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