Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
STATISTIC IN SPORT SCIENCES 75783003 3 + 0 3.0 7.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery Verbal and visual expression, discussing.
Course Coordinator
Instructors Cengiz AKALAN
Assistants
Goals Philosophy of science, research, population, sample, and the problem of selection, data collection, analysis and report writing topics öğrenmek.sp science are often used in statistical methods detailed analysis and statistical analysis packages programs analyzed with the aid can do, results and reports to prepare.
Course Content Teaching the research methods and statistics science
Learning Outcomes 1) Understanding philosophy and principles of research techniques and statistical concept in general terms
2) Examining and learning of scientific research techniques and methods which are frequently used in sport sciences
3) To learn various data collection and analysis methods
4) To understand and create research ethics in sports science, which often requires the use of human subjects
5) To be able to make statistical analysis in computer environment and write research proposal and report

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week "Introduction to the course, superficial information about the content, meaning and importance of research, science and research, research nedir, what are the purposes of research, types of research. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
2. Week Planning principles of research, Problem and idea generation, Identification of problem, Determination of research method. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
3. Week Universe and sample information and problems, Universe and sampling concepts, Sampling methods, Determination of the most appropriate number of samples, Minimizing the number of samples Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
4. Week Data types and data collection methods, Data definition and sources, Data types, Data collection methods. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
5. Week Data collection and editing, Data recording, Sorting and grouping, SPSS APPLICATION, Data recording, Sorting and grouping, Lost or incorrect data checking and correction. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
6. Week Statistical introduction and hypothesis analysis, Normal distribution, Change interval, Arithmetic mean, Variance, Standard error, Confidence interval, SPSS APPLICATION. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
7. Week Descriptive Statistics, Percentage distributions, Group distributions, SPSS APPLICATIONS. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
8. Week mid term exam Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
9. Week Preparation of tables, preparation of graphics, general research proposal and report writing, SPSS APPLICATION. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
10. Week ."Average Comparisons, Average, Single-Sample T-Test, Independent Sample T-Test, Dependent Sample T-Test, Creating Result Table and Report Writing, SPSS APPLICATION." Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
11. Week Single and multiple variance analysis, Identification of different groups, Creating result sheet and report writing, SPSS APPLICATION. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
12. Week Correlation Analysis, Correlation Coefficient, Correlation Matrix, SPSS APPLICATION. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
13. Week Project proposals and discussion. Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)
14. Week "Assessment of the course and the instructor by the students" Final Project "deadline." Lecture; Question Answer
Brainstorming; Six Hats Thinking
Project Based Learning; Problem Based Learning 		Homework Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
Araştırma Teknikleri ve Rapor Yazma. Arıkan, R.Tutibay Yayın. Ankara (1995)
Basic Statistics. Spatz, C.Brooks&Cole., USA (1997)
Biyoistatistik (3.Baskı).Sümbüloğlu.K. Sümbüloğlu, V.Hatipoğlu yayın.Ankara (1990)
İstatistik (5.baskı). Köksal.B.A.Çağlayan Kitabevi., İstanbul (1998)
Modern Araştırmacı (Çeviri).Graff, H.F. Barzun, J (Çeviri Dilber, F.).TUBİTAK. (1992)
Spor Bilimlerinde Uygulamalı İstatistik. Alpar.R.Bağırgan yayın., Ankara (2006)
SPSS Uygulamaları, Çok Değişkenli İstatistik Teknikleri. Editör: Doç. Dr. Şeref KALAYCI. Ankara (2010)

Assessment
Measurement and Evaluation Methods and Techniques
PRACTICING
Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5
PY1500000
PY2500000
PY3500000
PY4500000
PY5500000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 2
Homework 5 3
Presentation (Including Preparation Time) 5 10
Project (Including Preparation and presentation Time) 2 5
Report (Including Preparation and presentation Time) 2 6
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 12
Final Exam 1 30
Time to prepare for Final Exam 1 20
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload