This course provides an overview of multivariate data analysis methods for research in the area of international management. The course will present the procedures for applying basic multivariate statistical techniques and provide hands-on experience with many of those techniques.
The primary objectives for this course are (1) to provide the student with the knowledge and confidence to properly identify appropriate data analysis tools for testing theories that are relevant to their field of research and (2) to equip the student with the background needed to develop deeper knowledge of techniques that are not explicitly covered in this class, or are not covered in sufficient detail for their particular research projects. The major techniques introduced in this course are:
1. Introduction (Basic statistics, brief overview)
2. Selection of Multivariate Tools
3. Examination of data for Multivariate Analysis
4. Factor analysis
5. Regression and Multiple Regression
6. Logistic Regression
7. Discriminant analysis
8. ANOVA & MANOVA
9. Cluster analysis
10. Brief introduction to Structural equations
11. Brief introduction to time series analysis (Stationary and non-stationary variables, Autoregressive distributed lag Approach (ARDL)
Each of these areas could easily be an entire course. Therefore, in our limited time we will only be able to introduce and demonstrate each of these topics. That is a trade-off between depth and breadth consciously made so that the students can obtain a holistic view of the data analysis component of the research process in international management.
Following statistical packages will be used in the lab session:
1. SPSS will be used to cover the topics from (1-9).
2. STATA will be introduced to discuss the last topic and may be used as an alternative to SPSS.
A combination of lectures and SPSS laboratory exercises. Lectures will normally be 3 hours once a week followed by either laboratory assignments or laboratory exercises or a combination of the two.
Workload (135 hours for students already grounded in statistics. Others may need more)
Lectures: 36 hours
Lab exercises and lab assignments: 36 hours
Reading and self-study: 25 hours
Take home exam and preparation for exams: 38 hours
School of Business and Law