Spring 2023

POLSCI 231C 001 - SEM 001

Quantitative Analysis in Political Research

Kirk C Bansak

Jan 17, 2023 - May 05, 2023

09:00 am - 11:59 am

Social Sciences Building 791

Class #:33278

Units: 4

Instruction Mode: In-Person Instruction

Offered through Charles & Louise Travers Dept of Political Science

Current Enrollment

Total Open Seats: 11

Enrolled: 4

Waitlisted: 0

Capacity: 15

Waitlist Max: 5

Open Reserved Seats:

10 unreserved seats

1 reserved for Political Science: Graduate Students

2 to 1 hours of the exchange of opinions or questions on course material per week, 8 hours of outside work hours per week, and 3 to 4 hours of student-instructor coverage of course materials per week.

Other classes by Kirk C Bansak

Course Catalog Description

Learn about model-based statistical inference and its applications to political science research. The course will cover multiple approaches to model-based inference. First, students will learn about maximum likelihood estimation, which proceeds by assuming the data were generated by a specified probability model. Second, students will learn a collection of methods in machine learning, which employ algorithmic models to optimize fit to the data without relying on assumptions about the data mechanism. Along the way, students will learn about the strengths and limitations of these different approaches, how to interpret the outputs of different types of models, and how to assess the value of estimated models in different situations.