Week 8: Cross-Classified Models
Multilevel Models for Experimental Data
Week Learning Objectives
By the end of this module, you will be able to
- Identify the correct levels with experimental studies
- Describe designs with crossed random levels
- Assign variables to appropriate levels, and tell which variables can have random slopes at which levels
- Compute a version of effect size (\(R^2\)) for experimental data
Note
In the video, the slides use \(d\) for effect size, but we will use \(R^2\) in this class.
Task List
- Review the resources (lecture videos and slides)
- Complete the assigned readings
- Snijders & Bosker 13.1
- Hoffman & Rovine (2007)
- Attend the Tuesday session and participate in the class exercise
- Complete the project prospectus
- Complete Homework 7 (due in two weeks)
- Additional resources for learning MLM for experimental designs
- This paper by Judd et al.
Lecture
Slides
Unconditional model
When there is > 1 observations per cell, one can (and should) estimate the interaction variance component using
~ (1 | id) + (1 | Item) + (1 | id:Item) lg_rt
Full Model
Please check out the slides and the examples in the R code