qualitative analysis

Existing computational tools for qualitative data analysis often fall short in effectively supporting causal exploration due to insufficient contextual awareness, limited trustworthiness, or overly complex outputs. To address these limitations, this work proposes QualCausal, the first interactive causal analysis system grounded in user research–driven design principles. Developed through formative user studies, QualCausal integrates context-aware processing, cognitive scaffolding, and explainability mechanisms to facilitate efficient exploration and validation of causal hypotheses within qualitative datasets. The system enables researchers to extract causal relationships, construct interactive causal networks, and examine findings through coordinated multi-view visualizations. User evaluations demonstrate that QualCausal significantly reduces analytical burden, provides robust cognitive support, and prompts critical reflection on how computational tools can be meaningfully integrated into social science research practices, thereby bridging the gap between computational assistance and qualitative inquiry paradigms.

This study examines the tension between efficiency gains and researcher autonomy arising from AI-assisted analysis in qualitative research. Through in-depth interviews with 16 qualitative researchers, it comparatively analyzes acceptance and underlying mechanisms across three coding paradigms: fully manual, human-initiated AI-assisted, and AI-initiated. The study innovatively conceptualizes AI explicitly as a “supporter”—neither collaborator nor supervisor—and identifies three core determinants of adoption: efficiency enhancement, attribution of interpretive ownership, and algorithmic trust. Findings indicate broad acceptance of AI for accelerating coding and thematic analysis, yet strong consensus on human primacy in meaning-making and interpretive authority. Enhancing procedural transparency, researcher control, and structured human–AI collaboration significantly strengthens trust and mitigates bias risks. The work provides theoretical grounding and actionable guidelines for developing human-centered, accountable AI-augmented qualitative research workflows.

This study addresses the lack of systematic understanding regarding the types and motivations of visual representations in qualitative research. Building upon and extending Verdinelli & Scagnoli’s (2013) work through a data-driven literature review, it conducts a content analysis of articles and their visualizations published between 2020 and 2022 in three leading qualitative methods journals. Integrating epistemological stance classification with visualization-type coding, the study innovatively combines correspondence analysis and cognitive network analysis for the first time. Findings indicate that while visualizations remain underutilized in qualitative research, their typological diversity is increasing, and the choice of graphical representation appears largely independent of the authors’ epistemological positions. These results offer both empirical grounding and methodological innovation for integrating interdisciplinary visualization tools into qualitative inquiry.

Current open-source text analysis tools exhibit significant limitations in scalability, statistical modeling capabilities, and alignment with social science research paradigms, hindering paradigm-driven qualitative analysis of large-scale textual data. This study introduces an open-source Python framework designed specifically for computational social science, integrating sociological and anthropological research logic with scalable NLP architectures. It supports visual exploration and pattern discovery across heterogeneous qualitative sources—including field notes and web-based texts. Methodologically, the framework adopts a problem-oriented—rather than technology-driven—design, embedding core qualitative analysis workflows; leverages PyTorch and SciPy ecosystems to enable efficient distributed processing of document collections exceeding one million items; and provides low-code parameter interfaces alongside modular architecture to facilitate iterative, theory-informed validation bridging qualitative reasoning and quantitative modeling. The framework addresses critical gaps in the open-source ecosystem concerning openness, reproducibility, and methodological sensitivity.

Narrative-driven data exploration faces core challenges—including contextual discontinuity across views, difficulty in tracing analytical reasoning paths, and insufficient externalization of intermediate interpretations. Method: We conducted a qualitative empirical study with 48 participants, combining in-depth interviews and task-based observations, to code and thematically analyze multi-stage dynamic analytical behaviors. Contribution/Results: The study systematically identifies three critical impediments and derives three design principles for supporting narrative evolution in visual analytics: (1) enforcing cross-view contextual consistency, (2) explicitly tracking reasoning trajectories, and (3) structurally externalizing intermediate interpretations. These principles are operationalized into concrete interaction mechanisms and practical guidelines. The work advances visual analytics systems from static chart presentation toward next-generation tools that actively support dynamic, iterative narrative construction.

This study addresses the responsible integration of large language models (LLMs) into qualitative research workflows while upholding core epistemological principles such as reflexivity, situatedness, and interpretive judgment. By bridging qualitative methodology with explainable AI through an interdisciplinary lens, it systematically aligns key LLM technical parameters—including context window, temperature, top-p sampling, prompt design, and system cards—with the epistemological foundations of qualitative inquiry. The work demonstrates how LLMs differ fundamentally from traditional NLP tools in terms of transparency and interpretability. It further proposes a practical framework for qualitative researchers that explicitly links technical configurations to research ethics and methodological rigor, thereby advancing a critical and responsible synthesis of AI technologies with humanities and social science methodologies.

This study addresses the absence of empirically validated quality metrics for evaluating the contribution of interview responses to qualitative research objectives. Building a corpus of 343 interview transcripts comprising 16,940 responses, the authors systematically assess the predictive validity of ten established quality indicators with respect to their research utility. Integrating qualitative content analysis, natural language processing, and statistical modeling, the findings demonstrate that direct relevance to the core research question is the strongest predictor of a response’s value, whereas commonly used NLP-based metrics—such as clarity and surprise-based informativeness—show no significant predictive power. These results challenge the applicability of current automated evaluation approaches and provide empirical grounding for assessing response quality in qualitative inquiry.

Traditional questionnaires struggle to simultaneously capture qualitative depth and quantitative structure, limiting comprehensive understanding of complex social phenomena. This study proposes a dynamic survey platform powered by large language models (LLMs) that, for the first time, enables real-time semantic clustering of open-ended responses. Through an interactive feedback mechanism, users can rate, rank, and reflect on these clusters, generating visual reports that integrate qualitative insights with quantitative analysis. Innovatively embedding LLMs within a closed-loop data collection framework, the approach facilitates dynamic comparisons between individual perspectives and group-level trends. Empirical validation across two field studies involving 93 participants demonstrates that the platform significantly enhances data richness and user engagement compared to conventional survey tools, while effectively fostering collaborative sensemaking.

This study addresses a critical limitation in current learning analytics tools, wherein frequency-oriented visualizations often obscure rare yet educationally significant student feedback. To bridge the gap between quantitative visualization and qualitative educational research, the authors engaged STEM education researchers in analyzing student logs using the WordStream platform. Through an integrated approach combining thematic analysis, member checking, and mixed-methods user research, the study uncovered epistemological tensions educators face when repurposing quantitative codings for qualitative inquiry. Three core themes emerged: tool experience, disciplinary contextualization, and the integration of quantitative and qualitative paradigms. Building on these insights, the work proposes design principles for visualizations that support deep qualitative exploration, offering both theoretical grounding and practical guidance for the next generation of learning analytics tools.