Business Administration (DR) Preview
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code:

ISL6102

Course Name:

Multivariate Data Analysis

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Doç. Dr. MUSTAFA SUNDU

Course Lecturer(s):

Doç. Dr. MUSTAFA SUNDU
Dr. Öğr. Üy. FATİH KİRAZ

Course Assistants:

Course Objective and Content

Course Objectives:	The primary objective of this course is to provide the students with a preliminary overview of research process and type of research design, a summary of univariate data analysis methods and selected methods of multivariate data analysis.
Course Content:	Major themes addressed include the research design, data collection methods, attitude measurement, overview of univariate analysis, data examination for testing the assumptions of multivariate analysis, and various multivariate data analysis methods.

Learning Outcomes

The students who have succeeded in this course;
1) Development of the ability to understand and apply the statistical analysis presented in the literature.
2) Development of the ability to critically evaluate the pros and cons of each multivariate data analysis method.
3) Acquisition of the knowledge and skills to decide when to use each data analysis technique and how to interpret the results.

Course Flow Plan

Week	Subject	Related Preparation
1)	Research Design, Exploratory Research, and Qualitative Data
2)	Descriptive Research and Causal Designs
3)	Data Collection, Survey Questionnaires and Data Coll. Forms
4)	Attitude Measurement, Sampling Procedures
5)	Overview of Univariate Analysis Techniques
6)	Preparing For a MV Analysis: Data Examination
7)	Factor Analysis
8)	Multiple Regression Analysis
9)	Multiple Discriminate Analysis and Logistic Regression
10)	MANOVA
11)	Cluster Analysis
12)	Structural Equation Modeling: Overview
13)	CFA: Confirmatory Factor Analysis
14)	SEM: Testing A Structural Model
15)	Final Exam

Sources

Course Notes / Textbooks:	Öğretim elemanı tarafından paylaşılan güncel sunum ve bağlantı Updated presentations and linksprovided by the instructor
References:	Öğretim elemanı tarafından paylaşılan güncel sunum ve bağlantı Updated presentations and linksprovided by the instructor

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3
Program Outcomes
1) Developing current and advanced knowledge in the field based on master's qualifications at the level of expertise with original thought and/or research,
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3) To comprehend the interdisciplinary interaction that the field is related to.
4) To be able to interpret the knowledge gained in the field by integrating it with the knowledge from different disciplines.
5) Being able to contribute to the progress in the field by independently carrying out an original work that brings innovations to the field, develops a new idea, method, design and / or application or applies a known idea, method, design and / or application to a different field.
6) To be able to develop new thoughts and methods in the field by using high-level mental processes such as creative and critical thinking, problem solving and decision maki
7) To be able to expand the limits of knowledge in the field by publishing at least one scientific article related to the field in national and/or international refereed journals and/or by producing or interpreting an original work.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Developing current and advanced knowledge in the field based on master's qualifications at the level of expertise with original thought and/or research,
2)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3)	To comprehend the interdisciplinary interaction that the field is related to.
4)	To be able to interpret the knowledge gained in the field by integrating it with the knowledge from different disciplines.
5)	Being able to contribute to the progress in the field by independently carrying out an original work that brings innovations to the field, develops a new idea, method, design and / or application or applies a known idea, method, design and / or application to a different field.
6)	To be able to develop new thoughts and methods in the field by using high-level mental processes such as creative and critical thinking, problem solving and decision maki
7)	To be able to expand the limits of knowledge in the field by publishing at least one scientific article related to the field in national and/or international refereed journals and/or by producing or interpreting an original work.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 50
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	14	0	3		42
Application	14	0	4		56
Homework Assignments	1	0	20		20
Midterms	1	0	30		30
Final	1	30	20		50
Total Workload					198