Type of Credit: Elective
Credit(s)
Number of Students
This course is intended to help you understand quantitative research and learn quantitative methods in international relations (IR). It is intended for those who have had no prior exposure to statistics. Statistics has played an increasingly large role in social science research, so it is essential to understand how statistics can be used in published research and controversies, even for those who do not rely on statistical methods. You will learn basic statistical concepts and models and how they can be applied in IR studies. Although the emphasis will be on statistical methods, most of the principles we will learn apply to all types of systematic research, regardless of whether it relies on qualitative or quantitative comparisons.
The statistical software used in this course is R, which can be downloaded for free for Windows, Macintosh, and Linux operating systems from http://www.r-project.org (make sure to download the latest version). Every week, we will have a one-hour R session, in which you will learn how to use R to analyze data or finish some tasks related to the topic in that week. All the homework that includes data analyses should be done in R. Your statistical analyses for the research paper should also be done in R unless you know how to use other software (such as STATA or SPSS) to specify the same model.
能力項目說明
Our goals are two-fold: We will introduce basic and important statistical concepts based on which statistical methods are developed; we will also emphasize how to use quantitative methods to analyze empirical data and how to substantively interpret and use the results of such analyses. The course assumes no prior knowledge of statistics or mathematics beyond a high school level, although some willingness to learn along the way will be essential.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次 Week |
日期 Date |
課程主題 Topic |
指定閱讀 Required Readings |
作業 Homework |
|
1 |
2/20 |
Course introduction |
No readings |
|
|
2 |
2/27 |
Research design |
Agresti & Finlay, Chapter 1 Shively, Chapter 2 |
HW1 |
|
3 |
3/6 |
Sampling and measurement |
Agresti & Finlay, Chapter 2 |
HW2 |
|
4 |
3/13 |
Descriptive statistics |
Agresti & Finlay, Chapter 3 |
HW3 |
|
5 |
3/20 |
Probability and distribution |
Agresti & Finlay, Chapter 4 |
HW4 |
|
6 |
3/27 |
Confidence interval |
Agresti & Finlay, Chapters 5 |
HW5 |
|
7 |
4/3 |
No class (public holiday) |
|
|
|
8 |
4/10 |
Significance test |
Agresti & Finlay, Chapters 6 |
|
|
9 |
4/17 |
Midterm exam |
|
|
|
10 |
4/24 |
Linear regression |
Agresti & Finlay, Chapter 9 |
HW6 |
|
11 |
5/1 |
Multiple regression |
Agresti & Finlay, Chapters 10 & 11 |
HW7 |
|
12 |
5/8 |
Time-series and cross-national analysis |
Bell & Jones (2015) |
HW8 |
|
13 |
5/15 |
Guest lecture |
|
(topic confirmed) |
|
14 |
5/22 |
Logistic regression |
Agresti & Finlay, Chapter 15 |
HW9 |
|
15 |
5/29 |
Count data analysis |
Monogan, Chapter 7 |
HW10 |
|
16 |
6/5 |
Presentations of term papers |
|
|
|
17 |
6/12 |
Flexible teaching week |
|
|
|
18 |
6/19 |
Flexible teaching week |
|
|
Aside from the obvious requirements—class attendance, punctuality, and reading ahead in preparation for lectures, you are required to work on 10 problem sets. Each problem set accounts for 3% of the total grades. I will distribute problem sets every Thursday, and will expect to receive your homework by next Thursday prior to class (submitted to Moodle). Late assignments will not be accepted. Some problem sets are analytical and theoretical, so you are allowed to hand write the answers, although a typed one will be preferred. Some problems sets require you to use the statistical software, and in this case you have to type up your answers. Students are encouraged to work in groups to solve the homework problems, although your submitted homework should be done by yourself.
Students are also expected to form teams with 2-3 persons to produce a research paper, which applies quantitative methods from this course. However, PhD students should finish a paper by their own efforts. The structure of the research paper is given in the appendix. The paper should be no longer than 4,500 words. You need to turn in a list of your team members and the paper topic in Week 13 and present your paper in Week 16. The paper is due on Friday, June 6, 11:59pm via Moodle. All students in the same team will get identical grades for the paper, so be sure to collaborate and don’t free ride.
Distribution of final grade:
Weekly homework: 30%; Research paper & presentation: 30%; Midterm exam: 40%
Required readings:
Supplementary reading: