ChristianGeiser
Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective
Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective
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Excellent- More about Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective
This book provides a comprehensive guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus,using latent state–trait (LST) theory as a unifying conceptual framework. It covers traditional and LST models, measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, with user-friendly features and a companion website.
Format: Paperback / softback
Length: 344 pages
Publication date: 04 November 2020
Publisher: Guilford Publications
An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this comprehensive book employs latent state–trait (LST) theory as a unifying conceptual framework, encompassing the relevant coefficients of consistency, occasion specificity, and reliability.
Chapters meticulously review the theoretical underpinnings, strengths, and limitations of the diverse models, presenting data examples and demonstrating each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage spans both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data.
The book delves into measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, offering 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 boasts data sets, annotated syntax files, and output for all of the examples, enhancing the reader's experience.
This book is a valuable resource for researchers and practitioners seeking to employ advanced statistical techniques to analyze longitudinal data and uncover meaningful insights into complex relationships and processes.
Weight: 504g
Dimension: 155 x 236 x 20 (mm)
ISBN-13: 9781462538782
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