2018 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)
Yamamoto Hilofumi  Kuriyama Naoko  Inohara Takehiro 
Course component(s)
Lecture
Day/Period(Room No.)
Tue5-6(W9-707)  
Group
-
Course number
SHS.M462
Credits
2
Academic year
2018
Offered quarter
3-4Q
Syllabus updated
2018/4/9
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 fundation 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 in order to cooperate with real problems; decide what necessary data is; develop an effective research plan.

Student learning outcomes

At the end of this course, students will be able to:
1) Understand characteristics and merits of various mathematical methods.
2) Select proper methods for practical complicated problem solving.
3) Develop mathematical research plan which is appropriate for objects and goals.

Keywords

research methods, statistics, research design, classification, descrimination, multivariate analysis, tokenization, parsing, graph theory, protocol analysis, semantic differential method, self-organization map

Competencies that will be developed

Intercultural skills Communication skills Specialist 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 mathematical 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 Distribution and its characteristics, Scale and its characteristics Join a presentation and discussions according to assignments.
Class 3 Experimental design and analysis techniques Develop a research plan which utilizes simple regression, multiple regression, or mathematical quantification theory class I.
Class 4 Statistical methods for the classification Develop a research plan which utilizes factor analysis or principal component analysis.
Class 5 Multivariate analysis: multidimensional scaling method Develop a research plan which utilizes mathematical quantification theory class III.
Class 6 Multivariate analysis: principle component anaysis Develop a research plan which utilizes covariance Structure Analysis
Class 7 Morphology and syntax parser Develop a research plan which utilizes discriminant analysis or mathematical quantification theory class II.
Class 8 Speech sound analysis and transcription Develop a research plan which utilizes multi-dimensional scaling.
Class 9 Theory of graph: graph tools Develop a research plan which utilizes Cluster analysis.
Class 10 How to organize interview data: Protocol analysis Develop a research plan which utilizes network representation.
Class 11 Semantic differencial method and rikert method Develop a research plan which utilizes concepts of sub graph or creek.
Class 12 Self-organizing map, and personal attitude construct Develop a research plan which utilizes Network centrality.
Class 13 How to summarize ideas, visualization, and KJ methods Develop a research plan which utilizes structural analysis of network.
Class 14 Summary and booklet making Making and completing your own summary booklet.
Class 15 What is the research methods that do not fail. 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 fundamental linear algebra.

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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