Hiroshima University Syllabus

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Academic Year 2024Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences International Economic Development Program
Lecture Code WMH00002 Subject Classification Specialized Education
Subject Name Quantitative and Analytical Social Science
Subject Name
Subject Name in
Quantitative and Analytical Social Science
サイジョウ ハルノブ
Campus Higashi-Hiroshima Semester/Term 1st-Year,  Second Semester,  3Term
Days, Periods, and Classrooms (3T) Tues1-4:IDEC 203
Lesson Style Lecture Lesson Style
(More Details)
Credits 2.0 Class Hours/Week   Language of Instruction E : English
Course Level 5 : Graduate Basic
Course Area(Area) 24 : Social Sciences
Course Area(Discipline) 02 : Political Science
Eligible Students
Keywords causal inference, statistics, data analysis, research design 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
(Applicable only to targeted subjects for undergraduate students)
Class Objectives
/Class Outline
Students are expected to learn the basic concepts and skills for making quantitative causal inferences for the social sciences, implementing these methods on R, and successfully writing and presenting a quantitative research proposal with a research design.  
Class Schedule lesson1
Introduction to Data Analysis
Exercises - Introduction to R and RStudio
Causality with Randomized Experiments
Exercises - Analyzing and Interpreting Experiments
Survey Research
Exercises - Survey Design and Analysis
Student Presentations
Student Presentations
Linear Regressions
Exercises - Linear Regressions
Causal Inference with Observational Data
Exercises - Causal Analysis with Observational Data
Uncertainty and Probability
Exercises - Estimating and Interpreting Uncertainty
Student Presentations

Students will submit and present a research paper or a research proposal that incorporates some of the quantitative methods presented in the class. This will provide an opportunity to develop the quantitative portions of students' thesis research or other research projects. Students are required to come to office hours in the first half of the quarter to discuss their topics and interests, and submit a short proposal or outline by the fifth week.  
Llaudet, Elena, and Kosuke Imai. Data analysis for social science: a friendly and practical introduction. Princeton University Press, 2022. 
PC or AV used in
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Learning techniques to be incorporated  
Suggestions on
Preparation and
This course has no prerequisites, though basic arithmetic and probability will be helpful. Prior knowledge of data science, statistics, and experiments will also be helpful but not required. This course is intended for first-year masters students without strong quantitative methodological backgrounds, and will cover the basic concepts and implementation for causal inference techniques.

Please try to install R and RStudio prior to the first class, so that the introductory part can proceed smoothly. Students are asked to read the relevant chapters prior to the lectures, so that they can get the most use out of them. This class would be more helpful for those who are looking to design and implement a research project that involves quantitative analysis.

This class is intended to introduce students to research design in the social sciences, especially those that incorporate causal inference. This course will also introduce students to basic data analysis using R, so that students can learn to implement the tools introduced in lectures during exercises so that they can apply them to their own research or other practical uses.

Students will be asked to submit short proposals for their research proposals or papers during the middle of the semester, and they will present their research at the end of the semester so that they can onbtain feedback from the instructor and/or their peers.
Grading Method (1) Class participation (20%)
(2) First research proposal and in-class presentation (20%)
(3) Take-home quizzes (20%)
(4) Final research proposal and in-class presentation (40%)

The research proposals will be 2-3 pages in English. The initial proposal should present a coherent research question, a literature review, and a tentative research design for a topic in the social sciences (economics, political science, sociology, etc). The student will revise the proposal to submit as the final 3-5 page research proposal/presentation with a concrete research design. Specific deadlines and grading criteria will be announced 2-4 weeks before the deadlines.  
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Other Class schedule and requirements are subject to change. 
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. 
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