OLC755 - MEASUREMENT APPLICATONS WITH R SOFTWARE
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| MEASUREMENT APPLICATONS WITH R SOFTWARE | OLC755 | Any Semester/Year | 2 | 2 | 3 | 10 |
| Prequisites | OLC 750 | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Question and Answer Drill and Practice | |||||
| Instructor (s) | Assoc. Prof. Dr. Burcu ATAR, Assist. Prof. Dr. Kübra ATALAY KABASAKAL | |||||
| Course objective | The purpose of this course, is to make students conduct psychometrical and statistical analysis by using R software and use R in their thesis and studies yourself without need for other software. | |||||
| Learning outcomes |
| |||||
| Course Content | Data simulation with R, Classical Test Theory and Item Response Theory Applications, Multidimensional Item Response Theory, Differential Item Functioning and Test Equating, Exploratory and Confirmatory Factor Analysis, Structural Equation Modeling, Multilevel Models, Diagnostic Models, Computer Adaptive Test Applications | |||||
| References | Torsten Hothorn and Brian S. Everitt. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC Press, Boca Raton, Florida, USA, 3rd edition, 2014. ISBN 978-1-4822-0458-2. Pierre-Andre Cornillon. R for Statistics. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-4398-8145-3. Damon M. Berridge. Multivariate Generalized Linear Mixed Models Using R. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4398-1326-3. Phil Spector. Data Manipulation with R. Springer, New York, 2008. ISBN 978-0-387-74730-9. John M. Chambers. Software for Data Analysis: Programming with R. Springer, New York, 2008. ISBN 978-0-387-75935-7. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Basics of R programing language |
| Week 2 | Data Simulation- Dichotomous |
| Week 3 | Data Simulation- Polychotomous |
| Week 4 | Classical Test Theory Applications |
| Week 5 | Item Response Theory |
| Week 6 | Differential Item Functioning |
| Week 7 | Test Equating |
| Week 8 | Mid Term |
| Week 9 | Exploratory and Confirmatory Factor Analysis |
| Week 10 | Structural Equation Modeling |
| Week 11 | Multilevel Models |
| Week 12 | Multidimensional IRT |
| Week 13 | Computer Adaptive Testing |
| Week 14 | Diagnostic Models |
| Week 15 | Preparation to exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 2 |
| Presentation | 0 | 0 |
| Project | 3 | 10 |
| Seminar | 0 | 0 |
| Midterms | 1 | 10 |
| Final exam | 1 | 50 |
| Total | 72 | |
| Percentage of semester activities contributing grade succes | 7 | 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) | 14 | 2 | 28 |
| Laboratory | 0 | 0 | 0 |
| Application | 14 | 2 | 28 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 4 | 56 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 3 | 20 | 60 |
| Homework assignment | 4 | 15 | 60 |
| Midterms (Study duration) | 1 | 34 | 34 |
| Final Exam (Study duration) | 1 | 34 | 34 |
| Total Workload | 51 | 111 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Be able to explain the fundamental concepts of the measurement and evaluation according to different theories and when it is necessary be able to use those concepts by relating them to each other. | X | ||||
| 2. To be able generate solutions to new and complex problems by connecting the field of measurement and evaluation to the other disciplines. | X | ||||
| 3. To be able to identify the problems that can be encountered in interpretation and administration of the national and international exams. | X | ||||
| 4. To be able to compare different techniques employed in the development and adaptation of measurement instruments. | X | ||||
| 5. To be able to provide solutions to the problems, that are encountered in the national and international exams. | X | ||||
| 6. To be able to develop methods and techniques that will contribute to the field and be able to use them in the solution of new problems. | X | ||||
| 7. To be able to critique and evaluate the theories in the field and be able synthesize those theories when it is necessary. | X | ||||
| 8. To be able to solve the problems in the field of measurement and evaluation and be able to prepare original publications which can be part of the national and international meetings. | X | ||||
| 9. To be able to prepare and carry out a project that requires interdisciplinary work. | X | ||||
| 10. To be able to describe the problems related to the field and be able to generate solutions by using higher order mental processes. | X | ||||
| 11. To be able to plan teaching processes in order to help students to acquire knowledge and skills related to the field, be able to guide students in the activities and obey the ethical rules. | X | ||||
| 12. To be able to critique the existing paradigms in the field and contribute by developing original ideas. | X | ||||
| 13. To be able to interpret the national and international exams by considering the differences among the cultures. | X | ||||
| 14. To be able to monitor the technological developments in the field, be able to use those developments in the solution of the problems, be able to share the results at the national and international arenas. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest