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Longitudinal Structural Equation Modeling
Todd D. Little
Prologue
* A personal introduction and what to expect
How statistics came into my life
My approach to the book
Key features of the book
Overview of the book
* Datasets and measures used
My dataset with the Inventory Felt Energy and Emotion in Life (I FEEL) measure
* The I FEEL
Gallagher and Johnson's MIDUS example
* Neuroticism
* Negative affect
Dorothy Espelage's bullying and victimization examples
* Peer victimization
* Substance use
* Family conflict
* Family closeness
* Bullying
* Homophobic teasing
* Overdue gratitude
* Prophylactic apologies
1. Overview and SEM Foundations
* An overview of the conceptual foundations of SEM
Concepts, constructs, and indicators
From concepts to constructs to indicators to good models
* Sources of variance in measurement
Classical test theorem
Expanding classical test theorem
* Characteristics of indicators and constructs
Types of indicators and constructs
Categorical versus metrical indicators and constructs
Types of correlation coefficients that can be modeled
* A simple taxonomy of indicators and their roles
* Rescaling variables
* Parceling
* What changes and how?
* Some advice for SEM programming
* Philosophical issues and how I approach research
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
2. Design Issues in Longitudinal Studies
* Timing of measurements and conceptualizing time
Cross-sectional design
Single-cohort longitudinal design
Cross-sequential design
Cohort-sequential design
Time-sequential design
Other validity concerns
Temporal design
Lags within the interval of measurement
Episodic and Experiential Time
* Missing data imputation and planned missing designs
Missing data mechanisms
Recommendations and caveats
Planned missing data designs in longitudinal research
* Modeling developmental processes in context
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
3. The Measurement Model
* Drawing and labeling conventions
* Defining the parameters of a construct
* Scale setting
* Identification
* Adding means to the model: Scale setting and identification with means
* Adding a longitudinal component to the CFA model
* Adding phantom constructs to the CFA model
* Summary
* Key terms and concepts introduced in this chapter
* Recommended Readingsá
4. Model Fit, Sample Size, and Power
* Model fit and types of fit indices
Statistical rationale
Modeling rationale
The longitudinal null model
Summary and cautions
* Sample Size
* Power
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
5. The Longitudinal CFA Model
* Factorial invariance
* A small (nearly perfect) data example
Configural factorial invariance
Weak factorial invariance
Strong factorial invariance
Evaluating invariance constraints
Model modification
Partial invariance
* A larger example followed by tests of the latent construct relations
Testing the latent construct parameters
* An application of a longitudinal SEM to a repeated-measures experiment
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
6. Specifying and Interpreting a Longitudinal Panel Model
* Basics of a panel model
* The basic simplex change process
* Building a panel model
Covariate/control variables
Building the panel model of positive and negative affect
* Illustrative examples of panel models
A simplex model of cognitive development
Two simplex models of non-longitudinal data
A panel model of bullying and homophobic teasing
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
7. Multiple-Group Models
* Multiple-group longitudinal SEM
Step 1: Estimate missing data and evaluate the descriptive statistics
Step 2: Perform any supplemental analysis to rule out potential confounds
Step 3: Fit an appropriate multiple-group longitudinal null model
Step 4: Fit the configurally invariant model across time and groups
Step 5: Test for weak factorial (loadings) invariance
Step 6: Test for strong factorial invariance
Step 7: Test for mean-level differences in the latent constructs
Step 8: Test for the homogeneity of the variance–covariance matrix among the latent constructs
Step 9: Test the longitudinal SEM model in each group
* A dynamic p-technique multiple-group longitudinal model
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
8. Multilevel Growth Curves and SEM
* Longitudinal growth curve model
* Multivariate growth curve models
* Multilevel longitudinal model
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
9. Mediation and Moderation
* Making the distinction between mediators and moderators
Cross-sectional mediation
Half-longitudinal mediation
Full longitudinal mediation
* Moderation
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
10. Jambalaya: Complex Construct Representations and Decompositions
* Multitrait–multimethod models
* Pseudo-MTMM models
* Bifactor and higher order factor models
* Contrasting different variance decompositions
* Digestif
* Key terms and concepts introduced in this chapter
* Recommended readings
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