Rapor Tarihi: 22.12.2025 22:56
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| - | EYD600 | Turkish | Compulsory | 3 + 0 | 3.0 | 10.0 |
| Prerequisite Courses | |
| Course Level | Graduate |
| Mode of delivery | Face to face |
| Course Coordinator | Doç. Dr. Taner ATMACA |
| Instructor(s) | Doç. Dr. Taner ATMACA (Güz) |
| Goals | The aim of this course is to enable doctoral students to comprehend, apply, and interpret advanced quantitative data analysis methods used in educational research. Students will gain proficiency in statistical modeling, advanced regression techniques, measurement models, structural equation modeling, and data-driven decision-making processes. |
| Course Content | This course covers basic and advanced quantitative analysis methods used in educational research. Topics include descriptive statistics, correlation and regression analyses, analysis of variance, multivariate statistics, factor analysis, structural equation modeling, quantitative aspects of scale development, and the use of data analysis software. Students will work with real datasets to test research questions, interpret results, and produce academic reports. |
| # | Öğrenme Kazanımı |
| 1 | |
| 2 | |
| 3 |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Foundations of Quantitative Research; data types and levels of measurement | Preparation, After Class Study, Research |
| 2. Week | Descriptive statistics; normality assessment; outlier detection | Preparation, After Class Study, Research |
| 3. Week | Correlation analysis and underlying assumptions | Preparation, After Class Study, Research |
| 4. Week | Simple regression analysis | |
| 5. Week | Multiple regression and model comparison techniques | Preparation, After Class Study, Research |
| 6. Week | Logistic regression and decision models | |
| 7. Week | Analysis of variance (ANOVA) and its extensions | |
| 8. Week | Introduction to multivariate statistics (MANOVA, MANCOVA) | |
| 9. Week | Exploratory Factor Analysis (EFA) | |
| 10. Week | Confirmatory Factor Analysis (CFA) | Preparation, After Class Study |
| 11. Week | Structural Equation Modeling (SEM) | |
| 12. Week | Quantitative techniques in scale development processes | Preparation, After Class Study, Research |
| 13. Week | Quantitative analysis with large datasets and software applications | |
| 14. Week | Student presentations and overall evaluation |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Based on master of science competencies of Educational Administration, he improves and deepens high level of knowledge with original thinking and research | ✔ | |||||
| 11 | Leads in original and interdisciplinary studies of Educational Administration | ✔ | |||||
| Program Requirements | DK1 |
|---|---|
| PY1 | 3 |
| PY11 | 3 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 1 | 3 | 3 |
|
Ders Dışı |
Homework | 1 | 1 | 1 |
| Preparation, After Class Study | 1 | 1 | 1 | |
| Practice | 1 | 1 | 1 | |
|
Sınavlar |
Final | 1 | 3 | 3 |
| Total Workload | 9 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 10.0 | ||
