Linear Mixed Effects Models are an extension of simple linear models (such as regression or ANOVA) that include both fixed and random effects, and are useful for analyzing data with observations that are non independent. Non-independence often occurs when data is clustered due to study design (i.e. students are grouped in to classrooms; or plants are grouped into agricultural blocks), or when data is collected repeatedly from the same subjects (over time, or under different conditions).
Topics covered include:
- How to recognize non-independence in your data
- Difference between “block” and “random” effects, and how to choose which approach to use
- Intra-Class Correlation (ICC)
- Nested and Cross Random Effects
- Appropriate R2 statistics for mixed models
The workshop is intended for participants who have the equivalent of two semesters of statistics and previous experience with ANOVA and linear regression. Please review https://cscu.cornell.edu/workshop/interpreting-linear-models-regression-and-anova/.