2025 (Current Year) Faculty Courses Liberal arts and basic science courses Teacher education courses
Introduction to Psychological and Educational Measurement
- Academic unit or major
- Teacher education courses
- Instructor(s)
- Naoko Kuriyama / Nobuko Hagiuda
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Thu
- Class
- -
- Course Code
- LAT.A401
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Mar 24, 2025
- Language
- Japanese
Syllabus
Course overview and goals
- Themes of course are statistical methods and multivariable analysis methods as well as the warp and woof model of problem-solving for statistical analysis.
Course description and aims
The goal of this course is to master the basic techniques of statistics often used in the areas of education and psychology.
Keywords
Introductory and intermediate statistics
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The lessons of this course consist of six paired lecture and homework exercise and final review lesson.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Basic Statistics and statistical tests (An E-mail from m-domain address is required until 12:00 on April 14th.) | Exercises by the gaming instructional material |
| Class 2 | Correlation and cross tabulation | Exercises by the gaming instructional material |
| Class 3 | Analysis of variance | Exercises by the gaming instructional material |
| Class 4 | Regression analysis | Exercises by the gaming instructional material |
| Class 5 | Principal component analysis and factor analysis | Exercises by the gaming instructional material |
| Class 6 | Cluster analysis | Exercises by the gaming instructional material |
| Class 7 | Review and Q/A | Review report |
Study advice (preparation and review)
Textbook(s)
Matsuda, T. and Hagiuda, N. (Eds.) (2021) Introduction to data science for problem-solving, Jikkyo Syuppan.
Reference books, course materials, etc.
E-learning materials
(Additional information will be introduced when necessary.)
Evaluation methods and criteria
Homework Exercises and final report (Students who did not submit any exercise or final report lose their credits.)
Related courses
- LAT.A403 ： Practices for Psychological and Educational Measurement
Prerequisites
None
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
kuriyama[at]isct.ac.jp
Office hours
Office hours by appointment.