 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.
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).
 
