Event: Analyzing Complex Behavioral, Social and Population Health Data for COVID-19 & New Opportunities for Behavioral and Social Science Research (BSSR) Data Science Training

October 15, 2020, 12:00–1:00 PM EST / 9:00–10:00 AM PST via Zoom

Overview: The inaugural edition of the TADA-BSSR Webinar Series will feature a brief introduction of the new NIH/OBBSR sponsored training program and highlight some of the exciting ongoing work at five of the training sites involving complex data related to the COVID-19 pandemic. The session will feature interactive audience Q&A. Ask your questions in the chat box during the live Zoom session.

Event: Avoiding the Pitfalls of Selection Bias

January 21, 2021, 1:00–2:00 p.m. EST / 10:00–11:00 a.m. PST via Zoom

Overview: Selection bias occurs when the method by which a statistical sample is obtained prevents the sample from accurately representing the population about which one wishes to draw inferences. As straightforward as the issue may seem, selection bias is among the most pernicious perils of statistical inference. In this lecture, Dr. Carl T. Bergstrom will discuss some of the many ways that selection bias and related phenomena, from right censoring to the “Will Rogers effect,” can arise in medical research and beyond. He will draw from a range of examples, including recent studies on COVID-19. The session will feature interactive audience questions and answers, using the chat function of the live Zoom session.

Event: Translating Domain Knowledge into Mechanistic Process Models: Illustrating the Need with a Just-in-Time Adaptive Intervention

March 18, 2021, 12:00–1:00 p.m. EDT / 9:00–10:00 a.m. PDT via Zoom

Overview: Digital technologies present radically new possibilities for studying and developing insights related to both advancing fundamental understanding of social and behavioral processes and, simultaneously, improving behavioral interventions built on said knowledge. These new possibilities force us to find more robust ways to translate domain knowledge about processes (e.g., insights from operant and classical learning, cognitive science, and affective learning) into robust process models that rigorously specify temporal understanding of the dynamics and context-dependencies of said processes. The possibilities and challenges are particularly evident in developing just-in-time adaptive interventions (JITAI), which seek to provide support during states when a person would have the opportunity to engage in a positive behavior and be receptive to receiving support and when engagement would result in positive internalized adaptation toward participating in the desired behavior, eventually without the need for the JITAI. In this webinar the team will describe efforts to establish more robust approaches for translating domain knowledge about processes into computational models that account for theorized dynamics and to offer some initial steps to advance the field.