COURSE UNIT TITLE

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR AYSUN KAPUÇUGIL IKIZ

Offered to

Business Administration (English)

Course Objective

This course provides a working knowledge of the basic concepts underlying the most important multivariate techniques, with an overview of actual applications in all fields of management: Marketing, Production, Human Resources, Finance, Accounting, Decision Making, etc. Students are also having experience in actually using the techniques on a problem of their own choosing. The course is designed to address both the underlying statistical theory and practical applications. A reasonable level of competence in both statistics and mathematics is needed.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

Text Books:
* Multivariate Data Analysis: Joseph F. Hair, William C. Black, Barry J. Babin, Rolph E. Anderson, Pearson Education, 8th Edition, 2019.
* Book's website: http://www.mvstats.com/
* Data sets: http://research.ed.asu.edu/multimedia/DrB/Default.htm
* SPSS : http://core.ecu.edu/psyc/wuenschk/SPSS/SPSS-MV.htm
* Summary: http://www.utdallas.edu/~herve/Abdi-MultivariateAnalysis-pretty.pdf
* Notes: http://faculty.chass.ncsu.edu/garson/PA765/statnote.htm

Software:
* Minitab
* IBM SPSS
* MS Excel

Planned Learning Activities and Teaching Methods

1. Lectures and Class Discussions
2. Computer Applications
3. Homeworks / Analyses
4. Article Reviews/Presentations
5. Implementation Project

Lessons are aimed at imparting basic and advanced multivariate techniques in an environment where comprehension is significantly reinforced with additional explanations and examples.

Assessment Methods

Further Notes About Assessment Methods

1. Exams will measure the ability to identify and apply appropriate statistics and / or methods to real research problems. Each exam will cover course materials and include problems similar to those assigned as homework, questions on lecture materials, and additional items covered in the class.
2. Assignments will be given frequently. Assignments will provide a great opportunity for students to conduct multivariate analysis and develop solutions to various research problems. It is essential for the student to work on and understand these assignments in order to successfully complete the course. By completing their assignments, each student will develop their analytical skills and at the same time increase their competence on a data analysis tool and / or a statistical package program for data entry and analysis.

Assessment Criteria

1. Each student must review and present at least one published article using one of the multivariate techniques covered in the class. Presentation must cover the following main headings:
i. Research Questions / Hypotheses,
ii. Collected Data,
iii. Data Analysis and Results (The focus here is on the relevant statistical method only.)
iv. The limitations of the published research
The presentation should last a maximum of 15 minutes.

2. Each student must complete an Implementation Project that allows them to apply their developed skills to a topic of personal or professional interest of their choice. Project work can be done individually or in teams of two. Project topics will be determined by the students and are subject to the approval of the instructor. Project reports will enable students improve their competency using the language of statistics to communicate the results. The reports will be evaluated for such factors as apparent understanding of the topic, originality of treatment and discussion, accuracy of results, comprehensiveness of the report s content and depth of the analysis, clarity and mechanics of presentation such as organization, format, punctuation, grammar, and quality of exhibits and charts.
3. A good attendance record and participation may bring the grade up one level, for grades on the boundary between two grade levels. Participation will depend on (i) class attendance, (ii) the quality of answers the student provides to questions posed by the instructor during class, and (iii) the general contribution the student makes to the creation of a positive learning environment.

Language of Instruction

English

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. Absence will not be considered an excuse for submitting homework assignments late.
4. Delayed research projects will suffer grade decay equivalent to one letter grade per day late.

Contact Details for the Lecturer(s)

Prof. Aysun KAPUÇUGIL IKIZ
aysun.kapucugil@deu.edu.tr

DEU Faculty of Business
Department of Business Administration
Division of Quantitative Methods

Office Hours

To be announced later

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes