Research Methods in Political Science I（政治学方法論 I）
School of Law and Graduate School of Law, Kobe University, Fall 2015
Class Materials
1. Introduction
- Lecture 1: Slides
- Installing R and RStudio
- Introduction to R
- Homework Assignment 1 (Due: 9am on 14 October 2015)
2. Introduction to Statistical Computing with R
- Lecture 2: Slides
- Statistics with R
- The t Test
- Homework Assignment 2 (Due: 9am on 21 October 2015)
3. Reproducible Research
- Lecture 3: Slides
- Introduction to Literate Programming with R Markdown
- Homework Assignment 3 (Due: 9am on 28 October 2015)
- Answers to Homework Assignment 3
4. Visualizing Data and Results of Analyses
- No slides for Lecture 4
- Creating Tables and Figures with R
- Homework Assignment 4 (Due: 9am on 4 November 2015)
5. Data Collection
- Lecture 5: Slides
- Data Collection with R
- Example Python Script for Web Scraping
- Result of Python Web Scraping (CSV file)
- Homework Assignment 5 (Due: 9am on 11 November 2015)
- Sample Answer
6-8. Linear Regression
- Lecture 6: Slides (revised on 16 November 2015)
- Linear Regression with R (1)
- Homework Assignment 6 (Due: 9am on 18 November 2015)
- Lecture 7: Slides
- Homework Assignment 7 (Due: 9am on 25 November 2015)
- Lecture 8: Slides
- Linear Regression with R (2)
- Homework Assignment 8 (Due: 9am on 2 December 2015)
9-12. Generalized Linear Models
- Lecture 9: Slides
- Logistic Regression with R (1)
- Homework Assignment 9 (Due: 9am on 9 December 2015. Typo corrected on Dec. 3)
- Lecture 10: Slides
- Likelihood
- Lecture 11: Slides
- Logistic Regression with R (2)
- Homework Assignment 10 (Due:
9am on 16 December 20159am on 12 January 2016) - Homework Assignment 11 (Due: 9am on 12 January 2016)
- Lecture 12: Slides
- Logit and Probit with R
- Homework Assignment 12 (Due: 9am on 20 January 2016)
13. Multiple Imputation: Dealing with Missing Values
Final Project
Miscellaneous
- Analysis of Clustered Data (prepared for Kobe Scientific IR/CP Seminar, May 27, 2016)