Multilevel and Longitudinal Modeling with IBM SPSS, Third Edition,
demonstrates how to use the multilevel and longitudinal modeling
techniques available in IBM SPSS Versions 25-27. Annotated
screenshots with all relevant output provide readers with a
step-by-step understanding of each technique as they are shown how
to navigate the program. Throughout, diagnostic tools, data
management issues, and related graphics are introduced. SPSS
commands show the flow of the menu structure and how to facilitate
model building, while annotated syntax is also available for those
who prefer this approach. Extended examples illustrating the logic
of model development and evaluation are included throughout the
book, demonstrating the context and rationale of the research
questions and the steps around which the analyses are structured.
The book opens with the conceptual and methodological issues
associated with multilevel and longitudinal modeling, followed by a
discussion of SPSS data management techniques that facilitate
working with multilevel, longitudinal, or cross-classified data
sets. The next few chapters introduce the basics of multilevel
modeling, developing a multilevel model, extensions of the basic
two-level model (e.g., three-level models, models for binary and
ordinal outcomes), and troubleshooting techniques for everyday-use
programming and modeling problems along with potential solutions.
Models for investigating individual and organizational change are
next developed, followed by models with multivariate outcomes and,
finally, models with cross-classified and multiple membership data
structures. The book concludes with thoughts about ways to expand
on the various multilevel and longitudinal modeling techniques
introduced and issues (e.g., missing data, sample weights) to keep
in mind in conducting multilevel analyses. Key features of the
third edition: Thoroughly updated throughout to reflect IBM SPSS
Versions 26-27. Introduction to fixed-effects regression for
examining change over time where random-effects modeling may not be
an optimal choice. Additional treatment of key topics specifically
aligned with multilevel modeling (e.g., models with binary and
ordinal outcomes). Expanded coverage of models with
cross-classified and multiple membership data structures. Added
discussion on model checking for improvement (e.g., examining
residuals, locating outliers). Further discussion of alternatives
for dealing with missing data and the use of sample weights within
multilevel data structures. Supported by online data sets, the
book's practical approach makes it an essential text for
graduate-level courses on multilevel, longitudinal, latent variable
modeling, multivariate statistics, or advanced quantitative
techniques taught in departments of business, education, health,
psychology, and sociology. The book will also prove appealing to
researchers in these fields. The book is designed to provide an
excellent supplement to Heck and Thomas's An Introduction to
Multilevel Modeling Techniques, Fourth Edition; however, it can
also be used with any multilevel or longitudinal modeling book or
as a stand-alone text.
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