シラバス情報

授業科目名
Seminar II (FA)
(英語名)
Seminar II (FA)
科目区分
専門教育科目
Major Courses
対象学生
社会科学研究科
学年
2年
ナンバリングコード
KCWMS6MCA3
単位数
4単位
ナンバリングコードは授業科目を管理する部局、学科、教養専門の別を表します。詳細は右上の?から別途マニュアルをダウンロードしてご確認ください。
授業の形態
演習 (Seminar)
開講時期
2026年度後期
(Spring semester)
担当教員
Bishnu Kumar Adhikary、Bishnu Kumar Adhikary
所属
Graduate School of Social Sciences
授業での使用言語
英語
None
関連するSDGs目標
目標1/目標3/目標4
オフィスアワー・場所
Friday
12.20-13.00
Room No. 317, Research Building A
連絡先
By appointment
adhikarykobejp@gmail.com

対応するディプロマ・ポリシー(DP)・教職課程の学修目標
二重丸は最も関連するDP番号を、丸は関連するDPを示します。
学部DP
1◎/3◎/4◎
研究科DP
1◎/3◎/4◎
全学DP
1-1◎/1-2◎/2-1◎
教職課程の学修目標
目標1:磨き続ける力/目標3:協働する力/ー

講義目的・到達目標
This seminar reviews students’ research topics and provides the necessary guidance to carry out their theses systematically and scientifically. The main objective of this seminar is to help students produce a high-quality master's thesis in accounting and finance. In this seminar, students will learn to systematically review prior literature, outline key research questions, identify critical theories, draft hypotheses, collect data, use appropriate univariate and multivariate analyses, present and discuss research results, and propose new avenues for research with policy implications. At the end, the students will produce an academic paper related to their research interests.
授業のサブタイトル・キーワード
Seminar in Accounting and Finance
Keywords: Financial Accounting, Earnings Management, Corporate Governance, Research Approach

講義内容・授業計画
Lecture 1
Review of  research topics selected by students
Lecture 2
Techniques to review an academic article
Lecture 3
Scientific presentation of academic papers
Lecture 4
Review of time series, cross-section, and panel data
Lecture 5
Introduction to univariate analysis: mean, median, standard deviation, variance
Lecture 6
Review of academic papers
Lecture 7
Understanding sampling
Lecture 8
Understanding hypotheses and testing of hypotheses
Lecture 9
Understanding correlation and regression
Lecture 10
Presentation  of academic papers by students
Lecture 11
Understanding qualitative data analysis
Lecture 12
Presentation of academic papers by students
Lecture 13
Understanding how to write econometric models and present research results
Lecture 14
Paper presentation by students
Lecture 15
Review of the seminar outcome/ final report
対面・遠隔の別
対面
実施方法及び遠隔上限適用対象の別
In-person
生成AIの利用
完全に禁止
生成AI注意点
The use of Generative AI is forbidden
教科書
There is no textbook for this seminar
参考文献
The instructor will provide all lecture materials.
事前・事後学習(予習・復習)の内容・時間の目安
Students need to attend class regularly. They should read the class materials at home to participate actively in classroomdiscussions. In total, students need to allocate at least 30 hours outside the class.
アクティブ・ラーニングの内容
Teaching pedagogy includes lectures, academic readings, group discussions, assignments, and presentations. This is aninteractive class. The instructor will provide the necessary materials in the classroom. Students must study outside the classfor about two hours before each lecture to participate actively in classroom discussions. Finally, students are required to writean academic paper at the end of the seminar.
成績評価の基準・方法
Class contribution, 20%
Presentation, 30%
Final report, 50%

課題・試験結果の開示方法
in the classroom
履修上の注意・履修要件
Must complete Seminar I
実践的教育
None
備考
英語版と日本語版との間に内容の相違が生じた場合は、日本語版を優先するものとします。