目录:
Preface
Overview
I. Foundations
- Background and Goals of Longitudinal Research
- Basics of Structural Equation Modeling
- Some Technical Details on Structural Equation Modeling
- Using the Simplified Reticular Action Model Notation
- Benefits and Problems Using Structural Equation Modeling in Longitudinal Research
II. Longitudinal SEM for the Direct Identification of Intraindividual Changes
- Alternative Definitions of Individual Changes
- Analyses Based on Latent Curve Models
- Analyses Based on Time Series Regression Models
- Analyses Based on Latent Change Score Models
- Analyses Based on Advanced Latent Change Score Models
III. Longitudinal SEM for Interindividual Differences in Intraindividual Changes
- Studying Interindividual Differences in Intraindividual Changes
- Repeated Measures Analysis of Variance as a Structural Model
- Multilevel Structural Equation Modeling Approaches to Group Differences
- Multiple Group Structural Equation Modeling Approaches to Group Differences
- Incomplete Data with Multiple Group Modeling of Changes
IV. Longitudinal SEM for the Interrelationships in Growth
- Considering Common Factors/Latent Variables in Models
- Considering Factorial Invariance in Longitudinal Structural Equation Modeling
- Alternative Common Factors With Multiple Longitudinal Observations
- More Alternative Factorial Solutions for Longitudinal Data
- Extensions to Longitudinal Categorical Factors
V. Longitudinal SEM for Causes (Determinants) of Intraindividual 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
VI. Longitudinal SEM for Interindividual Differences in Causes (Determinants) of Intraindividual 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
VII. 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
Index
About the Authors