Longitudinal Structural Equation Modeling with Mplus A Latent State-Trait Perspective

An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state–trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

Christian Geiser, PhD, is Professor of Psychology at Utah State University and Director of the Quantitative Psychology PhD Specialization. His research interests are in psychometrics and structural equation modeling, particularly in longitudinal data analysis and multitrait–multimethod modeling. As part of his methodological work, he has presented new longitudinal structural equation modeling approaches for examining effects of situations and person–situation interactions, as well as models for integrating information from multiple reporters or other methods in longitudinal analyses. He offers Mplus workshops at www.goquantfish.com.

List of Abbreviations
Guide to Statistical Symbols
1. A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State–Trait Theory
1.1 Introduction
1.2 Latent State–Trait Theory
1.3 Chapter Summary
1.4 Recommended Readings
2. Single-Factor Longitudinal Models for Single-Indicator Data
2.1 Introduction
2.2 The Random Intercept Model
2.3 The Random and Fixed Intercepts Model
2.4 The ξ-Congeneric Model
2.5 Chapter Summary
2.6 Recommended Reading
3. Multifactor Longitudinal Models for Single-Indicator Data
3.1 Introduction
3.2 The Simplex Model
3.3 The Latent Change Score Model
3.4 The Trait–State–Error Model
3.5 Latent Growth Curve Models
3.6 Chapter Summary
3.7 Recommended Readings
4. Testing Measurement Equivalence in Longitudinal Studies
4.1 Introduction
4.2 The Latent State (LS) Model
4.3 The Latent State Model with Indicator-Specific Residual Factors (LS-IS Model)
4.4 Chapter Summary
4.5 Recommended Readings
5. Multiple-Indicator Longitudinal Models
5.1 Introduction
5.2 Latent State Change (LSC) Models
5.3 The Latent Autoregressive/Cross-Lagged States (LACS) Model
5.4 Latent State–Trait (LST) Models
5.5 Latent Trait Change (LTC) Models
5.6 Chapter Summary
5.7 Recommended Readings
6. Modeling Intensive Longitudinal Data
6.1 Introduction
6.2 Special features of Intensive Longitudinal Data
6.3 Specifying Longitudinal SEMs for Intensive Longitudinal Data
6.4 Chapter Summary
6.5 Recommended Readings
7. Missing Data Handling
7.1 Introduction
7.2 Missing Data Mechanisms
7.3 Maximum Likelihood Missing Data Handling
7.4 Multiple Imputation (MI)
7.5 Planned Missing Data Designs
7.6 Chapter Summary
7.7 Recommended Readings
8. How to Choose between Models and Report the Results
8.1 Model Selection
8.2 Reporting Results
8.3 Chapter Summary
8.4 Recommended Readings
References
Author Index
Subject Index