Information Package / Course Catalogue
Incomplete Data Analysis
Course Code: BİS633
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

To teach students the definiton of incomplete data and how to analyze incomplete data.

Course Content

Definition of missing data, Analysis of variance for incomplete data, Incomplete data analysis in repeated data set, Categorical data analysis in incomplete data analysis, Examination of missing data in multivariate analysis, Incomplete data prediction, Applications with package programs

Name of Lecturer(s)
Learning Outcomes
1.To learn the definition of missing data and which method to apply
2.To understand different types of missing data
3.To learn how to transform data to be analyzed
4.To learn how to transform repeated data sets proper to analysis method
5.To learn how to handle missing data problems using package programs.
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
Definition of missing data
Week 1 - Practice
Applications with package programs
Week 2 - Theoretical
Analysis of variance for incomplete data
Week 2 - Practice
Applications with package programs
Week 3 - Theoretical
Analysis of variance for incomplete data
Week 3 - Practice
Applications with package programs
Week 4 - Theoretical
Incomplete data analysis in repeated data set
Week 4 - Practice
Applications with package programs
Week 5 - Theoretical
Incomplete data analysis in repeated data set
Week 5 - Practice
Applications with package programs
Week 6 - Theoretical
Categorical data analysis in incomplete data analysis
Week 6 - Practice
Applications with package programs
Week 7 - Theoretical
Categorical data analysis in incomplete data analysis
Week 7 - Practice
Applications with package programs
Week 8 - Theoretical
Literature review and discussion (Midterm exam)
Week 9 - Theoretical
Examination of missing data in multivariate analysis
Week 9 - Practice
Applications with package programs
Week 10 - Theoretical
Examination of missing data in multivariate analysis
Week 10 - Practice
Applications with package programs
Week 11 - Theoretical
Analysis methods for incomplete data
Week 11 - Practice
Applications with package programs
Week 12 - Theoretical
Analysis methods for incomplete data
Week 12 - Practice
Applications with package programs
Week 13 - Theoretical
Incomplete data prediction
Week 13 - Practice
Applications with package programs
Week 14 - Theoretical
Incomplete data prediction
Week 14 - Practice
Applications with package programs
Week 15 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Assignment1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140228
Assignment25214
Quiz42112
Midterm Examination120222
Final Examination120222
TOTAL WORKLOAD (hours)126
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
OÇ-1
5
5
5
4
4
5
4
OÇ-2
OÇ-3
4
5
4
5
4
5
4
OÇ-4
OÇ-5
3
4
3
5
3
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026