WMH00002 Quantitative and Analytical Social Science

Hiroshima University Syllabus

Japanese

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
（Katakana）シャカイカガクノタメノスウリ・ケイリョウブンセキ

Subject Name in
English Quantitative and Analytical Social Science

Instructor SAIJO HARUNOBU

Instructor
(Katakana) 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
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(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
lesson2
Exercises - Introduction to R and RStudio
lesson3
Causality with Randomized Experiments
lesson4
Exercises - Analyzing and Interpreting Experiments
lesson5
Survey Research
lesson6
Exercises - Survey Design and Analysis
lesson7
Student Presentations
lesson8
Student Presentations
lesson9
Linear Regressions
lesson10
Exercises - Linear Regressions
lesson11
Causal Inference with Observational Data
lesson12
Exercises - Causal Analysis with Observational Data
lesson13
Uncertainty and Probability
lesson14
Exercises - Estimating and Interpreting Uncertainty
lesson15
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.

Text/Reference
Books,etc. Llaudet, Elena, and Kosuke Imai. Data analysis for social science: a friendly and practical introduction. Princeton University Press, 2022.

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review 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.

Requirements

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

Message

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.

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 （Katakana）	シャカイカガクノタメノスウリ・ケイリョウブンセキ
Subject Name in English	Quantitative and Analytical Social Science
Instructor	SAIJO HARUNOBU
Instructor (Katakana)	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 Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (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 lesson2 Exercises - Introduction to R and RStudio lesson3 Causality with Randomized Experiments lesson4 Exercises - Analyzing and Interpreting Experiments lesson5 Survey Research lesson6 Exercises - Survey Design and Analysis lesson7 Student Presentations lesson8 Student Presentations lesson9 Linear Regressions lesson10 Exercises - Linear Regressions lesson11 Causal Inference with Observational Data lesson12 Exercises - Causal Analysis with Observational Data lesson13 Uncertainty and Probability lesson14 Exercises - Estimating and Interpreting Uncertainty lesson15 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.
Text/Reference Books,etc.	Llaudet, Elena, and Kosuke Imai. Data analysis for social science: a friendly and practical introduction. Princeton University Press, 2022.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	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.
Requirements
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
Message
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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