Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Graduate School of Sciences
  • Department of Physical Education and Sports Master with thesis
  • Course Structure Diagram with Credits
  • Research and Statistics in Sport Psychology
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title BES589 - Research and Statistics in Sport Psychology
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 1+2
ECTS 7.5
Course Instructor(s) ARAŞTIRMA GÖREVLİSİ DOKTOR UMUT SEZER
Mode of Delivery The mode of delivery of this course is Face – to – face
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended optional programme component for this course
Recommended Reading List • Raab, M., Wylleman, P., Seiler, R., Elbe, A. M., & Hatzigeorgiadis, A. (Eds.). (2016). Sport and exercise psychology research: From theory to practice. Academic Press.• Horn, T. S., & Smith, A. L. (2018). Advances in sport and exercise psychology. Human Kinetics.• Filho, & I. Basevitch (Eds.), Sport, exercise and performance psychology: Research directions to advance the field. Oxford University Press.• Llaudet, E., & Imai, K. (2022). Data analysis for social science: A friendly and practical introduction. Princeton University Press.• Whittaker, T. A., & Schumacker, R. E. (2004). A beginner's guide to structural equation modeling. Psychology Press.• Aşçı, F. H. (Ed). (2022). Spor ve egzersizde psikolojik ölçme ve değerlendirme. Nobel Akademik Yayıncılık.
Assessment methods and criteria 1 mid term-exam (% 40) 1 final exam (% 60)
Work Placement N/A
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Basic concepts
Week - 2 Areas of application
Week - 3 Introduction to Jamovi
Week - 4 Introduction to R studio
Week - 5 Lavaan package
Week - 6 semToools package
Week - 7 Confirmatory factor analysis: Goodness of fit, modifications, estimators and optimization methods
Week - 8 Measurement models
Week - 9 Unidimensional factor model - Correlated factors model
Week - 10 Bi-factor models and Hierarchical factor model
Week - 11 Structural equation modeling
Week - 12 Application
Week - 13 Application
Week - 14 Feedback on exercises

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Team/Group Work
  • Drill - Practise
  • Brain Storming
  • Report Preparation and/or Presentation
  • Competences
  • Productive
  • Rational
  • Questoning
  • Entrepreneur
  • Creative
  • Follow ethical and moral rules
  • Effective use of a foreign language
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Information Management
  • To work autonomously
  • Elementary computing skills
  • Decision making
  • To work in interdisciplinary projects

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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