Geiser C. Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective

The Guilford Press, 2021. — 371 p. — (Methodology in the Social Sciences). — ISBN 9781462544240.Продольное Структурное Уравнение, Моделирующее с Mplus: Скрытая Перспектива Государственной чертыAn 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.A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State–Trait Theory
Single‑Factor Longitudinal Models for Single-Indicator Data
Multifactor Longitudinal Models for Single‑Indicator Data
Latent State Models and Measurement Equivalence Testing in Longitudinal Studies
Multiple‑Indicator Longitudinal Models
Modeling Intensive Longitudinal Data
Missing Data Handling
How to Choose between Models and Report the Results