▶Japanese
Post to SNS
- Home
- > School of Environment and Society
- > Graduate major in Social and Human Sciences
- > Graduate Methodologies in Cognition, Mathematics and Information F1
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Social and Human Sciences
- Instructor(s)
- Sato Reiko Kuriyama Naoko
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-6(W9-707)
- Group
- -
- Course number
- SHS.M462
- Credits
- 2
- Academic year
- 2019
- Offered quarter
- 3-4Q
- Syllabus updated
- 2019/9/26
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
In this course, students will study research methods frequently used in the fields of cognitive, quantitative, and information sciences.
Through presentations by students, we will learn the foundation of statistics, multivariate analysis, data processing, the use of software, the methods of paper writing, and presentation.
This course aims to cultivate the students’ abilities to: select a proper methodology from various mathematical methods to cooperate with real problems; decide what necessary data is; develop an active research plan.
Student learning outcomes
At the end of this course, students will be able to:
1) Understand the characteristics and merits of various mathematical methods.
2) Select proper methods for practical complicated problem-solving.
3) Develop a scientific research plan which is appropriate for objects and goals.
Keywords
research methods, statistics, research design, classification, discrimination, multivariate analysis
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | ✔ Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
In the first half, the students investigate the basis of mathematical methods, summarize, and make presentations. In the second half, as an application of the numerical method, students will present us how to use each analysis method.
Materials will be distributed on the day online pdf, so prepare internet terminals such as notebook computers or tablets.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction | We exchanged opinions on the most appropriate method of presentation, investigation method, survey method and arrangement procedure. |
| Class 2 | chi-square, t-test | Join a presentation and discussions according to assignments. |
| Class 3 | chi-square, t-test | Join a presentation and discussions according to assignments. |
| Class 4 | Analysis of variance（１） | Join a presentation and discussions according to assignments. |
| Class 5 | Analysis of variance（２） | Join a presentation and discussions according to assignments. |
| Class 6 | correlation（１） | Join a presentation and discussions according to assignments. |
| Class 7 | correlation（２） | Join a presentation and discussions according to assignments. |
| Class 8 | How to create a questionnaire | Join a presentation and discussions according to assignments. |
| Class 9 | Factor analysis（１） | Join a presentation and discussions according to assignments. |
| Class 10 | Factor analysis（２） | Join a presentation and discussions according to assignments. |
| Class 11 | Multiple regression analysis | Join a presentation and discussions according to assignments. |
| Class 12 | Covariance Structure Analysis | Join a presentation and discussions according to assignments. |
| Class 13 | presentation（１） | Join a presentation and discussions according to assignments. |
| Class 14 | presentation（２） | Join a presentation and discussions according to assignments. |
| Class 15 | Summary | Review and discussion |
Textbook(s)
Nothing required.
Reference books, course materials, etc.
Eric D. Kolaczyk, “Statistical Analysis of Network Data: Methods and Models,” Springer
T. W. Anderson, “An Introduction to Multivariate Statistical Analysis,” Wiley-Interscience
Assessment criteria and methods
Presentation and handouts by groups or individuals in each lesson will be assessed as your own performance.
Related courses
- SHS.M441 ： Graduate Lecture in Cognition, Mathematics and Information S1A
- SHS.M442 ： Graduate Lecture in Cognition, Mathematics and Information S1B
- SHS.D463 ： Analyses and Modeling Techniques of Educational Data
- LAT.A401 ： Introduction to Psychological and Educational Measurement A
- LAT.A402 ： Introduction to Psychological and Educational Measurement B
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students should have successfully completed statistics.
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
This course consists of the content of science. However, it is still useful even for the students who want to study technology
This course is the methodological preparations for your master thesis.
