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).

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.

INSTRUCTORS: Ting Yan, NORC David Dutwin, NORC Surveys remain one of the most powerful tools for understanding what people know, do, and believe—but designing a survey that produces valid and reliable data is far from simple. Researchers face critical decisions at every stage: Which sampling approach ensures representativeness? How do you craft questions that minimize bias? What strategies maximize response rates and data quality? This course provides a comprehensive overview of the survey design process, blending literature with practical, real-world examples. Participants will learn best practices grounded in methodological rigor, including: -Selecting a scientific probability-based sample -Designing and testing effective questionnaires -Developing outreach and data collection protocols to boost participation -Applying appropriate post-survey processing and adjustments for accuracy Whether you’re new to surveys or seeking a refresher, this session equips you with actionable strategies to design surveys that deliver trustworthy insights. By the end, you’ll have a clear roadmap for making informed design decisions that elevate the quality and impact of your research.

Instructors: Lorri Many Rivers Johnson Santamaría, Mixteco Indígena Community Organizing Project (MICOP); (course director) Cristina Corrine Santamaria Graff, Indiana University - Purdue University at Indianapolis

The Emotion Coding Technique (Lustick, 2021) is a systematic method of qualitative analysis that captures emotions as they arise and helps us process them as information about us, our participants, and our research objectives. In this course, we will review the emotion coding technique, which applies a set of reflexive questions to a chunk of data (Lustick, 2021). We will then talk about some of the complexities of naming and reflecting on emotions during data analysis. We will share our own best practices and hear some additional strategies from the instructor, including an emotion wheel to choose from. Lastly, we will shift into independent work time to practice and reflect on the technique. The course is open to all qualitative and mixed methods researchers, with graduate students and early career researchers in mind. Please have basic qualitative and mixed methods training, including an understanding of positionality and reflexivity. You are advised, though not required, to have available original qualitative data, such as an interview transcript, with which to practice the technique.

This course provides faculty, students, and other researchers with a primer on use and analyses of the many free teacher and principal survey datasets available through the RAND American Educator Panels (AEP). Participants will: (1) understand key features of probability-based sampling that ensures nationally representative survey data; (2) identify key AEP datasets for addressing their interests and research questions; (3) produce basic descriptive data regarding survey items of interest to them; (4) examine subgroup comparisons for survey items of interest; and (5) consider appropriate data visualizations for displaying results.

This course will focus on preparing education researchers to use state-of-the-art tools in artificial intelligence (AI) and machine learning in educational contexts. The course will cover topics such as applications of AI in education for prediction and classification, model evaluation via performance metrics, human feedback in AI model development, randomized control trials, and cost-benefit analysis. There will also be a strong emphasis on data ethics and responsible AI throughout the session. The course will be intended for those who have at least some programming and statistics background. The goal for the course is to introduce core tools and concepts in artificial intelligence and machine learning and familiarize participants with potential use cases via examples in educational settings. Required material include an installation of Jupyter notebooks or a google colab account.

The purpose of this course is to introduce the definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. Participants will also learn how to use a user-friendly R package to conduct the analysis and visualize results. The method introduction and the package implementation will be illustrated with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.

This inquiry-based methods course will focus on how researchers can take a problem of practice or topic of interest and transform it into a set of researchable questions. Participants will come to the course with a research question, and we will focus on improving it by interrogating its main concepts and unit(s) of analysis. This course is particularly relevant to practitioner researchers, executive doctoral and master's students, and early-career educational researchers. This will be a creative, collaborative, and constructively critical space of inquiry and support. Taught by two established researchers who are faculty in doctoral programs in education, this seminar will be hands-on and supportively critical. The goal is for every participant to leave with a set of research questions and a plan for next steps in research design. Participants will be asked to submit their draft research questions or topics on a shared document prior to the course. Required material and software include a word-processing program and access to Padlet and Google Docs.

Qualitative meta-synthesis is a rigorous and innovative approach to analyzing findings from multiple qualitative education research studies that have been determined to meet pre-established criteria (e.g., area of research, methodology used). Instructors from the Institute for Meta-Synthesis will teach theory and techniques for qualitative meta-synthesis, with a main goal of preparing participants to interrogate their topics in education research towards equity. The instructors have successfully completed and published on multiple meta-synthesis projects on equity topics in STEM education; examples and activities will be based on their research data. Despite its potential to help address issues of equity in education and to provide policy guidance at the national level, meta-synthesis is a methodology that is rarely introduced to graduate students. This course will address this knowledge gap for graduate students by teaching participants how to conduct qualitative meta-synthesis research, with a particular emphasis on justice-oriented aims and equitable research practices using examples from STEM education. Furthermore, skills learned for meta-synthesis may be applied to other important research tasks, such as conducting searches for literature reviews. This course is geared towards graduate students and early career scholars. Those interested in participating in this course ideally should have familiarity with literature reviews and qualitative research literature, though that is not required.