Introduction to Discrete Choice Modeling in R<https://datamatters.org/course-descriptions/> (August 10, 2022) is part of Data Matters: Data Science Course Series<http://datamatters.org>, a five-day intensive course series for students, researchers, and industry professionals interested in gaining skills in data science, analytics, and visualization. This event, co-organized by the Odum Institute<https://odum.unc.edu/> and RENCI<https://renci.org/>, will be held remotely from August 8 to 12, 2022. Registration<https://datamatters.org/registration/> is now open!
Introduction to Discrete Choice Modeling in R<https://datamatters.org/course-descriptions/>
Summary
This course introduces participants to discrete choice models, econometric models of 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. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge is expected, and the course will cover logistic regression, multinomial logistic regression, and nested logistic regression. Hands-on exercises will be conducted in R.
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.
What Will Participants Learn?
This course will combine a lecture and hands-on coding experiences. Participants can expect to learn:
* The theory underlying random utility modeling
* How to interpret estimates from random utility models
* Common pitfalls in random utility modeling
* How to structure and estimate random utility models
Prerequisites
This course is introductory but having some background in linear regression will be helpful. The course will be most valuable to students with some experience in R and/or who have taken the introductory R class earlier in the week of Data Matters, but this experience is not necessary.
Prior to class, please install R, RStudio, and the Apollo package (http://www.apollochoicemodelling.com/).
Instructor
Matthew Bhagat-Conway<https://datamatters.org/instructors/> is an Assistant Professor in the Department of City and Regional Planning. His research interests are in travel behavior, urban transportation, and statistical methods for transportation data analysis. He is also jointly appointed in the Odum Institute<https://odum.unc.edu/> for Research in the Social Sciences, where he is available to assist researchers with statistics and data analysis.
Dr. Bhagat-Conway has a PhD and MA in Geography from Arizona State University, and a BA in Geography from the University of California, Santa Barbara. Prior to graduate school, he was a software developer and project manager for Conveyal, a public transport planning consulting firm, and a fellow in the Data Science for Social Good fellowship at the University of Chicago.
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