This course is designed to provide theoretical and applied understandings of multilevel modeling (MLM).
GSSS - Multilevel Modeling
Course Description
The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include, for example, students within classrooms, people within countries, or repeated observations from the same person. In each of these cases and many more, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling, which is also sometimes referred to as mixed modeling, hierarchical linear modeling, or random coefficients modeling. In this course we will cover: random effects (MLM) regression, centering, interactions, and longitudinal models.
Study Characteristics
- Discipline: Social Sciences
- Language: English
- ECTS: 3
- Type of education: In class
- Academic skill: Quantitative Methods
- Graduate School: Graduate School of Social Sciences
- Start date: 26 November 2026
- End date: 16 December 2026
- Schedule:
Monday 26 October, 14.00-17.00
Monday 2 November, 14.00-17.00
Monday 9 November, 14.00-17.00
Monday 16 November, 14.00-17.00 - Minimum number of students: 5
- Maximum number of students: 15
- Admission criteria: Some previous experience of linear regression, basic R skills
- Assessment type: Final data analysis project
- Concluding assessment: Yes
- With Certificate: Yes, upon request
- Registration deadline: 4 weeks before the start of the course
- Available to: PhD candidates interested in applying MLM in their research. Free of charge for VU-GSSS, AISSR, and ZU PhD candidates. A fee of €750 applies to other PhD candidates.
- Name of teacher: Dr. Mauricio Garnier-Villarreal
- Link to profile: https://research.vu.nl/en/persons/mauricio-garnier-villarreal/
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Course Description & Study Characteristics
Course Description
The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include, for example, students within classrooms, people within countries, or repeated observations from the same person. In each of these cases and many more, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling, which is also sometimes referred to as mixed modeling, hierarchical linear modeling, or random coefficients modeling. In this course we will cover: random effects (MLM) regression, centering, interactions, and longitudinal models.
Study Characteristics
- Discipline: Social Sciences
- Language: English
- ECTS: 3
- Type of education: In class
- Academic skill: Quantitative Methods
- Graduate School: Graduate School of Social Sciences
- Start date: 26 November 2026
- End date: 16 December 2026
- Schedule:
Monday 26 October, 14.00-17.00
Monday 2 November, 14.00-17.00
Monday 9 November, 14.00-17.00
Monday 16 November, 14.00-17.00 - Minimum number of students: 5
- Maximum number of students: 15
- Admission criteria: Some previous experience of linear regression, basic R skills
- Assessment type: Final data analysis project
- Concluding assessment: Yes
- With Certificate: Yes, upon request
- Registration deadline: 4 weeks before the start of the course
- Available to: PhD candidates interested in applying MLM in their research. Free of charge for VU-GSSS, AISSR, and ZU PhD candidates. A fee of €750 applies to other PhD candidates.
- Name of teacher: Dr. Mauricio Garnier-Villarreal
- Link to profile: https://research.vu.nl/en/persons/mauricio-garnier-villarreal/