Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective
okt, 2020 · Guilford Publications
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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.
Muallif haqida
Christian Geiser, PhD, is a former professor of quantitative psychology. He currently works as an instructor and statistical consultant. His areas of expertise are in structural equation modeling, longitudinal data analysis, latent class modeling, multitrait–multimethod analysis, and measurement. His website is https://christiangeiser.com/.
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