COURSE UNIT TITLE

: MULTIVARIATE DATA ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
TUI 6194 MULTIVARIATE DATA ANALYSIS ELECTIVE 3 0 0 9

Offered By

Tourism Management

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR YILMAZ AKGÜNDÜZ

Offered to

Tourism Management

Course Objective

The fundamental objective of this course is to define, classify, relate one another and analyze multivariate data.

Learning Outcomes of the Course Unit

1   To be able to define fundamental concepts related to multivariate structures.
2   To be able to explain data collection methods related to multivariate structures in research process.
3   To be able to construct hypothesis related multivariate structures in research process.
4   To be able to design multivariate research.
5   To be able to analyze multivariate data.
6   To be able to explain the results of multivariate data.
7   To be able to design survey research incorporating multivariate data.
8   To be able to analyze multivariate data using computer programmes.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Multivariate Analysis
2 Research Process: Variable, Identification of The Theoretical Structure, Construction of Hypothesis
3 Research and Mesurement Design
4 Validity Analysis I
5 Validity Analysis II
6 Introduction to Correlation and Regression
7 MIDTERM EXAM / Logistic and Hiearchical Regression
8 Logistic and Hiearchical Regression
9 Generalized Linear Models: Factorial ANOVA and ANCOVA
10 Generalized Linear Models: Factorial MANOVA
11 Generalized Linear Models: Factorial MANCOVA
12 Discriminant Analysis
13 Canonical Correlation
14 Structural Equation Modelling
15 Presentation
16 FINAL EXAM

Recomended or Required Reading

Hair, Joseph F., Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black, Multivariate Data Analysis, 6th edition. Upper Saddle River, New Jersey: Prentice-Hall, Incorporated , 2006.
Tabachnick, Barbara and Linda S. Fidell, Using Multivariate Statistics, 4th ed. Allyn & Bacon, 2001.
Uma Sekaran, Research Methods for Business: A Skill Building Approach, Wiley, 2003.
Sheridan J. Coakes vd., SPSS: Analysis without anguish using SPSS version 13.0 for Windows, Wiley, 2006.
Ann Bowling, Research Methods in Health, Maidenhead, Open University Pres, 2005.
Yahşi Yazıcıoğlu, Samiye Erdoğan, SPSS Uygulamalı Bilimsel Araştırma Yöntemleri, Detay Yayıncılık, Ankara, 2004
Ibrahim Kılıç, Ayhan Ural, Bilimsel Araştırma Süreci ve SPSS ile Veri Analizi, Detay Yayıncılık, Ankara, 2004
Supplementary Book(s) / References / Materials:
International Journal of Social Research Methodology
Quality and Quantity, International Journal of Methodology

Planned Learning Activities and Teaching Methods

Question answer, presentation, case analysis, group projects, field study.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 + STT * 0.20 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + STT * 0.20 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. Bell-curve can be used in the calculation of grades based on the initiative of the course lecturer and overall sucsess level of students. In this case, students need to get at least 25 points in each exam to be able to participate bell-curve calculations. If any of the questions left unanswered, its value will be subtracted from the exam result. If the question is just simply answered (without showing the calculations reaching the result), then it will be graded with %25 of the question value.
2. The grade obtained from the participation of the student will depend on (i) participation to the courses, (ii) quality of the given answers asked by the course lecturer during the course and (iii) contribution of the student to create an affirmative learning environment.
3. A decent participation will help the grades between the border of two grades to increase it to the upper level.
4. Case analysis requires a cooperative effort. The group is responsible from ensuring each members' approximate contribution to the group work. case studies will be graded by the lecturer of the course and group members. Each member of the group will be asked to evaluate the contribution of themselves and other group members at the end of the semester.
5. Case studies, field studies and working papers will be evaluated in terms of the subject's being clearly understood, approach and the authenticity of the discussion, accuracy of results, comprehensiveness of the report content and depth of the analysis, presentation skills such as clarity and organization, format, punctuation, grammar and quality of the images.

Language of Instruction

Turkish

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Violations of Plagiarism of any kind will result in disciplinary steps being taken.
3. Being absent in a class can not be an excuse for the late submission of assignments.
4. Cell phones can not be used as a calculator in examinations.

Contact Details for the Lecturer(s)

Prof. Dr.Yılmaz Akgündüz

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 9 126
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Preparing assignments 1 10 10
Preparing presentations 1 10 10
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 227

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.1553534555
LO.2545534453
LO.3555544554
LO.4544454454
LO.5545434343
LO.6545355445
LO.7554554543
LO.8545345445