TY - BOOK AU - McArdle,John J. AU - Nesselroade,John R. TI - Longitudinal data analysis using structural equation models SN - 9781433817151 U1 - 000SA.05 23 PY - 2014/// CY - Washington, D.C. : PB - American Psychological Association, KW - Longitudinal method KW - Psychology KW - Research N1 - Includes bibliographical references (pages 373-400) and index; Preface -- Overview -- I. Foundations -- 1. Background and goals of longitudinal research -- 2. Basics of structural equation modeling -- 3. Some technical details on structural equation modeling -- 4. Using the simplified ram notation -- 5. Benefits and problems of longitudinal structure modeling -- II. Longitudinal SEM : direct identification of intra-individual changes -- 6. Alternative definitions of individual changes -- 7. Analyses based on latent curve models (LCM) -- 8. Analyses based on time series regression (TSR) -- 9. Analyses based on latent change score (LCS) models -- 10. Analyses based on advanced latent change score models -- III. Longitudinal SEM for identification of differences in intra-individual changes -- 11. Studying inter-individual differences in intra-individual changes -- 12. Repeated measures analysis of variance as a structural model -- 13. Multi-level structural equation modeling approaches to group differences -- 14. Multi-group structural equation modeling approaches to group differences -- 15. Incomplete data with multiple group modeling of changes -- IV. Longitudinal SEM for the interrelationships in growth -- 16. Considering common factors/latent variables in models -- 17. Considering factorial invariance in longitudinal SEM -- 18. Alternative common factors with multiple longitudinal observations -- 19. More alternative factorial solutions for longitudinal data -- 20. Extensions to longitudinal categorical factors -- V. Longitudinal SEM for causes (determinants) of intra-individual changes -- 21. Analyses based on cross-lagged regression and changes -- 22. Analyses based on cross-lagged regression in changes of factors -- 23. Current models for multiple longitudinal outcome scores -- 24. The bivariate latent change score model for multiple occasions -- 25. Plotting bivariate latent change score results -- VI. Longitudinalsem SEM for interindividual differences in causes (determinants) of intra-individual changes -- 26. Dynamic processes over groups -- 27. Dynamic influences over groups -- 28. Applying a bivariate change model with multiple groups -- 29. Notes on the inclusion of randomization in longitudinal studies -- 30. The popular repeated measures analysis of variance -- VII. Summary and discussion -- 31. Contemporary data analyses based on planned incompleteness -- 32. Factor invariance in longitudinal research -- 33. Variance components for longitudinal factor models -- 34. Models for intensively repeated measures -- 35. CODA : the future is yours! -- References-- Index N2 - The authors identify five basic purposes of longitudinal structural equation modeling. For each purpose, they present the most useful strategies and models. Two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores ER -