Features Shows how to properly model data structures to avoid incorrect parameter and standard error estimates Explains how multilevel models provide insights into your data that otherwise might not be detected Illustrates helpful graphical options in R appropriate for multilevel data Presents models for categorical dependent variables in single level and multilevel contexts Discusses multilevel modeling within the Bayesian framework Offers an introduction to R in the appendix for R novices Uses various R packages to conduct the analyses and interpret the results, with the code available online Summary A powerful tool for analyzing nested designs in a variety of fields, multilevel/hierarchical modeling allows researchers to account for data collected at multiple levels. Multilevel Modeling Using R provides you with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models. For those new to R, the appendix provides an introduction to this system that covers basic R knowledge necessary to run the models in the book. Through the R code and detailed explanations provided, this book gives you the tools to launch your own investigations in multilevel modeling and gain insight into your research.