| Learning Outcomes |
PO |
MME |
| The students who succeeded in this course: |
|
|
| LO-1 |
Understanding the philosophy and principles of scientific research techniques and statistics |
PO-1 Understands scientific research methods and processes and can apply these methods in the field of sports sciences.
|
Examination
|
| LO-2 |
Learning scientific research techniques and methods commonly used in sport sciences |
PO-1 Understands scientific research methods and processes and can apply these methods in the field of sports sciences.
|
Examination
|
| LO-3 |
To be able to apply various data collection and analysis methods |
PO-1 Understands scientific research methods and processes and can apply these methods in the field of sports sciences.
|
Examination
|
| LO-4 |
Understanding research ethics and conducting research in accordance with ethical rules |
PO-1 Understands scientific research methods and processes and can apply these methods in the field of sports sciences.
|
Examination
|
| LO-5 |
To be able to perform statistical analysis in computer environment |
PO-1 Understands scientific research methods and processes and can apply these methods in the field of sports sciences.
|
Examination
|
| LO-6 |
To be able to prepare a research proposal and report |
PO-1 Understands scientific research methods and processes and can apply these methods in the field of sports sciences.
|
Examination
|
PO: Programme Outcomes
MME:Method of measurement & Evaluation |
| Course Contents |
| Science is a systematic process of acquiring knowledge and involves collecting, analyzing and interpreting data using scientific methods. The scientific research process consists of stages such as problem definition, hypothesis formulation, data collection, analysis and interpretation of results. In research, the population refers to all individuals who are the subject of investigation, while the sample is a subset representing this population. Data collection methods include techniques such as questionnaires, observations and interviews depending on the nature of the research. Data analysis is carried out through parametric and non-parametric tests. It is possible to examine cause-and-effect relationships, structure data through factor and cluster analyses, and assess the consistency of measurement instruments through reliability analyses. In addition, computer-aided statistical programs such as SPSS and R are used to speed up and enable these processes |
| Weekly Course Content |
| Week |
Subject |
Learning Activities and Teaching Methods |
| 1 |
Introduction to scientific research: Problem definition and hypothesis formation |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 2 |
Research methods: Population, sample, and data collection techniques |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 3 |
Statistical concepts: Basic definitions and types of data |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 4 |
Data organization and descriptive statistics (mean, standard deviation, etc.) |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 5 |
Parametric tests: t-test and analysis of variance (ANOVA) |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 6 |
Non-parametric tests: Chi-square test and Mann-Whitney U test |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 7 |
Introduction to SPSS and basic data analysis |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 8 |
mid-term exam |
|
| 9 |
Regression and correlation analysis: Examining cause-effect relationships |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 10 |
Factor and cluster analysis: Structuring data |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 11 |
Reliability analysis: Consistency of measurement tools |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 12 |
Practical analysis with sample datasets in sports sciences |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 13 |
Introduction to R software and basic statistical analyses |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 14 |
Interpretation and reporting of research results |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 15 |
General review and case studies |
Lecture and Discussion,
Computer Applications,
Group Work,
Practical Data Analyses,
Case Studies and Project Development |
| 16 |
final exam |
|
| Recommend Course Book / Supplementary Book/Reading |
| Required Course instruments and materials |
| SPSS, R, Textbook, PC |
