Multi-level Modeling
NYU-APSTA-GE 2042
Preface
Course description
Important links
Quiz links
Assignment links
Class links
R programming Review
R basic
Functional programming
Tidyverse
ggplot
Theoratical Review
Introduction to ANOVA
Maximum Likelihood
Longitudinal data
1
Chapter 1: Nested grouping structure
1.1
Introduction
1.2
Modeling: first pass
1.3
Simpler baseline models
1.4
Two levels of nesting (schools and classrooms)
1.5
Appendix
1.5.1
OLS & MLE (sketch)
1.6
Coding
1.7
Quiz
1.7.1
Self-Assessment
1.7.2
Lab Quiz
1.7.3
Homework
2
: MLM conceptualization
2.1
MLM conceptualization, notation and effects
2.1.1
Classroom data example
2.1.2
A constructive approach to random effects, or, what are these things we call ‘effects’?
2.1.3
Model Selection
2.2
Appendix
2.3
Coding
2.4
Quiz
2.4.1
Self-Assessment
2.4.2
Lab Quiz
2.4.3
Homework
3
: Random Slopes, Wald Tests, a Re-examination of Inference
3.1
Random Slopes
3.2
Sampling variance & Wald tests; random slopes (redux)
3.3
Appendix
3.4
Coding
3.5
Quiz
3.5.1
Self-Assessment
3.5.2
Lab Quiz
3.5.3
Homework
4
: BLUPs, Residuals, Information Criteria
4.1
Predicting Random Effects: BLUPs
4.2
Examining residuals to uncover non-linearity
4.3
Information Criteria
4.4
Appendix
4.5
Coding
4.6
Quiz
4.6.1
Self-Assessment
4.6.2
Lab Quiz
4.6.3
Homework
5
: Level Notation, Psuedo-R^2, Nested Longitudinal Data
5.1
Level Notation and targeting variation – one example
5.2
Appendix
5.3
Coding
5.4
Quiz
5.4.1
Self-Assessment
5.4.2
Lab Quiz
5.4.3
Homework
6
: Centering; ecological fallacy; hybrid model; FE v. RE
6.1
Centering
6.2
Hybrid model
6.3
Fixed vs. random effects
6.4
Appendix
6.5
Coding
6.6
Quiz
6.6.1
Self-Assessment
6.6.2
Lab Quiz
6.6.3
Homework
References
By Joseph Shim
Multi-level Modeling: Nested and Longitudinal data
References