Longitudinal data analysis using structural equation models / John J. McArdle and John R. Nesselroade.
Material type: TextPublication details: Washington, D.C. : American Psychological Association, 2014.Description: xi, 426 p. : illustrations ; 25 cmISBN:- 9781433817151
- 000SA.05 23 M115
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000SA.05 In39h Handbook for practical work in statistics | 000SA.05 In39h Handbook for practical work in statistics | 000SA.05 L579 Analysis of mixed data : | 000SA.05 M115 Longitudinal data analysis using structural equation models / | 000SA.05 R179 Dynamic data analysis : | 000SA.05 Su957 Statistical analysis of panel count data / | 000SA.05 V628 Analysis and modeling of complex data in behavioral and social sciences / |
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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