WNF08050 Application of Statistical Analysis in Education

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program

Lecture Code WNF08050 Subject Classification Specialized Education

Subject Name 教育における統計分析の応用

Subject Name
（Katakana）

Subject Name in
English Application of Statistical Analysis in Education

Instructor To be announced.,HASHIMOTO JUNYA,TOYA AKIHIRO

Instructor
(Katakana) タントウキョウインミテイ,ハシモト　ジュンヤ,トヤ　アキヒロ

Campus Higashi-Hiroshima Semester/Term 1st-Year,  Second Semester,  3Term

Days, Periods, and Classrooms (3T) Inte：EDU K115

Lesson Style Seminar Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

Lecture-oriented, Practice-oriented,

Credits 2.0 Class Hours/Week 　 Language of Instruction J : Japanese

Course Level 5 : Graduate Basic

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 07 : Education

Eligible Students

Keywords Evidence-based education, Statistical analysis, Open data

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline The goal of this course is to learn advanced statistical methods used in fields such as education and psychology, and to acquire practical data analysis skills for educational data science. This is an intensive course that combines lectures and hands-on exercises, taking place over three Saturdays in October

Class Schedule lesson1 Applying Statistical Analysis in Education
lesson2 Principal Component Analysis and Factor Analysis (1)
lesson3 Principal Component Analysis and Factor Analysis (2)
lesson4 Generalizability Theory
lesson5 Summary 1: Creating, Setting, and Modifying Indicators
lesson6 Cross-Lagged Effect Models (1)
lesson7 Cross-Lagged Effect Models (2)
lesson8 Interaction Between Individual and Group Levels (1)
lesson9 Interaction Between Individual and Group Levels (2)
lesson10 Summary 2: Applied Educational Statistical Analysis
lesson11 Utilizing Open Data
lesson12 Practical Analysis Using Open Data (1)
lesson13 Practical Analysis Using Open Data (2)
lesson14 How Can Statistical Analysis Be Applied in Education?
lesson15 Decision-Making Based on the Results of Statistical Analysis in Education

This version is suitable for a syllabus or academic context and keeps the focus on the key aspects of each session.

Text/Reference
Books,etc. Nothing

PC or AV used in
Class,etc. Handouts, Visual Materials, Microsoft Teams, Zoom

(More Details)

Learning techniques to be incorporated Discussions, Quizzes/ Quiz format

Suggestions on
Preparation and
Review Lesson 1: Let’s explore what can be achieved by applying statistical analysis in education.
Lesson 2: Understand the differences between factor analysis and similar analytical methods.
Lesson 3: Learn the methods for measuring latent variables.
Lesson 4: Understand how to analyze data and continuously improve the reliability of evaluations and the significance of this process.
Lesson 5: Summarize the process of creating and setting valid indicators, making necessary adjustments, and collecting data.
Lesson  6: Understand what causal inference is.
Lesson  7: Acquire methods for causal inference.
Lesson  8: Understand the hierarchical structure of data commonly seen in educational data.
Lesson  9: Understand the relationship between aptitude-treatment interaction and interactions
between levels.
Lesson  10: Summarize the significance of constructing and analyzing appropriate models.
Lesson  11: Consider what kind of research can be conducted by using open data from international educational assessments.
Session 12: Understand the necessary preprocessing steps for analyzing open data from international educational assessments.
Session 13: Reproduce research using open data from international educational assessments.
Session 14: Think about how the content covered in previous lessons can be applied in real-world educational settings.
Session 15: Reflect on how statistical analysis can be applied in decision-making, based on the course content.

Requirements This is an intensive course that combines lectures and hands-on exercises, taking place over three Saturdays in October. Due to the instructor’s availability, schedule changes cannot be accommodated. Please note that alternative dates or individual participation via on-demand materials (e.g., recorded lectures) are not currently available.

It is preferable to have already completed the lecture of basic statistical analysis (course code WNF02000) or to have the equivalent knowledge and skills in statistical analysis and the use of R.

Grading Method At the end of the course, a report will be required and will be converted to a percentage grade. For example, if there are two reports, each will be worth 50% of the final grade.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program
Lecture Code	WNF08050	Subject Classification	Specialized Education
Subject Name	教育における統計分析の応用
Subject Name （Katakana）
Subject Name in English	Application of Statistical Analysis in Education
Instructor	To be announced.,HASHIMOTO JUNYA,TOYA AKIHIRO
Instructor (Katakana)	タントウキョウインミテイ,ハシモト　ジュンヤ,トヤ　アキヒロ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Inte：EDU K115
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
Lecture-oriented, Practice-oriented,
Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	5 : Graduate Basic
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	07 : Education
Eligible Students
Keywords	Evidence-based education, Statistical analysis, Open data
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	The goal of this course is to learn advanced statistical methods used in fields such as education and psychology, and to acquire practical data analysis skills for educational data science. This is an intensive course that combines lectures and hands-on exercises, taking place over three Saturdays in October
Class Schedule	lesson1 Applying Statistical Analysis in Education lesson2 Principal Component Analysis and Factor Analysis (1) lesson3 Principal Component Analysis and Factor Analysis (2) lesson4 Generalizability Theory lesson5 Summary 1: Creating, Setting, and Modifying Indicators lesson6 Cross-Lagged Effect Models (1) lesson7 Cross-Lagged Effect Models (2) lesson8 Interaction Between Individual and Group Levels (1) lesson9 Interaction Between Individual and Group Levels (2) lesson10 Summary 2: Applied Educational Statistical Analysis lesson11 Utilizing Open Data lesson12 Practical Analysis Using Open Data (1) lesson13 Practical Analysis Using Open Data (2) lesson14 How Can Statistical Analysis Be Applied in Education? lesson15 Decision-Making Based on the Results of Statistical Analysis in Education This version is suitable for a syllabus or academic context and keeps the focus on the key aspects of each session.
Text/Reference Books,etc.	Nothing
PC or AV used in Class,etc.	Handouts, Visual Materials, Microsoft Teams, Zoom
(More Details)
Learning techniques to be incorporated	Discussions, Quizzes/ Quiz format
Suggestions on Preparation and Review	Lesson 1: Let’s explore what can be achieved by applying statistical analysis in education. Lesson 2: Understand the differences between factor analysis and similar analytical methods. Lesson 3: Learn the methods for measuring latent variables. Lesson 4: Understand how to analyze data and continuously improve the reliability of evaluations and the significance of this process. Lesson 5: Summarize the process of creating and setting valid indicators, making necessary adjustments, and collecting data. Lesson 6: Understand what causal inference is. Lesson 7: Acquire methods for causal inference. Lesson 8: Understand the hierarchical structure of data commonly seen in educational data. Lesson 9: Understand the relationship between aptitude-treatment interaction and interactions between levels. Lesson 10: Summarize the significance of constructing and analyzing appropriate models. Lesson 11: Consider what kind of research can be conducted by using open data from international educational assessments. Session 12: Understand the necessary preprocessing steps for analyzing open data from international educational assessments. Session 13: Reproduce research using open data from international educational assessments. Session 14: Think about how the content covered in previous lessons can be applied in real-world educational settings. Session 15: Reflect on how statistical analysis can be applied in decision-making, based on the course content.
Requirements	This is an intensive course that combines lectures and hands-on exercises, taking place over three Saturdays in October. Due to the instructor’s availability, schedule changes cannot be accommodated. Please note that alternative dates or individual participation via on-demand materials (e.g., recorded lectures) are not currently available. It is preferable to have already completed the lecture of basic statistical analysis (course code WNF02000) or to have the equivalent knowledge and skills in statistical analysis and the use of R.
Grading Method	At the end of the course, a report will be required and will be converted to a percentage grade. For example, if there are two reports, each will be worth 50% of the final grade.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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