Methodology in Higher Education Research B
| Numbering Code | G-EDU50 56358 SJ47 | Year/Term | 2022 ・ Intensive, Second semester | |
|---|---|---|---|---|
| Number of Credits | Course Type | topics seminar | ||
| Target Year | Master's students | Target Student | ||
| Language | Japanese | Day/Period | Intensive | |
| Instructor name | SAITO YUGO (Part-time Lecturer) | |||
| Outline and Purpose of the Course |
Statistics is an extremely powerful tool for conducting quantitative data research. A certain level of knowledge and understanding of statistics is necessary, both for conducting one’s own research and for reading and understanding the research of others. In this course, we will be aware of the process of quantitative research in higher education in particular and cover the statistical analysis methods most commonly used. In this way, the course aims to build students’ knowledge of data collection and analysis methods that they can use to inquire into their own problem awareness and to build their knowledge of the thought processes involved in method selection. The course also covers how to describe and interpret research results. Although this course will revolve mainly around content related to higher education, the skills and knowledge this course aims to teach can be applied to a wide variety of fields. This course is geared towards students who are unfamiliar or uncomfortable with quantitative research methods and statistics; I welcome all students who have the desire to take on the challenge of quantitative research. |
|||
| Course Goals |
Gain the skills and abilities needed for investigating one’s own awareness of problem using statistical methods. The goal is for students to acquire a holistic understanding of the basic knowledge and skills required to use the statistical analysis methods relied on in education-related research and be able to apply these methods in their own inquiries. Practically speaking, the goal of this course is for students to ① gain the knowledge/skills necessary for quantitative research methods, especially in the field of higher education ② be able to conduct analysis using statistical analysis software ③ be able to apply such analysis to critical investigation of quantitative research conducted by oneself and others and make appropriate judgments |
|||
| Schedule and Contents |
<1: Univariate Analysis> Class 1 Can you make it to the end of the course? (basic statistics) <2: Bivariate Analysis> Class 2 What is your GPA an index of? (correlation and association 1, inferential statistics) Class 3 Can self-assessment be trusted? (correlation and association 2, effect size and confidence intervals) Class 4 Is active learning effective? (unpaired t-test, effect size, and confidence intervals) Class 5 Do college students grow up? (paired t-test, effect size, and confidence intervals) <3: Trivariate analysis and above/analysis of variance> Class 6 Once again, is active learning effective? (one-factor ANOVA) Class 7 How can we measure “effects” in the first place? (two-factor ANOVA 1, interaction) Class 8 In what cases is active learning “effective”? (two-factor ANOVA 2, aptitude treatment interaction) <4: Multivariate analysis/multiple regression analysis> Class 9 Can learning outcomes be visualized ? (vector representation, simple regression analysis) Class 10 “Holding back from eating the marshmallow leads to future success?” Wait a minute! (statistical control and partial correlation coefficient) Class 11 “The much you study, the higher your grades will be.” Isn't that just stating the obvious? (multiple regression analysis) <5: Multivariate analysis/factor analysis> Class 12 What does that test measure? (reliability/appropriateness) Class 13 We’ve measured too much. Can we back off? (factor analysis) <6: Meta-analysis> Class 14 Once again, what is your GPA an index of? (effect size and confidence interval, meta-analysis) Class 15 (feedback) What will you investigate, and how? (Statistical analysis concept map) |
|||
| Evaluation Methods and Policy |
【Grading Method】 1. Quizzes 30% (achievement of goal 1) 2. Real data analysis report 30％ (achievement of goals 1 & 2) 3. Critical investigation of existing research 40% (achievement of goal 3). 【Grading Policy】 Students will be assessed based on achievement goals under the Graduate School of Education grading policy. |
|||
| Course Requirements | None | |||
| Study outside of Class (preparation and review) |
Preparation: watch preparatory videos outside of class time and take quizzes. Review: actual data analysis practica. |
|||
| Textbooks | Textbooks/References | Others. Homework videos for review and related information will be distributed to students. | ||
| References, etc. | Introduced during class | |||
