Longitudinal data analysis using structural equation models - McArdle, John J.; Nesselroade, John R. (Washington : American Psychological Association, 2014)
Preface -- Overview -- Foundations -- Background and goals of longitudinal research -- Basics of structural equation modeling -- Some technical details on structural equation modeling -- Using the simplified ram notation -- Benefits and problems of longitudinal structure modeling -- The first purpose of LSEM : direct identification of intra-individual changes -- Alternative definitions of individual changes -- Analyses based on latent curve models (LCM) -- Analyses based on time series regression (TSR) -- Analyses based on latent change score (LCS) models -- Analyses based on advanced latent change score models -- The second purpose of LSEM : identification of inter-individual differences in intra-individual changes -- Studying inter-individual differences in intra-individual changes -- Repeated measures analysis of variance as a structural model -- Multi-level structural equation modeling approaches to group differences -- Multi-group structural equation modeling approaches to group differences -- Incomplete data with multiple group modeling of changes -- The third purpose of LSEM : identification of inter-relationships in growth -- Considering common factors/latent variables in models -- Considering factorial invariance in longitudinal SEM -- Alternative common factors with multiple longitudinal observations -- More alternative factorial solutions for longitudinal data -- Extensions to longitudinal categorical factors -- The fourth purpose of LSEM : identification of causes (determinants) of intra-individual changes -- Analyses based on cross-lagged regression and changes -- Analyses based on cross-lagged regression in changes of factors -- Current models for multiple longitudinal outcome scores -- The bivariate latent change score model for multiple occasions -- Plotting bivariate latent change score results -- The fifth purpose of lsem : identification of inter-individual differences in causes (determinants) of intra-individual changes -- Dynamic processes over groups -- Dynamic influences over groups -- Applying a bivariate change model with multiple groups -- Notes on the inclusion of randomization in longitudinal studies -- The popular repeated measures analysis of variance -- Summary and discussion -- Contemporary data analyses based on planned incompleteness -- Factor invariance in longitudinal research -- Variance components for longitudinal factor models -- Models for intensively repeated measures -- CODA : the future is yours! -- References.
Bibliografija: str. 373-400
Kazalo
Keywords: Longitudinal method; Psychology -- Research