Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ADVANCED STATISTICAL METHODS | - | Fall Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Required |
| Course Coordinator | Assist.Prof. Pakize YİĞİT |
| Name of Lecturer(s) | Prof.Dr. Hanefi ÖZBEK |
| Assistant(s) | |
| Aim | To show advanced statistical methods that can be used in scientific research. |
| Course Content | This course contains; 1. Introduction to multivariate statistical methods,2. r*c table chi-square tests,3. Multiple regression analysis methods,4. Logistic regression analysis 1,5. Logistic regression analysis 2,6. Probit analysis,7. ROC analysis MIDTERM,8. One-way repeated measures ANOVA,9. Two-way repeated measures ANOVA,10. One-way ANOVA for independent groups,11. Two-way ANOVA for independent groups,12. MANOVA,,13. Survival analysis methods 1,14. Survival analysis methods 2 FINAL EXAM. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Summarize the multivariate statistical methods. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 2. Apply multivariate statistical methods on a computer. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 3. Interpret the results of statistics. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 4: Inquiry-Based Learning, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | 1. Introduction to multivariate statistical methods | Preparation for the lecture notes previously issued. |
| 2 | 2. r*c table chi-square tests | Preparation for the lecture notes previously issued. |
| 3 | 3. Multiple regression analysis methods | Preparation for the lecture notes previously issued. |
| 4 | 4. Logistic regression analysis 1 | Preparation for the lecture notes previously issued. |
| 5 | 5. Logistic regression analysis 2 | Preparation for the lecture notes previously issued. |
| 6 | 6. Probit analysis | Preparation for the lecture notes previously issued. |
| 7 | 7. ROC analysis MIDTERM | Preparation for the lecture notes previously issued. |
| 8 | 8. One-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 9 | 9. Two-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 10 | 10. One-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 11 | 11. Two-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 12 | 12. MANOVA, | Preparation for the lecture notes previously issued. |
| 13 | 13. Survival analysis methods 1 | Preparation for the lecture notes previously issued. |
| 14 | 14. Survival analysis methods 2 FINAL EXAM | Preparation for the lecture notes previously issued. |
| Resources |
| Lecture notes wil be given to the students. |
| Advanced Statistics, Larry Stephens, McGraw Hill, 2004. Bilgisayar istatistik ve tıp Dr. Murat Hayran, Dr. Oktay Özdemir. Bilimsel araştırmalarda biyoistatistik prensip ve yöntemlerinin bilinçli kullanımı Kadir Sümbüloğlu, Vildan Sümbüloğlu. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-1. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-2. Sağlık alanına özel istatistiksel yöntemler Kadir Sümbüloğlu. Sağlık Araştırmaları İçin Temel İstatistik, Murat Hayran, Mutlu Hayran. Tıbbi araştırmalarda istatistiksel analiz teknikleri “SPSS uygulamaları” Aziz Akgül. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Has scienfic and basic knowledge based on evidence which he/she specializes in physical therapy and rehabilition area. | ||||||
| 2 | Has the knowledge to evaluate, plan treatments, practice and scientific perspective which he/she specializes in physical therapy and rehabilition area. | ||||||
| 3 | Solves problems related to practice by using the knowledge which he/she specializes in physical therapy and rehabilition area. | ||||||
| 4 | Evaluates patients that require physical therapy in which area he/she specializes and pracitice protective, preventive and rehabilitative treatments. | ||||||
| 5 | Works independently and takes responsibility in terms of the knowledge and skills he/she gets in physical therapy and rehabilition area he/she specializes. | ||||||
| 6 | Evaluates Professional knowledge in which area he/she specializes universally and use it effectively. | ||||||
| 7 | Practice working performance by using communication Technologies in which area he/she specializes | ||||||
| 8 | Uses Professional knowledge directed to national policies by regarding to interdisciplinary communication. | ||||||
| 9 | Practices the responsiblity of informing and raising awareness of the society in terms of protecting health according to ethical principles in which area he/she specializes. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 0 | 0 | 0 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 0 | 0 | 0 | |||
| General Exam | 0 | 0 | 0 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 0 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(0/30) | 0 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ADVANCED STATISTICAL METHODS | - | Fall Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Required |
| Course Coordinator | Assist.Prof. Pakize YİĞİT |
| Name of Lecturer(s) | Prof.Dr. Hanefi ÖZBEK |
| Assistant(s) | |
| Aim | To show advanced statistical methods that can be used in scientific research. |
| Course Content | This course contains; 1. Introduction to multivariate statistical methods,2. r*c table chi-square tests,3. Multiple regression analysis methods,4. Logistic regression analysis 1,5. Logistic regression analysis 2,6. Probit analysis,7. ROC analysis MIDTERM,8. One-way repeated measures ANOVA,9. Two-way repeated measures ANOVA,10. One-way ANOVA for independent groups,11. Two-way ANOVA for independent groups,12. MANOVA,,13. Survival analysis methods 1,14. Survival analysis methods 2 FINAL EXAM. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Summarize the multivariate statistical methods. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 2. Apply multivariate statistical methods on a computer. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 3. Interpret the results of statistics. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 4: Inquiry-Based Learning, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | 1. Introduction to multivariate statistical methods | Preparation for the lecture notes previously issued. |
| 2 | 2. r*c table chi-square tests | Preparation for the lecture notes previously issued. |
| 3 | 3. Multiple regression analysis methods | Preparation for the lecture notes previously issued. |
| 4 | 4. Logistic regression analysis 1 | Preparation for the lecture notes previously issued. |
| 5 | 5. Logistic regression analysis 2 | Preparation for the lecture notes previously issued. |
| 6 | 6. Probit analysis | Preparation for the lecture notes previously issued. |
| 7 | 7. ROC analysis MIDTERM | Preparation for the lecture notes previously issued. |
| 8 | 8. One-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 9 | 9. Two-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 10 | 10. One-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 11 | 11. Two-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 12 | 12. MANOVA, | Preparation for the lecture notes previously issued. |
| 13 | 13. Survival analysis methods 1 | Preparation for the lecture notes previously issued. |
| 14 | 14. Survival analysis methods 2 FINAL EXAM | Preparation for the lecture notes previously issued. |
| Resources |
| Lecture notes wil be given to the students. |
| Advanced Statistics, Larry Stephens, McGraw Hill, 2004. Bilgisayar istatistik ve tıp Dr. Murat Hayran, Dr. Oktay Özdemir. Bilimsel araştırmalarda biyoistatistik prensip ve yöntemlerinin bilinçli kullanımı Kadir Sümbüloğlu, Vildan Sümbüloğlu. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-1. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-2. Sağlık alanına özel istatistiksel yöntemler Kadir Sümbüloğlu. Sağlık Araştırmaları İçin Temel İstatistik, Murat Hayran, Mutlu Hayran. Tıbbi araştırmalarda istatistiksel analiz teknikleri “SPSS uygulamaları” Aziz Akgül. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Has scienfic and basic knowledge based on evidence which he/she specializes in physical therapy and rehabilition area. | ||||||
| 2 | Has the knowledge to evaluate, plan treatments, practice and scientific perspective which he/she specializes in physical therapy and rehabilition area. | ||||||
| 3 | Solves problems related to practice by using the knowledge which he/she specializes in physical therapy and rehabilition area. | ||||||
| 4 | Evaluates patients that require physical therapy in which area he/she specializes and pracitice protective, preventive and rehabilitative treatments. | ||||||
| 5 | Works independently and takes responsibility in terms of the knowledge and skills he/she gets in physical therapy and rehabilition area he/she specializes. | ||||||
| 6 | Evaluates Professional knowledge in which area he/she specializes universally and use it effectively. | ||||||
| 7 | Practice working performance by using communication Technologies in which area he/she specializes | ||||||
| 8 | Uses Professional knowledge directed to national policies by regarding to interdisciplinary communication. | ||||||
| 9 | Practices the responsiblity of informing and raising awareness of the society in terms of protecting health according to ethical principles in which area he/she specializes. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |