McArdle J.J., Nesselroade J.R. Longitudinal Data Analysis Using Structural Equation Models

NY: APA, 2014. — 426 p. — ISBN: 978-1-4338-1715-1.When determining the most appropriate method for analyzing longitudinal data, you must first consider what research question you want to answer. In this book, McArdle and Nesselroade 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. The book covers a wealth of models in a straightforward, understandable manner. Rather than overwhelm the reader with an extensive amount of algebra, the authors use path diagrams and emphasize methods that are appropriate for many uses.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
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
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
Longitudinal SEM for the Interrelationships in Growth
Considering Common Factors/Latent Variables in Structural 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
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
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
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!