| Course Code : | 2005TEWREM | | Study domain: | Research Methodology | | Semester: | Semester: 1st semester
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| | Contact hours: | 60 | | Credits: | 6 | | Study load (hours): | 168 | | Contract restrictions: | No contract restriction
| | Language of instruction : | English
| | Exam period: | exam in the 1st semester
| | Tutor(s) | Heidi Arnouts
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1. Prerequisites
At the start of this course the student should have acquired the following competences:
An active knowlegde of :Specific prerequisites for this course:
General competences
Students are assumed to be familiar with basic statistical theory and applicatons (probability theory, hypothesis testing and related subjects). In addtion the have a sufficient insight in linear algebra and matrix operations. To follow this course students should already have obtained a credit in "Inleiding tot de econometrie" or "Kwantitatieve beleidsmethoden". Econometric modeling and regression analysis are therefore not covered and are considered as general competences. Sequencing
2. Learning outcomes
Given a statistical problem students must be able:
- to identify the most appropriate multivariate technique
- to analyze the data in statistical software (SPSS and/or SAS)
- to interpret the generated outputs
- to formulate accurately the conclusion for the given problem
3. Course contents
Traditionally, multivariate data analyis makes a distinction between dependency methods and interdependency methods. Some of the dependency methods that are covered in this course are: discriminant analysis, canonical correlation, multivariate analysis of variance. Some of the interdependency methods that are addressed are: principal component analysis, factor analysis (exploratory and confirmatory), correspondence analysis and cluster analysis.
4. Teaching method
Class contact teaching: LecturesPractice sessions
Personal work: ExercisesAssignments:In group
5. Assessment method and criteria
Examination: Written without oral presentationOpen book
Continuous assessment: Assignments
6. Study material
Required reading
J. Lattin, J. D. Carroll and P.E. Green. Analyzing Multivariate Data. Thomson Learning, 2003
Optional reading
The following study material can be studied on a voluntary basis:
J. G Hair, W. C. Black, B. J. Babin and R. E. Anderson. Multivariate Data Analysis. Pearson, 2010
Books on SPSS
7. Contact information
(+)last update: 02/10/2012 10:56 heidi.arnouts
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