BÄ°S621 - EXPERIMENTAL DESIGN
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| EXPERIMENTAL DESIGN | BÄ°S621 | 2nd Semester | 3 | 0 | 3 | 7 |
| Prequisites | Having successfully completed the lectures BIS 605 or BIS 735 | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion | |||||
| Instructor (s) | ASSIST. PROF. SEVÄ°LAY KARAHAN, PHD | |||||
| Course objective | To teach experimental design techniques and to enable students to choose the most appropriate design technique that minimizes the experimental error, to model the design and to test hypothesis on model parameters. To enable students to use different softwares and by using these programs to test hypothesis about one factor and factorial designs with random, fixed and mixed effect models on different data sets and interpret the results. | |||||
| Learning outcomes |
| |||||
| Course Content | Principles of experimental design, randomization, experimental error and ways of minimizing experimental error, experiments with one factor, block designs, Latin and GrecoLatin Square techniques, factorial experiments, cross over designs and covariance analysis. | |||||
| References | 1. Montgomery, DC., Design and Analysis of Experiments, John Wiley and Sons, Inc., New York, 1984. 2. Mason, RL., Gunst, RF., Hess, JL., Statistical Design and Analysis of Experiments, John Wiley and Sons, Inc., New York, 1989. 3. Diaz, AG., Phillips, DT., Principles of Experimental Design and Analysis. Chapman and Hall, London, 1985. 4. Hicks, CR., Fundamental Concepts in the Design of Experiments. 2nd. ed., Holt, Rinehart and Winston, New York, 1973. 5. Conover, WJ., Practical Nonparametric Statistics. 2nd ed., John Wiley and Sos Inc., New York, 1982. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction, sources of experimental error, randomization and constraints on randomization. |
| Week 2 | Single factor experiment-One way analysis of variance |
| Week 3 | Single factor, completely randomized design. Assumptions, testing the validity of assumptions, alternative solutions in case of failure in validating assumptios. |
| Week 4 | Complete randomized block design, parametric and nonparametric approaches. |
| Week 5 | Incomplete randomized block design. |
| Week 6 | Midterm exam |
| Week 7 | Latin Square and greko latin square designs. |
| Week 8 | Factorial experiments. Interaction. |
| Week 9 | Fixed, random and mixed effect models. |
| Week 10 | 2n factorial experiment. |
| Week 11 | Midterm exam |
| Week 12 | 3n factorial experiment |
| Week 13 | Cross over designs. |
| Week 14 | Covariance analysis |
| Week 15 | Preparation to final 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 | 6 | 10 |
| Presentation | 2 | 15 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 25 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 10 | 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 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 2 | 7 | 14 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 2 | 15 | 30 |
| Project | 0 | 0 | 0 |
| Homework assignment | 6 | 4 | 24 |
| Midterms (Study duration) | 2 | 10 | 20 |
| Final Exam (Study duration) | 1 | 10 | 10 |
| Total Workload | 41 | 54 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. A specialist with a graduate diploma in biostatistics: Has the knowledge to lead research planning, execution, and finalization, staying updated on literature and current studies. | X | ||||
| 2. Critically evaluates studies and scientific papers in presentations, courses, seminars, and conferences, encouraging a critical perspective. | X | ||||
| 3. Has the sufficient theoretical and practical knowledge of statistics to determine the appropriate statistical analysis and to grasp the results correctly. | X | ||||
| 4. Be proficient in computer use and statistical software, ensuring data suitability and recommending solutions for data management and analysis methods. | X | ||||
| 5. Effectively communicates analysis issues through active participation in discussions, exchanging information with the advisor, and presenting seminars. | X | ||||
| 6. Provides method suggestions in consultancy, does research planning, prepares research reports. | X | ||||
| 7. Maintains scientific accuracy and ethical values, remaining careful against any conscious or unconscious biases throughout the study. | X | ||||
| 8. Supports a counseling service under faculty supervision, may handle independent projects, and participates in conferences, presenting papers or posters with the academic advisor. | X | ||||
| 9. Be ready for multidisciplinary studies, collaborating professionally in group settings and gains the ability to assign individuals in the group. | X | ||||
| 10. Integrates diverse disciplines to analyze and synthesize information, offering solutions. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest