Featuring actual datasets as illustrative examples, this book reveals
numerous ways to apply structural equation modeling (SEM) to any
repeated-measures study. Initial chapters lay the groundwork for
modeling a longitudinal change process, from measurement, design, and
specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation.
User-friendly
features include equation boxes that clearly explain the elements in
every equation, end-of-chapter glossaries, and annotated suggestions for
further reading. The companion website
(www.guilford.com/little-materials) provides datasets for all of the
examples--which include studies of bullying, adolescent students'
emotions, and healthy aging--with syntax and output from LISREL, Mplus,
and R (lavaan).
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