Objectives and competences
The main goal of the course is to acquaint the students with the basics of multivariate analysis and present teh most frequently used multivariate methods.
Content (Syllabus outline)
Material of multivariate analysis with examples.
Matrix algebra.
Displaying multivariate data.
Measuring and testing multivariate distances.
Principal components analysis.
Factor analysis.
Discriminant function analysis.
Cluster analysis.
Canonical correlation analysis.
Multidimensional scaling.
Learning and teaching methods
Lectures
The information and communications technology is used for educational purposes in the teaching and learning process.
Intended learning outcomes - knowledge and understanding
Students will understand and know the basic concepts and classical methods of multivariate analysis.
Students will understand and correctly use different methods of multivariate analysis.
Students wil know how to use the appropriate software for statistical research of multivariate data.
Intended learning outcomes - transferable/key skills and other attributes
Students will upgrade the obtained knowledge of fundamental statistical analyses with complex data analysis, where data obtained by empirical research is used.
Readings
1. L. G. Grimm: Reading and Understanding Multivariate Statistics, American Psychological Association, 2004.
2. R. Lowry: Concepts and Applications of Inferential Statistics. Spletni učbenik: http://faculty.vassar.edu/lowry/webtext.html
3. B.F.J. Manly: Multivariate Statistical Methods. A primer. London: Chapman & Hall, 1995.
Prerequisits
Prerequisites for attending the course:
Preknowledge from subject Statistics for Psychologists.
Prerequisites for completing the course:
At least 50% attendance at lectures and 80% attendance at tutorials is required.
