YRM-60306 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.

Contents:

This is an advanced course which is part of the Research Master Variant in the MSc programmes MID and MME. The aim of the course is to enhance students knowledge and skills in quantitative scientific research by focusing on knowledge and skills with regard to some multivariate data-analysis techniques that are very common in quantitative research in social sciences. These data analysis techniques are principal component analysis, exploratory and confirmatory factor analysis, structural equations modelling, cluster and discriminant analysis, and repeated and multivariate analysis of variance.

Learning outcomes:

By the end of this course you will be able to:
- understand the characteristics of multivariate quantitative research, including strengths and weaknesses;
- understand the principles and characteristics of the multivariate data analysis techniques: PCA, EFA, CFA; SEM, cluster and discriminant analysis, repeated and multivariate ANOVA;
- judge whether these techniques are useful for a particular research question given the characteristics of the collected data;
- apply these techniques by using statistical software;
- interpret correctly the results from these techniques.

Activities:

- plenary sessions on theory;
- practice in working groups;
- computer practicals using SPSS and Lisrel.

Examination:

- exam (60%) ;
- assignments (40%).
To pass this course, one needs both the exam and the assignment to have a grade of at least 5.5.

Literature:

James Lattin, J. Douglas Carroll, Paul E. Green. (2003). Analyzing multivariate data. Pacific Grove: Brooks/Cole Thomson Learning Inc. ISBN 0534349749.