YRM-50806 Quantitative Data Analysis: Multivariate Techniques

Course

Credits 6.00

Language of instruction:

English

Assumed knowledge on:

YSS-20306 Quantitative and Qualitative Research Techniques in the Social Sciences or MAT-22306 Quantitative Research Methodology and Statistics, or equivalent courses

Contents:

This is an advanced course which is part of the WASS graduate programme aimed at students in the MSc programmes MME, MID, MDR and MCH and PhD students. The course is designed for students that have the ambition to be able to fully appreciate research papers in which more advanced techniques are applied and/or to be able to apply these techniques in their own research. Students should not be afraid of mathematical formulae. The aim of the course is to enhance knowledge and skills with regard to multivariate data-analysis techniques common in quantitative research in the social sciences, including principal component analysis (PCA), exploratory (EFA) and confirmatory factor analysis (CFA), structural equations modelling (SEM), cluster and discriminant analysis, and repeated and multivariate analysis of variance.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- understand the characteristics of multivariate quantitative research;
- explain the principles of the multivariate data analysis techniques: PCA, EFA, CFA, SEM, cluster and discriminant analysis, repeated and multivariate ANOVA;
- judge whether these techniques are useful given the collected data;
- apply these techniques by using statistical software (SPSS and Lavaan);
- interpret the results of multivariate analyses.

Activities:

Lectures, computer practical, discussion in tutorials, individual assignment and self-study.

Examination:

- exam (60%);
- assignments (40%).
To pass this course, one needs both the exam and the assignment to have a grade of at least 5.50.

Literature:

Literature to be announced.