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Longitudinal data analysis using structural equation models / John J. McArdle and John R. Nesselroade.

By: Contributor(s): Material type: TextTextPublication details: Washington, D.C. : American Psychological Association, 2014.Description: xi, 426 p. : illustrations ; 25 cmISBN:
  • 9781433817151
Subject(s): DDC classification:
  • 000SA.05 23 M115
Contents:
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.
Summary: 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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.05 M115 (Browse shelf(Opens below)) Available 136083
Total holds: 0

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.

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.

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