| Academic Year |
2025Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA219001 |
Subject Classification |
Specialized Education |
| Subject Name |
行動計量学 |
Subject Name
（Katakana） |
コウドウケイリョウガク |
Subject Name in
English |
Behaviormetrics |
| Instructor |
HIRAKAWA MAKOTO |
Instructor
(Katakana) |
ヒラカワ マコト |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Fri5-8：ENG 220 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face, Online (simultaneous interactive) |
| Lecture-oriented |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
B
:
Japanese／English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
psychometrics, experimental design, statistical modeling |
| 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) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. |
|---|
Class Objectives
/Class Outline |
In this course you will learn about how to collect behavioral data and how to analyses these data with statistical model.The aim of this course is to understand the points that need to be taken into account in order to properly interpret about human behavior based on the statistical analysis.
|
| Class Schedule |
Section 1: Guidance
Section 2: Scientific method and statistical inference
Section 3: Psychological methodology
Section 4: Use of statistical inference in psychology
Section 5: Questionable research practices (QRPs): p-hacking
Section 6: Questionable research practices (QRPs): HARKing
Section 7: Cautions when using null hypothesis testing
Section 8: effect size and sample size Justification
Section 9: Preregistration of research
Section 10: Research transparency: open science
Section 11: Causal inference from experimental research
Section 12: Experimental design
Section 13: Validity of manipulations and measurements
Section 14: Reproducibility and generalizability of findings
Section 15: Summary and final exam
Final exam will be held on section 15. |
Text/Reference
Books,etc. |
none |
PC or AV used in
Class,etc. |
Handouts, Microsoft Teams, moodle |
| (More Details) |
slide, Hikkei PC |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Please make sure that you have a deep understanding of the content of each lecture. For example, read some of the literature referenced in the lectures. |
| Requirements |
|
| Grading Method |
Final exam will be evaluated 100% |
| 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. |