Master’s students Psychology with specialisation Methodology and Statistics.
In this course the general focus is on nonparametric multivariate analysis. In the first part of the course a review on null hypothesis significance testing procedures is given that identifies several statistical and interpretation difficulties concerning these procedures and p-values. Alternatives are presented for alleviating these problems, like cross validation, bootstrapping, permutation testing, and the Bayes factor.
In the second part, the focus is on two major methods for multivariate analysis, multiple regression analysis (MR) and principal component analysis (PCA). Classical MR and PCA are applicable to quantitative data and rely on assumptions of normality and linear relationships. More general methods, CATREG and CATPCA, will be discussed, that can handle both quantitative and qualitative data and do not rely on normality and linearity assumptions.
The third part of the course consists of multidimensional scaling. In the first meeting, some historical aspects concerning classical and least squares scaling, the specific data requirements, and the taxonomy of models are discussed. In the final two meetings, one mode models (PROXSCAL) and two mode models (PREFSCAL) are discussed in detail. The assignments consist of programming simple models in R and analysing own data with complex models using IBM SPSS and specialized software.
acquire knowledge of, and insight into, the assumptions of multivariate data analysis techniques
acquire knowledge of, and insight into, statistical techniques which require a minimal set of assumptions
learn to use statistical software for nonparametric multivariate analysis
Applied Multivariate Analysis (2012-2013):
Mode of instruction
Graded assignments during course.
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Information on blackboard.leidenuniv.nl
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