| Dozent/in |
Dr. rer. nat. Hanna Bettine Fechner |
| Veranstaltungsart |
Masterseminar |
| Code |
HS251279 |
| Semester |
Herbstsemester 2025 |
| Durchführender Fachbereich |
Gesundheitswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Di, 16.09.2025, 14:15 - 16:00 Uhr, E.508
Di, 23.09.2025, 14:15 - 16:00 Uhr, E.508
Di, 30.09.2025, 14:15 - 16:00 Uhr, E.508
Di, 07.10.2025, 14:15 - 16:00 Uhr, E.508
Di, 14.10.2025, 14:15 - 16:00 Uhr, E.508
Di, 28.10.2025, 14:15 - 16:00 Uhr, E.508
Di, 04.11.2025, 14:15 - 16:00 Uhr, E.508
Di, 11.11.2025, 14:15 - 16:00 Uhr, E.508
Di, 18.11.2025, 14:15 - 16:00 Uhr, E.508
Di, 25.11.2025, 14:15 - 16:00 Uhr, E.508
Di, 02.12.2025, 14:15 - 16:00 Uhr, E.508
Di, 09.12.2025, 14:15 - 16:00 Uhr, E.508
Di, 16.12.2025, 14:15 - 16:00 Uhr, E.508 |
| Weitere Daten |
For the exercises, the students will work on their own laptops on which they have installed the software R, R Studio, and topic-specific R packages.
For M. Sc. students of the Health Sciences, the course can be credited in the major Health Data Science or for the other majors in the electives. |
| Umfang |
2 Semesterwochenstunden |
| Inhalt |
For research questions and data analyses in the health sciences, the context of the data is often of critical importance. This means that the data is grouped or clustered according to specific contexts or levels (e.g., repeated measurements within the same patients or medical devices, measurements on grouped individuals in different hospitals or regions). Multilevel models are suitable statistical methods for analyzing such data and offer a wide range of possible applications in various fields of the health sciences.
After a brief review of regression models with quantitative and qualitative predictors, central principles of multilevel models are introduced (e.g., data structures with multiple levels, advantages of using multilevel models, specification of suitable models using fixed and random effects). Multilevel models for different research designs and data structures are then discussed. Topics include linear multilevel models for cross-sectional and longitudinal designs, cross-level interactions, generalized multilevel models, models with an extended number of levels, model diagnostics and modifications, and recommendations for the presentation of results from multilevel modeling.
For each aspect of multilevel modeling, there will be a short theoretical introduction, followed by a practical implementation in R und the interpretation of the resulting R output. Complementary exercise sheets will be provided for students to gain hands-on experience with the modeling techniques; students will present their solutions to each other. In the end of the course, students will apply their knowledge by presenting and discussing scientific research papers from various fields of the health sciences that contextualize the multilevel-modeling techniques covered. |
| E-Learning |
Course materials are made available or linked on the e-learning platform Moodle, and solutions to the exercises and presentation slides are submitted via Moodle. |
| Lernziele |
Upon completion of the course, students will be able to
• describe, explain, and evaluate central principles of multilevel models
• specify suitable models for data sets that were collected using different research designs and have different data structures
• implement important steps for data analysis with multilevel models using R and interpret the results obtained
• read, present, and critically evaluate scientific research papers from the health sciences that use multilevel-modeling techniques |
| Voraussetzungen |
Knowledge in the areas of data visualization and statistics / quantitative methods (e.g., linear and logistic regression). Experience with the software R and R Studio or the willingness to acquire this knowledge before the start of the course.
Please bring your own laptops with an installation of R and R Studio. |
| Sprache |
Englisch |
| Begrenzung |
The course is limited to 14 participants. The limit will be administered via Moodle according to the chronological order of registration. From 1st September 2025, 12:00 p.m. (noon), it will be possible to register via Moodle. As soon as 14 participants are registered, the registration window will close automatically. If you would like to be put on the waiting list, please send |
| Anmeldung |
https://elearning.hsm-unilu.ch/course/view.php?id=942 |
| Prüfung |
Grading will be based on 1) the coding solutions and presentation of these solutions for an exercise sheet in R (50%), and 2) the slides and presentation of a scientific article in which multilevel modeling was used (50%). An overall grade of 4.0 or better is required to successfully complete the course.
IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/ |
| Abschlussform / Credits |
Coding solutions in R and their presentation, slides and presentation of scientific article / 3 Credits
|
| Hinweise |
Teaching methods:
Theoretical inputs, demonstrations, exercises, presentations, group work and discussions by students. |
| Hörer-/innen |
Nein |
| Kontakt |
hanna.fechner@unilu.ch |
| Material |
Course materials are made available or linked on the e-learning platform Moodle, and solutions to the exercises and presentation slides are submitted via Moodle. |
| Literatur |
• Leyland, A. H., & Groenewegen, P. P. (2020). Multilevel modelling for public health and health services research: Health in context. Springer Open. https://doi.org/10.1007/978-3-030-34801-4_1
• Luke, D. A. (2020). Multilevel Modeling (Quantitative Applications in the Social Sciences, Band 143) (2nd ed.). Sage. https://doi.org/10.4135/9781544310305
• Pinheiro, J., & Bates, D. (2000). Mixed-effects models in S and S-PLUS. Springer. https://doi.org/10.1007/b98882 |