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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 Turkish Language and Literature
  3. Master of Science (MSc) in Contemporary Turkish Language
  4. Course Structure Diagram with Credits
  5. Bibliometric Analysis
  6. Description
  • Description
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Last Updated: 30/09/2026

Code - Course Title Course Type Laboratory + Practice ECTS
TDE589 - Bibliometric Analysis I. SEMESTER 3 +0 6.0
Language of Instruction Türkçe
Course Type Elective Courses
Course Instructor(s) PROF. DR. EMİNE KOLAÇ
Mode of Delivery The mode of delivery of this course is face-to-face
Place of Delivery
Used Educational Platforms
Prerequisites None
Courses Recomended None
Recommended Resources Andres, A. (2009). Measuring academic research: How to undertake a bibliometric study. Chandos Publishing. https://doi.org/10.1533/9781780630182Geiger, N., & Kufenko, V. (2019). Business cycles and economic crises: A bibliometric and economic history. Routledge.Briner, R. B. ve Denyer, D. (2012). Systematic review and evidence synthesis as a practice and scholarship tool. The Oxford Handbook of Evidence-Based Management içinde (ss. 112–129). Oxford University Press. doi:10.1093/oxfordhb/9780199763986.013.0007Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007Chatterjee, P., Saha, A., Kadry, S., & Demir, G. (Eds.). (2025). Bibliometric analyses in data-driven decision-making. John Wiley & Sons. https://doi.org/10.1002/9781394302581Murat, D. A. L., & İlhami, A. Y. (Eds.). (2025). Bibliyometrik Analiz III. Akademisyen Kitabevi.
Work Placement None
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 Bibliometric Analysis: Data collection, data organization, basic concepts
Week - 2 Data Sources: Databases
Week - 3 Source selection
Week - 4 Data collection process
Week - 5 Publication and Citation Analysis: Publication count, citation count, impact level
Week - 6 Publication Distribution: Author, institution and country-based analysis
Week - 7 Trend Analysis: Keywords, research topics and temporal changes
Week - 8 Network Analysis: Co-authorship, collaboration and connection structures
Week - 9 AI-supported analysis tools: Data organization, visualization
Week - 10 Use in the analysis process
Week - 11 Validity and Reliability: Data accuracy
Week - 12 Consistency and data diversity
Week - 13 Reporting: Presentation of findings, use of tables and figures
Week - 14 Academic writing

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Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100

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