TILM3616 Longitudinal Data Analysis (5 cr)
Cooperation network course
Network: Cross-institutional studies in advanced courses in mathematics and statistics
This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:
- Bachelor's Degree Programme in Mathematics
- Master's Degree Programme in Mathematics
- Bachelor's Degree Programme in Mathematics (Subject Teacher)
- Master's Degree Programme in Mathematics (Subject Teacher)
- Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Doctoral Programme in Mathematics and Statistics
- Doctoral Programme in Mathematics and Science (Specialication in Mathematics)
Grading scale:
0-5
Description
Longitudinal data are characterised by a typically smallish number of repeated observations of the same outcome variable from a number of independent individuals. The course deals with the following concepts: repeated measurements; general linear model; generalised linear model for longitudinal data; correlation structures; marginal models; random effects modes; transition models; generalised estimation equations; missing data
Learning outcomes
The student learns to choose an appropriate linear or generalised linear model to analyse longitudinal data. The students can interpret the estimated parameters and the analysis results and understands the role of correlation structures in the analysis of longitudinal data. The student can formulate and analyse research questions about between-unit as well as within-unit differences. The student understands how different missing data mechanisms affect the ability to conduct unbiased estimation.
Additional information
The course is organised every other year. The next round will be in 2026.
Description of prerequisites
Recommended preliminary courses: Statistical Inference I; Linear and Generalised Linear Models.