科目ナンバー

GM-OM3-SI03

履修条件・関連科目等

Students must have passed both Seminar I and II. Students should have their data for analyses before the course begin.

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

Based on the research questions and obtained data, students will do their analyses and estimations with the data to test their hypotheses.

科目目的

Students are expected to present their preliminary results and data analyses by the end of the semester.

到達目標

Students should be able to fulfill the basic data analyses by the end of the course.

授業計画と内容

Course content & schedule (subject to change):

1. Introduction. Show me your data for your graduation thesis.
2. Data management (1): Managing your files and setting your project’s material. How to use AI appropriately.
3. Data management (2): RStudio Markdown. Import data and data visualization. Subset data.
4. Data management (3): Handle the variables, outliers, duplication, and missing information.
5. Students’ presentation (1): Research progress report. Data management (4): Data structure, merge/append the data. Descriptive statistics and simple OLS estimation.
6. Students’ presentation (2): Research progress report. Data management (5): Practice 1.
7. Data management (6): Practice 2. Hypothesis testing and confidence intervals.
8. Students’ presentation (3): Research progress report. Panel data and panel fixed effects. Data management (7): Practice 3.
9. Students’ presentation (4): Research progress report. FAQ on RStudio usage. Descriptive statistics with RStudio (2).
10. Students’ presentation (5): Research progress report. How to improve your hypothesis.
11. Students’ presentation (6): Research progress report.
12. Students’ presentation (7): Research progress report.
13. Students’ presentation (8): Research progress report.
14. Students’ presentation (9): Research progress report.

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）

Notes:

Students must declare whether they use AI in the preparation of each presentation and must specify the exact prompts or sentences submitted to any AI chatbot. Students will not receive credit if they rely primarily on AI to complete their work or if they fail to properly acknowledge AI usage.

Students who do not wish to continue to Seminar IV and V (applicable to students with IDs starting with 23F and later cohorts) must submit written confirmation of their decision by the specified deadline.

Students who do not have a feasible research topic and appropriate data are not qualified 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

ディスカッション、ディベート／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. 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. Frorian Heiss. (2020). Using R for Introductory Econometrics, Second Edition. http://www.urfie.net (free access)

3. American Psychological Association. (2020). Publication Manual of the American Psychological Association, Seventh Edition.

Reference books

1. 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 .

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

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).

4. William Strunk Jr. The Elements of Style.

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.

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/