s course give valid inference under the different mechanisms.
- Be able to identify which are the hypotheses of interest, which model parameters are involved in these hypotheses, and which tests are appropriate?
- Be able to decide upon a proper strategy for model building and
- Be able to validate model assumptions.
- Be able to use software, e.g. R, to perform an analysis with an LMM or GLMM, by the ANOVA method, ML, REML, or GEE.
- Be able to interpret output from the software in terms of the practical problem.
- Know the strengths and limitations of various procedures for statistical inference with a mixed model.
For the course days, course location and class hours check the Time Table under the tab “StatSci Students -> Program Schedule” at http://www.math.leidenuniv.nl/statisticalscience
Mode of Instruction
The course will consist of a mix of lectures and practicals. Students will discuss some practical case studies in groups in class with respect to choice of model and aspects of statistical inference. PowerPoint presentations are leading and notes are supplied that provide details, worked case studies, R programs and output, and exercises with solutions for self-study. Some books are suggested (optional) for further details. Study material, including data setsfor the case studies mentioned, is available from Blackboard.
About halfway down the second part of the course (the part about longitudinal data) students will start working in groups on case studies that are handed out, under supervision of the teachers. Each group of students will hand in a written report about their case study. This report will be graded and together with the grade of the written exam determines the final grade of an individual student. Before a group of students hand in their final report, they will present the results of their case study in a plenary session, and be able to use feedback from the other students and teachers to improve upon the report.
A written exam (2/3) with open questions, presentation of the case study and case study report (1/3) (pass / fail). The case study report and the written exam should each be assessed with a minimum grade of 5 to obtain the course credits. The final grade should be at least 5.5 (which will be rounded to 6) to get a pass. Students may take a written re-exam following the university rules. Unless the student decides to follow the course again in a next year, the final grade for the case study is binding. The date for the oral presentation of the report and the date for handing in the case study report will be agreed upon during the course.
Date information about the exam and resit can be found in the Time Table pdf document under the tab “Masters Programme” at http://www.math.leidenuniv.nl/statscience. The room and building for the exam will be announced on the electronic billboard, to be found at the opposite of the entrance, the content can also be viewed here http://info.liacs.nl/math/.
The following books are occasionally referred to for further reading, but they are not compulsory reading for the exam.
- Faraway (2006). Extending the linear model with R. generalized linear, mixed effects and nonparametric regression models. Chapman & Hall/CRC
- Fitzmaurice, Laird & Ware (2004). Applied longitudinal analysis. John Wiley & Sons.
- McCulloch, Searle & Neuhaus (2008) Generalized, linear and mixed models. Wiley Blackwell.
The first two books are indicative for the applied level of this course. The third book is more technical and intended as a reference book. The Faraway book is relevant for the course about linear and generalized linear models as well. These books are occasionally referred to for further reading, but they are not compulsory reading for the exam.
Enroll in Blackboard for the course materials and course updates.
To be able to obtain a grade and the ECTS for the course, sign up for the (re-)exam in uSis ten calendar days before the actual (re-)exam will take place. Note, the student is expected to participate actively in all activities of the program and therefore uses and regist