OLC629 - DATA ANALYSIS METHODS IN EDUCATION
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DATA ANALYSIS METHODS IN EDUCATION | OLC629 | Service | 3 | 0 | 3 | 8 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Must | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Problem Solving | |||||
| Instructor (s) | Prof. Dr. Selahattin GELBAL | |||||
| Course objective | Understanding the definition, purpose, and types of statistics. Calculation and interpretation of descriptive statistics. Identification of the types of correlation | |||||
| Learning outcomes |
| |||||
| Course Content | Basic concepts, Statistics and its meaning, Scale and types of scale, Variables and types of variables, Descriptive statistics, Central distribution measures, Variance measures, Normal distribution and Binom distribution, Correlation and its meaning and types (Pearson, biserial, point biserial, tetrachoric, rank differences) and calculations, Partial and multiple correlation, Regression, Linear regression. | |||||
| References | Arıcı, H. (2000). İstatistik: Yöntemler ve uygulamalar. ANKARA: Meteksan Ltd. Şti. Baykul, Y. (1999), İstatistik metodlar ve uygulamalar (3. baskı). ANKARA: Anı Yayıncılık. Freund, J. E. & Simon, G. A. (1992). Modern elementary statistics (8th ed.). New Jersey: Prentice Hall. Gravetter, F. J. & Wallnau, L. B. (1985). Statistics for the behavioral sciences: A first course for students (2nd ed.). St. Paul: West Publishing Company. Sümbüllüoğlu, K. & Sümbüllüoğlu, V. (1998). Biyoistatistik, ANKARA: Hatipoğlu Basım ve Yayım San. Ldt. Şti. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Basic concepts |
| Week 2 | Statistics and its meaning |
| Week 3 | Scale and types of scale |
| Week 4 | Variables and types of variables |
| Week 5 | Descriptive statistics |
| Week 6 | Central distribution measures |
| Week 7 | Variance measures |
| Week 8 | Normal distribution and Binom distribution |
| Week 9 | Correlation, its meaning and types |
| Week 10 | Pearson, biserial, point biserial correlations and their calculations |
| Week 11 | Tetrachoric, rank differences correlations and their calculations |
| Week 12 | Partial and multiple correlation |
| Week 13 | Regression |
| Week 14 | Linear regression |
| Week 15 | Preparation to exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 10 | 0 |
| Laboratory | 14 | 0 |
| Application | 2 | 0 |
| Field activities | 2 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 7 | 0 |
| Presentation | 0 | 0 |
| Project | 1 | 50 |
| Seminar | 0 | 0 |
| Midterms | 0 | 0 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 1 | 0 |
| Percentage of final exam contributing grade succes | 1 | 0 |
| Total | 0 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 14 | 3 | 42 |
| Application | 10 | 2 | 20 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 4 | 3 | 12 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 3 | 42 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 20 | 20 |
| Homework assignment | 7 | 5 | 35 |
| Midterms (Study duration) | 0 | 0 | 0 |
| Final Exam (Study duration) | 1 | 20 | 20 |
| Total Workload | 65 | 59 | 233 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. To have the fundamental knowledge of concepts related to measurement and evaluation, statistics and research methods. When it is necessary, to be able to use these concepts by associating them to each other. | X | ||||
| 2. To be able to explain distinguishing features and characteristics of fundamental theories of measurement and evaluation. | X | ||||
| 3. To be able to explain the research methods and statistical methods that will be used with these methods. | X | ||||
| 4. To be able to interpret the results of the national and international exams by relating them to the concepts of measurement and evaluation. | X | ||||
| 5. To be able to develop or adapt measurement instruments, which are aligned with the objectives, by employing measurement techniques. | X | ||||
| 6. To be able to evaluate the results of the national and international exams. | X | ||||
| 7. To be able to identify a research problem related to measurement and evaluation, plan and administer scientific research methods for the solution of the research problem. | X | ||||
| 8. To provide a technical support for a solution of the problems faced by the other areas of social sciences. | X | ||||
| 9. To be able to interpret and critique the recent developments related to measurement and evaluation. | X | ||||
| 10. To be able to apply the knowledge related to measurement and evaluation in interdisciplinary studies. | X | ||||
| 11. To be able to monitor new software related to measurement and evaluation and use appropriate software for research problems. | X | ||||
| 12. To be able to carry out a research related to measurement and evaluation and present it in national and international meetings. | X | ||||
| 13. To comply with the ethical rules in the research studies related to measurement and evaluation. | X | ||||
| 14. To be able to share effectively the knowledge and skills related to measurement and evaluation with the co-workers. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest