Practical Multilevel Modeling Using R

Francis Huang, Ph.D. is an Associate Professor at the University of Missouri (MU) in the Statistics, Measurement, and Evaluation in Education program in the Department of Educational, School, and Counseling Psychology of the College of Education. He teaches courses on multilevel modeling, program evaluation, and data management and is the co-director of the methodology branch of the Missouri Prevention Science Institute. Dr. Huang’s research has been funded by federal agencies such as the U.S. Department of Education and the National Institute of Justice. His research focuses on both methodological (e.g., analysis of clustered data) and substantive (e.g., school climate, bullying, disparities in disciplinary sanctions) areas of interest. His work has been cited in outlets such as the New York Times, the Washington Post, and National Public Radio (among others). He has published in journals such as the Journal of Educational and Behavioral Statistics, Behavior Research Methods, and Educational Researcher. Prior to joining MU, he was a Senior Scientist at the University of Virginia and has worked at the American Institutes for Research, providing technical expertise on survey methods and the analysis of large-scale secondary datasets. He has worked as a management consultant and a high school teacher. He has an MA from Teachers College, Columbia University and a PhD from the University of Virginia. He is a father of two and married to his best friend. Francis does not take himself too seriously, plays the guitar, and dreams of being in a jazz trio in his retirement.

1Introduction
2The unconditional means model
3Adding predictors to a random intercepts model
4Investigating cross-level interactions and random slope models
5Understanding growth models
6Centering in multilevel models
7Multilevel modeling diagnostics
8Multilevel logistic regression models
9Modeling data structures with three (or more) levels
10Missing data in multilevel models
11Basic power analyses for multilevel models
12Alternatives to multilevel models