In this course, students will learn to evaluate the quality of quantitative data, gain insight into the strengths and limitations of various multivariate analysis techniques, learn how to perform analysis using R and how to interpret and communicate its output.
Hester van Herk
Hester van Herk is professor of Cross-Cultural Marketing Research at VU and Adjunct Senior Research Fellow at the University of Western Australia in Perth. Hester holds a PhD (2000) in marketing and cross-cultural psychology from Tilburg University. She studied psychology at Leiden University with a major in research methodology and minors in social and organizational psychology and in mathematics. After obtaining her MSc, she worked as a scientific researcher at Statistics Netherlands, Solvay Duphar, ABN Amro, and MarketResponse. At Vrije Universiteit Amsterdam she was Program Director of the Bachelor of Business Administration (2010-2013) and Acting Department Chair at the Marketing department (2013-2015).
Course Description
This course will emphasize understanding, implementation, and interpretation of multivariate statistical methods. The course will involve both lectures and work tutorials. First, we discuss how to clean data, recap some fundamental multivariate techniques such as analysis of (co)variance and regression analysis and go in-depth into factor analysis. Second, the main part of the course will be focused on learning some more advanced MDA techniques such as confirmatory factor analysis, structural equation modeling (SEM) and multi-level models (MLMs, also known as linear mixed models, hierarchical linear models, or mixed-effect models). We end with a short introduction into recent developments in the field, including network analysis.
In the course high-quality existing datasets will be introduced such as the European Social Survey (ESS), the European Values Survey (EVS), the World Values Survey (WVS) and the Dutch LISS panel data.
This course prepares the student for analyzing datasets using the freely available programming language R. R is a platform for which many scholars write packages. The basis enables you to manipulate data, clean data, and test hypotheses. The packages enable you to use advanced methods such as structural equation modeling and multilevel modeling. You will learn how to read various datasets (including files from SPSS or SAS) into R, and how to conduct manipulations such as transforming data.
Please download the course manual here.
Study Characteristics
- Study period: February 2026 – March 2026 (Period 4)
- Credits: 5 ECTS
- Tuition fee: €1250 (20% discount for early bird registration)
- Registration deadline: 18-01-2026 (early bird registration: 04-01-2026)
- Prerequisite knowledge: prior knowledge on quantitative research methods and R (e.g., the ABRI course R for Business and management Research).
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Course Description & Study Characteristics
Course Description
This course will emphasize understanding, implementation, and interpretation of multivariate statistical methods. The course will involve both lectures and work tutorials. First, we discuss how to clean data, recap some fundamental multivariate techniques such as analysis of (co)variance and regression analysis and go in-depth into factor analysis. Second, the main part of the course will be focused on learning some more advanced MDA techniques such as confirmatory factor analysis, structural equation modeling (SEM) and multi-level models (MLMs, also known as linear mixed models, hierarchical linear models, or mixed-effect models). We end with a short introduction into recent developments in the field, including network analysis.
In the course high-quality existing datasets will be introduced such as the European Social Survey (ESS), the European Values Survey (EVS), the World Values Survey (WVS) and the Dutch LISS panel data.
This course prepares the student for analyzing datasets using the freely available programming language R. R is a platform for which many scholars write packages. The basis enables you to manipulate data, clean data, and test hypotheses. The packages enable you to use advanced methods such as structural equation modeling and multilevel modeling. You will learn how to read various datasets (including files from SPSS or SAS) into R, and how to conduct manipulations such as transforming data.
Please download the course manual here.
Study Characteristics
- Study period: February 2026 – March 2026 (Period 4)
- Credits: 5 ECTS
- Tuition fee: €1250 (20% discount for early bird registration)
- Registration deadline: 18-01-2026 (early bird registration: 04-01-2026)
- Prerequisite knowledge: prior knowledge on quantitative research methods and R (e.g., the ABRI course R for Business and management Research).
Would you like to register or want to know more?
Please register with the Apply Now button at the top of this page. For more info please contact the course coordinator prof. dr. Hester van Herk: