科目ナンバー

GM-OM2-SB01

履修条件・関連科目等

Students must have passed Introductory economics (Macroeconomics) and Microeconomics.

授業で使用する言語

英語

授業で使用する言語（その他の言語）

授業の概要

In the seminars, students explore socio-economic, firm-level, and managerial issues of their own interest. They learn to independently identify research questions and propose appropriate solutions based on evidence derived from data analysis. Students begin by studying basic statistics, economic reports, and academic papers. They also learn how to analyze data, conduct empirical estimations, and present results effectively.
The curriculum combines the study of applied methods with the reading of research papers across a wide range of economic topics. Students then select a topic of interest, conduct a literature review, formulate their own hypotheses, collect data to test them, analyze the data, and report their findings. Through this step-by-step process, students gradually acquire the knowledge and skills necessary to produce a meaningful and high-quality graduation thesis.

The graduation thesis is written in English. The use of quantitative methods and micro-level data to address research questions is strongly encouraged. Learning to use statistical software effectively (self-study) is an essential component of the seminar.

Specifically, in Seminar I, students will study or review basic statistical concepts and read and analyze economic research papers.

科目目的

The course is to review basic concepts in statistics and causal inference, and to provide students with tools to identify problems and to study economic issues of their interest.

到達目標

By the end of the Seminar I, students should be able to identify socio-economic problems and how they are solved in the previous studies which are of their interest. Using concepts in statistics and basic causal inference, students should be able to find, read, comment, and do presentations on academic papers.

授業計画と内容

Course content & schedule (subject to change):

1. Introduction. RStudio installation.
2. Data and decision making. How to talk in your presentation. How to use AI efficiently.
3. Introduction to Data. Academic research papers: What to read and how to read.
4. Students’ presentation (1). Review basic statistics (1). How to read a specific paper.
5. Students’ presentation (2). Review basic statistics (2). Summarizing data.
6. Students’ presentation (3). How to start with a topic/problem for your graduation thesis. RStudio: Import data from various sources.
7. Students’ presentation (4). How to search papers efficiently. Sampling.
8. Basic causal inference. RStudio: variables and hypothesis.
9. How to move from topic to research questions. RStudio: Variable types.
10. Selection bias, Simpson’s paradox, and regression.
11. Students’ presentation (5). Simple descriptive statistics with RStudio.
12. Students’ presentation (6). How to move from problems to the sources.
13. Students’ presentation (7).
14. Students’ presentation (8).

授業時間外の学修の内容

指定したテキストやレジュメを事前に読み込むこと／その他

授業時間外の学修の内容（その他の内容等）

RStudio

授業時間外の学修に必要な時間数／週

・毎週１回の授業が半期（前期または後期）または通年で完結するもの。１週間あたり４時間の学修を基本とします。
・毎週２回の授業が半期（前期または後期）で完結するもの。１週間あたり８時間の学修を基本とします。

成績評価の方法・基準

成績評価の方法・基準（備考）

Students must disclose any use of AI in preparing presentations and list the prompts submitted to AI tools. Failure to properly acknowledge AI usage or excessive reliance on AI will result in no credit.
Students without a feasible research topic and suitable data are not eligible to continue to Seminar IV and V.

課題や試験のフィードバック方法

授業時間内で講評・解説の時間を設ける

課題や試験のフィードバック方法（その他の内容等）

アクティブ・ラーニングの実施内容

プレゼンテーション

アクティブ・ラーニングの実施内容（その他の内容等）

授業におけるICTの活用方法

実施しない

授業におけるICTの活用方法（その他の内容等）

実務経験のある教員による授業

いいえ

【実務経験有の場合】実務経験の内容

【実務経験有の場合】実務経験に関連する授業内容

テキスト・参考文献等

Textbooks

1. Mesquita and Flower. (2021). Thinking clearly with data. A guide to quantitative reasoning and analysis. https://ufinity.library.chuo-u.ac.jp/iwjs0002opc/BB01704706.

2. Elena Llaudet and Kosuke Imai. (2023). Data analysis for social science : a friendly and practical introduction. Princeton University Press. https://ufinity.library.chuo-u.ac.jp/iwjs0002opc/BB01676196 .

3. OpenIntro Statistics (4th Ed). (2019). Diez, Barr, Çetinkaya-Rundel
CreateSpace (ISBN: 978-1943450077). Free access at https://www.openintro.org/book/os/
(Japanese translation textbook is also available for free).

Reference books

1. Booth, Wayne C., et al. (2024). The Craft of Research, Fifth Edition, University of Chicago Press. ProQuest Ebook Central, https://www.proquest.com/legacydocview/EBC/31597372?accountid=26790

2. Jonathan Schwabish. (2017). Better Presentations: A guide for scholars, researchers, and wonks. Columbia University Press: New York. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/chuouniv-ebooks/detail.action?docID=4723060&query=Better%20Presentations:%20A%20guide%20for%20scholars,%20researchers,%20and%20wonks

その他特記事項

Class attendance is mandatory. Absences without valid reasons are strongly discouraged. The seminars are cumulative and build on prior knowledge. Without a solid understanding of fundamental concepts and consistent participation, it will be extremely difficult to follow the course material and complete the graduation thesis.

All materials introduced in class are directly applied to the graduation thesis. Insufficient attention during classes—including other students’ presentations—or poor attendance will prevent students from properly applying the material and may delay graduation. Students who miss a class are fully responsible for covering the missed content on their own. Students who miss more than five classes will not be eligible to receive course credit.

Pasting course materials into AI chatbots or uploading them to the public domain is strictly prohibited. Such actions would violate copyright laws.

Independent learning of statistical software is an essential component of this course. Simply observing RStudio demonstrations in class without self-practice is ineffective. Students are required to practice independently and re-run the provided RStudio code after each class. Without first working with simple datasets, it is nearly impossible to manage real and complex data.

All presentations, lectures, discussions, and the graduation thesis (research project) are conducted entirely in English. The use of quantitative methods and microdata to address research questions is strongly encouraged.

Students are required to regularly check their university email and Manaba for important announcements. Clear and timely communication is essential for resolving issues. When contacting the instructor, emails must include a clear subject line, the student’s full name, student ID, and a statement indicating that the inquiry concerns this course. Emails should be concise, clear, and proofread before sending.

It is assumed that all students will continue for Seminar IV and V. However, if students opt this out (student ID with 23F, 24F, 25F... only), the students must notify the advisor as early as possible.

参考URL

Seminar's page

https://sites.google.com/g.chuo-u.ac.jp/tienmanhvu/

Instructor's site

https://sites.google.com/view/tienmanhvu/