From Marginalization to Resilience: Understanding the Effects of Stress and Identifying Culturally Responsive Interventions | UW CSSS SEMINAR
In this presentation, I summarize a systematic, multi-method research program that examines how sociocultural stressors, including racial discrimination and acculturative stress, shape mental health and substance use among people of color. This research integrates complementary designs and statistical analysis methods to strengthen causal inference and measurement precision. Racial marginalization elevates stress and negative emotions and varies across contexts and individuals, and data suggest modifiable resilience factors such as social support and bicultural self-acceptance. By combining rigorous statistical approaches with diverse study designs, this research advances cumulative science and informs culturally responsive interventions. Priscilla Lui is an Associate Professor in the Department of Psychology at the University of Washington (UW) and a licensed clinical psychologist (WA, PY61473662). She received her B.S. in biology and psychology from the UW, and M.A. in general psychology from the…
Event Types: Academics. Lectures/Seminars.
Wednesday, April 8, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. Reciprocal Relationships, Reverse Causality, and Temporal Ordering: Testing Theories with Cross-lagged Panel Models | UW CSSS SEMINAR
Abstract:
Reciprocal causal relationships are a common feature of criminological theories. For example, stable employment may reduce offending while offending may lead to job loss, and perceived disorder may increase fear of crime while fear of crime may increase sensitivity to signs of disorder. When multiple observations over time are available, cross-lagged panel models are commonly used to estimate these reciprocal effects. Yet this is often done without careful attention to how they map on to the theoretical process they are meant to capture or whether key assumptions of the models are satisfied. This may result in estimates that are not substantively meaningful or are biased or even reversed in sign. Reciprocal relationships also pose challenges for causal assumptions based on graphical tools; theories that posit reciprocal causation often rely on underlying macro–micro mechanisms not explicitly represented in empirical models. We provide guidance on how to align theory, model specification, and choic…
Event Types: Academics. Lectures/Seminars.
Wednesday, April 15, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. How Militarization Impacts the Climate Crisis: A Global Perspective | UW CSSS SEMINAR
Abstract:
In this talk I will provide a broad overview of my collaborative research concerning the ways in which militarization, as a form of coercive power, contributes to anthropogenic carbon emissions for nations throughout the world. First, I will summarize research on the short-run and long-run effects of militarization on national carbon emissions. Second, I will describe research that focuses on how militarization shapes the effect of economic growth on nations’ carbon emissions. Third, I will summarize research on militarization facilitating and supporting transnational capital in Global North nations outsourcing their carbon pollution to Global South nations. This body of empirical work serves as the foundation for a book in progress. Andrew Jorgenson is a Professor of Sociology and Founding Director of the Climate & Society Lab at the University of British Columbia, and a Senior Research Fellow in the Department of Theoretical Economics at Vilnius University. As an environmental sociologist…
Event Types: Academics. Lectures/Seminars.
Wednesday, April 22, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. Marginalized Regression for Bounded Outcomes with Floor and Ceiling Effects | UW CSSS SEMINAR
Abstract: Outcomes studied in social science and health research frequently take the form of fractions or percentages with a defined lower and upper limit, such as the percentage of medication doses taken, the rate of condom-protected sex, and the number of substance use-related problems endorsed in a screening questionnaire. Such data commonly exhibit floor and ceiling effects due to many participants that never engage in the outcome (e.g. never take prescribed medication) or are consistently at the upper limit (e.g. take all prescribed doses). Prevailing statistical approaches used to analyze such data do not fully account for clusters of responses at the lower and upper limits, which risk invalid conclusions about the effectiveness of interventions and theoretical models. We introduce an accessible extension to zero-inflated regression, the marginalized zero- and N-inflated binomial (MZNIB) model, that can analyze fractions and percentage data on the entire range between zero and 100% with greater…
Event Types: Academics. Lectures/Seminars.
Wednesday, April 29, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. Using Multilevel Modeling to Investigate Agitation in Long-Term Care: Evidence from Older Chinese Residents | UW CSSS SEMINAR
Abstract: Agitation is one of the most common and distressing behavioral symptoms among older adults living in long-term care facilities, particularly among residents with cognitive impairment. Despite its clinical importance, limited research has examined how individual and facility-level factors jointly contribute to agitation among older Chinese residents in institutional settings. In this seminar, Dr. Wang will introduce the application of multilevel modeling to investigate agitation in long-term care facilities serving older Chinese adults. The presentation will discuss conceptual considerations in studying behavioral symptoms across nested care environments, methodological decisions in multilevel analysis, and challenges encountered when working with facility-based data. The study underscores the need for person-centered strategies that consider residents’ psychosocial needs, family engagement, and organizational support within long-term care settings. Kaipeng Wang joined the University of…
Event Types: Academics. Lectures/Seminars.
Wednesday, May 6, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. From Estimands to Robust Inference of Treatment Effects in Master Protocol Trials | UW CSSS SEMINAR
Abstract: A platform trial is an innovative clinical trial design that uses a master protocol to evaluate multiple treatments, where patients are often assigned to different subsets of treatment arms based on individual characteristics, enrollment timing, and treatment availability. While offering increased flexibility, this constrained and non-uniform treatment assignment poses inferential challenges, with two fundamental ones being the precise definition of treatment effects and robust, efficient inference on these effects. Such challenges arise primarily because some commonly used analysis approaches may target estimands defined on populations inadvertently depending on randomization ratios or trial operation format, thereby undermining interpretability. This article, for the first time, presents a formal framework for constructing a clinically meaningful estimand with precise specification of the population of interest. Specifically, the proposed entire concurrently eligible (ECE) population not only pre…
Event Types: Academics. Lectures/Seminars.
Wednesday, May 13, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. Almost Magic: The Promise and Pitfalls of AI-Assisted Coding | UW CSSS SEMINAR
Abstract: Artificial intelligence tools are democratizing programming, making computational research accessible to researchers who have little or no formal programming background. This seminar offers a practical introduction to programming with AI assistance, beginning with a brief history of how AI—and AI coding tools in particular—came to be. We then discuss practical considerations for programming with AI: how to work effectively with AI assistants, how to frame problems clearly, and how to evaluate the code they produce. The foregoing skills are essential in addressing “technical debt” in AI-assisted programming, where generated code does not generalize easily to new features. The talk should provide insights into what AI-assisted programming can and cannot do, and a foundation for using AI tools responsibly. Joseph L. Hellerstein received his PhD in Computer Science from the University of California at Los Angeles. He has thirty years of experience in research and software engineering at the IBM TJ…
Event Types: Academics. Lectures/Seminars.
Wednesday, May 20, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. One Model, Many Methods: NIMBLE for Hierarchical Statistical Modeling in Social and Other Sciences | UW CSSS SEMINAR
Abstract: People often need to customize statistical models for particular problems and then consider a variety of methods for estimation and inference. Customizations may include adding components across space, time, repeated sampling, networks, non-parametric relationships or distributions, or multiple data sources, among others. Methods may include MCMC with potentially many kinds of samplers, empirical Bayes or marginal maximum likelihood, Laplace approximation and its extension to adaptive Gauss-Hermite quadrature, integrated nested Laplace approximation and related methods, sequential Monte Carlo, and others. Some methods represent hybrids, such as Particle MCMC combining particle filtering and MCMC. I will give an overview of the NIMBLE framework (R package nimble) for such problems. NIMBLE combines a language for writing models (an extension of the BUGS/JAGS language) and an algorithm programming system from R, in which all built-in algorithms are written and users can write new algorithms. Models an…
Event Types: Academics. Lectures/Seminars.
Wednesday, May 27, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu. Addressing Measurement Error Bias in Grouped Continuous Data for Causal Inferences | UW CSSS SEMINAR
Abstract: Applied researchers often analyze ordered categories that discretize continuous quantities (income, time frequencies, biomarkers, exposures). Treating such indices as continuous or imputing bin midpoints are convenient but misleading strategies to estimate marginal effects in regression analyses. This paper characterizes a form of measurement error that arises in those strategies by design, from the sampling mechanism, which induces biased and inconsistent estimations that are model-dependent and a priori unpredictable. I provide a solution to this problem, a calibration method - regularized interval regression - that treats responses as intervals of a latent distribution, and predicts calibrated proxies robust to measurement error biases in downstream linear regressions. Monte Carlo evidence shows that, relative to midpoint imputation and “ordinal-as-continuous,” the calibrated proxy yields unbiased linear estimates, especially in the presence of right-censoring/top-coding. An example based on su…
Event Types: Academics. Lectures/Seminars.
Wednesday, June 3, 2026, 12:30 PM – 1:30 PM.
SAV 409.
For more info visit csss.uw.edu.