Categorical Data Analysis

Course Syllabus

Instructor: Sewon Park

Email: swpark0413@sookmyung.ac.kr

Semester: Fall / 2025

Class Time & Location: Tuesdays & Thursdays, 16:30–17:45, Room B116, Changhak B

Office Hours: Tuesdays, 14:30-15:30 or by appointment

Course Description

This course covers the fundamental theories and practical methods of categorical data analysis, which is a core subject in statistical data analysis alongside regression analysis. While regression analysis (and ANOVA) focuses on continuous response variables, categorical data analysis addresses discrete response variables such as binary, multinomial, and count data. We will learn a wide range of methodologies for analyzing categorical outcomes, with emphasis on both theoretical foundations and practical applications.

Learning Objectives

By the end of the course, students will be able to:

• Perform categorical data analyses using R (or Python) and interpret the results from real-world datasets.
• Extend their knowledge of regression analysis to the framework of Generalized Linear Models (GLMs), with an emphasis on categorical response models.
• Understand and compare methods for analyzing binary, multinomial, ordinal, and count data.
• Evaluate model fit, interpret parameter estimates, and apply categorical data analysis techniques to practical data analysis problems.

Prerequisites

• Mathematical Statistics I, II
• Linear Regression

Course Materials

• Textbook:
  • Alan Agresti 지음 | 박태성, 이승연 옮김, 범주형 자료분석 개론, 자유아카데미.
  • Alan Agresti, An introduction to categorical data analysis, Wiley

Grading Policy

Weekly Schedule