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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Graduate School
  2. Department of Educational Sciences
  3. Master of Arts (MA) in Educational Sciences
  4. Program in Educational Measurement and Evaluation
  5. Course Structure Diagram with Credits
  6. Introduction to Statistical Programming with R
  7. Description
  • Description
  • Learning Outcomes
  • ECTS Credit Load
  • Course's Contribution to Program
  • Learning Outcomes & Program Qualifications
Last Updated: 05/03/2026

Code - Course Title Course Type Laboratory + Practice ECTS
EÖD510 - Introduction to Statistical Programming with R II. SEMESTER 3 +0 6.0
Language of Instruction Türkçe
Course Type Elective Courses
Course Instructor(s) DOÇ. DR. BAŞAK ERDEM KARA
Mode of Delivery Face to face
Place of Delivery
Used Educational Platforms
Prerequisites There is no prerequisite for this course
Courses Recomended
Recommended Resources • Atar, B., Atalay-Kabasakal, K., Ünsal-Özberk E. B., Özberk, E. H. & Kıbrıslıoğlu-Uysal, N. (2021) (4. Bs). R ile veri analizi ve psikometriUygulamaları. Pegem Akademi.• Aydın, B., Algina, J., Leite, W. L., & Atılgan, H. (2018). Sosyal bilimler için R’a giriş. Anı Yayıncılık.• Desjardins, C. D. & Bulut, O. (2017). Handbook of educational measurement and psychometrics using R. Chapman and Hall/CRC• Field. A, Miles. J & Field. Z, (2012). Discovering statistics using R. Sage Publications.• Koğar, H. (2020). R ile geçerlik ve güvenirlik analizleri: Klasik test kuramı, faktör analizi ve madde tepki kuramı uygulamaları. Pegem Akademi.• Learn R in R: https://swirlstats.com/
Work Placement There is no work placement in the context of this course.
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 Introduction to R: Installation of R and RStudio, RStudio Environment and Main Operations
Week - 2 Main Commands in RStudio, Installing Packages and Creating Working Directory
Week - 3 Data Structures and Sample Applications: Vectors and Matrices
Week - 4 Data Structures and Sample Applications: Data Frames, Factors and Lists
Week - 5 Transformations between Data Structures and Sample Applications
Week - 6 Data Transfer and Manipulation
Week - 7 Data Manipulation
Week - 8 Main Descriptive Statistics and Hypothesis Testing
Week - 9 Data Visualization: Basic Level Graphs
Week - 10 Data Visualization: Advanced Level Graphs
Week - 11 Functions and Loops
Week - 12 Data Generation and Manipulation
Week - 13 Measurement Theory Applications
Week - 14 Measurement Theory Applications

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