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2021　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 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Tue3-4()  
Group
-
Course number
SHS.M462
Credits
2
Academic year
2021
Offered quarter
3-4Q
Syllabus updated
2021/3/19
Lecture notes updated
-
Language used
Japanese
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Course description and aims

In this course, students will learn research methods frequently used in the fields of cognitive, quantitative, and information sciences. 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, research design, statistics, analysis methods

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 basics of statistical and analytical methods will be introduced. In the second half, the students will critically read and discuss the literature. As an application of the statistical method, students will present a research plan using the methods they have learned. Materials will be distributed on the day.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to statistics Exchange opinions and summarize research methods, survey procedures, and presentation methods.
Class 2 Chi-square test Read papers in their own or similar fields that use the chi-square test and examine how it is used.
Class 3 Tests for the difference of means Read papers in their own or similar fields that use t-test and examine how it is used.
Class 4 Analysis of variance（１） Read papers in their own or similar fields that use analysis of variance and examine how it is used. Prepare for the presentation.
Class 5 Analysis of variance（２） Read papers in their own or similar fields that use analysis of variance and examine how it is used.
Class 6 How to create a questionnaire (1) Discuss and summarize the structure of the questionnaire, creation of scale items and face sheets, scale grading methods, and commissioning and conducting the survey.
Class 7 How to create a questionnaire (2) Examine methods for organizing and entering data obtained from questionnaires, and points to be noted when preparing questionnaires.
Class 8 Factor analysis (1) Read papers in their own or similar fields that use factor analysis and examine how it is used.
Class 9 Factor analysis (2) Read papers in their own or similar fields that use factor analysis and examine how it is used.
Class 10 Correlation analysis Read papers in their own or similar fields that use correlation analysis and examine how it is used.
Class 11 Multiple regression analysis Read papers in their own or similar fields that use multiple regression analysis and examine how it is used. Prepare for the presentation.
Class 12 Covariance Structure Analysis Read papers in their own or similar fields that use covariance structure analysis and examine how it is used. Prepare for the presentation.
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.

Course materials will be provided during the class.

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.

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

Reiko Sato（sato.r.ae[at]m.titech.ac.jp）
Midori Komatsu（komatsu.m.ae[at]m.titech.ac.jp）
Naoko Kuriyama（kuriyama[at]ila.titech.ac.jp）

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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