The NC TraCS Professional Development Seminar Series is open to anyone seeking exposure to foundational concepts in clinical/translational research such as communication skills, finding funding, career planning, and implementing research. The Finding Funding module focuses on what you need to know before applying for funding for biomedical research.

Seminars in the Finding Funding module are presented every 2 weeks from September 30 - November 14, in-person on Tuesdays from 12 - 2 p.m. ET and repeated via Zoom on Fridays from 12 - 2 p.m ET.

In-person | Bondurant Hall, room 2030
Tuesday, September 30: Introduction to Sponsored Research
Tuesday, October 14: NIH 101, or Anatomy of a Request for Funding Announcement
Tuesday, October 28: Working with Foundation/Industry Sponsors/ SPIN database
Tuesday, November 11: What is a pilot study?

Virtual | Zoom
Friday, October 3: Introduction to Sponsored Research
Friday, October 17: NIH 101, or Anatomy of a Request for Funding Announcement
Friday, October 31: Working with Foundation/Industry Sponsors/ SPIN database
Friday, November 14: What is a pilot study?

Join for the topics that interest you and on the days that work for you. Please register for the Zoom-only option if you are unlikely to participate in-person as space for the in-person option is limited.

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Discrete Choice Modeling

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.

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.

Participants should be familiar with linear regression. Some understanding of binary logistic regression, as well as experience using Python, is recommended not required.

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