Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
2MAN 602Advanced Research Methods II3+0+038

Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program PhD Program in Management (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The objectives of the course is to provide the students with necessary knowhow and skills

• To describe basic concepts and techniques of scientific inquiry
• To apply basic qualitative and quantitative research techniques
• To design a survey to conduct a research about a specific research question
• To evaluate the reliability and validity of a measurement scale and analyze the information collected through a survey via SPSS software
• To apply ethical codes of conduct in academic writing
To write and present the results of in a structured manner in compliance with academic conventions
Course Content This course is on data analysis techniques for both manufacturing and service enterprises. The course is designed for senior graduate students. This course will cover some important and popular data analysis techniques such as regression, classification, clustering, structural equation modelling and validation. Although some theoretical aspects of these techniques will be discussed, the emphasis will be on how to apply and integrate these techniques for solving problems in manufacturing, service organizations.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Selim Zaim
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources • Multivariate Data Analysis by Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, 7/E, Pearson, 2010. • Applied Multivariate Techniques by Subhash Sharma. John Wiley & Sons, Inc. 1996. • Structural Equation Modelling with AMOS, Barbara M. Bryne, Routletge, Taylor & Francis Group. • Using Multivariate Statistics by Barbara G. Tabachnick, Linda S. Fidell. Pearson, 2007.
• Multivariate Data Analysis by Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, 7/E, Pearson, 2010. • Applied Multivariate Techniques by Subhash Sharma. John Wiley & Sons, Inc. 1996. • Structural Equation Modelling with AMOS, Barbara M. Bryne, Routletge, Taylor & Francis Group. • Using Multivariate Statistics by Barbara G. Tabachnick, Linda S. Fidell. Pearson, 2007.

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Assignment 4 % 30
Project 1 % 30
Total
5
% 60

 
ECTS Allocated Based on Student Workload
Veri yok

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Develop statistical knowledge necessary for multivariate data analysis techniques
2 Explain the application areas of structural equation modelling
3 Utilize data analysis software on the researches
4 Decide advanced multivariate data analysis tools


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Data Analysis
2 Variance Based Structural Equation Modelling-1
3 Variance Based Structural Equation Modelling -2
4 Modeator And Mediating Analysis-1
5 Modeator And Mediating Analysis-2
6 Binary Logistic Regression Analysis
7 Multinominal Logistic Regression Analysis
8 Discriminant Analysis-1
9 Discriminant Analysis-2
10 Cluster Analysis
11 Correspondence Analysis
12 Multidimentional Scaling
13 Multidimentional Scaling-2
14 Time Series Analysis


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8
C1
C2
C3
C4

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://obs.ihu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=245405&lang=en