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