This valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data the random intercepts cross-lagged panel model (RI-CLPM) longitudinal mixture modeling and Bayesian SEM. Emphasizing a decision-making approach leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis longitudinal panel models and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation tips on what does and doesn't work end-of-chapter glossaries and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization adolescents' emotions and healthy aging--along with syntax and output chapter quizzes and the bookâs figures. New to This Edition: *Chapter on missing data with a spotlight on planned missing data designs and the R-based package PcAux. *Chapter on longitudinal mixture modeling with Whitney Moore. *Chapter on the random intercept cross-lagged panel model (RI-CLPM) with Danny Osborne. *Chapter on Bayesian SEM with Mauricio Garnier. *Revised throughout with new developments and discussions such as how to test models of experimental effects. . Language: English