Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
SEN4016 Multivariate Data Analysis Spring 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. MEHMET ALPER TUNGA
Recommended Optional Program Components: None.
Course Objectives: The students will have the ability of applying specific techniques included in multivariate analysis such as principle component analysis, factor analysis, linear regression to specific problems.

Learning Outcomes

The students who have succeeded in this course;
1. Describe multivariate data analysis concepts
2. Define the properties and limitations of PCA and compute PCA through different ways
3. Describe the types of factoring and factor computation
4. Define metric and non-metric scales
5. Describe simple and multiple correspondence analysis and chi squared distances
6. Define variations of MANOVA
7. Evaluate regression coefficients, parameter estimation, hypothesis testing
8. Describe deduction, induction, estimation, tests, correlation
9. Define univariate and multivariate filters

Course Content

The course content is composed of principle component analysis (pca), factor analysis, multidimensional scaling, correspondence analysis, multivariate analysis of variance (manova), multiple linear regression, statistical inference, feature subset selection.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction
2) Principle Component Analysis (PCA)
3) Principle Component Analysis (PCA)
4) Factor Analysis
5) Factor Analysis
6) Multidimensional Scaling
7) Correspondence Analysis
8) Multivariate Analysis of Variance (MANOVA)
9) Multiple Linear Regression
10) Multiple Linear Regression
11) Statistical Inference
12) Statistical Inference
13) Feature Subset Selection
14) Feature Subset Selection


Course Notes / Textbooks: Multivariate Data Analysis, 7/E, Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, Pearson, 2010, 9780138132637
References: Yok - None.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Quizzes 4 % 20
Homework Assignments 2 % 10
Midterms 1 % 30
Final 1 % 40
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 4 5 20
Homework Assignments 2 5 10
Quizzes 4 3 12
Midterms 1 15 15
Final 1 17 17
Total Workload 116

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) To be able to critically interpret and discuss the theories, the concepts, the traditions, and the developments in the history of thought which are fundamental for the field of new media, journalism and communication.
2) To be able to attain written, oral and visual knowledge about technical equipment and software used in the process of news and the content production in new media, and to be able to acquire effective abilities to use them on a professional level.
3) To be able to get information about the institutional agents and generally about the sector operating in the field of new media, journalism and communication, and to be able to critically evaluate them.
4) To be able to comprehend the reactions of the readers, the listeners, the audiences and the users to the changing roles of media environments, and to be able to provide and circulate an original contents for them and to predict future trends.
5) To be able to apprehend the basic theories, the concepts and the thoughts related to neighbouring fields of new media and journalism in a critical manner.
6) To be able to grasp global and technological changes in the field of communication, and the relations due to with their effects on the local agents.
7) To be able to develop skills on gathering necessary data by using scientific methods, analyzing and circulating them in order to produce content.
8) To be able to develop acquired knowledge, skills and competence upon social aims by being legally and ethically responsible for a lifetime, and to be able to use them in order to provide social benefit.
9) To be able to operate collaborative projects with national/international colleagues in the field of new media, journalism and communication.
10) To be able to improve skills on creating works in various formats and which are qualified to be published on the prestigious national and international channels.