This book provides students with a step-by-step guide for running their
own multilevel analyses. Detailed examples illustrate the conceptual and
statistical issues that multilevel modeling addresses in a way that is
clear and relevant to students in applied disciplines. Clearly annotated
R syntax illustrates how multilevel modeling (MLM) can be used, and
real-world examples show why and how modeling decisions can affect
results. The book covers all the basics but also important advanced
topics such as diagnostics, detecting and handling heteroscedasticity,
and missing data handling methods. Unlike other detailed texts on MLM
which are written at a very high level, this text with its applied focus
and use of R software to run the analyses is much more suitable for
students who have substantive research areas but are not training to be
methodologists or statisticians. Each chapter concludes with a "Test
Yourself" section, and solutions are available on the instructor website
for the book. A companion R package is available for use with this text.