PhD Principles & Methods of Design Research

With more open data available to researchers than ever before, a proliferation of new tools to make sense of it, increasing sensitivity to power dynamics and vulnerability in data capture, and growing rigor and comprehensiveness expected of social science researchers in the wake of the replication crisis, today’s design research landscape is undergoing rapid and substantial changes. This course challenges PhD scholars to embrace these new resources and changing expectations to more effectively ground their pioneering insights, structure responsible and effective research protocols, and defend nuanced research claims.

While probing outdated dichotomies such as quantitative:qualitative, objective:subjective, and correlation:causation, the course begins with a grounding in essential contemporary research methods – data collection, analysis, interpretation, and visualization. After this introduction, each week of the course focuses on a different potential insight source for researchers, predicated on the concepts of data provenance, verifiability, and vulnerability. These discussions proceed through personal data derived from self-tracking and autoethnography, direct observation and sensor-based evidence, network analysis, community-led participatory data capture, population-level datasets, and the creation of hypothetical data through simulations and predictive modeling. This journey through data provenance mirrors the path data often takes in the real world, from intimate personal observations through to large-scale societal understandings and speculative forecasts.

Students will discuss how to gather, analyze, interpret, and share data from each of these sources responsibly, building a comprehensive set of skills to align their research protocols with their dissertation objectives.

• Session 1: Introduction
• Session 2: Personal Data: Self-tracking methods, direct observation, & autoethnography techniques
• Session 3: Observed Data: Thick descriptions, wearable sensors, & spatial instrumentation
• Session 4: Community Data: Web-scraping, participatory logging, social media & network analysis
• Session 5: Population Data: Geospatial information, large public & private datasets
• Session 6: Modeled Data: Simulations, predictive & correlative models & synthetic data creation
• Session 7: Review