Course Title Course Code Program Level

Course Term
(Course Semester)
Teaching and Learning Methods
Theory Practice Lab Projects/Field Work Seminars/Workshops Other Total Credits ECTS Credits
28 14 96 138 3 5

Teaching Staff
Language of Instruction Türkçe (Turkish)
Type Of Course Compulsory
Prerequisites None
Recommended Optional Programme Component
Course Objectives To teach Analysis Methods in relation to Categorical Data
Course Content Probability Distributions and Measures of Association for Count Data, Inferences for Two-way Contingency Tables, Generalized Linear Models, Logistic Regression and Loglinear Models.
Learning Outcomes (LO) To be able to distinguish between the Categorical Data Structure and the statistical analysis appropriate to these data, and to gain the ability to analyze categorical data using SPSS.
Mode of Delivery Face to face
Course Outline
Week Topics
1. Week Basic Definition & Concepts
2. Week Probability Distributions of Categorical Coefficients
3. Week Estimation & Goodness of Fit Tests
4. Week Contingency Tables for Two Categorical Coefficients
5. Week 2x2 and RxC type Contingency Tables
6. Week Homogeneity & Independence Tests and Fisher's Certainty Test
7. Week Correlation Measures
8. Week Contingency Tables for Three Categorical Coefficients
9. Week Generalized Linear Models
10. Week Log Lİnear Models for Contingency Tables
11. Week Model Selection & Analysis
12. Week Lojit and Probit Models & Analysis
13. Week Logistics Regression Analysis
14. Week Sample Applications on Real Data
Mid-term (%) 40
Quizes (%)
Homeworks/Term papers (%)
Practice (%)
Labs (%)
Projects/Field Work (%)
Seminars/Workshops (%)
Final (%) 60
Other (%)
Total(%) 100
Course Book (s) and/or References 1. Agresti, A., 2002, Categorical Data Analysis, John Wiley&Sons. 2. Lawal B.,2003,Categorical Data Analysis with SAS and SPSS Applications, Lawrance Erlbaum Associates.
Work Placement(s)
The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO)