The SAGE Handbook of Multilevel Modeling

Edited by Marc A. Scott, Jeffrey S. Simonoff and Brian D. Marx

In The SAGE Handbook of Multilevel Modeling, Professor Jeffrey Simonoff and his co-editors, Marc A. Scott at NYU and Brian D. Marx at Louisiana State University, gather a range of leading contributors to introduce the theory and practice of multilevel modeling. Multilevel models are statistical models designed for longitudinal or panel data and clustered data, which have become increasingly important in "Big Data." The book establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice. It forges connections that cross traditional disciplinary divides and introduces best practices in the field.

The book is divided into four sections:
  • Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference.
  • Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models.
  • Part III includes discussion of missing data and robust methods, assessment of fit and software.
  • Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines.
The handbook is intended for students and researchers who aim to apply multilevel techniques in their own research.

To learn more, visit the book's website

Jeffrey S. Simonoff holds the Toyota Term Professorship in Statistics.