| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Advanced Data Analysis Methods | PDR621 | 3 + 0 | 3.0 | 6.0 |
| Prerequisites | None |
| Language of Instruction | Turkish |
| Course Level | Graduate |
| Course Type | |
| Mode of delivery | Formal |
| Course Coordinator |
Assist. Prof. Dr. Ahmet SAPANCI |
| Instructors |
Ahmet SAPANCI |
| Assistants | |
| Goals | The Advanced Data Analysis course aims to provide doctoral-level students with a comprehensive understanding of complex statistical analysis methods and the ability to effectively apply these methods in scientific research. This course seeks to equip students with the theoretical knowledge and practical skills necessary to analyze, interpret, and report datasets, fostering their capacity to conduct high-quality analyses in interdisciplinary research. |
| Course Content | The course covers foundational concepts of statistics and research, including hypothesis testing, regression analyses, and variance analyses (ANOVA, ANCOVA, MANOVA), as well as non-parametric tests. It also focuses on advanced statistical techniques such as mediation, moderation analyses, and structural equation modeling. Additionally, the course emphasizes measurement methods, including validity, reliability, and factor analyses, to ensure that statistical methods are applied in accordance with scientific standards throughout the research process. |
| Learning Outcomes |
- Understands and applies advanced statistical analysis techniques. - Comprehends differences between statistical tests and models and applies appropriate analysis methods. - Understands the fundamental principles of validity, reliability, and factor analyses and applies them in analysis processes. - Grasps the logic of mediation and moderation analyses and applies them in complex models. - Understands and develops the ability to apply structural equation modeling and multivariate analysis techniques. - Understands and applies ethical principles in reporting research findings and analysis processes. |
| Week | Topics | Learning Methods |
|---|---|---|
| 1. Week | Basic Concepts of Statistics and Research, Hypothesis Testing | Course Hours Visual Presentation Verbal Expression |
| 2. Week | Correlation Analysis, Simple Linear Regression, Multiple Regression | Course Hours Verbal Expression Preparation, After Class Study |
| 3. Week | Independent Samples T-Test, Mann-Whitney U Test, Paired Samples T-Test, Wilcoxon Test | Preparation, After Class Study Other Activities Course Hours Verbal Expression |
| 4. Week | One-Way Analysis of Variance for Independent Samples and Post-Hoc Tests, Kruskal-Wallis H Test | Preparation, After Class Study Other Activities Course Hours Verbal Expression |
| 5. Week | One-Way Repeated Measures Analysis of Variance, Friedman Test | Preparation, After Class Study Course Hours Verbal Expression Other Activities |
| 6. Week | Mixed Design (Split-Plot) ANOVA (Two-Way ANOVA with Repeated Measures on One Factor) | Verbal Expression Course Hours |
| 7. Week | Analysis of Covariance (ANCOVA) | Course Hours Verbal Expression |
| 8. Week | Multivariate Analysis of Variance (MANOVA) | Course Hours Preparation, After Class Study Other Activities |
| 9. Week | Validity and Reliability Concepts and Analyses, Internal Consistency in Psychological Tests, Item-Total Correlation | Course Hours Other Activities Preparation, After Class Study |
| 10. Week | Exploratory Factor Analysis | Course Hours Verbal Expression Preparation, After Class Study |
| 10. Week | Confirmatory Factor Analysis | Course Hours Preparation, After Class Study Verbal Expression |
| 11. Week | Mediation Analyses | Preparation, After Class Study Course Hours Other Activities |
| 12. Week | Moderation Analyses | Verbal Expression Course Hours Preparation, After Class Study |
| 13. Week | Structural Equation Modeling | Preparation, After Class Study Verbal Expression Course Hours |
| 14. Week | Structural Equation Modeling | Verbal Expression Preparation, After Class Study Course Hours |
| Tatlıdil, H. (1992). Applied Multivariate Statistics. Ankara: Akademi Matbaası. |
| Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). Uppersaddle River, N.J.: Pearson Prentice Hall. |
| Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling (2nd ed.). New York: Guilford Press |
| Tabachnick, B. G., & Fidell, L. S. (1996). Using Multivariate Statistics (3rd ed.). New York: HarperCollins. |
| Program Requirements | Contribution Level | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | Measurement Method |
|---|
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| Course's Level of contribution | None | Very Low | Low | Fair | High | Very High |
| Method of assessment/evaluation | Written exam | Oral Exams | Assignment/Project | Laboratory work | Presentation/Seminar |
| Event | Quantity | Duration (Hour) | Total Workload (Hour) |
|---|---|---|---|
| Course Hours | 14 | 4 | 56 |
| Research | 1 | 8 | 8 |
| Other Activities | 1 | 35 | 35 |
| Midterm 1 | 1 | 3 | 3 |
| Homework 1 | 1 | 38.5 | 38.5 |
| Final | 1 | 3 | 3 |
| Classroom Activities | 1 | 35 | 35 |
| Total Workload | 178.5 | ||
| ECTS Credit of the Course | 6.0 | ||
