Longitudinal Structural Equation Modeling
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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2 Design Issues in Longitudinal Studies
3 The Measurement Model
4 Model Fit Sample Size and Power
5 The Longitudinal CFA Model
6 Specifying and Interpreting a Longitudinal Panel Model
7 MultipleGroup Models
8 Multilevel Growth Curves and Multilevel SEM
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aggression analysis approach associations assumption autoregressive behavior bifactor bullying CFA model Chapter cohort confidence interval context cross-lagged cross-time dataset degrees of freedom dummy codes effects coding method evaluate example fixed factor method freely estimated groups growth curve model homophobic teasing imputation indicators influence intercept labeled latent construct latent-variable loadings longitudinal model lower order constructs marker variable mean measurement model measurement occasions mediation method of scaling metric missing data model fit moderation Mplus MTMM multilevel models multiple-group negative affect nested neuroticism null model observed occasion of measurement omnibus test outcome p-value panel model parameter estimates parcels path perfect shuffles phantom constructs Positive Affect predict predictor regression relations relationships residual variances RMSEA sample saturated model scores significant simplex slope specified standard deviation statistical strong invariance structural equation modeling sufficient statistics Table tion TLI/NNFI Toeplitz matrix trait unique