Statistics: Mixed-Effects Models
Course infoSchedule
Course moduleSOW-BS083
Credits (ECTS)4
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Behavioural Science;
dr. B.C. Figner
Other course modules lecturer
Contactperson for the course
dr. B.C. Figner
Other course modules lecturer
dr. B.C. Figner
Other course modules lecturer
Academic year2018
PER3  (04/02/2019 to 14/04/2019)
Starting block
Course mode
RemarksFor Behavioural Science RM students only, non-BSRM students interested in the course, please mail to
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
Upon successful completion of the course, students will be able to apply a statistical technique called linear mixed-effects modeling (including multilevel analysis and hierarchical linear modeling) to address specific research questions. Students will be able to recognize, evaluate, and interpret various types of parameter estimates, and be able to judge the appropriateness of the use of mixed-effects models presented in the scientific literature. Students who successfully complete the course will be able to describe the theoretical underpinnings of mixed-effects models, utilize these methods in their subsequent research, and report such analyses and their results in scientific journal articles.
How does error-related slowing in a Stroop task change over time in participants having received brain stimulation versus controls? How does daily drinking on weekdays versus weekends differ between females and males? How do developmental trajectories in a longitudinal study differ between children with versus without ADHD? How are income level of school districts and teachers' teaching style related to students' math scores?
These are all examples of research questions that are analyzed with so-called linear mixed-effects models (LMMs; closely related terms are multilevel analysis and hierarchical linear modeling), as they involve one dependent variable and so-called "clustered errors" (due to repeated measures or hierarchical "nesting" of data such as students "nested" in schools). After having successfully finished this class, you will be able to use this type of statistical model in your own research and report such analyses and their results in scientific articles. This relatively novel analysis approach is quickly being adopted in virtually all fields of psychology and other behavioral sciences because they are flexible, powerful, can handle well unbalanced data and missing observations and, compared to more traditional analyses, avoid inflated Type I errors and allow to make inferences about processes that traditional analysis cannot.
You will also learn the theoretical basis of linear mixed-effects models, and their use not only with normally distributed data, but also other data types (e.g., binary). The main statistical package in R that will be used is lme4.

Teaching methods: Lectures, computer lab sessions, and homework assignments.

Exam information
Course grade is based on the results of a (1) the take-home portion, which requires students to perform the appropriate analyses, and write up the results in APA style; and (2) the theory-based portion, which will be completed at a designated time and place, will include multiple choice questions concerning theoretical and practical aspects of mixed-effects models, and functionality and terminology used in R.
Assumed previous knowledge
Knowledge of and experience with the program R is required. It is highly recommended to follow the course: ‘Advanced Statistics in R (SOWBS86)' or a similar course.

Recommended materials
To be announced
Will be announced

Instructional modes
Computer Practicals


Home work assignments
Test weight0
OpportunitiesBlock PER3, Block PER4

Take home exam
Test weight5
Test typeExam
OpportunitiesBlock PER3, Block PER4

Written exam
Test weight5
Test typeExam
OpportunitiesBlock PER3, Block PER4