E-posta :nuranb@uludag.edu.tr Telefon: 0 224 29 41 126 Adres: Uludağ Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, Ekonometri Bölümü,16059, Görükle/Bursa.
18
Website:
19
Objective of the Course:
It aims to examine various methods in categorical data analysis and to examine, interpret and report the research process of categorical data through case studies.
20
Contribution of the Course to Professional Development
It has a contribution towards forming a basis for the development of students' professional skills related to categorical data analysis.
21
Learning Outcomes:
1
Understanding categorical data properties;
2
To be able to collect data about researches containing categorical data;
3
Ability to identify appropriate techniques to be applied to different types of data;
4
Ability to offer analytical solutions to identified problems;
5
Ability to analyze categorical data with appropriate methods;
6
Ability to create a categorical research proposal for a problem situation;
7
Being able to interpret the results of the analysis of a research containing categorical data.;
8
Ability to conduct research on the subject;
22
Course Content:
Week
Theoretical
Practical
1
Basic concepts of categorical data analysis
2
Distributions in categorical data
3
Generalized linear models
4
ANOCOR
5
HOMALS
6
HOMALS
7
CATREG
8
CATREG
9
nonlinear canonical correlation analysis
10
nonlinear canonical correlation analysis
11
Categorical principal component analysis
12
Categorical principal component analysis
13
Model selection, analysis summary and interpretation
14
Example applications with R
23
Textbooks, References and/or Other Materials:
Agresti, A. (2013). Categorical Data Analysis, New Jersey: Wiley Interscience Publication Bilder, J.R. & Loughin, T.M. (2015). Analysis of Categorical Data with R. London: CRC Press. Everitt BS. (1977). The Analysis of ContingencyTables, Institute of Psychiatry, London.
24
Assesment
TERM LEARNING ACTIVITIES
NUMBER
PERCENT
Midterm Exam
0
0
Quiz
0
0
Homeworks, Performances
0
0
Final Exam
1
100
Total
1
100
Contribution of Term (Year) Learning Activities to Success Grade
0
Contribution of Final Exam to Success Grade
100
Total
100
Measurement and Evaluation Techniques Used in the Course
Measurement and evaluation are made with multiple choice test questions and written questions.
Information
This course is evaluated with an absolute evaluation system.
25
ECTS / WORK LOAD TABLE
Activites
NUMBER
TIME [Hour]
Total WorkLoad [Hour]
Theoretical
14
2
28
Practicals/Labs
0
0
0
Self Study and Preparation
14
6
84
Homeworks, Performances
0
7
7
Projects
0
0
0
Field Studies
0
0
0
Midtermexams
0
0
0
Others
0
0
0
Final Exams
1
1
1
Total WorkLoad
120
Total workload/ 30 hr
4
ECTS Credit of the Course
4
26
CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
