- Introduction and overview of HLM applications (S&B, Chs 1, 2)
- Review of simple and multiple regression, fixed and random effects ANOVA
- Statistical treatment of clustered data (S&B, Ch 3)
- Random intercept models (S&B, Ch 4)
- Random slope models (S&B, Ch 5)
- Hypothesis testing and model specification (S&B, Ch 6)
- Explained variance (S&B, Ch 7)
- Heteroscedasticity (S&B, Ch 8)
- Missing data (S&B, Ch 9)
- Evaluation of model assumptions (S&B, Ch 10)
- Designing multilevel studies; power analysis (S&B, Ch 11)
- Alternative estimation methods (S&B, Ch 12)
- Crossed random effects and multiple memberships (S&B, Ch 13)
- Survey weights (S&B, Ch 14)
- Longitudinal data analysis (S&B, Ch 15)
- Multivariate multilevel models (S&B, Ch 16)
- Discrete dependent variables (S&B, Ch 17)
- Latent growth curve models, meta analysis
Snijders, T.A.B., & Bosker, R.J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. (2nd ed.) Sage. [S&B]