RL2026-2
Data Science, Interactive Visualizations, and Generative AI Tools for the Analysis of Qualitative, Mixed-Methods, and Multimodal Evidence
Wednesday, June 17, 2026
  
INSTRUCTOR

Manuel S. González Canché, University of Pennsylvania

TEACHING ASSISTANT

Chelsea Zhang, University of Pennsylvania

What if researchers could use advanced data science and AI tools without coding, without expensive monthly fees, and without giving up control of sensitive data?

Too many researchers are currently being asked to make an impossible choice: either remain outside the world of advanced data science and AI, or enter it by learning programming, relying on expensive proprietary platforms, and uploading sensitive data to external servers. This course begins from a different premise: researchers should not have to choose between rigor, accessibility, privacy, and interpretive depth.

This hands-on course introduces an integrated methodological ecosystem for ethical and equity-fueled data science in qualitative and mixed-methods research. It is designed for scholars working with textual, relational, temporal, affective, spatial, 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. Participants will be introduced to a fully local, no-code ecosystem of tools for analyzing complex evidence across multiple layers of inquiry, from language and structure to time, emotion, and context.

Special attention will be devoted 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. The course positions ISARI not as a substitute for interpretation, but as part of a broader local analytic ecosystem in which computational outputs remain accountable to researchers’ judgment and to participants’ original evidence.

This is not a course about replacing researchers with AI. It is a course about giving researchers ethical, equity-fueled access to advanced analytic tools that have too often remained restricted to those with programming expertise or privileged institutional support. If you want to expand your analytic toolkit without compromising ethics, privacy, transparency, or scholarly control, this course is for you.

The content of this course aligns with a forthcoming book titled “Data Science, Interactive Visualizations, and Generative AI Tools for the Analysis of Qualitative, Mixed-Methods, and Multimodal Evidence” See https://cutt.ly/RtZ1ZOfq for more details.

  
RL2026-3
Developing Theory Through Qualitative Inquiry
Wednesday, June 24, 2026
  
INSTRUCTOR

Johnny Saldaña, Arizona State University

This course provides focused guidance for developing theory by reviewing a qualitatively-derived theoretical statement’s six elements: concepts, propositional logic, parameters and/or variation, causation, generalization and/or transferability, and the improvement of social life. After a review of foundation principles in theory and theorizing, participants will explore skill-building activities in each of the six elements, followed by synthesis and visual modeling exercises, and recommended criteria for evaluating theoretical statements. The course is targeted to graduate students, novices to qualitative research, and early career scholars. Participants should have an introductory knowledge (e.g., a one semester or one quarter course) of research design or qualitative research methods. No pre-course assignments or special materials are needed. Course content is based on Saldaña’s textbook, Developing Theory Through Qualitative Inquiry (Sage Publications, 2025).

  
RL2026-4
Using the Interview Quality Reflection Tool (IQRT) to Hone the Craft of Interviewing
Monday, July 13, 2026
  
INSTRUCTORS

James L. Huff, University of Georgia

Jerrod Henderson, University of Houston
Sindia M. Rivera-Jiménez, University of Florida

In this course, we train attendees to evaluate the quality of their semi-structured or unstructured interviews using a novel process-based mechanism, the Interview Quality Reflection Tool (IQRT). This tool enables interviewers to reflect on how they can adaptively respond in interview settings, beyond the prescribed text of a protocol. Following a brief presentation, this course will use two learning activities to foster experiential immersion coupled with individual and group reflection, which includes a mock-interview experience and reflective analysis. The learning objectives for this course include: 1) develop novel understandings of interview quality by examining how interviewers adapt to the interview situation; 2) apply the IQRT to effectively reflect on how they elicit data in qualitative interviews; and 3) understand how they can use the IQRT to facilitate mentoring relationships in qualitative research. While no prior skills or knowledge are required, this course will be most effective for those who are actively engaged in interview-based research.