Category: Monthly Webinars

Abstract: Count data in social science studies exhibit various issues, including truncation, inflation, and under- or over-dispersion issues, making them unsuitable for standard Poisson and Negative Binomial models. Conway-Maxwell-Poisson (CMP) model has been recommended for its flexibility and interpretability of regression coefficients. However, empirical implementations remain limited, particularly in widely used commercial software familiar to… Continue reading A Generalized Conway–Maxwell–Poisson Model for Handling Inflation, Truncation, and Dispersion

Abstract: Jobs today ask us to “wear more hats” than ever, drawing academic attention to the emerging trend of skill diversification in the labor market. Most research on changing job skills focuses on high-paying occupations and treats skills as isolated boxes to check, ignoring how skills connect to one another and ignoring the possible skill… Continue reading Skill Diversification Beyond High-Paying Jobs

Abstract: This presentation highlights how deep neural networks enable new studies about protest dynamics. Images can be used to study the role of emotions, media behavior, and violence. New models significantly increase the precision and recall of protest images, expanding the scope of events under study. These models also improve the understanding of protester and… Continue reading Using Images to Study Protest Dynamics

Abstract: Large language models offer business researchers two forms of leverage: synthetic decision-makers for social simulations, and predictive labels for large-scale empirical analysis. Yet AI outputs are systematically biased, rarely meeting rigorous statistical or theoretical standards. Calibration, I argue, turns abundant-but-imperfect AI into trustworthy research artifacts, illustrated at two stages of the empirical pipeline. For… Continue reading Calibrating GenAI for Simulation and Inference in Business Research

Abstract: AI Agents with persistent memory, tool access, and specialist skills can now execute multi-step reasoning across the entire research pipeline, from idea to submission. This is not merely the fourth wave of research automation — following statistical computation, digital trace data, and machine learning — but the first to automate reasoning itself, and it… Continue reading Vibe Researching with Agentic AI for Social Sciences

Abstract: Text analysis has been a hot topic in the social sciences over the past decade. In comparison, the volume of visual data, such as social media images, street-view photos, and digitized maps, has grown even more rapidly, yet methods for analyzing them remain underdeveloped. This talk draws on my recent work to illustrate how… Continue reading Using Visual Data in Social Science Research

Abstract: How can one understand the spread of ideas across text data? This is a key measurement problem in sociological inquiry, from the study of how interest groups shape media discourse, to the spread of policy across institutions, to the diffusion of organizational structures and institution themselves. To study how ideas and narratives diffuse across… Continue reading Quantifying Narrative Similarity Across Languages

Abstract: This paper introduces the concept of the silicon gaze to explain how large language models (LLMs) reproduce and amplify long-standing spatial inequalities. Drawing on a 20.3-million-query audit of ChatGPT, we map systematic biases in the model’s representations of countries, states, cities and neighbourhoods. From these empirics, we argue that bias is not a correctable… Continue reading The Silicon Gaze: A Typology of Biases and Inequality in LLMs Through the Lens of Place

Abstract: “Digital twins” is becoming a paradigm for research and applications in various disciplines. To avoid jumping on the bandwagon without a thorough understanding of what it means and what it takes to address the genuine needs in our investigations, this seminar targets the nature and necessity of developing digital twins for social science research.… Continue reading Digital Twins for Social Science Research?

Abstract: We formulate factorial difference-in-differences (FDID) as a research design that extends the canonical difference-in-differences (DID) to settings without clean controls. Such situations often arise when researchers exploit cross-sectional variation in a baseline factor and temporal variation in an event affecting all units. In these applications, the exact estimand is often unspecified and justification for… Continue reading Factorial Difference-in-Differences