Multilevel Modeling Methods with Introductory and Advanced Applications

Multilevel Modeling Methods with Introductory and Advanced Applications by Ann A. O’Connell, published by Emerald Publishing Limited on March 17, 2022, is a comprehensive resource that introduces multilevel modeling to methodological and applied researchers, as well as advanced graduate students. Spanning 644 pages, this book serves as a potential textbook for courses in multilevel modeling, covering a wide range of topics from basic principles to advanced applications.
Readers will find that the book is structured into seventeen chapters, each designed to function independently while emphasizing application and interpretation. It addresses foundational topics relevant to organizational and longitudinal research, and delves into advanced extensions such as cross-classified random effects models and mixed effects location scale models. Additionally, the text discusses critical design and analytic issues, including missing data and causal inference, and explores special topics like the use of large-scale complex sample datasets.
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Multilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation.
In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs.
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