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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 Tourism Management
  3. Master of Arts (MA) in Tourism Management
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  5. Data Collecting and Statistical Analysis in Quntitative Rese
  6. Description
  • Description
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  • ECTS Credit Load
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Last Updated: 17/10/2026

Code - Course Title Course Type Laboratory + Practice ECTS
ARY523 - Data Collecting and Statistical Analysis in Quntitative Rese I. SEMESTER 3 +0 6.0
Language of Instruction Türkçe
Course Type Required Courses
Course Instructor(s) PROF. DR. ALİ ÖZDEMİR
Mode of Delivery Face to Face
Place of Delivery On Campus
Used Educational Platforms Face-to-face
Prerequisites There are no prerequisite courses for this course.
Courses Recomended No courses are recommended.
Recommended Resources Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods (3rd edition). Thousand Oaks, CA: Sage.
Work Placement There is no internship requirement.
Aims of the Course This course aims to demonstrate how numerical data in quantitative research are defined, how they are collected using appropriate measurement tools, and how the obtained data are analyzed using statistical methods in an objective, reliable, and generalizable manner.

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 LO-1 History of Quantitative research Lecture Question & Answer Discussion
Week - 2 LO-1 LO-2 Introduction to Quantitative Research: Characteristics of quantitative research, research designs, research questions, and hypotheses Lecture Question & Answer Discussion
Week - 3 LO-3 Variables and Measurement in Quantitative Research Lecture Question & Answer Discussion
Week - 4 LO-3 Quantitative Data Collection Methods: Survey, structured observation, experimental methods, primary and secondary data Lecture Question & Answer Discussion
Week - 5 LO-3 Population, Sample, and Sampling Methods Lecture Question & Answer Discussion
Week - 6 LO-5 Validity and Reliability Lecture Question & Answer Discussion
Week - 7 LO-3 LO-4 LO-5 Application Discussion Problem Solving Research and Inquiry
Week - 8 LO-6 Data Preparation and Preprocessing: Data encoding, missing data analysis, outliers, and data transformations Lecture Question & Answer Discussion Problem Solving
Week - 9 LO-6 Descriptive Statistics and Data Visualization: Frequency, percentage, mean, median, mode, standard deviation, tables, and graphs Lecture Question & Answer Discussion Problem Solving
Week - 10 LO-6 Hypothesis development and inference making Lecture Question & Answer Discussion Problem Solving
Week - 11 LO-6 Relationships between variables Lecture Question & Answer Discussion Problem Solving
Week - 12 LO-6 Relationships Between Variables I (Numerical Variables: Regression and Correlation Analysis) Lecture Question & Answer Discussion Problem Solving
Week - 13 LO-6 Relationships Between Variables II (Categorical Data) Lecture Question & Answer Problem Solving
Week - 14 LO-7 LO-8 Application Case Study Problem Solving Brain Storming

  • Quality Education

  • Data Science

  • Open-Ended Test Homework Presentation

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 20
Final Exam 1 60
Homework 1 20
Toplam (%) 100

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