Multilevel models

This is a curated list of the sources related to multilevel modeling.

Software

R

• glmm: https://rdrr.io/cran/glmm/
• lme4: https://rdrr.io/cran/lme4/
• lmerTest: https://rdrr.io/cran/lmerTest/
• nlme: https://rdrr.io/cran/nlme/
• glmmsr: https://rdrr.io/cran/glmmsr/
• MASS: https://rdrr.io/cran/MASS/ (glmmPQL function)
• glmmTMB: https://rdrr.io/cran/glmmTMB/
• mcemGLM: https://rdrr.io/cran/mcemGLM/

Python

• statsmodels: https://www.statsmodels.org/stable/mixed_linear.html

Books and Monographies

• Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press.

• Lazega, E., & Snijders, T. A. (2016). Multilevel network analysis for the social sciences. Cham, CH: Springer.

• Galwey, N. W. (2014). Introduction to mixed modelling: beyond regression and analysis of variance. John Wiley & Sons.

• Lavielle, M. (2014). Mixed effects models for the population approach: models, tasks, methods and tools. CRC press.

• Gałecki, A., & Burzykowski, T. (2013). Linear Mixed-Effects Models Using R. Springer, New York, NY.

• Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). London: Wiley.

• Snijders, T. A., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.

• Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2010). Multilevel analysis: Techniques and applications. Routledge.

• Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. Springer Science & Business Media.

• De Leeuw, J., Meijer, E., & Goldstein, H. (2008). Handbook of multilevel analysis. New York: Springer.

• Gelman, A., & Hill, J. (2007). Data analysis using regression and hierarchical/multilevel models. New York, NY: Cambridge.

• Pinheiro, J., & Bates, D. (2006). Mixed-effects models in S and S-PLUS. Springer Science & Business Media.

• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Sage.

• Stephen, R., & Anthony, B. (2002). Hierarchical linear models. Sage Publications, Thousand Oaks, CA.

Chapters in Books

• Field, A., Miles, J., & Field, Z. (2012). Multilevel linear models. In: Discovering statistics using R. Sage publications.

Presentations and Lectures

• Crash Course on Multilevel Modeling: https://www.ctsi.ucla.edu/education/files/view/training/docs/Ponce_Multilevel_modeling_CTSI_seminar_012313.pdf

• Introduction to Mixed Models in R (by Galin Jones, University of Minnesota): http://users.stat.umn.edu/~galin/IntroductionToMixedModelsInR.pdf

Manuals, Tutorials, and Blog Posts

• Introduction to Linear Mixed Models: https://stats.idre.ucla.edu/other/mult-pkg/introduction-to-linear-mixed-models/

• Introduction to Linear Mixed Models (2): https://ourcodingclub.github.io/tutorials/mixed-models/

• Multilevel Modeling: https://ademos.people.uic.edu/Chapter16.html

• Multilevel Modeling Tutorial: https://stat.utexas.edu/images/SSC/documents/SoftwareTutorials/MultilevelModeling.pdf

• Mixed Effects Modeling: https://ademos.people.uic.edu/Chapter17.html

• Using Mixed-Effects Models For Linear Regression: https://towardsdatascience.com/using-mixed-effects-models-for-linear-regression-7b7941d249b

• Linear Mixed-Effects Models: https://www.mathworks.com/help/stats/linear-mixed-effects-models.html

• Linear Mixed-Effects Models (2): http://sia.webpopix.org/lme.html

• Linear Mixed-Effects Models (3): https://www.zoology.ubc.ca/~bio501/R/workshops/lme.html

• A tutorial for using the lme function from the nlme package: https://crumplab.github.io/psyc7709/book/docs/a-tutorial-for-using-the-lme-function-from-the-nlme-package-.html

• Linear mixed-effect models in R: https://www.r-bloggers.com/linear-mixed-effect-models-in-r/

• Linear models and linear mixed effects models in R with linguistic applications: https://arxiv.org/pdf/1308.5499.pdf

• Mixed-effects models (in R): http://home.cc.umanitoba.ca/~krussll/stats/mixed-effects.html

• GLMM FAQ: https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html

Scientific Articles

• Meteyard, L., & Davies, R. A. (2020). Best practice guidance for linear mixed-effects models in psychological science. Journal of Memory and Language, 112, 104092.

• Hair Jr, J. F., & Fávero, L. P. (2019). Multilevel modeling for longitudinal data: concepts and applications. RAUSP Management Journal.

• Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 82(13).

• Sommet, N., & Morselli, D. (2017). Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS. International Review of Social Psychology, 30, 203-218.

• Magezi, D. A. (2015). Linear mixed-effects models for within-participant psychology experiments: an introductory tutorial and free, graphical user interface (LMMgui). Frontiers in psychology, 6, 2.

• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823.

• Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of school psychology, 48(1), 85-112.

• Hayes, A. F. (2006). A primer on multilevel modeling. Human communication research, 32(4), 385-410.