学部・大学院区分
Undergraduate / Graduate | | 開・博前 | | 時間割コード
Registration Code | | 3062310 | | 科目区分
Course Category | | 専門・プログラム
Program | | 科目名 【日本語】
Course Title | | 包摂的な社会と国家特論B（計量社会科学） | | 科目名 【英語】
Course Title | | Lecture on Inclusive Society and State B （Quantitative Social Science） | | コースナンバリングコード
Course Numbering Code | | INT2L6231E | | 担当教員 【日本語】
Instructor | | 関 能徳 ○ | | 担当教員 【英語】
Instructor | | SEKI Katsunori ○ | | 単位数
Credits | | 2 | | 開講期・開講時間帯
Term / Day / Period | | 春 火曜日 ５時限
Spring Tue 5 | | 授業形態
Course style | | 講義
Lecture | |
授業の目的 【日本語】
Goals of the Course(JPN) | | | | 授業の目的 【英語】
Goals of the Course | | This course introduces students to the fundamental of causal inference that constitutes the core of quantitative social sciences today. The course is divided into three parts: (1) conceptual discussion of causal inference, (2) elementary statistical theory, and (3) programming with statistical software. We are using a free software, R, which has been widely used by both scientists and major multinational corporations.
In this course, lectures proceed with data analyses examples with R. Students are expected to actively practice data analyses during the lectures in order to improve their programming skills, learn how to conduct quantitative research, and better understand causal inference. Data analyses examples are drawn from different fields of social sciences including political science, economics, and public policy. Quizzes, programing assignments, and data analysis practices are used to assess how well students understand materials covered in this course. |
| | 到達目標 【日本語】
Objectives of the Course(JPN) | | | | 到達目標 【英語】
Objectives of the Course | | By the end of the semester, students are expected to learn:
(1) fundamentals of causal inference,
(2) elementary statistical theory,
(3) programming with R, and
(4) how to start conducting quantitative analysis with their own topic of interest. |
| | 授業の内容や構成
Course Content / Plan | | Class 1: Course Introduction
Class 1-2: Introduction to R
- Agenda: Arithmetic operations, objects, vectors, functions, data files, saving objects, packages, programming and learning tips
Class 3-5: Causality
- Agenda: Loading a data file, cross-tabulation, subsetting, simple conditional statements, factor variables, causal effect and counterfactuals, randomized controlled trials, observational studies, confounding bias, difference-in-differences design, descriptive statistics for a single variable
Class 6-8: Measurement
- Agenda: Handling missing data, visualizing the univariate distribution, bar plot, histogram, box plot, printing and saving graphs, survey sampling, random sampling, nonresponse and other sources of bias, summarizing bivariate relationships, scatterplots, correlation, clustering, matrix, list
Class 9-13: Prediction
- Agenda: Loops, general conditional statements, linear regression, least squares, merging data sets, model fit, regression and causation, multiple regression, heterogeneous treatment effects, regression discontinuity design
Class 14-15: Uncertainty
- Agenda: Sample -> population inference, probability theory, statistical distributions, the central limit theorem, p-value, statistical inference, uncertainty in regression, confidence intervals, one- and two-tailed hypothesis tests |
| | 履修条件・関連する科目
Course Prerequisites and Related Courses | | | No course is required to take this course. |
| | 成績評価の方法と基準
Course Evaluation Method and Criteria | | (1) Programming assignments (20%)
(2) Data analysis exercises (30%)
(3) Two take-home exams (25% each)
Notes: Credit is given to C- or C (where applicable) or higher grade for each criterion. |
| | 教科書・参考書
Textbook/Reference Book | | [Textbook] Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press.
[Recommended reading] Kellstedt, Paul M. and Guy D. Whitten. 2018. The Fundamentals of Political Science Research (3rd edition). Cambridge University Press. |
| | 課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours) | | | Please read carefully relevant parts of the textbook before and after class. It is very important that students practice programming in R regularly inside and outside of class. |
| | 注意事項
Notice for Students | | Students must bring their own laptop that can install R and RStudio. Students will download and install them during the first class meeting if they have not done so.
[R] https://cran.r-project.org/
[RStudio] https://posit.co/download/rstudio-desktop/ |
| | 使用言語
Language(s) for Instruction & Discussion | | | | 授業開講形態等
Lecture format, etc. | | 対面で実施します。
Classes will be held in-person. |
| | 遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class) | | | |
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