NC TraCS Institute is sponsoring a Responsible Conduct of Research (RCR) course on July 17-19, 2024 from 8 a.m. - 12 p.m. each day. This course covers all NIH-required topics for the first phase of responsible conduct of research training, including topics covered in an NIH notice (NOT-OD-22-055) issued on February 17, 2022.

Individuals who have had prior RCR training may volunteer to serve as discussion leaders for the course to document ongoing RCR exposure. For example, if you are a K Scholar who needs ongoing RCR experiences for your grant, this an opportunity that would qualify. If you are interested, please email Susan Pusek at This email address is being protected from spambots. You need JavaScript enabled to view it..

To receive credit for course completion, participants must attend all three sessions.

Course Location by Date
July 17: via Zoom
July 18: Bioinformatics room #1131
July 19: Bioinformatics room #1131

Please note: On July 17, the course will be held via Zoom. On July 18 and 19, the course will be held in-person in Bioinformatics room #1131. Requests for remote participation on July 18 and 19 will be considered on a case-by-case basis.

If you have questions regarding this event or need to request a remote participation option for July 18 or July 19, please contact Susan Pusek at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

This 2-part (7/15/24 & 7/17/24) 5-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.

This course introduces participants to discrete choice models. These econometric models are used to explain how people choose between discrete outcomes, such as mode of travel to work or type of treatment for pain. The course will cover the subset of discrete choice models known as random utility models, namely the multinomial logit and nested logit. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge of discrete choice modeling is expected. Hands-on exercises will be conducted in Python.

Why Take This Course?
Random utility models are used across many disciplines. They allow one to use regression techniques to model choices between multiple outcomes, something not possible with many other models. Unlike many other models of discrete outcomes, random utility models are interpretable—it is easy to see which predictor variables are associated with which choices. Random utility models are also consistent with rational economic theory, meaning that properly specified estimates can be interpreted as willingness-to-pay and transformed into dollar amounts to understand the welfare impacts of policy. This course will prepare participants both to estimate these models and to interpret and evaluate them when encountered in practice.

Register