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 6035 MULTIVARIATE DATA ANALYSIS COMPULSORY 3 0 0 6

Offered By

Tourism Management

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR ÖZKAN TÜTÜNCÜ

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 Logistic and Hiearchical Regression
8 Field Study and Midterm Exam
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 Structural Equation Modelling
14 Presentations and Discussions

Recomended or Required Reading

Textbook(s):
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

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 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.50 + FIN* 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.50 + RST* 0.50

Further Notes About Assessment Methods

To be announced soon.

Assessment Criteria

1. Bell-curve calculations might be used in the transformation of the notes depending upon the overall success of students and initiative of the lecturer. In this case, it is required for the student to take at least 25 points for each exam to be able to participate in the calculation of the bell curve. If an exam question is left unanswered then the value of that problem will be subtracted from exam result. If there is only the answer itself and there is no calculations showing how the result is reached, then the question will be graded with 25% of that question's value.
2. The grade obtained from the participation of student will depend on (i) the status of attending the classes, (ii) the quality of the answers of the questions asked by the lecturer during the class, (iii) the contribution of the student to the creation of a positive learning environment.
3. A well participation will move the note into a better turnout for those having a note on the border between the two notes.
4. Case study analysis requires a collaborative effort. Ensuring the approximate equal contribution of each group member to the group work is the responsibility of group itself. Case studies will be graded by lecturer and group members. At the end of the semester, each member of the group will be asked to evaluate the contribution of themselves and the other group members.
5. Case studies, field research and study reports will be evaluated through the issue's clearly be understood, handling and discussion of the authenticity, accuracy of results, report content comprehensiveness and depth of the analysis, presentation mechanics and visual quality such as the clarity and organization, format, punctuation and grammar.

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)

ozkan.tutuncu@deu.edu.tr

Office Hours

To be announced soon.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for midterm exam 1 30 30
Preparation for final exam 1 20 20
Preparing assignments 1 12 12
Preparing presentations 1 10 10
Preparations before/after weekly lectures 14 2 28
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 146

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.155
LO.25
LO.34
LO.455
LO.54
LO.64
LO.755
LO.85