30 lines
1.3 KiB
Markdown
30 lines
1.3 KiB
Markdown
# Abstract
|
|
|
|
This workshop will give a short introduction to mixed-effects models for
|
|
longitudinal data. We will look at data sets where data was collected for
|
|
subjects over several time points. The first part will be a basic introduction
|
|
of the concept of random effects and how they extend linear regression. This is
|
|
meant to get everybody on the same page and introduce some notation. We will fit
|
|
the models together in R using the lme4 package. In the second part, we will
|
|
extend the models into growth curve models to model time trends within and
|
|
between subjects. Special focus will be on parameter interpretation and what
|
|
should be kept in mind when fitting and interpreting growth curve models. This
|
|
course is suited for novices to mixed-effects models who want to understand the
|
|
basic concepts, but also for people with a bit more expertise who want to dig
|
|
into more details and deepen their understanding of parameter interpretation.
|
|
|
|
# Instructor
|
|
|
|
Dr. Nora Wickelmaier, statistics consultant at the Leibniz-Institut für
|
|
Wissensmedien (IWM), Tübingen
|
|
|
|
# Prerequisites
|
|
|
|
Participants will need to have installed:
|
|
|
|
* a current R version (https://cran.r-project.org/)
|
|
* an IDE for R (like RStudio or VSCode) or a text editor with syntax
|
|
highlighting (like Vim or Notepad++)
|
|
* the R package lme4 (https://cran.r-project.org/package=lme4)
|
|
|