Japanese

Academic Year	2024Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Psychology Program
Lecture Code	WMC10050	Subject Classification	Specialized Education
Subject Name	心理学特講B
Subject Name （Katakana）
Subject Name in English	Special Lecture on Psychology B
Instructor	SHIMIZU HIROSHI,NAKASHIMA KENICHIRO
Instructor (Katakana)	シミズ　ヒロシ,ナカシマ　ケンイチロウ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year,  Second Semester,  Second Semester
Days, Periods, and Classrooms	(2nd) Inte：EDU K203
Lesson Style	Lecture	Lesson Style (More Details)
			Lecture and exercise lesson with PC This is a face-to-face class in a lecture hall. Teams are also used for online interaction.
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）	06 : Psychology
Eligible Students
Keywords	Bayesian statistics, statistic modeling, psychometrics
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	In order to carry out research in psychology and to become an expert in advanced practice, it is essential to learn basic and applied research methods. The goal of this class is to provide students with specialized knowledge and skills related to psychological research methods such as experiments, surveys, observations, and interviews, as well as the analysis of the results. Specifically, these research and analysis methods will be explained and discussed to deepen the understanding of these methods. Students will also discuss and consider research and analysis methods appropriate for their own research. In this year,  we introduce statistical modeling and Bayesian estimation. The purpose of this class is to learn theory of Bayesian statistical modeling and how to estimate probability models with Stan.
Class Schedule	lesson1  Orientation lesson2  The trends of Bayesian statistics lesson3  Bayesian statistical modeling. lesson4  Probability distribution and likelihood lesson5  Model evaluation lesson6  Models with normal distribution lesson7  Probabilistic programming languages lesson8  Stan lesson9  Generalized linear model lesson10  Random effect model lesson11  Generalized linear mixed model lesson12  Statistical modeling in psychology lesson13  Psychometrics and statistical modeling lesson14  How to create original model lesson15　General comment
Text/Reference Books,etc.	Reference books are introduced when we ask students to prepare before the class begins. We will then give instructions in class as needed.
PC or AV used in Class,etc.
	(More Details)	Handouts，image (video/PC/other image material)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Students will be informed of the content of the preparation prior to the start of class. Instructions will then be given in class as needed from the first lesson to the fifteenth lesson.
Requirements	Statistical analysis has a strong technical learning component. Active self-study is strongly encouraged.
Grading Method	A comprehensive evaluation will be made on the basis of the attitude of participation in the class and the content of the reports (assignments).
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Through the explanation of Bayesian statistics and statistical modeling, We would like to convey the key points for conducting research and writing articles.
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.