This workshop will develop participants’ understanding of the principles, methods, and interpretation of statistical models for longitudinal data (i.e. repeated measures over time) using R. The course will cover the principles of Linear Mixed Models from simple models to more complex ones and includes practical sessions getting hands-on experience of longitudinal analysis in R.
Researchers who are dealing with longitudinal data and wish to understand how to analyse them effectively. The workshop is relevant for all disciplines, although examples and exercises will focus on biological datasets. Prior expertise with R and the command line interface is required to a level equivalent of that provided by the R for Reproducible Scientific Research workshop, as the basics of R will not be covered. Participants are expected to have a basic familiarity with the concepts of statistical hypothesis testing and regression analysis.
- Recognise longitudinal datasets and identify the different types of longitudinal data
- Understand the difference between linear regression and linear mixed models, and know when to apply each
- Generate a range of descriptive statistics for longitudinal data using R
- Chose and apply the appropriate R package for different types of linear mixed model analysis
- Interpret and evaluate the output from R for linear mixed model predictions
- Introduction to principles of longitudinal data analysis
- Introduction to linear mixed models using specific R packages
- Understanding the basics of fixed and random effects, and how to distinguish between them
- Running models in R and interpretation and visualisation from such analyses