PSL726 - ANALYSIS of LONGITUDINAL DATA
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ANALYSIS of LONGITUDINAL DATA | PSL726 | 2nd Semester | 2 | 2 | 3 | 10 |
| Prequisites | - | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Drill and Practice | |||||
| Instructor (s) | Department Staff | |||||
| Course objective | Acquisition of knowledge and skills on analysis of parametric and non-parametric longitudinal data. | |||||
| Learning outcomes |
| |||||
| Course Content | Course Level: Graduate Course Coordinator: Prof. Dr. Zehra UÇANOK Course Supervisor: Prof. Dr. Zehra UÇANOK Course Assistants: It will be given by the course instructor by the department. Internship Status: None Longitudinal studies, methodological problems of longitudinal studies, statistical methods for longitudinal data and computer implementation of them. | |||||
| References | Singer, J. D. & Willet, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press. Long, J. D. (2011). Longitudinal data analysis for the behavioral sciences using R. New York: Sage. Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2004) Applied longitudinal analysis (Wiley series in probability and statistics). New York: Wiley-Interscience. Wu, H. & Zhang, J. (2006). Nonparametric regression methods for longitudinal data analysis. New Jersey: John Wiley & Sons. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Longitudinal studies and the problems of the design |
| Week 2 | Exploring the longitudinal data with graphical presentations / practice with computer |
| Week 3 | ANOVA methods in longitudinal data / practice with computer |
| Week 4 | Parametric modeling of longitudinal data / practice with computer |
| Week 5 | Generalized Linear Models / practice with computer |
| Week 6 | Generalized Linear Models / practice with computer |
| Week 7 | Mid-term exam |
| Week 8 | Growth Curve Models / practice with computer |
| Week 9 | Growth Curve Models / practice with computer |
| Week 10 | Hierarchical Linear Models / practice with computer |
| Week 11 | Hierarchical Linear Models / practice with computer |
| Week 12 | Mid-term exam |
| Week 13 | Non-parametric regression methods for longitudinal data / practice with computer |
| Week 14 | Missing values in longitudinal data / practice with computer |
| Week 15 | Missing values in longitudinal data / practice with computer |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 12 | 24 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 26 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 14 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 2 | 32 |
| Laboratory | 0 | 0 | 0 |
| Application | 16 | 2 | 32 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 16 | 11 | 176 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 0 | 0 | 0 |
| Midterms (Study duration) | 2 | 15 | 30 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 51 | 60 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest