazing space’ cultivated during data collection.
In this paper, we report an exploratory collaboration in data analysis between a human researcher and Generative AI (GenAI). Specifically, we reveal how human-AI collaboration might contribute to alternative interpretations in theory-informed policy analysis. We draw on Karen Barad’s philosophy of agential realism as an anchor point to consider the ethico-onto-epistem-ological affordances and limitations of incorporating GenAI in social sciences research to guide the human-AI collaborative analysis. The exploratory collaboration reported is based on a case involving how a human researcher (the first author) engaged with Copilot, drawing upon a relevant analytical method, to analyse a Chinese education policy document. Through this example, we illustrate how a more intra-active human-AI collaboration could enable the human researcher to think the unthinkable , that is to think beyond the discursive or conceptual boundaries of his own understanding of the policy logics through the analytical framework. We argue that we can benefit from the analytical conversational collaboration process between AI and human intelligence, rather than simply the AI-generated output per se. This study is significant as it demonstrates a much more ethical, interdependent, and indeed intimate form of human-AI collaboration, in which the agency between humans and GenAI is recognised and respected. This more intimate form of human engagement with GenAI might also yield new ways of knowing and being as researchers in conducting qualitative policy analysis.
This paper compares two popular R packages for structural equation modelling (SEM) – lavaan and seminr – to help researchers understand not only how they work, but also when each is most appropriate. Although both tools allow users to estimate the same structural models, they are grounded in different methodological traditions and are designed to support different research goals. Using an identical model, we estimated results with both covariance-based SEM ( lavaan ) and variance-based SEM ( seminr ) and compared their outputs, including model specification syntax, evaluation criteria, and reporting conventions. The results show that both approaches lead to substantively similar conclusions regarding the relationships between constructs, while differing in emphasis: lavaan provides richer global model-fit diagnostics, whereas seminr places greater emphasis on prediction-oriented assessment and convenient access to latent variable scores. The contribution of this study lies in its practical, hands-on demonstration rather than in a theoretical or simulation-based comparison. The findings reinforce that there is no universally “better” SEM approach; instead, methodological choice should be guided by the research objective. Researchers focussed on theory testing may benefit more from lavaan , while those prioritising prediction or exploratory analysis may find seminr more suitable. Ultimately, considering both perspectives can support more transparent, robust, and methodologically appropriate SEM applications.
The rapid development of generative artificial intelligence (gen-AI) confronts the social sciences in the Global North and South with uncertainties along a spectrum between hopes for previously unimagined, leapfrogging potentials and fears of unprecedented risks that deepen divides. Lack of empirical data made it difficult to comprehend the situation and how to respond to it. To remedy this predicament, it is necessary to obtain empirical data to understand the extent and the ways in which these innovations are being used in research, teaching, and learning. This article compares evidence from pioneering surveys in Denmark and Mexico, of how gen-AI is used and perceived by faculty and students at institutions within these two strongly contrasting countries. The comparison reveals distinct patterns of usage by country, academic status, and gender. Normative assessments among respondents vary across different research phases and tasks in which gen-AI is deployed. Open survey items suggest uncertainty about gen-AI’s ethical and methodological suitability and a contrast between faculty emphasis on regulation and student prioritization of training. The comparison of the two data sets aims to provide a foundation for reflection and discussion about normative frameworks, facilitative initiatives, and collective interventions at this historical juncture of enormous methodological innovation.
The social composition of online spaces such as social media platforms has undergone a recent dramatic transformation with the advent of generative AI. Online material generated by AI includes human-like chatbot interactions and manufactured text and images purporting to represent real-life people, places and events. Efforts to estimate the extent of such material are hampered by the considerable difficulty of reliably detecting machine-generated material. For some commentators, these developments threaten the ability of online spaces to offer meaningful social engagement. This paper explores whether, in this context, it is still possible to conduct online ethnography aimed at understanding culturally-embedded meaning-making. The paper argues against a generalised methodological exceptionalism for generative AI. Instead, some promising strategies are found in existing methodological approaches that treat authenticity as a problem experienced by ethnographers and participants. A reflexive approach to ethnographic treatment of authenticity remains a valuable stance in situations where suspicions about the presence of generative AI are raised. In particular, multi-sited approaches allow experience of varying and cross-contextual understandings of authenticity and autoethnography focuses attention on how we navigate the lived experience of uncertainty about the nature of online content. Second, the paper turns to more-than-human ethnographic approaches and finds ethnography positioned as an immersive means to embrace non-human actors, including AI-generated features, as an intrinsic part of online experience. Such approaches ask for reflexivity around what is at stake in making judgements about the ontological state of materials encountered online. The methodological strategies reviewed here suggest that there is a future for online ethnography in the face of generative AI involving ongoing methodological innovation without wholesale methodological exceptionalism, but that this requires both a multi-faceted reflexivity and caution in adopting human-centric approaches founded on principled separability of human and machine.
Generative Artificial Intelligence methods using large language models can rapidly generate and process text, fuelling interest in qualitative research applications. To explore these possibilities, we developed a chatbot to interview social scientists at a Swedish university ( n = 74) and then conducted follow-up email exchanges about their experiences ( n = 23). This article presents an empirical example of a Gen AI–mediated qualitative interviewing setup, in which a chatbot conducts simultaneous, adaptive interviews to elicit in-depth engagement. We also show that around half of the social scientists in our sample are already experimenting with Gen AI in their academic work, primarily for writing support, literature engagement, transcription and, to a lesser extent, data analysis. We argue that chatbots can be powerful tools for qualitative research, but caution that they require careful oversight, critical reflexivity, and ongoing methodological development. This knowledge adds a nuanced, empirically grounded example of Gen AI–mediated interviewing, relevant for social scientists seeking to integrate Generative AI into their research practice.
The lack of research on why certain locations are perceived as unsafe has often been attributed to the scarcity of suitable data on place-based fear of crime. To address this gap, this study explores two spatial data collection methods applied in semi-rural areas of Kristianstad, Sweden. The first method uses a digital sketch map integrated with open-ended questions, accessed via an online web map platform, allowing participants to directly mark unsafe locations and resulting in geographically precise data on perceived fear of crime. The second method involves a random sample survey, employing open-ended questions to identify unsafe locations and circumstances, with responses geocoded into points, lines, or polygons for spatial inquiry. The study identifies similarities and differences in the geography of fear of crime derived from these methods. Strong alignment between the web map and survey is observed in both locations, and Spearman’s rank correlation also reveals a significant link between signal crimes and fear of crime. Kernel Density Estimation and Nearest Neighbour analysis reveal significant clusters of fear of crime in both areas, often overlapping with crime hotspots. The findings suggest that a convenience sample collected via a web application performs comparably to a traditional open-ended survey with a random sample, indicating that simpler and more cost-effective methods may be sufficient for capturing place-based fear of crime. This methodological insight supports pragmatic data collection and informs targeted, place-based fear of crime prevention strategies in both rural and urban contexts.
This article presents a rigorous and replicable methodological alternative for articulating quantitative and qualitative analyses within mixed-methods research, contributing to interpretive coherence by relating quantitative proximity structures to inductively derived qualitative codes. The proposed approach integrates minimum-distance and mean-value analysis (employing WSSA1 and POSAC via HUDAP software) with qualitative content analysis in ATLAS.ti to construct cohesive interpretive categories. It enables the alignment and meaningful links of Euclidean clusters of quantitative variables with inductively derived qualitative codes, enhancing the interpretive coherence of complex datasets. Through a case study in Technology and Informatics teacher education, the article demonstrates how a didactic unit based on scientific instrument construction fosters professional competencies in pre-service educators. Grounded in abductive reasoning, this integrated strategy offers a methodological contribution to mixed-methods research by bridging geometric and semantic analyses within a transferable educational framework. • Provides a systematic, replicable model linking Euclidean clusters (HUDAP: WSSA1/POSAC) with inductively coded qualitative categories (ATLAS.ti). • Contributes to interpretive depth and internal validity through methodological triangulation. • Offers a transferable application for STEM teacher education by fostering competencies through hands-on instrument design.
This paper compares two popular R packages for structural equation modelling (SEM) – lavaan and seminr – to help researchers understand not only how they work, but also when each is most appropriate. Although both tools allow users to estimate the same structural models, they are grounded in different methodological traditions and are designed to support different research goals. Using an identical model, we estimated results with both covariance-based SEM ( lavaan ) and variance-based SEM ( seminr ) and compared their outputs, including model specification syntax, evaluation criteria, and reporting conventions. The results show that both approaches lead to substantively similar conclusions regarding the relationships between constructs, while differing in emphasis: lavaan provides richer global model-fit diagnostics, whereas seminr places greater emphasis on prediction-oriented assessment and convenient access to latent variable scores. The contribution of this study lies in its practical, hands-on demonstration rather than in a theoretical or simulation-based comparison. The findings reinforce that there is no universally “better” SEM approach; instead, methodological choice should be guided by the research objective. Researchers focussed on theory testing may benefit more from lavaan , while those prioritising prediction or exploratory analysis may find seminr more suitable. Ultimately, considering both perspectives can support more transparent, robust, and methodologically appropriate SEM applications.
While debates on the rationale for Mixed Methods Research (MMR) persist, its practical application faces challenges, particularly concerning the quality of integration, which hinges on the successful combination of compatible paradigms with qualitative and quantitative methods. This article contributes to mixed methods research by outlining an integrative strategy within a convergent mixed methods design. It offers a comprehensive framework for studying complex social phenomena, such as the political participation and social movements of Afro-descendant women in Chocó, Colombia. This design is supported by a clearly defined purpose, a specific philosophical orientation, and a strong rationale for mixing. Notably, it uniquely integrates Intersectionality Theory and the Capabilities Approach within a Critical Realism paradigm, shaping both data collection and analytical strategy.
A goal in implementing health research is to change behavior and workplace culture. Realist methods are being used increasingly in healthcare research. While these methods provide context-based pragmatic recommendations, the data can be dense. Thus, making the transfer of knowledge into practice challenging for researchers and clinicians. This paper offers a novel approach to studying complex issues, communicating and motivating behavior change in healthcare using a realist approach to narrative analysis and producing synthesized narratives. A realist lens to research enables researchers to understand what works, with whom, and under what conditions. When used alone, realist methods can result in complex findings that can prove challenging to translate into practice. However, combining a realist approach with narrative analysis can enable a better understanding of the topic and promote practice change. This paper employs a case study on speaking up in healthcare to illustrate this novel method. This case study reports on a longitudinal interview study with a cohort of allied health new graduates to illustrate these methods and discuss the benefits and limitations of their application. We will provide researchers with clear steps to support replication. We argue that this method can aid the implementation of research findings across various contexts within and outside healthcare settings.
Structural Equation Modelling (SEM) has been widely applied in information systems, psychology, marketing, management, and other social science disciplines, providing a powerful framework for analysing relationships among latent variables. However, traditional SEM methods rely on assumptions of linearity and normality, which may limit their ability to represent complex or nonlinear data patterns. Recent advances in computational modelling have introduced Deep SEM (or Neural SEM), an approach that integrates deep learning components within SEM. This hybrid framework combines SEM’s theoretical and explanatory strengths with the representational flexibility of neural networks. In this paper, we provide an overview of Deep SEM, demonstrate its implementation in R using the SEMdeep package, and compare its explanatory and predictive behaviour with that of a traditional covariance-based SEM under identical data conditions. Using an illustrative and parsimonious neural architecture, the results show that Deep SEM yields higher in-sample explained variance across endogenous constructs while preserving the dominant theoretical pathways identified by SEM. These findings suggest that Deep SEM offers a complementary extension to conventional SEM, enabling researchers to explore potential nonlinearities while maintaining interpretability and theoretical coherence.
In response to Van Hoogdalem and Bosman (2024) who advocate abandoning the use of intelligence tests at the individual level, we argue that their conclusions are too absolute and insufficiently substantiated. While we acknowledge some of the concerns they raise regarding measurement error and contextual influences, we argue that the complete dismissal of standardized intelligence tests overlooks their practical utility. Drawing on empirical evidence and established psychometric theory, we demonstrate that intelligence tests provide valuable information for clinical and educational decision-making. We further argue that replacing standardized testing with purely non-standardized methods introduces greater subjectivity and risk of error, ultimately undermining the quality of professional judgments. We argue that intelligence tests remain a valuable source of information in many cases of clinical and educational decision making, when interpreted carefully and integrated with other relevant sources of valid information using predefined decision rules.
This article introduces an original methodological contribution to inclusive research design: the Neuro-Cognitive Trait Interaction Model (NCTIM). Developed to address the exclusion and misunderstanding of neurodivergent people in traditional research, NCTIM provides a flexible, values-based framework that centres lived experience. Building on earlier work on neuro-cognitive trait interaction and inclusive research methods, NCTIM encourages researchers to respond to how neurodivergent people process information, interact, and communicate, rather than relying on diagnostic labels. Grounded in the neurodiversity paradigm and informed by epistemic justice, NCTIM focuses on how cognitive processing traits such as communication preferences, executive functioning, sensory processing, and attention styles shape research participation. It follows a three-stage process: mapping study demands, identifying potential trait interactions, and embedding inclusive features. The model’s application is demonstrated through my doctoral research with autistic women in the workplace. NCTIM offers a timely, neurodivergent-led contribution to reimagining research design, supporting greater inclusion, authenticity, and respect for neurodivergent expression and engagement in research.
This article explores how moments of silence, hesitation, and misrecognition can become methodologically generative in participatory research. Drawing on empirical work in the context of health, care, and socio-technical design, and grounded in feminist epistemology, postcolonial critique, and care ethics, we approach methodology not as a fixed set of tools, but as a n ethical and relational practice. Through a dialogical structure, we trace field encounters that unsettled our assumptions about participation, inclusion, and evidence—moments when participants withdraw, resist alignment, or are not legible within the formats research protocols provide. Rather than interpreting such instances as breakdowns, we propose a stance of attunement: a way of staying with epistemic friction, atmospheres, and forms of presence that escapes categorization. We argue that methods must adapt to the needs, rhythms, and expressions of participants—not the other way around—and that what remains unspoken in research is not a gap, but a form of presence. By staying with what remains untranslatable, we contribute to methodological discussions in qualitative research that challenge proceduralism and open space for epistemic elasticity, affective responsiveness, and situated accountability.
Social responsibility has emerged as one of the predominant topics in public discourse since the beginning of the century, not only at the level of social organizations but also within corporate spheres. Since the 1990s, many methods have been developed to measure social impact, showing the extent to which there is a need to evaluate this complex and multidimensional reality. However, despite these multiple efforts, none of these methodologies has become a reference standard. An exhaustive literature review was conducted to carry out a classification of SIA models, aimed at understanding their strengths and weaknesses, as well as identifying any gaps within the sector. The work encompassed both mapping and classifying SIA models along with identifying indicators related to these models. Later, efforts were made to harmonize indicator nomenclature through a combination of manual and automated methods. 144 methodologies and 1361 indicators were listed. First, the study concluded that there is no universally accepted definition of the concept of Social Impact. Second, the study concluded that no existing measurement fully meets the three fundamental characteristics outlined in the literature: producing a quantitative output , being exhaustive (i.e., considering all stakeholders), and enabling comparability over time and across (social and business) organizations or projects with differing characteristics.
This paper offers a reflexive, practice-based process evaluation of arts-based participatory methods (ABM) within a community-led violence prevention project in an Indigenous Andean community on Amantaní Island, Peru. Working within a Participatory Action Research (PAR) framework, the project engaged women and men through six creative methods – problem and solution trees, life history mapping with stones and petals, role play, body mapping, visioning drawings and Most Significant Change storytelling – drawing on visual, narrative and embodied modalities. These methods supported participants to articulate structural harms, strengthen confidence and public voice and co-design locally grounded solutions. By tracing how these activities unfolded in practice, we explore the relational, affective and political dimensions of ABM, showing how creative practices enabled trust-building, emotional expression and shifts in dialogue and power. We also reflect critically on the ethical tensions that emerged when working within externally funded, time-limited structures in a setting marked by high levels of gendered violence and marginalisation. This study contributes to growing scholarship on feminist and decolonial approaches to participatory research, offering concrete insights into the transformative potential and limitations of arts-based practice in under-resourced contexts marked by gendered and structural violence.
This article presents a qualitative methodological framework for exploring global socio-technological innovation in urban youth’s appropriation of public space. The proposed framework attempts to provide a heuristic approach for urban and youth studies scholars to better understand urban youth as key actors in the hybridisation of urban public space, without forgetting that access to and need for public space is not equally shared. Addressing gaps in intersectional youth studies and public space research, we propose a flexible, transdisciplinary approach based on intersectionality, starting questions and methodological principles. Designed to be flexible and adaptable to varying urban contexts, the proposed framework requires only minimal consensus on core concepts such as relational space and youthhood as a relational, intersectional category. Its main aim is to encourage collaboration between geographically diverse research teams to stimulate future studies on socio-technological changes in the appropriation of public space by youth around the world.
Diary methods are recognized as a valuable tool in qualitative research, however researchers have yet to take advantage of the many possibilities offered by social media platforms. This article demonstrates the potential of social media-based diary methods, specifically WhatsApp and Facebook diary groups, for researching everyday lived experience, by outlining the key advantages that this methodological innovation offers. Researchers can obtain insight into everyday repetitive behaviors, like eating and cooking, they can benefit from real-time reporting since participants can share information as and when it happens, and they can gain access to multi-modal data, as social media platforms support a variety of data types, including text, photos, audio, and video. Social media-based diary methods also make data collection more practical since the platforms are widely known and interacted with, and where they are supported by both smartphones and computers, transcription becomes a straightforward process too. A final benefit which could be taken advantage of is group discussions, so this article also explores the benefits of conducting diary methods in a group setting. This article is based on a research project exploring the everyday lived experiences of faith vegans which made use of private WhatsApp and Facebook groups to collect diary data from participants, including photos of meals, reflections on events and experiences, and group discussions. The findings in this article are thus grounded in methodological innovation and advance current scholarship on diary methods in a way that aligns well with contemporary trends and preferences.
