Too many qualitative and mixed-methods researchers are currently being asked to make an impossible choice: either remain outside the world of advanced data science and artificial intelligence, or enter it by learning programming, relying on expensive proprietary platforms, and uploading sensitive data to external servers. This book begins from a different premise: researchers should not have to choose between rigor, accessibility, privacy, and interpretive depth.
Data Science, Interactive Visualizations, and Generative AI Tools for the Analysis of Qualitative, Mixed-Methods, and Multimodal Evidence presents an integrated methodological ecosystem for ethical and equity-driven data science in qualitative and mixed-methods research. It is designed for scholars working with textual, relational, temporal, affective, spatial, visual, and multimodal evidence who want access to rigorous data science and AI-supported analytic tools without needing to master programming, pay recurring fees, or surrender control of sensitive materials.
The book introduces a fully local, no-code ecosystem of software tools for analyzing complex evidence across multiple layers of inquiry―from language and structure to time, emotion, interaction, and context. Special attention is given to ISARI (Intelligent Systems for Academic Research Integration), a fully offline, open-source, multimodal brainstorming partner designed to support scholarly memoing, comparison, synthesis, and evidence-grounded writing. ISARI is presented not as a substitute for interpretation, but as part of a broader local analytic environment in which computational outputs remain accountable to researchers’ judgment and to participants’ original evidence.
This is not a book about replacing researchers with AI. It is a book about giving researchers ethical, privacy-conscious, and equity-driven access to advanced analytic tools that have too often remained restricted to those with programming expertise or privileged institutional support. By bringing together interactive visualizations, machine learning, natural language processing, geocontextualization, temporal analysis, relational modeling, and local generative AI, this book offers a practical and forward-looking vision for doing rigorous research without compromising transparency, scholarly control, or data sovereignty. It is intended for researchers, faculty, graduate students, institutional analysts, and interdisciplinary scholars interested in expanding their analytic toolkit while preserving methodological accountability and interpretive authority.
- Provides access to no-code software that enables the dynamic analysis of knowledge production and democratizes access to data science tools
- Discusses analytic frameworks that overcome aggregation bias in text classification via machine learning
- Covers the integration of qualitative and quantitative methods in fully equal, status mixed methods design
Dr. Manuel S. González Canché is Professor in the Policy, Organization, Leadership, and Systems Division of the University of Pennsylvania, where he holds a tenured appointment. Dr. González Canché also serves as affiliated faculty with the Human Development and Quantitative Methods division and the International Educational Development Program. In addition, he is a senior scholar in the Alliance for Higher Education and Democracy. In his research, Dr. González Canché employs econometric, quasi-experimental, spatial statistics, and visualization methods for big and geocoded data, including geographical information systems, representation of real-world networks, and text-mining techniques. In related work, he aims to harness the mathematical power of network analysis to find structure in written content. He is developing an analytic method (Network Analysis of Qualitative Data) that blends quantitative, mathematical, and qualitative principles to analyze text data. Similarly, he is also developing the implementation of geographical network analyses that merge network principles and spatial econometrics to model spatial dependence of the outcome variables before making inferential claims. Dr. González Canché is currently teaching courses that rely heavily on computer programming code for PhD students. The no-code tools included in the proposed book have translated into grant funding and peer-reviewed publications in The Journal of Mixed Methods Research, The International Journal of Qualitative Methods, Expert Systems with Applications, and Methodological Innovations. Additionally, he has been offering professional development workshops for the American Educational Research Association. Dr. González Canché has a PhD in Higher Education Policy with cognates in Sociology, Economics, and Biostatistics from the University of Arizona.
