Linear Mixed Effects Modeling
Course infoSchedule
Course moduleLET-REMA-LC1803
Credits (ECTS)3
Language of instructionEnglish
Offered byRadboud University; Faculty of Arts; Graduate School;
dr. L.F.M. ten Bosch
Other course modules lecturer
dr. L.F.M. ten Bosch
Other course modules lecturer
Contactperson for the course
dr. L.F.M. ten Bosch
Other course modules lecturer
dr. N.G. Levshina
Other course modules lecturer
Academic year2023
PER 4  (08/04/2024 to 01/09/2024)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
This course will provide a practical guide and theoretical background of linear mixed effects modeling (LMER).

LMER is a statistical technique that has become more and more popular during the last couple of years.  During the course, aspects related to exploratory versus confirmatory research, model selection, fixed and random effects, anova versus AIC/BIC, the optimization of statistical models, and model prediction will be discussed. The computational part of the course is based on numerous realistic data examples. 

We will use R (R stdudio) as platform, but this course is not a course on R itself. Familiarity with R is assumed.
The course will consist of two (intertwined) parts.

In the theoretical part we will discuss the ins-and-outs of LMER models, including the concept of significance, the "p-value", the way how these models try to optimize model parameters so as to minimize some error criterion, the balance between fixed and random terms, contrast coding, and the significance of predictors as a function of the presence of other predictors.  We will also discuss blame analysis: properties of LMER models that, e.g., may help to understand which participants can be blamed for a certain effect, and touch on more advanced issues such as quantile regressions, and GAMS.

The practical part will be based on the application of various models on existing datasets or your own dataset. The course will start with simple examples. The platform we will use is R.
Knowledge of R and of Statistics and Experimental Methods1 and 2 is assumed.
Presumed foreknowledge
Knowledge of R and of Statistics and Experimental Methods 1 and 2 is assumed.
Test information
The assessment takes place via an individual short thesis. All information will be provided at the beginning of the course. 

Required materials
In consultation with teacher
This course will be based on papers and materials from the web, and will use and re-use various data sets available from research projects on the campus. Books and literature will be recommended during the course.

Instructional modes

Project paper
Test weight100
Test typeProject
OpportunitiesBlock PER 4, Block PER 4

Minimum grade