2020 Graduate Methodologies in Cognition, Mathematics and Information F1

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Academic unit or major
Graduate major in Social and Human Sciences
Instructor(s)
Sato Reiko  Komatsu Midori  Kuriyama Naoko 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Tue3-4(Zoom)  
Group
-
Course number
SHS.M462
Credits
2
Academic year
2020
Offered quarter
3-4Q
Syllabus updated
2020/9/18
Lecture notes updated
-
Language used
Japanese
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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. 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.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to statistics 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.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Nothing required.

Reference books, course materials, etc.

T. W. Anderson, “An Introduction to Multivariate Statistical Analysis,” Wiley-Interscience

Assessment criteria and methods

Presentations by groups or individuals 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 basic statistical concepts.

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.

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