DAY 1
Welcome, Background, Data, and R
- Welcome
- Things you should know before analysis
- What’s correlated data?
- Modeling techniques for correlated data: traditional vs advanced
- What’s Multi-Level Modeling (MLM)?
- MLM assumptions
- Your research question in hypothesis testing framework
- Data preparation for longitudinal data analysis
- Introduction to example cortisol data
- A brief R tutorial
DAY 2
Random Intercept/Slope Models (Exploratory Data Analysis, Model Fitting, and Diagnostics in R) Interpretations of Results
- Introduction to MLM for correlated data
- Random intercept model
- Random intercept/slope model
- Exploration of different covariance structures for the repeated measure
- Variations of random intercept/slope models
- Cortisol toy data example
DAY 3
Additional Modeling Techniques, Individualized Data Analysis, Trouble Shooting, and Presenting Outputs
- A brief review of additional modeling techniques such as non-linear and spline mixed effect modeling
- Example MLM research papers
- Getting to know your data
- Assistance with individualized model fitting
- Trouble shooting errors
- Assistance with interpretation and presenting outputs