Research methods: Linear mixed effects models in SPSS 3201-LST-OC-RM5
We will learn how to organize data in the long format so it is suitable
for mixed models
2. We will learn what multi-level data is.
3. We will learn the difference between fixed effects and random
effects
4. We will learn how to conduct mixed models using different types of
dependent variables.
5. We will discuss different modelling techniques (and how to steer
clear of p hacking).
6. We will discuss model assumptions, e.g., eBLUPS (Empirical Best
Linear Unbiased Predictions) and Pearson-residual plots.
7. We will visualize the predicted results.
Type of course
Mode
Course coordinators
Learning outcomes
After completing the course, a participant will:
Have basic knowledge of linear mixed-effects models: theory
Be able to run simple mixed-effects models
Be able to diagnose the models
Be able to visualize results
Assessment criteria
Work methods (underline the relevant points, or suggest others):
individual work,
pairwork,
teamwork,
audiovisual material,
work on case studies,
presentations,
brainstorming,
conceptual exercises,
group discussions,
other (specify) ……………..
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: